Abteilung Siedlungswasserwirtschaft

Publikationen von SWW

Peer Reviewed Publikationen

Beutler, P., Larsen, T. A., Maurer, M., Staufer, P., & Lienert, J. (2024). A participatory multi-criteria decision analysis framework reveals transition potential towards non-grid wastewater management. Journal of Environmental Management, 367, 121962 (15 pp.). doi:10.1016/j.jenvman.2024.121962, Institutional Repository

Many public environmental decisions are wicked problems due to high complexity and uncertainty. We test a participatory value-based framework based on multi-criteria decision analysis (MCDA) to tackle such problems. Our framework addresses two important gaps identified in reviews of MCDA applications to environmental problems: including stakeholders and treating uncertainty. We applied our framework in two complex real-world cases concerning a paradigm shift in the wastewater sector; the transition from centralized wastewater systems to decentralized non-grid systems. Non-grid systems may solve some problems of centralized systems by reducing costs, increasing flexibility, and addressing growing demands on environmental issues, especially in rural areas. But non-grid systems have rarely been implemented in OECD countries, because it is unclear whether a transition is recommendable, and whether stakeholders would accept this shift. This problem allows addressing several fundamental research questions. As theoretical contribution, we found that stakeholder participation in MCDA is necessary, because different preferences of stakeholders can lead to different best-performing options in the assessments. Compared to the typical integrated assessment (IA) approach that excludes stakeholders' preferences, the MCDA process led to clearer outcomes. Results indicate that including the uncertainty of predicted consequences of options with Monte Carlo simulation helped discriminate between options and identify best-performing options. Challenging the uncertainty of elicited stakeholder preferences with sensitivity analyses, we found that best-performing options were especially sensitive to the MCDA aggregation model. Despite the high uncertainty, it was possible to suggest robust consensus options that would perform reasonably well for all stakeholders. As practical contribution, results indicated that a transition from the centralized to decentralized non-grid systems seems feasible. Most stakeholders assigned highest weights to environmental protection objectives in decision-making workshops. These stakeholder preferences implemented in MCDA led to a generally better assessment of innovative non-grid systems, especially when including urine source separation. Stakeholders perceived the MCDA process as beneficial and found results plausible. We conclude that the proposed participatory value-based framework is rigorous, but still feasible in practice. The framework is certainly transferable to any context and is open to testing and refinement in various applications to wicked decision problems.
Blackman, R., Couton, M., Keck, F., Kirschner, D., Carraro, L., Cereghetti, E., … Altermatt, F. (2024). Environmental DNA: the next chapter. Molecular Ecology, 33(11), e17355 (17 pp.). doi:10.1111/mec.17355, Institutional Repository

Molecular tools are an indispensable part of ecology and biodiversity sciences and implemented across all biomes. About a decade ago, the use and implementation of environmental DNA (eDNA) to detect biodiversity signals extracted from environmental samples opened new avenues of research. Initial eDNA research focused on understanding population dynamics of target species. Its scope thereafter broadened, uncovering previously unrecorded biodiversity via metabarcoding in both well-studied and understudied ecosystems across all taxonomic groups. The application of eDNA rapidly became an established part of biodiversity research, and a research field by its own. Here, we revisit key expectations made in a land-mark special issue on eDNA in Molecular Ecology in 2012 to frame the development in six key areas: (1) sample collection, (2) primer development, (3) biomonitoring, (4) quantification, (5) behaviour of DNA in the environment and (6) reference database development. We pinpoint the success of eDNA, yet also discuss shortfalls and expectations not met, highlighting areas of research priority and identify the unexpected developments. In parallel, our retrospective couples a screening of the peer-reviewed literature with a survey of eDNA users including academics, end-users and commercial providers, in which we address the priority areas to focus research efforts to advance the field of eDNA. With the rapid and ever-increasing pace of new technical advances, the future of eDNA looks bright, yet successful applications and best practices must become more interdisciplinary to reach its full potential. Our retrospect gives the tools and expectations towards concretely moving the field forward.
Cavadini, G. B., Rodriguez, M., Nguyen, T., & Cook, L. M. (2024). Can blue-green infrastructure counteract the effects of climate change on combined sewer overflows? Study of a swiss catchment. Environmental Research Letters, 19(9), 094025 (12 pp.). doi:10.1088/1748-9326/ad6462, Institutional Repository

Combined sewer overflows (CSOs), the discharge of untreated sewage mixed with stormwater into surface waters, are expected to increase under climate change as a result of more extreme rainfall. Blue-green infrastructure (BGI), such as bioretention cells and porous pavements, can help to reduce the amount of stormwater entering combined sewer systems, thus reducing CSO discharge. However, our understanding of the potential for BGI to mitigate CSOs in a future climate is still lacking, as performance is typically evaluated for individual BGI elements with fixed implementation areas under historical climate conditions or limited future scenarios. In response, this study investigates the performance of 30 combinations of BGI elements and implementation rates to prevent increases in CSOs under a range of future climate scenarios in an urban catchment near Zurich, Switzerland. Median total annual rainfall, projected to increase by as much as 46%, could double the median annual CSO volume and increase median annual CSO frequency by up to 52%. Four BGI combinations that include bioretention cells show the most promise to prevent increases in CSO volume and frequency in a future climate; and given the diverse responses of BGI elements to distinct rainfall patterns, their combinations can enhance CSO discharge reduction across varying climate patterns. BGI is also likely to become more cost-effective under future climatic conditions as projected increases in total rainfall led to larger CSO volume reductions obtained through BGI. However, there is a trade-off between robustness to climate change and cost-effectiveness, since CSO volume reduction capacity scales with BGI implementation rate but cost-effectiveness declines. Our study illustrates the effectiveness of various BGI combinations to prevent increases in CSOs in a future climate, calling for a range of BGI elements and implementation areas to be considered for urban drainage adaptation.
Cavadini, G. B., Rodriguez, M., & Cook, L. M. (2024). Connecting blue-green infrastructure elements to reduce combined sewer overflows. Journal of Environmental Management, 365, 121465 (15 pp.). doi:10.1016/j.jenvman.2024.121465, Institutional Repository

By infiltrating and retaining stormwater, Blue-Green Infrastructure (BGI) can help to reduce Combined Sewer Overflows (CSOs), one of the main causes of urban water pollution. Several studies have evaluated the ability of individual BGI types to reduce CSOs; however, the effect of combining these elements, likely to occur in reality, has not yet been thoroughly evaluated. Moreover, the CSO volume reduction potential of relevant components of the urban drainage system, such as detention ponds, has not been quantified using hydrological models. This study presents a systematic way to assess the potential of BGI combinations to mitigate CSO discharge in a catchment near Zurich (Switzerland). Sixty BGI combinations, including four BGI elements (bioretention cells, permeable pavement, green roofs, and detention ponds) and four different implementation rates (25%, 50%, 75%, and 100% of the available sewer catchment area) are evaluated for four runoff routing schemes. Results reveal that BGI combinations can provide substantial CSO volume reductions; however, combinations including detention ponds can potentially increase CSO frequency, due to runoff prolongation. When runoff from upstream areas is routed to the BGI, the CSO discharge reductions from combinations of BGI elements differ from the cumulative CSO discharge reductions achieved by individual BGI types, indicating that the sum of effects from individual BGI types cannot accurately predict CSO discharge in combined BGI scenarios. Moreover, larger BGI implementation areas are not consistently more cost-effective than small implementation areas, since the additional CSO volume reduction does not outweigh the additional costs. The best-performing BGI combination depends on the desired objective, being CSO volume reduction, CSO frequency reduction or cost-effectiveness. This study emphasizes the importance of BGI combinations and detention ponds in CSO mitigation plans, highlighting their critical factors—BGI types, implementation area, and runoff routing— and offering a novel and systematic approach to develop tailored BGI strategies for urban catchments facing CSO challenges.
Chaudhary, P., Leitão, J. P., Schindler, K., & Wegner, J. D. (2024). Flood water depth prediction with convolutional temporal attention networks. Water, 16(9), 1286 (19 pp.). doi:10.3390/w16091286, Institutional Repository

Robust and accurate flood hazard maps are essential for early warning systems and flood risk management. Although physically based models are effective in estimating pluvial flooding, the computational burden makes them difficult to use for real-time flood prediction. In contrast, data-driven models can provide faster flood predictions if trained offline. While most studies have focused on predicting maximum water depth, in this study, we predict pixel-wise water depth maps for entire catchments at a lead time of 2 h. To that end, we propose a deep learning approach that uses a sequence encoding network with temporal self-attention. We also adapt the popular hydrological performance metric Nash–Sutcliffe efficiency (NSE) as our loss function. We test the effectiveness and generalizability of our method using a new dataset called SwissFlood, which consists of 100 catchments and 1500 rainfall events extracted from real observations in Switzerland. Our method produces 2 m spatial resolution flood maps with absolute error as low as 27 cm for water depth exceeding 1 m.
 
Chen, J., Bach, P. M., Nice, K. A., & Leitão, J. P. (2024). Investigating the efficacy of a fast urban climate model for spatial planning of green and blue spaces for heat mitigation. Science of the Total Environment, 955, 176925 (13 pp.). doi:10.1016/j.scitotenv.2024.176925, Institutional Repository

Problems caused by urban heat have prompted the exploration of urban greenery and blue spaces for heat mitigation. Various numerical models can simulate heat-related processes, but their use as support-tools to urban planners remains underexplored, particularly at the city-scale, due to high computational demand and complexity of such models. This study investigates the spatial relationships between urban heat, urban form and urban green and blue spaces with the fast climate model TARGET (The Air-temperature Response to Green/blue-infrastructure Evaluation Tool), which only requires minimal inputs of standard meteorological data, land cover and building geometry data. Using the City of Zurich as our case study, we: (i) validated the TARGET model against air temperature measurements from private sensor networks, (ii) performed a sensitivity analysis to identify key variables affecting urban heat, and (iii) investigated urban heat relationships with blue-green cover at locations frequented by pedestrians. Presence of urban green and blue spaces across the region shows potential for reducing local air temperatures by up to 5.2 °C (with urban forest). Investigating this relationship at different locations in the city revealed key districts that should potentially be targeted for reduction of pedestrian heat-impacts, due to their high pedestrian traffic, fewer green and blue spaces and high daytime air temperatures. Our results not only provide insights into the cooling effect of different amounts of green and blue features in the urban environment, but also demonstrates the application and integration potential of simplified models like TARGET to support the planning of more liveable future cities.
Cook, L. M., Good, K. D., Moretti, M., Kremer, P., Wadzuk, B., Traver, R., & Smith, V. (2024). Towards the intentional multifunctionality of urban green infrastructure: a paradox of choice?. npj Urban Sustainability, 4(1), 12 (13 pp.). doi:10.1038/s42949-024-00145-0, Institutional Repository

Decades of research on multifunctional Green Infrastructure (GI) has yet to translate into holistic implementation in the built environment. This oversight stems from assumptions that many ecosystem services occur passively and thus potential synergies are overlooked during planning and design. This study offers specific guidance for coordinating GI planning, design, and construction by examining the current state of academic literature on these aspects. It identifies 15 GI elements (e.g., green roofs) and 15 objectives (e.g., biodiversity) to collectively consider before implementation. The literature tends to isolate discussions of “engineered” GI elements with water-related objectives, while more “natural” GI are linked to biodiversity and human well-being. Coordinating across GI objectives and elements remains imperative, but evaluating too many options risks a paradox of choice. This study recommends short-term adherence to principles of adaptive design and, in the long-term, reemphasizes multifunctionality assessments, inter and transdisciplinary collaboration, and political will.
Dietzel, A., Moretti, M., & Cook, L. M. (2024). Shrinkage-based Bayesian variable selection for species distribution modelling in complex environments: an application to urban biodiversity. Ecological Informatics, 81, 102561 (9 pp.). doi:10.1016/j.ecoinf.2024.102561, Institutional Repository

Robust, quantitative understanding of the diverse ecological needs of species is needed to inform effective biodiversity conservation, now and in the future, but is lacking for most species. The advent of "big data" in ecology presents unprecedented opportunities to fill this gap and to disentangle the diverse drivers of biodiversity. Variable and model selection in sparse (small sample sizes for most species), high-dimensional (large pool of candidate predictors) problems is, however, non-trivial. Here, we employ cross-validated Bayesian projection predictive variable selection and shrinkage priors to identify, from a list of 70 ecological and biophysical candidate predictor variables, the minimal subset that best predicts the habitat preferences and distributions of 103 species of amphibians, birds, butterflies, dragonflies, and grasshoppers using the city of Zurich, Switzerland, as a case study. We contrast the predictive performance and ecological inference of models fit with the full set of predictors using shrinkage priors (exhaustive models) to models fit with a limited number of predictors obtained by compiling predictors from the full list of predictors using weakly informative priors (selective models). We show that exhaustive models excel in predictive performance, albeit at the cost of greater model complexity compared to selective models. Results from the selective models reveal the importance of access to aquatic habitat for a wide range of taxa, relative to other drivers such as urbanisation, vegetation and environmental hazards. These results are complemented by more nuanced insights from the exhaustive models into the importance of specific types of aquatic habitat (ponds, lakes, streams) and vegetation (herb, shrub, canopy cover) for the distribution of urban biodiversity, as well as the different spatial scales at which drivers are of predictive relevance. Our findings demonstrate the potential of shrinkage-based Bayesian variable selection to leverage big ecological data for species distribution modelling, and contribute to the development of concrete guidelines for urban planning and infrastructure design that account for biodiversity conservation.
Duque, N., Scholten, L., & Maurer, M. (2024). Exploring transitions of sewer wastewater infrastructure towards decentralisation using the modular model TURN-Sewers. Water Research, 257, 121640 (14 pp.). doi:10.1016/j.watres.2024.121640, Institutional Repository

We present a new modular model called TURN-Sewers for exploring different adaptations of centralised wastewater infrastructure towards more decentralised wastewater systems under different urban development scenarios. The modular model is flexible and computationally efficient in exploring transitions at the city scale, allowing for the comparison of different policies and management strategies for sanitary wastewater infrastructure. TURN-Sewers includes independent modules that simulate the generation, dimensioning, deterioration, management, and calculation of performance indicators for different wastewater systems. This model can use readily available spatial information to support infrastructure planners and other stakeholders in exploring different transition pathways from centralised to decentralised wastewater infrastructure. An illustrative example demonstrates how TURN-Sewers can generate multiple future alternatives, define different infrastructure management strategies regarding system expansion, rehabilitation and transition, and assess the economic, hydraulic and structural impacts.
Duque, N., Scholten, L., & Maurer, M. (2024). When does infrastructure hybridisation outperform centralised infrastructure paradigms? – Exploring economic and hydraulic impacts of decentralised urban wastewater system expansion. Water Research, 254, 121327 (13 pp.). doi:10.1016/j.watres.2024.121327, Institutional Repository

We explore the dynamics of centralised and decentralised wastewater infrastructure across various scenarios and introduce novel insights into their performance regarding structural vulnerability, hydraulic capacity, and costs. This study determines circumstances under which infrastructure hybridisation outperforms traditional centralised infrastructure paradigms. We combined system analysis to map out the modelling problem with the model-based exploration of the transition space using the novel TURN-Sewers model. System diagramming was used to identify the parameters or combinations of parameters that significantly influence the performance indicators being assessed. This allowed the creation of relevant simulation scenarios to identify circumstances where a decentralised sewer system could outperform a centralised one. TURN-Sewers was applied to model the infrastructure maintenance and generation of new infrastructure over 20 years for a municipality on the Swiss Plateau, considering a population growth rate of 0.03 a−1. Results show that decentralisation in expansion areas with higher densification can outperform the hydraulic performance and structural vulnerability of expanding centralised sanitary wastewater infrastructure. Decentralised systems can also offer economic advantages when capital expenditure costs for small-scale wastewater treatment plants are significantly reduced compared to current costs, particularly at higher discount rates, e.g. reaping effects of economies of scale. The findings of this study emphasise the potential of transition pathways towards decentralisation in urban water infrastructures and the value of models that allow the exploration of this transition space.
Fappiano, F., Maurer, M., & Leitão, J. P. (2024). Coupling sewers to the surface: systematic approaches to correcting data discrepancies for 1D-2D drainage modelling. Journal of Hydrology, 645, 132239 (14 pp.). doi:10.1016/j.jhydrol.2024.132239, Institutional Repository

One-dimensional (1D) drainage models coupled with two-dimensional (2D) surface models have been recognised as most suitable for simulating pluvial floods in urban areas. However, discrepancies between the 1D drainage network and the 2D Digital Terrain Model (DTM) surface elevation datasets are often observed. These incompatibilities are commonly adjusted manually, impacting the transparency and reproducibility of flood modelling results. No automated and reproducible methods are currently available to ensure compatibility and consistency between a 1D drainage network and the 2D DTM elevation dataset. We present two methods for automatically correcting these discrepancies: Method 1, which corrects the discrepancies by editing the 1D drainage network, and Method 2, which corrects the discrepancies by editing the elevation of the 2D surface. Both methods ensure 1D–2D data compatibility. The two methods were evaluated through hydraulic and hydrological simulations using a synthetic case study, demonstrating strong performance for both approaches. Additionally, we propose a procedure to guide practitioners and modellers in discerning between 1D data and 2D data errors and choosing which method to use. This procedure is demonstrated for a real case study. The two presented methodologies can improve the process of creating 1D-2D urban drainage models and their reproducibility.
Hassoun, L., & Cook, L. M. (2024). Global analysis of combined photovoltaic green and cool roofs under climate change. Advanced Sustainable Systems. doi:10.1002/adsu.202400097, Institutional Repository

Sustainable roofing configurations, including green and white roofs, can reduce rooftop surface temperatures compared to conventional surfaces and can therefore enhance photovoltaic (PV) system performance due to the temperature dependence of PV cells. Previous research, primarily experimental, recognized the synergy of combining PV with green or cool roofs. However, the influence of geographic and climatic factors on the performance of these combined systems, particularly in future climates affected by climate change, remains unclear. This work integrates three roof configurations (gravel, green, and white) into rooftop solar energy modeling across thirteen cities with different climate types, under current and future climate scenarios. Results indicate limited efficiency gains (< 2%) across all cities and climates, challenging previous findings. Yield is expected to increase in some cities receiving more solar irradiation in the future but decrease in others due to rising temperatures. Green and cool roofs can partially offset the effects of climate change on yield. PV-white roofs consistently outperform PV-green roofs, with the performance gap expected to widen in future climates. PV-green roofs excel in tropical climates with high irradiation and precipitation levels. Overall, the outcomes of this study inform the design and planning of sustainable buildings in response to climate change challenges.
Ho, H. C., & Altermatt, F. (2024). Predicted community consequences of spatially explicit global change-induced processes on plant–insect networks. Ecology and Evolution, 14(9), e70272 (13 pp.). doi:10.1002/ece3.70272, Institutional Repository

Plant–insect trophic systems should be particularly sensitive to processes altering species spatial co-occurrences, as impacts on one level can cascade effectively through the strong trophic reliance to the other level. Here, we predicted the biogeography of Lepidoptera–plant communities under global-change scenarios, exploiting spatially resolved data on 423 Lepidoptera species and their 848 food plants across the German state of Baden-Württemberg (ca. 36,000 km2). We performed simulations of plant extinction and Lepidoptera expansion, and respectively assessed their cascading consequences—namely secondary extinction of Lepidoptera and change in functional distance of plants—on the interaction networks. Importantly, the simulations were spatially explicit, as we accounted for realistic landscape contexts of both processes: Plant extinctions were simulated as "regional" (a species goes extinct in the whole region at once) vs. "isolation-driven" (a species gradually goes extinct from the peripheral or isolated localities according to its real regional distribution); Lepidoptera expansions were simulated with random, northward, and upward directions according to real topography. The consequences were assessed based on empirical community composition and trophic relationships. When evaluated by regional richness, the robustness of Lepidoptera assemblages against secondary extinctions was higher under isolation-driven plant extinctions than regional plant extinction; however, this relationship was reversed when evaluated by averaged local richness. Also, with isolation-driven plant extinctions, Lepidoptera at the central sub-region of Baden-Württemberg appeared to be especially vulnerable. With Lepidoptera expansions, plants' functional distances in local communities dropped, indicating a possible increase of competition among plants, yet to a lesser extent particularly with upward movements. Together, our results suggested that the communities' composition context at the landscape scale (i.e., how communities, with respective species composition, are arranged within the landscape) matters when assessing global-change influences on interaction systems; spatially explicit consideration of such context can reveal localised consequences that are not necessarily captured via a spatially implicit, regional perspective.
Jensen, D. M. R., Mutzner, L., Wei, Y., Mikkelsen, P. S., & Vezzaro, L. (2024). Temporal variations in micropollutant inlet concentrations matter when planning the design and compliance assessment of stormwater control measures. Journal of Environmental Management, 356, 120583 (10 pp.). doi:10.1016/j.jenvman.2024.120583, Institutional Repository

Stormwater Control Measures (SCMs) contribute to reducing micropollutant emissions from separate sewer systems. SCM planning and design are often performed by looking at the hydrological performance. Assessment of pollutant removal and the ability to comply with discharge concentration limits is often simplified due to a lack of data and limited monitoring resources. This study analyses the impact of using different time resolutions of input stormwater concentrations when assessing the compliance of SCMs against water quality standards. The behaviour of three indicator micropollutants (MP - Copper, Diuron, Benzo[a]pyrene) was assessed in four SCM archetypes, which were defined to represent typical SCM removal processes. High resolution MP data were extrapolated by using high resolution (2 min) measurements of TSS over a long period (343 events). The compliance assessment showed that high resolution input concentrations can result in a different level of compliance with water quality standards, especially when discharged concentrations are close to the limit values. This study underlines the importance of considering the high temporal variability of stormwater micropollutants when planning and designing SCMs to identify the most effective solutions for stormwater pollution management and to ensure a thorough consideration of all the environmental implications.
Julian, T. R., Devaux, A. J., Brülisauer, L., Conforti, S., Rusch, J. C., Gan, C., … Ort, C. (2024). Monitoring an emergent pathogen at low incidence in wastewater using qPCR: Mpox in Switzerland. Food and Environmental Virology, 16, 269-279. doi:10.1007/s12560-024-09603-5, Institutional Repository

Wastewater-based epidemiology offers a complementary approach to clinical case-based surveillance of emergent diseases and can help identify regions with infected people to prioritize clinical surveillance strategies. However, tracking emergent diseases in wastewater requires reliance on novel testing assays with uncertain sensitivity and specificity. Limited pathogen shedding may cause detection to be below the limit of quantification or bordering the limit of detection. Here, we investigated how the definition of limit of detection for quantitative polymerase chain reaction (qPCR) impacts epidemiological insights during an mpox outbreak in Switzerland. 365 wastewater samples from three wastewater treatment plants in Switzerland from 9 March through 31 October 2022 were analyzed for mpox DNA using qPCR. We detected mpox DNA in 22% (79 of 365) wastewater samples based on a liberal definition of qPCR detection as any exponentially increasing fluorescence above the threshold. Based on a more restrictive definition as the lowest concentration at which there is 95% likelihood of detection, detection was 1% (5 of 365). The liberal definition shows high specificity (90%) and accuracy (78%), but moderate sensitivity (64%) when benchmarked against available clinical case reporting, which contrasts with higher specificity (98%) but lower sensitivity (10%) and accuracy (56%) of the 95% likelihood definition. Wastewater-based epidemiology applied to an emergent pathogen will require optimizing public health trade-offs between reporting data with high degrees of uncertainty and delaying communication and associated action. Information sharing with relevant public health stakeholders could couple early results with clear descriptions of uncertainty. Impact Statement: When a novel pathogen threatens to enter a community, wastewater-based epidemiology offers an opportunity to track its emergence and spread. However, rapid deployment of methods for to detect a novel pathogen may rely on assays with uncertain sensitivity and specificity. Benchmarking the detection of mpox DNA in Swiss wastewaters with reported clinical cases in 2022, we demonstrate how definitions of detection of a qPCR assay influence epidemiological insights from wastewater. The results highlight the need for information sharing between public health stakeholders that couple early insights from wastewater with descriptions of methodological uncertainty to optimize public health actions.
Lechevallier, P., Villez, K., Felsheim, C., & Rieckermann, J. (2024). Towards non-contact pollution monitoring in sewers with hyperspectral imaging. Environmental Science: Water Research and Technology, 10(5), 1160-1170. doi:10.1039/D3EW00541K, Institutional Repository

Monitoring water quality in sewers is challenging, particularly because state-of-the-art technologies require contact with the raw wastewater. The presence of fat, oil, grease, and solids makes automated grab sampling difficult and causes sensor fouling. To overcome these limitations, non-contact methods based on light reflectance, such as hyperspectral imaging (HSI), are gaining attention. However, HSI has never been tested for raw wastewater. To assess its accuracy for measuring pollution, we developed a laboratory setup and performed targeted experiments with a combination of raw and diluted wastewater, as well as synthetic turbidity stock solutions. We measured seven pollution variables: chemical oxygen demand, turbidity, dissolved organic compounds, ammonium, total nitrogen, phosphate, and sulphates. We used automated pixel selection and partial least squares regression to retrieve pollution information from the hyperspectral images. Our results, based on 144 samples, suggest that HSI can estimate pollution levels with a precision in the range of state-of-the-art absorbance spectrophotometric methods. Additionally, we found that the combination of pixel and wavelength selection, enabled by the hyperspectral data structure, significantly influences the performance of partial least square modelling. Overall, our findings indicate that HSI is a promising technology for non-contact monitoring of water quality in raw wastewater.
Li, S., Leitão, J. P., Wang, Z., & Bach, P. M. (2024). A drainage network-based impact matrix to support targeted blue-green-grey stormwater management solutions. Science of the Total Environment, 912, 168623 (10 pp.). doi:10.1016/j.scitotenv.2023.168623, Institutional Repository

Urban floods will continue to be an alarming issue worldwide due to climate change and urban expansion. The costly and less environmentally friendly grey infrastructure is not always the most adequate solution to resolve urban pluvial flooding issues. The combination of grey and blue-green infrastructures, also called hybrid infrastructure, has been considered a promising solution for urban stormwater management. Existing approaches for identifying suitable hybrid solutions frequently rely on global multi-objective optimization algorithms. We developed a pre-screening method that decomposes a drainage network into clusters of pipes connected to sub-catchments, based on pipe hydraulic characteristic that allows for the impact of infrastructure combinations (blue-green and grey) to be mapped. Four impact matrices are proposed to map the total, local, upstream, and downstream flood reduction of all possible blue-green, grey, and hybrid solutions. Using an urban catchment in Guangzhou (China) as a case study, results showed that such an exercise could identify prime candidate locations for blue-green and grey infrastructure while filtering out ineffective locations for flood reduction. Furthermore, the impact matrices enabled the identification of flood zones where blue-green infrastructure could handle flood mitigation without the need of local grey infrastructure upgrades. As such, they are not only useful for quick screening of suitable interventions for each flooded zone, but can also potentially serve as a priori knowledge before diving into the data and computationally expensive process of finding the most effective flood mitigation solutions.
Liu, H., Kong, F., Yin, H., Cook, L. M., Huang, J., Lensky, I. M., & Tan, T. (2024). Substrate microorganisms can be an ideal tool for improving green roof sustainability. Urban Forestry and Urban Greening, 91, 128179 (11 pp.). doi:10.1016/j.ufug.2023.128179, Institutional Repository

Green roof (GR) ecosystems function as a result of a series of complex interactions between plants and substrates, which are poorly understood. This ambiguity hinders the scientific application of GRs to tackle urban eco-environmental problems. The goal of this study was to clarify the response of plants to various abiotic and biotic characteristics of GR substrates, including physical properties, nutrients, and microbes. We systematically analyzed the statistical association between 43 indicators of plant and substrate characteristics using four GR substrates and one common plant type (Sedum lineare Thunb) in a subtropical monsoon climate. Height, coverage, root-shoot ratio, total carbon, total nitrogen, total phosphorus, total potassium, root vitality, and malondialdehyde levels were considered sensitive indicators of the plant's response to the substrate. Substrate nutrients and microbial biomass were most closely associated with the plant response, whereas the material properties of the substrate could indirectly affect plant growth by influencing substrate microbes and plant nutrients. Our results suggest that when optimizing GR substrates for plant responses, it is necessary to respect the plants and account for the abiotic and biotic relationships. It may be possible to manage GR performance in a more scientific and effective manner through the control of substrate microorganisms.
Martínez-Núñez, C., Casanelles Abella, J., Frey, D., Zanetta, A., & Moretti, M. (2024). Local and landscape factors shape alpha and beta trophic interaction diversity in urban gardens. Proceedings of the Royal Society B: Biological Sciences, 291(2023), 20232501 (12 pp.). doi:10.1098/rspb.2023.2501, Institutional Repository

Promoting urban green spaces is an effective strategy to increase biodiversity in cities. However, our understanding of how local and landscape factors influence trophic interactions in these urban contexts remains limited. Here, we sampled cavity-nesting bees and wasps and their natural enemies within 85 urban gardens in Zurich (Switzerland) to identify factors associated with the diversity and dissimilarity of antagonistic interactions in these communities. The proportions of built-up area and urban green area at small landscape scales (50 m radius), as well as the management intensity, sun exposure, plant richness and proportion of agricultural land at the landscape scale (250 m radius), were key drivers of interaction diversity. This increased interaction diversity resulted not only from the higher richness of host and natural enemy species, but also from species participating in more interactions. Furthermore, dissimilarity in community structure and interactions across gardens (beta-diversity) were primarily influenced by differences in built-up areas and urban green areas at the landscape scale, as well as by management intensity. Our study offers crucial insights for urban planning and conservation strategies, supporting sustainability goals by helping to understand the factors that shape insect communities and their trophic interactions in urban gardens.
Micev, K., Steiner, J., Aydin, A., Rieckermann, J., & Delbruck, T. (2024). Measuring diameters and velocities of artificial raindrops with a neuromorphic event camera. Atmospheric Measurement Techniques, 17(1), 335-357. doi:10.5194/amt-17-335-2024, Institutional Repository

Hydrometers that measure size and velocity distributions of precipitation are needed for research and corrections of rainfall estimates from weather radars and microwave links. Existing optical disdrometers measure droplet size distributions, but underestimate small raindrops and are impractical for widespread always-on IoT deployment. We study the feasibility of measuring droplet size and velocity using a neuromorphic event camera. These dynamic vision sensors asynchronously output a sparse stream of pixel brightness changes. Droplets falling through the plane of focus of a steeply down-looking camera create events generated by the motion of the droplet across the field of view. Droplet size and speed are inferred from the hourglass-shaped stream of events. Using an improved hard disk arm actuator to reliably generate artificial raindrops with a range of small sizes, our experiments show maximum errors of 7% (mean absolute percentage error) for droplet sizes from 0.3 to 2.5mm and speeds from 1.3 to 8.0ms-1. Measurements with the same setup from a commercial PARSIVEL disdrometer show similar results. Both devices slightly overestimate the small droplet volume with a volume overestimation of 25% from the event camera measurements and 50% from the PARSIVEL instrument. Each droplet requires processing of 5000 to 50000 brightness change events, potentially enabling low-power always-on disdrometers that consume power proportional to the rainfall rate. Data and code are available at the paper website https://sites.google.com/view/dvs-disdrometer/home .
Nadeau, S., Devaux, A. J., Bagutti, C., Alt, M., Hampe, E. I., Kraus, M., … Stadler, T. (2024). Influenza transmission dynamics quantified from RNA in wastewater in Switzerland. Swiss Medical Weekly, 154(1), 3503 (20 pp.). doi:10.57187/s.3503, Institutional Repository

INTRODUCTION: Influenza infections are challenging to monitor at the population level due to many mild and asymptomatic cases and similar symptoms to other common circulating respiratory diseases, including COVID-19. Methods for tracking cases outside of typical reporting infrastructure could improve monitoring of influenza transmission dynamics. Influenza shedding into wastewater represents a promising source of information where quantification is unbiased by testing or treatment-seeking behaviours. METHODS: We quantified influenza A and B virus loads from influent at Switzerland's three largest wastewater treatment plants, serving about 14% of the Swiss population (1.2 million individuals). We estimated trends in infection incidence and the effective reproductive number (Re) in these catchments during a 2021/22 epidemic and compared our estimates to typical influenza surveillance data. RESULTS: Wastewater data captured the same overall trends in infection incidence as laboratory-confirmed case data at the catchment level. However, the wastewater data were more sensitive in capturing a transient peak in incidence in December 2021 than the case data. The Reestimated from the wastewater data was roughly at or below the epidemic threshold of 1 during work-from-home measures in December 2021 but increased to at or above the epidemic threshold in two of the three catchments after the relaxation of these measures. The third catchment yielded qualitatively the same results but with wider confidence intervals. The confirmed case data at the catchment level yielded comparatively less precise R_e estimates before and during the work-from-home period, with confidence intervals that included one before and during the work-fromhome period. DISCUSSION: Overall, we show that influenza RNA in wastewater can help monitor nationwide influenza transmission dynamics. Based on this research, we developed an online dashboard for ongoing wastewater-based influenza surveillance in Switzerland.
Peleg, N., Wright, D. B., Fowler, H. J., Leitão, J. P., Sharma, A., & Marra, F. (2024). A simple and robust approach for adapting design storms to assess climate-induced changes in flash flood hazard. Advances in Water Resources, 193, 104823 (9 pp.). doi:10.1016/j.advwatres.2024.104823, Institutional Repository

Hydrologists and civil engineers often use design storms to assess flash flood hazards in urban, rural, and mountainous catchments. These synthetic storms are not representations of real extreme rainfall events, but rather simplified versions parameterized to mimic extreme precipitation statistics often obtained from intensity–duration–frequency (IDF) curves. To construct design storms for the future climate, it is thus necessary first to recalculate IDF curves to represent rainfall under warmer conditions. We propose a framework for adjusting IDF curves and design storms to future climate conditions using the TENAX model, a novel statistical approach that can provide future short-duration precipitation return levels based on projected temperature changes. For most applications, information from climate models at the daily scale can be used to construct design storms at the sub-hourly scale without any downscaling or bias adjustment. Our approach is illustrated through a re-parameterization of the Chicago Design Storm (CDS) in the context of climate change. As a case study demonstration, we apply the TENAX model to data from the city of Zurich to calculate changes in the historical IDF curve for durations ranging from 10 min to 3 h. We then construct synthetic 100-year return period design storms based on the CDS for present and future climates and use the CAFlood model to produce flood inundation maps to assess changes in flood hazard. The codes for adapting design storms to climate change are simple to implement, easily applicable by practitioners, and made freely available.
Perrelet, K., Moretti, M., Dietzel, A., Altermatt, F., & Cook, L. M. (2024). Engineering blue-green infrastructure for and with biodiversity in cities. npj Urban Sustainability, 4(1), 27 (11 pp.). doi:10.1038/s42949-024-00163-y, Institutional Repository

Blue-green infrastructure (BGI), combining semi-natural and engineered elements, offers multifaceted benefits like stormwater management, water purification, heat mitigation, and habitat provision. However, current BGI designs prioritize engineering goals, overlooking its ecological potential. Here we advocate for integrating engineering and ecological objectives into BGI design to enhance performance and biodiversity. Through an interdisciplinary literature review, we emphasize the importance of species diversity, abundance, and ecological processes, to improve engineering performance and resilience, and lower management costs. We emphasize the importance of interdisciplinary collaboration to navigate trade-offs between engineering and ecological objectives, ultimately enabling us to engineer both for and with biodiversity.
Poggioli, M., Cavadini, G. B., Zheng, Z., Rodriguez, M., & Mutzner, L. (2024). The impact of blue-green infrastructure on trace contaminants: a catchment-wide assessment. Water Research X, 25, 100261 (11 pp.). doi:10.1016/j.wroa.2024.100261, Institutional Repository

Blue-green infrastructure (BGI) reduce urban combined sewer overflows (CSOs) and stormwater outlets (SWOs). However, most conventional BGI are not designed to remove trace organic contaminants. Little is known about the potential of conventional BGI to improve surface water quality by reducing the discharge of trace organic contaminants. We derived wash-off loads for street runoff (6PPD-q, DPG, and HMMM), construction materials (diuron), and wastewater-derived contaminants (diclofenac) based on measurements in the combined sewer system. Subsequently, the performance of four BGI types (bioretention cells, green roofs, porous pavements, and urban wetlands) to reduce the discharge of trace organic contaminants via SWOs and CSOs was quantified with a hydrodynamic SWMM model. Moreover, the catchment-wide impact of SWOs and CSOs on surface water was assessed using risk quotients. We found that the annually discharged load can be considerably reduced by implementing BGI. Among the studied BGI types, bioretention cells are the most effective, with a load reduction of up to 80% to surface waters, mainly due to a larger suitable implementation area and a substantial stormwater infiltration. BGI implemented in the separate sewer system are more effective in reducing stormwater contaminant loads than BGI in the combined system. The assessment of the risk quotient in the surface water showed that the concentrations during SWO and CSO discharges exceed the acute environmental threshold in the surface water for 6PPD-q, DPG, diuron, and diclofenac during several events. The implementation of BGI reduced the hours of exceeded risk quotient in the surface water by 93% for bioretention cells. These findings underscore the need for a catchment-wide assessment of future BGI implementations to quantify, manage, and mitigate the impacts of urban pollution.
Regueiro-Picallo, M., Langeveld, J., Wei, H., Bertrand-Krajewski, J. L., & Rieckermann, J. (2024). Combining a daily temperature pattern analysis and a heat-pulse system to estimate sediment depths in sewer systems. Environmental Science: Water Research and Technology, 10(4), 922-935. doi:10.1039/d3ew00825h, Institutional Repository

Sediments in urban drainage systems (UDS) significantly impact their operation, so effective strategies are required to reduce their negative effects. Monitoring sediment accumulation provides valuable insights into sediment characteristics, sediment transport dynamics, and system performance. However, the effectiveness of monitoring systems is limited due to cost constraints and installation challenges. This study describes the development and application of a new system based on temperature dynamics to measure sediment depths in sewer systems. The methodology involves the analysis of temperature time series under dry weather flow conditions to identify harmonic patterns between wastewater and sediment-bed temperatures. These patterns are increasingly attenuated by increasing sediment depth. This study combines a system called MONitoring Temperatures in SEdiments (MONTSE), which integrates a dual-probe heat-pulse (DPHP) method to characterize sediment thermal properties, and a surrogate model, which includes temperature pattern analysis, to estimate sediment depths. Likewise, laboratory-scale experiments were performed to validate the temperature monitoring system and the surrogate model performance. The maximum absolute errors in measured sediment depths were less than 22 mm, and the uncertainty of the system was estimated at ±7.3 mm. Groundbreaking measurements of thermal properties of UDS sediments were also reported. Reliable information on sediment depths and properties was provided, so the system could significantly optimize sewer system operation and cleaning strategies.
Rodriguez, M., Cavadini, G. B., & Cook, L. M. (2024). Do baseline assumptions alter the efficacy of green stormwater infrastructure to reduce combined sewer overflows?. Water Research, 253, 121284 (12 pp.). doi:10.1016/j.watres.2024.121284, Institutional Repository

Green stormwater infrastructure (GSI) is growing in popularity to reduce combined sewer overflows (CSOs) and hydrologic simulation models are a tool to assess their reduction potential. Given the numerous and interacting water flows that contribute to CSOs, such as evapotranspiration (ET) and groundwater (GW), these models should ideally account for them. However, due to the complexity, simplified models are often used, and it is currently unknown how these assumptions affect estimates of CSOs, GSI effectiveness, and ultimately planning guidance. This study evaluates the effect on estimates of CSOs and GSI effectiveness when different flows and hydrologic processes are neglected. We modified an existing EPA SWMM model of a combined sewer system in Switzerland to include ET, GW, and upstream inflows. Historical rainfall data over 30 years are used to assess volume and duration of CSOs with and without three types of GSI (bioretention basins, permeable pavements and green roofs). Results demonstrate that neglect of certain flows in modelling can alter CSO volumes from -15 % to 40 %. GSI effectiveness also varies considerably, resulting in differences in simulated percent of CSO volume reduced from 8 % to 35 %, depending on the GSI type and modeled flow or process. Representation of GW within models is particularly crucial when infiltrating GSI are present, as CSOs could increase in certain subcatchments due to higher GW levels from increased infiltration. When basing GSI planning decisions on modeled estimates of CSOs, all relevant hydrologic processes should be included to the extent possible, and uncertainty and assumptions should always be considered.
Rodriguez, M., Fu, G., Butler, D., Yuan, Z., & Cook, L. (2024). The effect of green infrastructure on resilience performance in combined sewer systems under climate change. Journal of Environmental Management, 353, 120229 (12 pp.). doi:10.1016/j.jenvman.2024.120229, Institutional Repository

Climate change is currently reshaping precipitation patterns, intensifying extremes, and altering runoff dynamics. Particularly susceptible to these impacts are combined sewer systems (CSS), which convey both stormwater and wastewater and can lead to combined sewer overflow (CSO) discharges during heavy rainfall. Green infrastructure (GI) can help mitigate these discharges and enhance system resilience under historical conditions; however, the quantification of its effect on resilience in a future climate remains unknown in the literature. This study employs a modified Global Resilience Analysis (GRA) framework for continuous simulation to quantify the impact of climate change on CSS resilience, particularly CSOs. The study assesses the efficacy of GI interventions (green roofs, permeable pavements, and bioretention cells) under diverse future rainfall scenarios based on EURO-CORDEX regional climate models (2085–2099) and three Representative Concentration Pathways (2.6, 4.5, 8.5 W/m2). The findings underscore a general decline in resilience indices across the future rainfall scenarios considered. Notably, the total yearly CSO discharge volume increases by a range of 145 % to 256 % in response to different rainfall scenarios. While GI proves effective in increasing resilience, it falls short of offsetting the impacts of climate change. Among the GI options assessed, green roofs routed to pervious areas exhibit the highest adaptive capacity, ranging from 9 % to 22 % at a system level, followed by permeable pavements with an adaptation capacity between 7 and 13 %. By linking the effects of future rainfall scenarios on CSO performance, this study contributes to understanding GI's potential as a strategic tool for enhancing urban resilience.
Salgueiro-Gonzalez, N., Béen, F., Bijlsma, L., Boogaerts, T., Covaci, A., Baz-Lomba, J. A., … Castiglioni, S. (2024). Influent wastewater analysis to investigate emerging trends of new psychoactive substances use in Europe. Water Research, 254, 121390 (11 pp.). doi:10.1016/j.watres.2024.121390, Institutional Repository

Wastewater-based epidemiology (WBE) can provide objective and timely information on the use of new psychoactive substances (NPS), originally designed as legal alternatives of internationally controlled drugs. NPS have rapidly emerged on the global drug market, posing a challenge to drug policy and constituting a risk to public health. In this study, a WBE approach was applied to monitor the use of more than 300 NPS, together with fentanyl and its main metabolite norfentanyl, in influent wastewater collected from 12 European cities during March-June 2021. Quantitative and qualitative analysis of NPS in composite 24 h influent wastewater samples were based on solid phase extraction and liquid chromatography-mass spectrometry. In-sample stability tests demonstrated the suitability of most investigated biomarkers, except for a few synthetic opioids, synthetic cannabinoids and phenetylamines. Fentanyl, norfentanyl and eight NPS were quantified in influent wastewater and at least three substances were found in each city, demonstrating their use in Europe. N,N-dimethyltryptamine and 3-methylmethcathinone (3-MMC) were the most common NPS found, with the latter having the highest mass loads (up to 24.8 mg/day/1000 inhabitants). Seven additional substances, belonging to five categories of NPS, were identified in different cities. Spatial trends of NPS use were observed between cities and countries, and a changing weekly profile of use was observed for 3-MMC. WBE is a useful tool to rapidly evaluate emerging trends of NPS use, complementing common indicators (i.e. population surveys, seizures) and helping to establish measures for public health protection.
Van Wichelen, N., Burgard, D., Campos-Mañas, M. C., Simarro-Gimeno, C., Hernández, F., Ort, C., … Bijlsma, L. (2024). A key factor in monitoring cannabis consumption trends through wastewater analysis: partitioning of THC[sbnd]COOH between the liquid and solid phase of influent wastewater. Water Research, 267, 122462 (9 pp.). doi:10.1016/j.watres.2024.122462, Institutional Repository

Current wastewater-based epidemiology (WBE) studies are predominantly focused on the analysis of urinary biomarkers present in the liquid phase of influent wastewater (IWW). This approach systematically underestimates less polar metabolites, such as cannabis biomarkers. These biomarkers can potentially sorb to and desorb from suspended particulate matter (SPM) present in IWW. This study investigates the bidirectional partitioning of THC[sbnd]COOH between the liquid phase and SPM of IWW by performing multiple sorption experiments using THC[sbnd]COOH-D9 as a surrogate due to the unavailability of blank SPM and blank IWW. In addition, this study involves the analysis of IWW collected from eight wastewater treatment plants (WWTP) (n = 56) across four European countries, where raw IWW, the liquid phase and SPM were separately analysed to identify potential trends in the fraction of THC[sbnd]COOH in the SPM between and withing the examined locations. Based on the performed sorption experiments, bidirectional partitioning of THC[sbnd]COOH between the liquid phase and SPM was noted, showing partition between both phases when only one of the two phases was spiked. It was illustrated that the concentration of SPM had a notable influence on the THC[sbnd]COOH partitioning between both phases. In addition to the inter location variability (average THC[sbnd]COOH present in the SPM ranged 31–59 %), a substantial intra location variation was also observed, e.g., in one location ranged 17–58 %. While the determination of a correction factor for the amount of THC[sbnd]COOH present in SPM would be ideal, this is challenging, since the amount of SPM is not fixed in all IWW samples. Although SPM has influence on the THC[sbnd]COOH partition, no correlation (p value Spearman correlation = 0.3160) was observed between the SPM concentration and the fraction of THC[sbnd]COOH in the solid phase. Moreover, the collection of homogenized samples is difficult, and the time required to reach an equilibration in partitioning of THC[sbnd]COOH between both IWW phases remains unclear. Due to i) the large inter-and intra-location variation of THC[sbnd]COOH present in the SPM, ii) the variability in SPM concentration in IWW samples, and iii) the time required to reach a partitioning equilibration, an analytical procedure based on liquid-liquid extraction (LLE) that considers both the liquid phase and SPM of IWW is recommended to reduce the overall uncertainty for THC[sbnd]COOH measurement in IWW. It was illustrated that this extraction method is capable to recover the total concentration of THC[sbnd]COOH in both phases.
Wang, W., Wang, Z., Guan, M., Wani, O., Bai, Y., Wang, K., & Leitao, J. P. (2024). Evaluation of stormwater mitigation performance with LID infrastructures, in-sewer space, and real-time control strategies. Journal of Hydrologic Engineering, 29(5), 05024016 (14 pp.). doi:10.1061/JHYEFF.HEENG-6185, Institutional Repository

Because low-impact development (LID) infrastructures are effective in capturing and utilizing urban stormwater runoff, real-time control (RTC) strategies can be viewed as soft infrastructures, offering an additional suite of opportunities to improve the stormwater drainage system performance. However, there is a paucity of analyses that disentangle the contributing sources of performance enhancement in these joint hard-soft stormwater infrastructures. This study evaluated the stormwater system performance with integrated LID infrastructures, in-sewer storage capacity, and RTC strategies. Our results showed that LID infrastructures play a major role in mitigating the flow peaks and high volumes, whereas adding RTC to the storage unit and in-sewer flow control mostly improves the performance under relatively small rainfall events, e.g., those with a 3-year return period. The critical contribution of RTC is realized by utilizing storage units as much as possible during the rainfall event. A downstream storage unit that is too close to the outfall may not be suitable for RTC application together with in-sewer capacity utilization. In the present case, the combination of a midstream storage unit and upstream control site at branch pipes worked better than those at other locations. The results suggest that the integration of LID and RTC on a simulation-system level can enhance the reduction of the peak runoff and total discharged volume only if the storage site and control locations are selected carefully.
Yin, Y., Zhang, D., Zhu, M., Zhang, H., Liu, D., Sun, Y., … Wang, P. (2024). Improving land-use efficiency of solar power in China and policy implications. Solar Energy, 280, 112867 (11 pp.). doi:10.1016/j.solener.2024.112867, Institutional Repository

Improving the power output of solar photovoltaic (PV) farms is critical to maximize the potential of PV power and reduce extensive land use in the context of large-scale deployment of renewable energy. In this paper we developed an integrated solar power potential assessment framework to quantify the gap between technical potential and actual generation of solar PV farms on national, provincial, and plant scales, and identify the key factors that cause the underperformance of PV farms. Specifically, to assess technical potential, hourly meteorological reanalysis data is adopted and the power generation-related parameters are calculated (i.e. optimal panel tilt, array spacing, packing factor etc.) with consideration of their spatial variability. For actual power generation, a detailed plant-level dataset is first established by this study which integrates technical, operational, and geospatial information from 145 solar farms across seven provinces in China. Our results show that the actual PV power generation per square meter is only 1/3 of the estimated technical potential. Technological factor is the primary factor, accounting for 48.43% of the underperformance, followed by engineering and management factors, accounting for 38.55% and 13.02%, respectively. The novelty of our study is revealing the underperformance level of solar farms in reality, identifying the causing factors, and highlighting the importance of integrating land-use efficiency indicators into solar power planning and development.
Zhang, K., Zheng, Z., Mutzner, L., Shi, B., McCarthy, D., Le-Clech, P., … Deletic, A. (2024). Review of trace organic chemicals in urban stormwater: concentrations, distributions, risks, and drivers. Water Research, 258, 121782 (11 pp.). doi:10.1016/j.watres.2024.121782, Institutional Repository

Urban stormwater, increasingly seen as a potential water resource for cities and towns, contains various trace organic chemicals (TrOCs). This study, conducted through a comprehensive literature review of 116 publications, provides a detailed report on the occurrence, concentration distribution, health, and ecological risks of TrOCs, as well as the impact of land use and rainfall characteristics on their concentrations. The review uncovers a total of 629 TrOCs detected at least once in urban stormwater, including 228 pesticides, 132 pharmaceutical and personal care products (PPCPs), 29 polycyclic aromatic hydrocarbons (PAHs), 30 per- and polyfluorinated substances (PFAS), 28 flame retardants, 24 plasticizers, 22 polychlorinated biphenyls (PCBs), nine corrosion inhibitors, and 127 other industrial chemicals/intermediates/solvents. Concentration distributions were explored, with the best fit being log-normal distribution. Risk assessment highlighted 82 TrOCs with high ecological risk quotients (ERQ > 1.0) and three with potential health risk quotients (HQ > 1.0). Notably, 14 TrOCs (including six PAHs, five pesticides, three flame-retardants, and one plasticizer) out of 68 analyzed were significantly influenced by land-use type. Relatively weak relationships were observed between rainfall characteristics and pollutant concentrations, warranting further investigation. This study provides essential information about the occurrence and risks of TrOCs in urban stormwater, offering valuable insights for managing these emerging chemicals of concern.
Back, Y., Kumar, P., Bach, P. M., Rauch, W., & Kleidorfer, M. (2023). Integrating CFD-GIS modelling to refine urban heat and thermal comfort assessment. Science of the Total Environment, 858(1), 159729 (14 pp.). doi:10.1016/j.scitotenv.2022.159729, Institutional Repository

Constant urban growth exacerbates the demand for residential, commercial and traffic areas, leading to progressive surface sealing and urban densification. With climate change altering precipitation and temperature patterns worldwide, cities are exposed to multiple risks, demanding holistic and anticipatory urban planning strategies and adaptive measures that are multi-beneficial. Sustainable urban planning requires comprehensive tools that account for different aspects and boundary conditions and are capable of mapping and assessing crucial processes of land-atmosphere interactions and the impacts of adaptation measures on the urban climate system. Here, we combine Computational Fluid Dynamics (CFD) and Geographic Information System (GIS) capabilities to refine an existing 2D urban micro- and bioclimatic modelling approach. In particular, we account for the vertical and horizontal variability in wind speed and air temperature patterns in the urban canopy layer. Our results highlight the importance of variability of these patterns in analysing urban heat development, intensity and thermal comfort at multiple heights from the ground surface. Neglecting vertical and horizontal variability, non-integrated CFD modelling underestimates mean land surface temperature by 7.8 °C and the Universal Thermal Climate Index by 6.9 °C compared to CFD-integrated modelling. Due to the strong implications of wind and air temperature patterns on the relationship between surface temperature and human thermal comfort, we urge caution when relying on studies solely based on surface temperatures for urban heat assessment and hot spot analysis as this could lead to misinterpretations of hot and cool spots in cities and, thus, mask the anticipated effects of adaptation measures. The integrated CFD-GIS modelling approach, which we demonstrate, improves urban climate studies and supports more comprehensive assessments of urban heat and human thermal comfort to sustainably develop resilient cities.
Blach, T., Lechevallier, P., & Engelhart, M. (2023). Effect of temperature during the hydrothermal carbonization of sewage sludge on the aerobic treatment of the produced process waters. Journal of Water Process Engineering, 51, 103368 (9 pp.). doi:10.1016/j.jwpe.2022.103368, Institutional Repository

The treatment of the highly contaminated process water produced during the hydrothermal carbonization (HTC) of waste activated sludge is of major concern for the full-scale implementation of the HTC process. So far, no satisfying treatment strategies have been elaborated and the biodegradability under aerobic conditions has hardly been studied. To fill these gaps, aerobic tests were first carried out in batches with HTC process waters produced at 190 °C, 218 °C and 249 °C, and two parallel sequencing batch reactors (SBR) were operated to treat process waters produced at 190 °C and 217 °C. Both experiments show that the HTC temperature has only a little effect on the elimination of dissolved organic carbon (DOC). In the aerobic batch tests, DOC removal were 80.5–81.9 %. In the SBR, 28 % of the initial DOC was found to be recalcitrant, and 25–28 % of the initial nitrogen. In the SBR experiments, the nitrification was also monitored, and nitrification inhibition test were conducted on both process waters obtained at 190 °C and 217 °C. Nitrification the initial SBR reactors was only possible after dilution of the process waters, which indicates the presence of inhibiting substances. The inhibition tests validated those observations, and showed that process waters derived at 217 °C had a higher inhibition potential. This study demonstrates that aerobically treated HTC process waters are still too polluted to be discharged in a wastewater treatment plant: model calculations showed an increase in effluent DOC of 8.3 mg C/L.
Boebel, M., Frei, F., Blumensaat, F., Ebi, C., Meli, M. L., & Rüst, A. (2023). Batteryless sensor devices for underground infrastructure - a long-term experiment on urban water pipes. Journal of Low Power Electronics and Applications, 13(2), 31 (23 pp.). doi:10.3390/jlpea13020031, Institutional Repository

Drinking water is becoming increasingly scarce as the world’s population grows and climate change continues. However, there is great potential to improve drinking water pipelines, as 30% of fresh water is lost between the supplier and consumer. While systematic process monitoring could play a crucial role in the early detection and repair of leaks, current practice requires manual inspection, which is both time-consuming and costly. This project envisages maintenance-free measurements at numerous locations within the underground infrastructure, a goal that is to be achieved through the use of a harvesting device mounted on the water pipe. This device extracts energy from the temperature difference between the water pipe and the soil using a TEG (thermoelectric generator), takes sensor measurements, processes the data and transmits it wirelessly via LoRaWAN. We built 16 harvesting devices, installed them in four locations and continuously evaluated their performance throughout the project. In this paper, we focus on two devices of a particular type. The data for a full year show that enough energy was available on 94% of the days, on average, to take measurements and transmit data. This study demonstrates that it is possible to power highly constrained sensing devices with energy harvesting in underground environments.
Devanand, V. B., Mubeen, A., Vojinovic, Z., Sanchez Torres, A., Paliaga, G., Abdullah, A. F., … Fröhle, P. (2023). Innovative methods for mapping the suitability of nature-based solutions for landslide risk reduction. Land, 12(7), 1357 (15 pp.). doi:10.3390/land12071357, Institutional Repository

