Assessment and Modelling of Groundwater Quality Data by Environmetric Methods in the Context of Public Health

Abstract

Various chemometric methods were used to analyze and model potable water quality data. Twenty water quality parameters were measured at 164 different sites in three representative areas (low land, semi-mountainous, and coastal) of the Thessaly region (Greece), for a 3-month period (September to November 2006). Hierarchical cluster analysis (CA) grouped the 164 sample sites into two clusters (CA-group 1 and CA-group 2) based on the similarities of potable water quality characteristics. Discriminant analysis was assigned about 94.5% of the cases grouped by CA. Factor analysis (FA) was applied to standardized log-transformed data sets to examine the differences between the above clusters and identify their latent factors. For each of the above two clusters (CA-group 1 and CA-group 2), FA yielded six latent factors that explain 68.7% and 73.4% of the total variance, respectively. FA was also identified the latent factors that characterize each cluster. The identification was obtained, using (a) descriptive statistics, (b) t test for equality of cluster means, (c) box plot, (d) error bar, (e) factors score plots, (f) matrix scatter score means plot and (g) scatter plot of the six significant latent factors from the factor set of all samples group. The classification scheme obtained through cluster analysis was confirmed by discriminant analysis and explained by factor analysis.