The quality of surface water is a very important issue to use various demands like as drinking water, industrial, agricultural and recreational usages. There has been an increasing demand for monitoring water quality of many rivers by regular measurements of various water quality variables. However precise and effective monitoring is not enough, if the acquired dataset is not analyzed thoroughly. Therefore, the aim of this study was to estimate differences of seasonal and regional water quality using multivariate data analysis for each investing tributaries in Han River. Statistical analysis was applied to the data concerning 11 mainly parameters (flow, water temperature, pH, EC, DO, BOD, COD, SS, TN, TP and TOC) for the time period 2012∼2016 from 12 sampling sites. The seasonal water quality variations showed that each of BOD, TN, TP and TOC average concentration in spring and winter was higher than that of summer and fall, respectively.
In summer each flow rate and average concentration of SS was higher than any other seasons, respectively. The correlation analysis were explained that EC had a strong relationship with BOD (r=0.857), COD (r=0.854), TN (r=0.899) and TOC (r=0.910). According to principal component analysis, five principal components (Eigenvalue > 1) are controlled 98.0% of variations in water quality. The first component included TP, DO, pH. The second component included EC, TN. The third component included SS. The fourth component included flow. The last component included Temp. Cluster analysis classified that spring is similar to fall and winter with water quality parameters. AnyA, WangsA, JungrA and TancA were identified as affected by organic pollution.
Cluster analysis derived seasonal differences with investigating sites and better explained the principal component analysis results.