环境科学
概率逻辑
毒物
人类健康
风险评估
地下水
地下水污染
风险分析(工程)
环境资源管理
计算机科学
污染
健康风险
水资源管理
环境规划
多元统计
环境卫生
健康风险评估
多元插值
环境监测
不确定度分析
蒙特卡罗方法
污染
环境保护
概率风险评估
污染物
概念模型
环境工程
生态系统服务
作者
Futian Ren,Peng Wang,Zhao Guo,Danbing Mei,Jiaming Wang,Zenghui Li,Lei Huang
标识
DOI:10.1021/acs.est.5c14888
摘要
"One toxicant at a time" regulations overlook the joint risks posed by realistic coexposure to complex toxicant mixtures in groundwater. Here, 1,016,590 quarterly concentration records for 277 toxicants from 367 monitoring points were integrated into an adaptive zonation and classification framework for mixture-oriented risk management. Ecological mixture risk was quantified with species-sensitivity-distribution models combined in a tiered concentration addition → response addition (CA → RA) scheme, while Monte Carlo simulations captured probabilistic carcinogenic and noncarcinogenic human health risks. These dimensions were merged into an environmental health risk index (EHRI; 0.42 ecology: 0.58 health). Quadrant analysis identified four coexposure patterns and showed that ≤10% of detected toxicants─chiefly inorganics, metals, BTEXs, haloalkanes, and phenols─drive >95% of cumulative EHRI. Spatial interpolation converted point-level EHRIs to grid-scale and, when embedded in a "source-plume-buffer" treatment train, reduced the great concern zone (EHRI > 0.60) from 19 km2 to <0.1 km2 after removal of 45 highest-leverage toxicants. The framework unifies deterministic and probabilistic perspectives, pinpoints priority toxicants and zones, and supplies tier-graded actionable triggers for adaptive intervention. This study provides the first field-scale evidence that mixture-aware risk assessment can be directly coupled to risk-zoned management, offering a transferable blueprint for the sustainable regulation of complex, multipollutant groundwater systems worldwide and a framework potentially relevant to other management zones such as surface water.
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