Assessment of Surface Water Quality Using Principle Component Analy

This research was carried out using multivariate statistical techniques for analyzing the quality of water and monitoring the variables affecting its quality in Gharasou river, in Ardabil province in northwest of Iran. During a year, 28 physical and chemical parameters were sampled in 11 stations. Results of these measurements were analyzed by multivariate procedures such as Principal Component Analysis (PCA), Factor Analysis (FA) and Discriminant Analysis (DA). The amount of pollutants resulting from the PCA showed that the three first components accounted for %78 percent of differences altogether, the first component accounting for 51.6, the second for 14.2 and the third for 12.2 percent of contribution, respectively. The values of coefficients calculated for the first and second components show the degree to which the places are susceptible to pollution. These values also showed that the main reason for the differences among stations’ pollution level, in different periods, is the amount of parameters like NO , NH , Na, TDS, EC, Mg , 3 3 2+ Turb., PO , WT, COD, BOD, SO , Fe, Ca and diazinon pesticide compared to other parameters. The results 4 4 322+ of this analysis distinguished the stations as did CA. DA showed that NH Ca , Mg , EC parameters and 3, 2+ 2+ diazinon had the utmost importance in grouping the stations. The first two functions of DA accounted for 100% of changes. Therefore, DA allowed a reduction in the dimensionality of large data set, delineating a few indicator parameters responsible for large variation in water quality. This, in addition, confirmed that the model resulting from multi-linear regression analysis of the main component is a good indicator of each source or factor’s loading in the distribution of pollution in the river. Thus, this study illustrated the usefulness of multivariate statistical techniques for analysis and interpretation of complex data sets and in water quality assessment, identification of pollution sources/factors and understanding spatial variations in water quality for effective river water quality management. This study also showed the effectiveness of these techniques for getting better information about the water quality and design of monitoring network for effective management of water resources.