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<p>Accurately identifying the sources of heavy metals in agricultural soils with geogenic high background is crucial for distinguishing natural enrichment from anthropogenic pollution and for safeguarding agricultural production. Traditional multivariate methods have limitations with high-dimensional, nonlinear environmental data, whereas the Self-Organizing Map (SOM) framework offers a new approach. This study focuses on Changning County, southern Sichuan, China, where Permian–Triassic Emeishan basalts, carbonate-bearing sedimentary strata, and pure carbonate rocks are superimposed within a single small agricultural county. Based on 2935 grid samples collected in 2013–2014, the concentrations and spatial distribution of As, Cd, Cr, Cu, Ni, Pb and Zn were analyzed; pollution was assessed by the Geo-accumulation Index (<i>I</i><sub><i>geo</i></sub>) and Potential Ecological Risk Index (<i>RI</i>); health risks were evaluated by Monte Carlo simulation; and the SOM–K-means model was used for source apportionment. The average Cd concentration (0.42&#xa0;mg/kg) was 5.3 times the Sichuan background, with 65.2% of points exceeding the risk screening threshold, making Cd the primary ecological risk. Cr was the dominant non-carcinogenic risk element (53.68% probability of exceeding standards for children), and the combined probabilistic carcinogenic risk for As, Cd, Cr and Ni reached 56.08% for adults and 58.14% for children. SOM distinguished three dominant source assemblages rather than fixed quantitative contributions: Cd associated with combined carbonate-rock weathering and anthropogenic activities; Cr, Cu and Ni inherited from Emeishan basalt and associated sedimentary strata; and As, Pb and Zn from human activities. This study establishes a transferable “lithology–source–risk” framework that decouples superimposed natural and anthropogenic signals, providing a scientific basis for differentiated soil management in carbonate-dominated high-background regions of Southwest China.</p>

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Source apportionment and probabilistic health risk assessment of soil heavy metals in a high-background carbonate region of Southwest China

  • 期刊论文

  • 发表日期：

    2026年7月18日

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  • Ge Jin,
  • Zhaoyu Zhou,
  • Zeming Shi,
  • Yafeng Zhang,
  • Huan Deng,
  • Zhangliang Shi

摘要

Accurately identifying the sources of heavy metals in agricultural soils with geogenic high background is crucial for distinguishing natural enrichment from anthropogenic pollution and for safeguarding agricultural production. Traditional multivariate methods have limitations with high-dimensional, nonlinear environmental data, whereas the Self-Organizing Map (SOM) framework offers a new approach. This study focuses on Changning County, southern Sichuan, China, where Permian–Triassic Emeishan basalts, carbonate-bearing sedimentary strata, and pure carbonate rocks are superimposed within a single small agricultural county. Based on 2935 grid samples collected in 2013–2014, the concentrations and spatial distribution of As, Cd, Cr, Cu, Ni, Pb and Zn were analyzed; pollution was assessed by the Geo-accumulation Index (Igeo) and Potential Ecological Risk Index (RI); health risks were evaluated by Monte Carlo simulation; and the SOM–K-means model was used for source apportionment. The average Cd concentration (0.42 mg/kg) was 5.3 times the Sichuan background, with 65.2% of points exceeding the risk screening threshold, making Cd the primary ecological risk. Cr was the dominant non-carcinogenic risk element (53.68% probability of exceeding standards for children), and the combined probabilistic carcinogenic risk for As, Cd, Cr and Ni reached 56.08% for adults and 58.14% for children. SOM distinguished three dominant source assemblages rather than fixed quantitative contributions: Cd associated with combined carbonate-rock weathering and anthropogenic activities; Cr, Cu and Ni inherited from Emeishan basalt and associated sedimentary strata; and As, Pb and Zn from human activities. This study establishes a transferable “lithology–source–risk” framework that decouples superimposed natural and anthropogenic signals, providing a scientific basis for differentiated soil management in carbonate-dominated high-background regions of Southwest China.