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<p>Heavy metal contamination in lacustrine ecosystems has emerged as a global environmental challenge. Despite extensive research, many studies remain limited to surface sediment analysis, lacking systematic investigations into vertical metal distributions in columnar sediments and integrated source apportionment methodologies. Such gaps hinder the development of targeted pollution mitigation strategies. To address these limitations, this study employs a coupled self-organizing map (SOM) and principal component analysis (PCA) framework to characterize nine heavy metals (As, Hg, Cr, Ni, Cu, Zn, Cd, Pb, Mn) in 20 sediment cores from Honghu Lake. Key findings reveal that Cd concentrations average 3.1-fold higher than background levels, with significantly elevated pollution risks observed in shallow sediments (0–18&#xa0;cm) compared to deeper layers (18–50&#xa0;cm, <i>p</i> &lt; 0.05), indicative of intensified anthropogenic inputs from industrial activities. Spatial analysis shows higher metal concentrations in non-upstream junction sites, confirming anthropogenic dominance over natural sources. Source apportionment via PCA and positive matrix factorization (PMF) identifies industrial-chemical (40.31%), agricultural-electronic (24.81%), and natural (34.87%) contributions, with Cd accounting for the highest ecological risk (<i>p</i> &lt; 0.001). This integrated SOM–PCA–PMF approach provides a robust framework for linking vertical pollution profiles with source attribution, offering scientific basis for precision ecological restoration in freshwater systems.</p>

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Vertical distribution, source apportionment, and ecological risk assessment of heavy metals in columnar sediments of Honghu Lake: a coupled SOM–PCA–PMF approach

  • 期刊论文

  • 发表日期：

    2026年3月13日

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  • Yiting Cheng,
  • Yuanzheng Liu,
  • Xi Liu,
  • Xue Jiang,
  • Xiaoguo Chen,
  • Zhi Wang

摘要

Heavy metal contamination in lacustrine ecosystems has emerged as a global environmental challenge. Despite extensive research, many studies remain limited to surface sediment analysis, lacking systematic investigations into vertical metal distributions in columnar sediments and integrated source apportionment methodologies. Such gaps hinder the development of targeted pollution mitigation strategies. To address these limitations, this study employs a coupled self-organizing map (SOM) and principal component analysis (PCA) framework to characterize nine heavy metals (As, Hg, Cr, Ni, Cu, Zn, Cd, Pb, Mn) in 20 sediment cores from Honghu Lake. Key findings reveal that Cd concentrations average 3.1-fold higher than background levels, with significantly elevated pollution risks observed in shallow sediments (0–18 cm) compared to deeper layers (18–50 cm, p < 0.05), indicative of intensified anthropogenic inputs from industrial activities. Spatial analysis shows higher metal concentrations in non-upstream junction sites, confirming anthropogenic dominance over natural sources. Source apportionment via PCA and positive matrix factorization (PMF) identifies industrial-chemical (40.31%), agricultural-electronic (24.81%), and natural (34.87%) contributions, with Cd accounting for the highest ecological risk (p < 0.001). This integrated SOM–PCA–PMF approach provides a robust framework for linking vertical pollution profiles with source attribution, offering scientific basis for precision ecological restoration in freshwater systems.