A multivariate Statistical Analysis of Groundwater Chemistry Data

Document Type: Original Research Paper

Authors

1 Department of hydraulics, University Hadj Lakhdar 05000 Batna, Algeria

2 Research Laboratory in Applied Hydraulics, University Hadj Lakhdar 05000 Batna, Algeria

3 Departement de Génie des Procèdes, Faculté de Technologie, Université de Bejaia, Targa, Ouzemour 06000, Algeria

Abstract

Q-mode hierarchical cluster (HCA) and principal component analysis (PCA) were simultaneously
applied to groundwater hydrochemical data from the three times in 2004: June, September, and December,
along the Ain Azel aquifer, 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
aquifer scale. Hydrochemical data for 54 groundwater samples were subjected to Q-mode hierarchical cluster
and principal component analysis. The study finds, from Q-mode HCA that there are three main hydrochemical
facies namely the less saline water (group 1: Ca-Mg-HCO3), mixed water (group 2: Mg-Ca-HCO3-Cl) and
blended water (group 3: Mg-Ca-Cl-HCO3). In principal component analysis, the first 4 factors explain 72.14%
of the total variance, their loadings allowing the interpretation of hydrochemical processes that take place in
the area. The results of this study clearly demonstrate the usefulness of multivariate statistical analysis in
hydrochemical.

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