The impacts of climate change are becoming more widespread across the world, with hydro-meteorological extreme events on the rise, causing severe threats to nature and communities. Increasing trends in the frequency and intensity of floods and landslides have been projected by climate models. This necessitates the development of more effective measures such as nature-based solutions (NBS) which can complement grey infrastructures. Recent studies have identified knowledge gaps and limitations in existing research and tools that aid in spatial planning for the implementation of large-scale NBS and proposed new methodologies for the spatial allocation of large-scale NBS for flood risk reduction. This work presents a novel method for mapping the suitability of NBS addressing geo-hydrological hazards such as shallow landslides, debris flow, and rockfall, which are typically caused due to slope instability. This methodology incorporates landslide susceptibility mapping, and was used to create a toolbox ESRI ArcGIS environment to aid decision-makers in the planning and implementation of large-scale NBS. The spatial allocation toolbox was applied to the case study Portofino promontory, Liguria region, Italy, and 70% of the area was found to be highly susceptible to landslides. The produced suitability maps show that 41%, 33%, and 65% of the study area is suitable for the restoration of terraces, bio-engineering, and vegetative measures such as NBS for landslide risk reduction.
Figueroa, A., Hadengue, B., Leitão, J. P., & Blumensaat, F. (2023). A framework for modelling in-sewer thermal-hydraulic dynamic anomalies driven by stormwater runoff and seasonal effects. Water Research, 229, 119492 (10 pp.). doi:10.1016/j.watres.2022.119492, Institutional Repository

RRain-induced surface runoff and seasons lead to short- to medium-term anomalies in combined storm- and wastewater flows and temperatures, and influence treatment processes in wastewater resource recovery facilities (WRRF). Additionally, the implementation of decentralized heat recovery (HR) technologies for energy reuse in buildings affect energy-related processes across the urban water cycle and WRRFs heat inflows. However, quantitative insights on thermal-hydraulic dynamics in sewers at network scale and across different scales are very rare.
To enhance the understanding of thermal-hydraulic dynamics and the water-energy nexus across the urban water cycle we present a modular framework that couples thermal-hydraulic processes: i) on the surface, ii) in the public sewer network, iii) in households (including in-building HR systems), and iv) in lateral connections. We validate the proposed framework using field measurements at full network scale, present modelling results of extended time periods to illustrate the effect of seasons and precipitation events simultaneously, and quantify the impact of decentralized HR devices on thermal-hydraulics.
Simulation results suggest that the presented framework can predict temperature dynamics consistently all year long including short- to long-term variability of in-sewer temperature. The study provides quantitative evidence that the impact of household HR technologies on WRRF inflow heat budgets is reduced by approximately 20% during wet-weather periods in comparison to dry-weather conditions. The presented framework has potential to support multiple research initiatives that will improve the understanding of the water-energy nexus, pollutant dispersion and degradation, and support maintenance campaigns at network scale.
Furrer, V., Mutzner, L., Ort, C., & Singer, H. (2023). Micropollutant concentration fluctuations in combined sewer overflows require short sampling intervals. Water Research X, 21, 100202 (8 pp.). doi:10.1016/j.wroa.2023.100202, Institutional Repository

Combined sewer overflows (CSOs) are an important pathway of organic micropollutants from urban areas to open water bodies. Understanding the temporal dynamics of these micropollutants during overflow events is crucial for applying appropriate sampling methods and implementing effective management strategies. Yet, little is known about the dynamics of micropollutants in CSOs, because most studies report concentrations from single grab samples or event mean concentrations (EMCs). With unique high temporal resolution measurements (3 min), we show the real dynamics of polar organic micropollutants in CSOs of one small (2,700 people: P) and one large (159,000 P) urban catchment, for two micropollutant categories: (i) 33 micropollutants in municipal wastewater and (ii) 13 micropollutants from urban surface runoff. The concentration dynamics depend on the substance source and the catchment size. Indoor substances such as pharmaceuticals show high temporal dynamics with changes of 1 to 2 orders of magnitude within 9 min in the CSO of the small catchment. In contrast, outdoor substances at the small catchment and all substances at the large catchment display considerably lower variation. We tested various time-proportional sampling strategies to assess the range of error when estimating EMCs. We recommend an interval of 3 min to capture the dynamics of indoor substances in CSOs from small catchments. The results highlight that both future monitoring campaigns and the planning and management of urban wet-weather treatment systems will benefit from high temporal sampling resolutions, not only to understand dynamics but also to minimize errors of estimated EMCs.
Gobatti, L., & Leite, B. C. C. (2023). Unmanaged vegetated roofs hydrological performance in subtropical areas: an investigation in São Paulo, Brazil. Science of the Total Environment, 874, 162417 (11 pp.). doi:10.1016/j.scitotenv.2023.162417, Institutional Repository

Extensive vegetated roofs are Nature-based Solution with the ability to manage rainwater runoff in densely built spaces. Despite the large amount of research demonstrating its water management abilities, its performance is poorly quantified under subtropical climates and when using unmanaged vegetation. The present work aims at characterizing the runoff retention and detention of vegetated roofs under the climate of São Paulo, Brazil, accepting the growth of spontaneous species. By using real scale prototypes under natural rain, a vegetated roof hydrological performance was compared with a ceramic tiled roof. By using models with different substrate depths under artificial rain, changes in the hydrological performance were monitored for different antecedent soil moisture contents. Results from the prototypes showed that the (i) extensive roof attenuated from 30 % up to 100 % the peak rainfall runoff; (ii) delayed the peak runoff from 14 up to 37 min and (iii) retained from 34 % up to 100 % the total rainfall. Furthermore, results from the testbeds indicated that (iv) when comparing two rainfalls with same depths, the one with longer duration can saturate more the vegetated roof and thus undermine more its ability to retain water; and (v) when not managing the vegetation, the vegetated roof's soil moisture content loses correlation with the substrate depth, as plants will also develop more and will more effectively restore the substrate retention capacity. Conclusions point to extensive vegetated roofs as a relevant sustainable drainage system in subtropical areas, but demonstrate that its performance is highly dependent on structural factors, weather factors and level of maintenance. Such findings are expected to be useful for practitioners dimensioning these roofs as well as for policy makers towards a more accurate standardization of vegetated roofs in subtropical regions and Latin American developing countries.
Gobatti, L., Bach, P. M., Scheidegger, A., & Leitão, J. P. (2023). Using satellite imagery to investigate Blue-Green Infrastructure establishment time for urban cooling. Sustainable Cities and Society, 97, 104768 (11 pp.). doi:10.1016/j.scs.2023.104768, Institutional Repository

The process of urbanization can alter the local climate to the point that it threatens citizens’ well-being by creating heat-related hazards. The construction of Blue-Green Infrastructure (BGI) can improve the regulation of surface energy exchange processes and address this problem. However, the time needed for a BGI to deliver a stable cooling performance, referred to here as the Cooling Establishment Time (CET), is poorly understood and quantified in the literature and dependent on environmental, design and maintenance factors. Here, we analyze the feasibility of using satellite data to derive the CET for different BGIs across the city of Zurich, Switzerland. Results showed that remote sensing can quantify the land surface temperature impact of BGIs and assist in estimating their CET. BGI with trees or climbing plants required a longer CET (seven to ten years) before any notable shift in surface temperatures were visible, while grasses or artificial irrigated systems led to shorter CETs (one to three years). These results allow us to better account for BGI cooling establishment when planning for areas that need urgent action under warming climates. This work supports evidence-based urban greenery planning and design towards cooling our increasingly warming cities in a timely manner.
Keshaviah, A., Diamond, M. B., Wade, M. J., Scarpino, S. V., Ahmed, W., Amman, F., … Ort, C. (2023). Wastewater monitoring can anchor global disease surveillance systems. Lancet Global Health, 11(6), e976-e981. doi:10.1016/S2214-109X(23)00170-5, Institutional Repository

To inform the development of global wastewater monitoring systems, we surveyed programmes in 43 countries. Most programmes monitored predominantly urban populations. In high-income countries (HICs), composite sampling at centralised treatment plants was most common, whereas grab sampling from surface waters, open drains, and pit latrines was more typical in low-income and middle-income countries (LMICs). Almost all programmes analysed samples in-country, with an average processing time of 2·3 days in HICs and 4·5 days in LMICs. Whereas 59% of HICs regularly monitored wastewater for SARS-CoV-2 variants, only 13% of LMICs did so. Most programmes share their wastewater data internally, with partnering organisations, but not publicly. Our findings show the richness of the existing wastewater monitoring ecosystem. With additional leadership, funding, and implementation frameworks, thousands of individual wastewater initiatives can coalesce into an integrated, sustainable network for disease surveillance—one that minimises the risk of overlooking future global health threats.
Manny, L. (2023). Socio-technical challenges towards data-driven and integrated urban water management: a socio-technical network approach. Sustainable Cities and Society, 90, 104360 (28 pp.). doi:10.1016/j.scs.2022.104360, Institutional Repository

Data-driven and integrated urban water management have been proposed to reduce surface water pollution in light of climate change and urbanization impacts. Besides technological innovation, data-driven and integrated management require information exchange among many actors, e.g., operators, engineers, or authorities. With the aim of achieving a more profound understanding of socio-technical infrastructures, such as urban water systems, I draw on the approach of socio-technical networks to study actors and infrastructure elements as well as multiple relations in-between. In this article, I investigate whether underlying socio-technical dependencies influence social interactions such as information exchange. More specifically related to data-driven and integrated management, I analyze potential challenges, such as organizational fragmentation, data access, and diverging perceptions. Based on empirical data from three case studies in Switzerland, I provide inferential results obtained from fitting exponential random graph models. Findings showed that actors' relatedness to infrastructure elements affects their information exchange. Among the cases, the presence of the three challenges varied and is potentially contingent upon system size, organizational form, or progress in terms of data-driven and integrated management. Thus, incorporating a socio-technical perspective on social actors and infrastructure elements could help to improve policy design and implementation aiming to achieve more sustainable cities.
Maurer, B., Lienert, J., & Cook, L. M. (2023). Comparing PV-green and PV-cool roofs to diverse rooftop options using decision analysis. Building and Environment, 245, 110922 (15 pp.). doi:10.1016/j.buildenv.2023.110922, Institutional Repository

Flat roofs can employ a range of technologies to improve sustainability, such as photovoltaic (PV) panels, green roofs, cool roofs, or a combination of these options. Yet, weighing the benefits, costs, and performance of different roofing technologies is complex, especially when different stakeholders are involved. Decision analysis techniques, such as multi-criteria decision analysis (MCDA), can be used to systematically evaluate a diverse range of rooftop options to assess trade-offs in a quantitative way and avoid decision biases. This study offers a holistic comparison of different roof types, considering stakeholder preferences and uncertainty using MCDA. Ten flat roof options are compared, including black, gravel, cool, extensive green and semi-intensive green roofs, each with or without a rooftop PV installation, for nine objectives and three hypothetical stakeholder profiles. Performance is evaluated using building energy simulation, hydrologic modeling, and literature research. Uncertainty analyses are used to evaluate the effects of model assumptions on the MCDA results. For assumed preferences of an urban planner and environmentalist, semi-intensive green roofs with integrated PV installation are the best performing option; however, for a hypothetical building owner more concerned with costs, a gravel roof with PV ranks best. Uncertainty plays a role in the results, in particular, the uncertainty of the predicted outcome of options for the building owner, which can change the top-ranking options considerably. The uncertainty analyses are useful to identify consensus options over all three stakeholder types. Despite considerable uncertainty, extensive and semi-intensive green roofs with PV are recommended as relatively robust best-performing options.
McFadden, I. R., Sendek, A., Brosse, M., Bach, P. M., Baity‐Jesi, M., Bolliger, J., … Narwani, A. (2023). Linking human impacts to community processes in terrestrial and freshwater ecosystems. Ecology Letters, 26(2), 203-218. doi:10.1111/ele.14153, Institutional Repository

Human impacts such as habitat loss, climate change and biological invasions are radically altering biodiversity, with greater effects projected into the future. Evidence suggests human impacts may differ substantially between terrestrial and freshwater ecosystems, but the reasons for these differences are poorly understood. We propose an integrative approach to explain these differences by linking impacts to four fundamental processes that structure communities: dispersal, speciation, species-level selection and ecological drift. Our goal is to provide process-based insights into why human impacts, and responses to impacts, may differ across ecosystem types using a mechanistic, eco-evolutionary comparative framework. To enable these insights, we review and synthesise (i) how the four processes influence diversity and dynamics in terrestrial versus freshwater communities, specifically whether the relative importance of each process differs among ecosystems, and (ii) the pathways by which human impacts can produce divergent responses across ecosystems, due to differences in the strength of processes among ecosystems we identify. Finally, we highlight research gaps and next steps, and discuss how this approach can provide new insights for conservation. By focusing on the processes that shape diversity in communities, we aim to mechanistically link human impacts to ongoing and future changes in ecosystems.
Molné, F., Donati, G. F. A., Bolliger, J., Fischer, M., Maurer, M., & Bach, P. M. (2023). Supporting the planning of urban blue-green infrastructure for biodiversity: a multi-scale prioritisation framework. Journal of Environmental Management, 342, 118069 (12 pp.). doi:10.1016/j.jenvman.2023.118069, Institutional Repository

Primary considerations for urban blue-green infrastructure (BGI) encompass sustainable stormwater/urban heat management while biodiversity conservation is often considered an inherent benefit rather than a core planning requirement. However, ecological function of BGI as 'stepping stones' or linear corridors for otherwise fragmented habitats is undisputed. While quantitative approaches for modelling ecological connectivity in conservation planning are well established, mismatches in scope and scale with models that support the planning of BGI makes their adoption and integration difficult across disciplines. Technical complexities have led to ambiguity around circuit and network-based approaches, focal node placement, spatial extents, and resolution. Furthermore, these approaches are often computationally intensive, and considerable gaps remain in their use for identifying local-scale critical "pinch-points" that urban planners may respond to with the integration of BGI interventions that address biodiversity enhancement among other ecosystem services. Here, we present a framework that simplifies and integrates the merits of regional connectivity assessments with a focus on urban areas to prioritise BGI planning interventions while reducing computational demands. Our framework facilitates: (1) modelling potential ecological corridors at a coarse regional scale, (2) prioritising local-scale BGI interventions based on the relative contribution of individual nodes in this regional network, and (3) inferring connectivity hot- and cold-spots for local-scale BGI interventions. We illustrate this in the Swiss lowlands, demonstrating how, compared to previous work, we are able to identify and rank different priority locations across the region for BGI interventions in support of biodiversity enhancement and how their local-scale functional design may be benefited by addressing specific environmental variables.
Mutzner, L., Zhang, K., Luthy, R. G., Arp, H. P. H., & Spahr, S. (2023). Urban stormwater capture for water supply: look out for persistent, mobile and toxic substances. Environmental Science: Water Research and Technology, 9, 3094-3102. doi:10.1039/D3EW00160A, Institutional Repository

Urban stormwater was traditionally managed for flood control and property protection. But this view is changing as urban stormwater is increasingly recognized as an underused water resource. As cities implement stormwater capture, treatment, and use practices, there is a growing need to address water quality criteria beyond traditional pollutants such as nutrients, total suspended solids, pathogens, and metals. Current use practice and regulations are lacking on the occurrence of persistent, mobile and toxic (PMT) and very persistent, very mobile (vPvM) organic substances. These substances are poorly removed in conventional stormwater treatment systems, e.g., urban blue-green infrastructure, and, thus, may pose a risk when urban runoff is used to augment drinking water sources or discharged to surface waters. We reviewed 97 stormwater monitoring studies and identified 49 PMT/vPvM substances detected in urban stormwater. Although detection does not equate with risk, this highlights the potential importance of urban stormwater as a source of PMT/vPvM substances released to water resources. There is a lack of surveillance data on PMT/vPvM substances in urban stormwater and their fate in stormwater control measures, which hinders reliable risk assessments of stormwater capture. Unified guidelines are needed to (i) monitor PMT/vPvM substances in urban stormwater runoff, (ii) assess the human and environmental risks PMT/vPvM substances may pose for urban stormwater capture and use, and (iii) establish improved stormwater management and use criteria to ensure safe stormwater capture for water supply.
Pakizer, K., Lieberherr, E., Farrelly, M., Bach, P. M., Saurí, D., March, H., … Binz, C. (2023). Policy sequencing for early-stage transition dynamics - a process model and comparative case study in the water sector. Environmental Innovation and Societal Transitions, 48, 100730 (20 pp.). doi:10.1016/j.eist.2023.100730, Institutional Repository

Sustainability scholars increasingly recognize that policy mixes can positively impact socio-technical transitions. However, the temporal dimension of policy interventions remains under-researched, especially in the context of early transition dynamics that typically emerge in niche contexts. In this article, we explore how policy sequencing can play a key role in supporting the scaling-up of early-stage transition dynamics to drive wider system change. We contribute to transition research by proposing a process model for analyzing policy sequences by focusing on the interplay between policy instruments and institutional barriers. We conceptualize two ideal-type policy sequencing patterns - strategic and reactive - which we illustrate with empirical examples from early transition dynamics in the urban water sector of San Francisco (United States) and Sant Cugat del Vallès (Spain). Applying the process model to these case studies reveals how different sequences of policy (instrument) mixes can assist in overcoming institutional barriers, thus supporting transition trajectories.
Peleg, N., Torelló-Sentelles, H., Mariéthoz, G., Benoit, L., Leitão, J. P., & Marra, F. (2023). Brief communication: the potential use of low-cost acoustic sensors to detect rainfall for short-term urban flood warnings. Natural Hazards and Earth System Sciences, 23(3), 1233-1240. doi:10.5194/nhess-23-1233-2023, Institutional Repository

Floods in urban areas are one of the most common natural hazards. Due to climate change enhancing extreme rainfall and cities becoming larger and denser, the frequency, magnitude, and impact of these events are expected to increase. Pluvial floods can occur in urban areas within minutes. A fast and reliable rainfall detection system should thus be implemented in flood-prone cities to warn the public of upcoming floods and save lives and reduce damage. The purpose of this brief communication is to discuss the potential implementation of low-cost acoustic rainfall sensors in short-term flood warning systems.
Regueiro-Picallo, M., Anta, J., Naves, A., Figueroa, A., & Rieckermann, J. (2023). Towards urban drainage sediment accumulation monitoring using temperature sensors. Environmental Science: Water Research and Technology, 9, 3200-3212. doi:10.1039/d2ew00820c, Institutional Repository

Sewer sediments are among the main concerns related to urban drainage system management as they represent the largest contribution of suspended solid loads during rainfall events due to their resuspension. This study presents a novel methodology to detect and assess bed deposits in urban drainage systems based on temperature monitoring by using well-known thermodynamics and sediment properties. To illustrate the heat transfer processes in a liquid–sediment system and their relation to accumulation, a lab-scale experimental campaign was performed using sewer sediments and simulating water temperature gradients in sewers. Wastewater temperatures showed a marked daily pattern, while the presence of sediment dampened dynamics. Sediment thickness could therefore be estimated from the time evolution of the temperature differences measured between the bottom of the sediment bed and the water phase. Likewise, experimental data were used to calibrate a 1D heat transfer model, from which several sediment accumulation scenarios were simulated by using real wastewater temperature series. Thus, the influence of sediment properties on accumulation processes was assessed, and the range of potentially measurable sediments within an optimal range of [5–20] cm was identified. As a conclusion, temperature measurements and heat transfer model analysis can be used to approximate and monitor the sediments deposited in urban drainage systems. Future studies will extend the method to spatially-resolved sediment monitoring and active temperature sensing to improve sediment accumulation monitoring capabilities.
Reynaert, E., Steiner, P., Yu, Q., D'Olif, L., Joller, N., Schneider, M. Y., & Morgenroth, E. (2023). Predicting microbial water quality in on-site water reuse systems with online sensors. Water Research, 240, 120075 (13 pp.). doi:10.1016/j.watres.2023.120075, Institutional Repository

Widespread implementation of on-site water reuse is hindered by the limited availability of monitoring approaches that ensure microbial quality during operation. In this study, we developed a methodology for monitoring microbial water quality in on-site water reuse systems using inexpensive and commercially available online sensors. An extensive dataset containing sensor and microbial water quality data for six of the most critical types of disruptions in membrane bioreactors with chlorination was collected. We then tested the ability of three typological machine learning algorithms - logistic regression, support-vector machine, and random forest - to predict the microbial water quality as "safe" or "unsafe" for reuse. The main criteria for model optimization was to ensure a low false positive rate (FPR) - the percentage of safe predictions when the actual condition is unsafe - which is essential to protect users health. This resulted in enforcing a fixed FPR ≤ 2%. Maximizing the true positive rate (TPR) - the percentage of safe predictions when the actual condition is safe - was given second priority. Our results show that logistic-regression-based models using only two out of the six sensors (free chlorine and oxidation-reduction potential) achieved the highest TPR. Including sensor slopes as engineered features allowed to reach similar TPRs using only one sensor instead of two. Analysis of the occurrence of false predictions showed that these were mostly early alarms, a characteristic that could be regarded as an asset in alarm management. In conclusion, the simplest algorithm in combination with only one or two sensors performed best at predicting the microbial water quality. This result provides useful insights for water quality modeling or for applications where small datasets are a common challenge and a general advantage might be gained by using simpler models that reduce the risk of overfitting, allow better interpretability, and require less computational power.
Rodriguez, M., Fu, G., Butler, D., Yuan, Z., & Cook, L. (2023). Global resilience analysis of combined sewer systems under continuous hydrologic simulation. Journal of Environmental Management, 344, 118607 (11 pp.). doi:10.1016/j.jenvman.2023.118607, Institutional Repository

Managing and reducing combined sewer overflow (CSO) discharges is crucial for enhancing the resilience of combined sewer systems (CSS). However, the absence of a standardised resilience analysis approach poses challenges in developing effective discharge reduction strategies. To address this, our study presents a top-down method that expands the existing Global Resilience Analysis to quantify resilience performance in CSS. This approach establishes a link between threats (e.g., rainfall) and impacts (e.g., CSOs) through continuous and long-term simulation, accommodating various rainfall patterns, including extreme events. We assess CSO discharge impacts from a resilience perspective by introducing eight new metrics. We conducted a case study in Fehraltorf, Switzerland, analysing the performance of three green infrastructure (GI) types (bioretention cells, green roofs, and permeable pavements) over 38 years. The results demonstrated that GI enhanced all resilience indices, with variations observed in individual CSO performance metrics and their system locations. Notably, in Fehraltorf, green roofs emerged as the most effective GI type for improving resilience, while the downstream outfall displayed the highest resilience enhancement. Overall, our proposed method enables a shift from event-based to continuous simulation analysis, providing a standardised approach for resilience assessment. This approach informs the development of strategies for CSO discharge reduction and the enhancement of CSS resilience.
Roldán-Valcarce, A., Jato-Espino, D., Manchado, C., Bach, P. M., & Kuller, M. (2023). Vulnerability to urban flooding assessed based on spatial demographic, socio-economic and infrastructure inequalities. International Journal of Disaster Risk Reduction, 95, 103894 (14 pp.). doi:10.1016/j.ijdrr.2023.103894, Institutional Repository

Urban flooding is a priority in natural risk management and mitigation because it is the most frequent natural disaster in densely urbanised environments. This research explores flood vulnerability in cities by developing an index that can be easily implemented across the world. Our methodology is based on the arrangement of a series variables into three different classes (demography, socioeconomics and infrastructure) and the determination of their spatial variability through a Principal Component Analysis (PCA). We tested the proposed approach in the city of Santander (Spain) where a vulnerability index map was generated based on the combination of the proposed classes. The analysis show that we can reduce complexity from an initially identified 159 relevant variables to 16 representative and impactful variables in terms of spatial variance. Classification of the variables into three different classes made it possible to quantify the main causes of vulnerability to flooding across space. We produce a flood risk map by integrating our findings with a flood hazard map for the same area. This flood risk map gives urban planners detailed information about the most affected areas and allows them to design measures that mitigate the severity and effects of floods optimising available resources.
Schneider, M. Y., Harada, H., Villez, K., & Maurer, M. (2023). Several small or single large? Quantifying the catchment-wide performance of on-site wastewater treatment plants with inaccurate sensors. Environmental Science and Technology, 57(2), 1114-1122. doi:10.1021/acs.est.2c05945, Institutional Repository

On-site wastewater treatment plants (OSTs) often lack monitoring, resulting in unreliable treatment performance. They thus appear to be a stopgap solution despite their potential contribution to circular water management. Low-maintenance but inaccurate soft sensors are emerging that address this concern. However, how their inaccuracy impacts the catchment-wide treatment performance of a system of many OSTs has not been quantified. We develop a stochastic model to estimate catchment-wide OST performances with a Monte Carlo simulation. In our study, soft sensors with a 70% accuracy improved the treatment performance from 66% of the time functional to 98%. Soft sensors optimized for specificity, indicating the true negative rate, improve the system performance, while sensors optimized for sensitivity, indicating the true positive rate, quantify the treatment performance more accurately. This new insight leads us to suggest programming two soft sensors in practical settings with the same hardware sensor data as input: one soft sensor geared to high specificity for maintenance scheduling and one geared to high sensitivity for performance quantification. Our findings suggest that a maintenance strategy combining inaccurate sensors with appropriate alarm management can vastly improve the mean catchment-wide treatment performance of a system of OSTs.
Tan, T., Kong, F., Yin, H., Cook, L. M., Middel, A., & Yang, S. (2023). Carbon dioxide reduction from green roofs: a comprehensive review of processes, factors, and quantitative methods. Renewable and Sustainable Energy Reviews, 182, 113412 (12 pp.). doi:10.1016/j.rser.2023.113412, Institutional Repository

Green roofs can mitigate climate change through various mechanisms, including carbon sequestration, thermal process regulation, and building energy savings. While those mechanisms have been well studied, it remains unclear how to quantify carbon dioxide reduction and design green roofs properly for optimal performance. This review summarizes the processes, factors, and quantitative methods for carbon dioxide reduction from peer-reviewed literature. Firstly, the processes that account for how green roofs reduce carbon dioxide directly (by absorbing ambient CO2 and storing it in biomass and substrate) and indirectly (thermal process regulation reduces energy consumption) are focused. The factors affecting CO2 reduction (i.e., vegetation, substrate, building environment, and maintenance) are then discussed to investigate how these factors impact the performance of green roofs. Lastly, quantitative methods, including field experiments and simulation models to evaluate direct and indirect CO2 reductions from green roofs, are discussed considering different spatial scales. The review found that a better understanding of green roofs' carbon reduction mechanisms is needed. Recommendations also should be developed on customized green roof system designs for various climate conditions. Lastly, there is a lack of green roof decarbonization evaluation at multiple spatial and temporal scales to effectively implement green roofs in urban areas. Overall, this comprehensive review facilitates an understanding of how green roofs reduce CO2, the driving factors, and quantitative methods, which will advance research on designing and building green roofs as effective climate mitigation solutions in urban areas.
Tondera, K., Brelot, E., Fontanel, F., Cherqui, F., Ellerbæk Nielsen, J., Brüggemann, T., … Anta, J. (2023). European stakeholders' visions and needs for stormwater in future urban drainage systems. Urban Water Journal, 20(7), 831-843. doi:10.1080/1573062X.2023.2211559, Institutional Repository

Transitioning urban drainage systems to serve water-smart societies requires the involvement of different disciplines and stakeholders. However, stakeholders have different visions and needs from the transitioning process (e.g in terms of financing, policy adaptation and system management) these also vary between regions and countries. Identifying such different needs for stakeholders is necessary to propose practical adaptation strategies. Therefore, evidence of needs as reflected in policy papers and legislation in seven European countries was collected. Knowledgeable individuals in the urban drainage community were asked about their visions. Results show that whilst there is consensus on the challenges, visions on how to transition are diverse, indicating that more interaction between the different stakeholder groups is required to develop consensus. Additionally, organisational and legislative structures often slow down the necessary change processes.
Caduff, L., Dreifuss, D., Schindler, T., Devaux, A. J., Ganesanandamoorthy, P., Kull, A., … Julian, T. R. (2022). Inferring transmission fitness advantage of SARS-CoV-2 variants of concern from wastewater samples using digital PCR, Switzerland, December 2020 through March 2021. Eurosurveillance: bulletin européen sur les maladies transmissibles, 27(10), 2100806 (20 pp.). doi:10.2807/1560-7917.ES.2022.27.10.2100806, Institutional Repository

BackgroundThroughout the COVID-19 pandemic, SARS-CoV-2 genetic variants of concern (VOCs) have repeatedly and independently arisen. VOCs are characterised by increased transmissibility, increased virulence or reduced neutralisation by antibodies obtained from prior infection or vaccination. Tracking the introduction and transmission of VOCs relies on sequencing, typically whole genome sequencing of clinical samples. Wastewater surveillance is increasingly used to track the introduction and spread of SARS-CoV-2 variants through sequencing approaches.AimHere, we adapt and apply a rapid, high-throughput method for detection and quantification of the relative frequency of two deletions characteristic of the Alpha, Beta, and Gamma VOCs in wastewater.MethodsWe developed drop-off RT-dPCR assays and an associated statistical approach implemented in the R package WWdPCR to analyse temporal dynamics of SARS-CoV-2 signature mutations (spike Δ69-70 and ORF1a Δ3675-3677) in wastewater and quantify transmission fitness advantage of the Alpha VOC.ResultsBased on analysis of Zurich wastewater samples, the estimated transmission fitness advantage of SARS-CoV-2 Alpha based on the spike Δ69-70 was 0.34 (95% confidence interval (CI): 0.30-0.39) and based on ORF1a Δ3675-3677 was 0.53 (95% CI: 0.49-0.57), aligning with the transmission fitness advantage of Alpha estimated by clinical sample sequencing in the surrounding canton of 0.49 (95% CI: 0.38-0.61).ConclusionDigital PCR assays targeting signature mutations in wastewater offer near real-time monitoring of SARS-CoV-2 VOCs and potentially earlier detection and inference on transmission fitness advantage than clinical sequencing.
Campos-Mañas, M. C., Van Wichelen, N., Covaci, A., van Nuijs, A. L. N., Ort, C., Béen, F., … Bijlsma, L. (2022). Analytical investigation of cannabis biomarkers in raw urban wastewater to refine consumption estimates. Water Research, 223, 119020 (8 pp.). doi:10.1016/j.watres.2022.119020, Institutional Repository

Wastewater analysis of Δ9-tetrahydrocannabinol (THC) biomarkers can provide essential information on trends in cannabis consumption. Although analysis is mostly focused on the aqueous phase, previous studies have illustrated the need of improving the measurements of raw influent wastewater (IWW) considering also suspended solids. This is important for cannabis biomarkers, because a substantial part of them is expected to be found in the suspended solids due to their more lipophilic character compared with other metabolites/drugs included in these types of studies. However, it remains open to which extent trend estimates might be affected by solely analysing the liquid phase. To investigate this aspect, robust analytical methodologies are required to measure both the liquid and solid phases of IWW. In this work, we firstly tested liquid-liquid extraction (LLE) for THC and its major metabolites (THC-OH, and THC-COOH). Using LLE, no filtration or centrifugation step was required for raw IWW analysis, and the three analytes were extracted from both the liquid and the solid phase simultaneously. In parallel, the raw IWW was centrifuged and the obtained solid and liquid phases were analyzed separately: the liquid phase by both LLE and solid phase extraction (SPE) for comparison of data, and the suspended solids by solid-liquid extraction (SLE). The separate analysis of both phases in a number of samples revealed that a significant amount of cannabis biomarkers (ranging from 42 to 90%) was found in the suspended solids. In addition, the total amount of cannabis biomarkers obtained by analysing raw IWW on the one hand, and by separate analysis of the liquid and the solid phases, on the other hand, was in good agreement. Data from this study show that the sole analysis of the liquid phase would lead to a notable underestimation of cannabis biomarkers concentrations in IWW.
Cariti, F., Tuñas Corzon, A., Fernandez-Cassi, X., Ganesanandamoorthy, P., Ort, C., Julian, T. R., & Kohn, T. (2022). Wastewater reveals the spatiotemporal spread of SARS-CoV-2 in the Canton of Ticino (Switzerland) during the onset of the COVID-19 pandemic. ACS ES&T Water, 2(11), 2194-2200. doi:10.1021/acsestwater.2c00082, Institutional Repository

Wastewater-based epidemiology (WBE) has emerged as an effective tool for monitoring SARS-CoV-2 dynamics during the COVID-19 pandemic. Here, we add a spatial component to WBE and use it to investigate SARS-CoV-2 spread in the canton of Ticino during the onset of the pandemic in Switzerland (end of February 2020 to beginning of March 2020). Ticino is located at the border to Northern Italy, where a large COVID-19 outbreak occurred in February 2020. Not surprisingly, Ticino was the site of the first clinically confirmed COVID-19 case in Switzerland. We retrospectively analyzed daily influent samples from nine wastewater treatment plants in Ticino that jointly cover an area of 20 km × 60 km and 351,000 people (>99% of the population). Our result is a fine-grained view of the spatiotemporal evolution of the COVID-19 pandemic in this canton. The wastewater analysis revealed that by February 29, 2020, SARS-CoV-2 had already spread to all catchments. At the same time, only four individual cases had been clinically confirmed across the region served by the treatment plants investigated. Our results demonstrate that WBE could serve as a versatile tool to monitor the introduction and spread of an infectious agent on a regional scale. To fully exploit its utility, WBE should be implemented in real time and become an integral part of future disease surveillance efforts.
Chaudhary, P., Leitão, J. P., Donauer, T., D’Aronco, S., Perraudin, N., Obozinski, G., … Russo, S. (2022). Flood uncertainty estimation using deep ensembles. Water, 14(19), 2980 (24 pp.). doi:10.3390/w14192980, Institutional Repository

We propose a probabilistic deep learning approach for the prediction of maximum water depth hazard maps at high spatial resolutions, which assigns well-calibrated uncertainty estimates to every predicted water depth. Efficient, accurate, and trustworthy methods for urban flood management have become increasingly important due to higher rainfall intensity caused by climate change, the expansion of cities, and changes in land use. While physically based flood models can provide reliable forecasts for water depth at every location of a catchment, their high computational burden is hindering their application to large urban areas at high spatial resolution. While deep learning models have been used to address this issue, a disadvantage is that they are often perceived as "black-box" models and are overconfident about their predictions, therefore decreasing their reliability. Our deep learning model learns the underlying phenomena a priori from simulated hydrodynamic data, obviating the need for manual parameter setting for every new rainfall event at test time. The only inputs needed at the test time are a rainfall forecast and parameters of the terrain such as a digital elevation model to predict the maximum water depth with uncertainty estimates for complete rainfall events. We validate the accuracy and generalisation capabilities of our approach through experiments on a dataset consisting of catchments within Switzerland and Portugal and 18 rainfall patterns. Our method produces flood hazard maps at 1 m resolution and achieves mean absolute errors as low as 21 cm for extreme flood cases with water above 1 m. Most importantly, we demonstrate that our approach is able to provide an uncertainty estimate for every water depth within the predicted hazard map, thus increasing the model's trustworthiness during flooding events.
Donati, G. F. A., Bolliger, J., Psomas, A., Maurer, M., & Bach, P. M. (2022). Reconciling cities with nature: Identifying local Blue-Green Infrastructure interventions for regional biodiversity enhancement. Journal of Environmental Management, 316, 115254 (14 pp.). doi:10.1016/j.jenvman.2022.115254, Institutional Repository

Increasing urbanization degrades quantity, quality, and the functionality of spatial cohesion of natural areas essential to biodiversity and ecosystem functioning worldwide. The uncontrolled pace of building activity and the erosion of blue (i.e., aquatic) and green (i.e., terrestrial) landscape elements threaten existing habitat ranges and movability of wildlife. Local scale measures, such as nature-inspired engineered Blue-Green Infrastructure (BGI) are emerging mitigation solutions. Originally planned to promote sustainable stormwater management, adaptation to climate change and improved human livability in cities, such instruments offer interesting synergies for biodiversity in support of existing ecological infrastructure. BGI are especially appealing for globally declining amphibians, a rich and diverse vertebrate assemblage sensitive to urbanization. We integrated biological and highly resolved urban-rural land-cover data, ensemble models of habitat suitability, and connectivity models based on circuit theory to improve multi-scale and multi-species protection of core habitats and ecological corridors in the Swiss lowlands. Considering a broad spectrum of amphibian biodiversity, we identified distributions of amphibian biodiversity hotspots and four landscape elements essential to amphibian movability at the regional scale, namely i) forest edges, ii) wet-forest habitats, iii) soils with variable moisture and iv) riparian zones. Our work shows that cities can make a substantial contribution (e.g., up to 15% of urban space in the study area) to wider landscape habitat connectivity. We highlight the importance of planning BGI locally in strategic locations across urban and peri-urban areas to promote the permeability and availability of ‘stepping stone’ habitats in densely populated landscapes, essential to the maintenance of regional habitat connectivity and thereby enhancing biodiversity and ecosystem functioning.
Duque, N., Bach, P. M., Scholten, L., Fappiano, F., & Maurer, M. (2022). A simplified sanitary sewer system generator for exploratory modelling at city-scale. Water Research, 209, 117903 (12 pp.). doi:10.1016/j.watres.2021.117903, Institutional Repository

Future climatic, demographic, technological, urban and socio-economic challenges call for more flexible and sustainable wastewater infrastructure systems. Exploratory modelling can help to investigate the consequences of these developments on the infrastructure. In order to explore large numbers of adaptation strategies, we need to re-balance the degree of realism of sewer network and ability to reflect key performance characteristics against the model's parsimony and computational efficiency. We present a spatially explicit algorithm for creating sanitary sewer networks that realistically represent key characteristics of a real system. Basic topographic, demographic and urban characteristics are abstracted into a squared grid of 'Blocks' which are the foundation for the sewer network's topology delineation. We compare three different pipe dimensioning approaches and found a good balance between detail and computational efficiency. With a basic hydraulic performance assessment, we demonstrate that we attain a computationally efficient and high-fidelity wastewater sewer network with adequate hydraulic performance. A spatial resolution of 250 m Block size in combination with a sequential Pipe-by-Pipe (PBP) design algorithm provides a sound trade-off between computational time and fidelity of relevant structural and hydraulic properties for exploratory modelling. We can generate a simplified sewer network (both topology and hydraulic design) in 18 s using PBP, versus 36 min using a highly detailed model or 1 s using a highly abstract model. Moreover, this simplification can cut up to 1/10th to 1/50th the computational time for the hydraulic simulations depending on the routing method implemented. We anticipate our model to be a starting point for sophisticated exploratory modelling into possible infrastructure adaptation measures of topological and loading changes of sewer systems for long-term planning.
Guo, Z., Moosavi, V., & Leitão, J. P. (2022). Data-driven rapid flood prediction mapping with catchment generalizability. Journal of Hydrology, 609, 127726 (12 pp.). doi:10.1016/j.jhydrol.2022.127726, Institutional Repository

Data-driven and machine learning models have recently received increasing interest to resolve the computational speed challenge faced by various physically-based simulations. A few studies have explored the application of these models to develop new, and fast, applications for fluvial and pluvial flood prediction, extent mapping, and flood susceptibility assessment. However, most studies have focused on model development for specific catchment areas, drainage networks or gauge stations. Hence, their results cannot be directly reused to other contexts unless extra data are available and the models are further trained. This study explores the generalizability potential of convolutional neural networks (CNNs) as flood prediction models. The study proposes a CNN-based model that can be reused in different catchment areas with different topography once the model is trained. The study investigates two options, patch- and resizing-based options, to process catchment areas of different sizes and different shapes. The results showed that the CNN-based model predicts accurately on "unseen" catchment areas with significantly less computational time when compared to physically-based models. The obtained results also suggest that the patch-based option is more effective than the resizing-based option in terms of prediction accuracy. In addition, all experiments have shown that the prediction of flow velocity is more accurate than water depth, suggesting that the water accumulation is more sensitive to global elevation information than flow velocity.
Hadengue, B., Morgenroth, E., & Larsen, T. A. (2022). How to get your feet wet: Integrating urban water and building engineering for low-energy domestic hot water systems. Energy and Buildings, 271, 112318 (6 pp.). doi:10.1016/j.enbuild.2022.112318, Institutional Repository

Thanks to intense research efforts, modern low-energy buildings consume only a fraction of the space heating loads of their older counterparts. However, reducing their energy footprint further requires optimization of domestic hot water systems. This is a difficult task, because the relationship between water and energy in domestic systems is intricate and complex. In general, the intrinsic uncertainty and variability of water demand makes the planning and assessment of new technologies challenging, but also risks related for instance to water pathogens like Legionella pneumophila impose trade-offs that diminish the performance of energy-saving measures. In this short communication, we argue that truly sustainable solutions will only be found if a multi-scale, multi-disciplinary mindset is adopted by the community. We show how the collaboration and knowledge integration between experts of various fields, notably urban water specialists and building engineers, is a prerequisite to resolve some of the most pressing issues on the way to optimizing domestic hot water systems. Through concrete examples, we describe how a new, common modeling framework has already triggered the required knowledge integration, and how it could be expanded further to integrate additional disciplines.
Hadengue, B., Morgenroth, E., Larsen, T. A., & Baldini, L. (2022). Performance and dynamics of active greywater heat recovery in buildings. Applied Energy, 305, 117677 (13 pp.). doi:10.1016/j.apenergy.2021.117677, Institutional Repository

In the effort to de-carbonize the building stock, heat pumps are increasingly utilized in Switzerland, with 70% of the fast-growing heat pump market using ambient air as heat source. Inexpensive and easy to implement, these heat pumps are, however, less efficient than their ground- or water-source counterparts. In this modeling study, we aim at increasing the efficiency of air-source heat pumps using domestic greywater-contained heat. We assess the performance improvement relative to standard heat pump configurations across various climates, seasons, building envelopes, and domestic hot water consumption patterns. The results show that the annually-averaged coefficient of performance improves by 4.1% on average - ranging from 0.6% to 7.5%. This efficiency gain translates on average to 1.8 kWh/week of compressor electricity savings. Although attractive due to its simplicity, the proposed open-loop configuration - preheating of an external heat source - only leads to moderate performance improvement of air-source heat pumps. Based on these results, we extensively discuss and compare alternative system configurations and identify several fundamental differences in the heat recovery dynamics of each configuration. We show that closed-loop systems - using greywater as direct heat source - show the largest performance improvement potential, although being more expensive and complex to implement.
Hadengue, B., Morgenroth, E., & Larsen, T. A. (2022). Screening innovative technologies for energy-efficient domestic hot water systems. Journal of Environmental Management, 320, 115713 (9 pp.). doi:10.1016/j.jenvman.2022.115713, Institutional Repository

Domestic hot water systems are large energy consumers. With the aim of reducing the energy footprint of these systems, we selected and simulated five technologies across a wide range of technology readiness levels: established technologies - pipe insulation and low-flow faucets -, relatively new technologies - shower drain heat exchangers and an innovative pipe system - and a novel experimental technology - a heat exchanger connected to membrane bioreactor for on-site greywater treatment. Using the WaterHub modeling framework, we simulated the technologies alone and in combination and compared the energetic performance of fifteen scenarios with a validated reference domestic hot water system. Low-flow appliances as standalone technologies performed best with 30% less energy required for the boiler tank, but combining low-flow appliances with a membrane bioreactor heat exchanger performed best overall (50% reduction). Deep insights into the temperature dynamics at all locations in the system led to the identification of technological competition patterns to prevent and synergies to exploit. Through our results, we are able to discuss and recommend further investigations regarding critical aspects like hygiene and economic performance.
Harpaz, C., Russo, S., Leitão, J. P., & Penn, R. (2022). Potential of supervised machine learning algorithms for estimating the impact of water efficient scenarios on solids accumulation in sewers. Water Research, 216, 118247 (16 pp.). doi:10.1016/j.watres.2022.118247, Institutional Repository

Understanding the negative effects of widespread implementation of optimal water efficient solutions may have on existing centralised sewer systems is still limited – one of these effects is the accumulation of solids in sewer pipes. Predicting these effects requires setting up and simulating complex detailed hydraulic sewer network models. Often, precise details of the sewer network layout and diurnal patterns of the wastewater flows are not available, limiting the applicability of using model predictions for such phenomena. In this study, the applicability of supervised machine learning (ML) algorithms for the development of a simplified surrogate model to predict solid accumulation in sewer pipes was investigated. A large number of highly variable sewer networks were synthetically generated and used to produce results that can be generalizable within the limitations of the current study. A hydrodynamic sewer model was set up and simulated for each synthetic sewer network and various scenarios in which different water-efficient solutions were considered. Simulation results indicated that the most impacts are expected to occur in the upstream part of the sewer networks, and that with 50% reduction in (waste-)water flows, 3–20% more pipes are expected to accumulate solids. It was further found that ML algorithms can be used to successfully predict locations of solids accumulation in sewer pipes without using hydrodynamic models. A simple tool based on the findings of this study, sparing the need to conduct complex hydraulic simulations, was developed. It allows the user to enter a set of pipe characteristics and the proportion of flow that is reduced due to the implementation of water efficient solutions, and it predicts whether the pipe will accumulate solids or not. The study results and the proposed ML algorithms can support the implementation of optimal water-efficient solutions that will promote designing and managing the water sensitive cities of the future.
Huisman, J. S., Scire, J., Caduff, L., Fernandez-Cassi, X., Ganesanandamoorthy, P., Kull, A., … Julian, T. R. (2022). Wastewater-based estimation of the effective reproductive number of SARS-CoV-2. Environmental Health Perspectives, 130(5), 057011 (12 pp.). doi:10.1289/EHP10050, Institutional Repository

BACKGROUND: The effective reproductive number, Re, is a critical indicator to monitor disease dynamics, inform regional and national policies, and estimate the effectiveness of interventions. It describes the average number of new infections caused by a single infectious person through time. To date, Re estimates are based on clinical data such as observed cases, hospitalizations, and/or deaths. These estimates are temporarily biased when clinical testing or reporting strategies change.
OBJECTIVES: We show that the dynamics of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA in wastewater can be used to estimate Re in near real time, independent of clinical data and without the associated biases.
METHODS: We collected longitudinal measurements of SARS-CoV-2 RNA in wastewater in Zurich, Switzerland, and San Jose, California, USA. We combined this data with information on the temporal dynamics of shedding (the shedding load distribution) to estimate a time series proportional to the daily COVID-19 infection incidence. We estimated a wastewater-based Re from this incidence.
RESULTS: The method to estimate Re from wastewater worked robustly on data from two different countries and two wastewater matrices. The resulting estimates were as similar to the Re estimates from case report data as Re estimates based on observed cases, hospitalizations, and deaths are among each other. We further provide details on the effect of sampling frequency and the shedding load distribution on the ability to infer Re.
DISCUSSION: To our knowledge, this is the first time Re has been estimated from wastewater. This method provides a low-cost, rapid, and independent way to inform SARS-CoV-2 monitoring during the ongoing pandemic and is applicable to future wastewater-based epidemiology targeting other pathogens.
Jahn, K., Dreifuss, D., Topolsky, I., Kull, A., Ganesanandamoorthy, P., Fernandez-Cassi, X., … Beerenwinkel, N. (2022). Early detection and surveillance of SARS-CoV-2 genomic variants in wastewater using COJAC. Nature Microbiology, 7, 1151-1160. doi:10.1038/s41564-022-01185-x, Institutional Repository

The continuing emergence of SARS-CoV-2 variants of concern and variants of interest emphasizes the need for early detection and epidemiological surveillance of novel variants. We used genomic sequencing of 122 wastewater samples from three locations in Switzerland to monitor the local spread of B.1.1.7 (Alpha), B.1.351 (Beta) and P.1 (Gamma) variants of SARS-CoV-2 at a population level. We devised a bioinformatics method named COJAC (Co-Occurrence adJusted Analysis and Calling) that uses read pairs carrying multiple variant-specific signature mutations as a robust indicator of low-frequency variants. Application of COJAC revealed that a local outbreak of the Alpha variant in two Swiss cities was observable in wastewater up to 13 d before being first reported in clinical samples. We further confirmed the ability of COJAC to detect emerging variants early for the Delta variant by analysing an additional 1,339 wastewater samples. While sequencing data of single wastewater samples provide limited precision for the quantification of relative prevalence of a variant, we show that replicate and close-meshed longitudinal sequencing allow for robust estimation not only of the local prevalence but also of the transmission fitness advantage of any variant. We conclude that genomic sequencing and our computational analysis can provide population-level estimates of prevalence and fitness of emerging variants from wastewater samples earlier and on the basis of substantially fewer samples than from clinical samples. Our framework is being routinely used in large national projects in Switzerland and the UK.
Kuller, M., Farrelly, M., Marthanty, D. R., Deletic, A., & Bach, P. M. (2022). Planning support systems for strategic implementation of nature-based solutions in the global south: current role and future potential in Indonesia. Cities, 126, 103693 (11 pp.). doi:10.1016/j.cities.2022.103693, Institutional Repository

Nature Based Solutions (NBS) are increasingly used for improving water quality, reducing urban flooding and providing ecological and amenity benefits. Although predominantly developed and implemented in industrialised countries, NBS are gaining traction in the Global South. Strategic planning is required to deliver the anticipated benefits and ensure successful integration into urban landscapes. Spatial software and planning support systems (PSS), can inform such decision-making. We seek to understand the efficacy of utilising PSS for advancing NBS practices within Indonesian urban settings. Through workshops and in-depth interviews with urban planning practitioners, we examined (i) the current NBS planning context, (ii) tacit experiences with contemporary PSS, and (iii) potential utility of PSS to address identified challenges with NBS planning. While strategic planning processes were perceived as improving over past decades, significant challenges remain including inadequate collaboration between actors, insufficient capacity and resources of local governments and limited access to high-quality spatial data. Although strategic planners employ contemporary PSS, further embedding those into decision-making processes requires rethinking the socio-institutional and political context within which they operate. Results revealed keen interest to adopt innovative PSS. However, without access to high-quality spatial information, strategic planning will continue to be compromised, potentially leading to ill-sited NBS interventions.
Langeveld, J. G., Cherqui, F., Tscheikner-Gratl, F., Muthanna, T. M., Fernandez-Delgado Juarez, M., Leitão, J. P., … Rulleau, B. (2022). Asset management for blue-green infrastructures: a scoping review. Blue-Green Systems, 4(2), 272-290. doi:10.2166/bgs.2022.019, Institutional Repository

Urban drainage systems have developed way beyond the traditional piped combined or separate sewer systems. Many 'new' systems are being introduced, ranging from stormwater infiltration facilities to green roofs. However, the widely advocated blue-green infrastructures are typically overlooked by asset managers, which will very likely have detrimental effects on their performance, service life, and wider adoption. In this paper, the working group on Urban Drainage Asset Management (UDAM - https://udam.home.blog/) of the IWA and IAHR Joint Committee on Urban Drainage discusses whether the state-of-the-art knowledge based on conventional sewer asset management is sufficient to develop asset management for blue-green infrastructures (BGIs). The discussion is structured around the five preconditions for effective control and asset management. Results show that asset management for BGIs is still underdeveloped due to a lack of monitoring techniques covering the broad range of BGI benefits and performance indicators, inspection techniques covering relevant failure mechanisms and models describing these mechanisms, maintenance and rehabilitation options, and sufficient support tools to aid inhabitants in the operation and maintenance of their individual BGIs such as green roofs or vegetated swales.
Manny, L., Angst, M., Rieckermann, J., & Fischer, M. (2022). Socio-technical networks of infrastructure management: network concepts and motifs for studying digitalization, decentralization, and integrated management. Journal of Environmental Management, 318, 115596 (14 pp.). doi:10.1016/j.jenvman.2022.115596, Institutional Repository

Networked infrastructure systems - including energy, transportation, water, and wastewater systems — provide essential services to society. Globally, these services are undergoing major transformative processes such as digitalization, decentralization, or integrated management. Such processes not only depend on technical changes in infrastructure systems but also include important social and socio-technical dimensions. In this article, we propose a socio-technical network perspective to study the ensemble of social actors and technical elements involved in an infrastructure system, and their complex relations. We conceptualize structurally explicit socio-technical networks of networked infrastructure systems based on methodological considerations from network analysis and draw on concepts from socio-technical system theories and social-ecological network studies. Based on these considerations, we suggest analytical methods to study basic network concepts such as density, reciprocity, and centrality in a socio-technical network. We illustrate socio-technical motifs, i.e., meaningful sub-structures in socio-technical networks of infrastructure management. Drawing on these, we describe how infrastructure systems can be analyzed in terms of digitalization, decentralization, and integrated management from a socio-technical network perspective. Using the example of urban wastewater systems, we illustrate an empirical application of our approach. The results of an empirical case study in Switzerland demonstrate the potential of socio-technical networks to promote a deeper understanding of complex socio-technical relations in networked infrastructure systems. We contend that such a deeper understanding could improve management practices of infrastructure systems and is becoming even more important for enabling future data-driven, decentralized, and more integrated infrastructure management.
Mutzner, L., Furrer, V., Castebrunet, H., Dittmer, U., Fuchs, S., Gernjak, W., … Vezzaro, L. (2022). A decade of monitoring micropollutants in urban wet-weather flows: what did we learn?. Water Research, 223, 118968 (14 pp.). doi:10.1016/j.watres.2022.118968, Institutional Repository

Urban wet-weather discharges from combined sewer overflows (CSO) and stormwater outlets (SWO) are a potential pathway for micropollutants (trace contaminants) to surface waters, posing a threat to the environment and possible water reuse applications. Despite large efforts to monitor micropollutants in the last decade, the gained information is still limited and scattered. In a metastudy we performed a data-driven analysis of measurements collected at 77 sites (683 events, 297 detected micropollutants) over the last decade to investigate which micropollutants are most relevant in terms of 1) occurrence and 2) potential risk for the aquatic environment, 3) estimate the minimum number of data to be collected in monitoring studies to reliably obtain concentration estimates, and 4) provide recommendations for future monitoring campaigns. We highlight micropollutants to be prioritized due to their high occurrence and critical concentration levels compared to environmental quality standards. These top-listed micropollutants include contaminants from all chemical classes (pesticides, heavy metals, polycyclic aromatic hydrocarbons, personal care products, pharmaceuticals, and industrial and household chemicals). Analysis of over 30,000 event mean concentrations shows a large fraction of measurements (> 50%) were below the limit of quantification, stressing the need for reliable, standard monitoring procedures. High variability was observed among events and sites, with differences between micropollutant classes. The number of events required for a reliable estimate of site mean concentrations (error bandwidth of 1 around the "true" value) depends on the individual micropollutant. The median minimum number of events is 7 for CSO (2 to 31, 80%-interquantile) and 6 for SWO (1 to 25 events, 80%-interquantile). Our analysis indicates the minimum number of sites needed to assess global pollution levels and our data collection and analysis can be used to estimate the required number of sites for an urban catchment. Our data-driven analysis demonstrates how future wet-weather monitoring programs will be more effective if the consequences of high variability inherent in urban wet-weather discharges are considered.
Nguyen, T. T., Bach, P. M., & Pahlow, M. (2022). Multi-scale stormwater harvesting to enhance urban resilience to climate change impacts and natural disasters. Blue-Green Systems, 4(1), 58-74. doi:10.2166/bgs.2022.008, Institutional Repository

Stormwater harvesting systems are a viable option to adapt cities to cope with climate change and reduce pressure on water supply services. This is particularly crucial in the event of natural disasters (e.g., earthquakes, floods), where large parts of cities may become disconnected from a secure water supply for prolonged time periods. We demonstrate how optimum location, density and storage size can be determined using UrbanBEATS, a spatial planning-support system for planning and design of sustainable Blue-Green Infrastructure strategies. We investigate the Otakaro/Avon River catchment, Christchurch, New Zealand for the time periods 2011-2020, 2041-2050 and 2091-2100 (for the RCP 8.5 climate change scenario). For targets of 30% of potable water substitution and 70% storage volumetric reliability, we found that stormwater harvesting systems in all climate scenarios required a larger capacity compared to the baseline. Most storages achieved their set targets and were larger than the municipality's recommended 9 m3 for flood inundation, indicating that the identified storages would also reduce minor flooding while ensuring water savings. A shift in the spatial layout of modelled systems from highly distributed to more centralised, however, raises a potential conflict with disaster resilience where more local solutions would be preferable.
Pastorek, J., Fencl, M., Rieckermann, J., & Bares, V. (2022). Precipitation estimates from commercial microwave links: practical approaches to wet-antenna correction. IEEE Transactions on Geoscience and Remote Sensing, 60, 4104409 (9 pp.). doi:10.1109/TGRS.2021.3110004, Institutional Repository

