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<p>Heavy metal (HM) contamination of groundwater has become a global problem, posing a significant risk to groundwater security and human health. This study employs Monte Carlo simulation coupled with Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and the Positive Matrix Factorization (PMF) model on 68 groundwater samples to identify potential sources of HM pollutants and assess source-specific health risks (SSHR) in a well-known industrialized site (Kanpur Nagar) in the middle Gangetic Basin, India. A series of pollution indices revealed that groundwater exhibited moderate to high levels of contamination from HM pollutants. PMF was preferred over the PCA-MLR model for quantitative source apportionment of pollution, as it provided a more refined and differentiated analysis of pollution sources. Six HM sources were identified by PMF, including tannery effluents (17.8%), coal combustion (22.4%), municipal/industrial leachate (11.9%), natural sources (14.9%), traffic-related pollutants (12.8%), and agricultural/agrochemical applications (20.8%). Health risk assessments indicated that non-carcinogenic risks for all susceptible sub-populations were negligible, whereas carcinogenic risks could not be ignored. The SSHR assessment identified tannery effluents as the most significant contributor to health risks for infants, followed by natural sources. For children, natural sources posed the highest health risk, followed by agricultural sources. These findings provide valuable insights for policymakers, local authorities, and environmental scientists in developing effective groundwater management and pollution mitigation strategies.</p>

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Groundwater Contamination Characterization and Source Apportionment of Heavy Metals and Associated Source-Specific Health Risk Appraisal Using Monte Carlo Simulation Coupled with PCA-MLR and PMF Models in the Middle Gangetic Basin, India

  • 期刊论文

  • 发表日期：

    2025年2月22日

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  • Akhtar Jahan,
  • Nachiketa Rai,
  • Mohd Usman Khan

摘要

Heavy metal (HM) contamination of groundwater has become a global problem, posing a significant risk to groundwater security and human health. This study employs Monte Carlo simulation coupled with Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and the Positive Matrix Factorization (PMF) model on 68 groundwater samples to identify potential sources of HM pollutants and assess source-specific health risks (SSHR) in a well-known industrialized site (Kanpur Nagar) in the middle Gangetic Basin, India. A series of pollution indices revealed that groundwater exhibited moderate to high levels of contamination from HM pollutants. PMF was preferred over the PCA-MLR model for quantitative source apportionment of pollution, as it provided a more refined and differentiated analysis of pollution sources. Six HM sources were identified by PMF, including tannery effluents (17.8%), coal combustion (22.4%), municipal/industrial leachate (11.9%), natural sources (14.9%), traffic-related pollutants (12.8%), and agricultural/agrochemical applications (20.8%). Health risk assessments indicated that non-carcinogenic risks for all susceptible sub-populations were negligible, whereas carcinogenic risks could not be ignored. The SSHR assessment identified tannery effluents as the most significant contributor to health risks for infants, followed by natural sources. For children, natural sources posed the highest health risk, followed by agricultural sources. These findings provide valuable insights for policymakers, local authorities, and environmental scientists in developing effective groundwater management and pollution mitigation strategies.