Testing a combined monitoring-modelling approach for quantifying micropollutant loads from separate stormwater systems

Reducing pollutant emissions from separate stormwater systems require a solid data background on the pollutant occurrence and the released fluxes at the catchment scale. However, stormwater quality at a single discharge point is characterized by a high inherent variability, driven by the intensity of the rain, and by the high variability of pollutant sources in the upstream catchment (for a review, see the overview in Müller et al., 2019).

Stormwater carries a wide range of micropollutants (e.g. heavy metals, PAH, etc.) which can negatively affect the chemical and environmental status of the receiving water body. Measuring these substances with traditional sampling approaches (e.g. autosamplers) and collecting a sufficient number of data to represent their variability in stormwater discharge, is a challenging task both financially and logistically.

Birch et al. (2013a) presented a novel monitoring approach, combining (a) high-time resolution data (from online sensors monitoring traditional indicators such as flow and turbidity), (b) passive chemical sampler (monitoring micropollutants) and (c) dynamic mathematical models. This approach has now been extended to two pilot sites, one located in Copenhagen (Denmark, 2019/2020) and one in Venice (Italy, 2020/2021), The aim of this study is to test an improved version of the passive samplers and combine the results with sensor data, to quantify micropollutant emissions from separate systems.