Using Multivariate Statistical Techniques to Evaluate the Quality of Water Reservoirs: Yamula Dam Lake Case Study

Year 2020, , 189 - 195, 20.05.2020

Musa Kar , Zeliha Leblebici

https://doi.org/10.35414/akufemubid.634550

Cited By: 5

Abstract

The aim of this study is to determine the changes in the water quality parameters of Kayseri Yamula Dam Lake using multivariate statistical methods. For this purpose, principal components analysis / factor analysis (PCA / FA) and cluster analysis (CA) techniques were used. Cluster analysis and principal components analysis are the most common statistical analyzes for determine water quality. The data used in the study were obtained by the State Hydraulic Works (DSI) Ankara Operation and Maintenance Department by seasonally sampling 125 samples from five different points determined from Yamula Dam Lake between September 2008 and April 2009. 16 physico-chemical parameters were selected to determine the water quality in the study area. These are; pH, electrical conductivity (EC), ammonia nitrogen (NH4-N), nitrite nitrogen (NO2-N), nitrate nitrogen (NO3-N), total phosphorus (TP), Sulfate (S04), chloride (Cl) , bicarbonate (HCO3), total hardness (TH), magnesium (Mg), organic matter (pV), Sodium (Na), potassium (K), orthophosphate (PO43-) and calcium (Ca). According to the cluster analysis, two main clusters were found depending on the pollution gradient. In the principal component analysis, different factors explained 100% of the total variance. It has been determined that the most important parameters to evaluate the water quality of Yamula Dam Lake are ammonia nitrogen, nitrite nitrogen, sulfate, chloride, sodium, orthophosphate, potassium, pH, organic matter, total phosphorus and nitrate nitrogen.determined as the most important parameters to evaluate the water quality of Yamula Dam Lake

Keywords

Yamula Dam Lake, PCA, Multivariable statistical methods, Water quality

Su Rezervuarlarının Kalitesinin Değerlendirilebilmesi İçin Çok Değişkenli İstatistiksel Tekniklerin Kullanılması: Yamula Baraj Gölü Örneği

Abstract

Bu çalışmanın amacı, Kayseri Yamula Baraj Gölü'nün su kalite parametrelerinde meydana gelen değişimleri çok değişkenli istatistiksel yöntemler kullanarak belirlemektir. Kümelenme analizi ve temel bileşenler analizi su kalitesinin belirlenmesi amacıyla kullanılan en yaygın istatistiksel analiz yöntemidir. Çalışmada kullanılan veriler, Devlet Su İşleri (DSİ) Ankara İşletme ve Bakım Daire Başkanlığı tarafından, Eylül 2008 ile Nisan 2009 tarihleri arasında Yamula Baraj Gölü’nden belirlenen beş ayrı noktadan mevsimsel olarak 125 adet örnekleme yapılarak elde edilmiştir. Çalışma alanındaki su kalitesini belirlemek için 16 fiziko-kimyasal parametre belirlenmiştir. Bunlar: pH, elektriksel iletkenlik (EC), amonyak azotu (NH4-N), nitrit azotu (NO2-N), nitrat azotu (NO3-N), toplam fosfor (TP), Sülfat (S04), klorür (Cl), bikarbonat (HCO3), toplam sertlik (TH), magnezyum (Mg), organik madde (pV), Sodyum (Na), potasyum (K), ortofosfat (PO43-) ve kalsiyum (Ca).olarak belirlenmiştir. Kümelenme analizine göre, kirlilik oranına bağlı olarak iki ana küme bulunmuştur. Temel bileşen analizinde farklı faktörler toplam varyansın % 100'ünü açıklamaktadır. Bu çalışmada, Yamula Baraj Gölü'nün su kalitesini değerlendirmek için en önemli parametrelerin amonyak azotu, nitrit azotu, sülfat, klorür, sodyum, ortofosfat, potasyum, pH, organik madde, toplam fosfor ve nitrat azotu olduğu belirlenmiştir.

