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人工灌调节低地农业池的重金属动态和风险

Shan Luo¹, Haijian Bing², Jiacong Huang³

¹State Key Laboratory of Lake and Watershed Science for Water Security, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 211135, China; University of Chinese Academy of Sciences, Beijing 100049, China; College of Water Resources Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China.

Journal of hazardous materials

|October 23, 2025

概括

一个新的机器学习框架为水生生态系统中的重金属 (HM) 提供了动态的,高分辨率的风险评估. 它确定人工灌是,和的关键驱动因素,改善了环境和公共卫生管理.

科学领域:

环境科学环境科学
评估水质水质的评估.
计算毒理学计算毒理学

背景情况:

重金属对水生生态系统和公共健康构成重大风险.
传统的监测方法缺乏有效的HM风险评估的解决方案和动态能力.
了解HM动态和环境因素之间的因果关系对于管理至关重要.

研究的目的:

开发和验证用于高分辨率,动态重金属度和风险评估的机器学习框架.
用SHAP分析量化环境因素对HM动态的影响.
为改善水资源管理的机制理解和实际的HM风险控制提供一个工具.

主要方法:

开发一个机器学习框架,集成沙普利增量解释 (SHAP) 分析.
应用随机森林算法来建模每日HM度和风险.
对环境因素影响的分析,包括值和相互作用模式.

主要成果:

该框架在预测日常HM动态方面取得了强的表现 (R2 > 0.69).
观察到 (As), (Cd) 和 (Pb) 的增加趋势和季节性峰值.
人工灌被确定为HM丰富的主要驱动因素,与各种环境因素关系.

结论:

拟议的框架允许在水生生态系统中进行微细,动态的HM风险评估.
人类致癌风险对整体HM风险有显著的贡献,即使在指导范围内.
这项研究为水资源管理和水资源污染控制提供了实际的好处.

关键词:

环境驱动因素环境驱动因素在HM风险评估中,HM的风险评估.低地农业池低地农业池机器学习机器学习根据SHAP方法,我们可以

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