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Effects of geographical and soil factors on soilś arsenic levels: a case study in typical arsenic-contaminated paddy fields based on machine learning

文献类型： 外文期刊

作者： Zhang, Renjie 1 ; Jiang, Liheng 3 ; Dong, Tianhao 3 ; Xie, Yunhe 1 ; Pan, Shufang 1 ; Liu, Saihua 1 ; Huang, Rui 1 ; Ji, Xionghui 1 ; Xue, Tao 1 ;

作者机构： 1.Hunan Acad Agr Sci, Hunan Inst Agroenvironm & Ecol, Changsha 410125, Peoples R China

2.Hunan Univ, Coll Biol, Longping Branch, Changsha 410125, Peoples R China

3.China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China

4.Minist Agr, Key Lab Prevent Control & Remediat Soil Heavy Met, Key Lab Agrienvironm Midstream Yangtze River Plain, Changsha 410125, Peoples R China

关键词： Soil arsenic; Machine learning; Geographical factors; Soil properties; Interactive effect; Paddy field

期刊名称：ENVIRONMENTAL MANAGEMENT （ 影响因子：3.0； 五年影响因子：3.5 ）

ISSN： 0364-152X

年卷期： 2025 年 75 卷 9 期

页码：

收录情况： SCI

摘要： Heavy metal pollution in agricultural land has emerged as a contemporary environmental issue of prominent concern. The concentration of heavy metals in soil is influenced not only by inherent soil properties but also by geographical factors. Moreover, the identification of its influencing factors is challenging because of the intricate interactive effects among them. Previous studies primarily focused on single-factor identification and spatial distribution characterization, neglecting the characteristics and spatial features of soil heavy metal concentration under the interactive effects of geographical factors and soil properties. This study assessed the influence of geographical factors, soil properties, and their interactive effects on the spatial distribution of soil arsenic (As), in a typical arsenic-contaminated paddy field area by employing machine learning, analysis of variance, and spatial analysis methods. The findings show that the prediction performance (R-2) of the random forest model for soil As concentration was 0.596, and the primary factors influencing the distribution of soil As are elevation, roads, rivers, soil pH, and cation exchange capacity (CEC). Moreover, the interactive effect between elevation and soil CEC had a significant effect on soil As (p < 0.05), exhibiting spatially homogeneous characteristics. The interactive effect between rivers and both soil pH and soil CEC exhibited spatially heterogeneous effects on soil As (p < 0.1). Additionally, the interactive effect between roads and soil pH affected soil As (p < 0.05), with spatially homogeneous characteristics. By identifying the main influencing factors of As in paddy soil, this study further explores the variation characteristics of soil As concentration under the interactive effects of geographical factors and soil properties. These insights can serve as a valuable reference for the precise prevention of As pollution in paddy field area.

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