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人类和环境对土壤污染的控制在一个倾向于灰尘的地区被随机森林和沙普利添加解释分析揭示

Zohre Ebrahimi-Khusfi¹, Shamsollah Ayoubi², Seyed Arman Samadi-Todar³

¹Department of Environmental Sciences and Engineering, Faculty of Natural Resources, University of Jiroft, Jiroft, Iran. Zohreebrahimi2018@ujiroft.ac.ir.

Scientific reports

|February 21, 2026

概括

本研究使用随机森林模型和各种环境因素预测伊朗土壤中的潜在有毒元素 (PTEs). 人类活动和土壤特性是PTE分布的关键驱动因素,对于干旱地区的风险管理至关重要.

科学领域:

环境科学环境科学
地质化学地质化学
土壤科学土壤科学

背景情况:

潜在有毒元素 (PTEs) 对人类健康和生态系统构成风险.
准确地对PTE进行空间预测,并了解其驱动因素,对于有效的环境管理至关重要.

研究的目的:

预测伊朗中部 (As), (Cd), (Co), (Cr) 和 (Pb) 的空间分布.
通过随机森林 (RF) 模型和夏普利添加式扩展 (SHAP) 来确定影响PTE分布的最重要的环境驱动因素.

主要方法:

该研究使用了RF模型,其中包含11种情景,结合了基于人类活动的因素 (HAF),基于陆地的因素 (LSF),土壤物理化学特性 (PSP),气象因素 (MF) 和遥感辅助数据 (RSAD).
对107个表面土壤样本的实验室分析确定了PTE度和土壤特性.
SHAP分析量化了每个因素对PTE预测的贡献.

主要成果:

射频模型在情景VI (HAF + PSP) 下显示了As,Co和Cr的最佳性能,在情景X (PSP + LSF + HAF + RSAD) 下显示了Cd的最佳性能.
在所有情景中, (Pb) 预测的表现都很弱.
基于人类活动的因素 (HAF) 和土壤物理化学性质 (PSP) 是预测PTE,特别是Cd的最有影响力的类别.

结论:

人类活动和土壤特性是干旱环境中PTE空间分布的关键因素.
确定关键驱动因素,如工业距离和特定的土壤特性,有助于制定有针对性的土壤管理策略.
这些发现支持在脆弱的干旱地区减轻PTE风险的努力.

关键词:

干旱地区是干旱地区.人类活动人类活动土地退化土地退化机器学习是机器学习.遥感是一种远程传感.土壤污染土壤污染

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