Prioritizing risk mitigation measures for binary heavy metal contamination emergencies at the watershed scale using...

Area of Science:

• Environmental Science
• Risk Assessment
• Water Resource Management

Background:

• Water pollution accidents, particularly heavy metal contamination, pose significant threats to ecosystems and human health.
• China has experienced severe heavy metal pollution events, highlighting the need for effective emergency decision-making.
• Existing risk mitigation strategies lack quantitative prioritization methods for complex pollution events.

Purpose of the Study:

• To develop a quantitative probabilistic model for prioritizing risk mitigation strategies in heavy metal pollution emergencies.
• To apply the model to a real-world case of chromium and mercury contamination in the Danshui River watershed.
• To provide a framework for optimizing emergency response and policy-making in watershed risk management.

Main Methods:

• A Bayesian Decision Network (BDN)-based probabilistic model was developed within the Drivers-Pressures-States-Impacts-Responses (DPSIR) framework.
• A Copula-based exposure risk model was integrated to simulate heavy metal ion fate and joint probability distributions.
• The model was applied to prioritize emergency response options for acute Cr(VI)-Hg(II) contamination.

Main Results:

• The comprehensive measure (M5) was identified as the most effective strategy for reducing ecological and human health risks.
• Risk source prevention (M1) emerged as the superior single mitigation strategy compared to exposure pathway interruption (M2) and receptor protection (M3-M4).
• The probabilistic method successfully addressed uncertainties and optimized risk management under the DPSIR framework.

Conclusions:

• The proposed probabilistic model provides a robust approach for quantitatively prioritizing heavy metal pollution mitigation strategies.
• The findings offer valuable insights for watershed-scale risk management, policy-making, and emergency response planning.
• Effective risk management requires a combination of strategies, with a strong emphasis on preventing pollution at its source.