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| Source apportionment of heavy metal pollution in farmland of the middle and lower reaches of the Zhengshui River basin based on PMF and machine learning models |
| Received:January 13, 2025 |
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| KeyWord:soil heavy metal;source apportionment;PMF model;SOM model;LightGBM model |
| Author Name | Affiliation | E-mail | | DONG Tianhao | Hunan Cultivated Land and Agricultural Eco-Environment Institute, Changsha 410125, China
Jinan Academy of Agricultural Sciences, Jinan 250100, China
Key Laboratory of Agro-Environment in Midstream of Yangtze Plain, Ministry of Agriculture and Rural Affairs, Changsha 410125, China
Key Lab of Prevention, Control and Remediation of Soil Heavy Metal Pollution in Hunan Province, Changsha 410125, China | | | REN Chuanmeng | Jinan Academy of Agricultural Sciences, Jinan 250100, China | | | REN Qingsheng | Jinan Academy of Agricultural Sciences, Jinan 250100, China | | | DONG Bei | Jinan Academy of Agricultural Sciences, Jinan 250100, China | | | ZHANG Renjie | Hunan Cultivated Land and Agricultural Eco-Environment Institute, Changsha 410125, China
Key Laboratory of Agro-Environment in Midstream of Yangtze Plain, Ministry of Agriculture and Rural Affairs, Changsha 410125, China
Key Lab of Prevention, Control and Remediation of Soil Heavy Metal Pollution in Hunan Province, Changsha 410125, China | | | LI Chengyong | Jinan Academy of Agricultural Sciences, Jinan 250100, China | | | PAN Shufang | Hunan Cultivated Land and Agricultural Eco-Environment Institute, Changsha 410125, China
Key Laboratory of Agro-Environment in Midstream of Yangtze Plain, Ministry of Agriculture and Rural Affairs, Changsha 410125, China
Key Lab of Prevention, Control and Remediation of Soil Heavy Metal Pollution in Hunan Province, Changsha 410125, China | | | GUO Yan | College of Land Science and Technology, China Agricultural University, Beijing 100193, China | | | JI Xionghui | Hunan Cultivated Land and Agricultural Eco-Environment Institute, Changsha 410125, China
Key Laboratory of Agro-Environment in Midstream of Yangtze Plain, Ministry of Agriculture and Rural Affairs, Changsha 410125, China
Key Lab of Prevention, Control and Remediation of Soil Heavy Metal Pollution in Hunan Province, Changsha 410125, China | | | XIE Yunhe | Hunan Cultivated Land and Agricultural Eco-Environment Institute, Changsha 410125, China
Key Laboratory of Agro-Environment in Midstream of Yangtze Plain, Ministry of Agriculture and Rural Affairs, Changsha 410125, China
Key Lab of Prevention, Control and Remediation of Soil Heavy Metal Pollution in Hunan Province, Changsha 410125, China | 45069794@qq.com |
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| Abstract: |
| To investigate the risk of heavy metal contamination in agricultural soils in the middle and lower reaches of the Zhengshui River, a tributary of the Xiangjiang River, soil sampling and pollution source apportionment were conducted. The results indicate the following： The study area exhibits a significant risk of cadmium（Cd）contamination in the soil, with partial risks of arsenic（As）, lead（Pb）, and copper （Cu）contamination. Four pollution sources were identified in the study area：a mixed natural-atmospheric deposition source, a natural source, an atmospheric deposition source, and an industrial source. The PMF（Positive Matrix Factorization）model determined that soil As and Hg are primarily influenced by the mixed natural-atmospheric deposition source, Cr, Ni and Cu are mainly affected by the natural source, Pb is predominantly influenced by the atmospheric deposition source, and Cd and Zn are primarily associated with the industrial source. The contribution rates of these four sources are 30.8%, 27.0%, 22.6%, and 19.6%, respectively. The SOM（Self-Organizing Map） model's classification results for pollution sources showed high consistency with the PMF model's source apportionment results. The LightGBM（Light Gradient Boosting Machine）model results indicated that the distance from the mainstream of the Zhengshui River has a significant impact on Cd, Pb, Ni, and Zn, while PM2.5 concentration has a notable influence on Cd and Pb. The most influential factor for As, Hg, and Cr is the parent rock type. Traffic-related factors have a considerable impact on Cu and Zn. The study reveals that the agricultural soils in the study area face certain risks of heavy metal contamination, with complex pollution sources. The LightGBM model can complement the PMF model results to some extent, and the combination of receptor models and machine learning models can more reasonably identify the primary sources of heavy metal contamination in the soil. |
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