An inadequate correction for wet-antenna attenuation (WAA) often causes a notable bias in quantitative precipitation estimates (QPEs) from commercial microwave links (CMLs) limiting the usability of these rainfall data in hydrological applications. This article analyzes how WAA can be corrected without dedicated rainfall monitoring for a set of 16 CMLs. Using data collected over 53 rainfall events, the performance of six empirical WAA models was studied, both when calibrated to rainfall observations from a permanent municipal rain gauge network and when using model parameters from the literature. The transferability of WAA model parameters among CMLs of various characteristics has also been addressed. The results show that high-quality QPEs with a bias below 5% and root-mean-square error (RMSE) of 1 mm/h in the median could be retrieved, even from subkilometer CMLs where WAA is relatively large compared to raindrop attenuation. Models in which WAA is proportional to rainfall intensity provide better WAA estimates than constant and time-dependent models. It is also shown that the parameters of models deriving WAA explicitly from rainfall intensity are independent of CML frequency and path length and, thus, transferable to other locations with CMLs of similar antenna properties.
Peleg, N., Ban, N., Gibson, M. J., Chen, A. S., Paschalis, A., Burlando, P., & Leitão, J. P. (2022). Mapping storm spatial profiles for flood impact assessments. Advances in Water Resources, 166, 104258 (11 pp.). doi:10.1016/j.advwatres.2022.104258, Institutional Repository

Synthetic design storms are often used to plan new drainage systems or assess flood impacts on infrastructure. To simulate extreme rainfall events under climate change, design storms can be modified to match a different return frequency of extreme rainfall events as well as a modified temporal distribution of rainfall intensities. However, the same magnitude of change to the rainfall intensities is often applied in space. Several hydrological applications are limited by this. Climate change impacts on urban pluvial floods, for example, require the use of 2D design storms (rainfall fields) at sub-kilometer and sub-hourly scales. Recent kilometer scale climate models, also known as convection-permitting climate models (CPM), provide rainfall outputs at a high spatial resolution, although rainfall simulations are still restricted to a limited number of climate scenarios and time periods. We nevertheless explored the potential use of rainfall data obtained from these models for hydrological flood impact studies by introducing a method of spatial quantile mapping (SQM). To demonstrate the new methodology, we extracted high-resolution rainfall simulations from a CPM for four domains representing different urban areas in Switzerland. Extreme storms that are plausible under the present climate conditions were simulated with a 2D stochastic rainfall model. Based on the CPM-informed stochastically generated rainfall fields, we modified the design storms to fit the future climate scenario using three different methods: the SQM, a uniform quantile mapping, and a uniform adjustment based on a rainfall–temperature relationship. Throughout all storms, the temporal distribution of rainfall was the same. Using a flood model, we assessed the impact of different rainfall adjustment methods on urban flooding. Significant differences were found in the flood water depths and areas between the three methods. In general, the SQM method results in a higher flood impact than the storms that were modified otherwise. The results suggest that spatial storm profiles may need to be re-adjusted when assessing flood impacts.
Probst, N., Bach, P. M., Cook, L. M., Maurer, M., & Leitão, J. P. (2022). Blue Green Systems for urban heat mitigation: mechanisms, effectiveness and research directions. Blue-Green Systems, 4(2), 348-376. doi:10.2166/bgs.2022.028, Institutional Repository

Reflected in the growing body of literature, urban heat mitigation is increasingly relevant as cities experience extreme heat, exacerbated by climate change and rapid urbanisation. Most studies focus on urban–rural temperature differences, known as the Urban Heat Island, which does not provide insight into urban heat dynamics. Here, we synthesise current knowledge on spatio-temporal variations of heat sources and sinks, showing that a targeted and absolute understanding of urban heat dynamics rather than an urban–rural comparison should be encouraged. We discuss mechanisms of heat sinks for microclimate control, provide a clear classification of Blue Green Systems and evaluate current knowledge of their effectiveness in urban heat mitigation. We consider planning and optimisation aspects of Blue Green Infrastructure (greenery and water bodies/features), interactions with hard surfaces and practices that ensure space and water availability. Blue Green Systems can positively affect urban microclimates, especially when strategically planned to achieve synergies. Effectiveness is governed by their dominant cooling mechanisms that show diurnal and seasonal variability and depend upon background climatic conditions and characteristics of surrounding urban areas. Situationally appropriate combination of various types of Blue Green Systems and their connectivity increases heat mitigation potential while providing multiple ecosystem services but requires further research.
Prodanovic, V., Jamali, B., Kuller, M., Wang, Y., Bach, P. M., Coleman, R. A., … Deletic, A. (2022). Calibration and sensitivity analysis of a novel water flow and pollution model for future city planning: Future Urban Stormwater Simulation (FUSS). Water Science and Technology, 85(4), 961-969. doi:10.2166/wst.2022.046, Institutional Repository

Planning for future urban development and water infrastructure is uncertain due to changing human activities and climate. To quantify these changes, we need adaptable and fast models that can reliably explore scenarios without requiring extensive data and inputs. While such models have been recently considered for urban development, they are lacking for stormwater pollution assessment. This work proposes a novel Future Urban Stormwater Simulation (FUSS) model, utilizing previously developed urban planning algorithm (UrbanBEATS) to dynamically assess pollution changes in urban catchments. By using minimal input data and adding stochastic point-source pollution to the build-up/wash-off approach, this study highlights calibration and sensitivity analysis of flow and pollution modules, across the range of common stormwater pollutants. The results highlight excellent fit to measured values in a continuous rainfall simulation for the flow model, with one significant calibration parameter. The pollution model was more variable, with TSS, TP and Pb showing high model efficiency, while TN was predicted well only across event-based assessment. The work further explores the framework for the model application in future pollution assessment, and points to the future work aiming to developing land-use dependent model parameter sets, to achieve flexibility for model application across varied urban catchments.
Robinson, C. T., Jolidon, C., Consoli, G., Bloem, S., & Ebi, C. (2022). Temporal dynamics in the physico-chemistry of a high-alpine stream network in the Swiss National Park. eco.mont, 14(2), 11-23. doi:10.1553/eco.mont-14-2s11, Institutional Repository

The Macun lakes form a high-alpine (> 2,600 m asl) cirque landscape (3.6 km2) in the Swiss National Park, comprising 26 small lakes together with a number of temporary ponds. Streams interconnect the four largest lakes, forming the drainage network that flows into the Inn River at the town of Lavin. The drainage network of Macun consists of a north and a south basin that overlie an ortho-gneiss, meta-granitoid bedrock. The south basin is influenced by various rock glaciers. The physico-chemistry of surface waters at 10 sites has been monitored annually in midsummer since 2001. Further, an YSI EXO2 Multiparameter Sonde (Exosonde) with various water quality sensors has been employed since 2016 at the last lake in the network to examine seasonal and diel patterns in physico-chemistry. Results showed clear physico-chemical differences between the two basins, which mostly reflect rockglacier inputs in the south basin. Nitrogen values were two-fold higher and particulate phosphorus values two-fold lower in the south basin than in the north basin. Over time, the physico-chemistry in the two basins became more homogeneous, with a reduction in rock-glacial inputs in the south basin and an overall decrease in nitrogen in the catchment. Data from 30 springs and tributaries sampled in 2002 and 2017 reflected the basin differences and temporal changes observed at the primary study sites. Continuous temperature records showed north basin streams to be ca. 3°C warmer than south-basin streams, but with high inter-annual variation that reflected annual differences in weather and no evidence of a general change over time (increase or decrease). Exosonde data revealed strong seasonality in measured parameters as well as seasonality in diel patterns (e. g., dissolved oxygen, temperature, chlorophyll-a); diel fluctuations were most pronounced in summer and least in winter. The results highlight the importance of long-term monitoring for understanding ecosystem state changes in alpine freshwaters, especially during periods of rapid environmental change.
Shi, B., Catsamas, S., Deletic, B., Wang, M., Bach, P. M., Lintern, A., … McCarthy, D. T. (2022). Illicit discharge detection in stormwater drains using an Arduino-based low-cost sensor network. Water Science and Technology, 85(5), 1372-1383. doi:10.2166/wst.2022.034, Institutional Repository

Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.
Back, Y., Bach, P. M., Jasper-Tönnies, A., Rauch, W., & Kleidorfer, M. (2021). A rapid fine-scale approach to modelling urban bioclimatic conditions. Science of the Total Environment, 756, 143732 (17 pp.). doi:10.1016/j.scitotenv.2020.143732, Institutional Repository

Surface characteristics play a vital role in simulations for urban bioclimatic conditions. Changing relationships and distribution patterns of sealed and vegetated surfaces as well as building geometry across different scales in urban environments influence surface temperatures. Cities comprise different urban forms, which, depending on their surface characteristics, enhance the heating process, increasing the emergence of urban heat islands (UHIs). Detecting priority areas to introduce multi-beneficial climate change adaptation measures is set to be a key task for the cities long-term strategies to improve climatic conditions across different urban structures and scales. We introduce a simple and fast spatial modelling approach to carry out fine-scale simulations for land surface temperature (LST), mean radiant temperature (MRT) and Universal Thermal Climate Index (UTCI) in a 2D environment. Capabilities of our modelling approach are demonstrated in evaluating urban thermal comfort in the alpine city of Innsbruck, the capital of Tyrol in western Austria. Results show a major contrast between sealed and vegetated surfaces reflected in the distributional patterns and values of LST, MRT and UTCI, correlating with the appearance and frequency of specific surface classes. We found the Sky View Factor to have a substantial impact on calculations for bioclimatic conditions and see high-albedo surfaces decrease LST but increase the apparent temperature (MRT and UTCI values) effecting human thermal comfort. Furthermore, MRT and UTCI are more sensitive to changes in emissivity values, whereas LST is more sensitive to changes in Bowen Ratio values. Application of our modelling approach can be used to identify priority areas and maximise multi-functionality of climate change adaptation measures, to support urban planning processes for heat mitigation and the implementation of policy suggestions to achieve sustainable development goals and other political objectives.
Browne, S., Lintern, A., Jamali, B., Leitão, J. P., & Bach, P. M. (2021). Stormwater management impacts of small urbanising towns: The necessity of investigating the 'devil in the detail'. Science of the Total Environment, 757, 143835 (13 pp.). doi:10.1016/j.scitotenv.2020.143835, Institutional Repository

In many parts of the world, small towns are experiencing high levels of population growth and development. However, there is little understanding of how urban growth in these regional towns will impact urban runoff. We used the case study of Wangaratta, located in South-East Australia, between 2006 and 2016, to investigate land cover changes and their impacts on urban runoff discharge. Detailed spatio-temporal analysis (including neighbourhood composition analysis and supervised classification of aerial imagery) identified that population, land use and land cover changes in Wangaratta, although subtle, were mostly driven by residential growth in the outskirts of the town, where there were large increases in impervious surface area. Overall, the urban growth was minimal. However, in spite of these small changes, a sub-catchment only SWMM model showed that the increase in impervious surface area nevertheless resulted in a statistically significant increase in total runoff across the town. Particularly, this increase was most pronounced for frequent and shorter storms. The analysis of urban development pattern changes coupled with urban hydrological modelling indicated that land cover changes in regional towns, especially when analysed in detail, may result in hydrological changes in the urban region (likely to be exacerbated in coming years by changing climate) and that adaptation efforts will need to adopt a variety of approaches in both existing and growth zones. Our findings highlight the necessity of detailed fine-scale analyses in small towns as even subtle changes will have substantial future implications and robust planning and adaptation decisions are even more important when compared to larger cities due to the greater economic constraints that small towns face and their important relationship with the surrounding hinterlands.
Cavadini, G. B., & Cook, L. M. (2021). Green and cool roof choices integrated into rooftop solar energy modelling. Applied Energy, 296, 117082 (13 pp.). doi:10.1016/j.apenergy.2021.117082, Institutional Repository

Due to their cooling ability, sustainable roofing configurations, such as green and cool roofs, have the potential to increase solar panel yield, which is temperature dependent. However, the influence of sustainable roofing configurations on panel yield is not yet considered in rooftop photovoltaic (PV) planning models; thus, the significance of these integrated systems cannot be evaluated. In order to quantify the potential benefits of sustainable rooftops on solar energy, the first goal of this study is to develop a method that systematically accounts for roof surface characteristics in the simulation of PV panel energy yield. To do so, a rooftop energy balance model is linked with a physically-based solar energy model (the System Advisor Model, SAM) to quantify the energy yield of PV installations on sustainable roofing configurations. Roof surface temperatures are first estimated using non-linear energy balance equations, then integrated into a revised version of SAM to simulate energy yield. This new method improves the accuracy of PV yield simulations, compared to prior assumptions of roof surface temperature equal to ambient temperature. This updated model is used for the second goal of the study, to understand how four roofing configurations (black membrane, rock ballasted, white membrane, and vegetated) influence PV panel yield, which is currently not well understood in cooler climates. For a flat rooftop PV installation near Zurich, Switzerland (temperate climate), results show that, compared to a conventional roof, green roofs can increase annual PV energy yield, on average, by 1.8%, whereas cool roofs can increase it by 3.4%. For the case-study installation, an inverse correlation between the 95th-quantile roof surface temperature and the PV energy yield was identified; an increase of 1 °C leads to a 71 kWh reduction in energy yield per year. Overall, cool roofs outperform green roofs in terms of increases in PV energy yield; however, potential improvements of both systems are non-negligible, even in relatively cooler climate regions like Switzerland. By providing a systematic method to evaluate the influence of the roofing configuration on PV energy yield, solar energy planners are able to differentiate between the benefits of traditional and sustainable rooftop configurations - the first step towards the coupling of distributed energy and sustainable building systems. In the future, this integrated method could be used as part of a holistic evaluation of the environmental, economic, and social objectives of green and cool roofs, as well as, other infrastructure systems.
Cook, L. M., & Larsen, T. A. (2021). Towards a performance-based approach for multifunctional green roofs: an interdisciplinary review. Building and Environment, 188, 107489 (14 pp.). doi:10.1016/j.buildenv.2020.107489, Institutional Repository

Green roofs have the potential to offer numerous ecosystem services; however, they are rarely designed to achieve them. Instead, design is restricted by perceived structural and maintenance constraints, which consequently diminish the achievable benefits. For green roofs to improve sustainability and resilience of cities, their design should match their promised multi-functional application using performance-based design. The first step towards a comprehensive performance model is to synthesize design recommendations across disciplines to identify synergies and trade-offs in design objectives for multiple benefits. This study discusses design strategies that could alter the energy and water balance in the green roof in order to attenuate urban stormwater, increase building energy performance, mitigate urban heat, and improve the output of solar panels placed on top of green roofs. These benefits are mathematically linked to quantifiable processes (discharge rate, water content, evapotranspiration, sensible heat, net radiation, insulation, and thermal mass), forming the foundation for a performance-based design model. Design recommendations are then summarized for each process, followed by a discussion of synergies, trade-offs, and research needs that arise when green roofs are designed to achieve multiple functions. Selecting vegetation with high leaf area and albedo improves multiple benefits without affecting structural constraints, whereas choosing plants with low stomatal resistance leads to trade-offs between higher evapotranspiration and higher irrigation requirements. Trade-offs in substrate depth and properties including organic matter and moisture are also apparent. Interdisciplinary collaborations are needed to simulate and optimize design parameters based on stakeholder preferences related to co-benefits and constraints.
Cook, L. M., VanBriesen, J. M., & Samaras, C. (2021). Using rainfall measures to evaluate hydrologic performance of green infrastructure systems under climate change. Sustainable and Resilient Infrastructure, 6(3-4), 156-180. doi:10.1080/23789689.2019.1681819, Institutional Repository

As climate change alters precipitation patterns, stakeholders will need to understand how performance of green stormwater infrastructure (GSI) could change in response. As an alternative to using on-site monitoring, which may not always feasible, we propose that changes in performance could be tracked using annual rainfall measures (e.g., maximum daily rainfall per year). We estimated performance of GSI in 17 U.S. cities using rainfall measures by establishing linear relationships with specific performance metrics (e.g., frequency of discharge). Prediction accuracy was evaluated in 2 cities for the period 2020 to 2060 by comparing performance predicted from rainfall trends from regional climate models (RCMs) with simulated performance in SWMM using the same RCMs as input. Findings suggest that tracking rainfall measures can provide insight into the hydrologic performance of green infrastructure by predicting the direction of change, as well as, the magnitude within 25% to 50% percent change.
Fernandez-Cassi, X., Scheidegger, A., Bänziger, C., Cariti, F., Tuñas Corzon, A., Ganesanandamoorthy, P., … Kohn, T. (2021). Wastewater monitoring outperforms case numbers as a tool to track COVID-19 incidence dynamics when test positivity rates are high. Water Research, 200, 117252 (9 pp.). doi:10.1016/j.watres.2021.117252, Institutional Repository

Wastewater-based epidemiology (WBE) has been shown to coincide with, or anticipate, confirmed COVID-19 case numbers. During periods with high test positivity rates, however, case numbers may be underreported, whereas wastewater does not suffer from this limitation. Here we investigated how the dynamics of new COVID-19 infections estimated based on wastewater monitoring or confirmed cases compare to true COVID-19 incidence dynamics. We focused on the first pandemic wave in Switzerland (February to April, 2020), when test positivity ranged up to 26%. SARS-CoV-2 RNA loads were determined 2–4 times per week in three Swiss wastewater treatment plants (Lugano, Lausanne and Zurich). Wastewater and case data were combined with a shedding load distribution and an infection-to-case confirmation delay distribution, respectively, to estimate infection incidence dynamics. Finally, the estimates were compared to reference incidence dynamics determined by a validated compartmental model. Incidence dynamics estimated based on wastewater data were found to better track the timing and shape of the reference infection peak compared to estimates based on confirmed cases. In contrast, case confirmations provided a better estimate of the subsequent decline in infections. Under a regime of high-test positivity rates, WBE thus provides critical information that is complementary to clinical data to monitor the pandemic trajectory.
Figueroa, A., Hadengue, B., Leitão, J. P., Rieckermann, J., & Blumensaat, F. (2021). A distributed heat transfer model for thermal-hydraulic analyses in sewer networks. Water Research, 204, 117649 (11 pp.). doi:10.1016/j.watres.2021.117649, Institutional Repository

Thermal-hydraulic considerations in urban drainage networks are essential to utilise available heat capacities from waste- and stormwater. However, available models are either too detailed or too coarse; fully coupled thermal-hydrodynamic modelling tools are lacking. To predict efficiently water-energy dynamics across an entire urban drainage network, we suggest the SWMM-HEAT model, which extends the EPA-StormWater Management Model with a heat-balance component. This enables conducting more advanced thermal-hydrodynamic simulation at full network scale than currently possible. We demonstrate the usefulness of the approach by predicting temperature dynamics in two independent real-world cases under dry weather conditions. We furthermore screen the sensitivity of the model parameters to guide the choice of suitable parameters in future studies. Comparison with measurements suggest that the model predicts temperature dynamics adequately, with RSR values ranging between 0.71 and 1.1. The results of our study show that modelled in-sewer wastewater temperatures are particularly sensitive to soil and headspace temperature, and headspace humidity. Simulation runs are generally fast; a five-day period simulation at high temporal resolution of a network with 415 nodes during dry weather was completed in a few minutes. Future work should assess the performance of the model for different applications and perform a more comprehensive sensitivity analysis under more scenarios. To facilitate the efficient estimation of available heat budgets in sewer networks and the integration into urban planning, the SWMM-HEAT code is made publicly available.
Figueroa, A., Jackiewicz, Z., & Löhner, R. (2021). Efficient two-step Runge-Kutta methods for fluid dynamics simulations. Applied Numerical Mathematics, 159, 1-20. doi:10.1016/j.apnum.2020.08.013, Institutional Repository

Explicit two-step Runge-Kutta (TSRK) methods offer an efficient alternative to traditional explicit Low-Storage Runge-Kutta (LSRK) schemes for solving the Navier-Stokes equations. A special class of TSRK methods that reduce requirement compared to previous TSRK schemes are derived. Schemes of fourth, fifth and sixth order are implemented and tested. The new schemes are evaluated with two common test cases, a 2D cylinder and a 3D Taylor-Green vortex. The results are compared with classical time discretization strategies. Timings obtained in three different hardware configurations show that the new TSRK methods of order four are 25% faster than LSRK schemes of the same order. Fifth and sixth order TSRK methods are tested with the same 3D test case and the results are compared to LSRK algorithms. Results show TSRK schemes of fifth and sixth order are competitive compared to LSRK methods of the same orders, as LSRK methods are of second order for non linear differential systems.
Gundlach, J., Bryla, M., Larsen, T. A., Kristoferitsch, L., Gründl, H., & Holzner, M. (2021). Novel NoMix toilet concept for efficient separation of urine and feces and its design optimization using computational fluid mechanics. Journal of Building Engineering, 33, 101500 (6 pp.). doi:10.1016/j.jobe.2020.101500, Institutional Repository

Separation of urine from feces in NoMix toilets bears a large potential for mitigating problems associated with the efficient reuse of excreta. A challenge lies in the design of these toilets that should maximize the amount of collected urine while at the same time minimizing its mixing with flush water that partly spills into the urine collector. We present a novel design idea that takes advantage of the so-called tea pot effect, which describes the attachment of liquids to a curved wall due to surface tension. This effect is used to efficiently guide the urine along a curved entrance into the collector. Computational Fluid Dynamics (CFD) simulations of the two phase (water/urine - air) system are carried out on different toilet geometries where we vary the inflow to the urine collector. In particular, we investigate two different radial curvatures and two V-profile grooves whose combinations result in four toilet geometries that are tested. We demonstrate that the urine collection can be dramatically improved by tailoring curvature radius and V-profile grooves, while keeping the amount of flush water in the urine collector at a minimum. A reduction to less the 2.5% of flush water entering the urine collector could be achieved by implementing a curvature radius of 15 mm and a V-profile groove width of 60 mm. The improved geometry's performance makes it an appealing candidate for broad application both in western and developing counties and we anticipate that this technology might replace traditional toilet designs in the future.
Guo, Z., Leitão, J. P., Simões, N. E., & Moosavi, V. (2021). Data-driven flood emulation: speeding up urban flood predictions by deep convolutional neural networks. Journal of Flood Risk Management, 14(1), e12684 (14 pp.). doi:10.1111/jfr3.12684, Institutional Repository

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image-to-image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data-driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood-safe urban layout planning.
Hadengue, B., Joshi, P., Figueroa, A., Larsen, T. A., & Blumensaat, F. (2021). In-building heat recovery mitigates adverse temperature effects on biological wastewater treatment: a network-scale analysis of thermal-hydraulics in sewers. Water Research, 204, 117552 (11 pp.). doi:10.1016/j.watres.2021.117552, Institutional Repository

Heat recovery from wastewater is a robust and straightforward strategy to reduce water-related energy consumption. Its implementation, though, requires a careful assessment of its impacts across the entire wastewater system as adverse effects on the water and resource recovery facility and competition among heat recovery strategies may arise. A model-based assessment of heat recovery from wastewater therefore implies extending the modeling spatial scope, with the aim of enabling thermal-hydraulic simulations from the household tap along its entire flow path down to the wastewater resource recovery facility. With this aim in mind, we propose a new modeling framework interfacing thermal-hydraulic simulations of (i) households, (ii) private lateral connections, and (iii) the main public sewer network. Applying this framework to analyze the fate of wastewater heat budgets in a Swiss catchment, we find that heat losses in lateral connections are large and cannot be overlooked in any thermal-hydraulic analysis, due to the high-temperature, low-flow wastewater characteristics maximizing heat losses to the environment. Further, we find that implementing shower drain heat recovery devices in 50% of the catchment's households lower the wastewater temperature at the recovery facility significantly less – only 0.3 K – than centralized in-sewer heat recovery, due to a significant thermal damping effect induced by lateral connections and secondary sewer lines. In-building technologies are thus less likely to adversely affect biological wastewater treatment processes. The proposed open-source modeling framework can be applied to any other catchment. We thereby hope to enable more efficient heat recovery strategies, maximizing energy harvesting while minimising impacts on biological wastewater treatment.
Hess, A., Baum, C., Schiessl, K., Besmer, M. D., Hammes, F., & Morgenroth, E. (2021). Stagnation leads to short-term fluctuations in the effluent water quality of biofilters: a problem for greywater reuse?. Water Research X, 13, 100120 (9 pp.). doi:10.1016/j.wroa.2021.100120, Institutional Repository

A key characteristic of decentralized greywater treatment and reuse is high variability in both nutrient concentrations and flow. This variability in flow leads to stagnant water in the system and causes short-term fluctuations in the effluent water quality. Automated monitoring tools provide data to understand the mechanisms underlying the dynamics and to adapt control strategies accordingly. We investigated the fluctuations in a building-scale greywater treatment system comprising a membrane bioreactor followed by a biological activated carbon filter. Short-term dynamics in the effluent of the biological activated carbon filter were monitored with automated flow cytometry and turbidity, and the impact of these fluctuations on various hygiene-relevant parameters in the reuse water was evaluated. Continuous biofilm detachment into the stagnant water in the biological activated carbon filter led to temporarily increased turbidity and cell concentrations in the effluent after periods of stagnation. The fluctuations in cell concentrations were consistent with a model assuming higher detachment rates during flow than during times with stagnant water. For this system, total cell concentration and turbidity were strongly correlated. We also showed that the observed increase in cell concentration was not related to either an increase of organic carbon concentration or the concentration of two opportunistic pathogens, P. aeruginosa and L. pneumophila. Our findings demonstrate that turbidity measurements are sensitive to changes in the effluent water quality and can be used to monitor the fluctuations caused by intermittent flow. Intermittent flow did not lead to an increase in opportunistic pathogens, and this study provides no indications that stagnant water in biological activated carbon filters need be prevented.
Jamali, B., Haghighat, E., Ignjatovic, A., Leitão, J. P., & Deletic, A. (2021). Machine learning for accelerating 2D flood models: potential and challenges. Hydrological Processes, 35(4), e14064 (14 pp.). doi:10.1002/hyp.14064, Institutional Repository

Two-dimensional hydrodynamic models numerically solve full Shallow Water Equations (SWEs). Despite their high accuracy, these models have long simulation run times and therefore are of limited use for exploratory or real-time flood predictions. We investigated the possibility of improving flood modelling speed using Machine Learning (ML). We propose a new method that replaces the computationally expensive parts of the hydrodynamic models with simple and efficient data-driven approximations. Our hypothesis is that by integrating ML with physics-based numerical methods, we can achieve improved generalization performance: that is, the trained model for one case study can be used in other studies without the need for new training. We tested two ML approaches: for the first, we integrated curve fitting, and, for the second, artificial neural networks (ANN) with a finite volume scheme to solve the local inertial approximation of the SWEs. The data-driven models approximated the Momentum Equation, which explicitly solved the time derivative of flow rates. Water depths were then updated by applying a water balance equation. We also tested two different training datasets: the simulated dataset, generated from the results of hydrodynamic model, and the random dataset, generated by directly solving the momentum equation on randomly sampled input data. Various combinations of input features, for example, water slope and depth, were explored. The proposed models were trained in a small hypothetical case and tested in a different hypothetical and in two real case studies. Results showed that the curve-fitting method can be implemented successfully, given sufficient training and input data. The ANN model trained with a random dataset was substantially more accurate than that of the model trained with the simulated dataset. However, it was not successful in the real case studies. The curve-fitting method resulted in better generalization performance and increased the simulation speed of the local inertial model by 23%. Future research should test the performance of ML in terms of an increase in stable time step size and approximation of the full SWEs.
Joshi, P., Leitão, J. P., Maurer, M., & Bach, P. M. (2021). Not all SuDS are created equal: impact of different approaches on combined sewer overflows. Water Research, 191, 116780 (13 pp.). doi:10.1016/j.watres.2020.116780, Institutional Repository

Sustainable urban drainage systems (SuDS) help in stormwater management by reducing runoff volume, increasing runoff concentration time and thereby improving the drainage system capacity. This study investigated the potential and cost-effectiveness of SuDS in reducing combined sewer overflows (CSOs). We simulated the performance of four SuDS techniques (bioretention cell, permeable pavement, rain barrel and green roof) at incremental levels of spatial coverage for a small urban catchment with a combined sewer system. We also used an Analytic Hierarchy Process (AHP) considering end-point CSO, land use, imperviousness, slope and elevation criteria to identify priority areas for SuDS deployment. Results show that CSO volume attenuation ranged a maximum of 50-99% for the catchment, depending on the deployment strategy and underlying mechanisms of each technology. We also found that deployment of SuDS in AHP-selected sub-catchments improved CSO reduction only for rain barrels and green roofs, but not for bioretention cells and permeable pavements. SuDS were also a cost-effective retrofit option: for a 40% volume reduction, the SuDS cost, at most, 25% of the equivalent cost required for a large CSO tank. Outcomes of this study demonstrate the efficacy of SuDS in controlling CSOs, adding yet another tangible benefit to their increasingly recognised multi-functionality.
Larsen, T. A., Riechmann, M. E., & Udert, K. M. (2021). State of the art of urine treatment technologies: a critical review. Water Research X, 13, 100114 (20 pp.). doi:10.1016/j.wroa.2021.100114, Institutional Repository

Over the last 15 years, urine treatment technologies have developed from lab studies of a few pioneers to an interesting innovation, attracting attention from a growing number of process engineers. In this broad review, we present literature from more than a decade on biological, physical-chemical and electrochemical urine treatment processes. Like in the first review on urine treatment from 2006, we categorize the technologies according to the following objectives: stabilization, volume reduction, targeted N-recovery, targeted P-recovery, nutrient removal, sanitization, and handling of organic micropollutants. We add energy recovery as a new objective, because extensive work has been done on electrochemical energy harvesting, especially with bio-electrochemical systems. Our review reveals that biological processes are a good choice for urine stabilization. They have the advantage of little demand for chemicals and energy. Due to instabilities, however, they are not suited for bathroom applications and they cannot provide the desired volume reduction on their own. A number of physical-chemical treatment technologies are applicable at bathroom scale and can provide the necessary volume reduction, but only with a steady supply of chemicals and often with high demand for energy and maintenance. Electrochemical processes is a recent, but rapidly growing field, which could give rise to exciting technologies at bathroom scale, although energy production might only be interesting for niche applications. The review includes a qualitative assessment of all unit processes. A quantitative comparison of treatment performance was not the goal of the study and could anyway only be done for complete treatment trains. An important next step in urine technology research and development will be the combination of unit processes to set up and test robust treatment trains. We hope that the present review will help guide these efforts to accelerate the development towards a mature technology with pilot scale and eventually full-scale implementations.
Larsen, T. A., Gruendl, H., & Binz, C. (2021). The potential contribution of urine source separation to the SDG agenda - a review of the progress so far and future development options. Environmental Science: Water Research and Technology, 7(7), 1161-1176. doi:10.1039/D0EW01064B, Institutional Repository

Sanitation and wastewater management are highly relevant for reaching a number of interconnected sustainable development goals (SDGs), especially SDG 6, the provision of safe drinking water and adequate sanitation for all as well as protection of water resources against pollution, and SDG 14.1, reducing nutrient emissions to the marine environment. Recent evidence increasingly shows that conventional sewer-based wastewater management will not be able to reach these targets. Rather than further optimizing and diffusing this ageing infrastructure paradigm, radical innovations like urine source separation technologies could help to leapfrog towards faster achievement of the SDGs. The technology would simplify on-site sanitation and develop a closed-loop nutrient cycle, thereby allowing for exceptionally high nutrient removal from wastewater and direct reuse in agriculture from the first day of implementation. Radical innovations, however, need decades to materialize. Based on a review of relevant academic and grey literature, we show how the past three decades of development of urine source separation have brought breakthroughs in toilet design and treatment processes, enabling the technology's value chain to reach the brink of maturity. In a short outlook, we discuss how the technology may reach global diffusion over the next decade, with the main remaining challenges relating to the creation of mass-markets for urine-diverting toilets, automation and mass-production of treatment systems, and the legitimation of fertilizer produced from urine in the agricultural sector.
Leite, A., & Leitão, J. (2021). The new town of Angra (Terceira, the Azores): confirming a contested urban planning history using reverse historical analysis and flood modelling tools. Urban History, 48(1), 20-36. doi:10.1017/S0963926819001093, Institutional Repository

The Portuguese discovered the uninhabited Azores archipelago in 1427 and started to settle it during the fifteenth century. Angra, located on Terceira Island, soon became the most important Azorean city, leading to rapid urban development. This article investigates the overlooked role of streams in the rise of Angra as a pivotal Atlantic urban centre. Through the intersection of historical and flood modelling methods, it makes a unique contribution to our understanding of Angra's urban morphologic development, highlighting the potential of applying urban flood modelling to analysis of the rise of coastal urban settlement.
Manny, L., Duygan, M., Fischer, M., & Rieckermann, J. (2021). Barriers to the digital transformation of infrastructure sectors. Policy Sciences, 54, 943-983. doi:10.1007/s11077-021-09438-y, Institutional Repository

Digital technologies can be important to policy-makers and public servants, as these technologies can increase infrastructure performance and reduce environmental impacts. For example, utilizing data from sensors in sewer systems can improve their management, which in turn may result in better surface water quality. Whether such big data from sensors is utilized is, however, not only a technical issue, but also depends on different types of social and institutional conditions. Our article identifies individual, organizational, and institutional barriers at the level of sub-states that hinder the evaluation of data from sewer systems. We employ fuzzy-set Qualitative Comparative Analysis (fsQCA) to compare 23 Swiss sub-states and find that two barriers at different levels can each hinder data evaluation on their own. More specifically, either a lack of vision at the individual level or a lack of resources at the organizational level hinder the evaluation of data. Findings suggest that taking into account different levels is crucial for understanding digital transformation in public organizations.
Merz, E., Kozakiewicz, T., Reyes, M., Ebi, C., Isles, P., Baity-Jesi, M., … Pomati, F. (2021). Underwater dual-magnification imaging for automated lake plankton monitoring. Water Research, 203, 117524 (12 pp.). doi:10.1016/j.watres.2021.117524, Institutional Repository

The Dual Scripps Plankton Camera (DSPC) is a new approach for automated in-situ monitoring of phyto- and zooplankton communities based on a dual magnification dark-field imaging microscope. Here, we present the DSPC and its associated image processing while evaluating its capabilities in i) detecting and characterizing plankton species of different size and taxonomic categories and ii) measuring their abundance in both laboratory and field applications. In the laboratory, body size and abundance estimates by the DSPC significantly and robustly scaled with measurements derived by microscopy. In the field, a DSPC installed permanently at 3 m depth in Lake Greifensee (Switzerland) delivered images of plankton individuals, colonies, and heterospecific aggregates at hourly timescales without disrupting natural arrangements of interacting organisms, their microenvironment or their behavior. The DSPC was able to track the dynamics of taxa, mostly at the genus level, in the size range between ∼10 μm to ∼ 1 cm, covering many components of the planktonic food web (including parasites and potentially toxic cyanobacteria). Comparing data from the field-deployed DSPC to traditional sampling and microscopy revealed a general overall agreement in estimates of plankton diversity and abundances. The most significant disagreements between traditional methods and the DSPC resided in the measurements of zooplankton community properties. Our data suggest that the DSPC is better equipped to study the dynamics and demography of heterogeneously distributed organisms such as zooplankton, because high temporal resolution and continuous sampling offer more information and less variability in taxa detection and quantification than traditional sampling. Time series collected by the DSPC depicted ecological succession patterns, algal bloom dynamics and diel fluctuations with a temporal frequency and morphological resolution that was never observed by traditional methods. Access to high frequency, reproducible and real-time data of a large spectrum of the planktonic ecosystem expands our understanding of both applied and fundamental plankton ecology. We conclude the DSPC is robust for both research and water quality monitoring and suitable for stable long-term deployments.
Narayan, A. S., Maurer, M., & Lüthi, C. (2021). The clean plan: analysing sanitation planning in India using the CWIS planning framework. Journal of Water Sanitation and Hygiene for Development, 11(6), 1036-1047. doi:10.2166/washdev.2021.130, Institutional Repository

Sanitation in India has received national attention for over a decade, especially with the Swachh Bharat Mission (SBM) making it a political priority. However, due to the lack of appropriate sanitation planning practices, there have been little long-term gains made in urban sanitation beyond the ending of open defaecation. In this paper, we analyse the key barriers to sanitation planning, in India, in the context of the emerging paradigm of Citywide Inclusive Sanitation (CWIS). A mixed method approach of shit flow diagrams, social network analysis, policy analysis, interviews and workshops at the national, state (2) and city (4) levels was conducted. Eight factors were identified as important barriers for planning including inadequate planning capacities, lack of ownership of city sanitation plans among city governments, poor community involvement, absence of a uniform planning framework, unreliable political and financial support, overlapping jurisdictions, and scheme-based funding. The paper also proposes the CWIS Planning Framework which offers a perspective at overcoming these barriers with the recommendation of bridging top-down and bottom-up planning approaches. While there is increasingly more clarity on what CWIS means, there is little understanding on how to plan for it. Therefore, this framework provides the theoretical basis for planning with the CWIS approach.
Penn, R., & Maurer, M. (2021). Effects of transition to water‐efficient solutions on existing centralized sewer systems - an integrated biophysical modelling approach. Water Resources Research, 57(9), e2020WR027616 (19 pp.). doi:10.1029/2020WR027616, Institutional Repository

Technical alternatives offering high water efficiency are being developed to complement existing centralized water and wastewater conveyance and treatment systems. Understanding of the negative effects wide-spread implementation of such alternative systems may have on existing systems is still limited, despite the importance of this inevitable transition. In this paper we present a novel approach to systematically identify these negative impacts. A comprehensive model integrating transport and transformation processes in sewer pipes was set up and simulated for various transition scenarios. A significant finding of this research is that reduced flows and increased chemical oxygen demand (COD) concentrations result in the accumulation of sediments and sulphides in sewer pipes. For the examined sewer system, when reducing the total daily flow by 50%, 30% of the pipes were found to accumulate sediments and 30% of the pipes experienced effects due to the presence of sulphides in different states depending on the wastewater temperature. It was found that an optimal spatial distribution of these water-efficient solutions will most likely require compromises between sediment and sulphide accumulation. The product of two design parameters, diameter and bed slope, was found to be a suitable predictor of the potential of a pipe to accumulate sediments. This research provides novel approaches to assessing the feasibility of transition to solutions offering higher water efficiency. The results indicate where the most affected points in the systems are expected to occur and might help in preparing an experimental approach to assess such negative effects, increasing urban water management efficiency.
Penn, R., Ward, B. J., Strande, L., & Maurer, M. (2021). Faecal sludge simulants: review of synthetic human faeces and faecal sludge for sanitation and wastewater research. In K. Velkushanova, L. Strande, M. Ronteltap, T. Koottatep, D. Brdjanovic, & C. Buckley (Eds.), Methods for faecal sludge analysis (pp. 195-234). London: IWA Publishing. , Institutional Repository
Ramgraber, M., Weatherl, R., Blumensaat, F., & Schirmer, M. (2021). Non‐Gaussian parameter inference for hydrogeological models using Stein Variational Gradient Descent. Water Resources Research, 57(4), e2020WR029339 (21 pp.). doi:10.1029/2020WR029339, Institutional Repository

The sustainable management of groundwater demands a faithful characterization of the subsurface. This, in turn, requires information which is generally not readily available. To bridge the gap between data need and availability, numerical models are often used to synthesize plausible scenarios not only from direct information but also additional, indirect data. Unfortunately, the resulting system characterizations will rarely be unique. This poses a challenge for practical parameter inference: Computational limitations often force modelers to resort to methods based on questionable assumptions of Gaussianity, which do not reproduce important facets of ambiguity such as Pareto fronts or multi‐modality. In search of a remedy, an alternative could be found in Stein Variational Gradient Descent (SVDG), a recent development in the field of statistics. This ensemble‐based method iteratively transforms a set of arbitrary particles into samples of a potentially non‐Gaussian posterior, provided the latter is sufficiently smooth. A prerequisite for this method is knowledge of the Jacobian, which is usually exceptionally expensive to evaluate. To address this issue, we propose an ensemble‐based, localized approximation of the Jacobian. We demonstrate the performance of the resulting algorithm in two cases: a simple, bimodal synthetic scenario, and a complex numerical model based on a real‐world, pre‐alpine catchment. Promising results in both cases - even when the ensemble size is smaller than the number of parameters - suggest that Stein Variational Gradient Descent can be a valuable addition to hydrogeological parameter inference.
Russo, S., Besmer, M. D., Blumensaat, F., Bouffard, D., Disch, A., Hammes, F., … Villez, K. (2021). The value of human data annotation for machine learning based anomaly detection in environmental systems. Water Research, 206, 117695 (10 pp.). doi:10.1016/j.watres.2021.117695, Institutional Repository

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.
Shi, B., Catsamas, S., Kolotelo, P., Wang, M., Lintern, A., Jovanovic, D., … McCarthy, D. T. (2021). A low‐cost water depth and electrical conductivity sensor for detecting inputs into urban stormwater networks. Sensors, 21(9), 3056 (22 pp.). doi:10.3390/s21093056, Institutional Repository

High‐resolution data collection of the urban stormwater network is crucial for future asset management and illicit discharge detection, but often too expensive as sensors and ongoing frequent maintenance works are not affordable. We developed an integrated water depth, electrical conductivity (EC), and temperature sensor that is inexpensive (USD 25), low power, and easily implemented in urban drainage networks. Our low‐cost sensor reliably measures the rate‐of‐change of water level without any re‐calibration by comparing with industry‐standard instruments such as HACH and HORIBA’s probes. To overcome the observed drift of level sensors, we developed an automated re‐calibration approach, which significantly improved its accuracy. For applications like monitoring stormwater drains, such an approach will make higher‐resolution sensing feasible from the budget control considerations, since the regular sensor re‐calibration will no longer be required. For other applications like monitoring wetlands or wastewater networks, a manual re‐calibration every two weeks is required to limit the sensor’s inaccuracies to ±10 mm. Apart from only being used as a calibrator for the level sensor, the conductivity sensor in this study adequately monitored EC between 0 and 10 mS/cm with a 17% relative uncertainty, which is sufficient for stormwater monitoring, especially for real‐time detection of poor stormwater quality inputs. Overall, our proposed sensor can be rapidly and densely deployed in the urban drainage network for revolutionised high‐density monitoring that cannot be achieved before with high‐end loggers and sensors.
Spuhler, D., Scheidegger, A., & Maurer, M. (2021). Ex-ante quantification of nutrient, total solids, and water flows in sanitation systems. Journal of Environmental Management, 280, 111785 (17 pp.). doi:10.1016/j.jenvman.2020.111785, Institutional Repository

To prioritise sustainable sanitation systems in strategic sanitation planning, indicators such as local appropriateness or resource recovery have to be known at the pre-planning phase. The quantification of resource recovery remains a challenge because existing substance flow models require large amounts of input data and can therefore only be applied for a few options at a time for which implementation examples exist. This paper aims to answer two questions: How can we predict resource recovery and losses of sanitation systems ex-ante at the pre-planning phase? And how can we do this efficiently to consider the entire sanitation system option space? The approach builds on an existing model to create all valid sanitation systems from a set of conventional and emerging technologies and to evaluate their appropriateness for a given application case. It complements the previous model with a Substance Flow Model (SFM) and with transfer coefficients from a technology library to quantify nutrients (phosphorus and nitrogen), total solids (as an indicator for energy and organics), and water flows in sanitation systems ex ante. The transfer coefficients are based on literature data and expert judgement. Uncertainties resulting from the variability of literature data or ignorance of experts are explicitly considered, allowing to assess the robustness of the model output. Any (future) technologies or additional products can easily be added to the library. The model is illustrated with a small didactic example showing how 12 valid system configurations are generated from a few technologies, and how substance flows, recovery ratios, and losses to soil, air, and water are quantified considering uncertainties. The recovery ratios vary between 0 and 28% for phosphorus, 0–10% for nitrogen, 0–26% for total solids, and 0–12% for water. The uncertainties reflect the high variability of the literature data but are comparable to those obtained in studies using a conventional post-ante material flow analysis (generally about 30% variability at the scale of a an urban area). Because the model is fully automated and based on literature data, it can be applied ex-ante to a large and diverse set of possible sanitation systems as shown with a real application case. From the 41 technologies available in the library, 101,548 systems are generated and substance flows are modelled. The resulting recovery ratios range from nothing to almost 100%. The two examples also show that recovery depend on technology interactions and has therefore to be assessed for all possible system configurations and not at the single technology level only. The examples also show that there exist trade-offs among different types of reuse (e.g. energy versus nutrients) or different sustainability indicators (e.g. local appropriateness versus resource recovery). These results show that there is a need for such an automated and generic approach that provides recovery data for all system configurations already at the pre-planning phase. The approach presented enables to integrate transparently the best available knowledge for a growing number of sanitation technologies into a planning process. The resulting resource recovery and loss ratios can be used to prioritise resource efficient systems in sanitation planning, either for the pre-selection or the detailed evaluation of options using e.g. MCDA. The results can also be used to guide future development of technology and system innovations. As resource recovery becomes more relevant and novel sanitation technologies and system options emerge, the approach presents itself as a useful tool for strategic sanitation planning in line with the Sustainable Development Goals (SDGs).
Strande, L., Englund, M., Andriessen, N., Carbajal, J. P., & Scheidegger, A. (2021). Estimating quantities and qualities (Q&Q) of faecal sludge at community to city‐wide scales. In K. Velkushanova, L. Strande, M. Ronteltap, T. Koottatep, D. Brdjanovic, & C. Buckley (Eds.), Methods for faecal sludge analysis (pp. 115-144). London: IWA Publishing. , Institutional Repository
Stravs, M. A., Stamm, C., Ort, C., & Singer, H. (2021). Transportable automated HRMS platform "mS2field" enables insights into water-quality dynamics in real time. Environmental Science and Technology Letters, 8(5), 373-380. doi:10.1021/acs.estlett.1c00066, Institutional Repository

Tracking the occurrence of a broad variety of chemicals in the aquatic environment at a high temporal resolution over extended time periods is challenging. Here, we present a transportable platform integrating sample preparation, liquid chromatography-high-resolution mass spectrometry measurement, automated data processing, and remote control and surveillance. The system is capable of quantifying pollutants at 20 min intervals with high sensitivity (limit of quantification down to 10 ng/L) and operated stably on-site over prolonged periods (<3 ppm mass drift over two weeks, <0.3 min retention time drift over six weeks). Field campaigns under different conditions demonstrated unprecedented insights into pollutant dynamics. Time-resolved measurements in a small creek revealed concentration peaks with up to 10-fold exceedances of environmental quality standards. The quantification of illicit drugs and their metabolites in raw wastewater distinguished fractions of consumed and discarded cocaine. Frequency analyses and data mining showed clusters of diurnal, episodic, and possibly industry-related pollutants, including newly identified substances. With this enabling technology the potential for researching and managing chemicals in natural and technical environments can be significantly expanded beyond our current capabilities, for example, real-time control in process engineering and sewer operation (water management and environmental toxicology), industrial surveillance, and wastewater-based epidemiology.
Wang, W., Leitão, J. P., & Wani, O. (2021). Is flow control in a space-constrained drainage network effective? A performance assessment for combined sewer overflow reduction. Environmental Research, 202, 111688 (11 pp.). doi:10.1016/j.envres.2021.111688, Institutional Repository

Recurring combined sewer overflows (CSOs) can have a significant impact on the ecological condition of receiving water bodies. There are several structural measures, like adding retention basins and switching to low impact development solutions, that have been proposed to reduce the number of sewage overflows. Besides, several flow control strategies have been discussed in scientific literature that take advantage of the space within urban drainage networks, which is assumed to be adequate, for temporary storage. The adequacy of such storage space, however, is not a universally valid assumption as a large fraction of drainage networks frequently operate close to their design discharge. In this paper, we investigate the efficacy of flow control for a space-constrained drainage network. We employ a low-cost, heuristic real time control strategy with the use of flow control devices (FCDs) and available in-sewer space to reduce the magnitude of CSOs. We consider the performance of the proposed control strategy and discuss the effect of FCD location on CSO reduction. Our results, based on over 300 rainfall-event simulations, show that the flow control strategy using limited sewer capacity is more efficient during relatively small rainfall events, where the CSO is large enough to enable reduction using the chosen control rules. The CSO is reduced, to varying degrees, for around 80% of rainfall events with peak intensity between 10 and 20 mm h−1. For larger rainfall events, the flow control is more unstable in response to abrupt water release during control operation, which seems to be unavoidable because of the water accumulation effect and the transition to pressurized pipe flow in space-constrained networks. We also found that the flow control performance is highly sensitive to the FCD location – as it depends on the interplay of the peak rainfall intensity and the water level condition immediately upstream of the FCD. The efficacy of a location for flow control is determined by the unfilled capacity (i.e., effective in-sewer storage potential) in the pipe upstream of the FCD during the rainfall peak; furthermore, the location also has to be resistant to the water accumulation effect. Using our analysis, we substantiate two anticipated caveats to flow control strategies when the storage space is limited in a drainage network: diminished performance in CSO reduction and the appearance of additional control-related challenges, which are otherwise mitigated in more spacious networks.
Špačková, A., Bareš, V., Fencl, M., Schleiss, M., Jaffrain, J., Berne, A., & Rieckermann, J. (2021). A year of attenuation data from a commercial dual-polarized duplex microwave link with concurrent disdrometer, rain gauge, and weather observations. Earth System Science Data, 13(8), 4219-4240. doi:10.5194/essd-13-4219-2021, Institutional Repository

Commercial microwave links (CMLs) in telecommunication networks can provide relevant information for remote sensing of precipitation and other environmental variables, such as path-averaged drop size distribution, evaporation, or humidity. The CoMMon field experiment (COmmercial Microwave links for urban rainfall MONitoring) mainly focused on the rainfall observations by monitoring a 38 GHz dual-polarized CML of 1.85 km path length at a high temporal resolution (4 s), as well as a co-located array of five disdrometers and three rain gauges over 1 year. The dataset is complemented with observations from five nearby weather stations. Raw and pre-processed data, which can be explored with a custom static HTML viewer, are available at https://doi.org/10.5281/zenodo.4923125 (Špačková et al., 2021). The data quality is generally satisfactory for further analysis, and potentially problematic measurements are flagged to help the analyst identify relevant periods for specific study purposes. Finally, we encourage potential applications and discuss open issues regarding future remote sensing with CMLs.
Bach, P. M., Kuller, M., McCarthy, D. T., & Deletic, A. (2020). A spatial planning-support system for generating decentralised urban stormwater management schemes. Science of the Total Environment, 726, 138282 (16 pp.). doi:10.1016/j.scitotenv.2020.138282, Institutional Repository

Current Water Sensitive Urban Design (WSUD) models are either purely technical or overly simplified, lacking consideration of urban planning and stakeholder preferences to adequately support stakeholders. We developed the Urban Biophysical Environments and Technologies Simulator (UrbanBEATS), which integrates stormwater management with urban planning to support the design and implementation of WSUD. This study specifically describes and tests UrbanBEATS' WSUD Planning Module, which combines spatial analysis, infrastructure design, preference elicitation and Monte Carlo methods to generate feasible stormwater management and harvesting infrastructure options in greenfield and existing urban environments. By applying UrbanBEATS to a real-world greenfield development case study in Melbourne, Australia (with data sourced from the project's water management plans and design consultants), we explore the variety of options generated by the model and analyse them collectively to demonstrate that UrbanBEATS can design similar WSUD systems (e.g. select suitable technology types, their sizes and locations) to actual infrastructure choices.
Chaudhary, P., D'Aronco, S., Leitão, J. P., Schindler, K., & Wegner, J. D. (2020). Water level prediction from social media images with a multi-task ranking approach. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 252-262. doi:10.1016/j.isprsjprs.2020.07.003, Institutional Repository

Floods are among the most frequent and catastrophic natural disasters and affect millions of people worldwide. It is important to create accurate flood maps to plan (offline) and conduct (real-time) flood mitigation and flood rescue operations. Arguably, images collected from social media can provide useful information for that task, which would otherwise be unavailable. We introduce a computer vision system that estimates water depth from social media images taken during flooding events, in order to build flood maps in (near) real-time. We propose a multi-task (deep) learning approach, where a model is trained using both a regression and a pairwise ranking loss. Our approach is motivated by the observation that a main bottleneck for image-based flood level estimation is training data: it is difficult and requires a lot of effort to annotate uncontrolled images with the correct water depth. We demonstrate how to efficiently learn a predictor from a small set of annotated water levels and a larger set of weaker annotations that only indicate in which of two images the water level is higher, and are much easier to obtain. Moreover, we provide a new dataset, named DEEPFLOOD, with 8145 annotated ground-level images, and show that the proposed multi-task approach can predict the water level from a single, crowd-sourced image with ≈11 cm root mean square error.
Cheng, T., Xu, Z., Yang, H., Hong, S., & Leitao, J. P. (2020). Analysis of effect of rainfall patterns on urban flood process by coupled hydrological and hydrodynamic modeling. Journal of Hydrologic Engineering, 25(1), 04019061 (14 pp.). doi:10.1061/(ASCE)HE.1943-5584.0001867, Institutional Repository

Urban flooding poses an increasing threat to citizens and property and has become a global challenge. The primary cause of urban flooding is rapid urbanization, which is outpacing infrastructure development and drainage system improvements. This paper utilizes a verified flood model that couples hydrological and hydrodynamic processes to investigate the flood response under rainfall events with different patterns. A sponge city pilot area in China is taken as a case study. Drainage system performance, surface inundation patterns, and relation between them are investigated by carrying out statistical analysis and Pearson correlation analysis, respectively. Results show that with increases in the rainfall recurrence interval (RRI) and peak position ratio (PPR), drainage system performance indicators (DSPIs) generally become worse, and the total inundation area (TIA) and surface inundation areas of larger depths become larger. The response time exhibits varied patterns but generally decreases with increasing recurrence intervals and peak ratios. For surface inundation, the recurrence interval presents an increasingly significant role compared with the peak ratio as rainfall becomes heavier. Compared with other DSPIs, the volume of overflowed manholes (VOMs) shows a delayed response to rainfall and has a stronger relationship with the inundation areas with greater water depths, which indicates a possible solution of utilizing manhole storage for water management and flood control. Analyzing the response of drainage systems and flood patterns to different rainfall events using a model coupling hydrological and hydrodynamic processes comprehensively reveals the specific problems of the study area and possible innovative solutions. The analytical method and knowledge gained can help support urban flood management in China and around the world.
Cook, L. M., McGinnis, S., & Samaras, C. (2020). The effect of modeling choices on updating intensity-duration-frequency curves and stormwater infrastructure designs for climate change. Climatic Change, 159, 289-308. doi:10.1007/s10584-019-02649-6, Institutional Repository

Intensity-duration-frequency (IDF) curves, commonly used in stormwater infrastructure design to represent characteristics of extreme rainfall, are gradually being updated to reflect expected changes in rainfall under climate change. The modeling choices used for updating lead to large uncertainties; however, it is unclear how much these uncertainties affect the design and cost of stormwater systems. This study investigates how the choice of spatial resolution of the regional climate model (RCM) ensemble and the spatial adjustment technique affect climate-corrected IDF curves and resulting stormwater infrastructure designs in 34 US cities for the period 2020 to 2099. In most cities, IDF values are significantly different between three spatial adjustment techniques and two RCM spatial resolutions. These differences have the potential to alter the size of stormwater systems designed using these choices and affect the results of climate impact modeling more broadly. The largest change in the engineering decision results when the design storm is selected from the upper bounds of the uncertainty distribution of the IDF curve, which changes the stormwater pipe design size by five increments in some cases, nearly doubling the cost. State and local agencies can help reduce some of this variability by setting guidelines, such as avoiding the use of the upper bound of the future uncertainty range as a design storm and instead accounting for uncertainty by tracking infrastructure performance over time and preparing for adaptation using a resilience plan.
Duque, N., Duque, D., Aguilar, A., & Saldarriaga, J. (2020). Sewer network layout selection and hydraulic design using a mathematical optimization framework. Water, 12(12), 3337 (22 pp.). doi:10.3390/w12123337, Institutional Repository

