Application of Receptor Models on Surface Water Quality and Source Apportionment

Abstract

Surface water plays an important role in carrying off different water wastes thereby affecting water quality used for different purposes. The Receptor Model (RM) development as a technique in the management of River water was used in this study, in identifying, separating and quantifying the major sources of water wastes flowing into River Musa, Bida, Nigeria. Twelve water variables were used in Principal Component Analysis. The generated variables of loaded components were used as independent variables and the Water Quality Index (WQI) as the dependent variable to estimate the quantity of identified pollutants sources using the Multiple Linear Regression Model (MLR). According to Canadian Council Ministers of Environments Water Quality Index (CCME WQI), the results determined for the five sample stations (Edokota location, Musa bridge location, Bida/Minna location, Ciriko location and Army Barrack location) were 74.4, 72.8, 64.6, 47.6, and 51.6 respectively. Among the five locations, three were investigated to be marginal and the remaining two were fair in rank. The principal component analysis (PCA) was adopted to separate the identified three major waste sources flowing into the river to be agricultural, municipal and industrial wastes. Pollutant levels were determined to be 0.936, 0.457 and 0.104 using RM at a high value of R2 (0.911). Agricultural waste was predicted to be the strongest pollutant contributor in the model, followed by municipal and the least contributor is industrial waste. It is strongly recommended that periodic monitoring and evaluation of the river water quality is carried out within the study area using the receptor model