S. HEMA1 AND T. SUBRAMANI2
Abstract
This paper presents a study of the application of different multivariate statistical approaches for the interpretation of water quality data. The data were obtained during a monitoring programme of the surface water quality of Cauvery River in Erode district, Tamil Nadu, India. A number of tanneries and textile industries have been established in this region since the past three decades. It is reported that the effluents from these industries are directly discharged onto the surrounding land, irrigation fields and surface water bodies. As a result, it degrades the quality of freshwater in the study area. Thirteen parameters including trace elements (Cd, As, Cu, Cr, Zn and Pb) have been monitored on 50 sampling points from a hydrogeochemical survey, conducted in the river stretch under study. The data set thus obtained was treated using R-mode factor analysis (FA) and principal component analysis (PCA). FA identified three factors responsible for data struc-ture explaining 91% of total variance. It allowed grouping of selected parameters according to common features. The results indicated that point source pollutants are responsible for affecting the water quality of this region. This study indicates the necessity and application of multivariate statistical techniques for evaluation and analysis of the data. It facilitates better information about the water quality and designs some remedial techniques to prevent future contamination.