Water quality assessment of river Ganga, India using water quality index and multivariate statistical techniques

The River Ganga is a vital lifeline for millions, and several authors reported that its quality has been deteriorating. Several initiatives have been taken by the government, but the situation is not up to the mark. Hence, water quality assessments are essential for sustainable river water management and restoring its ecological balance. This work employed spatiotemporal analysis of 20 hydrochemical variables, water quality indices (WQI), and multivariate statistics to assess the water quality of River Ganga. Water samples (n = 220) were collected at 20 locations (divided into four zones, namely upper (UZ), middle (MZ), lower (LZ), and estuarine (EZ)) based on the geographical nature and anthropogenic pressure. The study found that most of the water variables (specific conductivity (SC), pH, BOD, total alkalinity (TA), total hardness (TH), Ca-H, Mg-H, chlorinity (Cl-), salinity, nitrate-N, silicate and total dissolved solids (TDS)) were lowest reported at UZ while the highest at EZ. TH, salinity, Mg-H, Ca-H, TDS, Cl-, and SC in EZ, were above the drinking water limits and these variables markedly affect the river's water quality attributes, possibly increased by tidal influences. WQI indicated that the UZ was clean (26-50) in all seasons, MZ and LZ were good to poor (50-75) in all seasons, and EZ was unsuitable for drinking (> 100) in all seasons. Seasons were grouped into three clusters: less polluted (monsoon); slightly polluted (post-monsoon); and polluted (winter and pre-monsoon). The principal component analysis formed five clusters based on eigenvalue > 1: PC1 having TH, salinity, Mg-H, Ca-H, TDS, Cl-, and SC mainly influenced by tidal factor; PC2 having temperature, transparency, and DO was influenced by metrological source; PC3 (pH, TA, velocity) and PC4 (BOD, silicate) thought to be both natural as well as manmade; and PC5 was influenced by agricultural runoff (total phosphorus and NO₃-N) and sewage water (TN) discharge. The study emphasized the significance of multivariate statistical techniques in discerning the variability patterns of parameters, as well as in formulating management strategies to enhance river water quality by pinpointing the most impactful parameters contributing to water quality degradation.