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预测土壤水系统中各种有机化合物的吸收:元分析,机器学习建模和全球土壤绘图

Jiachun Sun¹, Kai Zhang¹, Huichun Zhang¹

¹Department of Civil and Environmental Engineering, Case Western Reserve University, Cleveland, OH 44106, USA.

Journal of hazardous materials

|February 5, 2025

概括

这项研究开发了先进的机器学习模型,以准确预测土壤中的有机化合物吸附,克服了各种化学结构和土壤类型的传统方法的局限性.

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:

传统的土壤水吸附模型无法充分反映复杂的实验条件,并与多功能,可电离的有机化合物 (OCs) 进行斗争.
现有的模型往往无法捕捉到影响环境命运的OCs和各种土壤属性的全部范围.

研究的目的:

创建一个全面的数据集,并开发先进的机器学习模型来预测土壤中的有机化合物吸收.
提高环境风险评估的土壤水吸附模型的准确性和范围.

主要方法:

编制了大量数据集 (20,945个点,419个OC,1037个土壤) 用于元分析和模型开发.
使用XGBoost算法与MACCS指纹和实验条件构建预测模型,用于阴离子,中性和阳离子OC物种.
从"协调世界土壤数据库" (Harmonized World Soil Database) 中纳入土壤属性,用于全球预测.

主要成果:

分析确定了土壤吸附与OC亚结构,土壤特性和溶液条件相关的关键趋势.
机器学习模型在log K_d上实现了0.32的根均平方误差,在不同的OC特异化中显示出高精度.
模型准确地捕获了对可电离OC吸附的异热非线性和pH效应,并根据全球土壤特性预测了吸附.

结论:

开发了强大的机器学习模型,大大提高了土壤中有机化合物吸附的预测.
这些模型为评估各种有机污染物的环境行为提供了更准确,更全面的工具.
这种方法提高了我们在各种环境场景下预测污染物的命运和运输的能力.

关键词:

化学物种的分类化学物种的分类模型解释模型解释有机污染物有机污染物土壤吸附吸附方式土壤吸附方式土壤水环境的环境.

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