Pattern recognition of water quality variance in Yamuna River (India) using hierarchical agglomerative cluster and principal component analyses.

Abstract

The monitoring and assessment of a river system is a complex process and not restricted to urban areas only. The discharge of wastewater drains in the river increases the river system complexity further. The abstraction of freshwater at regular intervals and the discharge of the wastewater from various sources cause significant spatial and temporal variation in water quality. The multivariate statistical analysis is performed to identify water quality parameters' variability on the 5-year dataset from four monitoring sites. Hierarchical agglomerative cluster analysis (HACA) and principal component analysis (PCA) are applied to characterize the water quality parameters and identify the significant pollution sources. The clusters are formed considering the similarities between parameters, and eigenvalues are determined from the covariance of parameters. The box plots are designed to identify the spatial and temporal variations. The highest variability of the first principal component is 60.78% of the total variance at the second sampling location, the ITO bridge. The significant varifactors obtained from the PCA indicate the parameters responsible for the maximum variation in water quality. The study reveals the importance of multivariate statistical techniques in identifying a pattern of variability of parameters and developing management strategies to improve river water quality by identifying dominant parameters causing the maximum degradation in water quality.