Source analysis and distribution prediction of soil heavy metals in a typical area of the Qinghai-Tibet Plateau
文献类型：期刊论文
| 作者 | Zha, Xinjie2; Deng, Liyuan2; Jiang, Wei2; An, Jialu2; Wang, Hongcai2; Tian, Yuan1 |
| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 2024-09-01 |
| 卷号 | 166页码:112460 |
| 关键词 | Qinghai-Tibet Plateau Soil Heavy metals Geodetector Random Forest |
| DOI | 10.1016/j.ecolind.2024.112460 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | The excessive presence of heavy metals (HMs) in soil poses a significant threat to both ecosystems and human health. Consequently, there is a compelling need for quantitative analysis of HMs concentration in soil and the prediction of potential contamination. In this study, 58 surface soil samples were systematically collected from 11 different townships in Luolong County. Using ArcGIS 10.7, the fishing net interpolation resampling was performed to obtain model data. The GeoDetector model was employed to determine the key driving factors and their interrelationships affecting soil composition. Subsequently, influential driving factors with higher explanatory power and employed a Random Forest (RF) model to generate a contamination prediction map. The results revealed that arsenic (As), cadmium (Cd) and lead (Pb) exceeded the risk screening values by 8.62%, 10.34%, and 10.34%, respectively. The GeoDetector model identified factors such as elevation, annual average precipitation, distance to the nearest river, geomorphic type in natural sources, geological type in geological sources, distance from roads, proximity to mining sites, per capita income of inhabitants, total potassium content and organic matter content in anthropogenic sources significantly influencing the spatial distribution of HMs concentration in the soil. The interactions among the primary driving factors increased their explanatory capacity. By using RF model to predict the spatial distribution of the main influencing factors of HMs, it was found that areas with a high probability of As contamination were mainly concentrated in the northern, central and southeast regions of Luolong County. Regions with Cd concentration exceeding the risk screening value were primarily concentrated in the east, northeast and a few northern areas of Luolong County, while the likelihood of Pb contamination was higher in the northern and southwestern regions of Luolong County. This study integrates spatial stratified heterogeneity with the random forest model to mitigate overfitting in the prediction of soil HM contamination, a common issue in traditional machine learning methods. This approach is essential for elucidating the environmental drivers of soil HM pollution, predicting high-risk areas in regions with complex environmental conditions and limited data, and ensuring the safety and stability of agricultural production as well as the well-being of local residents. |
| WOS关键词 | SPATIAL-DISTRIBUTION ; AGRICULTURAL SOIL ; ORGANIC-MATTER ; POLLUTION ; IDENTIFICATION ; CONTAMINATION ; PATTERNS ; CADMIUM ; REGION ; ZINC |
| WOS研究方向 | Biodiversity & Conservation ; Environmental Sciences & Ecology |
| WOS记录号 | WOS:001293086000001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/206892]  |
| 专题 | 生态系统网络观测与模拟院重点实验室_外文论文 |
| 通讯作者 | Tian, Yuan |
| 作者单位 | 1.Chinese Acad Sci, Inst Geograph Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modelling, Datun Str 11A, Beijing 100101, Peoples R China 2.Xian Univ Finance & Econ, Changning Str 360, Xian 710100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zha, Xinjie,Deng, Liyuan,Jiang, Wei,et al. Source analysis and distribution prediction of soil heavy metals in a typical area of the Qinghai-Tibet Plateau[J]. ECOLOGICAL INDICATORS,2024,166:112460. |
| APA | Zha, Xinjie,Deng, Liyuan,Jiang, Wei,An, Jialu,Wang, Hongcai,&Tian, Yuan.(2024).Source analysis and distribution prediction of soil heavy metals in a typical area of the Qinghai-Tibet Plateau.ECOLOGICAL INDICATORS,166,112460. |
| MLA | Zha, Xinjie,et al."Source analysis and distribution prediction of soil heavy metals in a typical area of the Qinghai-Tibet Plateau".ECOLOGICAL INDICATORS 166(2024):112460. |
入库方式： OAI收割
来源：地理科学与资源研究所
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