Projects
Modeling and Planning Blue-Green Infrastructure under Climate Change
This project seeks to safeguard one of Switzerland's most-valuable natural resources: clean, fresh water. As a safety mechanism to prevent flooding, urban sewer systems were designed to occasionally release polluted wastewater to rivers, lakes, and streams during infrequent, heavy rainstorms. However, both urban expansion and climate change increase how much water enter the sewers. Therefore, these spills will occur more frequently than intended. A solution to this could be blue-green infrastructure, which acts like a sponge for the city by absorbing rainwater before it gets to the sewer. We will investigate how implementing blue-green infrastructure can limit these spills and protect freshwater in the Limmattal and cities throughout Switzerland.
Improving Spatio-Temporal Monitoring and Modelling of Wastewater Pollution
Every time we use the toilet, shower, or do the dishes, we add pollutants to the sewer. How much ends up there, and where and when, depends on where people are throughout the day. In this project, we use anonymized mobile phone data to track how many people are present in different parts of a catchment, hour by hour. Combined with a sewer network model, this lets us estimate how pollutant loads vary across the network and over time. If successful, near-real-time population data could feed directly into the control of sewer systems, for example by deciding where to store wastewater during storms so that the most polluted water stays out of rivers and lakes in the Limmattal and beyond.
Unterstanding and Predicting Wastewater Flows during Rainfall
When it rains, large amounts of rainwater and groundwater can enter sewer systems, increasing the flow to wastewater treatment plants. These changes can make wastewater treatment more challenging and increase the risk of polluted water reaching rivers and streams. In this project, we use measurements from wastewater treatment plants across Switzerland, including the Limmattal treatment plant, to better understand and predict how wastewater flows and pollution levels change over time. By combining large datasets with machine learning, we aim to develop models that can support more efficient and resilient wastewater management.