This paper proposes an iterative mathematical optimization framework to solve the layout and hydraulic design problems of sewer networks. The layout selection model determines the flow rate and direction per pipe using mixed-integer programming, which results in a tree-like structured network. This network layout parametrizes a second model that determines hydraulic features including the diameter and the upstream and downstream invert elevations of pipes using a shortest path algorithm. These models are embedded in an iterative scheme that refines a cost function approximation for the first model upon learning the actual design cost from the second model. The framework was successfully tested on two sewer network benchmarks from the literature and a real sewer network located in Bogotá, Colombia, that is proposed as a new instance. For both benchmarks, the proposed methodology found a better solution with up to 42% cost reduction compared to the best methodologies reported in the literature. These are near-optimal solutions with respect to construction cost that satisfy all hydraulic and pipe connectivity constraints of a sewer system.
González‐Mariño, I., Baz‐Lomba, J. A., Alygizakis, N. A., Andrés‐Costa, M. J., Bade, R., Barron, L. P., … Ort, C. (2020). Spatio‐temporal assessment of illicit drug use at large scale: evidence from 7 years of international wastewater monitoring. Addiction, 115(1), 109-120. doi:10.1111/add.14767, Institutional Repository

Background and aims Wastewater‐based epidemiology is an additional indicator of drug use that is gaining reliability to complement the current established panel of indicators. The aims of this study were to: (i) assess spatial and temporal trends of population‐normalized mass loads of benzoylecgonine, amphetamine, methamphetamine and 3,4‐methylenedioxymethamphetamine (MDMA) in raw wastewater over 7 years (2011-17); (ii) address overall drug use by estimating the average number of combined doses consumed per day in each city; and (iii) compare these with existing prevalence and seizure data. Design Analysis of daily raw wastewater composite samples collected over 1 week per year from 2011 to 2017. Setting and Participants Catchment areas of 143 wastewater treatment plants in 120 cities in 37 countries. Measurements Parent substances (amphetamine, methamphetamine and MDMA) and the metabolites of cocaine (benzoylecgonine) and of Δ9‐tetrahydrocannabinol (11‐nor‐9‐carboxy‐Δ9‐tetrahydrocannabinol) were measured in wastewater using liquid chromatography-tandem mass spectrometry. Daily mass loads (mg/day) were normalized to catchment population (mg/1000 people/day) and converted to the number of combined doses consumed per day. Spatial differences were assessed world‐wide, and temporal trends were discerned at European level by comparing 2011-13 drug loads versus 2014-17 loads. Findings Benzoylecgonine was the stimulant metabolite detected at higher loads in southern and western Europe, and amphetamine, MDMA and methamphetamine in East and North-Central Europe. In other continents, methamphetamine showed the highest levels in the United States and Australia and benzoylecgonine in South America. During the reporting period, benzoylecgonine loads increased in general across Europe, amphetamine and methamphetamine levels fluctuated and MDMA underwent an intermittent upsurge. Conclusions The analysis of wastewater to quantify drug loads provides near real‐time drug use estimates that globally correspond to prevalence and seizure data.
Guozden, T., Carbajal, J. P., Bianchi, E., & Solarte, A. (2020). Optimized balance between electricity load and wind-solar energy production. Frontiers in Energy Research, 8, 16 (10 pp.). doi:10.3389/fenrg.2020.00016, Institutional Repository

One of the greatest obstacles in the exploitation of wind and solar resources is the uncertainty in their availability, usually known as intermittence. These effects can be greatly diminished by combining wind and solar resources from different locations. In this article we propose a numerical optimization of future renewable capacity additions aimed to minimize the dispersion of the residual power, which is the remaining electricity load after subtracting the contribution of renewables. Results show that penetration of wind and solar power may increase in another 10% of energy share while keeping the dispersion of the residual power constant, by adding capacity at sites most positively correlated with electricity load. For further increments, an optimized distribution of wind and solar facilities compensates variations between renewables. In this situation, wind sites that anticorrelate with solar cycle play an important role.
Hadengue, B., Scheidegger, A., Morgenroth, E., & Larsen, T. A. (2020). Modeling the water-energy nexus in households. Energy and Buildings, 225, 110262 (10 pp.). doi:10.1016/j.enbuild.2020.110262, Institutional Repository

One third of the global carbon emissions are emitted by the building sector. Over the last decades, space heating loads have decreased in modern buildings, and domestic hot water (DHW) is now oftentimes the largest energy consumer in the household. We developed the WaterHub modeling framework to assess the potential of technologies or measures targeting DHW energy demand. The framework combines process-based technological models and stochastic water demand modeling in a modular way to allow for holistic simulations of complex DHW systems. In two rigorous tests of the modeling framework, we demonstrated the importance of water consumption dynamics in the modeling of DHW systems, showing that static modeling leads to underestimated heat losses and wrong energy consumption predictions. In an exemplary case study, we identified and quantified the synergistic interactions between water boiler temperatures and a drain water heat recovery device, demonstrating the strength of this methodology for optimizing strategies targeting DHW systems. With its modular structure, this open-source modeling framework can be extended to include any DHW-related technology, providing a useful common platform for collaboration between technology developers and water experts.
Hoffmann, S., Feldmann, U., Bach, P. M., Binz, C., Farrelly, M., Frantzeskaki, N., … Udert, K. M. (2020). A research agenda for the future of urban water management: exploring the potential of non-grid, small-grid, and hybrid solutions. Environmental Science and Technology, 54(9), 5312-5322. doi:10.1021/acs.est.9b05222, Institutional Repository

Recent developments in high- and middle-income countries have exhibited a shift from conventional urban water systems to alternative solutions that are more diverse in source separation, decentralization, and modularization. These solutions include non-grid, small-grid, and hybrid systems to address such pressing global challenges as climate change, eutrophication, and rapid urbanization. They close loops, recover valuable resources, and adapt quickly to changing boundary conditions such as population size. Moving to such alternative solutions requires both technical and social innovations to co-evolve over time into integrated socio-technical urban water systems. Current implementations of alternative systems in high- and middle-income countries are promising, but they also underline the need for research questions to be addressed from technical, social, and transformative perspectives. Future research should apply a transdisciplinary research approach through socio-technical "lighthouse" projects that apply alternative urban water systems at scale. Such research should leverage experience from lighthouse projects in a range of socio-economic contexts, identify their potentials and limitations from an integrated perspective, and share their successes and failures across the urban water sector.
Jamali, B., Bach, P. M., & Deletic, A. (2020). Rainwater harvesting for urban flood management - an integrated modelling framework. Water Research, 171, 115372 (11 pp.). doi:10.1016/j.watres.2019.115372, Institutional Repository

It is well known that rainwater harvesting (RWH) can augment water supply and reduce stormwater pollutant discharges. Due to the lack of continuous 2D modelling of urban flood coverage and its associated damage, the ability of RWH to reduce urban flood risks has not been fully evaluated. Literature suggests that small distributed storage spaces using RWH tanks will reduce flood damage only during small to medium flooding events and therefore cumulative assessment of their benefits is needed. In this study we developed a new integrated modelling framework that implements a semi-continuous simulation approach to investigate flood prevention and water supply benefits of RWH tanks. The framework includes a continuous mass balance simulation model that considers antecedent rainfall conditions and water demand/usage of tanks and predicts the available storage prior to each storm event. To do so, this model couples a rainfall-runoff tank storage model with a detailed stochastic end-use water demand model. The available storage capacity of tanks is then used as a boundary condition for the novel rapid flood simulation model. This flood model was developed by coupling the U.S. EPA Storm Water Management Model (SWMM) to the Cellular-Automata Fast Flood Evaluation (CA-ffé) model to predict the inundation depth caused by surcharges over the capacity of the drainage network. The stage-depth damage curves method was used to calculate time series of flood damage, which are then directly used for flood risk and cost-benefit analysis. The model was tested through a case study in Melbourne, using a recorded rainfall time series of 85 years (after validating the flood model against 1D-2D MIKE-FLOOD). Results showed that extensive implementation of RWH tanks in the study area is economically feasible and can reduce expected annual damage in the catchment by up to approximately 30 percent. Availability of storage space and temporal distribution of rainfall within an event were important factors affecting tank performance for flood reduction.
Moy de Vitry, M., & Leitão, J. P. (2020). The potential of proxy water level measurements for calibrating urban pluvial flood models. Water Research, 175, 115669 (14 pp.). doi:10.1016/j.watres.2020.115669, Institutional Repository

Urban pluvial flood models need to be calibrated with data from actual flood events in order to validate and improve model performance. Due to the lack of conventional sensor solutions, alternative sources of data such as citizen science, social media, and surveillance cameras have been proposed in literature. Some of the methods proposed boast high scalability but without an on-site survey, they can only provide proxy measurements for physical flooding variables (such as water level). In this study, the potential value of such proxy measurements was evaluated by calibrating an urban pluvial flood model with data from experimental flood events conducted in a 25 × 25 m facility, monitored with surveillance cameras and conventional sensors in parallel. Both ideal proxy data and actual image-based proxy measurements with noise were tested, and the effects of measurement location and measurement noise were investigated separately. The results with error-free proxy data confirm the theoretic potential of such measurements, as in half of the calibration configurations tested, ideal proxy data increases model performance by at least 70% compared to sensor data. However, image-based proxy data can contain complex correlated errors, which have a complex and predominantly negative effect on performance.
Mutzner, L., Bohren, C., Mangold, S., Bloem, S., & Ort, C. (2020). Spatial differences among micropollutants in sewer overflows: a multisite analysis using passive samplers. Environmental Science and Technology, 54(11), 6584-6593. doi:10.1021/acs.est.9b05148, Institutional Repository

Untreated sewer overflows can contaminate receiving waters with micropollutants. Although concentrations of discharged micropollutants can be ecotoxicologically relevant, only limited data is available to assess occurrence and spatial differences among sewer overflow catchments. Therefore, we present an innovative type of data obtained with passive samplers at 20 combined sewer overflow sites (2-7 events per site; 95 events in total). The data sheds light on concentration ranges for 13 representative polar organic micropollutants and shows that micropollutants in both municipal wastewater and stormwater can be relevant sources of contaminants. We identify indicator micropollutants for further studies: benzotriazole (80% interquantile of time-weighted average concentration: 250–4800 ng/L), carbamazepine (33-910 ng/L), diclofenac (78-1000 ng/L), carbendazim (21-900 ng/L), diazinon (2.1-53 ng/L), diuron (22-1100 ng/L), mecoprop (98–5300 ng/L), metolachlor (6-230 ng/L), and terbutryn (29-810 ng/L). These concentration estimates are assumed to be on the safe side for comparison with environmental quality standards (EQS). A majority of sewer overflow sites (13 of 20) show discharge concentrations above acute EQS for at least one micropollutant and thus would have to rely on dilution by receiving waters to not exceed any EQS. The intersite variability among sewer overflows exceed the within-site variability. Hence, future monitoring studies should cover more sewer overflow sites. No correlation could be found with event durations, specific storage volume or land use data, thus showing the complexity of micropollutant occurrence and indicating that other factors led to the observed high spatial variability. In conclusion, our results clearly show the potential relevance of micropollutants in sewer overflows and the need to assess site-specific measures.
Nagel, J. B., Rieckermann, J., & Sudret, B. (2020). Principal component analysis and sparse polynomial chaos expansions for global sensitivity analysis and model calibration: application to urban drainage simulation. Reliability Engineering and System Safety, 195, 106737 (16 pp.). doi:10.1016/j.ress.2019.106737, Institutional Repository

This paper presents an efficient surrogate modeling strategy for the uncertainty quantification and Bayesian calibration of a hydrological model. In particular, a process-based dynamical urban drainage simulator that predicts the discharge from a catchment area during a precipitation event is considered. The goal of the case study is to perform a global sensitivity analysis and to identify the unknown model parameters as well as the measurement and prediction errors. These objectives can only be achieved by cheapening the incurred computational costs, that is, lowering the number of necessary model runs. With this in mind, a regularity-exploiting metamodeling technique is proposed that enables fast uncertainty quantification. Principal component analysis is used for output dimensionality reduction and sparse polynomial chaos expansions are used for the emulation of the reduced outputs. Sobol’ sensitivity indices are obtained directly from the expansion coefficients by a mere post-processing. Bayesian inference via Markov chain Monte Carlo posterior sampling is drastically accelerated.
Naves, J., Rieckermann, J., Cea, L., Puertas, J., & Anta, J. (2020). Global and local sensitivity analysis to improve the understanding of physically-based urban wash-off models from high-resolution laboratory experiments. Science of the Total Environment, 709, 136152 (15 pp.). doi:10.1016/j.scitotenv.2019.136152, Institutional Repository

Physically-based urban wash-off models are a promising means of studying the transport of finer suspended solids and their associated pollutants during rain events, considering spatial and temporal heterogeneities. This study contributes to the understanding of these models through an in-depth sensitivity analysis to provide the necessary information to simplify the model and deal with parameter identifiability. First, based on twelve tailored high-resolution experiments, the accurate measurement of input variables was used to study the parameters of the Hairsine-Rose sediment transport model through a global sensitivity analysis. Using Standardized Regression Coefficients (SRC) and Extended Fourier Amplitude Sensitivity Test (EFAST) methods, the analysis showed that both the total washed-off mass and the TSS peaks concentration are highly sensitive to the critical mass, which considers the reduction in the detachment of particles when the sediment available decreases and is scattered over the surface. In addition, the rain- and flow-driven detachment parameters were presented as key for smaller and larger sediment particles, respectively. Then, those uncertainties that are associated in field studies with the determination of the model input variables were also considered by conducting a local sensitivity analysis. The initial load of sediment and the mean grain size were seen to be the most important variables, thus underlining the need for very accurate measurements here. Moreover, a precise definition of Harsine-Rose parameters is also necessary to achieve reliable results in order to work on treatment and management techniques to minimize the impact of urban surface contaminants on urban environments.
Paillex, A., Siebers, A. R., Ebi, C., Mesman, J., & Robinson, C. T. (2020). High stream intermittency in an alpine fluvial network: Val Roseg, Switzerland. Limnology and Oceanography, 65, 557-568. doi:10.1002/lno.11324, Institutional Repository

More than one‐third of the world's rivers cease to flow and go dry on a periodic basis—so‐called intermittent rivers. The frequency and duration of flow intermittency in running waters are increasing due to climate change and water demands for human use. Intermittency effects on stream biodiversity and ecosystem functioning are dramatic and are expected to become increasingly prevalent in alpine landscapes in the near future. This project used modified field sensors to measure flow intermittency, temperature, and water origin (groundwater, precipitation, glacier) at high spatio‐temporal resolution throughout an alpine fluvial network (Val Roseg, Switzerland). We continuously recorded water presence in 30 tributary streams and validated sensor performance with field‐collected measures. Three different flow regimes were observed in the network, including periodically intermittent, seasonally intermittent, and permanently flowing streams. Twenty‐four streams (80% of recorded streams) dried at least once during the sampling period. Principal components analysis along with generalized additive models showed alpine streams with low average temperature and high conductivity (groundwater‐fed) were prone to permanent flow, whereas streams with higher average temperature and low conductivity (glacier‐fed) typically had intermittent flow. The field sensors proved precise for simultaneously measuring flow intermittency, temperature, and water origin at high resolution throughout the river network. Overall, this approach provides an effective way to develop eco‐hydrological models that examine the effects of flow intermittency on biodiversity and ecosystem functioning in riverine networks.
Schneider, M. Y., Furrer, V., Sprenger, E., Carbajal, J. P., Villez, K., & Maurer, M. (2020). Benchmarking soft sensors for remote monitoring of on-site wastewater treatment plants. Environmental Science and Technology, 54(17), 10840-10849. doi:10.1021/acs.est.9b07760, Institutional Repository

On-site wastewater treatment plants (OSTs) are usually unattended, so failures often remain undetected and lead to prolonged periods of reduced performance. To stabilize the performance of unattended plants, soft sensors could expose faults and failures to the operator. In a previous study, we developed soft sensors and showed that soft sensors with data from unmaintained physical sensors can be as accurate as soft sensors with data from maintained ones. The monitored variables were pH and dissolved oxygen (DO), and soft sensors were used to predict nitrification performance. In the present study, we use synthetic data and monitor three plants to test these soft sensors. We find that a long solids retention time and a moderate aeration rate improve the pH soft-sensor accuracy and that the aeration regime is the main operational parameter affecting the accuracy of the DO soft sensor. We demonstrate that integrated design of monitoring and control is necessary to achieve robustness when extrapolating from one OST to another in the absence of plant-specific fine-tuning. Additionally, we provide a unique labeled dataset for further feature and data-driven soft-sensor development. Our benchmarking results indicate that it is feasible to monitor OSTs with unmaintained sensors and without plant-specific tuning of the developed soft sensors. This is expected to drastically reduce monitoring costs for OST-based sanitation systems.
Sitzenfrei, R., Kleidorfer, M., Bach, P. M., & Bacchin, T. K. (2020). Green infrastructures for urban water system: balance between cities and nature. Water, 12(5), 1456 (4 pp.). doi:10.3390/w12051456, Institutional Repository

Urban water systems face severe challenges such as urbanisation, population growth and climate change. Traditional technical solutions, i.e., pipe-based, grey infrastructure, have a single purpose and are proven to be unsustainable compared to multi-purpose nature-based solutions. Green Infrastructure encompasses on-site stormwater management practices, which, in contrast to the centralised grey infrastructure, are often decentralised. Technologies such as green roofs, walls, trees, infiltration trenches, wetlands, rainwater harvesting and permeable pavements exhibit multi-functionality. They are capable of reducing stormwater runoff, retaining stormwater in the landscape, preserving the natural water balance, enhancing local climate resilience and also delivering ecological, social and community services. Creating multi-functional, multiple-benefit systems, however, also warrants multidisciplinary approaches involving landscape architects, urban planners, engineers and more to successfully create a balance between cities and nature. This Special Issue aims to bridge this multidisciplinary research gap by collecting recent challenges and opportunities from on-site systems up to the watershed scale.
Spuhler, D., Scheidegger, A., & Maurer, M. (2020). Comparative analysis of sanitation systems for resource recovery: influence of configurations and single technology components. Water Research, 186, 116281 (18 pp.). doi:10.1016/j.watres.2020.116281, Institutional Repository

Resource recovery and emissions from sanitation systems are critical sustainability indicators for strategic urban sanitation planning. In this context, sanitation systems are the most often structured using technology-driven templates rather than performance-based sustainability indicators. In this work, we answer two questions: Firstly, can we estimate generic resource recovery and loss potentials and their uncertainties for a diverse and large set of sanitation systems? And secondly, can we identify technological aspects of sanitation systems that indicate a better overall resource recovery performance? The aim is to obtain information that can be used as an input into any strategic planning process and to help shape technology development and system design for resource recovery in the future.
Starting from 41 technologies, which include novel and conventional options, we build 101,548 valid sanitation systems. For each system we quantify phosphorus, nitrogen, total solids, and water flows and use that to calculate recovery potentials and losses to the environment, i.e. the soil, air, or surface water. The four substances cover different properties and serve as a proxy for nutrient, organics, energy, and water resources. For modelling the flows ex-ante, we use a novel approach to consider a large range of international literature and expert data considering uncertainties. Thus all results are generic and can therefore be used as input into any strategic planning process or to help guide future technology development.
A detailed analysis of the results allows us to identify factors that influence recovery and losses. These factors include the type of source, the length of systems, and the level of containment in storage and treatment. The factors influencing recovery are related to interactions of different technologies in a system which shows the relevance of an modelling approach that allows to look at all possible system configurations systematically. Based on our analysis, we developed five recommendations for the optimization of resource recovery: (i) prioritize short systems that close the loop at the lowest possible level; (ii) separate waste streams as much as possible, because this allows for higher recovery potentials; (iii) use storage and treatment technologies that contain the products as much as possible, avoid leaching technologies (e.g. single pits) and technologies with high risk of volatilization (e.g. drying beds); (iv) design sinks to optimise recovery and avoid disposal sinks; and (v) combine various reuse options for different side streams (e.g. urine diversion systems that combine reuse of urine and production of biofuel from faeces).
Spuhler, D., Germann, V., Kassa, K., Abebe Ketema, A., Manandhar Sherpa, A., Gyalzen Sherpa, M., … Langergraber, G. (2020). Developing sanitation planning options: a tool for systematic consideration of novel technologies and systems. Journal of Environmental Management, 271, 111004 (21 pp.). doi:10.1016/j.jenvman.2020.111004, Institutional Repository

To provide access to sustainable sanitation for the entire world population, novel technologies and systems have been developed. These options are often independent of sewers, water, and energy and therefore promise to be more appropriate for fast-growing urban areas. They also allow for resource recovery and and are adaptable to changing environmental and demographic conditions what makes them more sustainable. More options, however, also enhance planning complexity. Structured decision making (SDM) can help balance opposing interests. Yet, most of the current research focuses on the selection of a preferred option, assuming that a set of appropriate options is available. There is a lack of reproducible methods for the identification of sanitation system planning options that can consider the growing number of available technology and the many possible system configurations. Additionally, there is a lack of data, particularly for novel options, to evaluate the various sustainability criteria for sanitation.To overcome this limitation, we present a novel software supported approach: the SANitation sysTem Alternative GeneratOr (Santiago). To be optimally effective, Santiago is required to be integrated into an SDM approach. In this paper, we present all the elements that such an integration requires and illustrate these methods at the case of Arba Minch, a fast growing town in Ethiopia. Based on this example and experiences from other cases, we discuss the lessons learnt and present the advantages potentially brought by Santiago for sanitation planning The integration requires four elements: a set of technologies to be looked at, decision objectives for sustainable sanitation, screening criteria to evalute technology appropriateness, and about the technologies and the casea. The main output is a set of sanitation system options that is locally appropriate, diverse in order to reveal trade-offs, and of a manageable size. To support the definition of decision objectives, we developed a generic objective hierarchy for sustainable sanitation. Because one of the main challenges lies in the quantification of screening criteria, we established the data for 27 criteria and 41 technologies in a library.The case studies showed, that if the integration is successful, then Santiago can provide substantial benefits: (i) it is systematic and reproducible; (ii) it opens up the decision space with novel and potentially more appropriate solutions; (iii) it makes international data accessible for more empirical decision making; (iv) it enables decisions based on strategic objectives in line with the sustainable development goals; (v) it allows to prioritise appropriate and resource efficient systems right from the beginning (vi) and it contributes to a more citywide inclusive approach by birding strategic objectives with an area-based appropriateness assessment. The here presented approach enables the prioritisation of appropriate and resource efficient sanitation technologies and systems in strategic planning. Thereby this approach contributes to SDG 6.2, 6.3, and 11, sustainable sanitation for all.
Spuhler, D., & Lüthi, C. (2020). Review of frameworks and tools for urban strategic sanitation planning: considering technology innovations and sustainability. Journal of Water Sanitation and Hygiene for Development, 10(4), 768-785. doi:10.2166/washdev.2020.062, Institutional Repository

To achieve citywide inclusive sanitation in developing countries, a strategic sanitation planning approach (SSA) needs to provide a variety of technical solutions that respond to different urban realities. Despite the development of various SSA frameworks, sanitation planning still often follows a 'one-size-fits-all' approach. Structured decision making (SDM) can help by balancing trade-offs among different solutions. But SDM requires a set of appropriate sanitation options to choose from. Because conventional sewer-based sanitation is often inappropriate, many novel technologies and systems have been developed (e.g. container-based sanitation). While these innovations enhance sustainability, they also increase planning complexity. In this review, we look at available frameworks and tools for SSA and discover a lack of systematic tools for the identification of planning options that are able to consider the growing portfolio of available solutions and multiple sustainability criteria. Therefore, we critically compare 15 tools from which we compile eight qualities that could help any future tool address the current sanitation challenge: it should be comprehensive, automated to deal with a large number of options, systematic, flexible towards future innovation and should consider all sustainability dimensions, make a contextualized evaluation, allow for participation, and consider uncertainties to be applicable ex-ante also for novel technologies.
Villez, K., Del Giudice, D., Neumann, M. B., & Rieckermann, J. (2020). Accounting for erroneous model structures in biokinetic process models. Reliability Engineering and System Safety, 203, 107075 (8 pp.). doi:10.1016/j.ress.2020.107075, Institutional Repository

In engineering practice, model-based design requires not only a good process-based model, but also a good description of stochastic disturbances and measurement errors to learn credible parameter values from observations. However, typical methods use Gaussian error models, which often cannot describe the complex temporal patterns of residuals. Consequently, this results in overconfidence in the identified parameters and, in turn, optimistic reactor designs. In this work, we assess the strengths and weaknesses of a method to statistically describe these patterns with autocorrelated error models. This method produces increased widths of the credible prediction intervals following the inclusion of the bias term, in turn leading to more conservative design choices. However, we also show that the augmented error model is not a universal tool, as its application cannot guarantee the desired reliability of the resulting wastewater reactor design.
Werner, T. T., Bach, P. M., Yellishetty, M., Amirpoorsaeed, F., Walsh, S., Miller, A., … Wu, X. (2020). A geospatial database for effective mine rehabilitation in Australia. Minerals, 10(9), 745 (21 pp.). doi:10.3390/min10090745, Institutional Repository

The Australian landscape is affected by abandoned mines that pose environmental, public health and safety risks. To promote the beneficial reuse, rehabilitation and/or remediation of these sites and understand their spatial arrangement, we compiled, classified and analysed a country-wide geospatial database of all known inactive hard rock mine sites. Following extensive review and classification of disparate records of such sites that have been terminated, neglected or classified as heritage, plus those under care and maintenance in Australia, we assessed state-by-state reporting and cross-border rehabilitation requirements. This was enabled by the development of the Mining Incidence Documentation & Assessment Scheme (MIDAS) that can be used to catalogue and compare active or inactive mine data regardless of reporting conventions. At a national level, and with four case studies, we performed GIS-based spatial analyses and environmental risk assessments to demonstrate potential uses of our database. Analyses considered the proximity of sites to factors such as infrastructure and sensitive environmental receptors. As Australia struggles to manage the ongoing technical, socioeconomic and environmental challenges of effective mine rehabilitation, the insights enabled by this national-level spatial database may be key to developing coordinated responses that extend beyond state boundaries. Our classification and methodology are easily transferable, thereby encouraging more formalized, systematic and widespread documentation of abandoned mines worldwide.
Zhang, K., Deletic, A., Dotto, C. B. S., Allen, R., & Bach, P. M. (2020). Modelling a 'business case' for blue-green infrastructure: lessons from the water sensitive cities toolkit. Blue-Green Systems, 2(1), 383-403. doi:10.2166/bgs.2020.018, Institutional Repository

Stormwater management through Blue-Green Infrastructure (BGI) delivers multiple benefits across urban environments. However, current integrated modelling tools fail to provide a simplified way of assessing these benefits. In this study, we reflected upon the development of an interdisciplinary BGI planning-support tool, known as the Water Sensitive Cities Toolkit (the WSC Toolkit) and offer guidance for effective tool development going forward. Based on interdisciplinary research, the WSC Toolkit incorporates a suite of independent sub-modules but can be connected together to provide integrated assessment, allowing evidence-based quantification of multiple benefits associated with BGI, e.g., stormwater treatment and harvesting, stream hydrology, erosion, minor flooding, urban microclimate, etc. Distinguished from other larger complex models, the WSC Toolkit was characterised by its simplicity, modularity and extensibility, providing scenario-based integrated assessment of these benefits. Through case studies, we demonstrated how the WSC Toolkit can be used to support improved decision-making towards maximising the benefits of BGI. We also showed how it can act as a platform for practical application of latest research outcomes and meanwhile encouraging interdisciplinary collaboration. We reflect upon five key lessons that could guide future researchers in developing effective integrated assessment tools, particularly within highly interdisciplinary fields such as BGI.
Zhang, K., Bach, P. M., Mathios, J., Dotto, C. B. S., & Deletic, A. (2020). Quantifying the benefits of stormwater harvesting for pollution mitigation. Water Research, 171, 115395 (12 pp.). doi:10.1016/j.watres.2019.115395, Institutional Repository

Stormwater harvesting (SWH) provides multiple benefits to urban water management. Other than providing water for human use, it also reduces the volume of polluted stormwater discharge to the environment. There are currently no methods available to quantify the additional environmental benefits, which could encourage greater uptake of the practice. This paper investigates a number of factors (climate and catchment characteristics, pollutant reduction targets, etc.) that could impact upon the benefits of SWH for pollution reduction through sensitivity analyses. A method was developed and tested for quantification of the pollution mitigation benefits by SWH under different scenarios. A novel indicator, Impervious Area Offset (IAO), was proposed to reflect the additional impervious area that can be left untreated to achieve the equivalent pollution load reduction targets due to the introduction of SWH. Results indicate significant correlations (p < 0.01) between IAO values and extraction rate (proportion of total annual runoff removed due to the harvesting system and water use substitution), system type, and pollutant reduction targets. The proposed linear empirical relationships between IAO values and extraction rate for different types of system configurations and pollution reduction targets were well represented by observed linear regression (average R2 = 0.98 for all tested scenarios). Empirical relationships were validated successfully against different scenarios, with differences between predicted IAO and baseline IAO values being only ±4.5% for the majority of the validation scenarios. Using this simple and reliable method to rapidly quantify SWH benefits can further add to the growing business case of adopting SWH practices.
Blumensaat, F., Leitão, J. P., Ort, C., Rieckermann, J., Scheidegger, A., Vanrolleghem, P. A., & Villez, K. (2019). How urban storm- and wastewater management prepares for emerging opportunities and threats: digital transformation, ubiquitous sensing, new data sources, and beyond – a horizon scan. Environmental Science and Technology, 53(15), 8488-8498. doi:10.1021/acs.est.8b06481, Institutional Repository

Ubiquitous sensing will create many opportunities and threats for urban water management, which are only poorly understood today. To identify the most relevant trends, we conducted a horizon scan regarding how ubiquitous sensing will shape the future of urban drainage and wastewater management. Our survey of the international urban water community received an active response from both the academics and the professionals from the water industry. The analysis of the responses demonstrates that emerging topics for urban water will often involve experts from different communities, including aquatic ecologists, urban water system engineers and managers, as well as information and communications technology professionals and computer scientists. Activities in topics that are identified as novel will either require (i) cross-disciplinary training, such as importing new developments from the IT sector, or (ii) research in new areas for urban water specialists, for example, to help solve open questions in aquatic ecology. These results are, therefore, a call for interdisciplinary research beyond our own discipline. They also demonstrate that the water management community is not yet prepared for the digital transformation, where we will experience a data demand, i.e. a “pull” of urban water data into external services. The results suggest that a lot remains to be done to harvest the upcoming opportunities. Horizon scanning should be repeated on a routine basis, under the umbrella of an experienced polling organization.
Boller, D., Moy de Vitry, M., Wegner, J. D., & Leitão, J. P. (2019). Automated localization of urban drainage infrastructure from public-access street-level images. Urban Water Journal, 16(7), 480-493. doi:10.1080/1573062X.2019.1687743, Institutional Repository

Comprehensive management of urban drainage network infrastructure is essential for sustaining the operation of these systems despite stresses from component deterioration, urban densification, and a predicted intensification of rainfall events. In this context, up-to-date and accurate urban drainage network data is key. However, such data is often absent, outdated, or incomplete. In this study, a new approach to localize manhole covers and storm drains, using deep learning to mine publicly available street-level images, is presented, tested, and assessed. Thus, the time-consuming and costly acquisition of the location of these system components can be avoided. The approach is evaluated using 5,000 high-resolution panoramas covering 500 km of public roads in Switzerland. The object detection approach proposed shows good performance and an improvement over state of the art image-based urban drainage infrastructure component detection. While the geographical localization of the detected objects still contains errors, the accuracy achieved is nevertheless sufficient for some applications, e.g. flood risk assessment.
Chaudhary, P., D'Aronco, S., Moy de Vitry, M., Leitão, J. P., & Wegner, J. D. (2019). Flood-water level estimation from social media images. In G. Vosselman, S. J. Oude Elberink, & M. Y. Yang (Eds.), ISPRS annals of the photogrammetry, remote sensing and spatial information sciences: Vol. IV-2/W5. ISPRS geospatial week (pp. 5-12). doi:10.5194/isprs-annals-IV-2-W5-5-2019, Institutional Repository

In the event of a flood, being able to build accurate flood level maps is essential for supporting emergency plan operations. In order to build such maps, it is important to collect observations from the disaster area. Social media platforms can be useful sources of information in this case, as people located in the flood area tend to share text and pictures depicting the current situation. Developing an effective and fully automatized method able to retrieve data from social media and extract useful information in real-time is crucial for a quick and proper response to these catastrophic events. In this paper, we propose a method to quantify flood-water from images gathered from social media. If no prior information about the zone where the picture was taken is available, one possible way to estimate the flood level consists of assessing how much the objects appearing in the image are submerged in water. There are various factors that make this task difficult: i) the precise size of the objects appearing in the image might not be known; ii) flood-water appearing in different zones of the image scene might have different height; iii) objects may be only partially visible as they can be submerged in water. In order to solve these problems, we propose a method that first locates selected classes of objects whose sizes are approximately known, then, it leverages this property to estimate the water level. To prove the validity of this approach, we first build a flood-water image dataset, then we use it to train a deep learning model. We finally show the ability of our trained model to recognize objects and at the same time predict correctly flood-water level.
Ebi, C., Schaltegger, F., Rust, A., & Blumensaat, F. (2019). Synchronous LoRa mesh network to monitor processes in underground infrastructure. IEEE Access, 7, 57663-57677. doi:10.1109/ACCESS.2019.2913985, Institutional Repository

Collecting precise real-time information on urban drainage system performance is essential to identify, predict and manage critical loading situations, such as urban flash floods and sewer overflows. Although emerging low-power wireless communication techniques allow efficient data transfers with great above-ground performance, for underground or indoor applications a large coverage range is difficult to achieve due to physical and topological limitations, particularly in dense urban areas. In this paper, we first discuss range limitations of the LoRaWAN standard based on a systematic evaluation of a long-term operation of a sensor network monitoring in-sewer process dynamics. Analyses reveal an –. on average – five-fold higher data packet loss for sub-surface nodes, which steadily grows with increasing distance to the gateway. Secondly, we present a novel LPWAN concept based on the LoRa. technology that i) enhances transmission reliability, efficiency and flexibility in range-critical situations through meshed multi-hop routing, and ii) ensures a precise time-synchronization through optional GPS or DCF77 long-wave time signaling. We thirdly illustrate the usefulness of the newly developed concept by evaluating the radio transmission performance for two independent full-scale field tests. Test results show that the synchronous LoRa mesh network approach clearly outperforms the standard LoRaWAN technique with regard to the reliability of packet delivery when transmitting from range-critical locations. Hence, the approach is expected to generally ease data collection from difficult-to-access locations, such as underground areas.
Ehlers, L. B., Wani, O., Koch, J., Sonnenborg, T. O., & Refsgaard, J. C. (2019). Using a simple post-processor to predict residual uncertainty for multiple hydrological model outputs. Advances in Water Resources, 129, 16-30. doi:10.1016/j.advwatres.2019.05.003, Institutional Repository

Regardless of the complexity of the hydrological model employed, uncertainty assessment (UA) is predominantly performed for the aggregated catchment response discharge. For coupled integrated models that simulate various hydrological states and fluxes on a grid cell basis, this represents a severe shortcoming. We test a simple data-driven technique (k-NN resampling) to evaluate its ability to provide reliable residual uncertainty estimates for the multi-variable (discharge, hydraulic head, soil moisture and actual evapotranspiration), deterministic output of two coupled groundwater-surface water models with different complexities. Being a nonparametric method, no explicit prior assumptions about the error distribution of different hydrological variables are required. When conditioning the algorithm, we propose to limit the number of error lags to be included based on inspection of the partial autocorrelation function (PACF). Our results confirm previous findings regarding reliability and robustness of the k-NN technique for discharge simulations and conclude that k-NN resampling also provides reliable and robust results for other variables like hydraulic head, soil moisture and actual evapotranspiration, even for underlying hydrological models with varying levels of performance. The 90% prediction intervals (PI) capture the observations in the testing period satisfactorily for all hydrological variables (92.6–97.3%), while Alpha indices (0.84–0.95) indicate very reliable PIs for all error quantiles. Differences in error structure between hydrological variables are successfully inferred from historical data and reflected in the results. We conclude that k-NN resampling represents a potent, cost-efficient UA technique for applications in operational hydrology, facilitating a near-simultaneous, easy uncertainty assessment for various outputs of computationally heavy hydrological models.
Gao, J., Li, J., Jiang, G., Shypanski, A. H., Nieradzik, L. M., Yuan, Z., … Thai, P. K. (2019). Systematic evaluation of biomarker stability in pilot scale sewer pipes. Water Research, 151, 447-455. doi:10.1016/j.watres.2018.12.032, Institutional Repository

Transformation of biomarkers (or their stability) during sewer transport is an important issue for wastewater-based epidemiology (WBE). Most studies so far have been conducted in the laboratory, which usually employed unrealistic conditions. In the present study, we utilized a pilot sewer system including a gravity pipe and a rising main pipe to investigate the fate of 24 pharmaceutical biomarkers. A programmable logic controller was used to control and monitor the system including sewer operational conditions and wastewater properties. Sequential samples were collected that can represent hydraulic retention time (HRT) of up to 8 h in a rising main and 4 h in a gravity sewer. Wastewater parameters and biomarker concentrations were analysed to evaluate the stability and transformation kinetics. The wastewater parameters of the pilot system were close to the conditions of real sewers. The findings of biomarker transformation were also close to real sewer data with seventeen biomarkers reported as stable while buprenorphine, caffeine, ethyl-sulfate, methadone, paracetamol, paraxanthine and salicylic acid degraded to variable extents. Both zero-order and first-order kinetics were used to model the degradation of unstable biomarkers and interestingly the goodness of fit R2 for the zero-order model was higher than the first-order model for all unstable biomarkers in the rising main. The pilot sewer system simulates more realistic conditions than benchtop laboratory setups and may provide a more accurate approach for assessing the in-sewer transformation kinetics and stability of biomarkers.
Haag, F., Reichert, P., Maurer, M., & Lienert, J. (2019). Integrating uncertainty of preferences and predictions in decision models: an application to regional wastewater planning. Journal of Environmental Management, 252, 109652 (16 pp.). doi:10.1016/j.jenvman.2019.109652, Institutional Repository

Decision-making in environmental management requires eliciting preferences of stakeholders and predicting outcomes of decision alternatives. Usually, preferences and predictions are both uncertain. Uncertainty of predictions can be tackled by multi-attribute utility theory, but the uncertainty of preferences remains a challenge. We demonstrate an approach for including both uncertainties in a multi-criteria decision analysis (MCDA), using utility theory and the concept of expected expected utility. For a decision regarding a regional merger of wastewater infrastructure in Switzerland, we constructed preference models for four stakeholders. These models also allowed for non-additive interactions between objectives. We evaluated the performance of eleven decision alternatives for which we predicted potential outcomes. Even though uncertainties were high, we could draw conclusions based on the expected expected utility of alternatives. Building a pipeline to discharge treated wastewater to a larger river emerged as a potential consensus alternative to mitigate the problem of micropollutants in a small stream. We investigated the robustness of the findings with sensitivity analysis regarding the preference parameters and the included objectives. In their actual decision, the stakeholders partly preferred other alternatives than those proposed by the model. Their choices could be explained by reduced decision models in which only few objectives were included. This may indicate the use of simplified choice heuristics by the stakeholders. The presented approach is feasible for supporting other difficult environmental or engineering decisions in practice, for which we give a number of recommendations.
Harder, R., Wielemaker, R., Larsen, T. A., Zeeman, G., & Öberg, G. (2019). Recycling nutrients contained in human excreta to agriculture: pathways, processes, and products. Critical Reviews in Environmental Science and Technology, 49(8), 696-743. doi:10.1080/10643389.2018.1558889, Institutional Repository

The need for better nutrient management has spurred efforts towards more comprehensive recycling of nutrients contained in human excreta to agriculture. Research in this direction has intensified throughout the past years, continuously unfolding new knowledge and technologies. The present review aspires to provide a systematic synthesis of the field by providing an accessible overview of terminology, recovery pathways and treatment options, and products rendered by treatment. Our synthesis suggests that, rather than focusing on a specific recovery pathway or product and on a limited set of nutrients, there is scope for exploring how to maximize nutrient recovery by combining individual pathways and products and including a broader range of nutrients. To this end, finding ways to more effectively share and consolidate knowledge and information on recovery pathways and products would be beneficial. The present review aims to provide a template that aims to facilitate designing human excreta management for maximum nutrient recovery, and that can serve as foundation for organizing and categorizing information for more effective sharing and consolidation.
Jamali, B., Bach, P. M., Cunningham, L., & Deletic, A. (2019). A cellular automata fast flood evaluation (CA‐ffé) model. Water Resources Research, 55(6), 4936-4953. doi:10.1029/2018WR023679, Institutional Repository

The simulation speed of two‐dimensional hydrodynamic flood models is a limiting factor when catchments are large, a considerable number of simulations is required (e.g., exploratory modeling, Monte‐Carlo flood simulations, or predicting probabilistic flood maps), or when there is a need for real‐time flood emergency management. Rapid Flood Models (RFMs) that rely only on topographic depressions and the water balance equation have been successfully implemented to predict maximum urban flood inundation depths within seconds to a few minutes. However, the preprocessing step (identification of depressions and their attributes) and the postprocessing step (marking up possible flow paths of flood water in between flooded depressions) of RFMs is time consuming. In this study, we developed a new fast flood inundation model based on the cellular automata (CA) approach. The new model does not require the preprocessing and postprocessing steps of RFMs and therefore can provide more simulation speed. The performance of our new model, referred to as Cellular Automata fast flood evaluation (CA‐ffé), was compared to two well‐known hydrodynamic flood models (HEC‐RAS and TUFLOW) in 20 simulation experiments conducted in five different urban subcatchments. CA‐ffé predicted maximum inundation depth with reasonable accuracy in a matter of seconds to a few minutes for a single rainfall event simulation. The CA‐ffé model performed exceptionally well in areas with low‐lying depressions. However, in areas where floodwaters had higher momentum and velocity, the model usually was not able to estimate inundation depths calculated by HEC‐RAS or TUFLOW. CA‐ffé's key drawback is also its inability to represent the temporal evolution of flooding and flow velocities. Nevertheless, its ability to provide spatial flood extents and depths in a fraction of the time compared to its hydrodynamic counterparts is a significant advancement toward exploratory approaches for water systems planning, model‐based predictive control, and real‐time flood management.
Kuller, M., Bach, P. M., Roberts, S., Browne, D., & Deletic, A. (2019). A planning-support tool for spatial suitability assessment of green urban stormwater infrastructure. Science of the Total Environment, 686, 856-868. doi:10.1016/j.scitotenv.2019.06.051, Institutional Repository

Distributed green stormwater management infrastructure is increasingly applied worldwide to counter the negative impacts of urbanisation and climate change, while providing a range of benefits related to ecosystem services. They are known as Water Sensitive Urban Design (WSUD) in Australia, Nature Based Solutions (NBS) in Europe, Low Impact Development (LID) in the USA, and Sponge City systems in China. Urban planning for WSUD has been ad-hoc, lacking strategy and resulting in sub-optimal outcomes. The purpose of this study is to help improve strategic WSUD planning and placement through the development of a Planning Support System. This paper presents the development of Spatial Suitability ANalysis TOol (SSANTO), a rapid GIS-based Multi-Criteria Decision Analysis tool using a flexible mix of techniques to map suitability for WSUD assets across urban areas. SSANTO applies a novel WSUD suitability framework, which conceptualises spatial suitability for WSUD implementation from two perspectives: 'Needs' and 'Opportunities' for WSUD. It combines biophysical as well as socio-economic, planning and governance criteria ('Opportunities') with criteria relating to ecosystem services ('Needs'). Testing SSANTO through comparing its results to work done by a WSUD consultancy successfully verified its algorithms and demonstrated its capability to reflect and potentially enhance the outcomes of planning processes. Manual GIS based suitability analysis is time and resource intensive. Through its rapid suitability analysis, SSANTO facilitates iterative spatial analysis for exploration of scenarios and stakeholder preferences. It thus facilitates collaborative planning and deeper understanding of the relationship between diverse and complex urban contexts and urban planning outcomes for WSUD.
McGrath, G., Kaeseberg, T., Reyes Silva, J. D., Jawitz, J. W., Blumensaat, F., Borchardt, D., … Rao, P. S. C. (2019). Network topology and rainfall controls on the variability of combined sewer overflows and loads. Water Resources Research, 55(11), 9578-9591. doi:10.1029/2019WR025336, Institutional Repository

Water and pollutant fluxes from combined sewer overflows (CSO) have a significant impact on receiving waters. The random nature of rainfall forcing dominates the variability of sewer discharges, pollutant loads, and concentrations. An analytical model developed here shows how sewer network topology and rainfall properties variously impact the stochasticity of CSO functioning. Probability distributions of sewer discharge and concentration compare well with the results from a calibrated Storm Water Management Model in an application to a sewershed located in Dresden, Germany. The model is determined by only four parameters, three of which can be predicted a priori, two from the rainfall record and one from the network topology using geomorphological flow recession theory, while the fourth can be estimated from a short discharge time series. The sensitivity of CSO and wastewater treatment loads to network structure suggests simple topologies may be more vulnerable to poor performance. The analytical model is useful for evaluating various CSO management strategies to reduce adverse impacts on receiving waters in a probabilistic setting.
Moy de Vitry, M., Kramer, S., Wegner, J. D., & Leitão, J. P. (2019). Scalable flood level trend monitoring with surveillance cameras using a deep convolutional neural network. Hydrology and Earth System Sciences, 23(11), 4621-4634. doi:10.5194/hess-23-4621-2019, Institutional Repository

In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods.
Moy de Vitry, M., Schneider, M. Y., Wani, O., Manny, L., Leitão, J. P., & Eggimann, S. (2019). Smart urban water systems: what could possibly go wrong?. Environmental Research Letters, 14(8), 081001 (4 pp.). doi:10.1088/1748-9326/ab3761, Institutional Repository

Urban water systems are being redefined for a digital age, promising substantial advantages for service users and providers, and for society as a whole. However, beside the much-discussed benefits of smart urban water systems in future smart cities, the transition will also bring unique challenges, particularly with respect to privacy and cybersecurity. A preemptive delineation of these risks and providing appropriate recommendations will help to guide digital transformations of urban water towards sustainable solutions. Faced with emerging risks of digitalization, urban water management needs to look beyond technology while recognizing the central and multifaceted role of research.
In smart city initiatives, urban water systems remain largely out of the digitalization spotlight compared to other types of infrastructure. Oftentimes, the management and planning of urban water systems still follow traditional, steel and concrete-based approaches that treat used water as waste and not as a resource [1]. The need for innovation, however, is significant: the infrastructure, operation, and maintenance cost of urban water systems around the world already ranges in the hundred billion dollars annually and water-related stresses are expected to continue rising. Digitalization could not only increase the flexibility and effectiveness of existing urban water systems, but could also allow for the provisions of new services to society. The envisioned transformation, as outlined for example in the literature [2–4], entails novel data collection and transmission techniques, analytical methods, models, and automation. For example, smart water meter data can be combined with noise loggers to detect and roughly locate leaks in water mains. Together with automated control infrastructure, pressure can be regulated to reduce water losses [4]. By this and numerous other smart solutions, not only water distribution but also sewers and wastewater treatment will be transformed. In particular, digitalization facilitates large-scale implementation of disruptive technologies like decentralized wastewater treatment [5] and direct potable reuse, which will help cities to cope with climate change and urbanization.
However, we argue that as the benefits of smart urban water systems are increasingly explored and showcased in literature, it is time to also give attention to potential risks that might emerge. Recurring data breaches and the growing use of cyber-attacks for geopolitical ends are a reminder that the digitalization of society indeed carries risks. Given the critical role of water services in society and within the water-energy-food nexus, these risks must be identified and managed pro-actively. In the following, we raise awareness and suggest ways forward in approaching the unique privacy and security issues that accompany the digitalization of urban water systems. Finally, we highlight the central role of researchers in assessing and mitigating the potential risks of the smart urban water solutions they propose.
Mutzner, L., Vermeirssen, E. L. M., & Ort, C. (2019). Passive samplers in sewers and rivers with highly fluctuating micropollutant concentrations – Better than we thought. Journal of Hazardous Materials, 361, 312-320. doi:10.1016/j.jhazmat.2018.07.040, Institutional Repository

Considerable pollutant loads can enter surface waters during rain events. Three factors challenge quantification of these pollutant fluxes using traditional sampling methods: (i) concentration fluctuations; (ii) unknown event duration; and (iii) placement, operation, and maintenance of equipment. Passive samplers offer the advantage of sampling in a continuous mode without power supply. However, variable uptake rates due to environmental factors and desorption in the case of fluctuating concentrations can affect the accuracy of time-weighted average (TWA) concentration estimates. While uncertainties related to environmental factors could be accounted for with additional effort, we can neither control nor quantify the concentration variability. We present measured and modelled concentration profiles at high temporal resolution and provide a systematic approach to assessing deviations from true TWA concentration due to fluctuating concentration profiles. We evaluate sampling of sewer overflows (0.3–14 h) with Chemcatcher and 1-week sampling in rivers. The uncertainty due to fluctuating concentrations is small, and other factors such as chemical analyses and sampler calibration have a similar or higher impact. The uncertainty due to fluctuations clearly increases with the sampling duration, particularly when exceeding the half-life of equilibrium. We conclude that passive sampling can also be used in wastewater systems with potentially high concentration variations.
Mutzner, L., Vermeirssen, E. L. M., Mangold, S., Maurer, M., Scheidegger, A., Singer, H., … Ort, C. (2019). Passive samplers to quantify micropollutants in sewer overflows: accumulation behaviour and field validation for short pollution events. Water Research, 160, 350-360. doi:10.1016/j.watres.2019.04.012, Institutional Repository

Contaminants in sewer overflows can contribute to exceedances of environmental quality standards, thus the quantification of contaminants during rainfall events is of relevance. However, monitoring is challenged by i) high spatiotemporal variability of contaminants in events of hard-to-predict durations, and ii) a large number of remote sites, which would imply enormous efforts with traditional sampling equipment. Therefore, we evaluate the applicability of passive samplers (Empore styrene-divinylbenzene reverse phase sulfonated (SDB-RPS)) to monitor a set of 13 polar organic contaminants. We present calibration experiments at high temporal resolution to assess the rate limiting accumulation mechanisms for short events (<36 h), report parameters for typical sewer conditions and compare passive samplers with composite water samples in a field study (three locations, total 10 events). With sampling rates of 0.35–3.5 L/d for 1 h reference time, our calibration results indicate a high sensitivity of passive samplers to sample short, highly variable sewer overflows. The contaminant uptake kinetic shows a fast initial accumulation, which is not well represented with the typical first-order model. Our results indicate that mass transfer to passive samplers is either controlled by the water boundary layer and the sorbent, or by the sorbent alone. Overall, passive sampler concentration estimates are within a factor 0.4 to 3.1 in comparison to composite water samples in the field study. We conclude that passive samplers are a promising approach to monitor a large number of discharge sites although it cannot replace traditional stormwater quality sampling in some cases (e.g. exact load estimates, high temporal resolution). Passive samplers facilitate identifying and prioritizing locations that may require more detailed investigations.
Muñoz, D. F., Simões, N. E., de Sousa, L. M., Maluf, L., Sá Marques, A., & Leitão, J. P. (2019). Generalizing multi-reward functions aimed at identifying the best locations to install flow control devices in sewer systems. Urban Water Journal, 16(8), 564-574. doi:10.1080/1573062X.2019.1700284, Institutional Repository

Multi-criteria decision models have been recently implemented in many urban flood risk management schemes due to the increasing demand of low-cost and reliable solutions to prevent and control overflow from sewer systems. Based on specific targets, these decision models encompass multiple reward (or objective) functions according to socio-economic requirements, vulnerability assessments and physical constraints on existing sewer systems. In this study, we present a simple algorithm to generalize reward functions aimed at locating the best sites to install in-sewer flow control devices, and thus controlling sewer overflow. The algorithm uses the mathematical structure of reward functions previously established, and adjust them based on hydraulic simulation results of two real sewer networks. Our results indicate that a single generalized reward function can efficiently identify strategic locations to guarantee (i) maximum in-sewer storage, (ii) minimum impact of possible flow control device failures and (iii) maximum flow discharge reduction upstream flood-prone urban areas.
O'Brien, J. W., Grant, S., Banks, A. P. W., Bruno, R., Carter, S., Choi, P. M., … Mueller, J. F. (2019). A national wastewater monitoring program for a better understanding of public health: a case study using the Australian census. Environment International, 122, 400-411. doi:10.1016/j.envint.2018.12.003, Institutional Repository

Wastewater contains a large range of biological and chemical markers of human activity and exposures. Through systematic collection and analysis of these markers within wastewater samples it is possible to measure the public health of whole populations. The analysis of effluent and biosolids can also be used to understand the release of chemicals from wastewater treatment plants into the environment. Wastewater analysis and comparison with catchment specific data (e.g. demographics) however remains largely unexplored. This manuscript describes a national wastewater monitoring study that combines influent, effluent and biosolids sampling with the Australian Census. An archiving program allows estimation of per capita exposure to and consumption of chemicals, public health information, as well as per capita release of chemicals into the environment. The paper discusses the study concept, critical steps in setting up a coordinated national approach and key logistical and other considerations with a focus on lessons learnt and future applications. The unique combination of archived samples, analytical data and associated census-derived population data will provide a baseline dataset that has wide and potentially increasing applications across many disciplines that include public health, epidemiology, criminology, toxicology and sociology.
Ohmura, K., Thürlimann, C. M., Kipf, M., Carbajal, J. P., & Villez, K. (2019). Characterizing long-term wear and tear of ion-selective pH sensors. Water Science and Technology, 80(3), 541-550. doi:10.2166/wst.2019.301, Institutional Repository

Today, the development and testing of methods for fault detection and identification in wastewater treatment research relies on two important assumptions: (i) that sensor faults appear at distinct times in different sensors and (ii) that any given sensor will function near-perfectly for a significant amount of time following installation. In this work, we show that such assumptions are unrealistic, at least for sensors built around an ion-selective measurement principle. Indeed, long-term exposure of sensors to treated wastewater shows that sensors exhibit fault symptoms that appear simultaneously and with similar intensity. Consequently, this suggests that future research should be reoriented towards methods that do not rely on the assumptions mentioned above. This study also provides the first empirically validated sensor fault model for wastewater treatment simulation, which is useful for effective benchmarking of both fault detection and identification methods and advanced control strategies. Finally, we evaluate the value of redundancy for remote sensor validation in decentralized wastewater treatment systems.
Pastorek, J., Fencl, M., Rieckermann, J., & Bareš, V. (2019). Commercial microwave links for urban drainage modelling: the effect of link characteristics and their position on runoff simulations. Journal of Environmental Management, 251, 109522 (11 pp.). doi:10.1016/j.jenvman.2019.109522, Institutional Repository

