Influence of urbanisation characteristics on the variability of particle-bound heavy metals build-up: A comparative study between China and Australia

Abstract

Heavy metal pollution of urban stormwater poses potential risks to human and ecosystem health. The design of reliable pollution mitigation strategies requires reliable stormwater modelling approaches. Current modelling practices do not consider the influence of urbanisation characteristics on stormwater quality. This could undermine the accuracy of stormwater quality modelling results. This research study used a database consisting of over 1000 datasets to compare the characteristics of heavy metal build-up (one of the most important stormwater pollutant processes) on urban surfaces under the influence of anthropogenic and natural factors specific to different urban regions from China (Shenzhen) and Australia (Gold Coast), using Bayesian Networks. The outcomes show that the differences in heavy metals build-up loads between the two regions (mean value for Shenzhen – mean value for Gold Coast)/mean value for Shenzhen) were 0.45 (Al), 0.88 (Cr), 0.99 (Mn), 0.68 (Fe), 0.98 (Ni), 0.24 (Cu), 0.47 (Zn) and 0.13 (Pb), respectively. The research outcomes also confirmed that the influence of traffic on the build-up of different sized particles varies between Shenzhen and Gold Coast, and traffic plays distinct roles as a source and as a factor that drives heavy metal re-distribution. The road surface roughness was also found to influence build-up process differently between the two regions. More importantly, the assessment of inherent process uncertainty revealed that heavy metal build-up between different road sites in Shenzhen varies over a wider range than in Gold Coast. The study highlighted a clear distinction in the influence of sources and key anthropogenic factors on the variability of particle-bound heavy metals build-up between geographically different urban regions. The study outcomes provide new knowledge to enhance the accuracy of urban stormwater quality modelling.