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A modeling approach for the suitability evaluation and human health risk assessment of heavy metals dispersion in groundwater resources

Author

Listed:
  • Shivam Saw

    (Indian Institute of Technology (ISM))

  • Prasoon Kumar Singh

    (Indian Institute of Technology (ISM))

  • Jaydev Kumar Mahato

    (Indian Institute of Technology (ISM))

  • Rohit Patel

    (Indian Institute of Technology (ISM))

  • Deep Shikha

    (Indian Institute of Technology (ISM))

Abstract

Groundwater is an essential natural resource that ensures people have access to safe drinking water, which is important for their health and well-being. The present study assessed the suitability of groundwater by using the Canadian Council of Ministers of the Environment Water Quality Index (CCMEWQI) and the Groundwater Water Quality Index (GWQI) model. The hydrogeochemical characterization in groundwater was elucidated by plotting the piper trilinear diagram of major cations (Ca2+, Na+, Mg2+, K+) and anions (HCO3−, Cl−, SO42−, F−). Performance evaluation of WQI models indicated that the CCMEWQI performed better than GWQI for assessing groundwater quality index with a comparatively higher R2 value of 0.97 and less NMSE, RMSE, and MAPE. Six heavy metals, viz. arsenic (As), iron (Fe), manganese (Mn), copper (Cu), lead (Pb), and nickel (Ni), were investigated in the groundwater from 65 locations in Ranchi city. Significant water-level fluctuation was observed (WLF—3.5–5.7 m) in the central and western parts of the study area. The non-carcinogenic and carcinogenic health risk of heavy metals hazardous quotient (HQ) was investigated and found that As (HQ > 1), followed by Mn > Pb > Ni > Fe > Cu (HQ 1).

Suggested Citation

  • Shivam Saw & Prasoon Kumar Singh & Jaydev Kumar Mahato & Rohit Patel & Deep Shikha, 2025. "A modeling approach for the suitability evaluation and human health risk assessment of heavy metals dispersion in groundwater resources," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 27(3), pages 7871-7895, March.
    • Handle: RePEc:spr:endesu:v:27:y:2025:i:3:d:10.1007_s10668-023-04227-4
    DOI: 10.1007/s10668-023-04227-4
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    References listed on IDEAS

    as
    1. Kusum Pandey & Shiv Kumar & Anurag Malik & Alban Kuriqi, 2020. "Artificial Neural Network Optimized with a Genetic Algorithm for Seasonal Groundwater Table Depth Prediction in Uttar Pradesh, India," Sustainability, MDPI, vol. 12(21), pages 1-24, October.
    2. Rohitashw Kumar & Saika Manzoor & Dinesh Kumar Vishwakarma & Nadhir Al-Ansari & Nand Lal Kushwaha & Ahmed Elbeltagi & Kallem Sushanth & Vishnu Prasad & Alban Kuriqi, 2022. "Assessment of Climate Change Impact on Snowmelt Runoff in Himalayan Region," Sustainability, MDPI, vol. 14(3), pages 1-23, January.
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