机器学习预测多种DOM来源的有机污染物的DOC-水分区系数
Ruyue Jin1,2, Yuzhen Liang1,2, Zhenqing Shi1,2
1School of Environment and Energy, South China University of Technology, Guangzhou, Guangdong 510006, People's Republic of China.
Environmental science. Processes & impacts
|June 4, 2025
概括
机器学习模型准确地预测有机污染物分解为溶解有机物质 (DOM). 诸如水性 (CrippenLogP) 和分子结构等关键因素影响了这一环境风险评估参数.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 毒理学 毒理学 毒理学
背景情况:
- 溶解有机碳水分离系数 (KDOC) 对于环境风险评估至关重要.
- 准确的KDOC预测对于了解水生和陆生系统中的有机污染物行为至关重要.
研究的目的:
- 开发和验证用于预测KDOC的机器学习模型.
- 确定影响有机污染物分解为溶解有机物质 (DOM) 的关键分子描述因素.
主要方法:
- 编制了709个数据点的数据集,用于190种有机污染物和各种DOM类型.
- 使用Multiwfn,PaDEL和RDKit计算的分子描述符.
- 开发了针对不同DOM源的单独机器学习模型,并使用Shapley增量解释分析了影响因素.
主要成果:
- 机器学习模型在不同的DOM类型中显示出出色的预测性能 (R^2 > 0.629).
- 鉴定出CrippenLogP (疏水性) 和MATS2m (分子紧性) 是最有影响力的描述因素.
- 发现分子描述物的特定范围与有机污染物在DOM中的分化增加有关.
结论:
- 机器学习模型为预测KDOC和理解污染物-DOM相互作用提供了强大的方法.
- 该研究通过改进对有机污染物分区的预测来加强环境风险评估.
- 这些发现强调了分子性质和DOM特征在污染物命运和运输中的重要性.
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