Marine Pollution Bulletin 68 (2013) 134–139
Contents lists available at SciVerse ScienceDirect
Marine Pollution Bulletin
journal homepage: www.elsevier.com/locate/marpolbul
Baseline
Chemometric evaluation of the heavy metals distribution in waters from the
Dilovası region in Kocaeli, Turkey
Deniz Bingöl a,⇑, Ümit Ay a, Seda Karayünlü Bozbasß a, Nevin Uzgören b
a
b
Department of Chemistry, Kocaeli University, Kocaeli, Turkey
Faculty of Economics and Administrative Sciences, Dumlupınar University, Kütahya, Turkey
a r t i c l e
i n f o
Keywords:
Heavy metals
Principal component analysis
Correlation analysis
Cluster analysis
Chemometrics
a b s t r a c t
The main objective of this study was to test water samples collected from 10 locations in the Dilovası area
(a town in the Kocaeli region of Turkey) for heavy metal contamination and to classify the heavy metal
(Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb and Hg) contents in water samples using chemometric methods. The
heavy metals in the water samples were identified using inductively coupled plasma-mass spectrometry
(ICP-MS). To ascertain the relationship among the water samples and their possible sources, the correlation analysis, principal component analysis (PCA), and cluster analysis (CA) were used as classification
techniques. About 10 water samples were classified into five groups using PCA. A very similar grouping
was obtained using CA.
Ó 2012 Elsevier Ltd. All rights reserved.
In Turkey, a developing country, environmental pollution problems have increased since 1960 due to the rapid growth of industry
and population increase in the Marmara region, specifically in Izmit Bay. Since the 1960s, more than 250 large industrial plants
have been built in the area surrounding the bay. Industrial activities in the region are mostly located along on the northern coast
of Izmit Bay and surrounded by residential neighborhoods (Demiray et al., 2012). With its 50,000 inhabitants, the Dilovası district of
Kocaeli is a symbol of Turkey’s uncontrolled industrialization.
There are 185 companies serving in 45 sectors mainly metal
(iron–steel, aluminum), chemistry (e.g., paint), and energy (coalfired electric power plant), in the Dilovası organized industrial
zone, which is located at the center of a bowl-like topographic
structure. This caused serious environmental problems in the region, for example, soil, air, and water pollution. Although serious
measures were taken to reduce and control pollution since the
beginning of 1990, pollution levels are still high in the region
(Karademir, 2006). In Dilovası, deaths caused by cancer have surpassed those caused by cardiovascular diseases, becoming the
leading cause of death (Tuncer, 2009). Dilovası’s sewer system is
directly connected to Dil Creek. This waste is not subject to any
purification. Dil Creek is located in the eastern Marmara region
and discharges into Izmit Bay. This water source is used for irrigation and as drinking water for animals. One of the most important
sources of pollution in Izmit Bay is Dil Creek, which flows into the
western part of the bay. An estimated 60% of the total waste water
⇑ Corresponding author. Tel.: +90 2623032030; fax: +90 2623032003.
E-mail
(D. Bingöl).
addresses:
[email protected],
[email protected]
0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved.
http://dx.doi.org/10.1016/j.marpolbul.2012.12.006
directly enters Izmit Bay (Telli-Karakoç et al., 2002). Large industrial plants (mainly paint and metal industries) around Dil Creek
to discharge their solid and liquid waste into Dil Creek after limited
treatment.
In this area, Izocam, DYO, Lever, Wishes, Olmuksa, Yazıcıoğlu,
Polisan and Çolakoğlu factories generate the most stream pollution. Factories such as the Porland porcelain factory are able to pollute the river and creek from a distance thanks to hundreds of
meters of pipes. Fifteen years ago, Dilovası was home to cherry, apple, and peach orchards and vineyards; however, unfortunately,
the Dil Creek pollution destroyed the orchards and vineyards.
Waste is discharged directly into the stream without a water treatment system causing the extinction of many species. In the past,
people swam and fished in Dil Creek’s clean water. Heavy metals
are deemed serious pollutants because of toxicity, persistence,
and non-degradability in the environment (Fang and Hong, 1999;
Klavins et al., 2000; Tam and Wong, 2000; Yuan et al., 2004). Over
the past century, heavy metals have been discharged into the
world’s rivers and estuaries as a result of rapid industrialization
(Chen et al., 2004; Cobelo-García and Prego, 2003; Pekey, 2006;
Tam and Wong, 2000).