Keywords

yamula Baraj Gölü, TBA, Çok değişkenli istatistiksel yöntemler, Su kalitesi

References

• Abazar, M., 2014. Application of Multivariate Statistical Methods and Water- Quality Index to Evaluation of Water Quality in the Kashkan River Abazar. Environ. Manage. 53, 865–881. https://doi.org/10.1007/s00267-014-0238-6Andrade, E.M. De, Araújo, L.D.F.P., Rosa, M.F., Disney, W., Alves, A.B., 2007. Seleção dos indicadores da qualidade das águas superficiais pelo emprego da análise multivariada. Eng. Agrícola 27, 683–690. https://doi.org/10.1590/S0100-69162007000400011Bilgin, A., 2015. An assessment of water quality in the Coruh Basin (Turkey) using multivariate statistical techniques. Environ. Monit. Assess. 187, 721. https://doi.org/10.1007/s10661-015-4904-9Boyacıoglu, H., 2006. Surface water quality assessment using factor analysis. Water SA 32, 389–393.Çevlik, H., Elibol, I., 2009. Yamula Baraj Gölü Limnolojisi. Devlet Su İşleri İşletme ve Bakımda Dairesi Başkanlığı, Ankara.Delegrange, A., Vincent, D., Courcot, L., Amara, R., 2015. Testing the vulnerability of juvenile sea bass (Dicentrarchus labrax) exposed to the harmful algal bloom (HAB) species Pseudo-nitzschia delicatissima. Aquaculture 437, 167–174. https://doi.org/10.1016/j.aquaculture.2014.11.023Duman, F., Kar, M., 2012. Temporal variation of metals in water, sediment and tissues of the European chup (Squalius cephalus L.). Bull. Environ. Contam. Toxicol. 89, 428–433. https://doi.org/10.1007/s00128-012-0679-7Filik Iscen, C., Emiroglu, Ö., Ilhan, S., Arslan, N., Yilmaz, V., Ahiska, S., 2008. Application of multivariate statistical techniques in the assessment of surface water quality in Uluabat Lake, Turkey. Environ. Monit. Assess. 144, 269–276. https://doi.org/10.1007/s10661-007-9989-3Işçen, C.F., Altin, A., Senoğlu, B., Yavuz, H.S., 2009. Evaluation of surface water quality characteristics by using multivariate statistical techniques: a case study of the Euphrates river basin, Turkey. Environ. Monit. Assess. 151, 259–264. https://doi.org/10.1007/s10661-008-0267-9Kıymaz, S., Karadavur, U., 2014. Application of Multivariate Statistical Analysis in the Assessment of Surface Water Quality in Seyfe Lake. J. Agric. Sci. 20, 150–163.Kuppusamy, M.R., Giridhar, V. V., 2006. Factor analysis of water quality characteristics including trace metal speciation in the coastal environmental system of Chennai Ennore, in: Environment International. pp. 174–179. https://doi.org/10.1016/j.envint.2005.08.008Li, S., Li, J., Zhang, Q., 2011. Water quality assessment in the rivers along the water conveyance system of the Middle Route of the South to North Water Transfer Project (China) using multivariate statistical techniques and receptor modeling. J. Hazard. Mater. 195, 306–17. https://doi.org/10.1016/j.jhazmat.2011.08.043Liu, C.-W., Lin, K.-H., Kuo, Y.-M., 2003. Application of factor analysis in the assessment of groundwater quality in a blackfoot disease area in Taiwan. Sci. Total Environ. 313, 77–89. https://doi.org/10.1016/S0048-9697(02)00683-6Liu, H., Benoit, G., Liu, T., Liu, Y., Guo, H., 2015. An integrated system dynamics model developed for managing lake water quality at the watershed scale. J. Environ. Manage. 155, 11–23. https://doi.org/10.1016/j.jenvman.2015.02.046Lopes, F.B., Andrade, E.M. De, Meireles, A.C.M., Becker, H., Batista, A.A., 2014. Assessment of the water quality in a large reservoir in semiarid region of Brazil. Rev. Bras. Eng. Agrícola e Ambient. 18, 437–445.Ouyang, Y., Nkedi-Kizza, P., Wu, Q.T., Shinde, D., Huang, C.H., 2006. Assessment of seasonal variations in surface water quality. Water Res. 40, 3800–10. https://doi.org/10.1016/j.watres.2006.08.030Shrestha, S., Kazama, F., 2007. Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji river basin, Japan. Environ. Model. Softw. 22, 464–475. https://doi.org/10.1016/j.envsoft.2006.02.001Tanriverdi, C., Alp, A., Demirkiran, A.R., Uçkardeş, F., 2010. Assessment of surface water quality of the Ceyhan River basin, Turkey. Environ. Monit. Assess. 167, 175–184. https://doi.org/10.1007/s10661-009-1040-4Venkatramanan, S., Chung, S.Y., Lee, S.Y., Park, N., 2014. Assessment of river water quality via environmentric multivariate statistical tools and water quality index: A case study of Nakdong River Basin, Korea. Carpathian J. Earth Environ. Sci. 9, 125–132.