基于机器学习的重金属化物对土壤污染的全球生态风险评估
Wenqi Jiao1, Mengting Wu1, Tao Hu1
1School of Resources and Safety Engineering, Central South University, Changsha, 410083, China.
Journal of environmental management
|December 27, 2025
概括
机器学习有效地识别了土壤重金属污染风险. 一个分类增强模型准确地预测风险水平,指导全球土地管理和恢复工作.
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:
- 土壤被持久性和生物积累性重金属化物污染是一个关键的全球环境问题.
- 准确识别污染风险水平对于生态系统和人类健康保护至关重要.
研究的目的:
- 开发和解释一个机器学习框架,以多类分类与土壤重金属化物污染相关的生态风险水平.
- 为此分类任务评估和比较各种机器学习模型的性能.
主要方法:
- 从全球9489个土壤样本构建了一个全面的数据集,包括土壤特性和金属含量.
- 对六种机器学习分类模型进行系统评估和超参数调整.
- 应用模型解释技术 (变换重要性,部分依赖图,沙普利增量解释) 来理解特征影响.
主要成果:
- 分类增强模型获得了最高的性能,测试组的精度为0.681和AUC为0.885.
- 影响风险分类的关键特征包括总金属化物含量,pH值,有机碳,粘土,阴离子交换能力和电子亲和力.
- 模型解释揭示了这些特征对预测的生态风险水平的非线性影响.
结论:
- 开发的机器学习框架为快速识别土壤重金属化物污染风险提供了有效和可解释的方法.
- 这些发现为全球污染管理和可持续土地管理策略提供了科学基础,包括优先恢复指导.
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