Multivariate Statistics

1. Environ Monit Assess (2010) 167:175–184
DOI 10.1007/s10661-009-1040-4
Assessment of surface water quality of the Ceyhan River
basin, Turkey
Çagatay Tanrıverdi · Ahmet Alp ·
˘
Ali Rıza Demirkıran · Fatih Üçkarde¸
s
Received: 22 January 2009 / Accepted: 3 June 2009 / Published online: 23 June 2009
© Springer Science + Business Media B.V. 2009
Abstract In this study, surface water quality of and Ca++ . The second factor (PC2) is significantly
the Ceyhan River basin were assessed and exam- related to pH, NH3− , and Mg++ , while water tem-
ined with 13 physico-chemical parameters in 31 perature (T) and NO3− accounted for the greatest
stations in 3 months during the period of 2005. loading for factor 3 (PC3). The stations were di-
Multivariate statistical techniques were applied to vided into three groups for PC1, two groups for
identify characteristics of the water quality in the PC2, and three groups for PC3 by hierarchical
studied stations. Nutrients, Cl− and Na+ affected cluster analysis. The stations in the vicinity of
mostly to the stations of Erkenez 2, Sır 2, and cities presented low dissolved oxygen and high
Sır 3 in the ordination diagram of correspondence concentration of physico-chemical parameter lev-
analysis. Three factors were extracted by principal els. The stations of Erkenez 2, Sır 2, Sır 3, and
component analysis, which explains 79.14% of the Aksu 4 located near the city of Kahramanmara¸ s
total variation. The first factor (PC1) captures were characterized by an extremely high pollution
variables of EC, DO, NO2− , PO4≡ , Cl− , SO4= , Na+ , due to discharge of wastewater from industry and
domestic. Pınarba¸ ı and Elbistan stations were
s
also influenced by household wastewater of the
Ç. Tanrıverdi city of Elbistan. According to criteria of Turkish
Department of Agricultural Structures and Irrigation, Water Pollution Control Regulation, Erkenez 2,
˙
Faculty of Agriculture, Kahramanmara¸ Sütçü Imam
s
University, Kahramanmara¸ , Turkey
s Sır 2, and Sır 3 stations have high polluted water.
This study suggests that it is urgent to control
A. Alp (B) point pollutions, and all wastewater should be pu-
Department of Fisheries, Faculty of Agriculture, rified before discharge to the Ceyhan River basin.
˙
Kahramanmara¸ Sütçü Imam University,
s
Kahramanmara¸ , Turkey
s
e-mail: aalp@ksu.edu.tr
Keywords Water quality · Correspondence
A. R. Demirkıran analysis · Principal component analysis ·
Department of Soil, Faculty of Agriculture, Cluster analysis · Ceyhan River
˙
Kahramanmara¸ Sütçü Imam University,
s
Kahramanmara¸ , Turkey
s
F. Üçkarde¸s
Introduction
Department of Animal Sciences, Faculty of
˙
Agriculture, Kahramanmara¸ Sütçü Imam University,
s Clean water is a vital commodity for the well-
Kahramanmara¸ , Turkey
s being of human societies, and damage of inland

2. 176 Environ Monit Assess (2010) 167:175–184
aquatic systems is one of the most serious envi- mara¸ increased and intensive use of water re-
s
ronmental problems of the last century (Tudesque sources, and surface water pollution has probably
et al. 2008). The quality of a river at any point become in the upper and middle basin. Mean-
reflects several major influences, including the while, wastewater is not purified and discharged
lithology of the basin, atmospheric inputs, climatic in the drainage basin.
conditions, and anthropogenic inputs (Shrestha In this study, physico-chemical parameters of
and Kazama 2007). Anthropogenic activities re- water quality in the 31 stations from the Ceyhan
sult in a significant decrease in surface water qual- River were determined. Three different multivari-
ity of aquatic systems in watersheds (Massoud ate statistical techniques [correspondence analysis
et al. 2006). River inflows contribute main pollu- (CA), principal component analysis (PCA) and
tants to most lakes in a watershed, thereby tending hierarchical cluster analysis (HCA)] were applied
to induce serious ecological and sanitary prob- to evaluate pollution sources and classification of
lems (Wang et al. 2007). Increasing water pollu- the stations on the river in terms of water quality.
tion causes not only the deterioration of water
quality but also threatens human health and the
balance of aquatic ecosystems, economic develop- Materials and methods
ment, and social prosperity (Milovanovic 2007).
