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

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 to 2.8ppm, with 40% of samples exceeding the World Health Organization's permissible limit (1.5ppm). Physicochemical analysis revealed significant differences in electrical conductivity (EC), total dissolved solids (TDS), total hardness (TH) and major ion concentrations (Na , HCO , Ca ) between fluoride-contaminated (FC) and fluoride-uncontaminated (FU) groups. Higher Na and HCO 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. [Abstract copyright: © 2024. The Author(s), under exclusive licence to Springer Nature B.V.]

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(10), 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: 2024-10
• Deposit Date: Aug 28, 2024
• Publicly Available Date: Aug 28, 2025
• Journal: Environmental geochemistry and health
• Print ISSN: 0269-4042
• Electronic ISSN: 1573-2983
• Publisher: Springer Verlag
• Peer Reviewed: Peer Reviewed
• Volume: 46
• Issue: 10
• Pages: 400
• DOI: https://doi.org/10.1007/s10653-024-02177-y
• Keywords: Mica-mining regions, Mining, Fluoride contamination, Self-organising maps, Human health risk, Humans, Child, Environmental Monitoring - methods, Water Pollutants, Chemical - analysis, Risk Assessment, Sobol sensitivity analysis, Aluminum Silicates, Fluorides - analysis, Groundwater - chemistry, Machine Learning
• Additional Information: This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10653-024-02177-y