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| Spatial dynamics of heavy metal pollution and their driving factors using coupled machine learning and PLS-SEM: a case study in Xiangtan，China |
| Received:March 06, 2025 |
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| KeyWord:heavy metal;paddy soil;random forest;partial least squares structural equation modeling;geographically weighted regression;spatial distribution |
| Author Name | Affiliation | E-mail | | ZHU Haipeng | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
School of Environmental Science and Safety Engineering, Tianjin University of Technology, Tianjin 300384, China | | | LIU Yuanyuan | Powerchina Kunming Engineering Corporation Limited, Kunming 610036, China | 52672780@qq.com | | YANG Chao | Shandong Agricultural Technology Extension Center, Jinan 250000, China | | | JIAO Le | Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Tianjin 300191, China
China-Mongolia Joint Laboratory for Multi-source Monitoring and Spatiotemporal Succession of Agricultural Environment, Tianjin 300191, China
Xiangtan Experimental Station of Agro-Environmental Protection Institute, Ministry of Agriculture and Rural Affairs, Xiangtan 411100, China | jiaole@caas.cn | | LIU Sheng | Powerchina Kunming Engineering Corporation Limited, Kunming 610036, China | | | FENG Junlin | Powerchina Kunming Engineering Corporation Limited, Kunming 610036, China | |
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| Abstract: |
| To quantitatively assess the impact of natural and socioeconomic factors on heavy metal distribution in rice paddy soils, this study integrated random forest（RF）, geographically weighted regression（GWR）, and partial least squares structural equation modeling （PLS-SEM）to construct an integrated framework for analyzing the driving pathways of heavy metal distribution in Xiangtan County, Hunan Province. Results showed that the primary heavy metal risks in paddy soils in Xiangtan were associated with cadmium（Cd）. We further analyzed the driving mechanisms of 7 heavy metals（excluding Ni）with concentrations exceeding the background values. RF model showed the highest characteristic importance value of irrigation water usage on Cd distribution, while precipitation and PM 10 had the highest characteristic importance value on mercury（Hg）, arsenic（As）, lead（Pb）, and zinc（Zn）. GWR results demonstrated the significantly spatial heterogeneity in the effects of various factors on the distribution of seven heavy metals. Pathway analysis based on PLSSEM revealed a directly positive effect of climatic factors on Cd, Pb, and Zn（β=0.50~0.74）. In contrast, soil properties showed a directly negative effect on Hg, As, Cr, and Cu（β=- 0.59~- 0.47）. Indirect effect analysis indicated that socioeconomic factors, mediated by soil properties, had significant positive effects on Cd, Pb, and Zn distribution（β=0.33~0.49）. However, terrain and hydrological factors showed significantly positive effects on Hg, As, Cr and Cu（β>0.30）. The mediating role of hydrological conditions moderated the directly positive effect of climatic factors on soil properties and the directly negative effect of terrain on soil properties. This study reveals the complex interactions between natural and socioeconomic factors in heavy metal distribution in paddy soils, providing scientific evidence to identify the driving mechanism of heavy metal distribution and formulate of effective remediation strategies. |
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