Emissions from urban drainage systems can have unwanted consequences for human and environmental health. Unfortunately, traditional water quality monitoring in sewers is expensive and not comprehensive enough to provide detailed data on pollution across an entire catchment. However, with the increasing digitization of society, alternative data sources such as mobile positioning data offer new opportunities to assess wastewater production and dynamics. In this study, we investigate the relation between mobile positioning data and wastewater flows in five catchments in Switzerland with different characteristics and sizes, using data from the largest Swiss telecom provider and simple multiple linear regression models. The initial results of this study are promising although the degree of correlation observed between mobile positioning data and wastewater production is rather low (R² between 0 and 0.73) and varies greatly from catchment to catchment. As expected, we find nonlinear effects in the data that indicate that advanced models - incorporating factors like flow distances and dynamic travel times - are needed for reliable predictions. Also, data privacy issues limit its use in small catchments, highlighting the need for domain-specific preprocessing. This approach holds potential for urban drainage, wastewater treatment, drinking water, epidemiology, and climate adaptation.

A major challenge in wastewater and sewer system monitoring is the development of advanced sensing technologies to improve standard pollutant measurement and allow real-time online detection of emerging contaminants. This study presents a dataset from a 25-week measurement campaign comparing a novel hyperspectral imaging system to state-of-the-art ultraviolet-visible (UV-vis) sensors. The dataset includes 5801 hyperspectral images of raw wastewater, measurements of temperature, ammonium, flow, turbidity, pH, and UV-vis absorbance spectra, as well as 533 grab samples analyzed for conventional pollutants. We also gathered 86 samples after four rain events and analyzed them for twenty organic chemicals, providing insights into the impact of wet weather on pollutant levels. The data collection and processing methodologies are detailed, along with visualizations and analysis. Despite difficulties in the maintenance of some sensors, in particular the ion-selective electrode for ammonium measurement, the dataset’s high temporal resolution, the time span of 25 weeks, and the extensive range of analyzed pollutants make it a valuable resource for advancing the field of urban water management.