Abstract

Multivariate statistical techniques such as cluster analysis (CA), principle component analysis (PCA), factor analysis (FA) were applied for the evolution of temporal variations and the interpretation of large complex water quality data set of the Hyderabad city, generating during year 2013-14 monitoring of 16 parameters at 23 different sites of an average depth of 1m. Hierarchical clustering analysis (CA) is first applied to distinguish the three general water quality patterns among the stations. Data set thus obtained was treated using R-mode factor analysis (FA) and followed by principle component analysis (PCA). Factor analysis identified five factors responsible for data structure explaining 75% of total variance and allowed to group selected parameters according to common futures. WT, EC, TSS and Na were associated and controlled by mixed origin with similar contribution from natural and anthropogenic sources. Whereas NO3, PO4, SO4, FC, TC, F-, K and B were derived from anthropogenic sources.

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