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 Bozbas ß a , Nevin Uzgören b a Department of Chemistry, Kocaeli University, Kocaeli, Turkey b Faculty of Economics and Administrative Sciences, Dumlupınar University, Kütahya, Turkey article info Keywords: Heavy metals Principal component analysis Correlation analysis Cluster analysis Chemometrics abstract 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 correla- tion 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 prob- lems have increased since 1960 due to the rapid growth of industry and population increase in the Marmara region, specifically in Iz- mit Bay. Since the 1960s, more than 250 large industrial plants have been built in the area surrounding the bay. Industrial activi- ties in the region are mostly located along on the northern coast of Izmit Bay and surrounded by residential neighborhoods (Demi- ray 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 (coal- fired 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 re- gion, 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 sur- passed 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 irriga- tion 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 directly enters Izmit Bay (Telli-Karakoç et al., 2002). Large indus- trial 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, Yazıcıog ˘lu, Polisan and Çolakog ˘lu factories generate the most stream pollu- tion. Factories such as the Porland porcelain factory are able to pol- lute the river and creek from a distance thanks to hundreds of meters of pipes. Fifteen years ago, Dilovası was home to cherry, ap- ple, 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 treat- ment 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 sta- tistics. 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 similar- ities between samples and groups of samples using PCA and CA. For example, researchers can use multivariate methods to evaluate 0025-326X/$ - see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpolbul.2012.12.006 ⇑ Corresponding author. Tel.: +90 2623032030; fax: +90 2623032003. E-mail addresses: [email protected], [email protected](D. Bingöl). Marine Pollution Bulletin 68 (2013) 134–139 Contents lists available at SciVerse ScienceDirect Marine Pollution Bulletin journal homepage: www.elsevier.com/locate/marpolbul
Transcript
Marine Pollution Bulletin 68 (2013) 134–139
Contents lists available at SciVerse ScienceDirect
Chemometric evaluation of the heavy metals distribution in waters from theDilovası region in Kocaeli, Turkey
Deniz Bingöl a,⇑, Ümit Ay a, Seda Karayünlü Bozbas� a, Nevin Uzgören b
a Department of Chemistry, Kocaeli University, Kocaeli, Turkeyb Faculty of Economics and Administrative Sciences, Dumlupınar University, Kütahya, Turkey
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. Theheavy 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 correla-tion analysis, principal component analysis (PCA), and cluster analysis (CA) were used as classificationtechniques. About 10 water samples were classified into five groups using PCA. A very similar groupingwas obtained using CA.
� 2012 Elsevier Ltd. All rights reserved.
In Turkey, a developing country, environmental pollution prob-lems have increased since 1960 due to the rapid growth of industryand population increase in the Marmara region, specifically in Iz-mit Bay. Since the 1960s, more than 250 large industrial plantshave been built in the area surrounding the bay. Industrial activi-ties in the region are mostly located along on the northern coastof Izmit Bay and surrounded by residential neighborhoods (Demi-ray et al., 2012). With its 50,000 inhabitants, the Dilovası district ofKocaeli 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 (coal-fired electric power plant), in the Dilovası organized industrialzone, which is located at the center of a bowl-like topographicstructure. This caused serious environmental problems in the re-gion, for example, soil, air, and water pollution. Although seriousmeasures were taken to reduce and control pollution since thebeginning of 1990, pollution levels are still high in the region(Karademir, 2006). In Dilovası, deaths caused by cancer have sur-passed those caused by cardiovascular diseases, becoming theleading cause of death (Tuncer, 2009). Dilovası’s sewer system isdirectly connected to Dil Creek. This waste is not subject to anypurification. Dil Creek is located in the eastern Marmara regionand discharges into Izmit Bay. This water source is used for irriga-tion and as drinking water for animals. One of the most importantsources of pollution in Izmit Bay is Dil Creek, which flows into thewestern part of the bay. An estimated 60% of the total waste water
directly enters Izmit Bay (Telli-Karakoç et al., 2002). Large indus-trial plants (mainly paint and metal industries) around Dil Creekto discharge their solid and liquid waste into Dil Creek after limitedtreatment.
