An Application of Multivariate Statistical Techniques to Evaluate Water Quality Across Dumpsites Along Otamiri-Ochie River, Etche

Abstract

The status of water quality in rural areas is attracting a great deal of attention on how suitable it is for public consumption, recreation and other purposes. There is however a lack of studies on water quality using multivariate statistical techniques to predict the sources of pollutant along Otamiri-Ochie River. Multivariate statistical approaches, including principal component analysis (PCA) and cluster analysis (CA) were employed to evaluate the water quality of the River. In this study, eight physico-chemical parameters were analysed in each water sample collected from four sampling sites surrounded by dumpsites along the River. Exploratory analysis of the dataset involved use of PCA, CA and water quality index (WQI) in attempt to identify the sources of variation measured in the samples. PCA was used to reduce the dataset to three components with predominantly dissolved oxygen (DO), total dissolved solids (TDS) and total suspended solids (TSS) contributing to over 55% of the total variance. CA classified the sites into two distinct groups identified as the upstream and downstream of the River. Chokocho (CHR) axis of the River was identified as being closer to the pollutant source and hence it is the most heavily polluted portion of the River. WQI value suggests that the water is unsuitable for drinking and may likely not be fit for domestic uses. The results prove multivariate statistics to be a powerful tool in identifying pollutant sources, which can be applied to both urban and rural water bodies.