Receptor Modeling Application on Surface Water Quality and Source Apportionment

Abstract

Abstract. There is need for regular monitoring of river water quality to determine specific pollutants in order to aid amelioration schemes. In this study, Principal Component Analysis (PCA) was applied on eighteen water quality parameters; pH, conductivity, dissolved oxygen(DO), turbidity, temperature ,total dissolved solids (TDS), total solids (TS),total hardness (TH), biochemical oxygen demand (BOD), carbon dioxide (CO2), ammonia (NH3), nitrate (NO3-), chloride (Cl-), lead (Pb), iron (Fe), chromium (Cr), copper (Cu) and manganese (Mn) to identify major sources of water pollution of river Asa. The generated Principal Components (PCs) were used as independent variables and water quality index (WQI) as dependent variable to predict the contribution of each of the sources using multiple linear regression model (MLR). The PCs results showed that the sources of pollution are storm water runoff, industrial effluent, erosion and municipal waste, while MLR identified storm water runoff (0.786) and industrial effluent (0.241) as the respective major contributors of pollution. The study showed that PC-MLR model gives good prediction (R2=0.8) for water quality index.