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在土壤上预测有机污染物的吸收,使用可解释的机器学习

Qian Wang¹, Jianmin Bian², Enze Ma³

¹School of Environmental Engineering, Xuzhou University of Technology, Xuzhou 221018, China.

Environmental pollution (Barking, Essex : 1987)

|June 14, 2025

概括

机器学习模型准确地预测土壤上的有机污染物吸收,识别电子效应和土壤有机物作为影响环境命运和风险的关键因素.

科学领域:

环境化学环境化学
土壤科学土壤科学
计算化学的计算化学

背景情况:

在土壤上吸收有机污染物对环境命运和运输至关重要.
了解吸附能力和影响因素之间的非线性关系是有限的.
由于复杂的相互作用,预测OP选需要先进的建模.

研究的目的:

开发和比较五种机器学习 (ML) 模型,用于预测土壤上的OPs吸收.
通过可解释性分析确定影响OPS吸附的关键因素.
绘制OPs在中国大陆的吸收能力,并评估环境风险.

主要方法:

使用了来自之前研究的352个数据点的数据集.
开发和评估了支持向量机 (SVM),深度神经网络 (DNN),极端梯度增强 (XGBT),随机森林 (RF) 和梯度增强决策树 (GBDT) 模型.
应用了Shapley添加式解释 (SHAP) 来实现模型的可解释性,并生成了OPs吸附能力的空间分布图.

主要成果:

该XGBT模型实现了优异的性能,R2为0.952和RMSE为0.103.
确定OP电子效应和土壤有机物 (SOM) 含量是最有影响力的因素.
高吸能力主要在中国南部和西南部发现,与环境风险降低相关.

结论:

开发了一种新的可解释的ML框架,用于预测OPs吸附潜力.
突出了 π-π 相互作用和 OPs 吸附机制中的疏水分区的主要作用.
该框架支持环境管理,风险评估,土地整治和土壤保护政策.

关键词:

可解释的机器学习模型全国范围的预测预测.有机污染物有机污染物土壤吸附吸附方式

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