Assessment of water quality of polluted lake using multivariate statistical techniques: a case study

Abstract

Multivariate statistical techniques, cluster analysis (CA) and principal component analysis (PCA) were applied to the data on water quality of Manchar Lake (Pakistan), generated during 2005-06, with monitoring at five different sites for 36 parameters. This study evaluated and interpreted complex water quality data sets and apportioned of pollution sources to get better information about water quality and to design a monitoring network. The chemical correlations were observed by PCA, which were used to classify the samples by CA, based on the PCA scores. Three significant sampling locations--(sites 1 and 2), (site 4) and (sites 3 and 5)--were detected on the basis of similarity of their water quality. The results revealed that the major causes of water quality deterioration were related to inflow of effluent from industrial, domestic, agricultural and saline seeps into the lake at site 1 and also resulting from people living in boats and fishing at sites 2 and 3.