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Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics

Nandi, Rupsha; Mondal, Sandip; Mandal, Jajati; Bhattacharyya, Pradip

Authors

Rupsha Nandi

Sandip Mondal

Pradip Bhattacharyya



Abstract

The contribution of mica mining activities to fluoride (F-) contamination in groundwater has been chased in this study. For the purpose, groundwater samples (n=40, replicated thrice) were collected during the post-monsoons (September-October) from a mica mining area in the Tisri block of Giridih district, Jharkhand. The study has employed a synergy of classical aquifer chemistry, statistical approaches, different indices, Self-Organising Maps (SOM), and Sobol sensitivity index (SSI) to unveil the underlying aquifer chemistry, identify the impacts of mining activities on groundwater quality and its associated health hazard. Fluoride levels varied from 0.34 ppm to 2.8 ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5 ppm). Physicochemical analysis revealed significant differences in electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and major ion concentrations (Na+, HCO3-, Ca2+) between fluoride-contaminated (FC) and fluoride-uncontaminated (FU) groups. Higher Na+ and HCO3- associated with F- contaminated samples, were indicative of silicate weathering and carbonate dissolution as primary geogenic sources for this ion. Health risk assessment (HRA) revealed hazard quotient (HQ) values exceeding unity, indicating non-carcinogenic risks, particularly for children in most samples from group FC. The mean Water Quality Index (WQI) of FC group (156.76 ± 7.30) was significantly higher (p<0.05) than group FU indicating of its unsuitability . SOM could accurately (80%) predict presence of fluoride in water samples based on other major ions. Sobol sensitivity analysis successfully identified fluoride concentration and body weight as most impactful parameters affecting human health. The integration of advanced modelling techniques and geospatial analysis as Inverse Distance Weightage (IDW) maps has provided a robust framework for ongoing groundwater quality monitoring in mining-affected regions and can help proactive intervention in risk-prone areas. Overall, this comprehensive study takes us a step ahead towards ensuring safe drinking water access for the global community.

Citation

Nandi, R., Mondal, S., Mandal, J., & Bhattacharyya, P. (2024). Deciphering geochemical fingerprints and health implications of groundwater fluoride contamination in mica mining regions using machine learning tactics. Environmental Geochemistry and Health, 46, Article 400. https://doi.org/10.1007/s10653-024-02177-y

Journal Article Type Article
Acceptance Date Aug 17, 2024
Online Publication Date Aug 27, 2024
Publication Date Aug 27, 2024
Deposit Date Nov 5, 2024
Publicly Available Date Aug 28, 2025
Print ISSN 0269-4042
Electronic ISSN 1573-2983
Publisher Springer Verlag
Peer Reviewed Peer Reviewed
Volume 46
Article Number 400
DOI https://doi.org/10.1007/s10653-024-02177-y