Application of multivariate statistical methods for groundwater physicochemical and biological quality assessment in the context of public health

Abstract

Three representative areas (lowland, semi-mountainous, and coastal) have been selected for the collection of drinking water samples, and a total number of 28 physical, chemical, and biological parameters per water sample have been determined and analyzed. The mean values of the physical and chemical parameters were found to be within the limits mentioned in the 98/83/EEC directive. The analysis of biological parameters shows that many of the water samples are inadequate for human consumption because of the presence of bacteria. Cluster analysis (CA) first was used to classify sample sites with similar properties and results in three groups of sites; discriminant analysis (DA) was used to construct the best discriminant functions to confirm the clusters determined by CA and evaluate the spatial variations in water quality. The standard mode discriminant functions, using 17 parameters, yielded classification matrix correctly assigning 96.97% of the cases. In the stepwise mode, the DA produced a classification matrix with 96.36% correct assignments using only ten parameters (EC, Cl (-aEuro parts per thousand), NO(3) (-aEuro parts per thousand), HCO(3) (-aEuro parts per thousand), CO(3) (-aEuro parts per thousand 2), Ca (+ 2), Na (+) , Zn, Mn, and Pb). CA and factor analysis (FA) are used to characterize water quality and assist in water quality monitoring planning. CA proved that two major groups of similarity (six subclusters) between 17 physicochemical parameters are formed, and FA extracts six factors that account for 66.478% of the total water quality variation, when all samples' physicochemical data set is considered. It is noteworthy that the classification scheme obtained by CA is completely confirmed by principal component analysis.