Water Quality Assessment, Possible Pollution Source Identification from Anthropogenically Stressed River Yamuna, India using Hydrochemical, Water Quality Indices and Multivariate Statistics Analysis

For effective and sustainable water management, assessing the water quality and identifying potential sources that threaten the river system are crucial steps. In the present study, spatiotemporal variation of 20 hydrochemical variables, water quality indices, and multivariate statistics were applied to evaluate the quality of Yamuna River water. In the middle and lower stretch, the levels of electric conductivity (EC), total dissolved solids (TDS), turbidity, dissolved organic matter (DOM), chemical oxygen demand (COD), and nutrients were higher than in the upper stretch. Based on the trophic state index, the upper, middle, and lower stretches were mesotrophic, moderate, and low eutrophic in nature, respectively. In the drinking water category, the water quality index (WQI) ranged from almost good (upper stretch) to inappropriate (middle and lower stretch). Nemerow pollution index (PI_Nemerow) and the comprehensive pollution index (CPI) indicated that most sites were strongly and moderately polluted, respectively. Various point and nonpoint sources of pollution deteriorated the quality of Yamuna water. Spatial cluster analysis divided eleven stations into three groups based on water variables similarity. Discriminate analysis indicated that water temperature, flow, turbidity, pH, dissolved oxygen (DO), magnesium hardness (Mg-H) and COD were the most influencing variables seasonally, while water flow, pH, chloride (Clˉ), DO, Mg-H, and nitrate–N were for spatial variation in Yamuna water quality. Five potential sources were identified using principal component analysis (PCA); anthropogenic, natural, agricultural non-point sources, metrological, and seasonal factors. This study emphasizes the importance of using multivariate statistical techniques to identify variability patterns and develop management plans to improve river water quality by identifying the key variables responsible for maximum deterioration.

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