As a response to these environmental concerns, The study area
there is an obligation and strong political pressure
for greatly increased emphasis on the control of The Ceyhan River basin is located in the eastern
pollution levels. It is along these lines that the Mediterranean region of Turkey, between lati-
recently agreed EC Water Framework Directive tudes of 36◦ 30 and longitudes of 35◦ and 20 . The
(European Parliament 2000) requires all inland Ceyhan basin is bordered by the Seyhan basin in
water to reach “good ecological status” by 2015 the west and northwest, the Asi in the south, and
(Tudesque et al. 2008). Turkish State Hydraulic the Euphrates in the east and northeast (Fig. 1).
Works is the only organization, which has regu- The Basin drains into the Mediterranean Sea in
lar systematic surface and ground water quality the south. The Ceyhan River rises in mountains of
monitoring network all over Turkey (Baltacı et al. 2,000–2,500 m to the north of the regional center
2008). However, these data have not been as- of Kahramanmaras and flows south west to the
sessed by statistical methods, and rivers have not Mediterranean Sea near Adana. The basin cov-
been classified according to point pollution so far. ers 20,670 km2 and includes mountains and three
Turkey has 26 river basins, and Gediz River basin major provinces, Kahramanmaras, Osmaniye, and
was only monitored within the detailed projects Adana. The computed mean annual flow volume
(Harmancioglu et al. 2008). is 7.18 billion cubic meter, and this corresponds
The Ceyhan River basin is one of the most de- to 4% of the total flow volume of Turkey (Agrin
veloped regions in Turkey, and the distribution of Co. Ltd. 1999). Kahramanmara¸ City and its eight
s
numerous streams and reservoirs strongly benefits towns are located in the middle and upper Ceyhan
the high speed development of the local society River basin, and the overall population of this
and economy. Although, heavy metal pollution area is about 1,000,000 inhabitants. Total area of
of the lower Ceyhan River basin (Adana-Turkey) the middle and upper river basin is 14,346 km2 .
was studied (Yımazer and Yaman 1999), there is The average density of the population in the re-
no knowledge about surface water pollution in gion is 70 inhabitants per square kilometer, and
the upper and middle basin of the Ceyhan River. 54% of the population inhabits in the urban areas
However, in recent years, rapid development of while 46% inhabits in the rural areas. Osmaniye
local industries such as textile (358 company), and some part of Adana provinces are located
metallurgy (48 company), food (59 company), in the lower Ceyhan River basin; and their total
chemical (18 company), and Af¸ in-Elbistan ther-
s area in the Ceyhan basin is about 6,324 km2 , and
mic power stations (the biggest thermic power their total populations in the lower basin is about
station in Turkey) in the region of Kahraman- 950,000 inhabitants.

3. Environ Monit Assess (2010) 167:175–184 177
Fig. 1 Location of the
Ceyhan River basin in
Turkey and sampling
stations
Stations:
1. Hurman 1
A 2. Hurman 2
3. Sögütlü
˘
4. Pınarbası
¸
5. Elbistan
6. Göksun
7. Menzelet 1
8. Menzelet 2
9. Menzelet 3
10. Menzelet 4
11. Menzelet 5
12. Menzelet 6
13. Menzelet 7
14. Menzelet 8
15. Güredin
16. Fırnız
17. Kılavuzlu
18. Sır 1
19. Sır 2
20. Sır 3
21. Sır 4
22. Sır 5
23. Sır 6
24. Erkenez 1
25. Erkenez 2
26. Aksu 1
27. Aksu 2
28. Aksu 3
29. Aksu 4
30. Körsulu 1
31. Körsulu 2
Sampling and physico-chemical analysis (standing waters) on the river system. Thirteen
physico-chemical parameters obtained from each
Surface waters from 31 different stations in the station were used for analysis. Water temperature
Ceyhan River basin were collected on April, (T), pH, conductivity (EC), and dissolved oxygen
August, and October during 2005. Seventeen sta- (DO) were all measured at each sampling site.
tions were selected from streams (flowing wa- Water temperature and pH were measured by a
ters), and 14 stations were selected from reservoirs pH meter Model SensION1 (Hach), conductivity

4. 178 Environ Monit Assess (2010) 167:175–184
was measured with Model Senso Direct Con200 applying Canoco 4.5, Systat 10.0, and SPSS 12.0
(Aqualytic), and DO was measured by an YSI software for windows.
