An integrated indexical and multivariate assessment of surface water quality within the Nag river, Maharashtra, India

Abstract The Nag River, flowing through the highly urbanized core of Nagpur, Maharashtra, serves as the primary drainage system for the city and is critically polluted due to rapid urban development and uncontrolled industrial discharges. While conventional physicochemical assessments exist, they fail to provide the quantitative, spatially resolved source apportionment necessary for targeted remediation. This study integrates WQI with PCA and CA to provide a structured, data-driven assessment of pollution sources along the Nag River, specifically Principal Component Analysis (PCA) and Cluster Analysis (CA) within a spatial framework to provide the first systematic differentiation of pollution sources (e.g. municipal sewage vs. specialized industrial effluent) and link them directly to specific land-use zones along the river’s 17 km urban corridor. The aim was to holistically assess the surface water quality and quantitatively identify, map, and attribute pollution sources along this critical stretch. Nine (9) surface water samples (S1-S9) were systematically collected during the pre-monsoon season (February 2023), covering segments influenced by diverse residential, commercial, and industrial land use. Twenty physicochemical and biological parameters were analyzed, and the reliability of the hydrochemical data was confirmed using the Ionic Balance Error (IBE) validation. WQI values ranged severely from 47.05 (Good) at the upstream baseline (S1) to a maximum of 6440.38 (Unfit for all practical uses) at Yashwant Stadium (S5), confirming chronic heavy pollution. This degradation is primarily attributed to untreated municipal sewage, as indicated by extreme BOD levels up to 216.28 mg/l and non-compliant specialized industrial discharges. PCA identified three primary Varifactors (VFs) explaining 87.935% of the total variance. Varifactor 1 (44.088%) confirmed the overwhelming dominance of untreated municipal sewage (organic load, total dissolved solids, and microbiological parameters). Varifactor 2 (16.666%) was strongly associated with specialized heavy metals (Nickel and Cadmium), indicating a distinct point source industrial effluent. CA successfully categorized sampling sites into four spatial pollution clusters (C1-C4), enabling the identification of high-priority pollution hotspots that correlate directly with land use. The present study integrates the WQI-PCA-CA approach, combined with land use assessment, to provide critical insights to support evidence-based river restoration and sustainable watershed management planning.