Application of multivariate statistical methods and inverse geochemical modeling for characterization of groundwater — A case study: Ain Azel plain (Algeria)

Multivariate statistical methods and inverse geochemical modeling were jointly used to define the variation and the genetic origin of chemical parameters of groundwater in the Ain Azel plain, Algeria. Interpretation of analytical data shows that the abundance of the major ions is as follows: Ca ≥ Mg > Na > K and HCO3 ≥ Cl > SO4. Q-mode hierarchical cluster analysis (HCA) was employed for partitioning the water samples into hydrochemical facies, also known as water groups or water types. Three major water groups resulted from the HCA analysis. The samples from the area were classified as recharge area waters (Group 1: Ca–Mg–HCO3 water), transition zone waters (Group 2: Ca–Mg–Cl–HCO3 water), and discharge area waters (Group 3: Mg–Ca–HCO3–Cl water). Inverse geochemical models of the statistical groups were developed using PHREEQC to elucidate the chemical reactions controlling water chemistry. The inverse geochemical modeling demonstrated that relatively few phases are required to derive water chemistry in the area. In a broad sense, the reactions responsible for the hydrochemical evolution in the area fall into three categories: (1) dissolution of evaporite minerals; (2) precipitation of carbonate minerals; and (3) weathering reactions of silicate minerals.

Research Highlights
► Combination of statistical methods and geochemical modeling.
► Hierarchical cluster analysis (HCA).
► Element ratios.
► Inverse modelling to formulate hypotheses on the element sources and sinks involved in the evolution from the initial to the final chemical composition.