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Turkish Journal of Fisheries and Aquatic Sciences 2019, Vol 19, Num, 9     (Pages: 727-737)

Determination of Spatial and Temporal Changes in Water Quality at Asi River Using Multivariate Statistical Techniques

Ece Kilic 1 ,Nebil Yucel 1

1 İskenderun Technical University, Faculty of Marine Sciences and Technology, Department of Water Resources Management and Organization, Hatay-Turkey DOI : 10.4194/1303-2712-v19_9_02 Viewed : 724 - Downloaded : 494 Water quality in surface waters is a critical issue since they are used in domestic, agricultural and industrial purposes. Therefore, proper water management strategies should be taken care of to protect water bodies. To accomplish this goal, ten years (2004-2014) seasonal water quality monitoring results consisting of 16 parameters (BOD5, COD, DO, NO2-, NO3-, NH4+, PO42-, SO42-, EC, SS, TDS, T, Na+, Mg2, Ca2+, Q ) measured at 5 stations taken from State of Hydraulic Works of Turkey was examined using multivariate statistical techniques like cluster analysis (CA), discriminant analysis (DA) and principal component / factor analysis (PCA/FA). Hierarchical CA grouped 5 monitoring stations and 4 seasons into two clusters as polluted/less polluted area and wet/dry season, respectively. DA showed that parameters responsible for temporal change in Asi River are Na+, Mg2+, Ca2+, Q, BOD, NH4+ and SS with 92.2% accuracy. Likewise, SO42-, DO and T were found as parameters responsible for temporal change with 90% accuracy. PCA revealed that mineral pollution, nutrient pollution, and organic pollution are major latent factors which influence the water quality of Asi River. It also showed that erosion, agricultural activities, domestic and industrial discharges are fundamental causes of water pollution in the study area. To conclude, the study revealed that multivariate statistical methods are beneficial tools for the evaluation of complex datasets like water quality monitoring data. Keywords : Orontes River, Environmental impact assessment, Cluster analysis, Discriminant analysis, Principal component analysis