Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach
文献类型：期刊论文
| 作者 | Zhou, Mengge3; Li, Yonghua2 |
| 刊名 | SCIENCE OF THE TOTAL ENVIRONMENT
 |
| 出版日期 | 2024-02-20 |
| 卷号 | 912页码:169092 |
| 关键词 | Random forest Source apportionment Shapley additive explanations t -Distributed random neighbor embedding Heavy metals |
| DOI | 10.1016/j.scitotenv.2023.169092 |
| 文献子类 | Article |
| 英文摘要 | Identifying the driving factors and quantifying the sources of potentially toxic elements (PTEs) are essential for protecting the ecological environment of the Yellow River Delta. In this study, data from 201 surface soil samples and 16 environmental variables were collected, and the random forest (RF) and Shapley additive explanations (SHAP) methods were then combined to explore the key factors affecting soil PTEs. An innovative t-distributed random neighbor embedding-RF-SHAP model was then constructed, based on the absolute principal component score and multivariate linear regression model, to quantitatively determine PTE sources. Although average PTE concentrations did not exceed the risk control values, PTE distributions exhibited significant differences. It was found that sodium, soil organic matter, and phosphorus contents were the three most important factors affecting PTEs, and human activities and natural environmental factors both influence PTE contents by altering the soil properties. The proposed model successfully determined PTE sources in the soil, outperforming the original linear regression model with a significantly lower RMSE. Source analysis revealed that the parent material was the main contributor to soil PTEs, accounting for more than half of the total PTE content. Industrial and agricultural activities also contributed to an increase in soil PTEs, with average contributions of 19.91 % and 17.44 %, respectively. Unknown sources accounted for 10.83 % of the total PTE content. Thus, the proposed model provides innovative perspectives on source parsing. These findings provide valuable scientific insights for policymakers seeking to develop effective environmental protection measures and improve the quality of saline -alkali land in the Yellow River Delta. |
| WOS关键词 | LLE ; MODELS ; RISK |
| WOS研究方向 | Environmental Sciences & Ecology |
| WOS记录号 | WOS:001133938000001 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/201568]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, 11A Datun Rd, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Key Lab Land Surface Pattern & Simulat, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhou, Mengge,Li, Yonghua. Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach[J]. SCIENCE OF THE TOTAL ENVIRONMENT,2024,912:169092. |
| APA | Zhou, Mengge,&Li, Yonghua.(2024).Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach.SCIENCE OF THE TOTAL ENVIRONMENT,912,169092. |
| MLA | Zhou, Mengge,et al."Spatial distribution and source identification of potentially toxic elements in Yellow River Delta soils, China: An interpretable machine-learning approach".SCIENCE OF THE TOTAL ENVIRONMENT 912(2024):169092. |
入库方式： OAI收割
来源：地理科学与资源研究所
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