A multivariate Statistical Analysis of Groundwater Chemistry Data

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 AM 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.