USING MULTIVARIATE STATISTICAL METHODS FOR THE ASSESSMENT OF THE SURFACE WATER QUALITY FOR A RIVER: A CASE STUDY

Multivariate statistical methods, such as principal component analysis (PCA) and cluster analysis (CA) were utilized to the surface water quality data of the Shatt Al Arab River (Iraq), over 4-year period on a monthly basis, with tracking at five different monitoring sites across the river for seven water quality parameters. The study area included the Shatt Al-Arab River, which is one of the most essential rivers that formed because of the confluence related to the Euphrates along with Tigris at the city of AlQurnah in Basrah province, southern Iraq. Water samples were analyzed for dissolved oxygen (DO), phosphate (PO4), calcium (Ca), magnesium (Mg), nitrate (NO3), chloride (Cl), and sulphate (SO4) were analyzed making use of standard methods. This research assessed and clarified the complex data sets of water quality and apportioned of pollution sources to obtain better information concerning water quality and to demonstrate the data structure as well as to analyze temporary and spatial variations regarding the water quality. The results of this research were exposed to principal component and factor analysis with three latent factors which were extracted with 98.9 % of the total variance explained. Based on gaining information, it will be possible to design a future, ideal sampling approach, which might minimize the number of monitoring sites and additionally associated cost.