Effects of geographical and soil factors on soilś arsenic levels: a case study in typical arsenic-contaminated paddy fields based on machine learning.
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...
| Publicado en: | Environmental Management Vol. 75; no. 9; pp. 2272 - 2287 |
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| Autores principales: | , , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Sep2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187765487&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187765487 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0364152X O5H jtl: Environmental Management issn: 0364152X maglogo: N pubinfo: dt: Sep2025 vid: 75 iid: 9 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187765487 184536329 10.1007/s00267-025-02160-y 187765487 ppf: 2272 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Effects of geographical and soil factors on soilś arsenic levels: a case study in typical arsenic-contaminated paddy fields based on machine learning. aug: au: Zhang, Renjie Jiang, Liheng Dong, Tianhao Xie, Yunhe Pan, Shufang Liu, Saihua Huang, Rui Ji, Xionghui Xue, Tao affil: https://ror.org/01fj5gf64 Hunan Institute of Agro-Environment and Ecology, Hunan Academy of Agricultural Sciences, 410125, Changsha, China sug: ab: 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 (R2) 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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