Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using bayesian decision networks

Jing Liu; Renzhi Liu; Zhifeng Yang; Lixiao Zhang; Sakari Kuikka

doi:10.1016/j.jenvman.2021.113640

Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using bayesian decision networks

J Environ Manage. 2021 Dec 1:299:113640. doi: 10.1016/j.jenvman.2021.113640. Epub 2021 Aug 31.

Authors

Jing Liu¹, Renzhi Liu², Zhifeng Yang³, Lixiao Zhang⁴, Sakari Kuikka⁵

Affiliations

¹ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China; Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People's Republic of China, Xuanwu District, Nanjing, China. Electronic address: 201731180008@mail.bnu.edu.cn.
² State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China. Electronic address: liurenzhi@bnu.edu.cn.
³ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China. Electronic address: zfyang@bnu.edu.cn.
⁴ State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, No. 19, Xinjiekouwai Street, Haidian District, Beijing, 100875, China. Electronic address: zhanglixiao@bnu.edu.cn.
⁵ Fisheries and Environmental Management Group (FEM), Ecosystems and Environment Research Programme, Faculty of Biological and Environmental Sciences, P.O Box 65, Viikinkaari 1, FI-00014 University of Helsinki, Finland. Electronic address: sakari.kuikka@helsinki.fi.

PMID: 34479155
DOI: 10.1016/j.jenvman.2021.113640

Abstract

Water pollution accidents have the characteristics of high uncertainty, rapid evolution and are difficult to control, thus posing great threats to human health, ecological security, and social stability. During the last 10 years, China has faced the occurrence of six extraordinarily serious heavy metal contamination pollution events at the watershed scale. This has alerted governments and enterprises of the significance of emergency decision-making. To quantitatively prioritize risk mitigation strategies for heavy metal emergencies, a Bayesian Decision Network-based probabilistic model is proposed under the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework. A Copula-based exposure risk model is embedded to simulate the fate of heavy metal ions for each risk reduction option, whose joint probability distributions can then be used as input parameters in the Bayesian Decision Network. This method was applied to the emergency response prioritization for acute Cr(VI)-Hg(II) contamination accidents in the Danshui River watershed. The results indicated that comprehensive measure (M5) was the best option for decreasing ecological and human health risks. As for a single risk mitigation strategy, risk source prevention (M1) was the best alternative compared to exposure pathway interruption (M2) and human/ecological receptor protection (M3-M4). This probabilistic method can not only address the uncertainties between certain risk sources and receptors in the BDN structure, but also realize the risk system optimization in a satisfactory/preferred mode under the DPSIR framework. Overall, it provides the probabilistic risk estimates for watershed-scale risk management and policy making for local risk managers and stakeholders.

Keywords: Acute Cr(VI)–Hg(II) contamination emergencies; Bayesian decision network; Copula-based exposure risk model; DPSIR; Risk mitigation measures.

MeSH terms

Bayes Theorem
China
Emergencies
Environmental Monitoring
Humans
Metals, Heavy* / analysis
Risk Assessment
Rivers
Water Pollutants, Chemical* / analysis
Water Pollution

Substances

Metals, Heavy
Water Pollutants, Chemical