Abstract
To 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.
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Dalal, S.G., Shirodkar, P.V., Jagtap, T.G. et al. Evaluation of significant sources influencing the variation of water quality of Kandla creek, Gulf of Katchchh, using PCA. Environ Monit Assess 163, 49–56 (2010). https://doi.org/10.1007/s10661-009-0815-y
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DOI: https://doi.org/10.1007/s10661-009-0815-y