Multivariate methods are being increasingly used because a
large amount of information can be compared in graphical form,
which is very difficult to do using number tables or univariate statistics. There are many well established multivariate methods for
classification, of which the most commonly used are correlation
analysis, principal component analysis (PCA), and cluster analysis
(CA) (Brereton, 2007). Some researchers have determined similarities between samples and groups of samples using PCA and CA.
For example, researchers can use multivariate methods to evaluate
D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
trace metal concentrations in some spices and herbs (Karadas and
Kara, 2012); to the assessment of the level of some heavy metals in
sediments (Idris, 2008), to determine the levels of essential, trace
and toxic elements in citrus honeys from different regions (Yücel
and Sultanoğlu, 2012); to determine trace elements in commonly
consumed medicinal herbs (Tokalıoğlu, 2012); to evaluate the mineral content of medicinal herbs (Kolasani et al., 2011); to evaluate
trace metal concentration in some herbs and herbal teas (Kara,
2009); to evaluate heavy metals in street dust samples (Tokalıoğlu
and Kartal, 2006); to determine concentrations of key heavy metals
in street dust and analyze their potential sources (Lu et al., 2010);
to identify heavy metals in pastureland (Franco-Uría et al., 2009);
to identify source of eight hazardous heavy metals in agricultural
soils (Cai et al., 2012); to classify sea cucumber according to region
of origin (Liu et al., 2012a); and to evaluate the heavy metal contamination of surface soil (Yaylalı-Abanuz, 2011).
The aim of this study was to apply the chemometric techniques
of correlation analysis, principal component analysis (PCA), and
cluster analysis (CA) to results obtained from inductively coupled
plasma-mass spectrometry (ICP-MS) of water samples, and to
identify similarities in heavy metal content.
An ICP-MS inductively coupled plasma-mass spectrometry
instrument (Perkin Elmer DRC-e/Cetax ADX-500) was used to
determine Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb, and Hg content in each
region. A Hanna pH 211 Microprocessor pH-meter was used to
measure the pH values of the solutions. The pH-meter was standardized with NBS buffers prior to each measurement.
A total of 10 sites were selected on Dil creek and Hereke Port
near the Dilovası area, and water samples were collected after
monsoon (October 2011) season (Fig. 1).
Water samples, namely d1-steel and aluminun (direct unloading point), d2-steel and aluminun (inside Dil Creek), d3-steel and
metal (inside Dil creek), d4-steel (sea water, Hereke), d5-sea water
(between Hereke Port and Dil creek), d6-chemistry and cosmetic,
d7, d8-sea waters, d9, d10-Dil Creek (direct unloading point), and
sea water, were filtered using Whatman filter paper (No. 40) and
refrigerated at approximately 4 °C until laboratory analysis. Water
samples used for total metals analyses were acidified to pH 2 with
ultra purified 6 M HNO3, and stored at 4 °C. Contents of 10 heavy
135
metals (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb and Hg) were measured
using ICP-MS. All results given are the average values of three replicate analyses.
The analytical data obtained from the water samples were classified using correlation analysis, PCA, and CA to evaluate whether
there are any relationships between the heavy metals in the water
samples. All statistical calculations were made using PASW Statistics 18 and Minitab 16 software.
The aim of a correlation analysis is to measure the relationship
between variables. Pearson’s correlation coefficient (r for sample)
is the most common correlation coefficient. The correlation coefficients can range from 1 to +1 and are independent of the units of
measurement. Usually, |r| > 0.75 indicates that there is a significant
relationship between the variables. In this study, first the relationships between variables are examined using Pearson’s correlation
coefficient. In the statistical analysis, a significant correlation
among the variables is not required. In such cases, the correlations
should be removed from the data set. The p number of relevant
variables can be expressed as the k number (k 6 p) of new artificial
variables, which are linear components of these variables do not
correlate within them. This function performs the PCA (Özdamar,
2002).
PCA is probably the most widespread multivariate statistical
technique used in chemometrics, and because of the importance
of multivariate measurements in chemistry, it is regarded by many
as the technique that most significantly changes chemist’s view of
data analysis. Exploratory data analysis such as PCA is primarily
used to determine general relationships between data. Sometimes,
more complex questions need to be answered; for example, do the
samples fall into groups? The aims of PCA are to determine underlying information from multivariate raw data (Brereton, 2007).
