Decentralized urban runoff management requires detailed information about pollutant sources and pathways. However, scarce data of local water quality compel simplified approaches in water quality modelling. This study investigated the use of constant source concentrations in modelling pollutant loads. The source area contributions of total suspended solids, total phosphorus, total nitrogen, lead, copper and zinc were modelled with SWMM based on literature event mean concentrations (EMCs) for different land cover types and on-site rainfall and discharge data for a residential area in southern Finland. The simulated pollutant loads were compared with loads measured at the catchment outlet. Large differences were evident in the modelled catchment-scale and land cover specific loads, depending on the EMC data source. The simulated loads exceeded the measured loads especially during wet conditions, which was explained by the dilution effect of large stormwater volumes on measured EMCs. In addition, the mismatch was explained by the lack of local data for the source area EMCs and by the unaccountability of the mechanisms affecting loads along the pollutant pathways from source areas to sewer outlet. The spatial simulation of stormwater pollutant loads enabled the assessment of source area contributions at the catchment scale, as well as the pollutant pathways and the total diffuse pollution load. For a single pollutant, one or two important pollutant sources contributed the majority of the catchment load, which provides useful information for stormwater management. However, for a group of pollutants, no single land cover type dominated the pollutant loads, reflecting the challenges in decentralized water quality management in the scale of a residential area. Overall, the results emphasize that the widely used stormwater quality modelling with constant EMCs is uncertain even when on-site water quality and rainfall-runoff data from a catchment outlet are available.
01/09/2015 → 31/12/2018
Projekti: Business Finland: Other research funding