Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks

The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005-2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr6+-Hg2+ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises. (C) 2020 Elsevier Ltd. All rights reserved.Peer reviewe