Commercial microwave links (CMLs), radio connections widely used in telecommunication networks, can provide path-integrated quantitative precipitation estimates (QPEs) which could complement traditional precipitation observations. This paper assesses the ability of individual CMLs to provide relevant QPEs for urban rainfall-runoff simulations and specifically investigates the influence of CML characteristics and position on the predicted runoff. The analysis is based on a 3-year-long experimental data set from a small (1.3 km2) urban catchment located in Prague, Czech Republic. QPEs from real world CMLs are used as inputs for urban rainfall-runoff predictions and subsequent modelling performance is assessed by comparing simulated runoffs with measured stormwater discharges. The results show that model performance is related to both the sensitivity of CML to rainfall and CML position. The bias propagated into the runoff predictions is inversely proportional to CML path length. The effect of CML position is especially pronounced during heavy rainfalls, when QPEs from shorter CMLs, located within or close to catchment boundaries, better reproduce runoff dynamics than QPEs from longer CMLs extending far beyond the catchment boundaries. Interestingly, QPEs averaged from all available CMLs best reproduce the runoff temporal dynamics. Adjusting CML QPEs to three rain gauges located 2–3 km outside of the catchment substantially reduces the bias in CML QPEs. Unfortunately, this compromises the ability of the CML QPEs to reproduce runoff dynamics during heavy rainfalls. More experimental case studies are necessary to provide specific recommendations on CML preprocessing methods tailored to different water management tasks, catchments and CML networks.
Penn, R., Maurer, M., Michalec, F. G., Scheidegger, A., Zhou, J., & Holzner, M. (2019). Quantifying physical disintegration of faeces in sewers: stochastic model and flow reactor experiments. Water Research, 152, 159-170. doi:10.1016/j.watres.2018.12.037, Institutional Repository

We present a novel stochastic model for quantifying gross solids (GS) physical disintegration under varying turbulent flow conditions and used a unique experimental setup for model calibration and validation. The stochastic deterioration model predicts faeces size evolution over time. It conceptually entails the two main processes of solid fragmentation, namely breakage and erosion. Model parameters were calibrated on synthetic faeces and validated with real human ones. A cylindrical reactor was used, where turbulent flow was forced by an array of water jets and the physical disintegration of the faeces was monitored using a high speed camera. Image analysis of breakage experiments obtained under backlight illumination allowed determination of the evolution of the solids’ size over time. The flow field in the reactor was characterised by particle image velocimetry (PIV) using tracer particles seeded into the water. We found different disintegration behaviours depending on turbulence intensity and water content of the solid. In conditions of low shear stress, dense solids hardly disintegrated. Generally, the model predictions mirrored the broad range in the solids disintegration rate imparted by the high variability in flow conditions and in solids characteristics. It is expected that, similar to our experiments, also in real sewer systems both flow conditions and solid characteristics are highly variable and the stochastic model can be tailored to capture this variability. We thus anticipate that the model can be integrated into existing sewer models predicting sewer flows and solids’ movement. From these, shear stress, flow velocities and transport of individual solids can be inferred. The integration of the present solids disintegration model may provide better predictions of hot-spots for solids accumulation and blockages in sewers.
Schertenleib, A., Sigrist, J., Friedrich, M. N. D., Ebi, C., Hammes, F., & Marks, S. J. (2019). Construction of a low-cost mobile incubator for field and laboratory use. Journal of Visualized Experiments (145), e58443 (17 pp.). doi:10.3791/58443, Institutional Repository

Incubators are essential for a range of culture-based microbial methods, such as membrane filtration followed by cultivation for assessing drinking water quality. However, commercially available incubators are often costly, difficult to transport, not flexible in terms of volume, and/or poorly adapted to local field conditions where access to electricity is unreliable. The purpose of this study was to develop an adaptable, low-cost and transportable incubator that can be constructed using readily available components. The electronic core of the incubator was first developed. These components were then tested under a range of ambient temperature conditions (3.5 °C - 39 °C) using three types of incubator shells (polystyrene foam box, hard cooler box, and cardboard box covered with a survival blanket). The electronic core showed comparable performance to a standard laboratory incubator in terms of the time required to reach the set temperature, inner temperature stability and spatial dispersion, power consumption, and microbial growth. The incubator set-ups were also effective at moderate and low ambient temperatures (between 3.5 °C and 27 °C), and at high temperatures (39 °C) when the incubator set temperature was higher. This incubator prototype is low-cost (< 300 USD) and adaptable to a variety of materials and volumes. Its demountable structure makes it easy to transport. It can be used in both established laboratories with grid power or in remote settings powered by solar energy or a car battery. It is particularly useful as an equipment option for field laboratories in areas with limited access to resources for water quality monitoring.
Schneider, M. Y., Carbajal, J. P., Furrer, V., Sterkele, B., Maurer, M., & Villez, K. (2019). Beyond signal quality: the value of unmaintained pH, dissolved oxygen, and oxidation-reduction potential sensors for remote performance monitoring of on-site sequencing batch reactors. Water Research, 161, 639-651. doi:10.1016/j.watres.2019.06.007, Institutional Repository

Sensor maintenance is time-consuming and is a bottleneck for monitoring on-site wastewater treatment systems. Hence, we compare maintained and unmaintained sensors to monitor the biological performance of a small-scale sequencing batch reactor (SBR). The sensor types are ion-selective pH, optical dissolved oxygen (DO), and oxidation-reduction potential (ORP) with platinum electrode. We created soft sensors using engineered features: ammonium valley for pH, oxidation ramp for DO, and nitrite ramp for the ORP. Four soft sensors based on unmaintained pH sensors correctly identified the completion of the ammonium oxidation (89–91 out of 107 cycles), about as many times as soft sensors based on a maintained pH sensor (91 out of 107 cycles). In contrast, the DO soft sensor using data from a maintained sensor showed slightly better (89 out of 96 cycles) detection performance than that using data from two unmaintained sensors (77, respectively 82 out of 96 correct). Furthermore, the DO soft sensor using maintained data is much less sensitive to the optimisation of cut-off frequency and slope tolerance than the soft sensor using unmaintained data. The nitrite ramp provided no useful information on the state of nitrite oxidation, so no comparison of maintained and unmaintained ORP sensors was possible in this case. We identified two hurdles when designing soft sensors for unmaintained sensors: i) Sensors’ type- and design-specific deterioration affects performance. ii) Feature engineering for soft sensors is sensor type specific, and the outcome is strongly influenced by operational parameters such as the aeration rate. In summary, the results with the provided soft sensors show that frequent sensor maintenance is not necessarily needed to monitor the performance of SBRs. Without sensor maintenance monitoring smalls-scale SBRs becomes practicable, which could improve the reliability of unstaffed on-site treatment systems substantially.
Shi, B., Bach, P. M., Lintern, A., Zhang, K., Coleman, R. A., Metzeling, L., … Deletic, A. (2019). Understanding spatiotemporal variability of in-stream water quality in urban environments – a case study of Melbourne, Australia. Journal of Environmental Management, 246, 203-213. doi:10.1016/j.jenvman.2019.06.006, Institutional Repository

To support sustainable urban planning and the design of water pollution mitigation strategies, the spatial and temporal trends of water quality in urban streams needs to be further understood. This study analyses over ten years of surface water quality data from 53 upstream catchments (20 of them predominated by a single type of land use) and two lowland sites across Greater Melbourne, Australia. We evaluated the impact of various catchment characteristics, especially urban land uses, on spatial and temporal urban water quality trends. Here, we focused on common urban pollutants: total suspended solids (TSS), total phosphorous (TP), total nitrogen (TN), zinc (Zn), copper (Cu) and nickel (Ni). Site median nutrient and heavy metal concentrations were negatively correlated with the catchment's elevation and its average annual rainfall. Further analysis shows that such trends were driven by the geographical pattern of Melbourne – i.e. low-laying sites tend to have less rainfall and be more urbanised. Only median concentrations of heavy metals (Zn and Cu) were correlated to catchment imperviousness. Further characterising of the urban environment was done into specific land uses (residential, industrial and commercial), yet median concentrations of all pollutants were not significantly correlated with land uses. This is because simple metrics, such as land use proportions, do not adequately reflect the significant variability in pollution sources that can exist even within the same land use type. Indeed, our temporal analysis found that the water quality difference between catchments with similar land uses is likely caused by their site-specific pollutant sources (construction and illegal discharge) and environmental management actions (wastewater management actions) regardless of similarities in land use. A 3-stage urbanisation cycle (development, operation and renewal) is suggested to further explain the urban water quality variance, but more data from small areas of an urban catchment is required to directly understand the unique impact of each urbanisation stage on water quality.
Tscharke, B. J., O’Brien, J. W., Ort, C., Grant, S., Gerber, C., Bade, R., … Mueller, J. F. (2019). Harnessing the power of the census: characterizing wastewater treatment plant  catchment populations for wastewater-based epidemiology. Environmental Science and Technology, 53(17), 10303-10311. doi:10.1021/acs.est.9b03447, Institutional Repository

Wastewater studies that provide per capita estimates of consumption (influent) or release (effluent) via wastewater systems rely heavily on accurate population data. This study evaluated the accuracy of Wastewater Treatment Plant (WWTP) reported populations, as well as hydrochemical parameters, against accurate populations from a population census. 104 catchment maps were received from WWTPs, geolocated in geospatial software and overlaid with the smallest area unit of the Australian census, equating to 14.9 million Australians or 64% of the national population. We characterized each catchment for population counts, as well as by age profile, income profile, and education level. For a subset of sites, population estimates using hydrochemical parameters BOD, COD, and dissolved ammonia were evaluated for accuracy against census populations. Population estimates provided by WWTP personnel were on average 18% higher than census-based populations. Furthermore, hydrochemical-based population estimates had high RSD (>44%) for BOD, COD, and ammonium between sites, suggesting that their applicability for use in population estimation may not be appropriate for every WWTP. Catchment age distributions were evaluated and 46% of catchments had skewed age distributions: 6% were skewed older, and 40% were skewed younger. Through this process WWTP catchment populations can be characterized in a way that will enhance the interpretations of per capita estimates.
Tscheikner-Gratl, F., Caradot, N., Cherqui, F., Leitão, J. P., Ahmadi, M., Langeveld, J. G., … Clemens, F. (2019). Sewer asset management - state of the art and research needs. Urban Water Journal, 16(9), 662-675. doi:10.1080/1573062X.2020.1713382, Institutional Repository

Sewer asset management gained momentum and importance in recent years due to economic considerations, since infrastructure maintenance and rehabilitation directly represent major investments. Because physical urban water infrastructure has life expectancies of up to 100 years or more, contemporary urban drainage systems are strongly influenced by historical decisions and implementations. The current decisions taken in sewer asset management will, therefore, have a long-lasting impact on the functionality and quality of future services provided by these networks. These decisions can be supported by different approaches ranging from various inspection techniques, deterioration models to assess the probability of failure or the technical service life, to sophisticated decision support systems crossing boundaries to other urban infrastructure. This paper presents the state of the art in sewer asset management in its manifold facets spanning a wide field of research and highlights existing research gaps while giving an outlook on future developments and research areas.
Wani, O., Scheidegger, A., Cecinati, F., Espadas, G., & Rieckermann, J. (2019). Exploring a copula-based alternative to additive error models—for non-negative and autocorrelated time series in hydrology. Journal of Hydrology, 575, 1031-1040. doi:10.1016/j.jhydrol.2019.06.006, Institutional Repository

Inaccurate description of uncertainty in the error models can cause biases in parameter estimation. When the parameters of the deterministic model and the error model are inferred jointly from the observations, the posterior converges to regions that reflect the processes in both high and low flows. If the nature of errors in low and high flows is different to the extent that the same error description cannot be used for both, biases in inference are introduced. In such cases, the parameter posterior will adjust to the region of the hydrograph with longer proportionate presence in the calibration time series. In this paper we demonstrate that the autoregressive order 1 (AR1) description of errors can lead to sub-optimally performing predictive models if the calibration period has substantial sections of inadequately modelled flows. Inference is performed within the Bayesian framework. We show this for a synthetic example as well as a case study. We also see that the predictive uncertainty bands that we get using the AR1 description can be overconfident and also admit negative values. To mitigate this, we analyze an alternative to additive error models. We use a distribution with a non-negative support, gamma in this study, reflecting the uncertainty in the system response at every time step. The gamma distribution is conditioned on the deterministic model output, which determines its mode and standard deviation. We capture autocorrelation in time using copulas. Given that copulas can capture dependence between different marginals, we use different specifications of the marginal distribution for high and low flows. The results show that 1) biases in parameter estimation can be reduced if a representative error description is attained using the flexibility of a copula-based likelihood. 2) The non-negative support allows to make more realistic uncertainty intervals for low flows. 3) However, the autocorrelation parameter in copulas severely interacts with the model and heteroscedasticity parameters. 4) While the formulation, in principle, should be of added value for parameter inference, in case of less informative priors, the flexibility of this description can produce non-robust inference.
Zhang, K., Manuelpillai, D., Raut, B., Deletic, A., & Bach, P. M. (2019). Evaluating the reliability of stormwater treatment systems under various future climate conditions. Journal of Hydrology, 568, 57-66. doi:10.1016/j.jhydrol.2018.10.056, Institutional Repository

Water Sensitive Urban Design (WSUD) stormwater systems, also known as Low Impact Development (LID) systems or Nature Based Solutions (NBS), are currently implemented based on the underlying assumption of statistical stationarity of rainfall, which threatens to become outdated under climatic uncertainty. This paper applies a new downscaling method to examine the implications of climate change on future rainfall and evaluate the reliability of WSUD stormwater infrastructure in pollution reduction, flow frequency mitigation and reliability as an alternative water supply. A variety of future atmospheric scenarios are considered as part of this comprehensive assessment by analysing an ensemble of eight different downscaled General Circulation Models (GCMs). High resolution catchment-scale rainfall projections for Melbourne, Australia were generated using a scheme called High-resolution Downscaling of Rainfall Using STEPS (HiDRUS) at a fine 1 km and 6-min scale for more precise analysis with uncertainty estimates. Statistical analyses show that, in general, the climate models predict a drier future with fewer rainfall events and longer dry periods when comparing the simulated near future (2040–2049) periods against the base-line period (1995–2004). The difference simulated between historical and future rainfall projections show minimum difference of WSUD performance in pollution removal and flow frequency reduction, with slightly lower harvesting reliability ( < 3%) observed under future climate; high variabilities, however, were observed across GCM simulations, indicating big uncertainties of system reliability under various conditions, e.g. design wetland sizes may vary from 2.5% to 4.0% of the impervious catchment area according to different future projects across GCMs. Larger WSUD systems are recommended to ensure reliable performance of pollution removal, as well as harvesting reliability under simulated future conditions. The significance of considering an ensemble of different GCMs as opposed to many scenarios generated by a single 'best' climate model was also demonstrated for the robust estimation of uncertainty in future WSUD reliability. This work highlights important considerations for the future design, management and quantitative evaluation of WSUD reliability.
Zhang, K., Deletic, A., Bach, P. M., Shi, B., Hathaway, J. M., & McCarthy, D. T. (2019). Testing of new stormwater pollution build-up algorithms informed by a genetic programming approach. Journal of Environmental Management, 241, 12-21. doi:10.1016/j.jenvman.2019.04.009, Institutional Repository

Pollution build-up and wash-off processes are often included in urban stormwater quality models. However, these models are often unreliable and have poor performance at large scales and in complicated catchments. This study tried to improve stormwater quality models by adopting the genetic programming (GP) approach to generate new build-up algorithms for three different pollutants (total suspend solids – TSS, total phosphorus – TP and total nitrogen – TN). This was followed by testing of the new models (also traditional build-up and wash-off models as benchmark) using data collected from different catchments in Australia and the USA. The GP approach informed new sets of build-up algorithms with the inclusion of not just the typical antecedent dry weather period (ADWP), but also other less 'traditional' variables - previous rainfall depth for TSS and maximum air temperatures for TP and TN simulation. The traditional models had relatively poor performance (Nash-Sutcliffe coefficient, E < 0.0), except for TP at Gilby Road (GR) (E = 0.21 in calibration and 0.43 in validation). Improved performance was observed using the models with new build-up algorithms informed by GP. Taking TP at GR for example, the best performing model had E of 0.46 in calibration and 0.54 in validation. The best performing models for TSS, TP, and TN are often different, suggesting that specific models shall be used for different pollutants. Insights into further improvements possible for stormwater quality models were given. It is recommended that in addition to the typical build-up and wash-off process, new generations of stormwater quality models should be able to account for the non-conventional pollutant sources (e.g. cross-connections, septic tank leakage, illegal discharges) through stochastic approaches. Emission inventories with information like intensity-frequency-duration (IFD) of pollutant loads from each type of non-conventional source are suggested to be built for stochastic modelling.
Zheng, Y., Mutzner, L., Ort, C., Kaegi, R., & Gottschalk, F. (2019). Modelling engineered nanomaterials in wet-weather discharges. NanoImpact, 16, 100188 (8 pp.). doi:10.1016/j.impact.2019.100188, Institutional Repository

The impacts of the unintended release of engineered nanomaterials (ENMs) and the resulting exposure to the ecosystems are still poorly understood and intensively debated. In current mass flow models, a global transfer coefficient is used to account for ENMs discharged from sewer systems to surface waters during rain events.
However, different types of sewer systems and specific ENM applications likely require a more elaborate approach to quantify ENM loads entering surface waters during rain events. Therefore, we evaluated the impact of distinguishing between different application areas of ENMs (indoor/outdoor) and different types of sewer systems (combined/separate) on the loads of selected ENMs passing through wastewater systems and reaching the surface water. We applied a dynamic mass flow analysis to eight substances (TiO2, ZnO, Ag, SiO2, FeOx, CuO, CeO2 and carbon black) for six different sewer system configurations. To estimate the runoff from streets and facades, we used a hydrological model developed for dissolved substances. For areas with separate sewer systems only and ENMs predominantly used in and released from outdoor applications (e.g. CuO, carbon black), up to ~80% of the annual loads entering the sewer systems never reach a sewage treatment plant. Those ENMs are directly discharged to surface waters via stormwater outlets. For combined sewer systems and ENMs mainly applied indoors (e.g. Ag, ZnO), the percentage of ENM bypassing sewage treatment plants remains below 2%. Although the model is setup for Switzerland and other geographical areas will show for example different rain characteristics, the relative importance of the different release pathways will broadly remain the same and the results should, therefore, be transferrable to other countries.
Amado, C., Carvalho, G., Brito, R. S., Coelho, S. T., & Leitão, J. P. (2018). Analysing the importance of variables for sewer failure prediction. Urban Water Journal, 15(4), 338-345. doi:10.1080/1573062X.2018.1459748, Institutional Repository

When defining the variables to predict sewer failure and therefore optimise sewer systems maintenance, it is important to identify the ones that most significantly influence the quality of the predictions or to define the smallest number of variables that is sufficient to obtain accurate predictions. In this study, three different statistical variable selection algorithms are applied for the first time to identify the most important variables for sewer failure prediction: the mutual information indicator, the out-of-bag samples concept, based on the random forest algorithm, and the stepwise search approach. The methods were applied to a real data-set that consists of the categorisation of sewer condition and associated physical characteristics. The mutual information and the stepwise search methods provided good predictions while those obtained using out-of-bag samples based on random forest were somewhat different, justified by the lack of robustness to imbalanced class distributions.
Bach, P. M., Deletic, A., Urich, C., & McCarthy, D. T. (2018). Modelling characteristics of the urban form to support water systems planning. Environmental Modelling and Software, 104, 249-269. doi:10.1016/j.envsoft.2018.02.012, Institutional Repository

A spatial model is presented, based on urban planning concepts for abstracting urban form characteristics in new and existing areas. Requiring input maps of land use, elevation, population and parameters from planning regulations, the model conceptualises (on a spatial grid) attributes including impervious fraction, allotment geometry and roof areas among other relevant characteristics for integrated urban water management. The model is calibrated to three different Melbourne districts, varying in size (10–60 km2) and land use. Performance was evaluated by comparing modelled outputs with observations of total dwelling count, employment and spatial distribution of impervious fraction and residential roof areas. Results not only highlight reasonably good prediction, particularly with spatially variable indicators such as imperviousness across all case studies, but also logical contrasts and consistency in the chosen planning parameters across the different case study districts. Discrepancies highlight aspects needing improvement and potential for exploring auto-calibration and model sensitivity.
Blumensaat, F., Scheidegger, A., Ebi, C., Dicht, S., Schaltegger, F., Rüst, A., … Maurer, M. (2018). Digitalization meets reality - concept and experiences from long-term wireless data collection with 50+ sewer monitors. In G. Mannina (Ed.), Proceedings of 11th international conference on urban drainage modelling (pp. 360-363). sine loco: IWA. , Institutional Repository

Blossoming visions foresee an ubiquitous availability of data enabled through emerging sensors, novel transmission technologies and revisited data analysis techniques. It is often expected that this likewise applies to urban water management (Kerkez et al., 2016; Eggimann et al., 2017). Contrary to the envisioned easiness to collect data, an in-creasing number of studies report about a declining effort to conduct experimental field re-search in catchment and urban hydrology, implying the risk of scientific advancement to be misdirected (Blume et al., 2017; van Emmerik et al., 2018). In this paper, we present re-sults from a unique sensor network that collects, transmits, and organizes very high resolu-tion status information from an urban drainage catchment. Our findings are based on a two-year operation at full-scale with more than 50 sensor nodes connected to a low-power wireless sensor network (LPWSN). In essence we i) illustrate the scalability of LPWSNs, ii) reveal telemetry limitations of the LoRaWAN standard (LoRa®Alliance 2015), iii) suggest a novel technique (LoRaMesh) to master this constraint, and iv) discuss challenges associ-ated with managing the amount of data collected from diverse sources. We find: 1. the proposed transmission technique considerably improves telemetry performance of the en-tire network without introducing additional gateway stations (total packet loss 2.2 %). 2. Dynamic and flexible data management solutions come at a price. 3. It is not the pure amount of data that is most challenging but the diversity of different signals. 4. Full metada-ta integration in the data warehouse is laborious but clearly consolidates data interpretation in the long-term run.
Caravelli, F., & Carbajal, J. P. (2018). Memristors for the curious outsiders. Technologies, 6(4), 118 (42 pp.). doi:10.3390/technologies6040118, Institutional Repository

We present both an overview and a perspective of recent experimental advances and proposed new approaches to performing computation using memristors. A memristor is a 2-terminal passive component with a dynamic resistance depending on an internal parameter. We provide an brief historical introduction, as well as an overview over the physical mechanism that lead to memristive behavior. This review is meant to guide nonpractitioners in the field of memristive circuits and their connection to machine learning and neural computation.
Castonguay, A. C., Iftekhar, M. S., Urich, C., Bach, P. M., & Deletic, A. (2018). Integrated modelling of stormwater treatment systems uptake. Water Research, 142, 301-312. doi:10.1016/j.watres.2018.05.037, Institutional Repository

Nature-based solutions provide a variety of benefits in growing cities, ranging from stormwater treatment to amenity provision such as aesthetics. However, the decision-making process involved in the installation of such green infrastructure is not straightforward, as much uncertainty around the location, size, costs and benefits impedes systematic decision-making. We developed a model to simulate decision rules used by local municipalities to install nature-based stormwater treatment systems, namely constructed wetlands, ponds/basins and raingardens. The model was used to test twenty-four scenarios of policy-making, by combining four asset selection, two location selection and three budget constraint decision rules. Based on the case study of a local municipality in Metropolitan Melbourne, Australia, the modelled uptake of stormwater treatment systems was compared with attributes of real-world systems for the simulation period. Results show that the actual budgeted funding is not reliable to predict systems’ uptake and that policy-makers are more likely to plan expenditures based on installation costs. The model was able to replicate the cumulative treatment capacity and the location of systems. As such, it offers a novel approach to investigate the impact of using different decision rules to provide environmental services considering biophysical and economic factors.
Castrignanò, E., Yang, Z., Bade, R., Baz-Lomba, J. A., Castiglioni, S., Causanilles, A., … Kasprzyk-Hordern, B. (2018). Enantiomeric profiling of chiral illicit drugs in a pan-European study. Water Research, 130, 151-160. doi:10.1016/j.watres.2017.11.051, Institutional Repository

The aim of this paper is to present the first study on spatial and temporal variation in the enantiomeric profile of chiral drugs in eight European cities. Wastewater-based epidemiology (WBE) and enantioselective analysis were combined to evaluate trends in illicit drug use in the context of their consumption vs direct disposal as well as their synthetic production routes. Spatial variations in amphetamine loads were observed with higher use in Northern European cities. Enantioselective analysis showed a general enrichment of amphetamine with the R-(−)-enantiomer in wastewater indicating its abuse. High loads of racemic methamphetamine were detected in Oslo (EF = 0.49 ± 0.02). This is in contrast to other European cities where S-(+)-methamphetamine was the predominant enantiomer. This indicates different methods of methamphetamine synthesis and/or trafficking routes in Oslo, compared with the other cities tested. An enrichment of MDMA with the R-(−)-enantiomer was observed in European wastewaters indicating MDMA consumption rather than disposal of unused drug. MDA's chiral signature indicated its enrichment with the S-(+)-enantiomer, which confirms its origin from MDMA metabolism in humans. HMMA was also detected at quantifiable concentrations in wastewater and was found to be a suitable biomarker for MDMA consumption. Mephedrone was only detected in wastewater from the United Kingdom with population-normalised loads up to 47.7 mg 1000 people−1 day−1. The enrichment of mephedrone in the R-(+)-enantiomer in wastewater suggests stereoselective metabolism in humans, hence consumption, rather than direct disposal of the drug. The investigation of drug precursors, such as ephedrine, showed that their presence was reasonably ascribed to their medical use.
Causanilles, A., Rojas Cantillano, D., Emke, E., Bade, R., Baz-Lomba, J. A., Castiglioni, S., … de Voogt, P. (2018). Comparison of phosphodiesterase type V inhibitors use in eight European cities through analysis of urban wastewater. Environment International, 115, 279-284. doi:10.1016/j.envint.2018.03.039, Institutional Repository

In this work a step forward in investigating the use of prescription drugs, namely erectile dysfunction products, at European level was taken by applying the wastewater-based epidemiology approach. 24-h composite samples of untreated wastewater were collected at the entrance of eight wastewater treatment plants serving the catchment within the cities of Bristol, Brussels, Castellón, Copenhagen, Milan, Oslo, Utrecht and Zurich. A validated analytical procedure with direct injection of filtered aliquots by liquid chromatography-tandem mass spectrometry was applied. The target list included the three active pharmaceutical ingredients (sildenafil, tadalafil and vardenafil) together with (bio)transformation products and other analogues. Only sildenafil and its two human urinary metabolites desmethyl- and desethylsildenafil were detected in the samples with concentrations reaching 60 ng L−1. The concentrations were transformed into normalized measured loads and the estimated actual consumption of sildenafil was back-calculated from these loads. In addition, national prescription data from five countries was gathered in the form of the number of prescribed daily doses and transformed into predicted loads for comparison. This comparison resulted in the evidence of a different spatial trend across Europe. In Utrecht and Brussels, prescription data could only partly explain the total amount found in wastewater; whereas in Bristol, the comparison was in agreement; and in Milan and Oslo a lower amount was found in wastewater than expected from the prescription data. This study illustrates the potential of wastewater-based epidemiology to investigate the use of counterfeit medication and rogue online pharmacy sales.
Eggimann, S., Truffer, B., Feldmann, U., & Maurer, M. (2018). Screening European market potentials for small modular wastewater treatment systems – an inroad to sustainability transitions in urban water management?. Land Use Policy, 78, 711-725. doi:10.1016/j.landusepol.2018.07.031, Institutional Repository

Urban water management represents a core economic sector exposed to global water-related challenges. Recently, small modular system configurations have been identified to enable a potential sustainability transition in this lasting and rather conservative sector. The identification of current market potentials of decentralised wastewater treatment is a first step to assess whether decentralised treatment technologies could potentially be deployed on a larger scale in Europe, which would allow current decentralised wastewater treatment technologies to develop and mature. The paper elaborates a method to assess the market potential for decentralised wastewater treatment systems by starting from a raster-based geospatial modelling framework, to determine the optimal degrees of centralisation for the case of Switzerland. The resulting market potential is shown to be twenty times higher than the current market share of decentralised systems. In order to extrapolate these findings to other countries, the calculated optimal degrees of centralisation were correlated with different spatial density measures to determine a reliable and widely available proxy: population density. Based on this indicator, the European market potentials for decentralised treatment systems are estimated to be about 100,000 units per annum serving around 35 million population equivalents. The paper concludes by discussing implications for future sustainability transitions in urban water management by large-scale installation of small modular wastewater treatment systems.
Hernández, F., Castiglioni, S., Covaci, A., de Voogt, P., Emke, E., Kasprzyk-Hordern, B., … Bijlsma, L. (2018). Mass spectrometric strategies for the investigation of biomarkers of illicit drug use in wastewater. Mass Spectrometry Reviews, 37(3), 258-280. doi:10.1002/mas.21525, Institutional Repository

The analysis of illicit drugs in urban wastewater is the basis of wastewater-based epidemiology (WBE), and has received much scientific attention because the concentrations measured can be used as a new non-intrusive tool to provide evidence-based and real-time estimates of community-wide drug consumption. Moreover, WBE allows monitoring patterns and spatial and temporal trends of drug use. Although information and expertise from other disciplines is required to refine and effectively apply WBE, analytical chemistry is the fundamental driver in this field. The use of advanced analytical techniques, commonly based on combined chromatography—mass spectrometry, is mandatory because the very low analyte concentration and the complexity of samples (raw wastewater) make quantification and identification/confirmation of illicit drug biomarkers (IDBs) troublesome. We review the most-recent literature available (mostly from the last 5 years) on the determination of IDBs in wastewater with particular emphasis on the different analytical strategies applied. The predominance of liquid chromatography coupled to tandem mass spectrometry to quantify target IDBs and the essence to produce reliable and comparable results is illustrated. Accordingly, the importance to perform inter-laboratory exercises and the need to analyze appropriate quality controls in each sample sequence is highlighted. Other crucial steps in WBE, such as sample collection and sample pre-treatment, are briefly and carefully discussed. The article further focuses on the potential of high-resolution mass spectrometry. Different approaches for target and non-target analysis are discussed, and the interest to perform experiments under laboratory-controlled conditions, as a complementary tool to investigate related compounds (e.g., minor metabolites and/or transformation products in wastewater) is treated. The article ends up with the trends and future perspectives in this field from the authors’ point of view.
Jamali, B., Löwe, R., Bach, P. M., Urich, C., Arnbjerg-Nielsen, K., & Deletic, A. (2018). A rapid urban flood inundation and damage assessment model. Journal of Hydrology, 564, 1085-1098. doi:10.1016/j.jhydrol.2018.07.064, Institutional Repository

Urban pluvial flooding is a global challenge that is frequently caused by the lack of available infiltration, retention and drainage capacity in cities. This paper presents RUFIDAM, an urban pluvial flood model, developed using GIS technology with the intention of rapidly estimating flood extent, depth and its associated damage. RUFIDAM integrates a 1D hydraulic drainage network model (SWMM or MOUSE) with an adapted version of rapid flood inundation models. One-metre resolution topographic data was used to identify depressions in an urban catchment. Volume-elevation relationships and minimum elevation between adjacent depressions were determined. Mass balance considerations were then used to simulate movement of water between depressions. Surcharge volumes from the 1D drainage network model were fed statically into the rapid inundation model. The model was tested on three urban catchments located in southeast Melbourne. Results of flood depth, extent and damage costs were compared to those produced using MIKE FLOOD; a well-known 1D-2D hydrodynamic model. Results showed that RUFIDAM can predict flood extent and accumulated damage cost with acceptable accuracy. Although some variations in the simulated location of flooding were observed, simulation time was reduced by two orders of magnitude compared to MIKE FLOOD. As such, RUFIDAM is suitable for large-scale flood studies and risk-based approaches that rely on a large number of simulations.
Kinyua, J., Negreira, N., McCall, A. K., Boogaerts, T., Ort, C., Covaci, A., & van Nuijs, A. L. N. (2018). Investigating in-sewer transformation products formed from synthetic cathinones and phenethylamines using liquid chromatography coupled to quadrupole time-of-flight mass spectrometry. Science of the Total Environment, 634, 331-340. doi:10.1016/j.scitotenv.2018.03.253, Institutional Repository

Recent studies have demonstrated the role of biofilms on the stability of drug residues in wastewater. These factors are pertinent in wastewater-based epidemiology (WBE) when estimating community-level drug use. However, there is scarce information on the biotransformation of drug residues in the presence of biofilms and the potential use of transformation products (TPs) as biomarkers in WBE.
The purpose of this work was to investigate the formation of TPs in sewage reactors in the presence of biofilm mimicking conditions during in-sewer transport. Synthetic cathinones (methylenedioxypyrovalerone, methylone, mephedrone) and phenethylamines (4-methoxy-methamphetamine and 4-methoxyamphetamine) were incubated in individual reactors over a 24 h period. Analysis of parent species and TPs was carried out using liquid chromatography coupled to quadrupole time-of-flight mass spectrometry (LC-QToFMS). Identification of TPs was done using suspect and non-target workflows.
In total, 18 TPs were detected and identified with reduction of β-keto group, demethylenation, demethylation, and hydroxylation reactions observed for the synthetic cathinones. For the phenethylamines, N- and O-demethylation reactions were identified. Overall, the experiments showed varying stability for the parent species in wastewater in the presence of biofilms. The newly identified isomeric forms of TPs particularly for methylone and mephedrone can be used as potential target biomarkers for WBE studies due to their specificity and detectability within a 24 h residence time.
Kuller, M., Farrelly, M., Deletic, A., & Bach, P. M. (2018). Building effective planning support systems for green urban water infrastructure – practitioners' perceptions. Environmental Science and Policy, 89, 153-162. doi:10.1016/j.envsci.2018.06.011, Institutional Repository

The multiple benefits of adopting distributed, green stormwater technologies in the local environment are increasingly recognised, particularly in relation to water quality, flood mitigation, amenity and aesthetics. To advance the integration of these systems into everyday decision-making practices, Planning Support Systems (PSS) are considered vital. Despite several PSS available to support planners and key decision-makers, their uptake remains constrained; a phenomenon known as the 'implementation gap'. While scholars have hypothesised why the adoption of PSS is limited, there remains little empirical investigation regarding the reasons why. This paper tests the hypotheses underlying the implementation gap in relation to water sensitive urban design (WSUD) planning. Drawing on the tacit experience of 24 key urban water planning professionals in the front-runner city of Melbourne, Australia, in-depth semi-structured interviews were undertaken to unpack the contemporary planning processes used and reveal characteristics leading to success and failure of PSS application. Data analysis revealed WSUD planning professionals regard the adoption of PSS as a significant step towards improving contemporary decision-making practices, which are regarded as opportunistic rather than strategic. PSS use was widespread, though the type, intensity and sophistication of use varied among interview participants. Confirming the hypotheses from planning literature, practitioners suggested PSS need to be user-friendly and align closely to planning practice. Additionally, however, it was found that it is crucial for PSS to meet industry conventions. Suggested improvements to current PSS included incorporating socio-economic factors alongside biophysical and planning factors, hence the role for GIS-based suitability analysis tools. Overall, this study provides current and future PSS-developers with critical insights regarding the type, function and characteristics of an 'ideal' PSS aimed at enhancing the usefulness and uptake of PSS, and thus improve planning that supports expediting green infrastructure implementation.
Kuller, M., Bach, P. M., Ramirez-Lovering, D., & Deletic, A. (2018). What drives the location choice for water sensitive infrastructure in Melbourne, Australia?. Landscape and Urban Planning, 175, 92-101. doi:10.1016/j.landurbplan.2018.03.018, Institutional Repository

Distributed and green urban drainage infrastructure known as Water Sensitive Urban Design (WSUD) is increasingly being implemented in cities globally to combat climate change and urbanisation effects. Rigorous consideration of the urban context in terms of biophysical, socio-economic and urban form related factors is crucial for optimal design outcomes. The extent to which the urban context is considered in current planning and decision-making processes remains unclear. This study investigates this relationship between current WSUD infrastructure in Melbourne (Australia) and each of the aforementioned factors for the first time. We obtained and pre-processed one of the most extensive and complete geo-located WSUD asset databases in the world (containing over 2000 WSUD assets), and undertook an evidence-based analysis of WSUD planning outcomes. Relationships were investigated using spatial analysis techniques (e.g. overlaying), as well as a number of statistical methods (e.g. exploratory regression). It was found that biophysical and urban form factors strongly explained variability in WSUD location choice, while socio-economic factors appeared to be overlooked. Our findings imply that the current WSUD planning practices are primarily governed by standard engineering design. Opportunistic WSUD planning leads to unintentional outcomes that fail to capitalise on the full potential of WSUD benefits. Increased investment in asset inventory development and analysis is critical to inform WSUD planning moving forward. Knowledge gained from this and additional studies can further planning through application in planning-support systems, to deal with the complexity and diversity of the broad set of decision criteria.
Leitão, J. P., Carbajal, J. P., Rieckermann, J., Simões, N. E., Sá Marques, A., & de Sousa, L. M. (2018). Identifying the best locations to install flow control devices in sewer networks to enable in-sewer storage. Journal of Hydrology, 556, 371-383. doi:10.1016/j.jhydrol.2017.11.020, Institutional Repository

The activation of available in-sewer storage volume has been suggested as a low-cost flood and combined sewer overflow mitigation measure. However, it is currently unknown what the attributes for suitable objective functions to identify the best location for flow control devices are and the impact of those attributes on the results. In this study, we present a novel location model and efficient algorithm to identify the best location(s) to install flow limiters. The model is a screening tool that does not require hydraulic simulations but rather considers steady state instead of simplistic static flow conditions. It also maximises in-sewer storage according to different reward functions that also considers the potential impact of flow control device failure. We demonstrate its usefulness on two real sewer networks, for which an in-sewer storage potential of approximately 2,000 m3 and 500 m3 was estimated with five flow control devices installed.
Leitão, J. P., & de Sousa, L. M. (2018). Towards the optimal fusion of high-resolution Digital Elevation Models for detailed urban flood assessment. Journal of Hydrology, 561, 651-661. doi:10.1016/j.jhydrol.2018.04.043, Institutional Repository

Newly available, more detailed and accurate elevation data sets, such as Digital Elevation Models (DEMs) generated on the basis of imagery from terrestrial LiDAR (Light Detection and Ranging) systems or Unmanned Aerial Vehicles (UAVs), can be used to improve flood-model input data and consequently increase the accuracy of the flood modelling results. This paper presents the first application of the MBlend merging method and assesses the impact of combining different DEMs on flood modelling results. It was demonstrated that different raster merging methods can have different and substantial impacts on these results. In addition to the influence associated with the method used to merge the original DEMs, the magnitude of the impact also depends on (i) the systematic horizontal and vertical differences of the DEMs, and (ii) the orientation between the DEM boundary and the terrain slope. The greater water depth and flow velocity differences between the flood modelling results obtained using the reference DEM and the merged DEMs ranged from −9.845 to 0.002 m, and from 0.003 to 0.024 m s−1 respectively; these differences can have a significant impact on flood hazard estimates. In most of the cases investigated in this study, the differences from the reference DEM results were smaller for the MBlend method than for the results of the two conventional methods. This study highlighted the importance of DEM merging when conducting flood modelling and provided hints on the best DEM merging methods to use.
Leitão, J. P., Peña-Haro, S., Lüthi, B., Scheidegger, A., & Moy de Vitry, M. (2018). Urban overland runoff velocity measurement with consumer-grade surveillance cameras and surface structure image velocimetry. Journal of Hydrology, 565, 791-804. doi:10.1016/j.jhydrol.2018.09.001, Institutional Repository

Physically-based models are important tools for evaluating the hydraulic behaviour of urban drainage systems and, more specifically, assessing flood risk. While it is well known that such models should be calibrated and validated with monitoring data, overland runoff information is seldom available for this purpose. This study investigates the potential of using surveillance camera footage to measure surface flow velocity thanks to an LSPIV-based method called Surface Structure Image Velocimetry (SSIV). Seven real-scale experiments conducted in a specialized flood training facility were used to test the SSIV method under varied and challenging conditions. SSIV performance was evaluated by benchmarking bulk (mean) velocity against that measured by a conventional sensor array. In the best conditions tested, SSIV and conventional flow sensors differed by only 1.7% (0.1% standard deviation). While the method proved sensitive to light conditions, our results suggest that infrared lighting could be used to increase measurement consistency. Our study concludes that for measuring overland flow velocity in urban areas, surveillance and traffic cameras can be considered as a low-maintenance and easy-to-install alternative to conventional sensor systems.
Mahmoodian, M., Carbajal, J. P., Bellos, V., Leopold, U., Schutz, G., & Clemens, F. (2018). A hybrid surrogate modelling strategy for simplification of detailed urban drainage simulators. Water Resources Management, 32(15), 5241-5256. doi:10.1007/s11269-018-2157-4, Institutional Repository

Urban drainage modelling typically requires development of highly detailed simulators due to the nature of various underlying surface and drainage processes, which makes them computationally too expensive. Application of such simulators is still challenging in activities such as real-time control (RTC), uncertainty quantification analysis or model calibration in which numerous simulations are required. The focus of this paper is to present a rather simple hybrid surrogate modelling (or emulation) strategy to simplify and accelerate a detailed urban drainage simulator (UDS). The proposed surrogate modelling strategy includes: a) identification of the variables to be emulated; b) development of a simplified conceptual model in which every component contributing to the variables identified in step (a) is replaced by a function; c) definition of these functions, either based on knowledge about the mechanisms of the simulator, or based on the data produced by the simulator; and finally, d) validation of the results produced by the surrogate model in comparison with the original detailed simulator. Herein, a detailed InfoWorks ICM simulator was selected for surrogate modelling. The case study area was a small urban drainage network in Luxembourg. An emulator was developed to map the rainfall time series, as input, to a storage tank volume and combined sewer overflow (CSO) in the case study network. The results showed that the introduced strategy provides a reliable method to simplify the simulator and reduce its run time significantly. For the specific case study, the emulator was approximately 1300 times faster than the original detailed simulator. For quantification of the emulation error, an ensemble of 500 rainfall scenarios with 1 month duration was generated by application of a multivariate autoregressive model for conditional simulation of rainfall time series. The results produced by the emulator were compared to the ones produced by the simulator. Finally, as an indicator of the emulation error, distributions of Nash-Sutcliffe efficiency (NSE) between the emulator and simulator results for prediction of storage tank volume and CSO flow time series were presented.
Moreira de Sousa, L., & Leitão, J. P. (2018). HexASCII: a file format for cartographical hexagonal rasters. Transactions in GIS, 22(1), 217-232. doi:10.1111/tgis.12304, Institutional Repository

Hexagonal segmentations of space have long been known for their advantages vis à vis squared partitions in discretizing spatial variables, be it natural phenomena or human-related features. However, readily available and easy-to-use tools to manipulate and interact with hexagonal rasters remain widely unavailable today. This article presents a first step to enable the use of hexagonal rasters in the GIS field. A format to encode cartographical hexagonal meshes as simple ASCII files is specified through a context-free grammar. Named HexASCII, this file format provides a simple means of storing and sharing such rasters. A set of simple tools based on the HexASCII format is presented, allowing their creation and basic interaction with traditional GIS software.
Moy de Vitry, M., Schindler, K., Rieckermann, J., & Leitão, J. P. (2018). Sewer inlet localization in UAV image clouds: improving performance with multiview detection. Remote Sensing, 10(5), 706 (18 pp.). doi:10.3390/rs10050706, Institutional Repository

Sewer and drainage infrastructure are often not as well catalogued as they should be, considering the immense investment they represent. In this work, we present a fully automatic framework for localizing sewer inlets from image clouds captured from an unmanned aerial vehicle (UAV). The framework exploits the high image overlap of UAV imaging surveys with a multiview approach to improve detection performance. The framework uses a Viola–Jones classifier trained to detect sewer inlets in aerial images with a ground sampling distance of 3–3.5 cm/pixel. The detections are then projected into three-dimensional space where they are clustered and reclassified to discard false positives. The method is evaluated by cross-validating results from an image cloud of 252 UAV images captured over a 0.57-km2 study area with 228 sewer inlets. Compared to an equivalent single-view detector, the multiview approach improves both recall and precision, increasing average precision from 0.65 to 0.73. The source code and case study data are publicly available for reuse.
Muthusamy, M., Wani, O., Schellart, A., & Tait, S. (2018). Accounting for variation in rainfall intensity and surface slope in wash-off model calibration and prediction within the Bayesian framework. Water Research, 143, 561-569. doi:10.1016/j.watres.2018.06.022, Institutional Repository

Exponential wash-off models are the most widely used method to predict sediment wash-off from urban surfaces. In spite of many studies, there is still a lack of knowledge on the effect of external drivers such as rainfall intensity and surface slope on wash-off predictions. In this study, a more physically realistic "structure" is added to the original exponential wash-off model (OEM) by replacing the invariant parameters with functions of rainfall intensity and catchment surface slope, so that the model can better represent catchment and rainfall conditions without the need for lookup tables and interpolation/extrapolation. In the proposed new exponential model (NEM), two such functions are introduced. One function describes the maximum fraction of the initial load that can be washed off by a rainfall event for a given slope and the other function describes the wash-off rate during a rainfall event for a given slope. The parameters of these functions are estimated using data collected from a series of laboratory experiments carried out using an artificial rainfall generator, a 1 m2 bituminous road surface and a continuous wash-off measuring system. These experimental data contain high temporal resolution measurements of wash-off fractions for combinations of five rainfall intensities ranging from 33 to 155 mm/h and three catchment slopes ranging from 2 to 8%. Bayesian inference, which allows the incorporation of prior knowledge, is implemented to estimate parameter values. Explicitly accounting for model bias and measurement errors, a likelihood function representative of the wash-off process is formulated, and the uncertainty in the prediction of the NEM is quantified. The results of this study show: 1) even when the OEM is calibrated for every experimental condition, the NEM's performance, with parameter values defined by functions, is comparable to the OEM. 2) Verification indices for estimates of uncertainty associated with the NEM suggest that the error model used in this study is able to capture the uncertainty well.
Novic, A. J., Ort, C., O'Brien, D. S., Lewis, S. E., Davis, A. M., & Mueller, J. F. (2018). Understanding the uncertainty of estimating herbicide and nutrient mass loads in a flood event with guidance on estimator selection. Water Research, 132, 99-110. doi:10.1016/j.watres.2017.12.055, Institutional Repository

The aim of this study was to understand the uncertainty of estimating loads for observed herbicides and nutrients during a flood event and provide guidance on estimator selection. A high-resolution grab sampling campaign (258 samples over 100 h) was conducted during a flood event in a tropical waterway in Queensland, Australia. Ten herbicides and three nutrient compounds were detected at elevated concentrations. Each had a unique chemograph with differences in transport processes (e.g. dependence on flow, dilution processes and timing of concentration pulses). Resampling from the data set was used to assess uncertainty. Bias existed at lower sampling efforts but depended on estimator properties as sampling effort increased: the interpolation, ratio and regression estimators became unbiased. Large differences were observed in precision and the importance of sampling effort and estimator selection depended on the relationship between the chemograph and hydrograph. The variety of transport processes observed and the resultant variability in uncertainty suggest that useful load estimates can only be obtained with sufficient samples and appropriate estimator selection. We provide a rationale to show the latter can be guided across sampling periods by selecting an estimator where the sampling regime or the relationship between the chemograph and hydrograph meet its assumptions: interpolation becomes more correct as sampling effort increases and the ratio becomes more correct as the r2 correlation between flux and flow increases (e.g. > 0.9); a stratified composite sampling approach, even with random samples, is a promising alternative.
Patsch, D., van Vliet, S., Garbani Marcantini, L., & Johnson, D. R. (2018). Generality of associations between biological richness and the rates of metabolic processes across microbial communities. Environmental Microbiology, 20(12), 4356-4368. doi:10.1111/1462-2920.14352, Institutional Repository

Biological richness is positively associated with the rates of some metabolic processes performed by microbial communities. It remains unclear, however, whether these positive associations are a general feature of the metabolic processes performed by microbial communities or whether they are specific to certain types of metabolic processes. For example, it was hypothesized that the strength of any particular positive association depends on how many different genotypes within a microbial community perform the metabolic process of interest (i.e. the 'rarity hypothesis'). We tested the generality of these positive associations by measuring the taxonomic richness, functional gene richness and rate constants for 71 different metabolic processes across 30 independent microbial communities. We found that both taxonomic and functional gene richness do indeed tend to positively associate with the rates of metabolic processes. In addition, we found that positive associations occur across a wide range of different environmental conditions. Counter to the 'rarity hypothesis', however, we did not detect a relationship between the strengths of the positive associations and the rarity of each metabolic process. Together, our data provide empirical evidence that positive associations with biological richness may indeed be a general feature of the metabolic processes performed by microbial communities.
Penn, R., Ward, B. J., Strande, L., & Maurer, M. (2018). Review of synthetic human faeces and faecal sludge for sanitation and wastewater research. Water Research, 132, 222-240. doi:10.1016/j.watres.2017.12.063, Institutional Repository

Investigations involving human faeces and faecal sludge are of great importance for urban sanitation, such as operation and maintenance of sewer systems, or implementation of faecal sludge management. However, working with real faecal matter is difficult as it not only involves working with a pathogenic, malodorous material but also individual faeces and faecal sludge samples are highly variable, making it difficult to execute repeatable experiments. Synthetic faeces and faecal sludge can provide consistently reproducible substrate and alleviate these challenges. A critical literature review of simulants developed for various wastewater and faecal sludge related research is provided. Most individual studies sought to develop a simulant representative of specific physical, chemical, or thermal properties depending on their research objectives. Based on the review, a suitable simulant can be chosen and used or further developed according to the research needs. As an example, the authors present such a modification for the development of a simulant that can be used for investigating the motion (movement, settling and sedimentation) of faeces and their physical and biological disintegration in sewers and in on-site sanitation systems.
Penn, R., Schuetze, M., Jens, A., & Friedler, E. (2018). Tracking and simulation of gross solids transport in sewers. Urban Water Journal, 15(6), 584-591. doi:10.1080/1573062X.2018.1529190, Institutional Repository

Investigation of various phenomena occurring in sewer systems is critical for increasing the sustainability of such systems. Such investigations also includes gross solids (GS) transport. GS transport may be affected by reduction of flows resulting from population decrease and/or reduced domestic water consumption. The present paper reports on the development of a model for describing GS velocity as a function of wastewater flow characteristics. The model was calibrated and validated with field experiments, in which two methods for GS tracking in sewers were developed, using small light sticks tracked by computerized light detector, and RFID based tracking. The model is integrated in a simulator which allows a description of the movement of individual GS through sewer systems. The developed model may assist in analysing existing sewer systems that are subject to changes in their input flows and prevent undesired failures (e.g. sedimentations, blockages, toxic gases) or in designing new systems.
Spuhler, D., Scheidegger, A., & Maurer, M. (2018). Generation of sanitation system options for urban planning considering novel technologies. Water Research, 145, 259-278. doi:10.1016/j.watres.2018.08.021, Institutional Repository

The identification of appropriate sanitation systems is particularly challenging in developing urban areas where local needs are not met by conventional solutions. While structured decision-making frameworks such as Community-Led Urban Environmental Sanitation (CLUES) can help facilitate this process, they require a set of sanitation system options as input. Given the large number of possible combinations of sanitation technologies, the generation of a good set of sanitation system options is far from trivial.
This paper presents a procedure for generating a set of locally appropriate sanitation system options, which can then be used in a structured decision-making process. The systematic and partly automated procedure was designed (i) to enhance the reproducibility of option generation; (ii) to consider all types of conventional and novel technologies; (iii) to provide a set of sanitation systems that is technologically diverse; and (iv) to formally account for uncertainties linked to technology specifications and local conditions.
We applied the procedure to an emerging small town in Nepal. We assessed the appropriateness of 40 technologies and generated 17,955 appropriate system options. These were classified into 16 system templates including on-site, urine-diverting, biogas, and blackwater templates. From these, a subset of 36 most appropriate sanitation system options were selected, which included both conventional and novel options.
We performed a sensitivity analysis to evaluate the impact of different elements on the diversity and appropriateness of the set of selected sanitation system options. We found that the use of system templates is most important, followed by the use of a weighted multiplicative aggregation function to quantify local appropriateness. We also show that the optimal size of the set of selected sanitation system options is equal to or slightly greater than the number of system templates.
As novel technologies are developed and added to the already large portfolio technology options, the procedure presented in this work may become an essential tool for generating and exploring appropriate sanitation system options.
Zeisl, P., Mair, M., Kastlunger, U., Bach, P. M., Rauch, W., Sitzenfrei, R., & Kleidorfer, M. (2018). Conceptual urban water balance model for water policy testing: an approach for large scale investigation. Sustainability, 10(3), 716 (24 pp.). doi:10.3390/su10030716, Institutional Repository

Urban water management will face various challenges in the future. Growing population in cities, changing climatic conditions and uncertain availability of water resources necessitate forward-looking water policy strategies. In this paper, we introduce a new water balance model to evaluate urban water strategies at a city scale. The aim is to evaluate decentralised water management measures within a large-scale investigation and to reduce external potable water demand. The upscaling process of local information (water demand, areal data) to a conceptual model approach is described. The modelling approach requires simplification of detailed processes to enable the execution with limited computing capacity. The model was applied to Greater Metropolitan Melbourne, Australia, a highly sprawled city with nearly four million inhabitants. Scenario analysis demonstrated the impact of using different water resources of different quality classes, the extensive implementation of water saving appliances and decentralised water storage strategies on the city’s water balance. Results indicate a potential reduction of potable water demand of up to 25% with a conservative rainwater reuse and, even 60% with widespread implementation of rain- and greywater recycling. Furthermore, we demonstrate that even small systems implemented at a local level can have noticeable effects when operated as clustered schemes.
Zheng, F., Tao, R., Maier, H. R., See, L., Savic, D., Zhang, T., … Popescu, I. (2018). Crowdsourcing methods for data collection in geophysics: state of the art, issues, and future directions. Reviews of Geophysics, 56(4), 698-740. doi:10.1029/2018RG000616, Institutional Repository

Data are essential in all areas of geophysics. They are used to better understand and manage systems, either directly or via models. Given the complexity and spatiotemporal variability of geophysical systems (e.g., precipitation), a lack of sufficient data is a perennial problem, which is exacerbated by various drivers, such as climate change and urbanization. In recent years, crowdsourcing has become increasingly prominent as a means of supplementing data obtained from more traditional sources, particularly due to its relatively low implementation cost and ability to increase the spatial and/or temporal resolution of data significantly. Given the proliferation of different crowdsourcing methods in geophysics and the promise they have shown, it is timely to assess the state of the art in this field, to identify potential issues and map out a way forward. In this paper, crowdsourcing-based data acquisition methods that have been used in seven domains of geophysics, including weather, precipitation, air pollution, geography, ecology, surface water, and natural hazard management, are discussed based on a review of 162 papers. In addition, a novel framework for categorizing these methods is introduced and applied to the methods used in the seven domains of geophysics considered in this review. This paper also features a review of 93 papers dealing with issues that are common to data acquisition methods in different domains of geophysics, including the management of crowdsourcing projects, data quality, data processing, and data privacy. In each of these areas, the current status is discussed and challenges and future directions are outlined.
van Nuijs, A. L. N., Lai, F. Y., Been, F., Andres-Costa, M. J., Barron, L., Baz-Lomba, J. A., … Ort, C. (2018). Multi-year inter-laboratory exercises for the analysis of illicit drugs and metabolites in wastewater: development of a quality control system. Trends in Analytical Chemistry, 103, 34-43. doi:10.1016/j.trac.2018.03.009, Institutional Repository

Thirty-seven laboratories from 25 countries present the development of an inter-laboratory testing scheme for the analysis of seven illicit drug residues in standard solutions, tap- and wastewater. Almost 10 000 concentration values were evaluated: triplicates of up to five samples and 26 laboratories per year. The setup was substantially improved with experiences gained across the six repetitions (e.g. matrix type, sample conditions, spiking levels). From this, (pre-)analytical issues (e.g. pH adjustment, filtration) were revealed for specific analytes which resulted in formulation of best-practice protocols for inter-laboratory setup and analytical procedures. The results illustrate the effectiveness of the inter-laboratory setup to assess laboratory performance in the framework of wastewater-based epidemiology. The exercise proved that measurements of laboratories were of high quality (>80% satisfactory results for six out of seven analytes) and that analytical follow-up is important to assist laboratories in improving robustness of wastewater-based epidemiology results.
Aymerich, I., Acuña, V., Ort, C., Rodríguez-Roda, I., & Corominas, L. (2017). Fate of organic microcontaminants in wastewater treatment and river systems: an uncertainty assessment in view of sampling strategy, and compound consumption rate and degradability. Water Research, 125, 152-161. doi:10.1016/j.watres.2017.08.011, Institutional Repository

