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| Machine learning coupled with multi-source variables predicts the distribution of heavy metals in farmland and ecological risks |
| Received:May 18, 2025 |
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| KeyWord:machine learning;multi-source variable;risk assessment;spatial prediction;soil heavy metals |
| Author Name | Affiliation | E-mail | | DENG Huiting | Guangdong Research Center for Agricultural Soil Pollution Prevention and Control Engineering Technology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Key Laboratory of Arable Land Conservation(South China), Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China | | | ZHAO Wanyu | Guangdong Research Center for Agricultural Soil Pollution Prevention and Control Engineering Technology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Key Laboratory of Arable Land Conservation(South China), Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China | | | LI Yingyuting | Guangdong Research Center for Agricultural Soil Pollution Prevention and Control Engineering Technology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Key Laboratory of Arable Land Conservation(South China), Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China | | | HU Tian | Guangdong Research Center for Agricultural Soil Pollution Prevention and Control Engineering Technology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Key Laboratory of Arable Land Conservation(South China), Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China | | | LI Wenyan | Guangdong Research Center for Agricultural Soil Pollution Prevention and Control Engineering Technology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Key Laboratory of Arable Land Conservation(South China), Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China | | | WANG Jinjin | Guangdong Research Center for Agricultural Soil Pollution Prevention and Control Engineering Technology, College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China
Key Laboratory of Arable Land Conservation(South China), Ministry of Agriculture and Rural Affairs, Guangzhou 510642, China | wangjinjin@scau.edu.cn |
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| Abstract: |
| To identify the spatial variation characteristics of heavy metals and their potential ecological risks, in this study, we collected 1 166 farmland soil samples in Taishan City, Guangdong Province, to measure concentrations of five heavy metals（Cr, Pb, As, Hg, Cd）and obtain 17 co-factors. Using recursive feature elimination（RFE）, the 10 most influential factors were identified and combined with three machine learning models（RF, SVM, ANN）to select the best prediction model. Geo-accumulation index was then used to assess pollution risk and generate a distribution map. The RF model performs the best, with R2 values above 0.940 on the training set and 0.583-0.766 on the test set（except for Cr）. In contrast, the SVM model had R2 values ranging from 0.275-0.533 on the training set and from 0.226-0.461 on the test set. The ANN model had R2 values ranging from 0.156-0.587 on the training set and from 0.183-0.489 on the test set. SHAP analysis identified key factors influencing predictions: precipitation, night light intensity, and distance to exploited metal mines for Cd and Pb; precipitation, distance to exploited metal mines, and distance to industrial enterprises for As and Hg. The study area had no strong pollution, but Cd and Hg showed moderate pollution over 5.6% and 35.5% of the area, respectively, indicating a need for focused pollution control. The RF model showed excellent prediction performance with strong generalization and application potential. Soil heavy metals in Taishan City were influenced by both natural and anthropogenic factors, with Cd and Hg being the main pollutants, primarily distributed in the southwest and northern areas. These results highlight the need to prioritize these regions for pollution control and remediation. |
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