water quality

principal component analysis (PCA)

fresh water

hierarchical cluster analysis (HCA)

Pearson’s correlation

hierarchical cluster analysis

(HCA)

Quality control

Pollution

Water monitoring

Yağlıdere Stream is one of the major waterways flowing into the eastern Black Sea. In this study, multivariate statistical techniques, hierarchical cluster analysis (HCA), and principal component analysis (PCA) were applied to data on Yağlıdere water quality. Thus, we aimed to determine main pollution factors and time risky polluted areas. During the study, water samples were taken by monitoring 23 physicochemical parameters at five different sites every month between June 2013 and May 2014. In addition, Pearson correlation was used to determine the relationships of all physico-chemical parameters. According to the results of HCA, five sampling areas were grouped into two clusters. From the PCA results, it may be estimated that river pollution is mainly from agricultural runoff and soil weathering, soil erosion, hydroelectric power plant installation activities, domestic disposal, and leaching from solid waste disposal sites. Consequently, the Yağlıdere is of good quality according to the physico-chemical data by national and international permissible limits, but it is under pressure. These temporal and spatial scale effects indicate that water-monitoring schemes need to be scaled-sensitive to water management for coming years.