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用可解释的机器学习模型揭示土壤重金属污染中的协同空间效应

Yibo Yan¹, Yong Yang¹

¹College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China; Key Laboratory of Arable Land Conservation (Middle and Lower Reaches of Yangtze River), Ministry of Agriculture, Wuhan 430070, China.

Journal of hazardous materials

|November 22, 2024

概括

了解土壤重金属污染需要确定关键的环境因素. 这项研究发现地质类型和企业密度显著影响,铜和水平,植被提供缓解作用.

科学领域:

环境科学环境科学
地质化学地质化学
空间分析空间分析

背景情况:

土壤重金属污染对生态系统和人类健康构成风险.
识别引起因素对于有效的污染控制和预防策略至关重要.

研究的目的:

评估16个环境因素对土壤重金属 (Cd,Cu,Pb) 的空间分布的影响.
评估自然和人为因素对土壤重金属度的协同作用.
为管理土壤重金属污染和促进可持续发展提供科学基础.

主要方法:

使用的地理探测器 (GD) 和极端梯度提升 (XGBoost) 采用沙普利增量解释 (SHAP) 模型.
分析了土壤中 (Cd),铜 (Cu) 和 (Pb) 的空间分布模式.
通过交叉检测分析研究了环境因素之间的相互作用.

主要成果:

在预测土壤重金属空间分布方面,XGBoost模型实现了高准确度 (R2 > 0.6).
地质类型和企业密度被确定为Cd,Cu和Pb度的重要驱动因素.
自然和人为因素的结合显示出强大的解释力;植被指数和企业密度的相互作用表明植被减轻了污染.

结论:

污染控制措施在经济发达地区是有效的,允许经济和环境平衡.
植物在减轻重金属污染方面发挥着作用,突出了其生态重要性.
结果支持全球土壤重金属管理和生态可持续性的战略决策.

关键词:

数据挖掘是一种数据挖掘.司机司机司机司机重金属是一种重金属.这就是 SHAP SHAP 的意思.空间分布的空间分布在XGBoost上使用.

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