oxygen meter Model 57 (YSI). Water samples
were collected in 1-l polyethylene bottles, trans-
ported to the laboratory, and analyzed within
48 h. The parameters of ammonium (NH− ), nitrite
3 Result and discussion
(NO− ), nitrate (NO− ), orthophosphate (PO≡ ),
2 3 4
chlorine (Cl− ), sulfate (SO= ), sodium (Na+ ), cal-
4 The average values of 13 parameters of water
cium (Ca++ ), and magnesium (Mg++ ) were deter- quality in the 31 stations of the Ceyhan River are
mined using the standard methods (APHA 1992; given in Table 1. Water quality is classified into
Baltacı 2000). The average data of each station, four classes in Turkish Water Pollution Control
based on pooled samples in the study period, were Regulation (Anonymous 1988). Water quality of
determined. class 1 is considered to be clean, water quality of
class 2 corresponds to low pollution status, water
quality of class 3 corresponds to pollution status,
Statistical methods and water quality of class 4 is considered to be
with high pollution status. According to this guide-
Multivariate statistical methods for evaluation line, Erkenez 2 station is considered as highly pol-
and classification of large datasets from environ- luted (class 4) based on the concentration of NO− ,2
mental monitoring programs allow the reduction PO≡ , Cl− , and SO= ; Sır 2 and Sır 3 stations can be
4 4
of the dimensionality of the data and the extrac- considered as highly polluted (class 4) based on
tion of information that will be helpful for the the concentration of PO≡ and NH− (class 4), while
4 3
water quality assessment and the management of other stations include low polluted water status
surface waters (Simeonova et al. 2003). In this (class 2).
study, multivariate analysis of the river water The mean water temperature (T) of April, Au-
quality data set was performed through CA, PCA, gust, and October varied from 11.5◦ C to 26.0◦ C.
and HCA techniques (Leps and Smilauer 2003; Water temperatures in reservoirs were highest
Helena et al. 2000; Bengraine and Marhaba 2003; than in streams and rivers. The mean pH values
Singh et al. 2004; Ouyang 2005; Ouyang et al. 2006; of the 31 stations in Table 1 showed less variation,
Kowalkowski et al. 2006; Wang et al. 2007; Astel ranging from 7.8 to 9.3, and the highest pH was
et al. 2007). The variables were standardized by z- determined in Erkenze 2 station. High pH value
transformation (Liu et al. 2003), and so PCA was in Erkenez 2 station may be the result of the in-
carried on it. In order to summarize proximity of dustry in Kahramanmara¸ . Average conductivity
s
the water quality parameters and river stations, values of all studied stations were in the range
CA was applied (Leps and Smilauer 2003). Thir- of 267–1,959 μS/cm, among which the Erkenez 2,
teen original variables, including T, pH, DO, EC, Sır 2, Sır 3, and Aksu 4 showed higher values of
NH− , NO− , NO− , PO≡ , Cl− , SO= , Na+ , Ca++ ,
3 2 3 4 4 conductivity than others. This can be described to
and Mg++ , were reduced fewer factors using PCA. the discharge of industrial and domestic sewage,
Any factor with an eigenvalue greater than unity which put large amounts of alkaline ions into the
(eigenvalue > 1) was considered significant. In river system, since conductivity depends mostly on
order to evaluate the similarities of the studied ion concentration in surface water (Bernard et al.
stations in terms of water quality, hierarchical 2004).
multivariate cluster analysis (Ward’s method) was The lowest DO level (3.9 mg/l) was found in
performed using the square root transformed data the Erkenez 2 station, and the DO values in the
