Browsing by Author "Uncumusaoglu, AA, Mutlu, E"

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Evaluating spatial and temporal variation in Tuzaklı Pond water using multivariate statistical analysis
(2019-01-01) Uncumusaoğlu A.A., Mutlu E.; Uncumusaoglu, AA, Mutlu, E
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.
Water quality assessment in karaboğaz stream basin (Turkey) from a multi-statistical perspective
(2021-01-01) Uncumusaoğlu A.A., Mutlu E.; Uncumusaoglu, AA, Mutlu, E
This study aims to evaluate the spatial and temporal changes in water quality of Karaboğaz Stream using statistical methods, to determine the main pollutant sources and to demonstrate the water quality classes. Water-quality data were obtained monthly (November 2016-October 2017) from 10 stations and considering 28 parameters. Temporal and spatial variations of Stream surface water quality were analyzed using multivariate statistical techniques on datasets, including agglomerative hierarchical clustering analysis (HCA) and principal component analysis (PCA). The analysis refers to the four main components responsible for the data structure and accounts for 87.41% of the total variance of the dataset. The root of these main components is generally related to the point source pollution (anthropogenic), nonpoint source pollution (agricultural activities) and natural processes (climate, soil and rock erosion). The temporal analysis of the water quality with HCA indicated that autumn is different from the other seasons. This study presents the practicality of various statistical methods in assessing and interpreting water-quality data to monitor and increase the management efficiency. When designing the most appropriate action plans for managers to control pollution, clearer, understandable information can be achieved using these methods and interpreting raw data.