Stochastic modelling of trace contaminants in wet-weather discharges

Urban wet-weather discharges (combined sewer overflows, CSO, and stormwater outlets from separate sewers, SWO) contain various trace contaminants which can pose a threat to receiving waters (e.g. Launay et al., 2016; Mutzner et al., 2020; Wicke et al., 2021b). The deterministic model prediction of trace contaminants loads and concentrations in wet-weather discharges is challenging due to the inherent high spatiotemporal variability (e.g.

Mutzner et al., 2020; Rippy et al., 2017; Wicke et al., 2021a). The observed high spatiotemporal variability is attributed to locally varying factors such as substance use behaviors, land use, and rainfall intensities, however to date no reliable correlation based on such local factors could be identified.

Stochastic model predictions based on available data have been previously used successfully to predict TSS concentration in wet-weather discharges (Rossi et al., 2005). In this study, we aim to predict the loads and concentrations of selected trace contaminants based on a large field monitoring data collection (> 60 sites, (Mutzner et al., in prep.) and integrate this information in a stochastic model.

The results will be directly useful for regulators and utilities as a first predictor of the influence of trace contaminants in urban wet-weather discharges on receiving waters.