Hydrochemical Characterization of Benin Formation in Benin-city and Environs, Nigeria using Multivariate Analyses

Abstract

Multivariate statistics was applied to some hydrochemical data from Benin Formation in Benin-city metropolis, Southern Nigeria with a view to characterizing and determining the main structures underlying groundwater chemistry in the area. Laboratory chemical analysis followed standard laboratory analytical procedures stipulated by American Society for Testing and Materials (ASTM). Hydrochemical data were standardized to z-scores and screened for outliers, normality and linearity prior to statistical analysis at significant level of 5%. Q-mode Hierarchical Cluster Analysis (HCA) grouped the groundwater samples into distinct clusters to represent different hydrochemical facies. R-mode Principal Component analysis (PCA) reduced bulk hydrochemical data to discrete principal components indicating possible dominant processes responsible for groundwater chemistry. Results of PCA revealed six principal components (PCs) which together explain 86.84 % of the total variance in the dataset. The results of PCA implied that Ca, total hardness, Mg, total dissolved solids, chloride, electrical conductivity and Na are the most significant parameters controlling groundwater chemistry in the area. The extracted components also indicated that atmospheric controls and silicate mineral weathering processes are the main factors responsible for variation in groundwater chemical variations in the area. Results of HCA which identified six significant clusters indicated high degree of spatial, statistical and chemical coherence, and validated grouping of groundwater into chemical clusters in the study area. Results from this study showed PCA and HCA are useful tools for analyzing dominant processes responsible for variation in groundwater chemistry in the study area.

Keywords: Principal component analysis, cluster analysis, groundwater chemistry, coefficient of variation.

DOI: 10.7176/JSTR/5-7-02