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<p>Source apportionment of heavy metals in waters have rarely been documented, contrasting to those vastly being conducted in soils and sediments; in addition, an integrated framework, such as combining correlation analysis (CA), Positive Matrix Factorization (PMF) and the GeoDetector model (GDM) in source apportionment of heavy metals in the regional surface and ground waters have not been examined. This gap impedes understanding aquatic contamination pathways, where source identification is critical for regional water management. In this study, the above mentioned framework was applied to quantify the contributions and spatial drivers of six heavy metals (As, Cd, Cr, Cu, Pb, Zn) in surface and groundwater across the Manas River Basin in Xinjiang, China. Analysis of 94 samples showed Surface water metal concentrations were within Class I limits of China’s environmental quality standards, while groundwater showed localized exceeding for Zn, As, and Cr. CA result indicated positive Cu–As and Pb–Zn associations, whereas Zn–Cd showed consistent negative correlations across surface and groundwater. PMF results revealed dominant sources in surface water as agriculture (34.25%) and transportation (23.72%), while groundwater metals were mainly from mixed natural-agricultural sources, with 43.53% attributed to farming activities. GDM further identified soil type (q = 0.32–0.43), husbandry density (q = 0.23–0.40), and precipitation (q &gt; 0.20) as primary spatial drivers, with notable geogenic-anthropogenic synergies in groundwater. The multi-model approach effectively locates heavy metal sources in coupled surface-groundwater systems and provided a quantitative assessment of the factors driving variations in heavy metal concentrations.</p>

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Integrated PMF-GeoDetector approach for source apportionment and spatial drivers of heavy metals in coupled surface-groundwater systems

  • 期刊论文

  • 发表日期：

    2025年10月21日

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  • Zhiying Li,
  • Chalachew Alemu,
  • Fen Yang,
  • Chaoyang Wei

摘要

Source apportionment of heavy metals in waters have rarely been documented, contrasting to those vastly being conducted in soils and sediments; in addition, an integrated framework, such as combining correlation analysis (CA), Positive Matrix Factorization (PMF) and the GeoDetector model (GDM) in source apportionment of heavy metals in the regional surface and ground waters have not been examined. This gap impedes understanding aquatic contamination pathways, where source identification is critical for regional water management. In this study, the above mentioned framework was applied to quantify the contributions and spatial drivers of six heavy metals (As, Cd, Cr, Cu, Pb, Zn) in surface and groundwater across the Manas River Basin in Xinjiang, China. Analysis of 94 samples showed Surface water metal concentrations were within Class I limits of China’s environmental quality standards, while groundwater showed localized exceeding for Zn, As, and Cr. CA result indicated positive Cu–As and Pb–Zn associations, whereas Zn–Cd showed consistent negative correlations across surface and groundwater. PMF results revealed dominant sources in surface water as agriculture (34.25%) and transportation (23.72%), while groundwater metals were mainly from mixed natural-agricultural sources, with 43.53% attributed to farming activities. GDM further identified soil type (q = 0.32–0.43), husbandry density (q = 0.23–0.40), and precipitation (q > 0.20) as primary spatial drivers, with notable geogenic-anthropogenic synergies in groundwater. The multi-model approach effectively locates heavy metal sources in coupled surface-groundwater systems and provided a quantitative assessment of the factors driving variations in heavy metal concentrations.