Assessment of surface water quality of the Ceyhan River basin, Turkey

Abstract

In this study, surface water quality of the Ceyhan River basin were assessed and examined with 13 physico-chemical parameters in 31 stations in 3 months during the period of 2005. Multivariate statistical techniques were applied to identify characteristics of the water quality in the studied stations. Nutrients, Cl- and Na+ affected mostly to the stations of Erkenez 2, Sir 2, and Sir 3 in the ordination diagram of correspondence analysis. Three factors were extracted by principal component analysis, which explains 79.14% of the total variation. The first factor (PC1) captures variables of EC, DO, NO2-, PO4triple bond, Cl-, SO4=, Na+, and Ca++. The second factor (PC2) is significantly related to pH, NH3-, and Mg++, while water temperature (T) and NO3- accounted for the greatest loading for factor 3 (PC3). The stations were divided into three groups for PC1, two groups for PC2, and three groups for PC3 by hierarchical cluster analysis. The stations in the vicinity of cities presented low dissolved oxygen and high concentration of physico-chemical parameter levels. The stations of Erkenez 2, Sir 2, Sir 3, and Aksu 4 located near the city of Kahramanmaraş were characterized by an extremely high pollution due to discharge of wastewater from industry and domestic. Pinarbaşi and Elbistan stations were also influenced by household wastewater of the city of Elbistan. According to criteria of Turkish Water Pollution Control Regulation, Erkenez 2, Sir 2, and Sir 3 stations have high polluted water. This study suggests that it is urgent to control point pollutions, and all wastewater should be purified before discharge to the Ceyhan River basin.

Assessment of the water quality in a large reservoir in semiarid region of Brazil

“…Tanriverdi & Demirkiran (2010), found similar results when studying the quality of surface water in the watershed of the River Ceyhan in Turkey; applying cluster analysis, the waters of the river were divided into three groups which are influenced by human activity (industrial and agricultural).…”

“…Multivariate statistics along with Principal Component Analysis (PCA) and Cluster Analysis (CA) have been widely used in data from the monitoring of water quality (Singh et al, 2004;Andrade et al, 2008;Fernandes et al, 2010;Tanriverdi & Demirkiran, 2010;Guedes et al, 2012). This type of analysis reduces the observational data and allows the interpretation of various components individually, since it can indicate associations between samples and/or variables, and also allows identification of possible factors and sources which influence the water system (Bouza-Deaño et al, 2008;Palácio et al, 2011;Guedes et al, 2012;Varol et al, 2012).…”

Spatio-Temporal Variations and Source Apportionment of Water Pollution in Danjiangkou Reservoir Basin, Central China

“…Thus, in recent years, there has been an increasing interest by researchers in analyzing such complex data using robust mathematics and statistical techniques, such as fuzzy comprehensive evaluation method (FCA), cluster analysis (CA), discriminant analysis (DA), and principal component analysis/factor analysis (PCA/FA), and absolute principal component score-multiple linear regression (APCS-MLR) [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. A literature review of these methods is described below.…”

“…Based on this information, they discovered that sea water intrusion, agricultural and industrial pollution, and geological weathering were responsible for the river pollution for both groups. In addition, Tanriverdi et al [21] applied PCA/FA to analyze and assess the surface water quality of Ceyhan River and suggested that the stations near cities were strongly affected by household wastewater, while the other stations were influenced by agricultural facilities. Moreover, Jha et al [22] identified major pollution sources influencing the physico-chemical variables in Aerial Bay using the FA technique, which included rivulet influx into the bay, land run-off, prevailing biological processes and tidal flow.…”

Use of Multivariate Analytical Methods in Assessment of River Water Quality

“…Rivers carry the municipal and industrial wastewater and run-off from agricultural land in their vast drainage basins and are the most vulnerable to pollution [2]. Human activities significantly decrease water quality and river inflows contribute pollutants to most riverine wetlands, thereby creating serious ecological problems [3]. For control of pollution effectively and water resources management, interpretation of a large number of monitoring data is required [1].…”