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Article

Multiscale Source Apportionment of Heavy Metals in Mining-Affected Farmland Soils Using PCA-PMF Modeling

1
Changsha General Survey of Natural Resources Center, Changsha 410600, China
2
Huangshan Observation and Research Station for Land-Water Resources, Huangshan 245400, China
3
Xiaogan Observation and Research Station for Natural Resources, Xiaogan 432000, China
4
Key Laboratory of Biodiversity Conservation and Characteristic Resource Utilization in Southwest Anhui, School of Life Sciences and Food Engineering, Anqing Normal University, Anqing 246133, China
5
Changsha Water Industry Group Co., Ltd., Changsha 410015, China
*
Author to whom correspondence should be addressed.
Toxics 2026, 14(7), 579; https://doi.org/10.3390/toxics14070579
Submission received: 20 May 2026 / Revised: 26 June 2026 / Accepted: 30 June 2026 / Published: 30 June 2026

Abstract

Polymetallic mining severely disrupts farmland soil ecosystems, yet the vertical migration of heavy metals, interlayer pollution disparities between topsoil and deep soil, and quantitative source apportionment of composite pollutants remain poorly understood in mining–agricultural overlapping zones. Two core hypotheses were accordingly proposed: mining-derived heavy metals can migrate downward and accumulate in deep soil layers, and the coupling of geostatistical analysis and receptor modeling enables reliable differentiation between geogenic and anthropogenic pollution sources. To test these hypotheses, 512 topsoil and 148 deep soil samples were collected from the Fenghuang Mining Area for quantification of eight metals and metalloids (including As). Geostatistical approaches, the single pollution index (Pi), and Nemerow comprehensive pollution index (PN) were utilized to characterize spatial heterogeneity and evaluate pollution severity, while a coupled PCA–PMF receptor model was adopted for quantitative source identification; vertical comparisons of element concentrations across soil profiles further validated the robustness of source apportionment outputs. The results revealed extensive heavy metal enrichment in both soil layers, with only topsoil Cd exceeding China’s risk screening value for agricultural land. Hg exhibited pronounced spatial variability and prominent anthropogenic fingerprints, and all target metals displayed consistent spatial distribution patterns along vertical soil profiles. Four distinct pollution sources were discriminated: geogenic sources dominating Cu, Zn, Cr, and Ni accumulation, mining-industrial emissions as the major contributor to Hg pollution, mixed industrial–agricultural inputs governing As and Pb enrichment, and traffic activities serving as the primary Cd source. Cd was identified as the priority pollutant threatening local farmland security. Confirmed downward percolation of anthropogenic metals creates persistent latent ecological risks across the study area, where mining and industrial discharges represent the dominant anthropogenic pollution inputs. This work systematically elucidates the geochemical signatures, vertical migration pathways, and quantitative source contributions of heavy metals in mining-disturbed farmlands, delivering solid scientific support for targeted source control, tiered risk management, and soil ecological remediation within the Fenghuang Mining Area. Moreover, the multi-method integrated analytical framework developed herein provides transferable guidance for heavy metal pollution mitigation in global polymetallic mining–agricultural regions with analogous geological and industrial backgrounds.
Keywords: heavy metals; potential ecological risk; source identification; soil; spatial distribution heavy metals; potential ecological risk; source identification; soil; spatial distribution

Share and Cite

MDPI and ACS Style

Deng, X.-Z.; Ma, Y.-H.; Wu, W.-Y.; Peng, Z.-G.; Zhao, Z.-H.; Gao, K.; Guo, J.-J.; Chen, W. Multiscale Source Apportionment of Heavy Metals in Mining-Affected Farmland Soils Using PCA-PMF Modeling. Toxics 2026, 14, 579. https://doi.org/10.3390/toxics14070579

AMA Style

Deng X-Z, Ma Y-H, Wu W-Y, Peng Z-G, Zhao Z-H, Gao K, Guo J-J, Chen W. Multiscale Source Apportionment of Heavy Metals in Mining-Affected Farmland Soils Using PCA-PMF Modeling. Toxics. 2026; 14(7):579. https://doi.org/10.3390/toxics14070579

Chicago/Turabian Style

Deng, Xiao-Zhou, Yong-Hong Ma, Wen-Ying Wu, Zhi-Gang Peng, Zhi-Hao Zhao, Kun Gao, Jia-Jia Guo, and Wei Chen. 2026. "Multiscale Source Apportionment of Heavy Metals in Mining-Affected Farmland Soils Using PCA-PMF Modeling" Toxics 14, no. 7: 579. https://doi.org/10.3390/toxics14070579

APA Style

Deng, X.-Z., Ma, Y.-H., Wu, W.-Y., Peng, Z.-G., Zhao, Z.-H., Gao, K., Guo, J.-J., & Chen, W. (2026). Multiscale Source Apportionment of Heavy Metals in Mining-Affected Farmland Soils Using PCA-PMF Modeling. Toxics, 14(7), 579. https://doi.org/10.3390/toxics14070579

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