based on each significant principal factor obtained Sır 2 and Sır 3 were also significantly lower than
by PCA, respectively. The clusters were formed the other stations. This suggests that the discharge
based on Sneath’s index: 2/3 Dmax , where Dmax is of industry and domestic wastewater induced seri-
the maximum distance (Astel et al. 2007). Mul- ous organic pollution in these stations, since the
tivariate statistics analyses were performed by decrease of DO was mainly caused by the de-

5. Environ Monit Assess (2010) 167:175–184 179
Table 1 Average concentration of physico-chemical parameters of water quality of the Ceyhan River
T pH EC DO NH−
3 NO−
2 NO−
3 PO≡
4 Cl− SO=
4 Na+ Ca++ Mg++
Hurman 1 14.0 8.6 336 9.4 0.005 0.005 0.79 0.015 8.6 17.6 1.23 44.8 12.8
Hurman 2 18.5 8.2 375 7.9 0.030 0.009 1.74 0.015 5.5 28.9 0.94 55.9 10.9
˘
Sögütlü 17.0 8.2 454 7.6 0.030 0.042 0.90 0.040 5.1 107.3 1.39 65.4 14.5
Pınarba¸ ı
s 14.7 8.3 454 5.7 0.003 0.011 4.36 0.014 4.0 93.1 0.58 56.2 20.7
Elbistan 16.0 8.8 495 7.6 0.028 0.018 1.77 0.024 6.3 64.8 2.36 66.9 18.0
Göksun 16.3 8.9 420 8.3 0.048 0.013 0.37 0.053 6.2 27.5 5.52 56.1 13.6
Menzelet 1 22.2 8.3 281 8.5 0.057 0.027 0.37 0.004 0.5 23.0 3.80 23.9 10.6
Menzelet 2 21.7 8.3 286 8.7 0.017 0.029 0.30 0.002 0.7 24.3 4.61 17.1 5.1
Menzelet 3 21.5 8.4 286 8.0 0.043 0.007 0.35 0.003 1.1 28.0 4.61 15.5 3.6
Menzelet 4 22.6 8.3 285 7.0 0.007 0.008 0.37 0.007 1.0 29.3 5.15 15.6 3.7
Menzelet 5 22.9 8.5 291 7.5 0.004 0.007 0.30 0.003 0.9 33.3 4.70 16.0 3.7
Menzelet 6 22.5 8.4 310 7.1 0.007 0.023 0.57 0.011 0.5 29.7 4.78 26.4 9.0
Menzelet 7 21.0 8.4 297 8.2 0.013 0.008 0.40 0.001 1.1 27.3 4.78 15.7 3.3
Menzelet 8 20.9 8.5 389 7.1 0.017 0.010 0.70 0.004 1.5 31.3 4.47 27.8 7.4
Güredin 17.0 8.9 267 7.9 0.018 0.011 1.62 0.017 5.1 21.7 0.73 34.5 10.8
Fırnız 10.0 8.9 273 9.7 0.041 0.005 0.01 0.030 1.3 17.7 0.57 35.5 10.9
Kılavuzlu 13.0 8.8 346 10.8 0.006 0.030 16.63 0.007 3.1 34.8 0.84 37.1 18.8
Sır 1 13.4 8.7 390 8.7 0.039 0.018 1.24 0.027 5.7 32.2 1.36 43.73 9.14
Sır 2 26.0 7.8 905 4.8 2.460 0.038 3.06 2.760 25.4 66.8 12.98 168.5 89.2
Sır 3 21.5 7.8 655 6.9 1.090 0.046 5.91 0.970 18.4 47.9 8.87 198.4 94.6
Sır 4 17.5 8.2 415 7.1 0.001 0.004 0.02 0.110 6.9 32.5 4.25 45.6 31.2
Sır 5 17.0 7.8 410 7.8 0.001 0.002 0.12 0.210 5.4 36.4 3.87 45.5 31.1
Sır 6 26.0 8.8 440 7.2 0.010 0.017 0.98 0.230 3.2 31.8 4.12 35.8 21.2
Erkenez 1 20.0 8.7 397 7.1 0.009 0.007 0.61 0.009 3.3 16.8 2.21 53.1 14.4
Erkenez 2 18.3 9.3 1,959 3.9 0.298 0.241 4.75 12.60 183.4 430.7 133.9 234.4 24.0
Aksu 1 13.5 8.7 388 6.1 0.023 0.024 22.20 0.006 2.6 27.4 0.83 59.3 10.2
Aksu 2 20.2 8.6 412 7.7 0.035 0.022 1.29 0.015 4.2 27.3 1.99 54.8 15.1
Aksu 3 19.0 8.7 369 7.5 0.041 0.026 7.38 0.014 5.9 27.0 2.44 46.1 14.9
Aksu 4 21.0 8.6 629 7.4 0.058 0.029 4.19 0.050 15.7 88.1 6.75 77.4 25.1
Körsulu 1 11.5 8.8 291 9.3 0.029 0.005 0.52 0.006 2.0 13.6 0.29 39.9 12.7
Körsulu 2 12.5 8.7 441 8.9 0.011 0.002 20.58 0.001 2.4 57.8 0.45 62.9 14.7
Water temperature (T) is in ◦ C, electrical conductivity (EC) is in μS/cm, and other parameters are mg/l
composition of organic compounds (Wang et al. The main reason for the high nutrient concen-
2007). Moreover, extremely low DO content usu- tration in Sır 2 and Sır 3 is the domestic waters
ally indicates the degradation of an aquatic system from the city of Kahramanmara¸ . This is because
s
(Wang et al. 2007). The highest DO contents were all wastewaters of 400,000 inhabitants in the city
found in Kılavuzlu (10.8 mg/l). Kılavuzlu station is are discharged into Sır 2 and Sır 3 stations without
located in the lower basin of Menzelet Reservoir, being purified.
