Department Surface Waters - Research and Management

Waterborne pathogens pose a serious threat to public health, emphasizing the need for reliable and efficient water treatment technologies. Wastewater treatment plants employ a range of processes to reduce microbial contamination, with membrane filtration emerging as a promising solution due to its ability to physically remove pathogens without the production of harmful chemical by-products. This study investigates the effectiveness of a gravity-driven membrane (GDM) filtration system for pathogen removal from wastewater and evaluates the associated public health risks with and without treatment. A quantitative microbial risk assessment model was employed to estimate infection probabilities for various waterborne pathogens. The results demonstrated a significant decrease in pathogen concentrations following treatment, with up to a 10⁴-fold reduction in norovirus infection risk. Three critical factors influencing membrane performance were identified: membrane integrity, pore size characteristics, and membrane fouling. Maintaining membrane integrity was found to be essential for ensuring consistent pathogen removal. While nominal pore size is commonly used to predict rejection efficiency, the overall pore size distribution was found to have a greater influence on virus retention. Additionally, although membrane fouling is often considered detrimental, it was shown to enhance virus removal by up to two orders of magnitude. These findings underscore the potential of GDM systems for effective virus removal and highlight the importance of proper membrane design, maintenance, and monitoring in ensuring long-term operational efficiency and maximizing public health protection in wastewater treatment applications.

Harmful algal blooms (HABs) caused by the dinoflagellate Alexandrium minutum can pose risks to human and ecosystem health. HABs of different species can coexist in coastal waters and accumulate near the shoreline, challenging their detection through Earth observation (EO). In this study, we use in situ geo-bio-optical and taxonomical data from the Rías Baixas (NW Spain) to develop a new method for identifying high-concentration blooms of A. minutum and its application to Sentinel-2 Multispectral Instrument (S2 MSI). Our approach named A. minutum index (AMI) was developed to capture the low absorption and high backscattering properties of A. minutum cells between 560 and 570 nm. We tested and validated the performance of three atmospheric correction algorithms (AC) (C2RCC, POLYMER and ACOLITE) using matchups between in situ and satellite-derived R_rs. Results show that C2RCC had the lowest error across most wavelengths. Applying AMI to S2 MSI indicates that our approach can accurately identify high-concentration blooms of A. minutum (F1 score: 70 %, Kappa: 68.3 %, balanced accuracy: 87.7 %, MCC: 68.3 %) and discriminate blooms of A. minutum from other phytoplankton species. We compared AMI with three existing indices for detecting HABs in coastal waters and found that our approach achieved a better performance, with the NDTI, RGCI and NDCI yielding F1 scores of 21.28, 21.74, and 0.0 % and MCC values of 15.0, 15.0 and 0.0 %, respectively. We also investigated the spatial resolution of S2 MSI to Sentinel-3 Ocean and Land Colour Instrument (S3 OLCI) for mapping fine-scale variations in A. minutum blooms. We found that the higher spatial resolution data from S2 MSI were highly useful for detecting small-scale variations in A. minutum in nearshore waters, enabling their detection in the mid-inner part of the Rías, where aquaculture activities are more prominent. This study also showcases the significance of accurate AC in near-shore waters, where high-concentration blooms can be more prevalent. Our findings show that greater errors in AC are observed in near-shore pixels, where the socio-economic and environmental impact of HABs are typically more severe.

Forest ecosystems are vital for biodiversity, climate regulation, and ecosystem services. Their resilience depends not only on species diversity but also on intraspecific variation—the genetic and phenotypic differences within species—which underpins adaptive capacity to environmental change. However, large-scale, continuous monitoring of intraspecific variation remains challenging. Here, we present a remote sensing approach using Sentinel-2 time series of five vegetation indices as proxies for pigment content, canopy structure, and water content to detect intraspecific variation in seven tree species across a broad environmental gradient in Switzerland. Using pure-species plot data from the Swiss National Forest Inventory, we decomposed variation into spatial, temporal, and spatiotemporal components. We found that spatial variation dominated in evergreen species (48–86%), while temporal variation was more pronounced in deciduous species (56–82%), reflecting their stronger seasonality. These findings demonstrate that species-specific Sentinel-2 time series can effectively track intraspecific variation, providing a scalable method for forest monitoring. This approach opens new pathways for studying forest adaptation, informing management strategies, and guiding species selection for conservation under changing climate conditions.