Abstract
Groundwater is the principal source of drinking water for the majority of India's population, but progressive deterioration of its physicochemical and microbiological quality poses an escalating public health risk. Multivariate statistical techniques can disentangle the dominant pollution sources and identify high-risk site clusters more effectively than univariate comparisons against regulatory limits. <em>Aim. </em>To identify the dominant factors driving variation in groundwater quality across four Indian states and to classify the constituent water-quality parameters into contamination-risk groupings using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA). Method: Secondary data on groundwater quality, collected through the National Water Monitoring Program of the Central Pollution Control Board, for the years 2021–2023, were extracted for Uttar Pradesh, Kerala, Maharashtra and Madhya Pradesh. The dataset comprised physical (temperature), chemical (pH, conductivity, dissolved oxygen, biochemical oxygen demand, nitrate) and microbiological (total coliform, fecal coliform, fecal streptococci) indicators, each reported as minimum and maximum values. Variables were standardised (Z-scores), inspected for normality (Kolmogorov–Smirnov test) and submitted to PCA after verifying sampling adequacy using the Kaiser–Meyer–Olkin (KMO) measure and Bartlett's test of sphericity. Components with eigenvalues greater than 1 were retained and rotated using the Varimax procedure. Hierarchical cluster analysis (Ward's linkage, Euclidean distance) of standardised parameters was used to identify groups of co-varying indicators. All twenty parameters deviated significantly from a normal distribution (Kolmogorov–Smirnov p < 0.001). Mean total coliform counts (11,162–127,068 MPN/100 mL) and biochemical oxygen demand (BOD; 3.7–6.8 mg/L) were well above potable-water thresholds, and conductivity reached extreme values in localised sites. Spearman correlations showed strong inverse association between dissolved oxygen (DO) and BOD (r = -0.60 to -0.67; p < 0.001) and strong positive association between BOD and microbial indicators (r = 0.40–0.65; p < 0.001). The KMO measure was 0.683 and Bartlett's test was significant (p < 0.001), justifying PCA. Six components were extracted, cumulatively explaining 70.5% of total variance. The components were interpreted as faecal microbial contamination, organic load / oxygen demand, ionic and acid–base chemistry, physical thermal regime, nutrient enrichment, and residual variation. HCA identified three coherent groupings of parameters representing high-, moderate- and low-risk contamination categories. Microbial and organic pollution were the dominant determinants of groundwater quality in this multi-state dataset, with chemical and nutrient enrichment as secondary drivers. The findings point to widespread untreated sewage infiltration and agricultural runoff as the principal anthropogenic sources, and underscore the need for source protection, structured wastewater management, point-of-use disinfection and continuous monitoring. Integration of emerging organic contaminants into future surveillance is essential to strengthen risk-based groundwater governance in India.