Assessment of PAHs in selected aquatic systems using GC-MS followed by ANN and PMF modelling

Authors

  • Soma Mukherjee Department of Environmental Science, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India https://orcid.org/0000-0002-5347-3931
  • Madhurima Kanji Department of Environmental Science, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India https://orcid.org/0009-0008-2094-8030
  • Susmita Bhowmick Department of Environmental Science, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India
  • Soumidipa Chatterjee Department of Environmental Science, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India
  • Anushka Das Department of Environmental Science, University of Kalyani, Kalyani, Nadia-741235, West Bengal, India
  • Somnath Sen ICMR-Centre for Ageing and Mental Health, Kolkata, India

DOI:

https://doi.org/10.47915/jiaem.v46i3.35320

Keywords:

Surface water, Polycyclic aromatic hydrocarbons, Gas Chromatography-Mass Spectrometry, Artificial Neural Network, Positive Matrix Factorization

Abstract

Surface water quality is a critical determinant of ecosystem health and public safety, especially in regions experiencing rapid urbanization and industrialization. This study represents the assessment of polycyclic aromatic hydrocarbons (PAHs) using gas chromatography-mass spectrometry (GC-MS) in nineteen surface water samples collected from selected peri-urban sites of West Bengal. Among 16 polycyclic aromatic hydrocarbons (PAHs), only fluorene (Fl), phenanthrene (Phe), chrysene (Chr) and benzo[a]pyrene (BaP) were detected by GC-MS at some locations.  The Water Quality Index (WQI) values were calculated and approximately 10.5% of the samples were classified as poor, 57.9% as very poor and 31.6% under the unsuitable category. Pearson correlation analysis was performed to understand the significant relationships among various parameters. ANN-based prediction and sensitivity analysis revealed that BOD and phenanthrene exerted the greatest influence on the availability of fluorene and chrysene. Positive Matrix Factorization (PMF) indicated three dominant sources such as industrial and vehicular emissions (39%), biomass and biogas burning (35%) and fossil fuel combustion (26%). These results emphasized the urgent need for regular monitoring and effective management to safeguard water resources and public health in rapidly developing regions.

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Published

2026-10-30

How to Cite

Assessment of PAHs in selected aquatic systems using GC-MS followed by ANN and PMF modelling. (2026). Journal of Indian Association for Environmental Management (JIAEM), 46(3), 57-73. https://doi.org/10.47915/jiaem.v46i3.35320

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