通过机器学习预测全球土壤中的重金属分布系数:矿物质异质性的影响
Wenping Zuo1, Huangling Gu1, Qinpeng Liao1
1School of Metallurgy and Environment, Central South University, Changsha 410083, China; Chinese National Engineering Research Center for Control & Treatment of Heavy Metal Pollution, Changsha 410083, China.
Environmental research
|February 8, 2026
概括
这项研究使用机器学习预测全球土壤中的重金属 (HM) 吸附,确定矿物质含量是关键因素. 它绘制了污染风险地区的地图,强调亚洲.
科学领域:
- 环境科学 环境科学
- 土壤科学 土壤科学
- 地质化学 地质化学
背景情况:
- 土壤颗粒的重金属吸附对于评估全球土壤污染风险至关重要.
- 大规模的土壤吸附数据很少,阻碍了全球风险评估.
- 现有的方法难以提供土壤吸附能力的全面全球分布.
研究的目的:
- 在全球范围内开发一种新的机器学习方法来预测HM的分布系数 (Kd).
- 确定影响HM吸附的关键土壤特性,并划定全球土壤污染风险区域.
- 评估矿物质异质性对HM吸附的影响,并为土壤修复策略提供信息.
主要方法:
- 利用机器学习模型,特别是随机森林 (RF),预测As,Cd,Cr和Pb的Kd值.
- 集成的土壤特性,吸附特性和矿物质含量数据用于模型培训和验证.
- 采用美国环保署的指标,根据预测的Kd值划分土壤污染风险区域.
主要成果:
- 使用射频模型实现了Kd值 (0.850.91) 的高预测精度.
- 确定矿物质含量,特别是氧化铁,化物和高化物,作为HM吸附的主要驱动因素.
- 发现全球超过20%的农田构成环境风险,亚洲的As,Cd和Cr比例最高.
结论:
- 拟议的机器学习方法可以准确预测土壤HM吸附和全球污染风险评估.
- 矿物成分显著影响HM吸附能力和分布,影响环境风险.
- 研究结果为全球环境风险评估和制定有针对性的土壤整治战略提供了宝贵的见解.
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