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dc.contributor.authorDalal, S.G.
dc.contributor.authorShirodkar, P.V.
dc.contributor.authorJagtap, T.G.
dc.contributor.authorNaik, B.G.
dc.contributor.authorRao, G.S.
dc.date.accessioned2010-04-04T14:18:03Z
dc.date.available2010-04-04T14:18:03Z
dc.date.issued2010
dc.identifier.citationEnvironmental monitoring and assessment, vol.163(1-3); 49-56
dc.identifier.urihttp://drs.nio.org/drs/handle/2264/3569
dc.description.abstractTo evaluate the significant sources contributing to water quality parameters, we used principal component analysis (PCA) for the interpretation of a large complex data matrix obtained from the Kandla creek environmental monitoring program. The data set consists of analytical results of a seasonal sampling survey conducted over 2 years at four stations. PCA indicates five principal components to be responsible for the data structure and explains 76% of the total variance of the data set. The study stresses the need to include new parameters in the analysis in order to make the interpretation of principal components more meaningful. The PCA could be applied as a useful tool to eliminate multi-collinearity problems and to remove the indirect effect of parameters
dc.language.isoen
dc.publisherSpringer
dc.rightsAn edited version of this paper was published by Springer. Copyright [2010] Springer...It is tried to respect the rights of the copyright holders to the best of the knowledge. If it is brought to our notice that the rights are violated then the item would be withdrawn.
dc.subjectwater quality
dc.subjectchemical analysis
dc.subjectseasonal variations
dc.subjectprincipal component analysis
dc.titleEvaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA
dc.typeJournal Article


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