在现场规模中改善了对土壤VOC污染的垂直分布预测,利用集成机器学习方法与分子描述器集成
Yu-Xuan Cai1, Hai-Yan Chen1, Ya-Jing Qu2
1State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China.
Journal of hazardous materials
|August 8, 2025
概括
合并数据集提高了机器学习准确度,用于预测污染工业场所的挥发性有机化合物 (VOC). 这种方法增强了环境监测和土壤污染的管理.
科学领域:
- 环境科学 环境科学
- 地质化学 地质化学
- 数据科学数据科学数据科学
背景情况:
- 现场规模的环境监测依赖于了解土壤污染物的行为和分布.
- 机器学习模型显示出有希望的结果,但由于数据有限,在工业环境中难以预测准确性.
- 挥发性有机化合物 (VOC) 是常见的工业污染物,需要有效的管理策略.
研究的目的:
- 提高在农药工厂中挥发性有机化合物 (VOC) 分布的预测准确度.
- 评估合并单个VOC数据集和整合数据驱动模型的分子描述符的有效性.
- 阐明影响VOC分布和迁移模式的相互作用效应.
主要方法:
- 单个VOC和集成分子描述器的合并数据集,以扩大输入数据.
- 训练并比较了四种广泛使用的机器学习模型.
- 构建了一个堆叠组合模型,并采用了沙普利相互作用量化 (SHAP-IQ) 分析.
主要成果:
- 与单个VOC数据集相比,合并的VOC数据集显著提高了预测准确性.
- 堆叠组合模型实现了0.809.80的高R2值.
- SHAP-IQ分析揭示了关键的相互作用,并确定了危险材料仓库作为主要污染源,解释了异构的VOC分布.
结论:
- 合并单个污染物的小型数据集是有效的增强数据驱动模型在现场规模.
- 这项研究提供了有关VOC垂直分布和由特定地点因素影响的迁移模式的宝贵见解.
- 这项研究为改善污染工业场所管理提供了科学基础.
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