Geochemical evolution, geostatistical mapping and machine learning predictive modeling of groundwater fluoride: a case study of western Balochistan, Quetta.

Abstract

Around 2.6 billion people are at risk of tooth carries and fluorosis worldwide. Quetta is the worst affected district in Balochistan plateau. Endemic abnormal groundwater fluoride (F^–) lacks spatiotemporal studies. This research integrates geospatial distribution, geochemical signatures, and data driven method for evaluating F^– levels and population at risk. Groundwater F^– ranged from 0 to 3.4 mg/l in (n = 100) with 52% samples found unfit for drinking. Through geospatial IDW tool hotspot areas affected with low and high groundwater F^– levels were identified. Geochemical distribution in geological setups recognized sediment variation leads to high F^– (NaHCO₃) and low F^– (CaHCO₃) water types in low elevation (central plain) and high elevation (mountain foot) respectively. Results of the modified water quality index identified 60% samples to be unsuitable for drinking. Support vector machine (SVM), random forest regression (RFR) and classification and regression tree (CART) machine learning models found , Salinity and CA⁺² and as important contributing variables in groundwater F^– prediction. CART model with R² value of 0.732 outperformed RFR and SVM in predicting F^–. Noncarcinogenic health risk vulnerability from F^– increased from Adults < Teens < Children < Infants. Infants and children with hazard quotient values of 11.3 and 4.2 were the most vulnerable population at risk for consuming F^– contaminated groundwater. The research emphasizes on both nutritional need and hazardous effect of F^–, and development of desirable limit for F^–.
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ABSTRACT ONLINE AT https://link.springer.com/article/10.1007/s10653-024-02335-2