Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China
文献类型：期刊论文
| 作者 | Li, Kai1,3; Guo, Guanghui1,3; Zhang, Degang4; Lei, Mei1,3; Wang, Yingying2 |
| 刊名 | JOURNAL OF HAZARDOUS MATERIALS
 |
| 出版日期 | 2024-10-05 |
| 卷号 | 478页码:135454 |
| 关键词 | Potentially toxic elements Machine learning Predictive accuracy SHAP analysis Relative importance analysis |
| DOI | 10.1016/j.jhazmat.2024.135454 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Accurate prediction of spatial distribution of potentially toxic elements (PTEs) is crucial for soil pollution prevention and risk control. Achieving accurate prediction of spatial distribution of soil PTEs at a large scale using conventional methods presents significant challenges. In this study, machine learning (ML) models, specially artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB), were used to predict spatial distribution of soil PTEs and identify associated key factors in mining and smelting area located in Yunnan Province, China, under the three scenarios: (1) natural + socioeconomic + spatial datasets (NS), (2) NS + irrigation pollution index (IPI) datasets, (3) NS + IPI + deposition (DEPO) datasets. The results highlighted the combination of NS+IPI+DEPO yielded the highest predictive accuracy across ML models. Particularly, XGB exhibited the highest performance for As (R2 =0.7939), Cd (R2 =0.6679), Cu (R2 =0.8519), Pb (R2 =0.8317), and Zn (R2 =0.7669), whereas RF performed the best for Ni (R2 =0.7146). The feature importance and Shapley additive explanation (SHAP) analysis revealed that DEPO and IPI were the pivotal factors influencing the distribution of soil PTEs. Our findings highlighted the important role of DEPO in spatial distribution prediction of soil PTEs, which has often been ignored in previous studies. |
| WOS关键词 | HEAVY-METALS ; RISK-ASSESSMENT ; POLLUTION ; RICE |
| WOS研究方向 | Engineering ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:001297384000001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206876]  |
| 专题 | 资源利用与环境修复重点实验室_外文论文 |
| 通讯作者 | Guo, Guanghui |
| 作者单位 | 1.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 2.Sichuan Ecoenvironm Monitoring Stn, Chengdu 610091, Peoples R China 3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 4.Honghe Univ, Mengzi 661100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Li, Kai,Guo, Guanghui,Zhang, Degang,et al. Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China[J]. JOURNAL OF HAZARDOUS MATERIALS,2024,478:135454. |
| APA | Li, Kai,Guo, Guanghui,Zhang, Degang,Lei, Mei,&Wang, Yingying.(2024).Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China.JOURNAL OF HAZARDOUS MATERIALS,478,135454. |
| MLA | Li, Kai,et al."Accurate prediction of spatial distribution of soil potentially toxic elements using machine learning and associated key influencing factors identification: A case study in mining and smelting area in southwestern China".JOURNAL OF HAZARDOUS MATERIALS 478(2024):135454. |
入库方式： OAI收割
来源：地理科学与资源研究所
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