The growing awareness of the relevance of organic microcontaminants on the environment has led to a growing number of studies on attenuation of these compounds in wastewater treatment plants (WWTP) and rivers. However, the effects of the sampling strategies (frequency and duration of composite samples) on the attenuation estimates are largely unknown. Our goal was to assess how frequency and duration of composite samples influence uncertainty of the attenuation estimates in WWTPs and rivers. Furthermore, we also assessed how compound consumption rate and degradability influence uncertainty. The assessment was conducted through simulating the integrated wastewater system of Puigcerdà (NE Iberian Peninsula) using a sewer pattern generator and a coupled model of WWTP and river. Results showed that the sampling strategy is especially critical at the influent of WWTP, particularly when the number of toilet flushes containing the compound of interest is small (≤100 toilet flushes with compound day-1), and less critical at the effluent of the WWTP and in the river due to the mixing effects of the WWTP. For example, at the WWTP, when evaluating a compound that is present in 50 pulses.d-1 using a sampling frequency of 15-min to collect a 24-h composite sample, the attenuation uncertainty can range from 94% (0% degradability) to 9% (90% degradability). The estimation of attenuation in rivers is less critical than in WWTPs, as the attenuation uncertainty was lower than 10% for all evaluated scenarios. Interestingly, the errors in the estimates of attenuation are usually lower than those of loads for most sampling strategies and compound characteristics (e.g. consumption and degradability), although the opposite occurs for compounds with low consumption and inappropriate sampling strategies at the WWTP. Hence, when designing a sampling campaign, one should consider the influence of compounds' consumption and degradability as well as the desired level of accuracy in attenuation estimations.
Bade, R., Bijlsma, L., Sancho, J. V., Baz-Lomba, J. A., Castiglioni, S., Castrignanò, E., … Hernández, F. (2017). Liquid chromatography-tandem mass spectrometry determination of synthetic cathinones and phenethylamines in influent wastewater of eight European cities. Chemosphere, 168, 1032-1041. doi:10.1016/j.chemosphere.2016.10.107, Institutional Repository

The popularity of new psychoactive substances (NPS) has grown in recent years, with certain NPS commonly and preferentially consumed even following the introduction of preventative legislation. With the objective to improve the knowledge on the use of NPS, a rapid and very sensitive method was developed for the determination of ten priority NPS (N-ethylcathinone, methylenedioxypyrovalerone (MDPV), methylone, butylone, methedrone, mephedrone, naphyrone, 25-C-NBOMe, 25-I-NBOMe and 25-B-NBOMe) in influent wastewater. Sample clean-up and pre-concentration was made by off-line solid phase extraction (SPE) with Oasis MCX cartridges. Isotopically labelled internal standards were used to correct for matrix effects and potential SPE losses. Following chromatographic separation on a C18 column within 6 min, the compounds were measured by tandem mass spectrometry in positive ionization mode. The method was optimised and validated for all compounds. Limits of quantification were evaluated by spiking influent wastewater samples at 1 or 5 ng/L. An investigation into the stability of these compounds in influent wastewater was also performed, showing that, following acidification at pH 2, all compounds were relatively stable for up to 7 days. The method was then applied to influent wastewater samples from eight European countries, in which mephedrone, methylone and MDPV were detected. This work reveals that although NPS use is not as extensive as for classic illicit drugs, the application of a highly sensitive analytical procedure makes their detection in wastewater possible. The developed analytical methodology forms the basis of a subsequent model-based back-calculation of abuse rate in urban areas (i.e. wastewater-based epidemiology).
Besmer, M. D., Hammes, F., Sigrist, J. A., & Ort, C. (2017). Evaluating monitoring strategies to detect precipitation-induced microbial contamination events in karstic springs used for drinking water. Frontiers in Microbiology, 8, 2229 (12 pp.). doi:10.3389/fmicb.2017.02229, Institutional Repository

Monitoring of microbial drinking water quality is a key component for ensuring safety and understanding risk, but conventional monitoring strategies are typically based on low sampling frequencies (e.g., quarterly or monthly). This is of concern because many drinking water sources, such as karstic springs are often subject to changes in bacterial concentrations on much shorter time scales (e.g., hours to days), for example after precipitation events. Microbial contamination events are crucial from a risk assessment perspective and should therefore be targeted by monitoring strategies to establish both the frequency of their occurrence and the magnitude of bacterial peak concentrations. In this study we used monitoring data from two specific karstic springs. We assessed the performance of conventional monitoring based on historical records and tested a number of alternative strategies based on a high-resolution data set of bacterial concentrations in spring water collected with online flow cytometry (FCM). We quantified the effect of increasing sampling frequency and found that for the specific case studied, at least bi-weekly sampling would be needed to detect precipitation events with a probability of >90%. We then proposed an optimized monitoring strategy with three targeted samples per event, triggered by precipitation measurements. This approach is more effective and efficient than simply increasing overall sampling frequency. It would enable the water utility to (1) analyze any relevant event and (2) limit median underestimation of peak concentrations to approximately 10%. We conclude with a generalized perspective on sampling optimization and argue that the assessment of short-term dynamics causing microbial peak loads initially requires increased sampling/analysis efforts, but can be optimized subsequently to account for limited resources. This offers water utilities and public health authorities systematic ways to evaluate and optimize their current monitoring strategies.
Blumensaat, F., Ebi, C., Dicht, S., Rieckermann, J., & Maurer, M. (2017). IoT driven low-power radio technology for highly distributed in-sewer monitoring - a data collection technique that shifts paradigms?. In 14th IWA/IAHR international conference on urban drainage. Conference proceedings (pp. 1542-1547). Prague: c-in. , Institutional Repository

Urban drainage systems traditionally lack adequate monitoring and evaluation technologies. Although weather radar and high-resolution remote sensing technologies promise vast advances for rainfall and land-use data collection, it is still challenging to deploy dense hydrological monitoring networks for sewers. Low-power data transmission technology, which is the communication backbone in the Internet-Of-Things (IoT), has the potential to contribute to a game change towards an evidence-based management of urban drainage systems. In this paper, we share our experiences with establishing and operating a wireless sensor network (WSN) at full-scale with approximately a 1 sensor per hectare connected catchment area and a monitoring frequency of 5 minutes or less. Specifically, we discuss i) the monitoring and deployment approach and ii) the adequacy of sensorand LoRaWANTM wireless data transmission technology for monitoring applications in sewer networks. A thorough evaluation of the telemetry performance provides evidence that the proposed technique allows a highly distributed monitoring of drainage capacity, sewer infiltration, performance of retention basins and overflows, with a far less effort compared to conventional methods. Remaining challenges concern the radio network architecture to allow better access to deep underground structures and efficient methods for automated data quality control to handle this type of data in the future.
Carbajal, J. P., Leitão, J. P., Albert, C., & Rieckermann, J. (2017). Appraisal of data-driven and mechanistic emulators of nonlinear simulators: the case of hydrodynamic urban drainage models. Environmental Modelling and Software, 92, 17-27. doi:10.1016/j.envsoft.2017.02.006, Institutional Repository

System identification, sensitivity analysis, optimization and control, require a large number of model evaluations. Accurate simulators are too slow for these applications. Fast emulators provide a solution to this efficiency demand, sacrificing unneeded accuracy for speed. There are many strategies for developing emulators but selecting one remains subjective. Herein we compare the performance of two kinds of emulators: mechanistic emulators that use knowledge of the simulator's equations, and purely data-driven emulators using matrix factorization. We borrow simulators from urban water management, because more stringent performance criteria on water utilities have made emulation a crucial tool within this field. Results suggest that naive data-driven emulation outperforms mechanistic emulation. We discuss scenarios in which mechanistic emulation seems favorable for extrapolation in time and dealing with sparse and unevenly sampled data. We also point to advances in Machine Learning that have not permeated yet into the environmental science community.
Causanilles, A., Baz-Lomba, J. A., Burgard, D. A., Emke, E., González-Mariño, I., Krizman-Matasic, I., … Bijlsma, L. (2017). Improving wastewater-based epidemiology to estimate cannabis use: focus on the initial aspects of the analytical procedure. Analytica Chimica Acta, 988, 27-33. doi:10.1016/j.aca.2017.08.011, Institutional Repository

Wastewater-based epidemiology is a promising and complementary tool for estimating drug use by the general population, based on the quantitative analysis of specific human metabolites of illicit drugs in urban wastewater. Cannabis is the most commonly used illicit drug and of high interest for epidemiologists. However, the inclusion of its main human urinary metabolite 11-nor-9-carboxy-Δ9-tetrahydrocannabinol (THC-COOH) in wastewater-based epidemiology has presented several challenges and concentrations seem to depend heavily on environmental factors, sample preparation and analyses, commonly resulting in an underestimation. The aim of the present study is to investigate, identify and diminish the source of bias when analysing THC-COOH in wastewater. Several experiments were performed to individually assess different aspects of THC-COOH determination in wastewater, such as the number of freeze-thaw cycles, filtration, sorption to different container materials and in-sample stability, and the most suitable order of preparatory steps. Results highlighted the filtration step and adjustment of the sample pH as the most critical parameters to take into account when analysing THC-COOH in wastewater. Furthermore, the order of these initial steps of the analytical procedure is crucial. Findings were translated into a recommended best-practice protocol and an inter-laboratory study was organized with eight laboratories that tested the performance of the proposed procedure. Results were found satisfactory with z-scores ≤ 2.
Cecinati, F., Wani, O., & Rico-Ramirez, M. A. (2017). Comparing approaches to deal with non-Gaussianity of rainfall data in kriging-based radar-gauge rainfall merging. Water Resources Research, 53(11), 8999-9018. doi:10.1002/2016WR020330, Institutional Repository

Merging radar and rain gauge rainfall data is a technique used to improve the quality of spatial rainfall estimates and in particular the use of Kriging with External Drift (KED) is a very effective radar-rain gauge rainfall merging technique. However, kriging interpolations assume Gaussianity of the process. Rainfall has a strongly skewed, positive, probability distribution, characterized by a discontinuity due to intermittency. In KED rainfall residuals are used, implicitly calculated as the difference between rain gauge data and a linear function of the radar estimates. Rainfall residuals are non-Gaussian as well. The aim of this work is to evaluate the impact of applying KED to non-Gaussian rainfall residuals, and to assess the best techniques to improve Gaussianity. We compare Box-Cox transformations with λ parameters equal to 0.5, 0.25, and 0.1, Box-Cox with time-variant optimization of λ, normal score transformation, and a singularity analysis technique. The results suggest that Box-Cox with λ=0.1 and the singularity analysis is not suitable for KED. Normal score transformation and Box-Cox with optimized λ, or λ=0.25 produce satisfactory results in terms of Gaussianity of the residuals, probability distribution of the merged rainfall products, and rainfall estimate quality, when validated through cross-validation. However, it is observed that Box-Cox transformations are strongly dependent on the temporal and spatial variability of rainfall and on the units used for the rainfall intensity. Overall, applying transformations results in a quantitative improvement of the rainfall estimates only if the correct transformations for the specific data set are used.
Chong, N., Bach, P. M., Moilleron, R., Bonhomme, C., & Deroubaix, J. F. (2017). Use and utility: exploring the diversity and design of water models at the science-policy interface. Water, 9(12), 983 (28 pp.). doi:10.3390/w9120983, Institutional Repository

Effort to narrow the gap between the production and use of scientific knowledge for environmental decision-making is gaining traction, yet in practice, supply and demand remains largely unbalanced. A qualitative study based on empirical analysis offers a novel approach to exploring key factors, focussing on seven water models in the context of two organisations at the science-policy interface: the PIREN-Seine in France and the CRC for Water Sensitive Cities in Australia. Tentative linkages drawn from these examples identify: (1) objective and expertise; (2) knowledge and tools; and (3) support structures as main drivers influencing the production of scientific knowledge which, in turn, affect the use and utility of modelling tools. Further insight is gained by highlighting the wide spectrum of uses and utilities existing in practice, suggesting that such 'boundary organisations' facilitate interactions and exchanges that give added value to scientific knowledge. Coordinated strategies that integrate inter-, extra-, and intra-boundary activities, framed through collaborative scenario building and the use of interactive modelling platforms, may offer ways to enhance the use and utility of scientific knowledge (and its tools) to better support water resources management, policy and planning decisions, thus promoting a more cohesive relationship between science and policy.
Eggimann, S., Mutzner, L., Wani, O., Schneider, M. Y., Spuhler, D., Moy de Vitry, M., … Maurer, M. (2017). The potential of knowing more: a review of data-driven urban water management. Environmental Science and Technology, 51(5), 2538-2553. doi:10.1021/acs.est.6b04267, Institutional Repository

The promise of collecting and utilising large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence assembled from research documented in the literature points towards a future UWM that offers significant potential benefits thanks to increased collection and utilisation of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and an extended functionality of today’s systems. However, generic challenges related to data-driven approaches (e.g. data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.
Fencl, M., Dohnal, M., Rieckermann, J., & Bareš, V. (2017). Gauge-adjusted rainfall estimates from commercial microwave links. Hydrology and Earth System Sciences, 21(1), 617-634. doi:10.5194/hess-21-617-2017, Institutional Repository

Increasing urbanization makes it more and more important to have accurate stormwater runoff predictions, especially with potentially severe weather and climatic changes on the horizon. Such stormwater predictions in turn require reliable rainfall information. Especially for urban centres, the problem is that the spatial and temporal resolution of rainfall observations should be substantially higher than commonly provided by weather services with their standard rainfall monitoring networks. Commercial microwave links (CMLs) are non-traditional sensors, which have been proposed about a decade ago as a promising solution. CMLs are line-of-sight radio connections widely used by operators of mobile telecommunication networks. They are typically very dense in urban areas and can provide path-integrated rainfall observations at sub-minute resolution. Unfortunately, quantitative precipitation estimates (QPEs) from CMLs are often highly biased due to several epistemic uncertainties, which significantly limit their usability. In this manuscript we therefore suggest a novel method to reduce this bias by adjusting QPEs to existing rain gauges. The method has been specifically designed to produce reliable results even with comparably distant rain gauges or cumulative observations. This eliminates the need to install reference gauges and makes it possible to work with existing information. First, the method is tested on data from a dedicated experiment, where a CML has been specifically set up for rainfall monitoring experiments, as well as operational CMLs from an existing cellular network. Second, we assess the performance for several experimental layouts of "ground truth" from rain gauges (RGs) with different spatial and temporal resolutions. The results suggest that CMLs adjusted by RGs with a temporal aggregation of up to 1 h (i) provide precise high-resolution QPEs (relative error <7%, Nash–Sutcliffe efficiency coefficient >0.75) and (ii) that the combination of both sensor types clearly outperforms each individual monitoring system. Unfortunately, adjusting CML observations to RGs with longer aggregation intervals of up to 24 h has drawbacks. Although it substantially reduces bias, it unfavourably smoothes out rainfall peaks of high intensities, which is undesirable for stormwater management. A similar, but less severe, effect occurs due to spatial averaging when CMLs are adjusted to remote RGs. Nevertheless, even here, adjusted CMLs perform better than RGs alone. Furthermore, we provide first evidence that the joint use of multiple CMLs together with RGs also reduces bias in their QPEs. In summary, we believe that our adjustment method has great potential to improve the space–time resolution of current urban rainfall monitoring networks. Nevertheless, future work should aim to better understand the reason for the observed systematic error in QPEs from CMLs.
Gracia-Lor, E., Rousis, N. I., Zuccato, E., Bade, R., Baz-Lomba, J. A., Castrignanò, E., … Castiglioni, S. (2017). Estimation of caffeine intake from analysis of caffeine metabolites in wastewater. Science of the Total Environment, 609, 1582-1588. doi:10.1016/j.scitotenv.2017.07.258, Institutional Repository

Caffeine metabolites in wastewater were investigated as potential biomarkers for assessing caffeine intake in a population. The main human urinary metabolites of caffeine were measured in the urban wastewater of ten European cities and the metabolic profiles in wastewater were compared with the human urinary excretion profile. A good match was found for 1,7-dimethyluric acid, an exclusive caffeine metabolite, suggesting that might be a suitable biomarker in wastewater for assessing population-level caffeine consumption. A correction factor was developed considering the percentage of excretion of this metabolite in humans, according to published pharmacokinetic studies. Daily caffeine intake estimated from wastewater analysis was compared with the average daily intake calculated from the average amount of coffee consumed by country per capita. Good agreement was found in some cities but further information is needed to standardize this approach. Wastewater analysis proved useful to providing additional local information on caffeine use.
Honti, M., Schuwirth, N., Rieckermann, J., & Stamm, C. (2017). Can integrative catchment management mitigate future water quality issues caused by climate change and socio-economic development?. Hydrology and Earth System Sciences, 21(3), 1593-1609. doi:10.5194/hess-21-1593-2017, Institutional Repository

The design and evaluation of solutions for integrated surface water quality management requires an integrated modelling approach. Integrated models have to be comprehensive enough to cover the aspects relevant for management decisions, allowing for mapping of larger-scale processes such as climate change to the regional and local contexts. Besides this, models have to be sufficiently simple and fast to apply proper methods of uncertainty analysis, covering model structure deficits and error propagation through the chain of sub-models. Here, we present a new integrated catchment model satisfying both conditions. The conceptual "iWaQa" model was developed to support the integrated management of small streams. It can be used to predict traditional water quality parameters, such as nutrients and a wide set of organic micropollutants (plant and material protection products), by considering all major pollutant pathways in urban and agricultural environments. Due to its simplicity, the model allows for a full, propagative analysis of predictive uncertainty, including certain structural and input errors. The usefulness of the model is demonstrated by predicting future surface water quality in a small catchment with mixed land use in the Swiss Plateau. We consider climate change, population growth or decline, socio-economic development, and the implementation of management strategies to tackle urban and agricultural point and non-point sources of pollution. Our results indicate that input and model structure uncertainties are the most influential factors for certain water quality parameters. In these cases model uncertainty is already high for present conditions. Nevertheless, accounting for today's uncertainty makes management fairly robust to the foreseen range of potential changes in the next decades. The assessment of total predictive uncertainty allows for selecting management strategies that show small sensitivity to poorly known boundary conditions. The identification of important sources of uncertainty helps to guide future monitoring efforts and pinpoints key indicators, whose evolution should be closely followed to adapt management. The possible impact of climate change is clearly demonstrated by water quality substantially changing depending on single climate model chains. However, when all climate trajectories are combined, the human land use and management decisions have a larger influence on water quality against a time horizon of 2050 in the study.
Leitão, J. P., Simões, N. E., Pina, R. D., Ochoa-Rodriguez, S., Onof, C., & Sá Marques, A. (2017). Stochastic evaluation of the impact of sewer inlets’ hydraulic capacity on urban pluvial flooding. Stochastic Environmental Research and Risk Assessment, 31(8), 1907-1922. doi:10.1007/s00477-016-1283-x, Institutional Repository

Sewer inlet structures are vital components of urban drainage systems and their operational conditions can largely affect the overall performance of the system. However, their hydraulic behaviour and the way in which it is affected by clogging is often overlooked in urban drainage models, thus leading to misrepresentation of system performance and, in particular, of flooding occurrence. In the present paper, a novel methodology is proposed to stochastically model stormwater urban drainage systems, taking the impact of sewer inlet operational conditions (e.g. clogging due to debris accumulation) on urban pluvial flooding into account. The proposed methodology comprises three main steps: (i) identification of sewer inlets most prone to clogging based upon a spatial analysis of their proximity to trees and evaluation of sewer inlet locations; (ii) Monte Carlo simulation of the capacity of inlets prone to clogging and subsequent simulation of flooding for each sewer inlet capacity scenario, and (iii) delineation of stochastic flood hazard maps. The proposed methodology was demonstrated using as case study design storms as well as two real storm events observed in the city of Coimbra (Portugal), which reportedly led to flooding in different areas of the catchment. The results show that sewer inlet capacity can indeed have a large impact on the occurrence of urban pluvial flooding and that it is essential to account for variations in sewer inlet capacity in urban drainage models. Overall, the stochastic methodology proposed in this study constitutes a useful tool for dealing with uncertainties in sewer inlet operational conditions and, as compared to more traditional deterministic approaches, it allows a more comprehensive assessment of urban pluvial flood hazard, which in turn enables better-informed flood risk assessment and management decisions.
McCall, A. K., Palmitessa, R., Blumensaat, F., Morgenroth, E., & Ort, C. (2017). Modeling in-sewer transformations at catchment scale – implications on drug consumption estimates in wastewater-based epidemiology. Water Research, 122, 655-668. doi:10.1016/j.watres.2017.05.034, Institutional Repository

To which extent illicit drugs are transformed during in-sewer transport, depends on a number of factors: i) substance-specific transformation rates, ii) environmental conditions, iii) point of discharge (location of drug user) and iv) sewer network properties, primarily hydraulic residence time (HRT) and the ratio of biofilm contact area to wastewater volume (A/Veq).
Assessing associated uncertainties typically requires numerous simulations. Therefore, we propose a new two-step modeling framework: 1) Quantify hydrodynamic conditions. This computationally demanding step was performed once in SWMM to derive HRT and A/Veq for each potential point of discharge (node) in three catchments of different size. 2) Estimate biomarker loss. In this step, Monte Carlo simulations were performed for defined scenarios. Depending on assumptions about drug user distribution and prevalence, a number of nodes was sampled. For each node an empirical first-order transformation model was applied with flow-path-corresponding HRT and A/Veq from step 1. Biotic and abiotic transformation rates were sampled from distributions combining variability of different biofilms.
In our modeling study, median losses were >30% for amphetamine, 6-monoacetylmorphine and 6-acetylcodeine in all three catchments with high uncertainty (5%–100% loss), which would imply a systematic underestimation of consumption when neglecting in-sewer processes. Median losses for 21 other investigated biomarkers were <10% with different uncertainty ranges – “no substantial transformation” was confirmed for nine substances in a real sewer segment with a 2-h residence time. Transferability of these results must be tested for other catchments. To further reduce uncertainty, mainly additional knowledge on transformation rates, particularly in biofilm, and their distribution across a sewer network is needed to update model input objectively. Our approach allows efficient testing and, furthermore, can be expanded for many other human biomarkers. Accounting for biomarker stability during in-sewer transport will avoid biased estimates and further improve wastewater-based epidemiology.
Moy de Vitry, M., Dicht, S., & Leitão, J. P. (2017). floodX: urban flash flood experiments monitored with conventional and alternative sensors. Earth System Science Data, 9(2), 657-666. doi:10.5194/essd-9-657-2017, Institutional Repository

The data sets described in this paper provide a basis for developing and testing new methods for monitoring and modelling urban pluvial flash floods. Pluvial flash floods are a growing hazard to property and inhabitants’ well-being in urban areas. However, the lack of appropriate data collection methods is often cited as an impediment for reliable flood modelling, thereby hindering the improvement of flood risk mapping and early warning systems. The potential of surveillance infrastructure and social media is starting to draw attention for this purpose. In the floodX project, 22 controlled urban flash floods were generated in a flood response training facility and monitored with state-of-the-art sensors as well as standard surveillance cameras. With these data, it is possible to explore the use of video data and computer vision for urban flood monitoring and modelling. The floodX project stands out as the largest documented flood experiment of its kind, providing both conventional measurements and video data in parallel and at high temporal resolution. The data set used in this paper is available at https://doi.org/10.5281/zenodo.830513.
O’Brien, J. W., Banks, A. P. W., Novic, A. J., Mueller, J. F., Jiang, G., Ort, C., … Thai, P. K. (2017). Impact of in-sewer degradation of pharmaceutical and personal care products (PPCPs) population markers on a population model. Environmental Science and Technology, 51(7), 3816-3823. doi:10.1021/acs.est.6b02755, Institutional Repository

A key uncertainty of wastewater-based epidemiology is the size of the population which contributed to a given wastewater sample. We previously developed and validated a Bayesian inference model to estimate population size based on 14 population markers which: (1) are easily measured and (2) have mass loads which correlate with population size. However, the potential uncertainty of the model prediction due to in-sewer degradation of these markers was not evaluated. In this study, we addressed this gap by testing their stability under sewer conditions and assessed whether degradation impacts the model estimates. Five markers, which formed the core of our model, were stable in the sewers while the others were not. Our evaluation showed that the presence of unstable population markers in the model did not decrease the precision of the population estimates providing that stable markers such as acesulfame remained in the model. However, to achieve the minimum uncertainty in population estimates, we propose that the core markers to be included in population models for other sites should meet two additional criteria: (3) negligible degradation in wastewater to ensure the stability of chemicals during collection; and (4) < 10% in-sewer degradation could occur during the mean residence time of the sewer network.
Peleg, N., Blumensaat, F., Molnar, P., Fatichi, S., & Burlando, P. (2017). Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling. Hydrology and Earth System Sciences, 21(3), 1559-1572. doi:10.5194/hess-21-1559-2017, Institutional Repository

The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP – Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM – the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.
Penn, R., Schütze, M., Alex, J., & Friedler, E. (2017). Impacts of onsite greywater reuse on wastewater systems. Water Science and Technology, 75(8), 1862-1872. doi:10.2166/wst.2017.057, Institutional Repository

Together with significant water savings that onsite greywater reuse (GWR) may provide, it may also affect the performance of urban sewer systems and wastewater treatment plants (WWTPs). In order to examine these effects, an integrated stochastic simulation system for GWR in urban areas was developed. The model includes stochastic generators of domestic wastewater streams and gross solids (GSs), a sewer network model which includes hydrodynamic simulation and a GS transport module, and a dynamic process model of the WWTP. The developed model was applied to a case study site in Israel. For the validation of the sewer simulator, field experiments in a real sewer segment were conducted. The paper presents the integration and implementation of these modules and depicts the results of the effects of various GWR scenarios on GS movement in sewers and on the performance of the WWTP.
Rousis, N. I., Gracia-Lor, E., Zuccato, E., Bade, R., Baz-Lomba, J. A., Castrignanò, E., … Castiglioni, S. (2017). Wastewater-based epidemiology to assess pan-European pesticide exposure. Water Research, 121, 270-279. doi:10.1016/j.watres.2017.05.044, Institutional Repository

Human biomonitoring, i.e. the determination of chemicals and/or their metabolites in human specimens, is the most common and potent tool for assessing human exposure to pesticides, but it suffers from limitations such as high costs and biases in sampling. Wastewater-based epidemiology (WBE) is an innovative approach based on the chemical analysis of specific human metabolic excretion products (biomarkers) in wastewater, and provides objective and real-time information on xenobiotics directly or indirectly ingested by a population. This study applied the WBE approach for the first time to evaluate human exposure to pesticides in eight cities across Europe. 24 h-composite wastewater samples were collected from the main wastewater treatment plants and analyzed for urinary metabolites of three classes of pesticides, namely triazines, organophosphates and pyrethroids, by liquid chromatography-tandem mass spectrometry. The mass loads (mg/day/1000 inhabitants) were highest for organophosphates and lowest for triazines. Different patterns were observed among the cities and for the various classes of pesticides. Population weighted loads of specific biomarkers indicated higher exposure in Castellon, Milan, Copenhagen and Bristol for pyrethroids, and in Castellon, Bristol and Zurich for organophosphates. The lowest mass loads (mg/day/1000 inhabitants) were found in Utrecht and Oslo. These results were in agreement with several national statistics related to pesticides exposure such as pesticides sales. The daily intake of pyrethroids was estimated in each city and it was found to exceed the acceptable daily intake (ADI) only in one city (Castellon, Spain). This was the first large-scale application of WBE to monitor population exposure to pesticides. The results indicated that WBE can give new information about the “average exposure” of the population to pesticides, and is a useful complementary biomonitoring tool to study population-wide exposure to pesticides.
Santos, P., Amado, C., Coelho, S. T., & Leitão, J. P. (2017). Stochastic data mining tools for pipe blockage failure prediction. Urban Water Journal, 14(4), 343-353. doi:10.1080/1573062X.2016.1148178, Institutional Repository

Failure prediction plays an important role in the management of urban water systems infrastructures. An accurate description of the deterioration of urban drainage systems is essential for optimal investment and rehabilitation planning. In the study presented in this paper, a new method to predict sewer pipe failure based on robust decision trees is proposed. Five other different stochastic failure prediction models – the non-homogeneous Poisson process, the zero-inflated non-homogeneous Poisson process, classical decision tress (CART and Random Forest algorithms), the Weibull accelerated lifetime model and the linear extended Yule process – are also implemented and explored in order to identify models that combine good failure prediction results with robustness. The six models were tested on the asset register and pipe failure register of a large US wastewater utility; only pipe blockage failures were considered in this study. The linear extended Yule process and the zero-inflated non-homogeneous Poisson process presented the overall best results throughout the models’ comparison, showing a good ability to detect pipes with high likelihood of blockage failure. Decision trees based on robust random forests only detected pipes with high likelihood of failure when considering a short-term prediction window; the accuracy of the predictions was one of the best when using the robust decision tree model. The Weibull accelerated lifetime model provided some of the best medium-term predictions but performed less well for shorter prediction windows.
Schmitt, R. J. P., Morgenroth, E., & Larsen, T. A. (2017). Robust planning of sanitation services in urban informal settlements: an analytical framework. Water Research, 110, 297-312. doi:10.1016/j.watres.2016.12.007, Institutional Repository

New types of sanitation services are emerging to tackle the sanitation crisis in informal settlements. These services link toilet facilities to semi-decentralized treatment plants via frequent, road-based transport of excreta. However, information for the planning of such sanitation services is scarce, and their future operating conditions are highly uncertain. The key questions of this paper are therefore: a) what are the drivers behind success or failure of a service-based sanitation system in informal settlements and b) on what scales and under which conditions can such a system operate successfully? To answer these questions, already at an early stage of the planning process, we introduce a stochastic model to analyze a wide range of system designs under varying technical designs, socio-economic factors, and spatial condition. Based on these initial results, we design a sanitation service and use the numeric model to study its reliability and costs over a wide range of scales, i.e., system capacities, from very few to many hundred users per semi-decentralized treatment unit. Key findings are that such a system can only operate within a narrow, but realistic range of conditions. Key requirements are toilet facilities, which can be serviced rapidly, and a flexible workforce. A high density of facilities will also lower the costs. Under these premises, we develop a road-based sanitation service and model its functionality in different settings and under many scenarios. Results show that the developed sanitation system using a single vehicle is scalable (100–700 users), can provide reliable service, and can be cheap (<1.5 c/p/day). Hence, this paper demonstrates opportunities for road-based sanitation in informal settlements and presents a quantitative framework for designing such systems.
Scholten, L., Maurer, M., & Lienert, J. (2017). Comparing multi-criteria decision analysis and integrated assessment to support long-term water supply planning. PLoS One, 12(5), e0176663 (30 pp.). doi:10.1371/journal.pone.0176663, Institutional Repository

We compare the use of multi-criteria decision analysis (MCDA)–or more precisely, models used in multi-attribute value theory (MAVT)–to integrated assessment (IA) models for supporting long-term water supply planning in a small town case study in Switzerland. They are used to evaluate thirteen system scale water supply alternatives in four future scenarios regarding forty-four objectives, covering technical, social, environmental, and economic aspects. The alternatives encompass both conventional and unconventional solutions and differ regarding technical, spatial and organizational characteristics. This paper focuses on the impact assessment and final evaluation step of the structured MCDA decision support process. We analyze the performance of the alternatives for ten stakeholders. We demonstrate the implications of model assumptions by comparing two IA and three MAVT evaluation model layouts of different complexity. For this comparison, we focus on the validity (ranking stability), desirability (value), and distinguishability (value range) of the alternatives given the five model layouts. These layouts exclude or include stakeholder preferences and uncertainties. Even though all five led us to identify the same best alternatives, they did not produce identical rankings. We found that the MAVT-type models provide higher distinguishability and a more robust basis for discussion than the IA-type models. The needed complexity of the model, however, should be determined based on the intended use of the model within the decision support process. The best-performing alternatives had consistently strong performance for all stakeholders and future scenarios, whereas the current water supply system was outperformed in all evaluation layouts. The best-performing alternatives comprise proactive pipe rehabilitation, adapted firefighting provisions, and decentralized water storage and/or treatment. We present recommendations for possible ways of improving water supply planning in the case study and beyond.
Tobias, R., O'Keefe, M., Künzle, R., Gebauer, H., Gründl, H., Morgenroth, E., … Larsen, T. A. (2017). Early testing of new sanitation technology for urban slums: the case of the Blue Diversion Toilet. Science of the Total Environment, 576, 264-272. doi:10.1016/j.scitotenv.2016.10.057, Institutional Repository

The toilets used most in informal urban settlements have detrimental consequences for the environment and human health due to the lack of proper collection and treatment of toilet waste. Concepts for safe, sustainable and affordable sanitation systems exist, but their feasibility and acceptance have to be investigated at an early stage of development, which is difficult due to the high costs of building working models. In this paper, we present an approach to estimate acceptance in a valid and representative form with only one working model, and apply it to test an innovative zero-emission toilet with recycling of wash water. Four basic principles were specified for investigation and nine hypotheses formulated to test the feasibility and acceptance of these principles: source separation of urine and feces with subsequent collection for resource recovery; provision of wash water in a separate cycle with on-site recovery through a membrane bioreactor; a convenient and attractive overall design; and a financially sustainable business plan. In Kampala (Uganda), in 2013, data was collected from 22 regular users, 308 one-time users and a representative sample of 1538 participants. Qualitative data was collected from the users, who evaluated their likes, perceived benefits, social norms and expected ease of use based on verbal and visual information. Most of the hypotheses were confirmed, indicating the feasibility and acceptance of the basic principles. Source separation and on-site water recovery were found to be feasible and accepted, provided users can be convinced that the emptying service and water recovery process work reliably. In the survey, the toilet was evaluated favorably and 51% of the participants agreed to be placed on a bogus waiting list. However, some design challenges were revealed, such as the size of the toilet, hiding feces from view and improving the separation of urine and water
Torres, M. N., Rodríguez, J. P., & Leitão, J. P. (2017). Geostatistical analysis to identify characteristics involved in sewer pipes and urban tree interactions. Urban Forestry and Urban Greening, 25, 36-42. doi:10.1016/j.ufug.2017.04.013, Institutional Repository

Tree roots cause significant and costly damage to sewer systems. However, the contributions made by tree and sewer characteristics to sewer system damage are not yet well understood. Previous research has proved inconclusive and there is a lack of agreement on the importance of the variables involved, which means that the results cannot be extrapolated globally. This study presents a methodology to improve the understanding of tree root-sewer conflicts through a geostatistical analysis of a tree inventory and CCTV video fault reports. The methodology is applied to a 11 km2 case study in Bogotá, Colombia. The analysis used tree and sewer pipe georeferenced data, principal component analysis, and linear, Logit, and Poisson regression models. The proposed methodology identifies the pipe characteristics and the tree species most responsible for the root intrusion events into sewers. As expected, larger trees are prone to cause more pipe damage; therefore tree-pipe distance is a relevant parameter control for reducing potential deterioration. Cumulative precipitation and pipe length showed no effect on observed root intrusion events, and pipes made from brick had higher odds to present root intrusion in contrast with concrete pipes. The presented methodology uses readily available information and software, and can be modified depending on the requirements of each particular case study. This approach will therefore allow for more efficient use of costly, site-specific pipe investigations, and is especially useful for cases where there is a general lack of knowledge regarding the characteristics that favour the negative interactions between pipes and tree roots.
Wani, O., Scheidegger, A., Carbajal, J. P., Rieckermann, J., & Blumensaat, F. (2017). Parameter estimation of hydrologic models using a likelihood function for censored and binary observations. Water Research, 121, 290-301. doi:10.1016/j.watres.2017.05.038, Institutional Repository

Observations of a hydrologic system response are needed to accurately model system behaviour. Nevertheless, often very few monitoring stations are operated because collecting such reference data adequately and accurately is laborious and costly. It has been recently suggested to use observations not only from dedicated flow meters but also from simpler sensors, such as level or event detectors, which are available more frequently but only provide censored information. Binary observations can be considered as extreme censoring. It is still unclear, however, how to use censored observations most effectively to learn about model parameters. To this end, we suggest a formal likelihood function that incorporates censored observations, while accounting for model structure deficits and uncertainty in input data. Using this likelihood function, the parameter inference is performed within the Bayesian framework. We demonstrate the implementation of our methodology on a case study of an urban catchment, where we estimate the parameters of a hydrodynamic rainfall-runoff model from binary observations of combined sewer overflows. Our results show, first, that censored observations make it possible to learn about model parameters, with an average decrease of 45% in parameter standard deviation from prior to posterior. Second, the inference substantially improves model predictions, providing higher Nash-Sutcliffe efficiency. Third, the gain in information largely depends on the experimental design, i.e. sensor placement. Given the advent of Internet of Things, we foresee that the plethora of censored data promised to be available can be used for parameter estimation within a formal Bayesian framework.
Wani, O., Beckers, J. V. L., Weerts, A. H., & Solomatine, D. P. (2017). Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting. Hydrology and Earth System Sciences, 21(8), 4021-4036. doi:10.5194/hess-21-4021-2017, Institutional Repository

A non-parametric method is applied to quantify residual uncertainty in hydrologic streamflow forecasting. This method acts as a post-processor on deterministic model forecasts and generates a residual uncertainty distribution. Based on instance-based learning, it uses a k nearest-neighbour search for similar historical hydrometeorological conditions to determine uncertainty intervals from a set of historical errors, i.e. discrepancies between past forecast and observation. The performance of this method is assessed using test cases of hydrologic forecasting in two UK rivers: the Severn and Brue. Forecasts in retrospect were made and their uncertainties were estimated using kNN resampling and two alternative uncertainty estimators: quantile regression (QR) and uncertainty estimation based on local errors and clustering (UNEEC). Results show that kNN uncertainty estimation produces accurate and narrow uncertainty intervals with good probability coverage. Analysis also shows that the performance of this technique depends on the choice of search space. Nevertheless, the accuracy and reliability of uncertainty intervals generated using kNN resampling are at least comparable to those produced by QR and UNEEC. It is concluded that kNN uncertainty estimation is an interesting alternative to other post-processors, like QR and UNEEC, for estimating forecast uncertainty. Apart from its concept being simple and well understood, an advantage of this method is that it is relatively easy to implement.
de Oliveira Girão, L. F., da Cruz Simões, N. E., Almeida de Sá Marques, J. A., Correia Leitão, J. P., & Pina, R. D. (2017). Modelação hidráulica e de qualidade da água dos sistemas de drenagem em meios urbanos. Hydraulic and water quality modelling of urban drainage systems. Engenharia Sanitária e Ambiental, 22(2), 351-360. doi:10.1590/s1413-41522016161318, Institutional Repository

Urban floods are becoming more frequent. Thus, the European Union published the Directive 2007/60/CE to mitigate costs related with this phenomenon and to impose limits on the concentration of pollutants in pluvial water. The purpose of this study was to present the modelling of drainage systems in flood situations, based on the software of the United States Environmental Protection Agency (EPA), the Storm Water Management Model (SWMM), in which the study case is to analyze a unitary urban drainage system of Zona Central catchment in Coimbra, Portugal. By using the Automatic Overland Flow Delineation (AOFD) methodology for the generation of a superficial drainage network and implementing it in the SWMM, it was obtained a dual drainage model that allows the flow analysis in flood conditions, including flow control between the surface and collectors network and also modeling of water quality at the surface. This model allows quantifying the pollution load at the surface, relatively to the parameter of total suspended solids, for any extreme rainfall event.
Banta-Green, C. J., Brewer, A. J., Ort, C., Helsel, D. R., Williams, J. R., & Field, J. A. (2016). Using wastewater-based epidemiology to estimate drug consumption—statistical analyses and data presentation. Science of the Total Environment, 568, 856-863. doi:10.1016/j.scitotenv.2016.06.052, Institutional Repository

Aim: Analysis of wastewater samples can be used to assess population drug use, but reporting and statistical issues have limited the utility of the approach for epidemiology due to analytical results that are below the limit of quantification or detection. Unobserved or non-quantifiable—censored—data are common and likely to persist as the methodology is applied to more municipalities and a broader array of substances. We demonstrate the use of censored data techniques and account for measurement errors to explore distributions and annual estimates of the daily mean level of drugs excreted per capita.
Measurements: Daily 24-hour composite wastewater samples for 56 days in 2009 were obtained using a random sample stratified by day of week and season for 19 municipalities in the Northwest region of the U.S.
Methods: Methamphetamine, benzoylecgonine (cocaine metabolite), 3,4 methylenedioxymethamphetamine (MDMA), methadone, oxycodone and hydrocodone were identified and quantified in wastewater samples. Four statistical approaches (reporting censoring, Maximum Likelihood Estimation, Kaplan-Meier estimates, or complete data calculations) were used to estimate an annual average, including confidence bounds where appropriate, dependent upon the amount of censoring in the data.
Findings: The proportion of days within a year with censored data varied greatly by drug across the 19 municipalities, with MDMA varying the most (4% to 94% of observations censored). The different statistical approaches each needed to be used given the levels of censoring of measured drug concentrations. Figures incorporating confidence bounds allow visualization of the data that facilitates appropriate comparisons across municipalities.
Conclusions: Results from wastewater sampling that are below detection or quantification limits contain important information and can be incorporated to create a more complete and valid estimate of drug excretion.
Baz-Lomba, J. A., Salvatore, S., Gracia-Lor, E., Bade, R., Castiglioni, S., Castrignanò, E., … Thomas, K. (2016). Comparison of pharmaceutical, illicit drug, alcohol, nicotine and caffeine levels in wastewater with sale, seizure and consumption data for 8 European cities. BMC Public Health, 16, 1035 (11 pp.). doi:10.1186/s12889-016-3686-5, Institutional Repository

Background: Monitoring the scale of pharmaceuticals, illicit and licit drugs consumption is important to assess the needs of law enforcement and public health, and provides more information about the different trends within different countries. Community drug use patterns are usually described by national surveys, sales and seizure data. Wastewater-based epidemiology (WBE) has been shown to be a reliable approach complementing such surveys.
Method: This study aims to compare and correlate the consumption estimates of pharmaceuticals, illicit drugs, alcohol, nicotine and caffeine from wastewater analysis and other sources of information. Wastewater samples were collected in 2015 from 8 different European cities over a one week period, representing a population of approximately 5 million people. Published pharmaceutical sale, illicit drug seizure and alcohol, tobacco and caffeine use data were used for the comparison.
Results: High agreement was found between wastewater and other data sources for pharmaceuticals and cocaine, whereas amphetamines, alcohol and caffeine showed a moderate correlation. methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA) and nicotine did not correlate with other sources of data. Most of the poor correlations were explained as part of the uncertainties related with the use estimates and were improved with other complementary sources of data.
Conclusions: This work confirms the promising future of WBE as a complementary approach to obtain a more accurate picture of substance use situation within different communities. Our findings suggest further improvements to reduce the uncertainties associated with both sources of information in order to make the data more comparable.
Been, F., Bijlsma, L., Benaglia, L., Berset, J. D., Botero-Coy, A. M., Castiglioni, S., … Ort, C. (2016). Assessing geographical differences in illicit drug consumption—a comparison of results from epidemiological and wastewater data in Germany and Switzerland. Drug and Alcohol Dependence, 161, 189-199. doi:10.1016/j.drugalcdep.2016.02.002, Institutional Repository

Background: Wastewater analysis is an innovative approach that allows monitoring illicit drug use at the community level. This study focused on investigating geographical differences in drug consumption by comparing epidemiological, crime and wastewater data.
Methods: Wastewater samples were collected in 19 cities across Germany and Switzerland during one week, covering a population of approximately 8.1 million people. Self-report data and consumption offences for the investigated areas were used for comparison and to investigate differences between the indicators.
Results: Good agreement between data sources was observed for cannabis and amphetamine-type stimulants, whereas substantial discrepancies were observed for cocaine. In Germany, an important distinction could be made between Berlin, Dortmund and Munich, where cocaine and particularly amphetamine were more prevalent, and Dresden, where methamphetamine consumption was clearly predominant. Cocaine consumption was relatively homogenous in the larger urban areas of Switzerland, although prevalence and offences data suggested a more heterogeneous picture. Conversely, marked regional differences in amphetamine and methamphetamine consumption could be highlighted.
Conclusions: Combining the available data allowed for a better understanding of the geographical differences regarding prevalence, typology and amounts of substances consumed. For cannabis and amphetamine-type stimulants, the complementarity of survey, police and wastewater data could be highlighted, although notable differences could be identified when considering more stigmatised drugs (i.e. cocaine and heroin). Understanding illicit drug consumption at the national scale remains a difficult task, yet this research illustrates the added value of combining complementary data sources to obtain a more comprehensive and accurate picture of the situation.
Brewer, A. J., Banta-Green, C. J., Ort, C., Robel, A. E., & Field, J. (2016). Wastewater testing compared with random urinalyses for the surveillance of illicit drug use in prisons. Drug and Alcohol Review, 35(2), 133-137. doi:10.1111/dar.12185, Institutional Repository

Introduction and Aims. Illicit drug use is known to occur among inmate populations of correctional (prison) facilities. Conventional approaches to monitor illicit drug use in prisons include random urinalyses (RUA). Conventional approaches are expected to be prone to bias because prisoners may be aware of which days of the week RUAs are conducted. Therefore, we wanted to compare wastewater loads for methamphetamine and cocaine during days with RUA testing and without. Design and Methods. We collected daily 24-h composite samples of wastewater by continuous sampling, computed daily loads for 1 month and compared the frequency of illicit drug detection to the number of positive RUAs. Diurnal data also were collected for 3 days to determine within-day patterns of illicit drugs excretion.Results. Methamphetamine was observed in each sample of prison wastewater with no significant difference in daily mass loads between RUA testing and non-testing days. Cocaine and its major metabolite, benzoylecgonine, were observed only at levels below quantification in prison wastewater. Six RUAs were positive for methamphetamine during the month while none were positive for cocaine out of the 243 RUAs conducted.Discussion and Conclusions. Wastewater analyses offer data regarding the frequency of illicit drug excretion inside the prison that RUAs alone could not detect.
Burdon, F. J., Reyes, M., Alder, A. C., Joss, A., Ort, C., Räsänen, K., … Stamm, C. (2016). Environmental context and magnitude of disturbance influence trait-mediated community responses to wastewater in streams. Ecology and Evolution, 6(12), 3923-3939. doi:10.1002/ece3.2165, Institutional Repository

Human land uses and population growth represent major global threats to biodiversity and ecosystem services. Understanding how biological communities respond to multiple drivers of human-induced environmental change is fundamental for conserving ecosystems and remediating degraded habitats. Here, we used a replicated ‘real-world experiment’ to study the responses of invertebrate communities to wastewater perturbations across a land-use intensity gradient in 12 Swiss streams. We used different taxonomy and trait-based community descriptors to establish the most sensitive indicators detecting impacts and to help elucidate potential causal mechanisms of change. First, we predicted that streams in catchments adversely impacted by human land-uses would be less impaired by wastewater inputs because their invertebrate communities should be dominated by pollution-tolerant taxa (‘environmental context’). Second, we predicted that the negative effects of wastewater on stream invertebrate communities should be larger in streams that receive proportionally more wastewater (‘magnitude of disturbance’). In support of the ‘environmental context’ hypothesis, we found that change in the Saprobic Index (a trait-based indicator of tolerance to organic pollution) was associated with upstream community composition; communities in catchments with intensive agricultural land uses (e.g., arable cropping and pasture) were generally more resistant to eutrophication associated with wastewater inputs. We also found support for the ‘magnitude of disturbance’ hypothesis. The SPEAR Index (a trait-based indicator of sensitivity to pesticides) was more sensitive to the relative input of effluent, suggesting that toxic influences of wastewater scale with dilution. Whilst freshwater pollution continues to be a major environmental problem, our findings highlight that the same anthropogenic pressure (i.e., inputs of wastewater) may induce different ecological responses depending on the environmental context and community metrics used. Thus, remediation strategies aiming to improve stream ecological status (e.g., rehabilitating degraded reaches) need to consider upstream anthropogenic influences and the most appropriate indicators of restoration success.
Del Giudice, D., Albert, C., Rieckermann, J., & Reichert, P. (2016). Describing the catchment-averaged precipitation as a stochastic process improves parameter and input estimation. Water Resources Research, 52(4), 3162-3186. doi:10.1002/2015WR017871, Institutional Repository

Rainfall input uncertainty is one of the major concerns in hydrological modeling. Unfortunately, during inference, input errors are usually neglected, which can lead to biased parameters and implausible predictions. Rainfall multipliers can reduce this problem but still fail when the observed input (precipitation) has a different temporal pattern from the true one or if the true nonzero input is not detected. In this study, we propose an improved input error model which is able to overcome these challenges and to assess and reduce input uncertainty. We formulate the average precipitation over the watershed as a stochastic input process (SIP) and, together with a model of the hydrosystem, include it in the likelihood function. During statistical inference, we use “noisy” input (rainfall) and output (runoff) data to learn about the “true” rainfall, model parameters, and runoff. We test the methodology with the rainfall-discharge dynamics of a small urban catchment. To assess its advantages, we compare SIP with simpler methods of describing uncertainty within statistical inference: (i) standard least squares (LS), (ii) bias description (BD), and (iii) rainfall multipliers (RM). We also compare two scenarios: accurate versus inaccurate forcing data. Results show that when inferring the input with SIP and using inaccurate forcing data, the whole-catchment precipitation can still be realistically estimated and thus physical parameters can be “protected” from the corrupting impact of input errors. While correcting the output rather than the input, BD inferred similarly unbiased parameters. This is not the case with LS and RM. During validation, SIP also delivers realistic uncertainty intervals for both rainfall and runoff. Thus, the technique presented is a significant step toward better quantifying input uncertainty in hydrological inference. As a next step, SIP will have to be combined with a technique addressing model structure uncertainty.
Eggimann, S., Truffer, B., & Maurer, M. (2016). Economies of density for on-site waste water treatment. Water Research, 101, 476-489. doi:10.1016/j.watres.2016.06.011, Institutional Repository

Decentralised wastewater treatment is increasingly gaining interest as a means of responding to sustainability challenges. Cost comparisons are a crucial element of any sustainability assessment. While the cost characteristics of centralised waste water management systems (WMS) have been studied extensively, the economics of decentralised WMS are less understood. A key motivation for studying the costs of decentralised WMS is to compare the cost of centralised and decentralised WMS in order to decide on cost-efficient sanitation solutions. This paper outlines a model designed to assess those costs which depend on the spatial density of decentralised wastewater treatment plants in a region. Density-related costs are mostly linked to operation and maintenance activities which depend on transportation, like sludge removal or the visits of professionals to the plants for control, servicing or repairs. We first specify a modelled cost-density relationship for a region in a geometric two-dimensional space by means of heuristic routing algorithms that consider time and load-capacity restrictions. The generic model is then applied to a Swiss case study for which we specify a broad range of modelling parameters. As a result, we identify a ‘hockey-stick’-shaped cost curve that is characterised by strong cost reductions at high density values which level out at around 1 to 1.5 plants per km2. Variations in the cost curves are mostly due to differences in management approaches (scheduled or unscheduled emptying). In addition to the well-known diseconomies of scale in the case of centralised sanitation, we find a similar generic cost behaviour for decentralised sanitation due to economies of density. Low densities in sparsely populated regions thus result in higher costs for both centralised and decentralised system. Policy implications are that efforts to introduce decentralised options in a region should consider the low-density/high-cost problem when comparing centralised and decentralised options.
Eggimann, S., Truffer, B., & Maurer, M. (2016). The cost of hybrid waste water systems: a systematic framework for specifying minimum cost-connection rates. Water Research, 103, 472-484. doi:10.1016/j.watres.2016.07.062, Institutional Repository

To determine the optimal connection rate (CR) for regional waste water treatment is a challenge that has recently gained the attention of academia and professional circles throughout the world. We contribute to this debate by proposing a framework for a total cost assessment of sanitation infrastructures in a given region for the whole range of possible CRs. The total costs comprise the treatment and transportation costs of centralised and on-site waste water management systems relative to specific CRs. We can then identify optimal CRs that either deliver waste water services at the lowest overall regional cost, or alternatively, CRs that result from households freely choosing whether they want to connect or not. We apply the framework to a Swiss region, derive a typology for regional cost curves and discuss whether and by how much the empirically observed CRs differ from the two optimal ones. Both optimal CRs may be reached by introducing specific regulatory incentive structures.
Gao, J., O'Brien, J., Du, P., Li, X., Ort, C., Mueller, J. F., & Thai, P. K. (2016). Measuring selected PPCPs in wastewater to estimate the population in different cities in China. Science of the Total Environment, 568, 164-170. doi:10.1016/j.scitotenv.2016.05.216, Institutional Repository

Sampling and analysis of wastewater from municipal wastewater treatment plants (WWTPs) has become a useful tool for understanding exposure to chemicals. Both wastewater based studies and management and planning of the catchment require information on catchment population in the time of monitoring. Recently, a model has been developed and calibrated using selected pharmaceutical and personal care products (PPCPs) measured in influent wastewater for estimating population in different catchments in Australia. The present study aimed at evaluating the feasibility of utilizing this population estimation approach in China. Twenty-four hour composite influent samples were collected from 31 WWTPs in 17 cities with catchment sizes from 200,000–3,450,000 people representing all seven regions of China. The samples were analyzed for 19 PPCPs using liquid chromatography coupled to tandem mass spectrometry in direct injection mode. Eight chemicals were detected in more than 50% of the samples. Significant positive correlations were found between individual PPCP mass loads and population estimates provided by WWTP operators. Using the PPCP mass load modeling approach calibrated with WWTP operator data, we estimated the population size of each catchment with good agreement with WWTP operator values (between 50–200% for all sites and 75–125% for 23 of the 31 sites). Overall, despite much lower detection and relatively high heterogeneity in PPCP consumption across China the model provided a good estimate of the population contributing to a given wastewater sample. Wastewater analysis could also provide objective PPCP consumption status in China.
Humphries, M. A., Bruno, R., Lai, F. Y., Thai, P. K., Holland, B. R., O’Brien, J. W., … Mueller, J. F. (2016). Evaluation of monitoring schemes for wastewater-based epidemiology to identify drug use trends using cocaine, methamphetamine, MDMA and methadone. Environmental Science and Technology, 50(9), 4760-4768. doi:10.1021/acs.est.5b06126, Institutional Repository

Wastewater-based epidemiology is increasingly being used as a tool to monitor drug use trends. To minimize costs, studies have typically monitored a small number of days. However, cycles of drug use may display weekly and seasonal trends that affect the accuracy of monthly or annual drug use estimates based on a limited number of samples. This study aimed to rationalize sampling methods for minimizing the number of samples required while maximizing information about temporal trends. A range of sampling strategies were examined: (i) targeted days (e.g., weekends), (ii) completely random or stratified random sampling, and (iii) a number of sampling strategies informed by known weekly cycles in drug use data. Using a time-series approach, analysis was performed for four drugs (MDMA, methamphetamine, cocaine, methadone) collected through a continuous sampling program over 14 months. Results showed, for drugs with weekly cycles (MDMA, methamphetamine and cocaine in this sample), sampling strategies which made use of those weekly cycles required fewer samples to obtain similar information as sampling 5 days per week and had better accuracy than stratified random sampling techniques.
Kuroda, K., Itten, R., Kovalova, L., Ort, C., Weissbrodt, D. G., & McArdell, C. S. (2016). Hospital-use pharmaceuticals in Swiss waters modeled at high spatial resolution. Environmental Science and Technology, 50(9), 4742-4751. doi:10.1021/acs.est.6b00653, Institutional Repository

