Abstract
Multivariate statistical techniques, such as cluster analysis and principal component analysis (PCA), were applied for evaluation of spatial variations and interpretation of large complex water quality data set of the Ganga river basin, generated during one year (2013-2014) monitoring of eight water parameters at seven different sites. Hierarchical cluster analysis grouped seven sampling sites into three clusters, i.e., relatively low polluted (LP), medium polluted (MP) and highly polluted (HP) sites based on the similarity of water quality characteristics. Principal component analysis produced three significant main components and explaining more than 82.9% of the variance (anthropogenic and industrial effect) that present 57.1%, 13.8% and 12% respectively of the total variance of water quality in Ganga river. The result reveals that Turbidity, Dissolved oxygen and Biochemical oxygen demand are the parameters that are most important in assessing variations of water quality. Water quality index based on eight parameters (Turbidity, DO, BOD, COD, pH, TS, TSS and TDS) calculated for all the sites are found to be medium to bad. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis, interpretation of complex data sets and understanding spatial variations in water quality for effective river water quality management. The study reveals that untreated industrial and municipal discharges are the major source of the pollution to the Ganga river. Implementation of suitable management plan along with proper sewerage treatment network, maintaining sufficient dilution flow, artificial aeration and watershed management will control the pollution in the Ganga river.
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