Drinking water quality assessment through multivariate statistical approach and formulated PCA-based contamination...

Ensuring safe drinking water remains a major public health and sustainability challenge in rapidly urbanizing, groundwater-stressed and semi-arid regions. Conventional monitoring lacks statistical integration and holistic assessment. This study evaluated 167 drinking water samples from surface (PHED supply from Bisalpur Dam) and groundwater sources, assessing parameters including physicochemical, heavy metals, bacteriological indicators and antibiotic resistance profiles of 141 E. coli-like isolates. To enhance interpretability and risk stratification, an advanced multivariate statistical framework was applied to derive a novel Principal Component Analysis-based Drinking Water Contamination Index (DWCI), supported by Mahalanobis Distance (MD) outlier detection and K-means clustering with ANOVA-derived F-values for variable importance. Key findings revealed significant contamination: multiple samples exceeded WHO limits for TDS, fluoride, lead and chromium. Microbial analysis showed high microbial load (3.00-6.44 log₁₀ CFU/mL), with 36.53% of samples contaminated by E. coli and total coliforms (1.8-1800 MPN/100 mL) frequently surpassing safe thresholds, highlighting fecal contamination and infrastructural vulnerabilities. 93.6% of isolates from E. coli-positive samples (n = 61) exhibited resistance to multiple antibiotics/drugs, with 96.45% surpassing high-risk multiple antibiotic resistance index (MARI) threshold. Groundwater consistently exhibited higher contamination across all assessed parameters. MD analysis identified 22 outlier samples (13 surface, 9 groundwater) with extreme contamination, overlapping with high DWCI values, while clustering revealed three contamination regimes, from baseline geogenic to severe anthropogenic pollution. This study demonstrates the effectiveness of integrated multivariate techniques in characterizing contamination patterns, offering a replicable, cost-effective framework for water quality monitoring, risk-based regulation, supporting SDG-6 targets for safe, equitable drinking water access in water-stressed environments.