Neuenhofer Nicolas, Disch Andy, Baumgartner Stephan, Ort Christoph, Rieckermann Jörg
Department of Urban Water Management, Eawag, Swiss Federal Institute of Aquatic Science and Technology, 8600 Dübendorf, Switzerland.
Institute of Civil, Environmental and Geomatic Engineering, ETH Zürich, 8093 Zurich, Switzerland.
Water Sci Technol. 2025 Sep;92(5):669-682. doi: 10.2166/wst.2025.119. Epub 2025 Aug 7.
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 ( 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.
城市排水系统的排放可能会对人类和环境健康产生不良影响。不幸的是，传统的下水道水质监测成本高昂，且不够全面，无法提供整个集水区污染的详细数据。然而，随着社会数字化程度的不断提高，移动定位数据等替代数据源为评估废水产量和动态提供了新的机会。在本研究中，我们利用瑞士最大电信提供商的数据和简单多元线性回归模型，调查了瑞士五个具有不同特征和规模的集水区中移动定位数据与废水流量之间的关系。尽管本研究的初步结果很有前景，但移动定位数据与废水产量之间的相关程度相当低（介于0至0.73之间），且各集水区之间差异很大。正如预期的那样，我们在数据中发现了非线性效应，这表明需要采用包含流量距离和动态行程时间等因素的先进模型才能进行可靠的预测。此外，数据隐私问题限制了其在小集水区的使用，凸显了进行特定领域预处理的必要性。这种方法在城市排水、废水处理、饮用水、流行病学和气候适应方面具有潜力。