Additional interpretations between heavy metals and water
samples may be obtained using more powerful chemometric techniques such as PCA. PCA is a projection method that allows easy
visualization of all the information contained in a data set. PCA
helps determine differences between samples and identifies which
variables contribute most to this difference (Liu et al., 2012b). PCA
is a process that transforms components of data matrix A (Anp),
including n samples and p, as shown in the following equation:
Fig. 1. A map of sampling locations/sites.
136
D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
A ¼ T B þ EA
ð1Þ
where T is n q score matrix, and B is q p PCA loading matrices. q
gives the minimum number of principal components needed for a
PCA analysis of matrix A. Each column vector of matrix T and each
row vector of matrix B is considered one principal component of
matrix A (Dıraman et al., 2009). In PCA, the information carried by
the original variables is projected onto a smaller number of underlying (‘‘latent’’) variables called principal components. The first
principal component covers as much of the variation in the data
as possible, the second principal component is orthogonal to the
first and covers as much of the remaining variation as possible,
and so on (Kara, 2009). Because the first and second principal component usually covers a large portion of the total, a clustering of
samples, according to the effect of all variables within the twodimensional plane, is possible by plotting against each of the first
two column vectors (the first two principle components: PC1 and
PC2) of matrix T. For a grouping that depends on the distribution
of variables in the system, the first two rows of matrix B is plotted
against each other (Dıraman et al., 2009). If the first m principal
component describes a large portion of the total variance, the rest
of the p–m principal component can be neglected. In this case, there
is a small variance (information) loss, and the work-space size is reduced to m from p (m < p) (reduction of dimension) (Tatlıdil, 1992).
Principal components (Yi) are independent, and their variances are
equal to the eigenvalue (ki) of correlation matrixes. The total variance of the original system is equal to the total variance of principal
components:
Total variance ¼ k1 þ k2 þ þ kp ¼
p
X
VarðY i Þ:
ð2Þ
i¼1
The total variability of the data matrix is equal to the total variability of principal components;
The variability ratio explained by the kth principal component
¼
kk
k1 þ k2 þ þ kp
ðk ¼ 1; 2; . . . ; pÞ:
ð3Þ
In applications, a few principal components describes a proportion larger than 80% of the total variance, without causing a loss of
information that can substitute for the original variable (Ersungur
et al., 2007). The number of eigenvalues greater than one value
when using standardized data matrix gives the value of m (Tatlıdil,
1992).
PCA allows users to see the results by themselves rather than as
a result of a property provider because the principal components
are capable of an intermediate step for more extensive investigations. In particular, CA uses principal component scores, which
are fairly common conditions (Özdamar, 2002).
The purpose of CA is to organize observations of a number of
groups/variables and determine if they share observed properties.
PCA is used primarily to determine general relationships between
data. CA is often coupled with PCA to check results and to group
individual parameters and variables. A dendrogram is the most
commonly used method of summarizing hierarchical clustering
(Lu et al., 2010). This technique is an unsupervised classification
procedure that involves a measurement of the similarity between
objects to be clustered.
The average of three results and standard deviations of analyses
obtained using ICP-MS are shown in Table 1. Using the data in Table 1, the heavy metals and water samples were classified using
correlation analysis, principal component analysis, and cluster
analysis.
In the first stage of this study, the findings obtained by calculating descriptive statistics of selected variables were interpreted
(Table 2). In the second stage, correlation analysis was applied to
determine whether the principal component analysis is appropriate to standardize data sets (Table 3). The results of the analysis
showed that there are significant correlations between the variables (heavy metals). Therefore, the principal component analysis is appropriate. In the third stage of study, the principal
component analysis was applied and the eigenvalues, which belong to the first four components, were found to be greater than
one and explained 91% of the total variance (Table 4). The water
samples were classified in accordance with the findings, graphics
were drawn, and variables (heavy metals) that are effective in
this classification were determined. In the final stage of the
study, water samples were classified using the Ward algorithm
with a hierarchical clustering analysis (HCA). HCA was used to
assess the spatial similarity or dissimilarity in water samples
according to heavy metals (Malik and Nadeem, 2011). HCA was
carried out using the first four principal components, explaining
91% of the total variance.