In this area, Izocam, DYO, Lever, Wishes, Olmuksa, Yazıcıoglu,Polisan and Çolakoglu factories generate the most stream pollu-tion. Factories such as the Porland porcelain factory are able to pol-lute the river and creek from a distance thanks to hundreds ofmeters of pipes. Fifteen years ago, Dilovası was home to cherry, ap-ple, 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 treat-ment system causing the extinction of many species. In the past,people swam and fished in Dil Creek’s clean water. Heavy metalsare 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). Overthe past century, heavy metals have been discharged into theworld’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 alarge amount of information can be compared in graphical form,which is very difficult to do using number tables or univariate sta-tistics. There are many well established multivariate methods forclassification, of which the most commonly used are correlationanalysis, principal component analysis (PCA), and cluster analysis(CA) (Brereton, 2007). Some researchers have determined similar-ities 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) 134–139 135
trace metal concentrations in some spices and herbs (Karadas andKara, 2012); to the assessment of the level of some heavy metals insediments (Idris, 2008), to determine the levels of essential, traceand toxic elements in citrus honeys from different regions (Yüceland Sultanoglu, 2012); to determine trace elements in commonlyconsumed medicinal herbs (Tokalıoglu, 2012); to evaluate the min-eral content of medicinal herbs (Kolasani et al., 2011); to evaluatetrace metal concentration in some herbs and herbal teas (Kara,2009); to evaluate heavy metals in street dust samples (Tokalıogluand Kartal, 2006); to determine concentrations of key heavy metalsin 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 agriculturalsoils (Cai et al., 2012); to classify sea cucumber according to regionof origin (Liu et al., 2012a); and to evaluate the heavy metal con-tamination of surface soil (Yaylalı-Abanuz, 2011).
The aim of this study was to apply the chemometric techniquesof correlation analysis, principal component analysis (PCA), andcluster analysis (CA) to results obtained from inductively coupledplasma-mass spectrometry (ICP-MS) of water samples, and toidentify similarities in heavy metal content.
An ICP-MS inductively coupled plasma-mass spectrometryinstrument (Perkin Elmer DRC-e/Cetax ADX-500) was used todetermine Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb, and Hg content in eachregion. A Hanna pH 211 Microprocessor pH-meter was used tomeasure the pH values of the solutions. The pH-meter was stan-dardized with NBS buffers prior to each measurement.
A total of 10 sites were selected on Dil creek and Hereke Portnear the Dilovası area, and water samples were collected aftermonsoon (October 2011) season (Fig. 1).
Water samples, namely d1-steel and aluminun (direct unload-ing point), d2-steel and aluminun (inside Dil Creek), d3-steel andmetal (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), andsea water, were filtered using Whatman filter paper (No. 40) andrefrigerated at approximately 4 �C until laboratory analysis. Watersamples used for total metals analyses were acidified to pH 2 withultra purified 6 M HNO3, and stored at 4 �C. Contents of 10 heavy
Fig. 1. A map of sampl
metals (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb and Hg) were measuredusing ICP-MS. All results given are the average values of three rep-licate analyses.
The analytical data obtained from the water samples were clas-sified using correlation analysis, PCA, and CA to evaluate whetherthere are any relationships between the heavy metals in the watersamples. All statistical calculations were made using PASW Statis-tics 18 and Minitab 16 software.
The aim of a correlation analysis is to measure the relationshipbetween variables. Pearson’s correlation coefficient (r for sample)is the most common correlation coefficient. The correlation coeffi-cients can range from �1 to +1 and are independent of the units ofmeasurement. Usually, |r| > 0.75 indicates that there is a significantrelationship between the variables. In this study, first the relation-ships between variables are examined using Pearson’s correlationcoefficient. In the statistical analysis, a significant correlationamong the variables is not required. In such cases, the correlationsshould be removed from the data set. The p number of relevantvariables can be expressed as the k number (k 6 p) of new artificialvariables, which are linear components of these variables do notcorrelate within them. This function performs the PCA (Özdamar,2002).
PCA is probably the most widespread multivariate statisticaltechnique used in chemometrics, and because of the importanceof multivariate measurements in chemistry, it is regarded by manyas the technique that most significantly changes chemist’s view ofdata analysis. Exploratory data analysis such as PCA is primarilyused to determine general relationships between data. Sometimes,more complex questions need to be answered; for example, do thesamples fall into groups? The aims of PCA are to determine under-lying information from multivariate raw data (Brereton, 2007).
Additional interpretations between heavy metals and watersamples may be obtained using more powerful chemometric tech-niques such as PCA. PCA is a projection method that allows easyvisualization of all the information contained in a data set. PCAhelps determine differences between samples and identifies whichvariables contribute most to this difference (Liu et al., 2012b). PCAis a process that transforms components of data matrix A (An�p),including n samples and p, as shown in the following equation:
ing locations/sites.
