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

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.

Authors

Institutions

Publication Details

Journal
Toxics
Published
2026-06-30
DOI
https://doi.org/10.3390/toxics14070579
Primary Topic
Heavy metals in environment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Wei Chen, Zhigang Peng, Deng Xiao-zhou, Wenying Wu et al.
Toxics
Heavy metals in environment
article

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

Wei Chen, Zhigang Peng, Deng Xiao-zhou, Wenying Wu, J Z Guo, Yong-Hong Ma, Kun Gao, Zhi-Hao Zhao
article en

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.

ToxicsVol. 14(7)
Changsha University (CN), Huangshan University (CN), The Central Hospital of Xiao gan (CN), Xiaogan First People's Hospital (CN), China Nonferrous Metals Changsha Investigation Design Institute (CN), Hubei Engineering University (CN), Anqing Normal University (CN)
China Geological Survey
Openalex Percentile: Top 15%
Heavy metals in environment
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.