Driving mechanisms and high-risk area prediction of arsenic pollution in surface water of the Shaanxi Wei River Basin
文献类型：期刊论文
| 作者 | Zha, Xinjie1; Xu, Hongzhao6; Tian, Yuan5; An, Jialu2,3,4; Di, Jin1; Wei, Yan1; Yang, Yizhuo1 |
| 刊名 | ENVIRONMENTAL POLLUTION
 |
| 出版日期 | 2025-12-15 |
| 卷号 | 387页码:127297 |
| 关键词 | Arsenic Spatial heterogeneity Driving mechanism Geographical detector Machine learning Weihe River Basin |
| ISSN号 | 0269-7491 |
| DOI | 10.1016/j.envpol.2025.127297 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | The Weihe River Basin, located within the Yellow River Basin, is an ecologically important region increasingly threatened by arsenic (As) contamination in surface water, which poses risks to both environmental security and public health. This study collected 186 surface water samples across the Shaanxi section of the basin and integrated 22 environmental factors-including hydrogeochemical, soil, climatic, and anthropogenic variables-into a comprehensive analysis. By combining the geographical detector model with a random forest algorithm, we identified the spatial heterogeneity of As concentrations and investigated the mechanisms driving its distribution. The results revealed significant spatial variability, with average and maximum As concentrations of 3.8 mu g/ L and 63.9 mu g/L, respectively. Localized exceedances of the 10 mu g/L guideline were observed in northern Yan'an and the Chang'an District of Xi'an. Geographical detector analysis identified mean annual precipitation (PREC, q = 0.629) and clay content (COS, q = 0.594) as the dominant individual factors influencing the spatial distribution of As, while the interaction between total nitrogen content and climate/topographic factors (e.g., N boolean AND TEMP, q = 0.754) amplified As spatial differentiation through nonlinear synergistic effects. The random forest-based risk model demonstrated strong predictive performance (accuracy: 98.92 %, AUC = 0.988), and spatial overlays with population density highlighted northern Yan'an and urban Xi'an as high-risk areas. These findings offer a methodological framework and scientific basis for targeted prevention and management of As pollution in river basins, thereby supporting ecological protection and public health strategies. |
| URL标识 | 查看原文 |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001606254000001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/217772]  |
| 专题 | 拉萨站高原生态系统研究中心_外文论文 |
| 通讯作者 | Tian, Yuan |
| 作者单位 | 1.Xian Univ Finance & Econ, Changning Str 360, Xian 710100, Peoples R China; 2.Changan Univ, Key Lab Ecohydrol & Water Secur Arid & Semiarid Re, Minist Water Resources, Xian 710054, Shaanxi, Peoples R China 3.Changan Univ, Key Lab Subsurface Hydrol & Ecol Effect Arid Reg, Minist Educ, Xian 710054, Shaanxi, Peoples R China; 4.Changan Univ, Sch Water & Environm, Xian 710054, Shaanxi, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modelling, Datun Str 11A, Beijing 100101, Peoples R China; 6.Second Geol Brigade Xizang Autonomous Reg Geol & M, Lhasa 850000, Tibet, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Zha, Xinjie,Xu, Hongzhao,Tian, Yuan,et al. Driving mechanisms and high-risk area prediction of arsenic pollution in surface water of the Shaanxi Wei River Basin[J]. ENVIRONMENTAL POLLUTION,2025,387:127297. |
| APA | Zha, Xinjie.,Xu, Hongzhao.,Tian, Yuan.,An, Jialu.,Di, Jin.,...&Yang, Yizhuo.(2025).Driving mechanisms and high-risk area prediction of arsenic pollution in surface water of the Shaanxi Wei River Basin.ENVIRONMENTAL POLLUTION,387,127297. |
| MLA | Zha, Xinjie,et al."Driving mechanisms and high-risk area prediction of arsenic pollution in surface water of the Shaanxi Wei River Basin".ENVIRONMENTAL POLLUTION 387(2025):127297. |
入库方式： OAI收割
来源：地理科学与资源研究所
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