136 D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
A ¼ T � Bþ EA ð1Þ
where T is n � q score matrix, and B is q � p PCA loading matrices. qgives the minimum number of principal components needed for aPCA analysis of matrix A. Each column vector of matrix T and eachrow vector of matrix B is considered one principal component ofmatrix A (Dıraman et al., 2009). In PCA, the information carried bythe original variables is projected onto a smaller number of under-lying (‘‘latent’’) variables called principal components. The firstprincipal component covers as much of the variation in the dataas possible, the second principal component is orthogonal to thefirst and covers as much of the remaining variation as possible,and so on (Kara, 2009). Because the first and second principal com-ponent usually covers a large portion of the total, a clustering ofsamples, according to the effect of all variables within the two-dimensional plane, is possible by plotting against each of the firsttwo column vectors (the first two principle components: PC1 andPC2) of matrix T. For a grouping that depends on the distributionof variables in the system, the first two rows of matrix B is plottedagainst each other (Dıraman et al., 2009). If the first m principalcomponent describes a large portion of the total variance, the restof the p–m principal component can be neglected. In this case, thereis a small variance (information) loss, and the work-space size is re-duced to m from p (m < p) (reduction of dimension) (Tatlıdil, 1992).Principal components (Yi) are independent, and their variances areequal to the eigenvalue (ki) of correlation matrixes. The total vari-ance of the original system is equal to the total variance of principalcomponents:
Total variance ¼ k1 þ k2 þ � � � þ kp ¼Xp
i¼1
VarðYiÞ: ð2Þ
The total variability of the data matrix is equal to the total var-iability of principal components;
The variability ratio explained by the kth principal component
¼ kk
k1 þ k2 þ � � � þ kpðk ¼ 1;2; . . . ;pÞ:
ð3Þ
In applications, a few principal components describes a propor-tion larger than 80% of the total variance, without causing a loss ofinformation that can substitute for the original variable (Ersunguret al., 2007). The number of eigenvalues greater than one valuewhen using standardized data matrix gives the value of m (Tatlıdil,1992).
PCA allows users to see the results by themselves rather than asa result of a property provider because the principal componentsare capable of an intermediate step for more extensive investiga-tions. In particular, CA uses principal component scores, whichare fairly common conditions (Özdamar, 2002).
The purpose of CA is to organize observations of a number ofgroups/variables and determine if they share observed properties.PCA is used primarily to determine general relationships betweendata. CA is often coupled with PCA to check results and to groupindividual parameters and variables. A dendrogram is the mostcommonly used method of summarizing hierarchical clustering(Lu et al., 2010). This technique is an unsupervised classificationprocedure that involves a measurement of the similarity betweenobjects to be clustered.
The average of three results and standard deviations of analysesobtained using ICP-MS are shown in Table 1. Using the data in Ta-ble 1, the heavy metals and water samples were classified usingcorrelation analysis, principal component analysis, and clusteranalysis.
In the first stage of this study, the findings obtained by calcu-lating descriptive statistics of selected variables were interpreted(Table 2). In the second stage, correlation analysis was applied todetermine whether the principal component analysis is appropri-ate to standardize data sets (Table 3). The results of the analysisshowed that there are significant correlations between the vari-ables (heavy metals). Therefore, the principal component analy-sis is appropriate. In the third stage of study, the principalcomponent analysis was applied and the eigenvalues, which be-long to the first four components, were found to be greater thanone and explained 91% of the total variance (Table 4). The watersamples were classified in accordance with the findings, graphicswere drawn, and variables (heavy metals) that are effective inthis classification were determined. In the final stage of thestudy, water samples were classified using the Ward algorithmwith a hierarchical clustering analysis (HCA). HCA was used toassess the spatial similarity or dissimilarity in water samplesaccording to heavy metals (Malik and Nadeem, 2011). HCA wascarried out using the first four principal components, explaining91% of the total variance.
The findings obtained by calculating the descriptive statistics ofthe selected variables were given in Table 2. Descriptive statisticsfor 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 thanthe average). The kurtosis values show that the series, except forCu and As, is sharper than normal, which means that the collectionof smaller than average values is higher than normal. These resultsshow that the series is not normally distributed. In addition, thestandard deviation values were very close to the average valuesfor many variables (Mn, Ni, Cu, Zn, and As), and some were evenfound to be higher (Co, Cd, Pb, and Hg). This situation indicatedthat the observation values were significantly different than themean (i.e., the variability was very high). This means that the watersamples taken from different locations were differently character-ized by their heavy metal content.
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).
D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139 137
In multivariate statistical analyzes, the statistical analysis mustbe used to standardize data instead of the original data when themeasurement units and variability of variables investigated weredifferent. However, the standard deviation values for the studywere quite different. The original data matrix was standardizedusing the following equation: z = (x � l)/r, and subsequent ana-lyzes were based on a standardized data matrix.
The Person’s correlation coefficients for 10 heavy metals arepresented in Table 3. The positive and negative correlation coeffi-cients indicate positive and negative correlations respectively, be-tween the two metals. A significantly positive correlation atp < 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, Mnis positively correlated with Cu at p < 0.05, and Cd is not correlatedto the other elements. There are statistically significant and high
correlations between variables. Therefore, the application of PCAto the data set is significant in eliminating the dependence struc-ture and/or reducing size.