The findings obtained by calculating the descriptive statistics of
the selected variables were given in Table 2. Descriptive statistics
for the data set of 10 variables were analyzed. For all the variables,
positive and strong asymmetry (skewness > 0.5) was found (i.e.,
the observation values collected had relatively smaller values than
the average). The kurtosis values show that the series, except for
Cu and As, is sharper than normal, which means that the collection
of smaller than average values is higher than normal. These results
show that the series is not normally distributed. In addition, the
standard deviation values were very close to the average values
for many variables (Mn, Ni, Cu, Zn, and As), and some were even
found to be higher (Co, Cd, Pb, and Hg). This situation indicated
that the observation values were significantly different than the
mean (i.e., the variability was very high). This means that the water
samples taken from different locations were differently characterized by their heavy metal content.
Table 1
Analysis data concerning water samples.
a
Water
samples
Average (lg/L ± StDeva)
Cr
Mn
Co
Ni
Cu
Zn
As
Cd
Pb
Hg
d1
d2
d3
d4
d5
d6
d7
d8
d9
d10
0.027 ± 0.07
0.023 ± 0.08
0.020 ± 0.09
0.263 ± 0.27
0.071 ± 0.04
0.081 ± 0.05
0.058 ± 0.04
0.027 ± 0.05
0.010 ± 0.04
0.015 ± 0.03
770.66 ± 4.06
364.75 ± 2.27
331.67 ± 2.37
160.13 ± 1.24
139.58 ± 2.26
311.76 ± 3.17
207.16 ± 2.47
159.02 ± 3.19
303.53 ± 2.17
303.00 ± 3.37
30.21 ± 2.20
554.15 ± 4.20
575.61 ± 4.07
211.67 ± 3.21
123.35 ± 1.23
113.67 ± 1.29
89.53 ± 1.07
21.13 ± 0.97
38.49 ± 1.11
17.84 ± 1.37
2.49 ± 1.25
10.42 ± 2.29
7.41 ± 2.07
8.17 ± 1.97
6.77 ± 1.20
5.27 ± 1.45
7.20 ± 1.86
5.08 ± 1.23
16.76 ± 3.27
16.25 ± 3.17
13.25 ± 2.43
12.92 ± 2.21
13.41 ± 2.57
1.66 ± 0.26
1.14 ± 0.17
2.77 ± 0.22
2.17 ± 0.19
4.74 ± 1.57
3.16 ± 1.20
2.99 ± 1.24
5.34 ± 1.19
6.26 ± 1.28
6.35 ± 1.47
5.34 ± 1.31
2.36 ± 1.22
3.96 ± 1.26
3.62 ± 1.19
2.87 ± 1.07
23.91 ± 2.29
21.86 ± 2.24
1.43 ± 0.37
10.51 ± 1.17
13.41 ± 1.26
8.13 ± 1.29
4.05 ± 0.31
4.63 ± 0.28
8.46 ± 0.27
4.34 ± 0.21
4.06 ± 0.19
1.04 ± 0.16
0.148 ± 0.24
0.094 ± 0.07
0.130 ± 0.20
0.353 ± 0.37
0.028 ± 0.06
0.864 ± 0.44
0.231 ± 0.27
0.045 ± 0.05
0.027 ± 0.03
0.020 ± 0.03
8.77 ± 2.01
2.84 ± 1.06
3.12 ± 1.20
18.15 ± 3.27
1.32 ± 0.27
2.08 ± 1.03
6.36 ± 1.19
0.852 ± 0.47
0.019 ± 0.04
0.595 ± 0.37
2.780 ± 1.27
0.072 ± 0.04
0.065 ± 0.05
0.023 ± 0.08
0.021 ± 0.07
0.013 ± 0.06
0.016 ± 0.03
0.020 ± 0.06
0.019 ± 0.04
0.008 ± 0.02
StDev: Standard deviation.
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D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
Table 2
Descriptive Statistics for Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb, Hg.
