No abstract

This study is focused on water quality of Melen River (Turkey) and evaluation of 26 physical and chemical pollution data obtained five monitoring stations during the period [1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006]. It presents the application of multivariate statistical methods to the data set, namely, principal component and factor analysis (PCA/FA), multiple regression analysis (MRA) and discriminant analysis (DA). The PCA/FA was employed to evaluate the high-low flow periods correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing high-low flow periods variations of river water quality. Latent factors were identified as responsible for data structure explaining 72-97% of the total variance of the each data sets. PCA/FA was supported with multiple regression analysis to determine the most important parameter in each factor. It examines the relation between a single dependent variable and a set of independent variables to best represent the relation in the each factor. Obtained important parameters provided us to determine the major pollution sources in Melen River Basin. So factors are conditionally named soil structure and erosion, domestic, municipal and industrial effluents, agricultural activities (fertilizer, irrigation water and livestock wastes), atmospheric deposition and seasonal effects factors. DA applied the data set to obtain the parameters responsible for temporal and spatial variations. Assessment of high-low flow period changes in surface water quality is an important aspect for evaluating temporal and spatial variations of river pollution. The aim of this study is illustration the usefulness of multivariate statistical analysis for evaluation of complex data sets, in Melen River water quality assessment identification of factors and pollution sources, for effective water quality management determination the spatial and temporal variations in water quality.

No abstract

This study was aimed to evaluate the water quality and pollution sources in Sapanca Lake and its tributaries by applying multivariate statistical techniques to physicochemical parameters and toxic metals. For this purpose, the multivariate statistical methods such as principal component analysis (PCA) and absolute principal component score-multiple linear regression (APCS-MLR) model have been employed. It was tried to determine the seasonal pollution sources of physicochemical parameters and toxic metals obtained from 22 different sampling points between the years of 2015 and 2017. PCA was applied to the datasets, and 6 varimax factors describing 84%, 80%, 76%, and 79% of the total variance for each season were extracted. The obtained factors were analyzed using the APCS-MLR model for the apportionment of various pollution sources affecting physicochemical parameters and toxic metals. The results show that the natural soil structure, municipal-industrial wastewater, agricultural-atmospheric runoff, highways, and seasonal effects are the major pollution sources for toxic metals and physicochemical parameters. The material contribution of pollutant sources to toxic metals and physicochemical parameters was calculated and verified by the concentrations analyzed. Consequently, multivariate statistical techniques are useful to determine the physicochemical parameters and toxic metals through reciprocal correlation and assess the seasonal impact of pollutant sources in the basin. This study also provides a basis for the creation of measurement programs, determination of pollution sources, and provision of sustainable watershed management regarding other water resources.

Boyarmaddelerin geniş kullanımı sebebiyle ortaya çıkan boyalı atıksuların arıtılması önemli bir çevre sorunudur. Sigara kullanımı da günümüzde birçok kişinin sahip olduğu zararlı bir alışkanlıktır. Sigara kullanımı sırasında ortaya çıkan atık küllere bir kullanım alanı yaratmak ve boyarmaddelerin adsorpsiyon yöntemi ile gideriminde yeni, alternatif ve düşük maliyetli bir adsorban ortaya koymak amacıyla yola çıkılan bu çalışmada, sigara külünün Remazol Brillant Blue R (RBBR) boyarmaddesini adsorplama yeteneği araştırılmıştır. Bu amaçla, RBBR boyarmaddesinin maliyetsiz bir adsorban olan sigara külü üzerine adsorbsiyon şartları pH, başlangıç boyarmadde konsantrasyonu, karıştırma süresi ve adsorbent dozuna bağlı olarak incelenmiş ve maksimum adsorbsiyon kapasitesi 178,57 mg/g olarak belirlenmiştir. Bunun yanında deneysel verilerin izoterm ve kinetik modellere uygunluğu araştırılmış ve bu modellere ait parametreler hesaplanmıştır. RBBR boyarmaddesinin sigara külü ile adsorpsiyonunun Langmiur izotermi ile daha iyi ifade edildiği görülmüştür.