Assessment of Spatio-Temporal Variations in Water Quality of Shailmari River, Khulna (Bangladesh) Using Multivariate Statistical Techniques (original) (raw)
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Statistical techniques were applied to analyse the water quality datasets of Kathmandu Basin, Nepal, to identify the major factors affecting water quality and to investigate the spatial and seasonal variations. Spatial variation was investigated using cluster analysis in which a total of 40 river monitoring sites were divided into two major clusters: low and high polluted zones. Discriminant analysis (DA), principal component analysis (PCA) and factor analysis (FA) were subjected to water quality parameters to identify the pollution sources. DA confirmed the seasonal variation of water quality data into three seasons providing 92.5% correct classification. Principal components (PCs) were extracted to distinguish the anthropogenic processes and the symmetrical correlation matrix computed with ten variables for winter, monsoon and post monsoon seasons. The significant components extracted have eigenvalues greater than 1, and account for 79%, 68% and 77% of the total variance in the winter, monsoon and post monsoon data respectively. Non-parametric tests were applied for each parameter to identify their significance differences spatially and temporally.
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