Prasoon Kumar Singh

Groundwater chemistry across semi-arid India is deteriorating under increasing abstraction, agricultural intensification, and urbanization, compromising the region's drinking water supply. Reliable assessment requires understanding both hydrogeochemical processes and uncertainty arising from natural hydrochemical variability. Most previous studies, however, have treated hydrogeochemical characterization and water quality indexing separately. This study addresses this gap by linking groundwater evolution with uncertainty in quality classification and distinguishing anthropogenic from geogenic sources using multiple analytical methods. The results show widespread salinization driven by enrichment of major ions, particularly sodium (Na+), magnesium (Mg2+), calcium (Ca2+), sulfate (SO42-), and chloride (Cl-). Hydrogeochemical methods (Piper and Gibbs diagrams) indicate that groundwater evolution is primarily due to evaporation, with only a small contribution from rock-water interaction, while multivariate techniques (Pearson correlation, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA)) provide further evidence of a geogenic mineralization signature and identify a localized anthropogenic potassium (K+) contamination signal. Almost 78.9% of the samples were rated as poor to extremely poor for drinking purposes by the deterministic Entropy-Weighted Water Quality Index (EWQI), with 51.5% classified as extremely poor. A Monte Carlo simulation (with 10,000 realizations) showed that groundwater quality classification is notably uncertain. The sensitivity analysis indicates that K+, Cl-, and total dissolved solids are major factors driving EWQI variability, with K+ serving as a local anthropogenic indicator in multivariate statistical studies and facies analyses. These results indicate that deterministic groundwater quality classification must account for uncertainty to provide a more reliable foundation for assessing groundwater quality in stressed semi-arid aquifers.

Groundwater is the primary source of freshwater for drinking, agriculture, and industrial activities in mining-intensive regions; however, its quality is increasingly threatened by acid mine drainage (AMD) and heavy metal contamination associated with coal mining. Despite the environmental significance of the Magadh-Amrapali coalfield, integrated post-monsoon investigations on groundwater quality, heavy metals and health risks remains limited. The post-monsoon season was selected because groundwater quality reflects the combined effects of monsoonal recharge, contaminant leaching and dilution. This study, covering 198 km² surrounding the mining area operated by Central Coalfields Limited, Jharkhand, India, analyzed thirty groundwater samples for 13 physicochemical parameters and six heavy metals (Fe, Cr, Ni, Cu, Zn, and As) using conventional analytical techniques and inductively coupled plasma-optical emission spectrometry( ICP-OES). Entropy-Weighted Water Quality Index (EWQI) and traditional Groundwater Quality Index (GWQI) were comparatively evaluated, with EWQI applying objective entropy-based parameter weighting to reduce subjectivity, while GIS-based Inverse Distance Weighting (IDW) described spatial contamination. Results revealed neutral to slightly alkaline groundwater (pH 7.13–7.70), with iron exceeding the WHO guideline in 63.3% of samples. EWQI demonstrated better performance over GWQI (R² = 0.87; MAPE = 6.67%). Principal Component Analysis explained 63.27% of the total variance, while Piper analysis identified Ca–Mg–Cl/SO₄ as the dominant hydrochemical facies. Health-risk assessment indicated non-carcinogenic risks, expressed as Hazard Quotient and Hazard Index (HI), with HI >1 and elevated carcinogenic risks (CR) from arsenic and hexavalent chromium, particularly among children. The integrated approach provides a robust framework for groundwater management and sustainable water-resource protection in coal-mining regions.

Groundwater Quality (GWQ) presents a significant global concern owing to extensive agricultural and industrial activities, necessitating proficient management strategies, as it constitutes approximately half of the world's potable water supply. Traditional methods of monitoring water quality are valuable for identifying pollution sources, but they do not adequately provide an overall view of water quality trends. Machine learning (ML) technologies provide reliable predictions of water quality by learning complicated patterns from data without preconceived equations. This paper provides a meta‐analysis and bibliographic review of ML applications in GWQ assessment, evaluating the accuracy, applicability, and usability of various models including artificial neural networks (ANN), support vector machines (SVM), k‐nearest neighbors (KNN), decision tree (DT), random forest (RF), and deep learning (DL) (including deep neural networks [DNN] architectures) approaches. It offers a thorough synopsis and conclusion that neural networks have traditionally been the most widely used ML model in GWQ modelling. India, China, and the United States are global leaders in groundwater modelling, benefiting from extensive historical data. The most widely modelled elements are nitrate and heavy metal pollution, which are present in about half of the studies. Despite significant progress, several research gaps remain, particularly in the modelling of lesser‐explored water quality parameters and in the integration of advanced ML techniques. Emerging approaches such as physics‐informed machine learning (PIML), graph neural networks (GNNs), transformer‐based architectures, and large language models (LLMs) show considerable potential for improving prediction accuracy and handling complex environmental datasets. These advancements may enable more robust and integrated GWQ management frameworks in future studies.