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, metal mixed pollution accidents comprised approximately 33% the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed quantitatively predict human health for at watershed scale. To estimate probability distributions joint exposure once a accident occurs, Copula-based calculation method, hydro-dynamic model, emergent transport Copula functions, embedded. This was applied risk assessment acute Cr6+-Hg2+ 76 electroplating 24 sub-watersheds Dongjiang River downstream watershed. results indicated that nine created high risks, while only five risks. In addition, levels were highest tributary (the Xizhijiang River), more critical river network, serious mainstream River. quantitative provides substantial support incident prevention control, management, as well regulatory decision making enterprises.