TITLE:
Multivariate Statistical Analysis for the Assessment of Hydrogeochemistry of Groundwater in Upper Kambo Watershed (Douala-Cameroon)
AUTHORS:
Rel Dechangue Tatou, Veronique Kamgang Kabeyene, Guillaume Ewodo Mboudou
KEYWORDS:
Principal Component Analysis, Cluster Analysis, Groundwater, Upper Kambo Watershed, Douala
JOURNAL NAME:
Journal of Geoscience and Environment Protection,
Vol.5 No.3,
March
31,
2017
ABSTRACT: The determination of the processes responsible for the mineralization of ground-water requires a large number of samples and physicochemical data, the treatment of which proves to be tedious. Many statistical methods are developed to facilitate the treatment of a certain volume of data. In this study, multivariate statistical analysis including principal components analysis (PCA) and hierarchical cluster analysis (HCA) have been applied on the groundwater physicochemical data of the upper Kambo watershed. The aim is to simplify the data in order to evaluate the relationships between the parameters and also to detect the factors that influence the water chemistry. For the PCA, three factors were retained representing 70% of the total inertia. Factor 1 shows the heterogeneity of sources of acquisition of the mineralization by the positive correlation with all the variables. Factor 2 indicates the dissolution of carbonates and anthropogenic input. Factor 3 highlights the silicate, anthropogenic and atmospheric or marine origin. The HCA enables to differentiate 4 groups of water according to the degree of mineralization. Groups 1 and 2 represent the most mineralized waters, dominated by calcium. The poor mineralized waters are groups 3 and 4 with the rather close contents in calcium and sodium.