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使用机器学习来预测重金属度和中国主要城市饮用水分配系统中诱导的健康风险

Hui Zhang¹, Tao Wan², Xuechun Li³

¹School of Environmental and Municipal Engineering, Xi'an University of Architecture and Technology, Xi'an 710055, China.

|December 21, 2025

概括

这项研究监测了中国饮用水配送系统 (DWDS) 中的重金属,发现风险在可接受水平内. 机器学习准确地预测了金属度,其中酸盐被确定为关键影响因素.

科学领域:

环境科学环境科学
公共卫生公共卫生
分析水质分析水质

背景情况:

饮用水配送系统 (DWDS) 中的残留重金属构成了重大的公共卫生挑战.
确保饮用水安全需要监测和预测重金属污染.
关于中国各大城市重金属含量和相关风险的数据有限.

研究的目的:

在31个中国城市的DWDS中调查重金属度 (Mn,Al,Zn,Cd,Pb,As).
开发和比较用于预测重金属含量的机器学习模型.
评估与重金属暴露相关的健康风险,并确定关键影响因素.

主要方法:

从中国31个主要城市 (2019-2024) 收集了水样和质量数据.
采用和优化随机森林 (RF),XGBoost和自适应提升 (ADB) 进行重金属预测.
使用USEPA方法和蒙特卡洛模拟进行健康风险评估;使用SHAP和PDP进行特征分析.

主要成果:

XGBoost和RF模型显示出高预测精度 (XGBoost的R2>0.80,RF的R2>0.55).
酸盐度被确定为Mn,Zn,Cd和As水平的主要驱动因素.
非致癌和致癌风险都在可接受的范围内,尽管儿童面临更高的非致癌风险,成人面临更高的致癌风险.

结论:

机器学习模型,特别是XGBoost和RF,是预测DWDS中重金属污染的有效工具.
酸盐水平是影响饮用水中重金属存在的关键因素.
虽然整体风险是可以接受的,但针对儿童和成人等弱势群体可能需要有针对性的干预措施.

关键词:

饮用水分配系统的饮用水分配系统.评估健康风险评估健康风险这是一种重金属,重金属.机器学习模型机器学习模型蒙特卡罗的蒙特卡罗是一个非常好的城市.

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