Department Urban Water Management

Climate change is increasing the frequency of extreme rainfall events while reducing baseline river flows across Switzerland. At the same time, ongoing urbanization is adding further pressure on wastewater infrastructure. Together, these trends are expected to amplify the impacts of urban wastewater system discharges — from combined sewer overflows as well as from WWTP effluent — on the aquatic environment, threatening both environmental quality and public health.

Meeting this challenge does not necessarily require costly upgrades to centralized treatment and network capacity. Existing wastewater infrastructure holds unused operational potential that, if better understood and exploited, could help counteract part of this growing pressure — offering a more sustainable alternative to expanding grey infrastructure. Realizing this potential requires a better understanding of the spatio-temporal dynamics of flow and pollution within the sewer network — where pollution and water originate, how they vary over time and how these dynamics differ between catchments.

One largely unexplored data source for improving this understanding is mobile positioning data, which captures the dynamic distribution of population within a catchment and offers a new way to link human activity to wastewater generation and pollution loads. In parallel, combining high-frequency flow time-series with rainfall, groundwater, and geospatial data can improve our understanding of groundwater infiltration and rainfall-derived inflow and infiltration. These processes remain difficult to quantify and predict, limiting the transferability of insights and management strategies between wastewater systems.

The EMPOWER-DD project addresses these gaps by developing data-driven tools and strategies for smarter, and more resilient wastewater management. It will improve our ability to characterize, predict, and manage flow and pollution dynamics across wastewater systems by exploring approaches such as pollution-based real-time control, decentralized wastewater system design, large-sample analysis and transferable inflow forecasting.

• Improve prediction of wastewater generation and pollution using real-time mobile positioning data.
• Characterize and predict non-sanitary flows across multiple wastewater systems using large-sample data analysis and machine learning.
• Develop integrated models and monitoring frameworks for system-wide flow and pollution assessment.
• Explore smart real-time control and waste design strategies to optimize wastewater system management.

Project 1: Predicting Wastewater Pollution with Mobile Phone Data

Pollution dynamics in a sewer system depend on when and where people move through a city and generate wastewater. This project explores the use of mobile positioning data (MPD) as a new data source for quantifying and modeling pollution in urban sewer networks. By analyzing anonymized, high-resolution mobility data from mobile phone networks, the project will investigate how population dynamics relate to wastewater flows and pollution loads and how they can be exploited to inform monitoring and control. These tools will ultimately enable more responsive, efficient wastewater management, especially under rapidly changing conditions caused by urbanization and climate change.

Project 2: Characterizing and Predicting Non-Sanitary Flows in Urban Sewer Systems Using Big Data

Non-sanitary wastewater flows, particularly stormwater and infiltration, reduce treatment efficiency and contribute to combined sewer overflows, aggravating environmental impacts. However, these extraneous components remain difficult to quantify and predict. This project will combine high-frequency influent measurements from Swiss wastewater treatment plants with rainfall, groundwater and geospatial data. Large-sample analyses will be used to identify the main environmental and catchment drivers on groundwater infiltration as well as rainfall derived inflow and infiltration. The project will also develop transferable short-term inflow forecasting models and methods to reconstruct high-frequency pollutant dynamics during rainfall events. By integrating data from multiple wastewater systems, the project aims to contribute to the development of large-sample, data-driven approaches in urban water management.

Project 3: Developing Smart Management Options for Integrated Wastewater Systems

Sewers and wastewater treatment plants are usually operated and optimized separately, even though they are highly interconnected. This part of the project investigates how they can be managed as an integrated system. Building on improved predictions of wastewater flows and pollution using mobile positioning data and stormwater modelling, it explores complementary strategies: using better influent forecast to fine-tune treatment plant operation during dry weather and coordinating pollution-based real-time control across sewers and treatment plants during storms. An integrated sewer-WWTP model will allow testing decentralized "waste design" measures, such as temporarily storing wastewater upstream, to smooth out pollution peaks before they reach the treatment plant and evaluating the impact on the receiving waters and greenhouse gas emissions. Together, these approaches aim to show how existing infrastructure can be operated more effectively as one connected system, reducing pollution discharges to rivers while limiting new costly construction.