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M. Radojevic and N. V. Bashkin, “Practical Environmental Analysis,” Royal Society of Chemistry, UK, 1999.

has been cited by the following article:

  • TITLE: Water Quality Assessment of River Ogun Using Multivariate Statistical Techniques

    AUTHORS: Adebola A. Oketola, Seun M. Adekolurejo, Oladele Osibanjo

    KEYWORDS: Surface Water; Heavy Metals; Water Quality; PCA; CA

    JOURNAL NAME: Journal of Environmental Protection, Vol.4 No.5, May 17, 2013

    ABSTRACT: Variations in water quality of River Ogun around the cattle market, Isheri along Lagos-Ibadan express road were evaluated using multivariate statistical techniques such as principal component analysis (PCA) and cluster analysis (CA) to analyze the similarities or dissimilarities among the sampling points so as to identify spatial and temporal variations in water quality and sources of contamination over time. Water quality data were generated from 8 sampling points during 6 year sampling periods (i.e., 2000, 2005, 2006, 2009, 2010, and 2011). The samples were analyzed for 14 physico-chemical parameters and heavy metals such as temperature, pH, total solids (TS), total dissolved solids (TDS), suspended solids (SS), oil and grease, dissolved oxygen (DO), chemical oxygen demand (COD), Cl-1, alkalinity, total hardness (TH), SO42-, NO3 -, PO43- and heavy metals (Cd, Cu, Fe, Ni, Pb, Mn, and Zn). Three zones were differentiated based on the cluster analysis results, and implied similar water quality features. Thus, the water quality around the site may be categorized as relatively less polluted, moderately polluted and highly polluted. The PCA assisted to extract and recognize the factors responsible for water quality variations over the years. The results showed that the index which changes the quality of the water differs. The natural, inorganic and organic parameters e.g., temperature, TS, and etc., were the most significant parameters contributing to the variations in the water quality over the years. This shows that a parameter that can be significant in contributing to water quality in one season may less or not be significant in another. This result may be used to reduce the number of samples analyzed both in space and time, without much loss of information. This will assist the decision makers in identifying priorities to improve water quality that has deteriorated due to pollution from various anthropogenic activities.