Modelling heavy metals build-up on urban road surfaces for effective stormwater reuse strategy implementation

Abstract

Urban road stormwater is an alternative water resource to mitigate water shortage issues in the worldwide. Heavy metals deposited (build-up) on urban road surface can enter road stormwater runoff, undermining stormwater reuse safety. As heavy metal build-up loads perform high variabilities in terms of spatial distribution and is strongly influenced by surrounding land uses, it is essential to develop an approach to identify hot-spots where stormwater runoff could include high heavy metal concentrations and hence cannot be reused if it is not properly treated. This study developed a robust modelling approach to estimating heavy metal build-up loads on urban roads using land use fractions (representing percentages of land uses within a given area) by an artificial neural network (ANN) model technique. Based on the modelling results, a series of heavy metal load spatial distribution maps and a comprehensive ecological risk map were generated. These maps provided a visualization platform to identify priority areas where the stormwater can be safely reused. Additionally, these maps can be utilized as an urban land use planning tool in the context of effective stormwater reuse strategy implementation.

Keywords: Artificial neural networks; Ecological risk; Heavy metal; Road stormwater runoff; Spatial distribution; Stormwater reuse.