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<p>Accurate source apportionment is essential for identifying major contributors to heavy-metal pollution and guiding effective water management. However, most receptor-model studies overlook the spatial dimension of pollution sources. To address this gap, this study integrates a GIS-based Urban–Rural Gradient Index (URGI) with Positive Matrix Factorization (PMF) to provide spatially explicit source interpretation. The URGI, derived from land-use and population rasters, quantified the degree of urbanization and human activity intensity and was coupled with the PMF contribution matrix to distinguish urban- and rural-related sources.</p><p>The framework was applied to the Nanchang section of the Fu River during both wet and dry periods, analyzing nine heavy metals (As, V, Mn, Fe, Zn, Sr, Ba, Cd, and Cr). Hotspots of V, Mn, Sr, and Ba were concentrated in highly urbanized areas, may not directly due to local emissions but because these elements share common occurrence patterns and broad migration pathways that spatially intersect with urban regions. In contrast, Fe, Zn, and As showed weak or negative responses to the URGI, with rural hotspots mainly driven by natural background inputs and hydrological mobilization rather than anthropogenic activities. PMF resolved five major sources—natural sources, urban-industrial sources, rural-composite sources, atmospheric deposition sources, and traffic sources—with contributions of 64.02%, 16.82%, 0.18%, 15.63%, and 3.35% in the dry period, and 42.97%, 27.34%, 3.76%, 14.41%, and 11.51% in the wet period. Natural sources dominated in both seasons, while urban-industrial and traffic sources increased during the wet period.</p><p>Coupling URGI with PMF strengthened the spatial interpretability of source factors, reduced subjectivity in factor identification, and revealed clear urban–rural contrasts in source contributions, supporting targeted prevention and precision management of heavy-metal pollution in complex watershed environments.</p>

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Coupled PMF and urban–rural gradient index (URGI) analysis for source apportionment of heavy metals in surface water: a case study of the Fu River, China

  • 期刊论文

  • 发表日期：

    2026年1月10日

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  • Kangcheng Deng,
  • Yanhong Zhang,
  • Junhua Chen,
  • Zhiyu Meng,
  • Kaixuan Sun,
  • Yue Luo

摘要

Accurate source apportionment is essential for identifying major contributors to heavy-metal pollution and guiding effective water management. However, most receptor-model studies overlook the spatial dimension of pollution sources. To address this gap, this study integrates a GIS-based Urban–Rural Gradient Index (URGI) with Positive Matrix Factorization (PMF) to provide spatially explicit source interpretation. The URGI, derived from land-use and population rasters, quantified the degree of urbanization and human activity intensity and was coupled with the PMF contribution matrix to distinguish urban- and rural-related sources.

The framework was applied to the Nanchang section of the Fu River during both wet and dry periods, analyzing nine heavy metals (As, V, Mn, Fe, Zn, Sr, Ba, Cd, and Cr). Hotspots of V, Mn, Sr, and Ba were concentrated in highly urbanized areas, may not directly due to local emissions but because these elements share common occurrence patterns and broad migration pathways that spatially intersect with urban regions. In contrast, Fe, Zn, and As showed weak or negative responses to the URGI, with rural hotspots mainly driven by natural background inputs and hydrological mobilization rather than anthropogenic activities. PMF resolved five major sources—natural sources, urban-industrial sources, rural-composite sources, atmospheric deposition sources, and traffic sources—with contributions of 64.02%, 16.82%, 0.18%, 15.63%, and 3.35% in the dry period, and 42.97%, 27.34%, 3.76%, 14.41%, and 11.51% in the wet period. Natural sources dominated in both seasons, while urban-industrial and traffic sources increased during the wet period.

Coupling URGI with PMF strengthened the spatial interpretability of source factors, reduced subjectivity in factor identification, and revealed clear urban–rural contrasts in source contributions, supporting targeted prevention and precision management of heavy-metal pollution in complex watershed environments.