Journal of Environment and Earth Science www.iiste.org ISSN 2224-3216 (Paper) ISSN 2225-0948 (Online) Vol 2, No.2, 2012 16 Application of Principal Component Analysis & Multiple Regression Models in Surface Water Quality Assessment Adamu Mustapha 1* Ado Abdu 2 1. Department of Environmental Science, Faculty of Environmental Studies, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. 2. Department of Resources Management & Consumer Studies, Faculty of Human Ecology, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor, Malaysia. *E-mail of the corresponding author: amustapha494@gmail.com Abstract Principal component analysis (PCA) and multiple linear regressions were applied on the surface water quality data with the aim of identifying the pollution sources and their contribution toward water quality variation. Surface water samples were collected from four different sampling points along Jakara River. Fifteen physico-chemical water quality parameters were selected for analysis: dissolved oxygen (DO), biochemical oxygen demand (BOD 5 ), chemical oxygen demand (COD), suspended solids (SS), pH, conductivity, salinity, temperature, nitrogen in the form of ammonia (NH 3 ), turbidity, dissolved solids (DS), total solids (TS), nitrates (NO 3 ), chloride (Cl) and phosphates (PO 4 3- ). PCA was used to investigate the origin of each water quality parameters and yielded five varimax factors with 83.1% total variance and in addition PCA identified five latent pollution sources namely: ionic, erosion, domestic, dilution effect and agricultural run-off. Multiple linear regressions identified the contribution of each variable with significant value (r 0.970, R 2 0.942, p < 0.01). Keywords: River, Stepwise regression, Varimax factor, Varimax rotation, Water pollution 1. Introduction With the growth of human populations, commercial and industrial activities, surface water has received large amount of pollutants from variety of sources (Satheeshkumar, and Anisa, 2011). The quality of surface water provides significant information about the available resources for supporting life in the ecosystem (Manikannan et al. 2011). The physical, chemical and biological compositions of surface water is controlled by many factors such as natural (precipitation, geology of the watershed, climate and topography) and anthropogenic (domestic, industrial activities and agricultural run-off). Increasing surface water pollution causes not only deterioration of water quality, but also threatens human health, balance of aquatic ecosystem, economic development and social prosperity (Milovanovic, 2007). It is imperative to prevent and control the surface water pollution and to have reliable information on its quality for effective management (Sing et al. 2005). Characterization of the spatial variation and source apportionment of water