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<p>Soil contamination by potentially toxic metals (PTMs) in dust-prone regions poses significant risks to public health and the environment. Accurate prediction of their spatial distribution and identification of controlling factors are essential for effective risk mitigation. This study determined the most accurate model–scenario combinations for predicting arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), lead (Pb), and zinc (Zn) concentrations in the northwestern Jazmourian Basin, southeastern Iran. PTMs and soil properties were measured in 104 surface soil samples (0–10&#xa0;cm) in the laboratory. Modeling was performed using Random Forest (RF) and Extreme Gradient Boosting (XGB) under five scenarios based on soil properties (SP), anthropogenic factors (AF), geo-based factors (GBF), meteorological elements (ME), and remote sensing auxiliary data (RSAD). Critical regions were identified by combining binary maps of the six PTMs, classified according to their crustal mean concentrations, where higher cumulative values indicated multi-metal contamination zones. The contribution of controlling factors was quantified using Shapley Additive Explanations (SHAP). Optimal predictions for As and Cr were obtained using XGB–Scenario (I) (RSAD) and XGB–Scenario (III) (RSAD + ME + GBF) with R² values of 0.40 and 0.55, respectively. Higher R² values of 0.52, 0.34, and 0.65 were achieved for Cd, Pb, and Zn using XGB–Scenario (V) (RSAD + ME + GBF + AF + SP). RF–Scenario (V) provided the best spatial prediction for Ni (R² = 0.31). Human settlements in the central region were identified as critical zones. SHAP analysis showed that RSAD contributed most to predicting As and Zn, whereas GBF, SP, and ME were dominant for Cr, Ni, Cd, and Pb. Among predictive variables, inverted difference vegetation index (IPVI), band ratio (3/4), geological formations, slope, silt, and wind speed were key controlling factors. These findings provide valuable guidance for environmental planning and soil contamination risk reduction in dust-prone areas.</p>

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Environmental drivers of soil potentially toxic metals identified through multi-scenario machine learning in an arid region of Southeastern Iran

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Zohre Ebrahimi-Khusfi,
  • Mojtaba Soleimani-Sardo,
  • Ghobad Jalali

摘要

Soil contamination by potentially toxic metals (PTMs) in dust-prone regions poses significant risks to public health and the environment. Accurate prediction of their spatial distribution and identification of controlling factors are essential for effective risk mitigation. This study determined the most accurate model–scenario combinations for predicting arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), lead (Pb), and zinc (Zn) concentrations in the northwestern Jazmourian Basin, southeastern Iran. PTMs and soil properties were measured in 104 surface soil samples (0–10 cm) in the laboratory. Modeling was performed using Random Forest (RF) and Extreme Gradient Boosting (XGB) under five scenarios based on soil properties (SP), anthropogenic factors (AF), geo-based factors (GBF), meteorological elements (ME), and remote sensing auxiliary data (RSAD). Critical regions were identified by combining binary maps of the six PTMs, classified according to their crustal mean concentrations, where higher cumulative values indicated multi-metal contamination zones. The contribution of controlling factors was quantified using Shapley Additive Explanations (SHAP). Optimal predictions for As and Cr were obtained using XGB–Scenario (I) (RSAD) and XGB–Scenario (III) (RSAD + ME + GBF) with R² values of 0.40 and 0.55, respectively. Higher R² values of 0.52, 0.34, and 0.65 were achieved for Cd, Pb, and Zn using XGB–Scenario (V) (RSAD + ME + GBF + AF + SP). RF–Scenario (V) provided the best spatial prediction for Ni (R² = 0.31). Human settlements in the central region were identified as critical zones. SHAP analysis showed that RSAD contributed most to predicting As and Zn, whereas GBF, SP, and ME were dominant for Cr, Ni, Cd, and Pb. Among predictive variables, inverted difference vegetation index (IPVI), band ratio (3/4), geological formations, slope, silt, and wind speed were key controlling factors. These findings provide valuable guidance for environmental planning and soil contamination risk reduction in dust-prone areas.