A model to predict the mass flows and concentrations of pharmaceuticals predominantly used in hospitals across a large number of sewage treatment plant (STP) effluents and river waters was developed at high spatial resolution. It comprised 427 geo-referenced hospitals and 742 STPs serving 98% of the general population in Switzerland. In the modeled base scenario, domestic, pharmaceutical use was geographically distributed according to the population size served by the respective STPs. Distinct hospital scenarios were set up to evaluate how the predicted results were modified when pharmaceutical use in hospitals was allocated differently; for example, in proportion to number of beds or number of treatments in hospitals. The hospital scenarios predicted the mass flows and concentrations up to 3.9 times greater than in the domestic scenario for iodinated X-ray contrast media (ICM) used in computed tomography (CT), and up to 6.7 times greater for gadolinium, a contrast medium used in magnetic resonance imaging (MRI). Field measurements showed that ICM and gadolinium were predicted best by the scenarios using number of beds or treatments in hospitals with the specific facilities (i.e., CT and/or MRI). Pharmaceuticals used both in hospitals and by the general population (e.g., cyclophosphamide, sulfamethoxazole, carbamazepine, diclofenac) were predicted best by the scenario using the number of beds in all hospitals, but the deviation from the domestic scenario values was only small. Our study demonstrated that the bed number-based hospital scenarios were effective in predicting the geographical distribution of a diverse range of pharmaceuticals in STP effluents and rivers, while the domestic scenario was similarly effective on the scale of large river-catchments.
Lai, F. Y., O'Brien, J. W., Thai, P. K., Hall, W., Chan, G., Bruno, R., … Mueller, J. F. (2016). Cocaine, MDMA and methamphetamine residues in wastewater: consumption trends (2009–2015) in South East Queensland, Australia. Science of the Total Environment, 568, 803-809. doi:10.1016/j.scitotenv.2016.05.181, Institutional Repository

Wastewater analysis, or wastewater-based epidemiology, has become a common tool to monitor trends of illicit drug consumption around the world. In this study, we examined trends in cocaine, 3,4-methylenedioxymethamphetamine (MDMA) and methamphetamine consumption by measuring their residues in wastewater from two wastewater treatment plants in Australia (specifically, an urban and a rural catchment, both in South East Queensland) between 2009 and 2015. With direct injection of the samples, target analytes were identified and quantified using liquid chromatography-mass spectrometry. Cocaine and MDMA residues and metabolites were mainly quantifiable in the urban catchment while methamphetamine residues were consistently detected in both urban and rural catchments. There was no consistent trend in the population normalised mass loads observed for cocaine and MDMA at the urban site between 2009 and 2015. In contrast, there was a five-fold increase in methamphetamine consumption over this period in this catchment. For methamphetamine consumption, the rural area showed a very similar trend as the urban catchment starting at a lower baseline. The observed increase in per capita loads of methamphetamine via wastewater analysis over the past six years in South East Queensland provides objective evidence for increased methamphetamine consumption in the Australian population while the use of other illicit stimulants remained relatively stable.
Larsen, T. A., Hoffmann, S., Lüthi, C., Truffer, B., & Maurer, M. (2016). Emerging solutions to the water challenges of an urbanizing world. Science, 352(6288), 928-933. doi:10.1126/science.aad8641, Institutional Repository

The top priorities for urban water sustainability include the provision of safe drinking water, wastewater handling for public health, and protection against flooding. However, rapidly aging infrastructure, population growth, and increasing urbanization call into question current urban water management strategies, especially in the fast-growing urban areas in Asia and Africa. We review innovative approaches in urban water management with the potential to provide locally adapted, resource-efficient alternative solutions. Promising examples include new concepts for stormwater drainage, increased water productivity, distributed or on-site treatment of wastewater, source separation of human waste, and institutional and organizational reforms. We conclude that there is an urgent need for major transdisciplinary efforts in research, policy, and practice to develop alternatives with implications for cities and aquatic ecosystems alike.
Leitão, J. P., Moy de Vitry, M., Scheidegger, A., & Rieckermann, J. (2016). Assessing the quality of digital elevation models obtained from mini unmanned aerial vehicles for overland flow modelling in urban areas. Hydrology and Earth System Sciences, 20(4), 1637-1653. doi:10.5194/hess-20-1637-2016, Institutional Repository

Precise and detailed digital elevation models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM, such as airplane light detection and ranging (lidar) DEMs and point and contour maps, remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of unmanned aerial vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions. In addition, we compared the best-quality UAV DEM to a conventional lidar-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to lidar-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g. buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional lidar-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local, and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.
Leitão, J. P., Prodanović, D., & Maksimović, Č. (2016). Improving merge methods for grid-based digital elevation models. Computers and Geosciences, 88, 115-131. doi:10.1016/j.cageo.2016.01.001, Institutional Repository

Digital Elevation Models (DEMs) are used to represent the terrain in applications such as, for example, overland flow modelling or viewshed analysis. DEMs generated from digitising contour lines or obtained by LiDAR or satellite data are now widely available. However, in some cases, the area of study is covered by more than one of the available elevation data sets. In these cases the relevant DEMs may need to be merged. The merged DEM must retain the most accurate elevation information available while generating consistent slopes and aspects. In this paper we present a thorough analysis of three conventional grid-based DEM merging methods that are available in commercial GIS software. These methods are evaluated for their applicability in merging DEMs and, based on evaluation results, a method for improving the merging of grid-based DEMs is proposed. DEMs generated by the proposed method, called MBlend, showed significant improvements when compared to DEMs produced by the three conventional methods in terms of elevation, slope and aspect accuracy, ensuring also smooth elevation transitions between the original DEMs. The results produced by the improved method are highly relevant different applications in terrain analysis, e.g., visibility, or spotting irregularities in landforms and for modelling terrain phenomena, such as overland flow.
Leitão, J. P., Coelho, S. T., Alegre, H., Cardoso, M. A., Silva, M. S., Ramalho, P., … Carriço, N. (2016). Moving urban water infrastructure asset management from science into practice. Urban Water Journal, 13(2), 133-141. doi:10.1080/1573062X.2014.939092, Institutional Repository

iGPI, the National Initiative for Infrastructure Asset Management is a Portuguese collaborative project led by LNEC (National Civil Engineering Laboratory, Portugal) through which 19 water utilities develop their own infrastructure asset management (IAM) systems and plans in a joint training and capacitation programme. Technical assistance to the participating utilities is ensured by LNEC, IST (Technical University of Lisbon) and Addition, a software development company. The water utilities get collective as well as one-on-one support and specific training. They benefit from networking with the other utilities in a common and simultaneous process, with similar difficulties and challenges, leading to an effective sharing of solutions. The developed products, including training materials, templates and guidelines for developing strategic and tactical IAM plans, are available to the general public. This project has greatly contributed to the establishment of reference methodologies and standards for IAM planning, in a range of utilities of widely diverse size and context, effectively defining an accepted best practice. This paper discusses the project's format and its advantages, and goes on to describe the main outcomes, including selected cases and final products.
McCall, A. K., Bade, R., Kinyua, J., Lai, F. Y., Thai, P. K., Covaci, A., … Ort, C. (2016). Critical review on the stability of illicit drugs in sewers and wastewater samples. Water Research, 88, 933-947. doi:10.1016/j.watres.2015.10.040, Institutional Repository

Wastewater-based epidemiology (WBE) applies advanced analytical methods to quantify drug residues in wastewater with the aim to estimate illicit drug use at the population level. Transformation processes during transport in sewers (chemical and biological reactors) and storage of wastewater samples before analysis are expected to change concentrations of different drugs to varying degrees. Ignoring transformation for drugs with low to medium stability will lead to an unknown degree of systematic under- or overestimation of drug use, which should be avoided. This review aims to summarize the current knowledge related to the stability of commonly investigated drugs and, furthermore, suggest a more effective approach to future experiments. From over 100 WBE studies, around 50 mentioned the importance of stability and 24 included tests in wastewater. Most focused on in-sample stability (i.e., sample preparation, preservation and storage) and some extrapolated to in-sewer stability (i.e., during transport in real sewers). While consistent results were reported for rather stable compounds (e.g., MDMA and methamphetamine), a varying range of stability under different or similar conditions was observed for other compounds (e.g., cocaine, amphetamine and morphine). Wastewater composition can vary considerably over time, and different conditions prevail in different sewer systems. In summary, this indicates that more systematic studies are needed to: i) cover the range of possible conditions in sewers and ii) compare results more objectively. To facilitate the latter, we propose a set of parameters that should be reported for in-sewer stability experiments. Finally, a best practice of sample collection, preservation, and preparation before analysis is suggested in order to minimize transformation during these steps.
McCall, A. K., Scheidegger, A., Madry, M. M., Steuer, A. E., Weissbrodt, D. G., Vanrolleghem, P. A., … Ort, C. (2016). Influence of different sewer biofilms on transformation rates of drugs. Environmental Science and Technology, 50(24), 13351-13360. doi:10.1021/acs.est.6b04200, Institutional Repository

To estimate drug consumption more reliably, wastewater-based epidemiology would benefit from a better understanding of drug residue stability during in-sewer transport. We conducted batch experiments with real, fresh wastewater and sewer biofilms. Experimental conditions mimic small to medium-sized gravity sewers with a relevant ratio of biofilm surface area to wastewater volume (33 m2 m–3). The influences of biological, chemical, and physical processes on the transformation of 30 illicit drug and pharmaceutical residues were quantified. Rates varied among locations and over time. Three substances were not stable—that is, >20% transformation, mainly due to biological processes—at least for one type of tested biofilm for a residence time ≤2 h: amphetamine, 6-acetylcodeine, and 6-monoacetylmorphine. Cocaine, ecgonine methyl ester, norcocaine, cocaethylene, and mephedrone were mainly transformed by chemical hydrolysis and, hence, also unstable in sewers. In contrast, ketamine, norketamine, O-desmethyltramadol, diclofenac, carbamazepine, and methoxetamine were not substantially affected by in-sewer processes under all tested conditions and residence times up to 12 h. Our transformation rates include careful quantification of uncertainty and can be used to identify situations in which specific compounds are not stable. This will improve accuracy and uncertainty estimates of drug consumption when applied to the back-calculation.
Mutzner, L., Staufer, P., & Ort, C. (2016). Model-based screening for critical wet-weather discharges related to micropollutants from urban areas. Water Research, 104, 547-557. doi:10.1016/j.watres.2016.08.003, Institutional Repository

Wet-weather discharges contribute to anthropogenic micropollutant loads entering the aquatic environment. Thousands of wet-weather discharges exist in Swiss sewer systems, and we do not have the capacity to monitor them all. We consequently propose a model-based approach designed to identify critical discharge points in order to support effective monitoring.
We applied a dynamic substance flow model to four substances representing different entry routes: indoor (Triclosan, Mecoprop, Copper) as well as rainfall-mobilized (Glyphosate, Mecoprop, Copper) inputs. The accumulation on different urban land-use surfaces in dry weather and subsequent substance-specific wash-off is taken into account. For evaluation, we use a conservative screening approach to detect critical discharge points. This approach considers only local dilution generated onsite from natural, unpolluted areas, i.e. excluding upstream dilution.
Despite our conservative assumptions, we find that the environmental quality standards for Glyphosate and Mecoprop are not exceeded during any 10-min time interval over a representative one-year simulation period for all 2500 Swiss municipalities. In contrast, the environmental quality standard is exceeded during at least 20% of the discharge time at 83% of all modelled discharge points for Copper and at 71% for Triclosan. For Copper, this corresponds to a total median duration of approximately 19 days per year. For Triclosan, discharged only via combined sewer overflows, this means a median duration of approximately 10 days per year. In general, stormwater outlets contribute more to the calculated effect than combined sewer overflows for rainfall-mobilized substances. We further evaluate the Urban Index (Aurban,impervious/Anatural) as a proxy for critical discharge points: catchments where Triclosan and Copper exceed the corresponding environmental quality standard often have an Urban Index >0.03.
A dynamic substance flow analysis allows us to identify the most critical discharge points to be prioritized for more detailed analyses and monitoring. This forms a basis for the efficient mitigation of pollution.
Randall, D. G., Krähenbühl, M., Köpping, I., Larsen, T. A., & Udert, K. M. (2016). A novel approach for stabilizing fresh urine by calcium hydroxide addition. Water Research, 95, 361-369. doi:10.1016/j.watres.2016.03.007, Institutional Repository

In this study, we investigated the prevention of enzymatic urea hydrolysis in fresh urine by increasing the pH with calcium hydroxide (Ca(OH)2) powder. The amount of Ca(OH)2 dissolving in fresh urine depends significantly on the composition of the urine. The different urine compositions used in our simulations showed that between 4.3 and 5.8 g Ca(OH)2 dissolved in 1 liter of urine at 25 °C. At this temperature, the pH at saturation is 12.5 and is far above the pH of 11, which we identified as the upper limit for enzymatic urea hydrolysis. However, temperature has a strong effect on the saturation pH, with higher values being achieved at lower temperatures. Based on our results, we recommend a dosage of 10 g Ca(OH)2·L−1 of fresh urine to ensure solid Ca(OH)2 always remains in the urine reactor which ensures sufficiently high pH values. Besides providing sufficient Ca(OH)2, the temperature has to be kept in a certain range to prevent chemical urea hydrolysis. At temperatures below 14 °C, the saturation pH is higher than 13, which favors chemical urea hydrolysis. We chose a precautionary upper temperature of 40 °C because the rate of chemical urea hydrolysis increases at higher temperatures but this should be confirmed with kinetic studies. By considering the boundaries for pH and temperature developed in this study, urine can be stabilized effectively with Ca(OH)2 thereby simplifying later treatment processes or making direct use easier.
Ryu, Y., Barceló, D., Barron, L. P., Bijlsma, L., Castiglioni, S., de Voogt, P., … Thomas, K. V. (2016). Comparative measurement and quantitative risk assessment of alcohol consumption through wastewater-based epidemiology: an international study in 20 cities. Science of the Total Environment, 565, 977-983. doi:10.1016/j.scitotenv.2016.04.138, Institutional Repository

Quantitative measurement of drug consumption biomarkers in wastewater can provide objective information on community drug use patterns and trends. This study presents the measurement of alcohol consumption in 20 cities across 11 countries through the use of wastewater-based epidemiology (WBE), and reports the application of these data for the risk assessment of alcohol on a population scale using the margin of exposure (MOE) approach. Raw 24-h composite wastewater samples were collected over a one-week period from 20 cities following a common protocol. For each sample a specific and stable alcohol consumption biomarker, ethyl sulfate (EtS) was determined by liquid chromatography coupled to tandem mass spectrometry. The EtS concentrations were used for estimation of per capita alcohol consumption in each city, which was further compared with international reports and applied for risk assessment by MOE. The average per capita consumption in 20 cities ranged between 6.4 and 44.3 L/day/1000 inhabitants. An increase in alcohol consumption during the weekend occurred in all cities, however the level of this increase was found to differ. In contrast to conventional data (sales statistics and interviews), WBE revealed geographical differences in the level and pattern of actual alcohol consumption at an inter-city level. All the sampled cities were in the "high risk" category (MOE < 10) and the average MOE for the whole population studied was 2.5. These results allowed direct comparisons of alcohol consumption levels, patterns and risks among the cities. This study shows that WBE can provide timely and complementary information on alcohol use and alcohol associated risks in terms of exposure at the community level.
Ryu, Y., Gracia-Lor, E., Bade, R., Baz-Lomba, J. A., Bramness, J. G., Castiglioni, S., … Thomas, K. V. (2016). Increased levels of the oxidative stress biomarker 8-iso-prostaglandin F2α in wastewater associated with tobacco use. Scientific Reports, 6, 1-7. doi:10.1038/srep39055, Institutional Repository

Wastewater analysis has been demonstrated to be a complementary approach for assessing the overall patterns of drug use by a population while the full potential of wastewater-based epidemiology has yet to be explored. F2-isoprostanes are a prototype wastewater biomarker to study the cumulative oxidative stress at a community level. In this work, 8-iso-prostaglandin F2α (8-iso-PGF2α) was analysed in raw 24 h-composite wastewater samples collected from 4 Norwegian and 7 other European cities in 2014 and 2015. Using the same samples, biomarkers of alcohol (ethyl sulfate) and tobacco (trans-3′-hydroxycotinine) use were also analysed to investigate any possible correlation between 8-iso-PGF2α and the consumption of the two drugs. The estimated per capita daily loads of 8-iso-PGF2α in the 11 cities ranged between 2.5 and 9.9 mg/day/1000 inhabitants with a population-weighted mean of 4.8 mg/day/1000 inhabitants. There were no temporal trends observed in the levels of 8-iso-PGF2α, however, spatial differences were found at the inter-city level correlating to the degree of urbanisation. The 8-iso-PGF2α mass load was found to be strongly associated with that of trans-3′-hydroxycotinine while it showed no correlation with ethyl sulfate. The present study shows the potential for 8-iso-PGF2α as a wastewater biomarker for the assessment of community public health.
Thomaidis, N. S., Gago-Ferrero, P., Ort, C., Maragou, N. C., Alygizakis, N. A., Borova, V. L., & Dasenaki, M. E. (2016). Reflection of socioeconomic changes in wastewater: licit and illicit drug use patterns. Environmental Science and Technology, 50(18), 10065-10072. doi:10.1021/acs.est.6b02417, Institutional Repository

The economic crisis plaguing Greece was expected to impact consumption of pharmaceuticals and illicit drugs – a priori to an unknown extent. We quantified the change of use for various classes of licit and illicit drugs by monitoring Athens’ wastewater from 2010 to 2014. A high increase in the use of psychoactive drugs was detected between 2010 and 2014, especially for antipsychotics (35-fold), benzodiazepines (19-fold), and antidepressants (11-fold). This directly reflects the perceived increase of incidences associated with mental illnesses in the population, as a consequence of severe socioeconomic changes. Other therapeutic classes, like antiepileptics, hypertensives, and gastric and ulcer drugs also showed an increase in use (from 2-fold increase for antiepileptics to 13-fold for hypertensives). In contrast, the overall use of antibiotics and NSAIDs decreased. For mefenamic acid, an almost 28-fold decrease was observed. This finding is likely related to the reduction in drug expenditure applied in public health. A 2-fold increase of methamphetamine use was detected, associated with a cheap street drug called ″sisa″ (related to marginal conducts), which is a health concern. MDMA (5-fold) and methadone (7-fold) use showed also an increase, while cocaine and cannabis estimates did not show a clear trend.
Van Dyken, E., Lai, F. Y., Thai, P. K., Ort, C., Bruno, R., Hall, W., … Prichard, J. (2016). Challenges and opportunities in using wastewater analysis to measure drug use in a small prison facility. Drug and Alcohol Review, 35(2), 138-147. doi:10.1111/dar.12156, Institutional Repository

Introduction and Aims. Wastewater analysis (WWA) is intended to be a direct and objective method of measuring substance use in large urban populations. It has also been used to measure prison substance use in two previous studies. The application of WWA in this context has raised questions as to how best it might be used to measure illicit drug use in prisons, and whether it can also be used to measure prescription misuse. We applied WWA to a small regional prison to measure the use of 12 licit and illicit substances. We attempted to measure the non-medical use of methadone and buprenorphine and to compare our findings with the results of the prison's mandatory drug testing (MDT). Design and Methods. Representative daily composite samples were collected for two periods of 12 consecutive days in May to July 2013 and analysed for 18 drug metabolites. Prescription data and MDT results were obtained from the prison and compared with the substance use estimates calculated from WWA data. Results. Daily use of methamphetamine, methadone, buprenorphine and codeine was detected, while sporadic detection of ketamine and methylone was also observed. Overall buprenorphine misuse appeared to be greater than methadone misuse. Discussion and Conclusions. Compared with MDT, WWA provides a more comprehensive picture of prison substance use. WWA also has the potential to measure the misuse of medically prescribed substances. However, a great deal of care must be exercised in quantifying the usage of any substance in small populations, such as in prisons.
Zheng, J., Egger, C., & Lienert, J. (2016). A scenario-based MCDA framework for wastewater infrastructure planning under uncertainty. Journal of Environmental Management, 183(3), 895-908. doi:10.1016/j.jenvman.2016.09.027, Institutional Repository

Wastewater infrastructure management is increasingly important because of urbanization, environmental pollutants, aging infrastructures, and climate change. We propose a scenario-based multi-criteria decision analysis (MCDA) framework to compare different infrastructure alternatives in terms of their sustainability. These range from the current centralized system to semi- and fully decentralized options. Various sources of uncertainty are considered, including external socio-economic uncertainty captured by future scenarios, uncertainty in predicting outcomes of alternatives, and incomplete preferences of stakeholders. Stochastic Multi-criteria Acceptability Analysis (SMAA) with Monte Carlo simulation is performed, and rank acceptability indices help identify robust alternatives. We propose step-wise local sensitivity analysis, which is useful for practitioners to effectively elicit preferences and identify major sources of uncertainty. The approach is demonstrated in a Swiss case study where ten stakeholders are involved throughout. Their preferences are quantitatively elicited by combining an online questionnaire with face-to-face interviews. The trade-off questions reveal a high concern about environmental and an unexpectedly low importance of economic criteria. This results in a surprisingly good ranking of high-tech decentralized wastewater alternatives using urine source separation for most stakeholders in all scenarios. Combining scenario planning and MCDA proves useful, as the performance of wastewater infrastructure systems is indeed sensitive to socio-economic boundary conditions and the other sources of uncertainty. The proposed sensitivity analysis suggests that a simplified elicitation procedure is sufficient in many cases. Elicitation of more information such as detailed marginal value functions should only follow if the sensitivity analysis finds this necessary. Moreover, the uncertainty of rankings can be considerably reduced by better predictions of the outcomes of alternatives. Although the results are case based, the proposed decision framework is generalizable to other decision contexts.
Čepický, J., & Moreira de Sousa, L. (2016). New implementation of OGC web processing service in Python programming language. PyWPS-4 and issues we are facing with processing of large raster data using OGC WPS. In L. Halounova, F. Sunar, M. Potůčková, L. Patková, M. Yoshimura, U. Soergel, … B. Zagajewski (Eds.), The international archives of the photogrammetry, remote sensing and spatial information sciences: Vol. XLI-B7. XXIII ISPRS congress, commission VII (pp. 927-930). doi:10.5194/isprs-archives-XLI-B7-927-2016, Institutional Repository

The OGC® Web Processing Service (WPS) Interface Standard provides rules for standardizing inputs and outputs (requests and responses) for geospatial processing services, such as polygon overlay. The standard also defines how a client can request the execution of a process, and how the output from the process is handled. It defines an interface that facilitates publishing of geospatial processes and client discovery of processes and and binding to those processes into workflows. Data required by a WPS can be delivered across a network or they can be available at a server.
PyWPS was one of the first implementations of OGC WPS on the server side. It is written in the Python programming language and it tries to connect to all existing tools for geospatial data analysis, available on the Python platform. During the last two years, the PyWPS development team has written a new version (called PyWPS-4) completely from scratch.
The analysis of large raster datasets poses several technical issues in implementing the WPS standard. The data format has to be defined and validated on the server side and binary data have to be encoded using some numeric representation. Pulling raster data from remote servers introduces security risks, in addition, running several processes in parallel has to be possible, so that system resources are used efficiently while preserving security. Here we discuss these topics and illustrate some of the solutions adopted within the PyWPS implementation.
Albert, C., Künsch, H. R., & Scheidegger, A. (2015). A simulated annealing approach to approximate Bayes computations. Statistics and Computing, 25(6), 1217-1232. doi:10.1007/s11222-014-9507-8, Institutional Repository

Approximate Bayes computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In particle ABC, an ensemble of particles in the product space of model outputs and parameters is propagated in such a way that its output marginal approaches a delta function at the data and its parameter marginal approaches the posterior distribution. Inspired by Simulated Annealing, we present a new class of particle algorithms for ABC, based on a sequence of Metropolis kernels, associated with a decreasing sequence of tolerances w.r.t. the data. Unlike other algorithms, our class of algorithms is not based on importance sampling. Hence, it does not suffer from a loss of effective sample size due to re-sampling. We prove convergence under a condition on the speed at which the tolerance is decreased. Furthermore, we present a scheme that adapts the tolerance and the jump distribution in parameter space according to some mean-fields of the ensemble, which preserves the statistical independence of the particles, in the limit of infinite sample size. This adaptive scheme aims at converging as close as possible to the correct result with as few system updates as possible via minimizing the entropy production of the process. The performance of this new class of algorithms is compared against two other recent algorithms on two toy examples as well as on a real-world example from genetics.
Del Giudice, D., Löwe, R., Madsen, H., Mikkelsen, P. S., & Rieckermann, J. (2015). Comparison of two stochastic techniques for reliable urban runoff prediction by modeling systematic errors. Water Resources Research, 51(7), 5004-5022. doi:10.1002/2014WR016678, Institutional Repository

In urban rainfall-runoff, commonly applied statistical techniques for uncertainty quantification mostly ignore systematic output errors originating from simplified models and erroneous inputs. Consequently, the resulting predictive uncertainty is often unreliable. Our objective is to present two approaches which use stochastic processes to describe systematic deviations and to discuss their advantages and drawbacks for urban drainage modeling. The two methodologies are an external bias description (EBD) and an internal noise description (IND, also known as stochastic gray-box modeling). They emerge from different fields and have not yet been compared in environmental modeling. To compare the two approaches, we develop a unifying terminology, evaluate them theoretically, and apply them to conceptual rainfall-runoff modeling in the same drainage system. Our results show that both approaches can provide probabilistic predictions of wastewater discharge in a similarly reliable way, both for periods ranging from a few hours up to more than 1 week ahead of time. The EBD produces more accurate predictions on long horizons but relies on computationally heavy MCMC routines for parameter inferences. These properties make it more suitable for off-line applications. The IND can help in diagnosing the causes of output errors and is computationally inexpensive. It produces best results on short forecast horizons that are typical for online applications.
Del Giudice, D., Reichert, P., Bareš, V., Albert, C., & Rieckermann, J. (2015). Model bias and complexity - understanding the effects of structural deficits and input errors on runoff predictions. Environmental Modelling and Software, 64, 205-214. doi:10.1016/j.envsoft.2014.11.006, Institutional Repository

Oversimplified models and erroneous inputs play a significant role in impairing environmental predictions. To assess the contribution of these errors to model uncertainties is still challenging. Our objective is to understand the effect of model complexity on systematic modeling errors. Our method consists of formulating alternative models with increasing detail and flexibility and describing their systematic deviations by an autoregressive bias process. We test the approach in an urban catchment with five drainage models. Our results show that a single bias description produces reliable predictions for all models. The bias decreases with increasing model complexity and then stabilizes. The bias decline can be associated with reduced structural deficits, while the remaining bias is probably dominated by input errors. Combining a bias description with a multimodel comparison is an effective way to assess the influence of structural and rainfall errors on flow forecasts.
Egger, C., & Maurer, M. (2015). Importance of anthropogenic climate impact, sampling error and urban development in sewer system design. Water Research, 73, 78-97. doi:10.1016/j.watres.2014.12.050, Institutional Repository

Urban drainage design relying on observed precipitation series neglects the uncertainties associated with current and indeed future climate variability. Urban drainage design is further affected by the large stochastic variability of precipitation extremes and sampling errors arising from the short observation periods of extreme precipitation. Stochastic downscaling addresses anthropogenic climate impact by allowing relevant precipitation characteristics to be derived from local observations and an ensemble of climate models. This multi-climate model approach seeks to reflect the uncertainties in the data due to structural errors of the climate models. An ensemble of outcomes from stochastic downscaling allows for addressing the sampling uncertainty. These uncertainties are clearly reflected in the precipitation-runoff predictions of three urban drainage systems. They were mostly due to the sampling uncertainty. The contribution of climate model uncertainty was found to be of minor importance. Under the applied greenhouse gas emission scenario (A1B) and within the period 2036–2065, the potential for urban flooding in our Swiss case study is slightly reduced on average compared to the reference period 1981–2010. Scenario planning was applied to consider urban development associated with future socio-economic factors affecting urban drainage. The impact of scenario uncertainty was to a large extent found to be case-specific, thus emphasizing the need for scenario planning in every individual case. The results represent a valuable basis for discussions of new drainage design standards aiming specifically to include considerations of uncertainty.
Eggimann, S., Truffer, B., & Maurer, M. (2015). To connect or not to connect? Modelling the optimal degree of centralisation for wastewater infrastructures. Water Research, 84, 218-231. doi:10.1016/j.watres.2015.07.004, Institutional Repository

The strong reliance of most utility services on centralised network infrastructures is becoming increasingly challenged by new technological advances in decentralised alternatives. However, not enough effort has been made to develop planning tools designed to address the implications of these new opportunities and to determine the optimal degree of centralisation of these infrastructures. We introduce a planning tool for sustainable network infrastructure planning (SNIP), a two-step techno-economic heuristic modelling approach based on shortest path-finding and hierarchical-agglomerative clustering algorithms to determine the optimal degree of centralisation in the field of wastewater management. This SNIP model optimises the distribution of wastewater treatment plants and the sewer network outlay relative to several cost and sewer-design parameters. Moreover, it allows us to construct alternative optimal wastewater system designs taking into account topography, economies of scale as well as the full size range of wastewater treatment plants. We quantify and confirm that the optimal degree of centralisation decreases with increasing terrain complexity and settlement dispersion while showing that the effect of the latter exceeds that of topography. Case study results for a Swiss community indicate that the calculated optimal degree of centralisation is substantially lower than the current level.
Fencl, M., Rieckermann, J., Sýkora, P., Stránský, D., & Bareš, V. (2015). Commercial microwave links instead of rain gauges: fiction or reality?. Water Science and Technology, 71(1), 31-37. doi:10.2166/wst.2014.466, Institutional Repository

Commercial microwave links (MWLs) were suggested about a decade ago as a new source for quantitative precipitation estimates (QPEs). Meanwhile, the theory is well understood and rainfall monitoring with MWLs is on its way to being a mature technology, with several well-documented case studies, which investigate QPEs from multiple MWLs on the mesoscale. However, the potential of MWLs to observe microscale rainfall variability, which is important for urban hydrology, has not been investigated yet. In this paper, we assess the potential of MWLs to capture the spatio-temporal rainfall dynamics over small catchments of a few square kilometres. Specifically, we investigate the influence of different MWL topologies on areal rainfall estimation, which is important for experimental design or to a priori check the feasibility of using MWLs. In a dedicated case study in Prague, Czech Republic, we collected a unique dataset of 14 MWL signals with a temporal resolution of a few seconds and compared the QPEs from the MWLs to reference rainfall from multiple rain gauges. Our results show that, although QPEs from most MWLs are probably positively biased, they capture spatio-temporal rainfall variability on the microscale very well. Thus, they have great potential to improve runoff predictions. This is especially beneficial for heavy rainfall, which is usually decisive for urban drainage design.
Helbling, D. E., Johnson, D. R., Lee, T. K., Scheidegger, A., & Fenner, K. (2015). A framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. Water Research, 70, 471-484. doi:10.1016/j.watres.2014.12.013, Institutional Repository

The rates at which wastewater treatment plant (WWTP) microbial communities biotransform specific substrates can differ by orders of magnitude among WWTP communities. Differences in taxonomic compositions among WWTP communities may predict differences in the rates of some types of biotransformations. In this work, we present a novel framework for establishing predictive relationships between specific bacterial 16S rRNA sequence abundances and biotransformation rates. We selected ten WWTPs with substantial variation in their environmental and operational metrics and measured the in situ ammonia biotransformation rate constants in nine of them. We isolated total RNA from samples from each WWTP and analyzed 16S rRNA sequence reads. We then developed multivariate models between the measured abundances of specific bacterial 16S rRNA sequence reads and the ammonia biotransformation rate constants. We constructed model scenarios that systematically explored the effects of model regularization, model linearity and non-linearity, and aggregation of 16S rRNA sequences into operational taxonomic units (OTUs) as a function of sequence dissimilarity threshold (SDT). A large percentage (greater than 80%) of model scenarios resulted in well-performing and significant models at intermediate SDTs of 0.13–0.14 and 0.26. The 16S rRNA sequences consistently selected into the well-performing and significant models at those SDTs were classified as Nitrosomonas and Nitrospira groups. We then extend the framework by applying it to the biotransformation rate constants of ten micropollutants measured in batch reactors seeded with the ten WWTP communities. We identified phylogenetic groups that were robustly selected into all well-performing and significant models constructed with biotransformation rates of isoproturon, propachlor, ranitidine, and venlafaxine. These phylogenetic groups can be used as predictive biomarkers of WWTP microbial community activity towards these specific micropollutants. This work is an important step towards developing tools to predict biotransformation rates in WWTPs based on taxonomic composition.
Kaeseberg, T., Blumensaat, F., Zhang, J., & Krebs, P. (2015). Assessing antibiotic resistance of microorganisms in sanitary sewage. Water Science and Technology, 71(2), 168-173. doi:10.2166/wst.2014.467, Institutional Repository

The release of antimicrobial substances into surface waters is of growing concern due to direct toxic effects on all trophic levels and the promotion of antibiotic resistance through sub-inhibitory concentration levels. This study showcases (1) the variation of antibiotics in sanitary sewage depending on different timescales and (2) a method to assess the antibiotic resistance based on an inhibition test. The test is based on the measurement of the oxygen uptake rate (OUR) in wastewater samples with increasing concentrations of the selected antibiotic agents. The following antibiotics were analysed in the present study: clarithromycin (CLA) was selected due to its high toxicity to many microorganisms (low EC50), ciprofloxacin (CIP) which is used to generally fight all bacteria concerning interstitial infections and doxycyclin (DOX) having a broad spectrum efficacy. Results show that CLA inhibited the OUR by approximately 50% at a concentration of about 10 mg L−1, because Gram-negative bacteria such as Escherichia coli are resistant, whereas CIP inhibited about 90% of the OUR at a concentration equal to or greater than 10 mg L−1. In the case of DOX, a moderate inhibition of about 38% at a concentration of 10 mg L−1 was identified, indicating a significant antibiotic resistance. The results are consistent with the corresponding findings from the Clinical and Laboratory Standards Institute. Thus, the presented inhibition test provides a simple but robust alternative method to assess antibiotic resistance in biofilms instead of more complex clinical tests.
Kinyua, J., Covaci, A., Maho, W., McCall, A. K., Neels, H., & van Nuijs, A. L. N. (2015). Sewage-based epidemiology in monitoring the use of new psychoactive substances: validation and application of an analytical method using LC-MS/MS. Drug Testing and Analysis, 7(9), 812-818. doi:10.1002/dta.1777, Institutional Repository

Sewage-based epidemiology (SBE) employs the analysis of sewage to detect and quantify drug use within a community. While SBE has been applied repeatedly for the estimation of classical illicit drugs, only few studies investigated new psychoactive substances (NPS). These compounds mimic effects of illicit drugs by introducing slight modifications to chemical structures of controlled illicit drugs. We describe the optimization, validation, and application of an analytical method using liquid chromatography coupled to positive electrospray tandem mass spectrometry (LC-ESI-MS/MS) for the determination of seven NPS in sewage: methoxetamine (MXE), butylone, ethylone, methylone, methiopropamine (MPA), 4-methoxymethamphetamine (PMMA), and 4-methoxyamphetamine (PMA). Sample preparation was performed using solid-phase extraction (SPE) with Oasis MCX cartridges. The LC separation was done with a HILIC (150 x 3 mm, 5 µm) column which ensured good resolution of the analytes with a total run time of 19 min. The lower limit of quantification (LLOQ) was between 0.5 and 5 ng/L for all compounds. The method was validated by evaluating the following parameters: sensitivity, selectivity, linearity, accuracy, precision, recoveries and matrix effects. The method was applied on sewage samples collected from sewage treatment plants in Belgium and Switzerland in which all investigated compounds were detected, except MPA and PMA. Furthermore, a consistent presence of MXE has been observed in most of the sewage samples at levels higher than LLOQ.
Künzle, R., Pronk, W., Morgenroth, E., & Larsen, T. A. (2015). An energy-efficient membrane bioreactor for on-site treatment and recovery of wastewater. Journal of Water Sanitation and Hygiene for Development, 5(3), 448-455. doi:10.2166/washdev.2015.116, Institutional Repository

The present study describes the development of a new type of aerated membrane bioreactor referred to as a biologically activated membrane bioreactor (BAMBi) for on-site treatment of high-strength wastewater. The treated wastewater is reused for flushing and personal hygiene. BAMBi is an adaptation of a gravity-driven membrane reactor, originally developed for the purpose of treating river water to drinking water quality. Initially, a series of reactor configurations were tested and it was found that the simplest possible configuration could treat the wastewater to an acceptable standard, provided that a polishing step for color removal and disinfection was introduced. A commercial electrolysis unit was utilized for polishing. The energy consumption of BAMBi is 0.8 kWh/m3 of water treated, which can be considered low for an on-site membrane bio reactor application.
Lai, F. Y., Anuj, S., Bruno, R., Carter, S., Gartner, C., Hall, W., … Ort, C. (2015). Systematic and day-to-day effects of chemical-derived population estimates on wastewater-based drug epidemiology. Environmental Science and Technology, 49(2), 999-1008. doi:10.1021/es503474d, Institutional Repository

Population size is crucial when estimating population-normalized drug consumption (PNDC) from wastewater-based drug epidemiology (WBDE). Three conceptually different population estimates can be used: de jure (common census, residence), de facto (all persons within a sewer catchment), and chemical loads (contributors to the sampled wastewater). De facto and chemical loads will be the same where all households contribute to a central sewer system without wastewater loss. This study explored the feasibility of determining a de facto population and its effect on estimating PNDC in an urban community over an extended period. Drugs and other chemicals were analyzed in 311 daily composite wastewater samples. The daily estimated de facto population (using chemical loads) was on average 32% higher than the de jure population. Consequently, using the latter would systemically overestimate PNDC by 22%. However, the relative day-to-day pattern of drug consumption was similar regardless of the type of normalization as daily illicit drug loads appeared to vary substantially more than the population. Using chemical loads population, we objectively quantified the total methodological uncertainty of PNDC and reduced it by a factor of 2. Our study illustrated the potential benefits of using chemical loads population for obtaining more robust PNDC data in WBDE.
Larsen, T. A., Gebauer, H., Gründl, H., Künzle, R., Lüthi, C., Messmer, U., … Ranner, B. (2015). Blue Diversion: a new approach to sanitation in informal settlements. Journal of Water Sanitation and Hygiene for Development, 5(1), 64-71. doi:10.2166/washdev.2014.115, Institutional Repository

The sanitation concept ‘Blue Diversion’ (www.bluediversiontoilet.com) was developed as a possible answer to the sanitation crisis in urban slums. It is based on two main elements: (1) diversion of urine, feces, and water at the source as the basis for efficient resource recovery, and (2) linking different scales (family toilets and semi-centralized resource recovery). Our objective was to develop an attractive ‘grid-free’ (i.e. functioning without piped water, sewer, and electrical grid) dry urine-diverting toilet, which provides water (through recycling on-site) for flushing, personal hygiene (anal cleansing and menstrual hygiene), and hand washing. This service, including the entire sanitation value chain, should eventually be made available as a profitable business with total user fees of 5 ¢/p/d. The results presented in this paper are (1) a toilet design model, (2) the development of a new type of membrane bioreactor for treating flush and wash water, (3) main results of a geographic information system-based stochastic service model to link the family-scale toilet to a community-scale Resource Recovery Plant, and (4) a business model that yields maximum profit for the local community. We conclude that the approach is feasible, but challenging from a technical as well as an organizational point of view.
Larsen, T. A. (2015). CO2-neutral wastewater treatment plants or robust, climate-friendly wastewater management? A systems perspective. Water Research, 87, 513-521. doi:10.1016/j.watres.2015.06.006, Institutional Repository

CO2-neutral wastewater treatment plants can be obtained by improving the recovery of internal wastewater energy resources (COD, nutrients, energy) and reducing energy demand as well as direct emissions of the greenhouse gases N2O and CH4. Climate-friendly wastewater management also includes the management of the heat resource, which is most efficiently recovered at the household level, and robust wastewater management must be able to cope with a possible resulting temperature decrease. At the treatment plant there is a substantial energy optimization potential, both from improving electromechanical devices and sludge treatment as well as through the implementation of more energy-efficient processes like the mainstream anammox process or nutrient recovery from urine. Whether CO2 neutrality can be achieved depends not only on the actual net electricity production, but also on the type of electricity replaced: the cleaner the marginal electricity the more difficult to compensate for the direct emissions, which can be substantial, depending on the stability of the biological processes. It is possible to combine heat recovery at the household scale and nutrient recovery from urine, which both have a large potential to improve the climate friendliness of wastewater management.
Lienert, J., Scholten, L., Egger, C., & Maurer, M. (2015). Structured decision-making for sustainable water infrastructure planning and four future scenarios. EURO Journal on Decision Processes, 3, 107-140. doi:10.1007/s40070-014-0030-0, Institutional Repository

Water supply and wastewater infrastructures are vital for human well-being and environmental protection; they adhere to the highest standards, are expensive and long-lived. Because they are also aging, substantial planning is required. Climate and socio-economic change create large planning uncertainties and simple projections of past developments are no longer adequate. This paper presents the initial phases of a structured decision-making (SDM) procedure which is designed to increase the sustainability of water infrastructure planning and includes various stakeholders in an exemplary Swiss case study. We evaluate the SDM approach critically based on stakeholder feedback, give general recommendations and provide ample material to make it applicable to other settings. We carried out 27 interviews and two stakeholder workshops. We identified important objectives for water infrastructure planning, including all three sustainability pillars and their respective attributes (indicators, benchmarks) to measure how well the objectives are achieved. We then created strategic decision alternatives, including "business-as-usual" upgrades of the central water supply and wastewater system as well as semi- to fully decentralized alternatives. To tackle future uncertainty, we developed four socio-demographic scenarios. We used these to test the robustness of decision alternatives in a later Multi-Attribute Utility Theory analysis. Additionally, we contribute to the topical discussion of combining scenario planning with multi-criteria decision analysis and demonstrate how various scenarios can stimulate creativity when generating decision alternatives. Their internal consistency is ensured by rigorously specifying them using a strategy generation table. Our SDM procedure can be adapted to inform decisions about sustainable water infrastructures in other contexts.
Neumann, M. B., Rieckermann, J., Hug, T., & Gujer, W. (2015). Adaptation in hindsight: dynamics and drivers shaping urban wastewater systems. Journal of Environmental Management, 151, 404-415. doi:10.1016/j.jenvman.2014.12.047, Institutional Repository

Well-planned urban infrastructure should meet critical loads during its design lifetime. In order to proceed with design, engineers are forced to make numerous assumptions with very little supporting information about the development of various drivers. For the wastewater sector, these drivers include the future amount and composition of the generated wastewater, effluent requirements, technologies, prices of inputs such as energy or chemicals, and the value of outputs produced such as nutrients for fertilizer use. When planning wastewater systems, there is a lack of methods to address discrepancies between the timescales at which fundamental changes in these drivers can occur, and the long physical life expectancy of infrastructure (on the order of 25–80 years). To explore these discrepancies, we take a hindsight perspective of the long-term development of wastewater infrastructure and assess the stability of assumptions made during previous designs. Repeatedly we find that the drivers influencing wastewater loads, environmental requirements or technological innovation can change at smaller timescales than the infrastructure design lifetime, often in less than a decade. Our analysis shows that i) built infrastructure is continuously confronted with challenges it was not conceived for, ii) significant adaptation occurs during a structure's lifetime, and iii) "muddling-through" is the pre-dominant strategy for adaptive management. As a consequence, we argue, there is a need to explore robust design strategies which require the systematic use of scenario planning methods and instruments to increase operational, structural, managerial, institutional and financial flexibility. Hindsight studies, such as this one, may inform the development of robust design strategies and assist in the transition to more explicit forms of adaptive management for urban infrastructures.
O'Brien, J. W., Thai, P. K., Brandsma, S. H., Leonards, P. E. G., Ort, C., & Mueller, J. F. (2015). Wastewater analysis of Census day samples to investigate per capita input of organophosphorus flame retardants and plasticizers into wastewater. Chemosphere, 138, 328-334. doi:10.1016/j.chemosphere.2015.06.014, Institutional Repository

The use of organophosphate esters (PFRs) as flame retardants and plasticizers has increased due to the ban of some brominated flame retardants. There is however some concern regarding the toxicity, particularly carcinogenicity and neurotoxicity, of some of the PFRs. In this study we applied wastewater analysis to assess use of PFRs by the Australian population. Influent samples were collected from eleven wastewater treatment plants (STPs) in Australia on Census day and analysed for PFRs using gas chromatography coupled with mass spectrometry (GC-MS). Per capita mass loads of PFRs were calculated using the accurate Census head counts. The results indicate that tris(2-butoxyethyl) phosphate (TBOEP) has the highest per capita input into wastewater followed by tris(2-chloroisopropyl) phosphate (TCIPP), tris(isobutyl) phosphate (TIBP), tris(2-chloroethyl) phosphate (TCEP) and tris(1,3-dichloroisopropyl) phosphate (TDCIPP). Similar PFR profiles were observed across the Australian STPs and a comparison with European and U.S. STPs indicated similar PFR concentrations. We estimate that approximately 2.1 mg person−1 day−1 of PFRs are input into Australian wastewater which equates to 16 tonnes per annum.
Scheidegger, A., Leitão, J. P., & Scholten, L. (2015). Statistical failure models for water distribution pipes – a review from a unified perspective. Water Research, 83, 237-247. doi:10.1016/j.watres.2015.06.027, Institutional Repository

This review describes and compares statistical failure models for water distribution pipes in a systematic way and from a unified perspective. The way the comparison is structured provides the information needed by scientists and practitioners to choose a suitable failure model for their specific needs.
The models are presented in a novel framework consisting of: 1) Clarification of model assumptions. The models originally formulated in different mathematical forms are all presented as failure rate. This enables to see differences and similarities across the models. Furthermore, we present a new conceptual failure rate that an optimal model would represent and to which the failure rate of each model can be compared. 2) Specification of the detailed data assumptions required for unbiased model calibration covering the structure and completeness of the data. 3) Presentation of the different types of probabilistic predictions available for each model.
Nine different models and their variations or further developments are presented in this review. For every model an overview of its applications published in scientific journals and the available software implementations is provided.
The unified view provides guidance to model selection. Furthermore, the model comparison presented herein enables to identify areas where further research is needed.
Scholten, L., Schuwirth, N., Reichert, P., & Lienert, J. (2015). Tackling uncertainty in multi-criteria decision analysis – an application to water supply infrastructure planning. European Journal of Operational Research, 242(1), 243-260. doi:10.1016/j.ejor.2014.09.044, Institutional Repository

We present a novel approach for practically tackling uncertainty in preference elicitation and predictive modeling to support complex multi-criteria decisions based on multi-attribute utility theory (MAUT). A simplified two-step elicitation procedure consisting of an online survey and face-to-face interviews is followed by an extensive uncertainty analysis. This covers uncertainty of the preference components (marginal value and utility functions, hierarchical aggregation functions, aggregation parameters) and the attribute predictions. Context uncertainties about future socio-economic developments are captured by combining MAUT with scenario planning. We perform a global sensitivity analysis (GSA) to assess the contribution of single uncertain preference parameters to the uncertainty of the ranking of alternatives. This is exemplified for sustainable water infrastructure planning in a case study in Switzerland. We compare 11 water supply alternatives ranging from conventional water supply systems to novel technologies and management schemes regarding 44 objectives. Their performance is assessed for four future scenarios and 10 stakeholders from different backgrounds and decision-making levels. Despite uncertainty in the ranking of alternatives, potential best and worst solutions could be identified. We demonstrate that a priori assumptions such as linear value functions or additive aggregation can result in misleading recommendations, unless thoroughly checked during preference elicitation and modeling. We suggest GSA to focus elicitation on most sensitive preference parameters. Our GSA results indicate that output uncertainty can be considerably reduced by additional elicitation of few parameters, e.g. the overall risk attitude and aggregation functions at higher-level nodes. Here, rough value function elicitation was sufficient, thereby substantially reducing elicitation time.
Sikorska, A. E., Del Giudice, D., Banasik, K., & Rieckermann, J. (2015). The value of streamflow data in improving TSS predictions - Bayesian multi-objective calibration. Journal of Hydrology, 530, 241-254. doi:10.1016/j.jhydrol.2015.09.051, Institutional Repository

The concentration of total suspended solids (TSS) in surface waters is a commonly used indicator of water quality impairments. Its accurate prediction remains, however, problematic because: i) TSS build-up, erosion, and wash-off are not easily identifiable; ii) calibrating a TSS model requires observations of sediment loads, which are rare, and streamflow observations to calculate concentrations; iii) predicted TSS usually deviate systematically from observations, an effect which is commonly neglected. Ignoring systematic errors during calibration can lead to overconfident (i.e. unreliable) uncertainty estimates during predictions. In this paper, we therefore investigate whether a statistical description of systematic model errors makes it possible to generate reliable predictions for TSS. In addition, we explore how the reliability of TSS predictions increases when streamflow data are additionally used in model calibration. A key aspect of our study is that we use a Bayesian multi-output calibration and a novel autoregressive error model, which describes the model predictive error as a sum of independent random noise and autocorrelated bias. Our results show that using a statistical description of model bias provides more reliable uncertainty estimates of TSS than before and including streamflow data into calibration makes TSS predictions more precise. For a case study of a small ungauged catchment, this improvement was as much as 15%. Our approach can be easily implemented for other water quality variables which are dependent on streamflow.
Simões, N. E., Ochoa-Rodríguez, S., Wang, L. P., Pina, R. D., Marques, A. S., Onof, C., & Leitão, J. P. (2015). Stochastic urban pluvial flood hazard maps based upon a spatial-temporal rainfall generator. Water, 7(7), 3396-3406. doi:10.3390/w7073396, Institutional Repository

It is a common practice to assign the return period of a given storm event to the urban pluvial flood event that such storm generates. However, this approach may be inappropriate as rainfall events with the same return period can produce different urban pluvial flooding events, i.e., with different associated flood extent, water levels and return periods. This depends on the characteristics of the rainfall events, such as spatial variability, and on other characteristics of the sewer system and the catchment. To address this, the paper presents an innovative contribution to produce stochastic urban pluvial flood hazard maps. A stochastic rainfall generator for urban-scale applications was employed to generate an ensemble of spatially—and temporally—variable design storms with similar return period. These were used as input to the urban drainage model of a pilot urban catchment (~9 km2) located in London, UK. Stochastic flood hazard maps were generated through a frequency analysis of the flooding generated by the various storm events. The stochastic flood hazard maps obtained show that rainfall spatial-temporal variability is an important factor in the estimation of flood likelihood in urban areas. Moreover, as compared to the flood hazard maps obtained by using a single spatially-uniform storm event, the stochastic maps generated in this study provide a more comprehensive assessment of flood hazard which enables better informed flood risk management decisions.
Tokarczyk, P., Leitao, J. P., Rieckermann, J., Schindler, K., & Blumensaat, F. (2015). High-quality observation of surface imperviousness for urban runoff modelling using UAV imagery. Hydrology and Earth System Sciences, 19(10), 4215-4228. doi:10.5194/hess-19-4215-2015, Institutional Repository

Modelling rainfall–runoff in urban areas is increasingly applied to support flood risk assessment, particularly against the background of a changing climate and an increasing urbanization. These models typically rely on high-quality data for rainfall and surface characteristics of the catchment area as model input.
While recent research in urban drainage has been focusing on providing spatially detailed rainfall data, the technological advances in remote sensing that ease the acquisition of detailed land-use information are less prominently discussed within the community. The relevance of such methods increases as in many parts of the globe, accurate land-use information is generally lacking, because detailed image data are often unavailable. Modern unmanned aerial vehicles (UAVs) allow one to acquire high-resolution images on a local level at comparably lower cost, performing on-demand repetitive measurements and obtaining a degree of detail tailored for the purpose of the study.
In this study, we investigate for the first time the possibility of deriving high-resolution imperviousness maps for urban areas from UAV imagery and of using this information as input for urban drainage models. To do so, an automatic processing pipeline with a modern classification method is proposed and evaluated in a state-of-the-art urban drainage modelling exercise. In a real-life case study (Lucerne, Switzerland), we compare imperviousness maps generated using a fixed-wing consumer micro-UAV and standard large-format aerial images acquired by the Swiss national mapping agency (swisstopo). After assessing their overall accuracy, we perform an end-to-end comparison, in which they are used as an input for an urban drainage model. Then, we evaluate the influence which different image data sources and their processing methods have on hydrological and hydraulic model performance. We analyse the surface runoff of the 307 individual subcatchments regarding relevant attributes, such as peak runoff and runoff volume. Finally, we evaluate the model's channel flow prediction performance through a cross-comparison with reference flow measured at the catchment outlet.
We show that imperviousness maps generated from UAV images processed with modern classification methods achieve an accuracy comparable to standard, off-the-shelf aerial imagery. In the examined case study, we find that the different imperviousness maps only have a limited influence on predicted surface runoff and pipe flows, when traditional workflows are used. We expect that they will have a substantial influence when more detailed modelling approaches are employed to characterize land use and to predict surface runoff. We conclude that UAV imagery represents a valuable alternative data source for urban drainage model applications due to the possibility of flexibly acquiring up-to-date aerial images at a quality compared with off-the-shelf image products and a competitive price at the same time. We believe that in the future, urban drainage models representing a higher degree of spatial detail will fully benefit from the strengths of UAV imagery.
Been, F., Rossi, L., Ort, C., Rudaz, S., Delémont, O., & Esseiva, P. (2014). Population normalization with ammonium in wastewater-based epidemiology: application to illicit drug monitoring. Environmental Science and Technology, 48(14), 8162-8169. doi:10.1021/es5008388, Institutional Repository

Fluctuations in ammonium (NH4 +), measured as NH 4-N loads using an ion-selective electrode installed at the inlet of a sewage treatment plant, showed a distinctive pattern which was associated to weekly (i.e., commuters) and seasonal (i.e., holidays) fluctuations of the population. Moreover, population size estimates based on NH4-N loads were lower compared to census data. Diurnal profiles of benzoylecgonine (BE) and 11-nor-9-carboxy-δ9-tetrahydrocannabinol (THC-COOH) were shown to be strongly correlated to NH4-N. Characteristic patterns, which reflect the prolonged nocturnal activity of people during the weekend, could be observed for BE, cocaine, and a major metabolite of MDMA (i.e., 4-hydroxy-3- methoxymethamphetamine). Additional 24 h composite samples were collected between February and September 2013. Per-capita loads (i.e., grams per day per 1000 inhabitants) were computed using census data and NH4-N measurements. Normalization with NH4-N did not modify the overall pattern, suggesting that the magnitude of fluctuations in the size of the population is negligible compared to those of illicit drug loads. Results show that fluctuations in the size of the population over longer periods of time or during major events can be monitored using NH4-N loads: either using raw NH4-N loads or population size estimates based on NH 4-N loads, if information about site-specific NH4-N population equivalents is available.
Blumensaat, F., Seydel, J., Krebs, P., & Vanrolleghem, P. A. (2014). Model structure sensitivity of river water quality models for urban drainage impact assessment. In D. P. Ames, N. W. T. Quinn, & A. E. Rizzoli (Eds.), Vol. 2. Proceedings of the 7th international congress on environmental modelling and software (pp. 609-618). San Diego, California, USA: International Environmental Modelling and Software Society. , Institutional Repository