and it contains water from hydroelectric power Chlorid, sulfate, sodium, calcium, and magne-
stations. Therefore, the water in this station is sium concentrations in the stations of Erkenez 2,
well aerated with oxygen. Sır 2 and Sır 3 stations Sır 2, and Sır 3 were found higher than other sta-
showed high NH− contents, while Erkenez 2, Sır 3,
3 tions. High sulfate values in these stations resulted
and Sögütlü stations showed high NO− and Aksu
˘ 2 from industrial and domestic water from the city
1, Körsulu 2, and Kılavuzlu stations showed higher of Kahramanmara¸ . Sulfate was also found to be
s
NO− contents than the others. The highest PO≡
3 4 high in Elbistan, Pınarba¸ ı, and Aksu 4 stations.
s
concentration was also determined in Erkenez 2, High sulfate concentration in Elbistan and Pı-
Sır 2, and Sır 3 stations. High concentration of narba¸ ı stations may have resulted from domes-
s
nutrient and phosphorus indicate urban pollution. tic and recreational facilities or wastewater and

6. 180 Environ Monit Assess (2010) 167:175–184
Table 2 Component loadings in Principal Component
Analysis (PCA)
Variables PC1 PC2 PCA3
T 0.009 −0.138 0.670∗
pH 0.496 −0.514∗ −0.216
EC 0.974∗ 0.069 0.012
DO 0.576∗ −0.267 0.270
NH3− 0.124 −0.916∗ 0.033
NO2− 0.963∗ 0.169 0.029
NO3− 0.116 0.097 0.739∗
PO4≡ 0.967∗ 0.082 −0.041
Cl− 0.977∗ 0.146 −0.027
SO4= 0.949∗ 0.219 −0.080
Na+ 0.913∗ 0.208 −0.081
Ca++ 0.904∗ −0.308 0.040
Mg++ 0.067 −0.877∗ −0.069
Variance explained 6.93 2.22 1.14
by components
Percent of total 53.31 17.07 8.76
variance explained
Fig. 2 Proximity of the water quality parameters and
stations in Ceyhan River with ordination diagram from Three significant factors (i.e., eigenvalue > 1)
correspondence analysis (Stations: 1 Hurman 1, 2 Hurman were extracted by PCA, which explains 79.14%
˘
2, 3 Sögütlü, 4 Pınarba¸ ı, 5 Elbistan, 6 Göksun, 7 Menzelet
s of the total variation. Singh et al. (2004) classified
1, 8 Menzelet 2, 9 Menzelet 3, 10 Menzelet 4, 11 Menzelet
5, 12 Menzelet 6, 13 Menzelet 7, 14 Menzelet 8, 15 Güredin,
16 Fırnız, 17 Kılavuzlu, 18 Sır 1, 19 Sır 2, 20 Sır 3, 21 Sır 4,
22 Sır 5, 23 Sır 6, 24 Erkenez 1, 25 Erkenez 2, 26 Aksu 1, 27
Aksu 2, 28 Aksu 3, 29 Aksu 4, 30 Körsulu 1, 31 Körsulu 2)
atmospheric inputs of SO= from thermic power
2
station in Elbistan (Af¸ in-Elbistan Thermic Power
s
Station is the biggest thermic power station in
Turkey). However, high sulfate value in Aksu 4
station may have resulted form agricultural and
industrial facilities.
The physico-chemical parameters and river sta-
tions were given with an ordination diagram from
CA in Fig. 2. The parameters PO≡ , Cl− , and Na+
4
affected mostly the Erkenez 2 station (25), while
NH− affected mostly the Sır 2 (19) and Sır 3 (20)
3
stations, and NO− affected mostly the Kılavuzlu
3
(17), Sır 4 (21), and Sır 5 (22) stations. Menzelet
Reservoir stations lay far from the PO≡ , NH− ,
4 3
Na+ , and Cl− , while the stream stations were
represented by water temperature, pH, DO, and
EC in the ordination diagram.
PCA was performed to identify characteris-
tics of water quality variables in all stations, and Fig. 3 Dendrogram of cluster analysis for factor 1 (PC 1)
the factor loading matrix is listed in Table 2. dataset (EC, DO, NO2 –N, O–PO4 , Cl, SO4 , Na, and Ca)

7. Environ Monit Assess (2010) 167:175–184 181
cantly related to factor 1 are presented for each
group in Table 3. Group 1 includes 23 stations,
and they are the cleanest stations in the Ceyhan
River system. Group 2 includes seven stations and
showed relatively higher PO≡ , Cl− , SO= , Na+ ,
4 4
and Ca++ concentrations than group 1. Group 3
includes one station, and it has high polluted water
(class 4) according to criteria of Turkish Water
Pollution Control Regulation (Anonymous 1988).
