Surface and groundwater water quality assessment using multivariate analytical methods: A case study of the Western Niger Delta, Nigeria

Abstract

This study investigates the natural and anthropogenic processes that influence the chemistry of surface and groundwater within the western Niger Delta region using multivariate statistical techniques. A total of 137 surface and groundwater samples were collected between 2003 and 2007 during the rainy and dry seasons, from 15 sites and analysed for their physico-chemical constituents. The chemical data set generated were subjected to Principal Component Analysis (PCA)/Factor Analysis (FA) and Hierarchic Cluster Analysis (HCA). PCA is a procedure for reducing data redundancy, while FA establishes the general relationship among variables. CA is used to detect spatial similarity among sampling sites. The results indicate five dominant processes or factors for surface water that explained 77.11% of the variance in the data set. In groundwater, the factors account for 80.55% of the total variance. Cluster analysis revealed a random spatial distribution of the chemical components investigated. This is consistent with the multipurpose nature of land use in the study area. The multiple natural and anthropogenic sources indicated by this study, and their unsystematic distribution show that proper land use planning and firm implementation of existing environmental laws is imperative in this oil producing region, in order to have effective surface water and groundwater resource management.