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<p>In tropical megacities undergoing rapid industrialization, comprehensive assessments of seasonal variability in heavy metal(loid)s sources and ecological risks in river–canal sediments remain limited, particularly in Southeast Asian urban systems. Urban sediments function as both secondary sinks and potential sources of heavy metal(loid)s, yet integrated evaluations linking geochemistry, source apportionment, and predictive risk modeling are still unexplored. A total of 144 sediment samples were collected during the dry and wet seasons of 2022. Here, we applied an integrated methodological framework combining geochemical characterization, sequential extraction for metal speciation, non-negative matrix factorization (NMF) for source apportionment, GIS-based risk mapping, and machine-learning prediction using Random Forest and XGBoost, with SHAP interpretation. Results demonstrated pronounced spatial and seasonal heterogeneity. Metal burdens were dominated by Fe, Cr, Cu, Zn, and As, primarily derived from crustal/soil inputs and traffic-related urban emissions. Sequential extraction indicated that Cr and Fe were largely associated with residual fractions, whereas Zn, Cu, and As were enriched in redox-sensitive phases. NMF resolved five source profiles, and risk mapping consistently identified high-risk hotspots (levels 4–5) in the Nhieu Loc–Thi Nghe canal, with additional concerns in Ben Nghe canal. Among predictive models, XGBoost exhibited superior performance, and SHAP analysis identified Cr, Fe, and As as the dominant risk drivers. Overall, this study establishes a data-driven, interpretable framework that supports evidence-based prioritization of remediation zones and strategic urban sediment management. Future work integrating metal bioavailability, ecotoxicological responses, and hydrodynamic processes would further refine risk prediction and sustainable intervention planning.</p>

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Environmental geochemical and machine learning assessment of heavy metal(loid)s sources and risks in urban river–canal sediments of the Saigon system

  • 期刊论文

  • 发表日期：

    2026年4月2日

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  • Nguyen Xuan Tong,
  • Phan Do Trong Nghia,
  • Cao Phuong Thao,
  • Truong Anh Tu,
  • Nguyen Kim Hoa,
  • Nguyen Duy Thanh,
  • Luu Tang Phuc Khang

摘要

In tropical megacities undergoing rapid industrialization, comprehensive assessments of seasonal variability in heavy metal(loid)s sources and ecological risks in river–canal sediments remain limited, particularly in Southeast Asian urban systems. Urban sediments function as both secondary sinks and potential sources of heavy metal(loid)s, yet integrated evaluations linking geochemistry, source apportionment, and predictive risk modeling are still unexplored. A total of 144 sediment samples were collected during the dry and wet seasons of 2022. Here, we applied an integrated methodological framework combining geochemical characterization, sequential extraction for metal speciation, non-negative matrix factorization (NMF) for source apportionment, GIS-based risk mapping, and machine-learning prediction using Random Forest and XGBoost, with SHAP interpretation. Results demonstrated pronounced spatial and seasonal heterogeneity. Metal burdens were dominated by Fe, Cr, Cu, Zn, and As, primarily derived from crustal/soil inputs and traffic-related urban emissions. Sequential extraction indicated that Cr and Fe were largely associated with residual fractions, whereas Zn, Cu, and As were enriched in redox-sensitive phases. NMF resolved five source profiles, and risk mapping consistently identified high-risk hotspots (levels 4–5) in the Nhieu Loc–Thi Nghe canal, with additional concerns in Ben Nghe canal. Among predictive models, XGBoost exhibited superior performance, and SHAP analysis identified Cr, Fe, and As as the dominant risk drivers. Overall, this study establishes a data-driven, interpretable framework that supports evidence-based prioritization of remediation zones and strategic urban sediment management. Future work integrating metal bioavailability, ecotoxicological responses, and hydrodynamic processes would further refine risk prediction and sustainable intervention planning.