As these stations in groups 2 and 3 are in the close
vicinity of the cities, rapid urbanization must be
the principal reason for the deterioration of water
quality in these groups.
Based on the second factor (PC2) dataset (pH,
NH− , and Mg++ ), all 31 stations were classified
3
into two groups (Fig. 4). Group 1 includes 20
stations, and it was the cleanest stations of Ceyhan
River system in terms of pH, NH− , and Mg++ .
3
Group 2 consists of 11 stations. Group 2 was influ-
enced by sewage from households and presented
a high value of NH− (Table 4).
3
Fig. 4 Dendrogram of cluster analysis for factor 2 (PC 2)
dataset (pH, NH3 –N, and Mg)
the factor loadings as “strong,” “moderate,” and
“weak” corresponding to absolute loading values
of >0.75, 0.75–0.50, and 0.50–0.30, respectively.
The first factor (PC1) accounts for 53.31% of the
total variance and has strong positive loadings on
EC, DO, NO− , PO≡ , Cl− , SO= , Na+ , and Ca and a
2 4 4
moderate loading of DO. The second factor (PC2)
accounts for 17.07% of the total variance and is
significantly related to pH, NH− , and Mg++ . Wa-
3
ter temperature (T) and NO− accounted for the
3
greatest loading for factor 3 (PC3), which explains
8.76% of the total variation. By using PCA, the
13 original variables were reduced to three key
independent factors.
Multivariate cluster analysis was applied to
classify the stations based on each significant fac-
tor obtained by PCA. Three dendrograms pro-
duced by multivariate cluster analysis for these
factors are shown in Figs. 3, 4, and 5, respectively.
For factor 1, the 31 stations were divided
into three groups by multivariate cluster analysis Fig. 5 Dendrogram of cluster analysis for factor 3 (PC 3)
(Fig. 3). The average values of variables signifi- dataset (T and NO3 –N)

8. 182 Environ Monit Assess (2010) 167:175–184
Table 3 Average values of the indicators related to factor 1 (PC1) for the three groups computed by cluster analysis and the
water quality guideline of the national water pollution control regulation
Group 1 Group 2 Group 3 Water quality guideline
23 stations (n = 69) 7 stations ( n = 21) 1 station ( n = 3) Class
I II III IV
EC (μS/cm) 343.5 ± 68.7b 544.5 ± 136.5c 2,260.7 ± 1,169.2d
(246–597) (310–905) (913–3,005)
DO (mg/l) 8.97 ± 1.53b 6.98 ± 1.36c 3.91 ± 1.31d 8 6 3 <3
(6.68–13.81) (4.5–9.6) (2.7–5.3)
NO2− (mg/l) 0.014 ± 0.016b 0.026 ± 0.023b 0.269 ± 0.214c 0.002 0.01 0.05 >0.05
(0.001–0.072) (0.001–0.083) (0.032–0.449)
PO4≡ (mg/l) 0.032 ± 0.057a 0.513 ± 0.976c 12.683 ± 11.861d 0.02 0.16 0.65 >0.65
(0.001–0.330) (0.001–2.960) (0.740–24.460)
Cl− (mg/l) 3.90 ± 2.32b 10.83 ± 8.37c 194.07 ± 149.94d 10 10 50 >50
(0.2–10.3) (1.20–26.40) (34.60–332.20)
SO4= (mg/l) 26.60 ± 6.83b 74.93 ± 41.71c 448.97 ± 184.10d 200 200 400 >400
(13.2–38.4) (20.0–189.0) (249.3–612.0)
Na+ (mg/l) 3.53 ± 2.85b 4.25 ± 4.39b 136.63 ± 28.95c 125 125 250 >250
(0.41–13.80) (0.21–12.98) (105.30–162.40)
Ca++ (mg/l) 38.48 ± 13.89b 96.00 ± 50.42c 260.27 ± 181.22d
(19.02–79.16) (41.3–198.4) (58.8–410.0)
The means are given with SD and bracket numbers are minimum and maximum values. Superscripted lowercase letters on
the numbers are the Tukey HSD test symbols
Table 4 Average values of the indicators related to factor 2 (PC2) for the 2 groups computed by cluster analysis and water
quality guideline of the national water pollution control regulation
Group 1 Group 2 Water quality guideline
20 stations (n = 60) 11 stations (n = 33) Class
I II III IV
pH 8.56 ± 0.26 8.37 ± 0.60 6.5–8.5 6.5–8.5 6.0–9.0 <6.0–9.0<
(7.88–9.10) (7.50–9.58)
NH3− (mg/l) 0.026 ± 0.030b 0.367 ± 0.760c 0.2 1.0 2.0 >2.0
(0.001–0.140) (0.001–2.680)
Mg++ (mg/l) 11.05 ± 4.59b 37.74 ± 29.91c
(2.80–21–40) (13.74–120.00)
The means are given with SD and bracket numbers are minimum and maximum values. Superscripted lowercase letters on
the numbers are the Tukey HSD test symbols
Table 5 Average values of the indicators related to factor 3 (PC3) for the four groups computed by cluster analysis and
water quality guideline of the national water pollution control regulation
Group 1 Group 2 Group 3 Water quality
guideline
13 stations ( n = 39) 12 stations ( n = 36) 6 stations ( n = 18) Class
I II III IV
T 15.26 ± 3.29 21.83 ± 5.75 20.489 ± 5.637 25 25 30 >30
(10.0–23.0) (12.4–31.2) (12.0–30.2)
