Application of multivariate statistical methods to enhance the water quality monitoring system of Kashmir Valley with special emphasis on side-stream pollution

Abstract

Surface waterbodies, on which the growing population of Kashmir Valley is reliant in a variety of ways, are increasingly deteriorated due to anthropogenic pollution from rapid economic development. This research aims to assess the quality of the surface waterbodies in the northeastern region of Kashmir Valley. Standard analytical procedures were used to analyze the water samples taken from 11 distinct sampling stations for 14 physicochemical parameters. The results were compared with the standard permissible levels which showed that the water quality of rivers and lakes in the north-east Himalayan region has steadily declined. Furthermore, multivariate statistical techniques were used with the goal of identifying key variables that influence seasonal and sectional water quality variations. The analysis of variance (ANOVA) analysis revealed that there is substantial spatio-temporal variability in the water quality parameters. According to principal component analysis (PCA) results, four primary components, which together accounted for 79.23% of the total variance, could be used to evaluate all data. Chemical, organic, and conventional pollutants were found to be significant latent factors influencing the water quality of rivers in the study region. The results indicate that PCA and ANOVA may be used as vital tools to identify crucial surface water quality indices and the most contaminated river sections.