蒙特卡罗方法
中国
环境科学
构造盆地
重金属
流域
风险评估
健康风险
水文学(农业)
水资源管理
地理
地质学
环境卫生
环境化学
统计
化学
地貌学
考古
医学
地图学
数学
计算机科学
计算机安全
岩土工程
作者
Wanjun Zhang,Cunlin Xin,Wenyue Du,Shi Yu
标识
DOI:10.1016/j.ecolind.2025.113620
摘要
This study investigates the hydrochemical characteristics of surface water in the Lijiang River Basin with complex pollution sources, focusing on tracing the origins and health risks of heavy metals and hydrochemical components. Systematic analysis of 120 surface water samples revealed that the water chemistry predominantly follows the HCO3-Ca·Mg type, demonstrating the dominant influence of carbonate rock weathering in the basin. Source analysis using the Positive Matrix Factorization (PMF) model indicated dual contamination drivers from natural geological processes and anthropogenic activities, with industrial and agricultural inputs being the most significant contributors. The Comprehensive Environmental Water Quality Index (CEWQI) ranged from 36.14 to 79.62, suggesting that the overall water quality in the study area is good. High-quality water is generally located in the upper and middle reaches of the basin, while the downstream water quality is comparatively poorer. Major pollutants identified include Cd, Fe, Mn, Cu, Al, Hg, and NO3−, based on entropy weight coefficients and standard limits. Monte Carlo risk assessments indicate that Al poses non-carcinogenic risks to both adults and children, whereas Cd presents non-carcinogenic risks specifically to children and significant carcinogenic risks to all populations. Source analysis demonstrated that non-carcinogenic risks predominantly originate from agricultural non-point source pollution, whereas carcinogenic risks correlate closely with endogenous sediment release. These findings provide scientific foundations for targeted water pollution management in river basins, strengthening agricultural surface source control and sediment management is important for safeguarding the health of the population and promoting sustainable regional development.
科研通智能强力驱动
Strongly Powered by AbleSci AI