NO3− (mg/l) 1.297 ± 1.419c 0.415 ± 0.378b 6.944 ± 10.051d 5 10 20 >20
(0.001–4.820) (0.001–1.685) (0.001–41.610)
The means are given with SD and bracket numbers are minimum and maximum values. Superscripted lowercase letters on
the numbers are the Tukey HSD test symbols

9. Environ Monit Assess (2010) 167:175–184 183
Cluster analysis based on the third factor Water Framework Directive, a national water
dataset (water temperature and NO− ) is pre-
3 quality monitoring network should be established
sented in Fig. 5. Average values of related vari- in Turkey as soon as possible (Baltacı et al. 2008).
ables are listed in Table 5. Three groups were
produced among the 31 stations. Group 1 includes
13 stations, group 2 includes 12 stations, and group
3 consists of six stations. Reservoir stations were
represented in the second group, and they differed References
from the other stations in terms of water temper-
ature. NO− value in the third group (Kılavuzlu,
3
Agrin, Co. Ltd. (1999). Aslanta¸ Dam and related aspects
s
of the Ceyhan River basin, Turkey. A WCD case study
Aksu 3, Aksu 4, Sır 2, Sır 3, and Erkenez 2) was
prepared as an input to the World Commission on
highest than the others. High NO− values in the
3 Dams, Cape Town. http://dams.org/kbase/studies/tr.
stations of Aksu and Sır may have resulted from Anonymous (1988). Turkish Water Pollution Control
agricultural and industrial facilities; however, high Regulation—Official Gazette, 19919.
APHA (1992). Standard methods for the examination of
NO− concentration in the Kılavuzlu station may
3 water and wastewater (18th ed.). Washington, DC:
probably have resulted from hypolimnetic waters APHA–AWWA–WPCF.
of Menzelet Reservoir. Astel, A., Tsakovski, S., Barbieri, P., & Simeonov, V.
(2007). Comparison of self-organizing maps classi-
fication approach with cluster and principal com-
ponents analysis for large environmental data sets.
Water Research, 41, 4566–4578. doi:10.1016/j.watres.
Conclusions 2007.06.030.
Baltacı, F. (2000). Su analiz metotları (Water analysis
˙s ˘ ˙
methods). Devlet Su I¸ leri Genel Müdürlügü, Içme
In this study, the variables of water quality in ˘
Suyu ve Kanalizasyon Dairesi Ba¸ kanlıgı. Ankara (334
s
the Ceyhan River were identified by multivariate pp.) (in Turkish).
statistical methods. Cluster analysis grouped the ˘
Baltacı, F., Onur, A. K., & Tahmiscioglu, S. (2008). Water
31 stations into three or two clusters with similar quality monitoring studies of Turkey with present and
probable future constraints and opportunities. Desali-
characteristics based on each factor obtained by
nation, 226, 321–327. doi:10.1016/j.desal.2007.02.114.
PCA. Bernard, P., Antoine, L., & Bernard, L. (2004). Principal
The results suggest that anthropogenic activi- component analysis: An appropriate tool for water
ties have had significant effects on water quality quality evaluation and management—Application to
a tropical lake system. Ecological Modelling, 178, 295–
in the river. The stations in the vicinity of cities
311. doi:10.1016/j.ecolmodel.2004.03.007.
strongly influenced by household wastewater pre- Bengraine, K., & Marhaba, T. F. (2003). Using prin-
sented the highest concentrations of nutrients. cipal component analysis to monitor spatial and
The stations of Erkenez 2, Sır 2, and Sır 3 located temporal changes in water quality. Journal of Haz-
ardous Materials, 100, 179–195. doi:10.1016/S0304-
near the city of Kahramanmara¸ were character-
s
3894(03)00104-3.
ized by an extremely high pollution due to the European Parliament (2000). Directive 2000/60/EC of the
discharge of wastewater from industry and domes- European Parliament and of the Council Establishing
tic. Pınarba¸ ı and Elbistan stations were also influ-
s a Framework for Community Action in the Field of
Water Policy. Off. L327, 72.
enced by the household wastewater from the city
Harmancioglu, N. B., Fedra, K., & Barbaros, F.
of Elbistan. The other stations were influenced (2008). Analysis for sustainability in management
especially by agricultural facilities; however, this of water scarce basins: The case of the Gediz
was less pollutant pressures than industrial and River Basin in Turkey. Desalination, 226, 175–182.
doi:10.1016/j.desal.2007.02.106.
household wastewaters.
