Application of Multivariate Statistical Analysis in the Assessment of Surface Water Quality in Seyfe Lake, Turkey

Multivariate statistical methods are successfully used in many areas. Cluster Analysis and Principal Component Analysis methods are the most used of these methods. In this study, water quality data prepared for Seyfe Lake were evaluated. For this purpose, principal components and cluster analysis techniques were used. The 26 selected parameters were gathered at 11 of the points and evaluated. The cluster analysis was obtained from three different groups. These sampling points have different physicochemical characteristics and the pollution levels. According to the results of Principal Component Analysis, the six factors explained 92.72% of the total variance. The first factor 29.52%, the second factor 17.89%, the third factor 16.75%, the fourth 13.65%, the fifth 9.73%, and the sixth 5.17% of the cumulative variances explained respectively. Results reveal that sulfate, nitrate, total phosphorus, hardness, electrical conductivity, total dissolved solid, magnesium, potassium, sodium, calcium, biochemical oxygen demand, chemical oxygen demand, aluminum, iron, chromium, and lead were the most important parameters used to evaluate changes in water quality of the lake.