Assessment of water quality of the Shatt Al-Arab River, using multivariate statistical technique

Abstract

This paper presents the results of statistical analysis of a set of physico-chemical water quality parameters, monthly collected from December 2012 to November 2013 at seven sampling stations spread over the Shatt Al-Arab River. Seventeen parameters were treated using Multivariate statistical technique; principal component analysis (PCA) and cluster analysis (CA) were applied for the evaluation and interpretation of a water quality data set for the Shatt Al-Arab River. The results of PCA identified four latent factors, which are responsible for the data structure explaining 78.64% of the total variance of the dataset these factors are: Water mineralization, Seasonal effect of temperature and organic pollution, Nutrients content and water visibility. CA showed four different groups of similarity between the sampling stations reflecting the different physicochemical characteristics features and natural background sources types. This study suggests that PCA and CA techniques are useful tools for identification of important surface water quality monitoring stations and parameters.