A computational platform for integrated modelling of micropollution in the urban environment
Anthropogenic chemical contaminants are found routinely in our water resources. Although most of these compounds are usually present at low concentrations, they are of concern due to their ecotoxicity, antibacterial resistance and potential harm to human health. Identification of the temporal dynamics of these substances is a key step towards better remediation strategies. This information is also necessary for ecotoxicologists, as the risk of a substance is largely governed by its temporal dynamics. The main goal of this work was to introduce a new computational framework to assess the dynamics of micropollutants at high temporal resolution (typically 10 min), in both wastewater and urban streams. The software combines two independent modules. One module is a hydrological transport model that is computational efficient for complex urban catchments, which can include both sewer networks and urban rivers. The second is a source pollution model. The latter is a user-defined highly flexible module that can be adapted to the properties of the substances considered. A degradation function was included to account for sorption and biogeochemical transformations. The model was calibrated and used to predict concentrations of different classes of micropollutants in Lausanne, Switzerland. It achieved good performance in (i) modelling biocide concentration in an urban stream, (ii) modelling biocide concentrations wastewater and, (iii) modelling antibiotic concentrations in wastewaters. Recent work has extended the model to simulation of illicit products such THC and heroin. The proposed computational framework was designed to be easily adaptable to other pollution sources and geographical contexts. It provides a scientifically consistent and practical water quality/quantity modelling tool that can be used in a systematic fashion in diverse urban environments.
Abstract_Software DAB.doc
openaccess
33.5 KB
Microsoft Word
b08fab955cbbce5b29b0703943d53619