62 Advancing the Understanding and Management of Groundwater Pollution with Arsenic and Other Geogenic Contaminants Using Geospatial Tools, Machine Learning, and Data Science (Posters)

The growing trend of data aggregation and analysis, along with emerging technologies like machine learning, has significantly improved our understanding of groundwater basins, enabling better assessments of water quality for direct consumption and treatment in groundwater supply management.

Sunday, 19 October 2025
8:00 AM - 5:30 PM
HBGCC, Hall 1

Hydrochemical Clustering and Contamination Assessment of Urban Rivers in North Tehran, Iran

Paper number: 62-0
Program: Technical Sessions
Time: 8:00 AM – 8:00 AM

Authors

  • Maryam Salimi
    Department of Minerals and Groundwater Resources, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Select One...
  • Hamid Reza Nassery
    Department of Minerals and Groundwater Resources, Faculty of Earth Sciences, Shahid Beheshti University, Tehran
  • Meysam Vadiati
    Hubert H. Humphrey Fellowship Program, Global Affairs, University of California,, Davis
  • Prosun Bhattacharya Presenting Author
    KTH-International Groundwater Arsenic Research Group, Department of Sustainable Development, Environmental Science and Engineering, KTH Royal Institute of Technology,, Stockholm

Abstract

Water quality management is an increasing concern in semi-arid urban regions. This study assessed the hydrochemical variability and diverse sources of contamination of six rivers in North Tehran, Iran. A total of 72 surface water samples collected in four seasons during 2020 were analyzed for major ions, nutrient elements, and microbial indicators to classify spatial-temporal water quality patterns, Hierarchical Clustering Analysis (HCA), Fuzzy C-Means (FCM), Genetic Algorithm Fuzzy C-Means (GAFCM), and Self-Organizing Map (SOM) were applied. The results revealed distinct seasonal patterns, with FCM and GAFCM categorizing the samples into two clusters during winter-spring and three clusters in summer-autumn. These patterns were consistent with HCA and SOM outputs, which identified groupings related to distinct river sections and varying contamination intensities. The dominant hydrochemical processes were mineral dissolution and weathering, while anthropogenic inputs, including wastewater discharge, led to elevated sulfate, nitrate, and phosphate levels  which was further supported by microbial analyses. The integration of clustering methods with artificial neural network (ANN)-based approaches  provided an effective approach for capturing spatial and seasonal dynamics of water quality., including Gibbs diagrams, Hounslow ion ratios, and saturation index calculations, highlighted the combined influence of geogenic and human-induced processes in shaping river chemistry. Supporting analyses including the Gibbs plots, Hounslow ion ratios, and saturation index calculations, demonstrated the combined influence of natural geogenic  and human-induced processes in controlling the hydrochemical characteristics of the north Tehran rivers. These findings provide valuable insights for sustainable water resource management and effective pollution control in rapidly urbanizing water-stressed regions.

Keywords: Urban rivers, Hydrochemistry, Water quality assessment, Clustering techniques, Contamination sources, Tehran

Geological Society of America Abstracts with Programs. Vol. 57, No. 6

doi: 10.1130/abs/2025AM-413205

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