Multivariate statistical characterization of groundwater quality in Ain Azel plain, Algeria

Abstract

Multivariate statistical techniques, cluster and principal component analysis were applied to the data on groundwater quality of Ain Azel plain (Algeria), to extract principal factors corresponding to the different sources of variation in the hydrochemistry, with the objective of defining the main controls on the hydrochemistry at the plain scale. Q-mode hierarchical cluster analysis grouped 54 groundwater samples into three clusters, that is, relatively less saline water (group 1), mixed water (group 2) and blended water (group 3), based on the similarity of groundwater quality characteristics. Principal component analysis, applied to the data sets of the three different groups obtained from hierarchical cluster analysis, resulted in four, five and three latent factors explaining 83.21, 83.36 and 87.30% of the total variance in groundwater quality data sets of group 1, group 2 and group 3, respectively. The varifactors obtained from PCA indicate that the parameters responsible for groundwater quality variations are mainly related to presence and dissolution of some carbonate, dolomitic and evaporite minerals; natural processes and water-rock interaction in the three water types. The results of this study clearly demonstrate the usefulness of multivariate statistical analysis in hydro chemical.