Two pieces of information are connected, namely, geographyand concentration. So, in many areas of multivariate analysis, oneaim 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 stan-dardized data, the correlation matrix was used for the analysis. ThePCA results are summarized in Table 4. The principal componentsthat have eigenvalues higher than one were extracted. The resultsindicate that there were four eigenvalues higher than one. The firstcomponent explains 29.7% of the total variance and loads heavilyon Ni and Zn. The second component, dominated Mn, Cu and Hg,accounts for 25.5% of the total variance. The third component isloaded by Co, Cu, and As, accounting for 22.4% of the total variance.The fourth component is dominated by Cr, Ni, Zn, and Pb, account-ing for 11.4% of the total variance.
First Component
Seco
nd C
ompo
nent
3210-1-2
4
3
2
1
0
-1
-2
-3
d10d9
d8
d7d6 d5
d4
d3 d2
d1
Score Plot of Cr; ...; Hg
Fig. 2. PCA score plot.
First Component
Seco
nd C
ompo
nent
0,50,40,30,20,10,0-0,1-0,2-0,3-0,4
0,50
0,25
0,00
-0,25
-0,50
Hg
Pb CdAs
Zn
Cu
NiCo
Mn
Cr
Loading Plot of Cr; ...; Hg
Fig. 3. PCA loading plot.
Fig. 4. Three way PCA score plot.
Observations
Sim
ilarit
y
d10d9d6d7d8d5d4d3d2d1
-34,40
10,40
55,20
100,00
Dendrogram with Ward Linkage and Euclidean Distance
Fig. 5. Dendrogram results obtained from Ward linkage method.
138 D. Bingöl et al. / Marine Pollution Bulletin 68 (2013) 134–139
PCA scores according to the first principal component as deter-mined by Ni and Zn content, and the second principal component,as determined by Mn, Cu, and Hg, are listed in Table 5.d10 to d9and d5 to d8 water samples listed in the top formed a cluster,and the d1 water sample was listed in the bottom when listingby the first main component. The d1 water sample was listed atthe top with a large score difference over the second componentin Mn, Cu and Hg content. According to the analysis of the d1 watersample, these three heavy metals are highly effective in this loca-tion. By plotting the principal component, the inter-relationshipsbetween different variables can be viewed, and interpreted forsample patterns, groupings, similarities, or differences (Kara,2009). The first and second principal component usually includesa large portion of the total variance; therefore, the first two princi-ple components (PC1 and PC2) are plotted against each other, andclustering of samples is possible in the effects of all variables with-in the two-dimensional plane. The PC1 and PC2 score vectors andthe PC1 and PC2 loading vectors from PCA are plotted against eachother in Figs. 2 and 3, respectively. The first two components ex-plain 57.2% of the variation in the data set.
When Figs. 1 and 2 are evaluated, d9 and d10 samples areshown to be characterized by Ni and Zn metals; the d1 sample isonly characterized by Cu, Hg, and Mn metals; and the d4 sampleis only characterized a Cr metal a cluster. The d2 and d3 watersamples were mainly characterized by Co metal and Cu, Co, Mn,and Hg metals in low levels. The d6 sample was situated nearthe center and was slightly affected by Cd, As and Pb metals. Sim-ilarly, the d5, d7, and d8 samples were situated close to the centerand were not characterized by any metal.
A biplot involves a superimposition of scores and a loadingsplot, with the variables and samples represented on the same dia-gram. Fig. 4 shows a graph of the PC1, PC2, and PC3 score vectorsfrom PCA against each other. The first three components explain79.6% 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 locationsgave similar results to the results of a two-dimensional graphand usually consisted of five groups.
At this stage of the study, the aim is to make HCA and the clas-sification of the water samples from different locations, and tocompare the findings obtained with the results obtained from
the PCA. HCA was applied to the score vectors obtained fromPCA. 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 thisstudy, 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) 134–139 139
Fig. 5. The Dendrogram results show that the appropriate numberof clusters was five, which was similar to the PCA results.
Chemometric methods were applied to classify water accordingto heavy metal contents. The chemometric evaluation showed thata relationship exists between their heavy metal contents and thewater samples from the Dilovası area. The water samples and hea-vy metals were classified into five groups by PCA and a clusteranalysis. From the chemometric evaluation of heavy metal content,the first group contains only the d1 sample, the second group con-tains the d2 and d3 samples, the third group consists of the d9 andd10 samples, the fourth group is composed of the d5, d6, d7 and d8samples, and the fifth group only contains the d4 water sample.
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