Assessment of water quality using principal component analysis: A case study of the river Ganges

Abstract

In present study multivariate statistical approaches are used; interpretation of large and complex data matrix obtained during a monitoring of the river Ganges in Varanasi. 16 physicochemical and bacteriological variables have been analyzed in water samples collected every three months for two years from six sampling sites where river affected by man made and seasonal influences. The dataset was treated using Principal Component Analysis (PCA) to extract the parameters that are most important in assessing variation in water quality. Four Principal Factor were identified as responsible for the data structure explaining 90% of the total variance of the dataset, in which nutrient factor (39.2%), sewage and feacal contamination (29.3%), physicochemical sources of variability (6.2%) and waste water pollution from industrial and organic load (5.8%) that represents total variance of water quality in the Ganges River. The present study suggests that PCA techniques are useful tools for identification of important surface water quality parameters. © 2010 Allerton Press, Inc.