Key Takeaways & Executive Findings
- •• • Mean concentrations of Pb, Ni, and Cu in paddy soils exceeded Jiangxi Province background values, with Ni showing the highest ecological risk: 62.9% of Ni samples exhibited moderate risk, making it the primary pollutant requiring priority remediation. • • PMF source apportionment quantified three sources: natural background (51.1%), industrial and mining activities (26.6%), and mixed agricultural/traffic emissions (22.3%), enabling targeted source-specific mitigation strategies. • • XGBoost-SHAP analysis revealed that soil Fe and P contents are the dominant drivers for Cr, Ni, and Cu spatial variation, while Pb is significantly influenced by industrial and mining activities, highlighting distinct control mechanisms. • • The integrated PMF and XGBoost-SHAP framework reduced uncertainty in source identification compared to single models, but limitations remain due to incomplete feature selection and lack of temporal dynamics, necessitating future long-term monitoring.
Abstract
Identifying the sources and driving factors of heavy metals (HMs) in paddy soils around mining areas is crucial for safeguarding regional agricultural production and food security. This study focused on paddy soils near a mining area in southern Jiangxi Province, integrating Spearman correlation analysis, positive matrix factorization (PMF), and extreme gradient boosting (XGBoost) coupled with SHapley Additive exPlanations (SHAP) to quantitatively apportion potential ecological risks, pollution sources, and driving factors. Results showed that mean concentrations of Pb, Ni, and Cu exceeded the soil background values of Jiangxi Province, except for Cr. The mean comprehensive potential ecological risk index (RI) was 17.4, with 25.7% of sampling sites exhibiting moderate risk, and Ni being the primary ecological risk factor. Source apportionment identified three main sources: natural background (51.1%), industrial and mining activities (26.6%), and a mixed source of agricultural activities and traffic emissions (22.3%). Spatial variation of Cr, Cu, and Ni was predominantly governed by soil physicochemical properties (Fe and P contents), whereas Pb distribution was significantly influenced by industrial and mining activities. The combination of PMF and XGBoost-SHAP effectively delineated sources and driving factors, providing a scientific basis for targeted soil management and pollution control in mining-affected agricultural regions.
1. Introduction
Heavy metal contamination in agricultural soils poses a critical threat to ecosystem health and food safety, particularly in regions where mining and farming coexist. Conventional assessment methods such as enrichment factor and geo-accumulation index focus on single-element enrichment and fail to capture the combined toxic effects of multiple metals. While receptor models like APCS-MLR and UNMIX have been used for source apportionment, they suffer from limitations such as negative contributions and sensitivity to collinearity, hindering accurate identification of complex pollution sources. PMF overcomes these issues through non-negative constraints and uncertainty weighting, yet it does not incorporate spatial or environmental factors, limiting its explanatory power in heterogeneous landscapes.
To address this bottleneck, this study integrates PMF with XGBoost-SHAP, a machine learning approach capable of capturing nonlinear relationships and providing interpretable feature attributions. This hybrid methodology not only quantifies source contributions but also reveals the key environmental drivers and their marginal effects on heavy metal distribution. By applying this framework to paddy soils around a mining area in Jiangxi Province, we demonstrate its efficacy in identifying pollution sources and driving factors, thereby offering a robust tool for soil management and pollution control in mining-affected agricultural regions.
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HU Jiaxue, LI Pei, SONG Shihong, WANG Xueping (2026). Source Apportionment and Driving Factors of Heavy Metal Pollution in Paddy Soils Around a Mining Area Using PMF and XGBoost-SHAP Models. Chinese Journal of Environmental Engineering. https://doi.org/10.12030/j.cjee.202511087
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Frequently Asked Questions
What are the limitations of traditional source apportionment methods that this study aims to overcome?
Traditional methods like APCS-MLR and UNMIX often produce negative source contributions or are sensitive to collinearity among source profiles, leading to inaccurate results. PMF addresses these issues but lacks spatial and environmental context. This study integrates PMF with XGBoost-SHAP to incorporate nonlinear relationships and provide interpretable driving factors, enhancing source identification accuracy.
How does the XGBoost-SHAP model improve the interpretation of heavy metal spatial variation compared to conventional statistical methods?
XGBoost-SHAP captures nonlinear interactions between environmental factors and heavy metal concentrations, and SHAP values quantify each factor's marginal contribution and direction. This reveals that Fe and P contents are dominant drivers for Cr, Ni, and Cu, while Pb is influenced by industrial activities, offering mechanistic insights beyond linear correlations.
What are the practical implications of the source apportionment results for soil remediation in the study area?
The results indicate that natural background contributes 51.1% of heavy metals, while industrial/mining activities (26.6%) and agricultural/traffic sources (22.3%) are significant. This suggests that remediation efforts should prioritize controlling industrial emissions and agricultural practices, while accounting for naturally elevated background levels.
How reliable are the findings given the limited sample size (35 sampling points)?
The sample size is relatively small, which may affect the robustness of the PMF and XGBoost models. However, the consistency between PMF and XGBoost-SHAP results reduces uncertainty. Future studies should increase sampling density and incorporate temporal monitoring to validate and refine these findings.
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