Variable
Mean
StDev
Minimum
Median
Maximum
Range
Skewness
Kurtosis
Cr
Mn
Co
Ni
Cu
Zn
As
Cd
Pb
Hg
0.0595
305.1
177.6
8.58
5.82
8.19
6.01
0.1940
4.41
0.304
0.0756
182.7
212.7
4.67
5.18
7.88
3.99
0.2581
5.55
0.870
0.0100
139.6
17.8
2.49
1.14
2.36
1.04
0.0200
0.0190
0.00800
0.0270
303.3
101.6
7.31
3.08
5.34
4.49
0.1120
2.46
0.0205
0.2630
770.7
575.6
16.76
13.41
23.91
13.41
0.8640
18.15
2.780
0.2530
631.1
557.8
14.27
12.27
21.55
12.37
0.8440
18.13
2.772
2.59
2.04
1.47
0.94
0.91
1.67
0.61
2.31
1.99
3.16
7.23
5.21
0.71
0.08
1.28
1.27
0.43
5.75
4.15
9.98
Table 3
Pearson’s correlations matrix for heavy metal contents in water samples.
Mn
Co
Ni
Cu
Zn
As
Cd
Pb
Hg
Cr
Mn
Co
Ni
Cu
Zn
As
Cd
Pb
0.358
0.004
0.198
0.417
0.319
0.162
0.417
0.841a
0.156
0.000
0.235
0.701b
0.080
0.250
0.003
0.082
0.901a
0.031
0.627
0.244
0.875a
0.028
0.060
0.220
0.255
0.907a
0.100
0.373
0.325
0.461
0.149
0.380
0.208
0.019
0.524
0.349
0.342
0.329
0.133
0.060
0.201
0.384
0.246
0.068
0.276
Cell contents: Pearson correlation.
a
Correlation is significant at the 0.01 level (2-tailed).
b
Correlation is significant at the 0.05 level (2-tailed).
Table 4
PCA results.
Heavy metals
Cr
Mn
Co
Ni
Cu
Zn
As
Cd
Pb
Hg
Eigenvalue
Explained variance (%)
Cumulative (%)
Table 5
PCA scores sorted according to the first and second main component.
Component
1
2
3
4
0.227355
0.198878
0.253415
0.477632
0.291689
0.465248
0.262640
0.228365
0.357822
0.256323
2.9707
29.7
29.7
0.441023
0.520239
0.028459
0.024688
0.417222
0.147935
0.197216
0.231913
0.200684
0.452239
2.7472
25.5
57.2
0.220162
0.126453
0.577028
0.187752
0.325137
0.029839
0.529704
0.206578
0.214403
0.302476
2.2385
22.4
79.6
0.398191
0.188798
0.123304
0.423878
0.097179
0.439124
0.080615
0.210712
0.578360
0.152479
1.139
11.4
91.0
In multivariate statistical analyzes, the statistical analysis must
be used to standardize data instead of the original data when the
measurement units and variability of variables investigated were
different. However, the standard deviation values for the study
were quite different. The original data matrix was standardized
using the following equation: z = (x l)/r, and subsequent analyzes were based on a standardized data matrix.
The Person’s correlation coefficients for 10 heavy metals are
presented in Table 3. The positive and negative correlation coefficients indicate positive and negative correlations respectively, between the two metals. A significantly positive correlation at
p < 0.01 was found between the heavy metal pairs Ni–Zn (0.907),
Co–As (0.875), Cr–Pb (0.841), and Mn–Hg (0.91). In addition, Mn
is positively correlated with Cu at p < 0.05, and Cd is not correlated
to the other elements. There are statistically significant and high
Water samples
PC1
Water samples
PC2
d10
d9
d5
d8
d7
d6
d2
d3
d4
d1
2.89167
2.87429
0.50237
0.38607
0.22454
0.67878
0.76770
1.31706
1.55420
2.11212
d1
d2
d10
d9
d3
d8
d5
d7
d6
d4
3.50864
0.66222
0.64893
0.59746
0.45042
0.17656
0.79765
1.00550
1.00645
2.88151
correlations between variables. Therefore, the application of PCA
to the data set is significant in eliminating the dependence structure and/or reducing size.
Two pieces of information are connected, namely, geography
and concentration. So, in many areas of multivariate analysis, one
aim may be to connect the samples (e.g., geographical location/
sampling site), which are represented by the scores to the variables
(e.g., chemical measurements), which are represented by loadings
(Brereton, 2007).
PCA was applied to the entire data set (Table 4). Due to the standardized data, the correlation matrix was used for the analysis. The
PCA results are summarized in Table 4. The principal components
that have eigenvalues higher than one were extracted. The results
indicate that there were four eigenvalues higher than one. The first
component explains 29.7% of the total variance and loads heavily
on Ni and Zn. The second component, dominated Mn, Cu and Hg,
accounts for 25.5% of the total variance. The third component is
loaded by Co, Cu, and As, accounting for 22.4% of the total variance.
