Assessment of Surface Water through Multivariate Analysis

Multivariate statistical techniques such as factor analysis (FA) and Discriminant analysis (DA), were applied for the evaluation of spatial variations and the interpretation of a large complex water quality data set of two rivers (Juru and Jejawi) in Malaysia, monitoring 10 parameters at 10 different sites each. Factor analysis resulted in two factors explaining more than 82% of the total variance in water quality data set. The factors indicate that the possible variances in water quality may be due to either sources of anthropogenic origin or due to different biochemical processes that are taking place in the system. The first factor called pseudo anthropogenic factor explained 59.29% of the total variance. The second factor called anthropogenic explained 23.03%. DA gave the best result to identify the relative contribution for all parameters in discriminating (distinguishing) the two rivers affording 100 % correct assignations. This study illustrates the benefit of multivariate statistical techniques for analyzing and interpretation of complex data sets, and to plan for future studies.