A Comprehensive Exploration of Groundwater Quality of Ambagarh Chowki Region, Chhattisgarh, India: Water Quality...

Access to safe and clean drinking water remains a critical global challenge, with groundwater as a primary source for billions of people. Further, toxic contaminants increasingly threaten groundwater quality, posing significant health risks. This study analyzed groundwater from 52 villages of Ambagarh Chowki region, Chhattisgarh, for 15 physicochemical parameters to evaluate spatial variation, develop inter-parameter correlations, and compute a water quality index (WQI) to classify water for drinking purposes. Human health risk assessment (HHRA) was performed to quantify non-carcinogenic and carcinogenic risks for adults and children through oral and dermal exposure pathways. Predictive modeling using artificial neural networks (ANN) was also conducted to enable efficient and accurate WQI prediction for large datasets. The regions have shown significant spatial variation of pH, TDS, Fe, As, and fluoride concentrations above their permissible limits. The findings revealed that 12% of the samples were of excellent quality, 36% good, 31.8% poor, 7% very poor, and 13.2% unfit for drinking. Carcinogenic risks were significant at 21.55% of sampling points for adults and 57.3% for children. The ANN model demonstrated high predictive accuracy (𝑅² = 0.99, MSE = 0.843), confirming its applicability for rapid WQI prediction. Further, strong correlation has not been observed between the water quality parameters. This study highlights the critical health risks of contaminated groundwater in the Ambagarh Chowki region. It demonstrates the potential of integrating WQI, HHRA, and ANN modeling for comprehensive water quality evaluation and effective decision-making. These findings contribute to addressing global water resource challenges by providing scalable and actionable solutions. SUMMARY: Developed a water quality index (WQI) to assess the suitability of groundwater for drinking purposes. WQI indicates that 48% of samples have water quality suitable for drinking purposes. Identified severe groundwater contamination by arsenic and iron. Human health risk assessment (HHRA) indicates significant carcinogenic risks. Artificial neural network (ANN) modeling was used to predict the WQI.