Helena, B., Pardo, R., Vega, M., Barrado, E.,
Consequently, this study suggests that it is ur- Fernandez, J. M., & Fernandez, L. (2000). Temporal
gent to control point pollutions, all wastewater evolution of groundwater composition in an
should be purified before discharge, and the rules alluvial aquifer (Pisuerga River, Spain) by principal
component analysis. Water Research, 34, 807–816.
of European Water Framework Directive should
doi:10.1016/S0043-1354(99)00225-0.
be applied for the Ceyhan River basin. Addi- Kowalkowski, T., Zbytniewski, R., Szpejna, J., &
tionally, in order to meet the demands of the Buszewski, B. (2006). Application of chemometrics in

10. 184 Environ Monit Assess (2010) 167:175–184
river water classification. Water Research, 40, 744–752. Shrestha, S., & Kazama, F. (2007). Assessment of sur-
doi:10.1016/j.watres.2005.11.042. face water quality using multivariate statistical tech-
Leps, J., & Smilauer, P. (2003). Multivariate analysis of niques: A case study of the Fuji river basin, Japan.
ecological data using CANOCO. The Edinburg Build- Environmental Modelling & Software, 22, 464–475.
ing, Cambridge CB2 2RU, UK: Cambridge University doi:10.1016/j.envsoft.2006.02.001.
Press. Simeonova, V., Stratis, J. A., Samara, C., Zachariadis,
Liu, C. W., Lin, K. H., & Kuo, Y. M. (2003). Applica- G., Voutsa, D., Anthemidis, A., et al. (2003).
tion of factor analysis in the assessment of ground- Assessment of the surface water quality in
water quality in a blackfoot disease area in Taiwan. Northern Greece. Water Research, 37, 4119–4124.
The Science of the Total Environment, 313, 77–89. doi:10.1016/S0043-1354(03)00398-1.
doi:10.1016/S0048-9697(02)00683-6. Singh, K. P., Malik, A., Mohan, D., & Sinha, S. (2004).
Massoud, M. A., El-Fadel, M., Scrimshaw, M. D., & Multivariate statistical techniques for the evaluation
Lester, J. N. (2006). Factors influencing develop- of spatial and temporal variations in water qualityof
ment of management strategies for the Abou Ali Gomti River (India)—A case study. Water Research,
River in Lebanon: I. Spatial variation and land use. 38, 3980–3992. doi:10.1016/j.watres.2004.06.011.
The Science of the Total Environment, 362, 15–30. Tudesque, L., Gevrey, M., Grenouillet, G., & Lek, S.
doi:10.1016/j.scitotenv.2005.09.079. (2008). Long-term changes in water physicochemistry
Milovanovic, M. (2007). Water quality assessment and in the Adour–Garonne hydrographic network during
determination of pollution sources along the Ax- the last three decades. Water Research, 42, 732–742.
ios/Vardar River, Southeastern Europe. Desalination, doi:10.1016/j.watres.2007.08.001.
213, 159–173. doi:10.1016/j.desal.2006.06.022. Wang, X., Lu, Y., Han, J., He, G., & Wang, T. (2007).
Ouyang, Y. (2005). Evaluation of river water quality Identification of anthropogenic influences on wa-
monitoring stations by principal component analysis. ter quality of rivers in Taihu watershed. Jour-
Water Research, 39, 2621–2635. doi:10.1016/j.watres. nal of Environmental Sciences (China), 19, 475–481.
2005.04.024. doi:10.1016/S1001-0742(07)60080-1.
Ouyang, Y., Nkedi-Kizza, P., Wu, Q. T., Shinde, D., & Yımazer, D., & Yaman, S. (1999). Heavy metal pollu-
Huang, C. H. (2006). Assessment of seasonal vari- tion and chemical profile of Ceyhan River (Adana–
ations in surface water quality. Water Research, 40, Turkey). Turkish Journal of Engineering and Environ-
3800–3810. doi:10.1016/j.watres.2006.08.030. mental Sciences, 23, 59–61.