The fourth component is dominated by Cr, Ni, Zn, and Pb, accounting for 11.4% of the total variance.
138
D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
Score Plot of Cr; ...; Hg
4
Loading Plot of Cr; ...; Hg
Mn
d1
0,50
Cu
2
1
d2
d3
d10
d9
d8
0
d6
-1
d5
d7
Second Component
Second Component
3
Hg
0,25
Zn
Ni
Co
0,00
Pb
As
Cd
-0,25
-2
Cr
d4
-3
-0,50
-2
-1
0
1
2
3
-0,4
-0,3
-0,2
First Component
Group
Group
Group
Group
Group
1:
2:
3:
4:
5:
d1.
d2 d3.
d9 d10.
d5 d6 d7 d8.
d4.
At this stage of the study, the aim is to make HCA and the classification of the water samples from different locations, and to
compare the findings obtained with the results obtained from
0,0
0,1
0,2
0,3
0,4
0,5
First Component
Fig. 2. PCA score plot.
Fig. 3. PCA loading plot.
Fig. 4. Three way PCA score plot.
Dendrogram with Ward Linkage and Euclidean Distance
-34,40
Similarity
PCA scores according to the first principal component as determined by Ni and Zn content, and the second principal component,
as determined by Mn, Cu, and Hg, are listed in Table 5.d10 to d9
and d5 to d8 water samples listed in the top formed a cluster,
and the d1 water sample was listed in the bottom when listing
by the first main component. The d1 water sample was listed at
the top with a large score difference over the second component
in Mn, Cu and Hg content. According to the analysis of the d1 water
sample, these three heavy metals are highly effective in this location. By plotting the principal component, the inter-relationships
between different variables can be viewed, and interpreted for
sample patterns, groupings, similarities, or differences (Kara,
2009). The first and second principal component usually includes
a large portion of the total variance; therefore, the first two principle components (PC1 and PC2) are plotted against each other, and
clustering of samples is possible in the effects of all variables within the two-dimensional plane. The PC1 and PC2 score vectors and
the PC1 and PC2 loading vectors from PCA are plotted against each
other in Figs. 2 and 3, respectively. The first two components explain 57.2% of the variation in the data set.
When Figs. 1 and 2 are evaluated, d9 and d10 samples are
shown to be characterized by Ni and Zn metals; the d1 sample is
only characterized by Cu, Hg, and Mn metals; and the d4 sample
is only characterized a Cr metal a cluster. The d2 and d3 water
samples were mainly characterized by Co metal and Cu, Co, Mn,
and Hg metals in low levels. The d6 sample was situated near
the center and was slightly affected by Cd, As and Pb metals. Similarly, the d5, d7, and d8 samples were situated close to the center
and were not characterized by any metal.
A biplot involves a superimposition of scores and a loadings
plot, with the variables and samples represented on the same diagram. Fig. 4 shows a graph of the PC1, PC2, and PC3 score vectors
from PCA against each other. The first three components explain
79.6% of the total variation in the data set.
Fig. 4 was plotted according to the first three main components,
which showed that water samples taken from different locations
gave similar results to the results of a two-dimensional graph
and usually consisted of five groups.
-0,1
10,40
55,20
100,00
d1
d2
d3
d4
d5
d8
d7
d6
d9
d10
Observations
Fig. 5. Dendrogram results obtained from Ward linkage method.
the PCA. HCA was applied to the score vectors obtained from
PCA. The score vectors of the first four principal components,
explaining 91% of the total change, were used in the analysis.
The measurement is based on squared Euclidean distance. In this
study, the clustering method used was the Ward linkage method.
Dendrogram obtained from the Ward linkage method is shown in
D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
Fig. 5. The Dendrogram results show that the appropriate number
of clusters was five, which was similar to the PCA results.
Chemometric methods were applied to classify water according
to heavy metal contents. The chemometric evaluation showed that
a relationship exists between their heavy metal contents and the
water samples from the Dilovası area. The water samples and heavy metals were classified into five groups by PCA and a cluster
analysis. From the chemometric evaluation of heavy metal content,
the first group contains only the d1 sample, the second group contains the d2 and d3 samples, the third group consists of the d9 and
d10 samples, the fourth group is composed of the d5, d6, d7 and d8
samples, and the fifth group only contains the d4 water sample.
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