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Research Paper | Environmental Engineering | India | Volume 6 Issue 6, June 2017
Assessment and Studies on Physiochemical and Biological Parameter of Ganga River at local Barrage using Environmental and Multivariate Statistical Techniques
Abstract: Water quality assessments are an essential procedure in monitoring programs and are used to collect baseline environmental data. They are particularly important in developing regions where people often cannot access adequate supplies of water and effective water resource management is critical for future development. Here multivariate statistical techniques such as cluster analysis (CA), Principal component analysis (PCA), Discriminant analysis (DA) and Factor analysis (FA) were applied for the temporal and spatial variation and the interpretation of large complex water quality data set to predict variation in the various physiochemical properties and their remedies. Primary objective of cluster analysis (CA) is to assemble the object based on their characteristics they possess. Principal component analysis (PCA) was used to investigate the origin of each water quality parameter in the GANGA Basin and identified the major factor affecting the water quality. The major variations are related to the anthropogenic activities such as irrigation variation, construction activities, clearing of land and domestic waste disposal and natural processes such as erosion of river bank and runoff. Discriminant analysis (DA) was applied to the dataset to maximise the similarities between groups relative to within group variance of the parameter. DA provides better result with great discriminatory ability. Thus, this study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective river water quality management.
Keywords: GANGA river, physiochemical parameter, cluster analysis, factor analysis, principal component analysis, discriminate analysis
Edition: Volume 6 Issue 6, June 2017,
Pages: 274 - 282