Combined sewer overflows (CSOs) can release toxic organic chemicals into surface waters during rain events. Currently, most overflow sites are not monitored because commonly used methods, such as automated grab sampling followed by laboratory analysis using liquid chromatography coupled with mass spectroscopy (LC-MS), are costly and time-consuming. Due to this monitoring gap, the dynamics of organic chemicals in CSOs remain poorly understood. This study explores the use of eight online sensor parameters as proxies for polar organic chemicals from different sources in combined sewer systems during wet weather. We used sensor and organic chemical data collected in three urban catchments of varying sizes. Correlations between chemicals from the same source and sensor parameters were calculated. In the largest catchment (160,000 inhabitants), indoor chemicals are strongly correlated with flow, electrical conductivity, spectral absorption coefficient at 254 nm (SAC_{254 nm}), and ammonium (NH₄-N). Additionally, linear regressions were developed to predict organic chemical concentrations from sensor data. Models based on SAC_{254 nm} and NH₄-N predict indoor chemical concentrations with median relative errors of 32% and 29%, respectively, in the large catchment. Prediction performance for road chemicals is independent of catchment size, with median relative errors ranging from 39% to 44%, using either level or flow measurements. However, the prediction of pesticide concentrations remains limited, as these chemicals exhibit diverse patterns across rain events. Overall, our results suggest that linear regression models can estimate indoor chemical concentrations in large catchments and road chemical concentrations in catchments of any size. However, for real-world implementation, further research is needed to refine calibration requirements and validate the models across diverse catchments. Nevertheless, these models are promising for cost-effective, long-term monitoring of organic chemicals and for mitigating the impact of CSO discharges.

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.