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1

Evaluating spatial and temporal variation in Tuzaklı Pond Water using multivariate statistical analysis

100%
Uncumusaoglu A. A., Mutlu E.
Polish Journal of Environmental Studies
|
2019
|
tom 28
|
nr 5
This study used multivariate statistical techniques to demonstrate the spatial and temporal changes in water quality, main pollutant sources and water quality classes in Tuzaklı Pond. The water quality datasets are obtained on a monthly basis (November 2014–October 2015) using the results of 28 parameters that are obtained from three stations in the pond. Datasets are spatially and temporally assessed using statistical techniques, including one-way analysis of variance (ANOVA), Pearson’s correlation, hierarchical agglomerative cluster analysis (HCA) and principal component analysis (PCA). PCA indicates the four main components responsible for the data structure, accounting for 88.31% of the total variance of the dataset. These main components are physical parameters, soluble salts (natural), ammonium and phosphorus (agricultural activity), which are nutrient elements. Furthermore, it can be temporally concluded using HCA that the summer and autumn seasons exhibit more similar characteristics as compared to those exhibited by the remaining seasons. According to the water quality and class criteria of Turkey Surface Water Management Regulation and the World Health Organisation (WHO), while this pond generally represents Class I, we observed PO₄³⁻ , SO₃²⁻, NO₂⁻ and NO₃⁻ (Class II), which resulted in slightly contaminated water.
2

Assessment of water quality of Yaglidere stream (Turkey) using multivariate statistical techniquess

100%
Uncumusaoglu A. A., Akkan T.
Polish Journal of Environmental Studies
|
2017
|
tom 26
|
nr 4
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 physico-chemical 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.
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