Numerical modeling of physicochemical conditions in rivers influenced by urban drainage and other pressures is increasingly used as supportive method for an integrative environmental impact assessment. In various European countries, protocols for water quality based impact assessment (WQA protocols) are in use, of which many propose the use of water quality models. Despite the increased effort to study uncertainty issues in river water quality modeling in recent years, identifying and differentiating model structure uncertainty remains a challenging task. This study elaborates upon a key conflict in model development: the need to simplify and to still ensure structural adequacy to obtain reliable modeling results. The paper evaluates the adequacy of diverse river water quality modeling approaches when subjected to a changing model structure. This evaluation is achieved by applying a set of calibrated candidate models to two different river case studies under varying pollution conditions. The term 'model structure sensitivity' is introduced to quantify the output variation as a result of a changing model structure. Sensitivity is here, in contrast to previous works, positively interpreted as model flexibility. The study illustrates that the interdependence bet ween model sensitivity and model error is conditional upon complexity, but model adequacy differs depending on the pollution dynamics in the modeled system. Results show that model structure uncertainty has a composite nature: effects related to transport and conversion model contribute with varying shares. Two actions should be considered to improve model structure characterization: i) use of a variety of model structures combined with an analysis of structure and parameter sensitivity, and ii) acquisition of high-resolution reference data to capture varying pollution load dynamics.
Dürrenmatt, D. J., & Wanner, O. (2014). A mathematical model to predict the effect of heat recovery on the wastewater temperature in sewers. Water Research, 48(1), 548-558. doi:10.1016/j.watres.2013.10.017, Institutional Repository

Raw wastewater contains considerable amounts of energy that can be recovered by means of a heat pump and a heat exchanger installed in the sewer. The technique is well established, and there are approximately 50 facilities in Switzerland, many of which have been successfully using this technique for years. The planning of new facilities requires predictions of the effect of heat recovery on the wastewater temperature in the sewer because altered wastewater temperatures may cause problems for the biological processes used in wastewater treatment plants and receiving waters. A mathematical model is presented that calculates the discharge in a sewer conduit and the spatial profiles and dynamics of the temperature in the wastewater, sewer headspace, pipe, and surrounding soil. The model was implemented in the simulation program TEMPEST and was used to evaluate measured time series of discharge and temperatures. It was found that the model adequately reproduces the measured data and that the temperature and thermal conductivity of the soil and the distance between the sewer pipe and undisturbed soil are the most sensitive model parameters. The temporary storage of heat in the pipe wall and the exchange of heat between wastewater and the pipe wall are the most important processes for heat transfer. The model can be used as a tool to determine the optimal site for heat recovery and the maximal amount of extractable heat.
Gulde, R., Helbling, D. E., Scheidegger, A., & Fenner, K. (2014). pH-dependent biotransformation of ionizable organic micropollutants in activated sludge. Environmental Science and Technology, 48(23), 13760-13768. doi:10.1021/es5037139, Institutional Repository

Removal of micropollutants (MPs) during activated sludge treatment can mainly be attributed to biotransformation and sorption to sludge flocs, whereby the latter process is known to be of minor importance for polar organic micropollutants. In this work, we investigated the influence of pH on the biotransformation of MPs with cationic-neutral speciation in an activated sludge microbial community. We performed batch biotransformation, sorption control, and abiotic control experiments for 15 MPs with cationic-neutral speciation, one control MP with neutral-anionic speciation, and two neutral MPs at pHs 6, 7, and 8. Biotransformation rate constants corrected for sorption and abiotic processes were estimated from measured concentration time series with Bayesian inference. We found that biotransformation is pH-dependent and correlates qualitatively with the neutral fraction of the ionizable MPs. However, a simple speciation model based on the assumption that only the neutral species is efficiently taken up and biotransformed by the cells tends to overpredict the effect of speciation. Therefore, additional mechanisms such as uptake of the ionic species and other more complex attenutation mechanisms are discussed. Finally, we observed that the sorption coefficients derived from our control experiments were small and showed no notable pH-dependence. From this we conclude that pH-dependent removal of polar, ionizable organic MPs in activated sludge systems is less likely an effect of pH-dependent sorption but rather of pH-dependent biotransformation. The latter has the potential to cause marked differences in the removal of polar, ionizable MPs at different operational pHs during activated sludge treatment.
Honti, M., Scheidegger, A., & Stamm, C. (2014). The importance of hydrological uncertainty assessment methods in climate change impact studies. Hydrology and Earth System Sciences, 18(8), 3301-3317. doi:10.5194/hess-18-3301-2014, Institutional Repository

Climate change impact assessments have become more and more popular in hydrology since the middle 1980s with a recent boost after the publication of the IPCC AR4 report. From hundreds of impact studies a quasi-standard methodology has emerged, to a large extent shaped by the growing public demand for predicting how water resources management or flood protection should change in the coming decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The choice of uncertainty assessment method actually determined what sources of uncertainty could be identified at all. This demonstrated that one could arrive at rather different conclusions about the causes behind predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.
Leidel, M., Hagemann, N., Seegert, J., Weigelt, C., Zakorchevna, N., & Blumensaat, F. (2014). Supporting decisions in water management by exploring information and capacity gaps: Experiences from an IWRM study in the Western Bug River Basin, Ukraine. Environmental Earth Sciences, 72(12), 4771-4786. doi:10.1007/s12665-014-3863-8, Institutional Repository

Key problems of Integrated Water Resources Management refer to interactions between various levels, scales and existing coordination gaps, such as inadequate governance structures and insufficient knowledge and capacities. In this study we describe a management framework that aligns model-based systems analysis with capacity assessments suggesting a concept for improving cross-scale interactions and thus for overcoming both, water-related pressures and coordination gaps. In the study we (i) identify the missing link between technical development approaches and capacity development, (ii) outline interrelations between environmental pressures on aquatic systems and capacity and information gaps in a transparent way, and (iii) introduce a practice-relevant method to combine model-based system planning with capacity assessments for deriving management options that support water management actors in reducing pressures and gaps. The results of the integrated analysis are made explicit by introducing a matrix approach that is inspired by an existing framework to systematically differentiate water quality-related pressures (cf. Blumensaat et al. 2013). The approach confronts pressures and gaps and so jointly addresses technical issues, institutional challenges, organizational development, information needs, and human resources development. The concept supports a transparent decision making process by identifying knowledge and capacities required for the implementation of corresponding technical intervention options and vice versa. The application of the method in the International Water Research Alliance Saxony model region 'Ukraine' is illustrated to demonstrate the added value as a boundary object between scales that is supporting actors in streamlining model-based planning and capacity development.
Logar, I., Brouwer, R., Maurer, M., & Ort, C. (2014). Cost-benefit analysis of the Swiss national policy on reducing micropollutants in treated wastewater. Environmental Science and Technology, 48(21), 12500-12508. doi:10.1021/es502338j, Institutional Repository

Contamination of freshwater with micropollutants (MPs) is a growing concern worldwide. Even at very low concentrations, MPs can have adverse effects on aquatic ecosystems and possibly also on human health. Switzerland is one of the first countries to start implementing a national policy to reduce MPs in the effluents of municipal sewage treatment plants (STPs). This paper estimates the benefits of upgrading STPs based on public’s stated preferences. To assess public demand for the reduction of the environmental and health risks of MPs, we conducted a choice experiment in a national online survey. The results indicate that the average willingness to pay per household is CHF 100 (US$ 73) annually for reducing the potential environmental risk of MPs to a low level. These benefits, aggregated over households in the catchment of the STPs to be upgraded, generate a total annual economic value of CHF 155 million (US$ 113 million). This compares with estimated annual costs for upgrading 123 STPs of CHF 133 million (US$ 97 million) or CHF 86 (US$ 63) per household connected to these STPs. Hence, a cost-benefit analysis justifies the investment decision from an economic point of view and supports the implementation of the national policy in the ongoing political discussion.
McConville, J. R., Künzle, R., Messmer, U., Udert, K. M., & Larsen, T. A. (2014). Decision support for redesigning wastewater treatment technologies. Environmental Science and Technology, 48(20), 12238-12246. doi:10.1021/es501854x, Institutional Repository

This paper offers a methodology for structuring the design space for innovative process engineering technology development. The methodology is exemplified in the evaluation of a wide variety of treatment technologies for source-separated domestic wastewater within the scope of the Reinvent the Toilet Challenge. It offers a methodology for narrowing down the decision-making field based on a strict interpretation of treatment objectives for undiluted urine and dry feces and macroenvironmental factors (STEEPLED analysis) which influence decision criteria. Such an evaluation identifies promising paths for technology development such as focusing on space-saving processes or the need for more innovation in low-cost, energy-efficient urine treatment methods. Critical macroenvironmental factors, such as housing density, transportation infrastructure, and climate conditions were found to affect technology decisions regarding reactor volume, weight of outputs, energy consumption, atmospheric emissions, investment cost, and net revenue. The analysis also identified a number of qualitative factors that should be carefully weighed when pursuing technology development; such as availability of O&M resources, health and safety goals, and other ethical issues. Use of this methodology allows for coevolution of innovative technology within context constraints; however, for full-scale technology choices in the field, only very mature technologies can be evaluated.
O'Brien, J. W., Thai, P. K., Eaglesham, G., Ort, C., Scheidegger, A., Carter, S., … Mueller, J. F. (2014). A model to estimate the population contributing to the wastewater using samples collected on census day. Environmental Science and Technology, 48(1), 517-525. doi:10.1021/es403251g, Institutional Repository

An important uncertainty when estimating per capita consumption of, for example, illicit drugs by means of wastewater analysis (sometimes referred to as "sewage epidemiology") relates to the size and variability of the de facto population in the catchment of interest. In the absence of a day-specific direct population count any indirect surrogate model to estimate population size lacks a standard to assess associated uncertainties. Therefore, the objective of this study was to collect wastewater samples at a unique opportunity, that is, on a census day, as a basis for a model to estimate the number of people contributing to a given wastewater sample. Mass loads for a wide range of pharmaceuticals and personal care products were quantified in influents of ten sewage treatment plants (STP) serving populations ranging from approximately 3500 to 500 000 people. Separate linear models for population size were estimated with the mass loads of the different chemical as the explanatory variable: 14 chemicals showed good, linear relationships, with highest correlations for acesulfame and gabapentin. De facto population was then estimated through Bayesian inference, by updating the population size provided by STP staff (prior knowledge) with measured chemical mass loads. Cross validation showed that large populations can be estimated fairly accurately with a few chemical mass loads quantified from 24-h composite samples. In contrast, the prior knowledge for small population sizes cannot be improved substantially despite the information of multiple chemical mass loads. In the future, observations other than chemical mass loads may improve this deficit, since Bayesian inference allows including any kind of information relating to population size.
Ort, C., Eppler, J. M., Scheidegger, A., Rieckermann, J., Kinzig, M., & Sörgel, F. (2014). Challenges of surveying wastewater drug loads of small populations and generalizable aspects on optimizing monitoring design. Addiction, 109(3), 472-481. doi:10.1111/add.12405, Institutional Repository

Aims. Quantifying illicit drug loads through wastewater analysis (WWA) is an alternative approach to estimating population drug use. This study investigated the variability of daily drug loads in wastewater and their relationships to environmental factors over an extended period to: (i) explore the suitability of WWA in small populations and (ii) optimize the monitoring design for future studies.
Design, Setting, Participants. Daily wastewater samples (n = 1369 consecutive days) from a German village with approximately 7160 inhabitants.
Measurements. Samples were analysed for cocaine and benzoylecgonine with liquid chromatography-tandem mass spectrometry. Time-series analysis was used to explore the effects of weather and other factors on daily cocaine loads. Subsampling was used to assess monitoring design.
Findings. Cocaine loads [mean = 652 mgCOC/day, standard deviation (SD) = 498 mgCOC/day] increased over the study period, with higher values during winter and spring. Despite high day-to-day variation, loads were significantly higher during weekends [+161 mgCOC/day, 95% confidence interval (CI) = 115–207 mgCOC/day, P < 10−4] and days with frost (+114 mgCOC/day, 95% CI = 6–223 mgCOC/day, P = 0.039) or snow (+150 mgCOC/day, 95% CI = 46–253 mgCOC/day, P = 0.005). Annual means estimated from 1-week periods were subject to approximately 60% relative error. Increasing sample size and changing sampling from consecutive days to stratified random decreased this uncertainty.
Conclusions. Day-to-day variation and seasonality of drug loads from the few long-term wastewater studies available to date suggest that up to 56 stratified random samples are required to obtain reliable (expected uncertainty around 10%) annual estimates of drug loads. Successfully assessing changes in consumption patterns or relationships to external factors requires larger sample sizes than estimating annual means, which holds true for high-prevalence drugs in small communities and low-prevalence drugs in big cities.
Ort, C., Banta-Green, C. J., Bijlsma, L., Castiglioni, S., Emke, E., Gartner, C., … van Nuijs, A. L. N. (2014). Sewage-based epidemiology requires a truly transdisciplinary approach. GAIA: Ecological Perspectives for Science and Society, 23(3), 266-268. doi:10.14512/gaia.23.3.12, Institutional Repository

If asked whether you had consumed illicit drugs recently, would you admit it? If yes, could you precisely recall types of drug, times and amounts used? If you were the person commissioned with the task of quantifying drug use, what approach would you use given the social stigma attached with such behavior? We measure drug residues in sewage, which represents urine of entire populations, to provide an objective estimate of total drug use in a region. In transdisciplinary projects, sewage-based results provide valuable information at unrivaled spatiotemporal resolution complementing traditional data.
Ort, C., van Nuijs, A. L. N., Berset, J. D., Bijlsma, L., Castiglioni, S., Covaci, A., … Thomas, K. V. (2014). Spatial differences and temporal changes in illicit drug use in Europe quantified by wastewater analysis. Addiction, 109(8), 1338-1352. doi:10.1111/add.12570, Institutional Repository

Aims. To perform wastewater analyses to assess spatial differences and temporal changes of illicit drug use in a large European population.
Design. Analyses of raw wastewater over a 1-week period in 2012 and 2013.
Setting and Participants. Catchment areas of wastewater treatment plants (WWTPs) across Europe, as follows: 2012: 25 WWTPs in 11 countries (23 cities, total population 11.50 million); 2013: 47 WWTPs in 21 countries (42 cities, total population 24.74 million).
Measurements. Excretion products of five illicit drugs (cocaine, amphetamine, ecstasy, methamphetamine, cannabis) were quantified in wastewater samples using methods based on liquid chromatography coupled to mass spectrometry.
Findings. Spatial differences were assessed and confirmed to vary greatly across European metropolitan areas. In general, results were in agreement with traditional surveillance data, where available. While temporal changes were substantial in individual cities and years (P ranging from insignificant to <10−3), overall means were relatively stable. The overall mean of methamphetamine was an exception (apparent decline in 2012), as it was influenced mainly by four cities.
Conclusions. Wastewater analysis performed across Europe provides complementary evidence on illicit drug consumption and generally concurs with traditional surveillance data. Wastewater analysis can measure total illicit drug use more quickly and regularly than is the current norm for national surveys, and creates estimates where such data does not exist.
Scholten, L., Scheidegger, A., Reichert, P., Mauer, M., & Lienert, J. (2014). Strategic rehabilitation planning of piped water networks using multi-criteria decision analysis. Water Research, 49(1), 124-143. doi:10.1016/j.watres.2013.11.017, Institutional Repository

To overcome the difficulties of strategic asset management of water distribution networks, a pipe failure and a rehabilitation model are combined to predict the long term performance of rehabilitation strategies. Bayesian parameter estimation is performed to calibrate the failure and replacement model based on a prior distribution inferred from three large water utilities in Switzerland. Multi criteria decision analysis (MCDA) and scenario planning build the framework for evaluating 18 strategic rehabilitation alternatives under future uncertainty. Outcomes for three fundamental objectives (low costs, high reliability, and high intergenerational equity) are assessed. Exploitation of stochastic dominance concepts helps to identify twelve non dominated alternatives and local sensitivity analysis of stakeholder preferences is used to rank them under four scenarios. Strategies with annual replacement of 1.5 2% of the network perform reasonably well under all scenarios. In contrast, the commonly used reactive replacement is not recommendable unless cost is the only relevant objective. Exemplified for a small Swiss water utility, this approach can readily be adapted to support strategic asset management for any utility size and based on objectives and preferences that matter to the respective decision makers.
Seegert, J., Berendonk, T. U., Bernhofer, C., Blumensaat, F., Dombrowsky, I., Fuehner, C., … Krebs, P. (2014). Integrated water resources management under different hydrological, climatic and socio-economic conditions: results and lessons learned from a transdisciplinary IWRM project IWAS. Environmental Earth Sciences, 72(12), 4677-4687. doi:10.1007/s12665-014-3877-2, Institutional Repository

The International Water Research Alliance Saxony (IWAS) is addressing the global challenges concerning water quality in the areas of drinking water and sanitation, agricultural irrigation and the quality of surface and ground waters, as well as developing specific ecosystem-relevant services to be implemented on an exemplary basis in selected model regions. Locations (model regions) have been selected in Eastern Europe (R1), Central and Southeast Asia (R2 and R3), the Middle East (R4) and Latin America (R5) that are representative international regions with respect to climate, land use and demographic change (Ibisch et al., Helmholtz Centre for Environmental Research—UFZ, Department of Aquatic Ecosystem analysis and management, 2013). The causes of water problems and the relevant boundary conditions vary from region to region (Borchardt and Ibisch, Integrated water resources management in a changing world : lessons learnt and innovative perspectives, pp 225, 2013). Mongolia and Vietnam were selected model regions in the first IWAS phase; the research was transferred and continued in one of the cross-cutting projects in IWAS II (Vietnam → capacity development) or in the frame of related project activities in Central Asia (R2 Mongolia, Karthe et al., Environ Earth Sci, doi:10.1007/s12665-014-3789-1, 2014). The IWAS consortium exists on scientific institutions like the Technische Universität Dresden and the Helmholtz Centre for Environmental Research—UFZ, Leipzig, as well as partners from industry like the Stadtentwässerung Dresden GmbH (SE-DD), DREBERIS—Dresden consulting for international strategies, and itwh—institute for technical-scientific Hydrology, Hanover. This thematic issue compiles the most important scientific results of the second phase of the IWAS project. The project itself and findings of the first phase were already introduced in a previous special issue by Kalbus et al. (Environ Earth Sci 65:1363–1366, 2012). Main results: The IWAS project is structured by the model regions (R) as well as by cross-cutting activities scenario analysis (Q1), technology development (Q2), governance (Q3) and capacity development (Q4).
Veronesi, M., Chawla, F., Maurer, M., & Lienert, J. (2014). Climate change and the willingness to pay to reduce ecological and health risks from wastewater flooding in urban centers and the environment. Ecological Economics, 98, 1-10. doi:10.1016/j.ecolecon.2013.12.005, Institutional Repository

Climate change scenarios predict an increase of extreme rain events, which will increase the risk of wastewater flooding and of missing legal water quality targets. This study elicits the willingness to pay to reduce ecological and health risks from combined sewer overflows (CSOs) in rivers and lakes, and wastewater flooding of residential and commercial zones under the uncertainty of climate change. We implement a discrete choice experiment on a large representative sample of the Swiss population. We find that about 71% of the respondents are willing to pay a higher annual local tax to reduce the risk of CSOs in rivers and lakes. Swiss households strongly value the protection of water bodies, and mostly, the avoidance of high ecological risks and health risks for children related to CSOs in rivers and lakes. Our findings also show that climate change perception has a significant effect on the willingness to pay to reduce these risks. These results are important to support policy makers' decisions on how to deal with emerging risks of climate change in the water sector and where to set priorities.
van Dyken, E., Thai, P., Lai, F. Y., Ort, C., Prichard, J., Bruno, R., … Mueller, J. F. (2014). Monitoring substance use in prisons: assessing the potential value of wastewater analysis. Science and Justice, 54(5), 338-345. doi:10.1016/j.scijus.2014.06.006, Institutional Repository

Prison substance use is a major concern for prison authorities and the wider community. Australia has responded to this problem by implementing the National Corrections Drug Strategy. Across Australia, the true extent of prison substance use cannot be determined. As a result, the effectiveness of the interventions employed as part of this strategy cannot be properly assessed. This has important implications for the allocation of corrective services resources and future policy development. This article explores the benefits and limitations, as well as the ethical and practical issues in using wastewater analysis (WWA) to measure levels of substance use in prisons. It reports results from the first application of WWA to an Australian prison, which supports the use of WWA in this context. Given the increasing concern for prescription misuse in prisons, we also highlight the novel use of WWA to measure the extent of prescription misuse by prisoners. The article concludes that as a result of its objectivity, sensitivity and cost-effectiveness, the use of WWA in prisons warrants further consideration in Australia.
Bianchi, B., Rieckermann, J., & Berne, A. (2013). Quality control of rain gauge measurements using telecommunication microwave links. Journal of Hydrology, 492, 15-23. doi:10.1016/j.jhydrol.2013.03.042, Institutional Repository

Accurate rain rate measurements are essential for many hydrological applications. Although rain gauge remains the reference instrument for the measurement of rain rate, the strong spatial and temporal variability of rainfall makes it difficult to spot faulty rain gauges. Due to the poor spatial representativeness of the point rainfall measurements, this is particularly difficult where their density is low. Taking advantage of the high density of telecommunication microwave links in urban areas, a consistency check is proposed to identify faulty rain gauges using nearby microwave links. The methodology is tested on a data set from operational rain gauges and microwave links, in Zürich (Switzerland). The malfunctioning of rain gauges leading to errors in the occurrence of dry/rainy periods are well identified. In addition, the gross errors affecting quantitative rain gauge measurements during rainy periods, such as blocking at a constant value, random noise and systematic bias, can be detected. The proposed approach can be implemented in real time.
Castiglioni, S., Bijlsma, L., Covaci, A., Emke, E., Hernández, F., Reid, M., … Zuccato, E. (2013). Evaluation of uncertainties associated with the determination of community drug use through the measurement of sewage drug biomarkers. Environmental Science and Technology, 47(3), 1452-1460. doi:10.1021/es302722f, Institutional Repository

The aim of this study was to integrally address the uncertainty associated with all the steps used to estimate community drug consumption through the chemical analysis of sewage biomarkers of illicit drugs. Uncertainty has been evaluated for sampling, chemical analysis, stability of drug biomarkers in sewage, back-calculation of drug use (specific case of cocaine), and estimation of population size in a catchment using data collected from a recent Europe-wide investigation and from the available literature. The quality of sampling protocols and analytical measurements has been evaluated by analyzing standardized questionnaires collected from 19 sewage treatments plants (STPs) and the results of an interlaboratory study (ILS), respectively. Extensive reviews of the available literature have been used to evaluate stability of drug biomarkers in sewage and the uncertainty related to back-calculation of cocaine use. Different methods for estimating population size in a catchment have been compared and the variability among the collected data was very high (7–55%). A reasonable strategy to reduce uncertainty was therefore to choose the most reliable estimation case by case. In the other cases, the highest uncertainties are related to the analysis of sewage drug biomarkers (uncertainty as relative standard deviation; RSD: 6–26% from ILS) and to the back-calculation of cocaine use (uncertainty; RSD: 26%). Uncertainty can be kept below 10% in the remaining steps, if specific requirements outlined in this work are considered. For each step, a best practice protocol has been suggested and discussed to reduce and keep to a minimum the uncertainty of the entire procedure and to improve the reliability of the estimates of drug use.
Clouzot, L., Choubert, J. M., Cloutier, F., Goel, R., Love, N. G., Melcer, H., … Vanrolleghem, P. A. (2013). Perspectives on modelling micropollutants in wastewater treatment plants. Water Science and Technology, 68(2), 448-461. doi:10.2166/wst.2013.272, Institutional Repository

Models for predicting the fate of micropollutants (MPs) in wastewater treatment plants (WWTPs) have been developed to provide engineers and decision-makers with tools that they can use to improve their understanding of, and evaluate how to optimize, the removal of MPs and determine their impact on the receiving waters. This paper provides an overview of such models, and discusses the impact of regulation, engineering practice and research on model development. A review of the current status of MP models reveals that a single model cannot represent the wide range of MPs that are present in wastewaters today, and that it is important to start considering classes of MPs based on their chemical structure or ecotoxicological effect, rather than the individual molecules. This paper identifies potential future research areas that comprise (i) considering transformation products in MP removal analysis, (ii) addressing advancements in WWTP treatment technologies, (iii) making use of common approaches to data acquisition for model calibration and (iv) integrating ecotoxicological effects of MPs in receiving waters.
Del Giudice, D., Honti, M., Scheidegger, A., Albert, C., Reichert, P., & Rieckermann, J. (2013). Improving uncertainty estimation in urban hydrological modeling by statistically describing bias. Hydrology and Earth System Sciences, 17(10), 4209-4225. doi:10.5194/hess-17-4209-2013, Institutional Repository

Hydrodynamic models are useful tools for urban water management. Unfortunately, it is still challenging to obtain accurate results and plausible uncertainty estimates when using these models. In particular, with the currently applied statistical techniques, flow predictions are usually overconfident and biased. In this study, we present a flexible and relatively efficient methodology (i) to obtain more reliable hydrological simulations in terms of coverage of validation data by the uncertainty bands and (ii) to separate prediction uncertainty into its components. Our approach acknowledges that urban drainage predictions are biased. This is mostly due to input errors and structural deficits of the model. We address this issue by describing model bias in a Bayesian framework. The bias becomes an autoregressive term additional to white measurement noise, the only error type accounted for in traditional uncertainty analysis. To allow for bigger discrepancies during wet weather, we make the variance of bias dependent on the input (rainfall) or/and output (runoff) of the system. Specifically, we present a structured approach to select, among five variants, the optimal bias description for a given urban or natural case study. We tested the methodology in a small monitored stormwater system described with a parsimonious model. Our results clearly show that flow simulations are much more reliable when bias is accounted for than when it is neglected. Furthermore, our probabilistic predictions can discriminate between three uncertainty contributions: parametric uncertainty, bias, and measurement errors. In our case study, the best performing bias description is the output-dependent bias using a log-sinh transformation of data and model results. The limitations of the framework presented are some ambiguity due to the subjective choice of priors for bias parameters and its inability to address the causes of model discrepancies. Further research should focus on quantifying and reducing the causes of bias by improving the model structure and propagating input uncertainty.
Dürrenmatt, D. J., Del Giudice, D., & Rieckermann, J. (2013). Dynamic time warping improves sewer flow monitoring. Water Research, 47(11), 3803-3816. doi:10.1016/j.watres.2013.03.051, Institutional Repository

Successful management and control of wastewater and storm water systems requires accurate sewer flow measurements. Unfortunately, the harsh sewer environment and insufficient flow meter calibration often lead to inaccurate and biased data. In this paper, we improve sewer flow monitoring by creating redundant information on sewer velocity from natural wastewater tracers. Continuous water quality measurements upstream and downstream of a sewer section are used to estimate the travel time based on i) cross-correlation (XCORR) and ii) dynamic time warping (DTW). DTW is a modern data mining technique that warps two measured time series non-linearly in the time domain so that the dissimilarity between the two is minimized. It has not been applied in this context before. From numerical experiments we can show that DTW outperforms XCORR, because it provides more accurate velocity estimates, with an error of about 7% under typical conditions, at a higher temporal resolution. In addition, we can show that pre-processing of the data is important and that tracer reaction in the sewer reach is critical. As dispersion is generally small, the distance between the sensors is less influential if it is known precisely. Considering these findings, we tested the methods on a real-world sewer to check the performance of two different sewer flow meters based on temperature measurements. Here, we were able to detect that one of two flow meters was not performing satisfactorily under a variety of flow conditions. Although theoretical analyses show that XCORR and DTW velocity estimates contain systematic errors due to dispersion and reaction processes, these are usually small and do not limit the applicability of the approach.
Egger, C., Scheidegger, A., Reichert, P., & Maurer, M. (2013). Sewer deterioration modeling with condition data lacking historical records. Water Research, 47(17), 6762-6779. doi:10.1016/j.watres.2013.09.010, Institutional Repository

Accurate predictions of future conditions of sewer systems are needed for efficient rehabilitation planning. For this purpose, a range of sewer deterioration models has been proposed which can be improved by calibration with observed sewer condition data. However, if datasets lack historical records, calibration requires a combination of deterioration and sewer rehabilitation models, as the current state of the sewer network reflects the combined effect of both processes. Otherwise, physical sewer lifespans are overestimated as pipes in poor condition that were rehabilitated are no longer represented in the dataset. We therefore propose the combination of a sewer deterioration model with a simple rehabilitation model which can be calibrated with datasets lacking historical information. We use Bayesian inference for parameter estimation due to the limited information content of the data and limited identifiability of the model parameters. A sensitivity analysis gives an insight into the model's robustness against the uncertainty of the prior. The analysis reveals that the model results are principally sensitive to the means of the priors of specific model parameters, which should therefore be elicited with care. The importance sampling technique applied for the sensitivity analysis permitted efficient implementation for regional sensitivity analysis with reasonable computational outlay. Application of the combined model with both simulated and real data shows that it effectively compensates for the bias induced by a lack of historical data. Thus, the novel approach makes it possible to calibrate sewer pipe deterioration models even when historical condition records are lacking. Since at least some prior knowledge of the model parameters is available, the strength of Bayesian inference is particularly evident in the case of small datasets.
Fencl, M., Rieckermann, J., Schleiss, M., Stránský, D., & Bareš, V. (2013). Assessing the potential of using telecommunication microwave links in urban drainage modelling. Water Science and Technology, 68(8), 1810-1818. doi:10.2166/wst.2013.429, Institutional Repository

The ability to predict the runoff response of an urban catchment to rainfall is crucial for managing drainage systems effectively and controlling discharges from urban areas. In this paper we assess the potential of commercial microwave links (MWL) to capture the spatio-temporal rainfall dynamics and thus improve urban rainfall-runoff modelling. Specifically, we perform numerical experiments with virtual rainfall fields and compare the results of MWL rainfall reconstructions to those of rain gauge (RG) observations. In a case study, we are able to show that MWL networks in urban areas are sufficiently dense to provide good information on spatio-temporal rainfall variability and can thus considerably improve pipe flow prediction, even in small subcatchments. In addition, the better spatial coverage also improves the control of discharges from urban areas. This is especially beneficial for heavy rainfall, which usually has a high spatial variability that cannot be accurately captured by RG point measurements.
Kaegi, R., Voegelin, A., Ort, C., Sinnet, B., Thalmann, B., Krismer, J., … Mueller, E. (2013). Fate and transformation of silver nanoparticles in urban wastewater systems. Water Research, 47(12), 3866-3877. doi:10.1016/j.watres.2012.11.060, Institutional Repository

Discharge of silver nanoparticles (Ag-NP) from textiles and cosmetics, todays major application areas for metallic Ag-NP, into wastewater is inevitable. Transformation and removal processes in sewers and wastewater treatment plants (WWTP) will determine the impact of Ag-NP on aquatic and terrestrial environments, via the effluents of the WWTP and via the use of digested sludge as fertilizer. We thus conducted experiments addressing the behavior of Ag-NP in sewers and in WWTP.
We spiked Ag-NP to a 5 km long main trunk sewer and collected 40 wastewater samples after 500 m, 2400 m and 5000 m each according to the expected travel times of the Ag-NP. Excellent mass closure of the Ag derived by multiplying the measured Ag concentrations times the volumetric flow rates indicate an efficient transport of the Ag-NP without substantial losses to the sewer biofilm. Ag-NP reacted with raw wastewater in batch experiments were sulfidized to roughly 15% after 5 h reaction time as revealed by X-ray absorption spectroscopy (XAS). However, acid volatile sulfide (AVS) concentrations were substantially higher in the sewer channel (100 μM) compared to the batch experiments (3 μM; still sufficient to sulfidize spiked 2 μM Ag) possibly resulting in a higher degree of sulfidation in the sewer channel. We further investigated the removal efficiency of 10 nm and 100 nm Ag- and gold (Au)-NP coated with citrate or polyvinylpyrrolidone in activated sludge batch experiments. We obtained very high removal efficiencies (∼99%) irrespective of size and coating for Ag- and Au-NP, the latter confirming that the particle type was of minor importance with respect to the degree of NP removal. We observed a strong size dependence of the sulfidation kinetics. We conclude that Ag-NP discharged to the wastewater stream will become sulfidized to various degrees in the sewer system and are efficiently transported to the WWTP. The sulfidation of the Ag-NP will continue in the WWTP, but primarily depending on the size the Ag-NP, may not be complete. Very high removal efficiencies in the WWTP will divert most of the Ag-NP mass flow to the digester and only a small fraction of the Ag will be released to surface waters.
Kenway, S. J., Scheidegger, R., Larsen, T. A., Lant, P., & Bader, H. P. (2013). Water-related energy in households: a model designed to understand the current state and simulate possible measures. Energy and Buildings, 58, 378-389. doi:10.1016/j.enbuild.2012.08.035, Institutional Repository

Energy use in households, including private transport, accounts for about 30% of primary energy use in industrialised countries. Therefore, households are important key drivers of energy use and related greenhouse gas emissions. In order to understand energy use related to water in households a detailed mathematical flow analysis of materials, energy, CO2 emissions and costs (MMFA) for household water use was set up and tested for a specific family household in Brisbane, Australia. The simulation results for the current state of this household were well within 20% of the monitored data. After calibration, a detailed scenario investigation determined the impact of (i) potential and (ii) realistic reduction values for all relevant (a) behavioural and (b) technical parameters, including a shift from gas to a solar hot-water system. The reduction potentials for water use, greenhouse gas emissions, water-related energy consumption, water costs and water-related energy costs were 4–77%, 14–85%, 15–93%, 1–31% and 13–90% respectively. The study showed that for this household, technical improvements alone, without changing to a solar hot-water system, result in less than a 15% change in terms of energy and greenhouse gas emissions. In contrast, combined behavioural and technical changes have a much higher reduction potential.
Lai, F. Y., Bruno, R., Leung, H. W., Thai, P. K., Ort, C., Carter, S., … Mueller, J. F. (2013). Estimating daily and diurnal variations of illicit drug use in Hong Kong: a pilot study of using wastewater analysis in an Asian metropolitan city. Forensic Science International, 233(1-3), 126-132. doi:10.1016/j.forsciint.2013.09.003, Institutional Repository

The measurement of illicit drug metabolites in raw wastewater is increasingly being adopted as an approach to objectively monitor population-level drug use, and is an effective complement to traditional epidemiological methods. As such, it has been widely applied in western countries. In this study, we utilised this approach to assess drug use patterns over nine days during April 2011 in Hong Kong. Raw wastewater samples were collected from the largest wastewater treatment plant serving a community of approximately 3.5 million people and analysed for excreted drug residues including cocaine, ketamine, methamphetamine, 3,4-methylenedioxymethamphetamine (MDMA) and key metabolites using liquid chromatography coupled with tandem mass spectrometry. The overall drug use pattern determined by wastewater analysis was consistent with that have seen amongst people coming into contact with services in relation to substance use; among our target drugs, ketamine (estimated consumption: 1400–1600 mg/day/1000 people) was the predominant drug followed by methamphetamine (180–200 mg/day/1000 people), cocaine (160–180 mg/day/1000 people) and MDMA (not detected). The levels of these drugs were relatively steady throughout the monitoring period. Analysing samples at higher temporal resolution provided data on diurnal variations of drug residue loads. Elevated ratios of cocaine to benzoylecgonine were identified unexpectedly in three samples during the evening and night, providing evidence for potential dumping events of cocaine. This study provides the first application of wastewater analysis to quantitatively evaluate daily drug use in an Asian metropolitan community. Our data reinforces the benefit of wastewater monitoring to health and law enforcement authorities for strategic planning and evaluation of drug intervention strategies.
Lai, F. Y., Bruno, R., Hall, W., Gartner, C., Ort, C., Kirkbride, P., … Mueller, J. F. (2013). Profiles of illicit drug use during annual key holiday and control periods in Australia: wastewater analysis in an urban, a semi-rural and a vacation area. Addiction, 108(3), 556-565. doi:10.1111/add.12006, Institutional Repository

Aims: To examine changes in illicit drug consumption between peak holiday season (23 December-3 January) in Australia and a control period two months later in a coastal urban area, an inland semi-rural area and an island populated predominantly by vacationers during holidays. Design: Analysis of representative daily composite wastewater samples collected from the inlet of the major wastewater treatment plant in each area. Setting: Three wastewater treatment plants. Participants: Wastewater treatment plants serviced approximately 350 000 persons in the urban area, 120 000 in the semi-rural area and 1100-2400 on the island. Measurements: Drug residues were analysed using liquid chromatography coupled to a tandem mass spectrometer. Per capita drug consumption was estimated. Changes in drug use were quantified using Hedges' g. Findings: During the holidays, cannabis consumption in the semi-rural area declined (g=-2.8) as did methamphetamine (-0.8), whereas cocaine (+1.5) and ecstasy (+1.6) use increased. In the urban area, consumption of all drugs increased during holidays (cannabis +1.6, cocaine +1.2, ecstasy +0.8 and methamphetamine +0.3). In the vacation area, methamphetamine (+0.7), ecstasy (+0.7) and cocaine (+1.1) use increased, but cannabis (-0.5) use decreased during holiday periods. Conclusions: While the peak holiday season in Australia is perceived as a period of increased drug use, this is not uniform across all drugs and areas. Substantial declines in drug use in the semi-rural area contrasted with substantial increases in urban and vacation areas. Per capita drug consumption in the vacation area was equivalent to that in the urban area, implying that these locations merit particular attention for drug use monitoring and harm minimisation measures.
Lai, F. Y., Thai, P. K., O'Brian, J., Gartner, C., Bruno, R., Kele, B., … Mueller, J. F. (2013). Using quantitative wastewater analysis to measure daily usage of conventional and emerging illicit drugs at an annual music festival. Drug and Alcohol Review, 32(6), 594-602. doi:10.1111/dar.12061, Institutional Repository

Introduction and Aims. Wastewater analysis provides a non-intrusive way of measuring drug use within a population. We used this approach to determine daily use of conventional illicit drugs [cannabis, cocaine, methamphetamine and 3,4-methylenedioxymethamphetamine (MDMA)] and emerging illicit psychostimulants (benzylpiperazine, mephedrone and methylone) in two consecutive years (2010 and 2011) at an annual music festival. Design and Methods. Daily composite wastewater samples, representative of the festival, were collected from the on-site wastewater treatment plant and analysed for drug metabolites. Data over 2 years were compared using Wilcoxon matched-pair test. Data from 2010 festival were compared with data collected at the same time from a nearby urban community using equivalent methods. Results. Conventional illicit drugs were detected in all samples whereas emerging illicit psychostimulants were found only on specific days. The estimated per capita consumption of MDMA, cocaine and cannabis was similar between the two festival years. Statistically significant (P < 0.05; Z = −2.0–2.2) decreases were observed in use of methamphetamine and one emerging illicit psychostimulant (benzyl piperazine). Only consumption of MDMA was elevated at the festival compared with the nearby urban community. Discussion and Conclusions. Rates of substance use at this festival remained relatively consistent over two monitoring years. Compared with the urban community, drug use among festival goers was only elevated for MDMA, confirming its popularity in music settings. Our study demonstrated that wastewater analysis can objectively capture changes in substance use at a music setting without raising major ethical issues. It would potentially allow effective assessments of drug prevention strategies in such settings in the future.
Maurer, M., Scheidegger, A., & Herlyn, A. (2013). Quantifying costs and lengths of urban drainage systems with a simple static sewer infrastructure model. Urban Water Journal, 10(4), 268-280. doi:10.1080/1573062X.2012.731072, Institutional Repository

A generic model is introduced that is capable of quantitatively representing the combined sewer infrastructure of a settlement. It consists of a catchment area module, which calculates the length and size distribution of the needed sewer pipes on the basis of rain, housing densities and area size. These results are fed into the sewer construction costs module in order to estimate the combined sewer costs of the entire catchment area.
The model could be successfully fitted to existing Swiss sewer systems, indicating that it can emulate their principal characteristics. It could also identify fundamental differences in sewer designs in cities with historic roots. The results confirm that there are economies of scale for combined sewer systems in Switzerland. The modelling approach proved to be an effective tool for understanding the factors underlying the cost structure for water network infrastructures.
Plósz, B. G., Benedetti, L., Daigger, G. T., Langford, K. H., Larsen, H. F., Monteith, H., … Vanrolleghem, P. A. (2013). Modelling micro-pollutant fate in wastewater collection and treatment systems: status and challenges. Water Science and Technology, 67(1), 1-15. doi:10.2166/wst.2012.562, Institutional Repository

This paper provides a comprehensive summary on modelling of micro-pollutants' (MPs) fate and transport in wastewater. It indicates the motivations of MP modelling and summarises and illustrates the current status. Finally, some recommendations are provided to improve and diffuse the use of such models. In brief, we conclude that, in order to predict the contaminant removal in centralised treatment works, considering the dramatic improvement in monitoring and detecting MPs in wastewater, more mechanistic approaches should be used to complement conventional, heuristic and other fate models. This is crucial, as regional risk assessments and model-based evaluations of pollution discharge from urban areas can potentially be used by decision makers to evaluate effluent quality regulation, and assess upgrading requirements, in the future.
Scheidegger, A., Scholten, L., Maurer, M., & Reichert, P. (2013). Extension of pipe failure models to consider the absence of data from replaced pipes. Water Research, 47(11), 3696-3705. doi:10.1016/j.watres.2013.04.017, Institutional Repository

Predictions of the expected number of failures of water distribution network pipes are important to develop an optimal management strategy. A number of probabilistic pipe failure models have been proposed in the literature for this purpose. They have to be calibrated on failure records. However, common data management practices mean that replaced pipes are often absent from available data sets. This leads to a 'survival selection bias', as pipes with frequent failures are more likely to be absent from the data.
To address this problem, we propose a formal statistical approach to extend the likelihood function of a pipe failure model by a replacement model. Frequentist maximum likelihood estimation or Bayesian inference can then be applied for parameter estimation. This approach is general and is not limited to a particular failure or replacement model.
We implemented this approach with a Weibull-exponential failure model and a simple constant probability replacement model. Based on this distribution assumptions, we illustrated our concept with two examples. First, we used simulated data to show how replacement causes a 'survival selection bias' and how to successfully correct for it. A second example with real data illustrates how a model can be extended to consider covariables.
Schleiss, M., Rieckermann, J., & Berne, A. (2013). Quantification and modeling of wet-antenna attenuation for commercial microwave links. IEEE Geoscience and Remote Sensing Letters, 10(5), 1195-1199. doi:10.1109/LGRS.2012.2236074, Institutional Repository

Data from a new experimental setup deployed in Dübendorf (Switzerland) are used to quantify and model the magnitude and dynamics of the wet-antenna attenuation (WAA) affecting a 1.85-km commercial microwave link at 38 GHz. The results show that the WAA exhibits the following properties: 1) It is bounded by a maximum value of about 2.3 dB; 2) it increases exponentially toward 2.3 dB during the first 5-20 min of rainfall; and 3) it decreases exponentially as soon as the rain stops. A new dynamic WAA model that reproduces these three features and can be calibrated using solely link measurements is proposed. Its performance is evaluated at different temporal resolutions and compared with other wet-antenna models from the literature. The results show that the dynamic model outperforms all other models and significantly reduces the uncertainty of the retrieved path-averaged rain rates.
Scholten, L., Scheidegger, A., Reichert, P., & Maurer, M. (2013). Combining expert knowledge and local data for improved service life modeling of water supply networks. Environmental Modelling and Software, 42, 1-16. doi:10.1016/j.envsoft.2012.11.013, Institutional Repository

The presented approach aims to overcome the scarce data problem in service life modeling of water networks by combining subjective expert knowledge and local replacement data. A procedure to elicit imprecise quantile estimates of survival functions from experts, considering common cognitive biases, was developed and applied. The individual expert priors of the parameters of the service life distribution are obtained by regression over the stated distribution quantiles and aggregated into a single prior distribution. Furthermore, a likelihood function for the commonly encountered censored and truncated pipe replacement data is formulated. The suitability of the suggested Bayesian approach based on elicitation data from eight experts and real network data is demonstrated. Robust parameter estimates could be derived in data situations where frequentist maximum likelihood estimation is unsatisfactory, and to show how the consideration of imprecision and in-between-variance of experts improves posterior inference.
Sikorska, A. E., Scheidegger, A., Banasik, K., & Rieckermann, J. (2013). Considering rating curve uncertainty in water level predictions. Hydrology and Earth System Sciences, 17(11), 4415-4427. doi:10.5194/hess-17-4415-2013, Institutional Repository

Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also influences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative approach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncertainty analysis is performed within a formal Bayesian framework and the contributions of rating curve versus rainfall-runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its independence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydrometric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) better assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hydrological models.
Blumensaat, F., Staufer, P., Heusch, S., Reußner, F., Schütze, M., Seiffert, S., … Rieckermann, J. (2012). Water quality-based assessment of urban drainage impacts in Europe - where do we stand today?. Water Science and Technology, 66(2), 304-313. doi:10.2166/wst.2012.178, Institutional Repository

Traditionally, design and optimisation of urban drainage systems was mainly driven by cost efficiency, surface flood prevention, and later by emission reduction. More recent procedures explicitly include ecological conditions of the receiving water in the definition of acceptable pollutant discharges via sewer system and treatment plant outlets. An ambient Water Quality based impact Assessment (WQA) principle therefore requires an integrative system optimisation. However, a broad range of mostly national WQA protocols exist across Europe varying in structure and complexity, assessment concept, spatial and temporal scope and handling of uncertainty. This variety inherently implies a considerable risk of subjectivity in the impact assessment with highly variable outcomes. The present review identifies differences and similarities of WQA protocols in use and discusses their strengths and weaknesses through: (i) a systematic comparison of WQA protocols by selected attributes, (ii) a review of real-life cases reported in the literature and expert interviews, and (iii) an illustration of our main findings by applying selected WQA in an instructive example. The review discusses differences in structure and concept, which are mainly identified for simplistic WQA protocols. The application of selected protocols to an example case shows a wide variety of numerical results and conclusive decisions. It is found that existing protocols target different questions within the decision making process, which users should be more aware of. Generally, to make assessments more reliable, further fundamental research is required to fully understand the relationship between stressors and stream ecosystem responses which will make assessments more reliable. Technically, tools suggested in WQA protocols show severe deficiencies and an uncertainty assessment should be mandatory.
Brewer, A. J., Ort, C., Banta-Green, C. J., Berset, J. D., & Field, J. A. (2012). Normalized diurnal and between-day trends in illicit and legal drug loads that account for changes in population. Environmental Science and Technology, 46(15), 8305-8314. doi:10.1021/es202447r, Institutional Repository

Drug concentrations in composite municipal wastewater samples and census-based estimates of population are used to derive daily loads of illicit substances that are indexed to population. However, such estimates do not provide information on the diurnal trends of substance excretion nor can they account for changes in population. To address these limitations, a series of 1 h composites created by sampling wastewater influent at 6 min intervals was collected over four consecutive days at a single wastewater treatment plant. Creatinine (a urinary indicator), caffeine, methamphetamine, benzoylecgonine (BZE), and cocaine were analyzed by liquid chromatography/tandem mass spectrometry (LC-MS/MS). Diurnal trends and between-day trends were substance specific and related to the number of estimated doses and excretory half-life. Normalization to creatinine yielded trends in substances that differed significantly from non-normalized trends by accounting for changes in population within the municipality studied. Increases in normalized substance excretion observed during early morning hours originate from individuals among the resident population of the municipality due to the absence of commuters.
Burkhardt, M., Zuleeg, S., Vonbank, R., Bester, K., Carmeliet, J., Boller, M., & Wangler, T. (2012). Leaching of biocides from Façades under natural weather conditions. Environmental Science and Technology, 46(10), 5497-5503. doi:10.1021/es2040009, Institutional Repository

Biocides are included in organic building façade coatings as protection against biological attack by algae and fungi but have the potential to enter the environment via leaching into runoff from wind driven rain. The following field study correlates wind driven rain to runoff and measured the release of several commonly used organic biocides (terbutryn, Irgarol 1051, diuron, isoproturon, OIT, DCOIT) in organic façade coatings from four coating systems. During one year of exposure of a west oriented model house façade in the Zurich, Switzerland area, an average of 62.7 L/m2, or 6.3% of annual precipitation came off the four façade panels installed as runoff. The ISO method for calculating wind driven rain loads is adapted to predict runoff and can be used in the calculation of emissions in the field. Biocide concentrations tend to be higher in the early lifetime of the coatings and then reach fairly consistent levels later, generally ranging on the order of mg/L or hundreds of μg/L. On the basis of the amount remaining in the film after exposure, the occurrence of transformation products, and the calculated amounts in the leachate, degradation plays a significant role in the overall mass balance.
Derlon, N., Peter-Varbanets, M., Scheidegger, A., Pronk, W., & Morgenroth, E. (2012). Predation influences the structure of biofilm developed on ultrafiltration membranes. Water Research, 46(10), 3323-3333. doi:10.1016/j.watres.2012.03.031, Institutional Repository

This study investigates the impact of predation by eukaryotes on the development of specific biofilm structures in gravity-driven dead-end ultrafiltration systems. Filtration systems were operated under ultra-low pressure conditions (65 mbar) without the control of biofilm formation. Three different levels of predation were evaluated: (1) inhibition of eukaryotic organisms, (2) addition of cultured protozoa (Tetrahymena pyriformis), and (3) no modification of microbial community as a control. The system performance was evaluated based on permeate flux and structures of the biofilm. It was found that predation had a significant influence on both the total amount and also the structure of the biofilm. An open and heterogeneous structure developed in systems with predation whereas a flat, compact, and thick structure that homogeneously covered the membrane surface developed in absence of predation. Permeate flux was correlated with the structure of the biofilm with increased fluxes for smaller membrane coverage. Permeate fluxes in the presence or absence of the predators was 10 and 5 L m−2 h−1, respectively. It was concluded that eukaryotic predation is a key factor influencing the performance of gravity-driven ultrafiltration systems.
Hall, W., Prichard, J., Kirkbride, P., Bruno, R., Thai, P. K., Gartner, C., … Mueller, J. F. (2012). An analysis of ethical issues in using wastewater analysis to monitor illicit drug use. Addiction, 107(10), 1767-1773. doi:10.1111/j.1360-0443.2012.03887.x, Institutional Repository

Aims: To discuss ethical issues that may arise in using WWA to monitor illicit drug use in the general population and in entertainment precincts, prisons, schools and work-places. Method: Review current applications of WWA and identify ethical and social issues that may be raised with current and projected future uses of this method. Results: Wastewater analysis (WWA) of drug residues is a promising method of monitoring illicit drug use that may overcome some limitations of other monitoring methods. When used for monitoring purposes in large populations, WWA does not raise major ethical concerns because individuals are not identified and the prospects of harming residents of catchment areas are remote. When WWA is used in smaller catchment areas (entertainment venues, prisons, schools or work-places) their results could, possibly, indirectly affect the occupants adversely. Researchers will need to take care in reporting their results to reduce media misreporting. Fears about possible use of WWA for mass individual surveillance by drug law enforcement officials are unlikely to be realized, but will need to be addressed because they may affect public support adversely for this type of research. Conclusions: Using wastewater analysis to monitor illicit drug use in large populations does not raise major ethical concerns, but researchers need to minimize possible adverse consequences in studying smaller populations, such as workers, prisoners and students.
Hering, J. G., Hoehn, E., Klinke, A., Maurer, M., Peter, A., Reichert, P., … Wehrli, B. (2012). Moving targets, long-lived infrastructure, and increasing needs for integration and adaptation in water management: an illustration from Switzerland. Environmental Science and Technology, 46(1), 112-118. doi:10.1021/es202189s, Institutional Repository
Hug, T., & Maurer, M. (2012). Stochastic modeling to identify requirements for centralized monitoring of distributed wastewater treatment. Water Science and Technology, 65(6), 1067-1075. doi:10.2166/wst.2012.945, Institutional Repository

Distributed wastewater treatment is increasingly considered as an alternative to the predominantly transport based combination of sewer network and centralized wastewater treatment plant. If substantial amount of wastewater of a particular area is processed in small distributed treatment units, the sum of these units must be considered as one system providing an overall service. This paper focuses on the monitoring for centralized operation of such distributed units. We present a simple stochastic model to calculate probability distributions of process performance. We thereby simulated a fleet of simple treatment units with stochastic failure rates, unreliable sensors and regular routine inspections. All calculated performance indicators exhibit a considerable difference between the true performance and the perceived that is based on sensor observations. The results also show that there is a trade-off between investing into more reliable technology or into more frequent human interventions to achieve a certain performance. The simulations can quantify both effects and thereby support the identification of requirements for centralized monitoring of distributed treatment units. The model approach is generic and can be extended and applied to various distributed wastewater treatment technologies and contexts.
Le Corre, K. S., Ort, C., Kateley, D., Allen, B., Escher, B. I., & Keller, J. (2012). Consumption-based approach for assessing the contribution of hospitals towards the load of pharmaceutical residues in municipal wastewater. Environment International, 45(1), 99-111. doi:10.1016/j.envint.2012.03.008, Institutional Repository

Hospitals are considered as major sources of pharmaceutical residues discharged to municipal wastewater, but recent experimental studies showed that the contribution of hospitals to the loads of selected, quantifiable pharmaceuticals in sewage treatment plant (STP) influents was limited. However such conclusions are made based on the experimental analysis of pharmaceuticals in hospital wastewater which is hindered by a number of factors such as access to suitable sampling sites, difficulties in obtaining representative samples and availability of analytical methods. Therefore, this study explores a refined and extended consumption-based approach to predict the contribution of six selected Australian hospitals to the loads of 589 pharmaceuticals in municipal wastewater. In addition, the possibility that hospital-specific substances are present at levels that may pose a risk for human health was evaluated. For 63 to 84% of the pharmaceuticals investigated, the selected hospitals are not a major point source with individual contributions likely to be less than 15% which is in line with previous experimental studies. In contrast, between 10 and 20% of the pharmaceuticals consumed in the selected hospitals are exclusively used in these hospitals. For these hospital-specific substances, 57 distinct pharmaceuticals may cause concerns for human health as concentrations predicted in hospital effluents are less than 100-fold lower than effect thresholds. However, when concentrations were predicted in the influent of the corresponding STP, only 12 compounds (including the antineoplastic vincristine, the antibiotics tazobactam and piperacillin) remain in concentration close to effect thresholds, but further decrease is expected after removal in STP, dilution in the receiving stream and drinking water treatment. The results of this study suggest that risks of human exposure to the pharmaceuticals exclusively administered in the investigated hospitals are limited and decentralised wastewater treatment at these sites would not have a substantial impact on pharmaceutical loads entering STPs, and finally the environment. Overall, our approach demonstrates a unique opportunity to screen for pharmaceuticals used in hospitals and identifying priority pollutants in hospital wastewater explicitly accounting for site-specific conditions. Being based on consumption and loads discharged by hospitals into municipal wastewater, it is not limited by 1) the big effort to obtain representative samples from sewers, 2) the availability of sensitive chemical analysis or 3) a pre-selection of consumption data (e.g. consumption volume).
Maurer, M., Bufardi, A., Tilley, E., Zurbrügg, C., & Truffer, B. (2012). A compatibility-based procedure designed to generate potential sanitation system alternatives. Journal of Environmental Management, 104(Aug), 51-61. doi:10.1016/j.jenvman.2012.03.023, Institutional Repository

Regarding multi-criteria decision analysis (MCDA), the problem of generating alternatives has not received the attention it deserves. Most research is currently devoted to the problem of alternative selection, where it is assumed that a set of appropriate alternatives is already given. This paper addresses the generation of potential alternatives in the domain of sanitation systems planning and decision-making. A compatibility assessment procedure is proposed to determine the set of technically feasible or potential sanitation system alternatives. This is based on a clear definition of such an alternative containing sub-processes that include a user interface, storage, conveyance treatment and reuse/disposal. A newly developed compatibility matrix is applied to identify incompatibilities between the options of the sub-processes.
A potential sanitation system alternative is therefore defined by the absence of two-by-two incompatibility between all its options.
The compatibility assessment acts as a first filter on the set of sanitation system alternatives to eliminate those that are inoperable before the feasibility assessment. The objective of both steps is to obtain a set of alternatives that are of reasonable and manageable size from which the final solution may be selected.
Prichard, J., Lai, F. Y., Kirkbride, P., Bruno, R., Ort, C., Carter, S., … Mueller, J. F. (2012). Measuring drug use patterns in Queensland through wastewater analysis. Trends & Issues in Crime and Criminal Justice, 442, 1-8. Retrieved from https://www.aic.gov.au/publications/tandi/tandi442, Institutional Repository