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Assessment of the environmental and physiological processesdetermining the accumulation of organochlorine compoundsin European mountain lake fish through multivariate analysis(PCA and PLS)
F OM. Felipe-Sotelo⁎, R. Tauler, I. Vives, J.O. GrimaltDepartment of Environmental Chemistry, IIQAB-CSIC, C/Jordi Girona 18-26, E-08034, Barcelona, Spain O
A R T I C L E I N F O
⁎ Corresponding author. Currently at the DepSpain. Fax: +34 932045904.
Please cite this article as: Felipe-Sotelo Mthe accumulation of organochlorine com
A B S T R A C T
Article history:Received 10 December 2007Received in revised form 3 June 2008Accepted 4 June 2008Available online xxxx
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EDPRThe presence of organochlorine compounds (OCs)–namely hexachlorobenzene (HCB),
hexachlorocyclohexanes (HCH), polychlorobiphenyls (PCBs #28, 52, 101, 118, 138, 153 and180), dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyldichloroethane (DDE)–was examined in various fish tissues (muscle and liver) sampled in 23 mountain lakes inEurope. The dependence of these organochlorine compounds on geographical parameters(altitude, longitude, latitude and temperature) and physiological parameters (lipid content,age, weight and size) was assessed. Principal components analysis (PCA) and partial leastsquares (PLS) models were used for the analyses.PCA results showed that organochlorine compound concentrations in fish tissues increasedwith increasing altitude and decreasing temperatures. This trend appeared to bemoremarkedfor the less volatile compounds. Some differenceswere found between themuscle and liver inthe effects of the percentage of lipids on the accumulation of organochlorine compounds andthe behaviour of HCB. Moreover, PCBs tended to accumulate more in liver rather than inmuscle. PCA scores clearly differentiated samples according to the lake of origin. PLS modelsconfirmed that temperature and altitude were themain factors influencing the accumulationof most organochlorine compounds in the lipids of the fish tissue.
Organochlorine compounds are distributed worldwide, asthey are rapidly transported from their emission sites todistant places through the atmosphere. This transfer involvessingular processes having in mind the currently acceptedenvironmental paradigms, i.e. besides dispersion and dilutionconcentration steps are required since some of these pollu-tants are found at higher concentrations in remote sites thanclose to their sources (Grimalt et al., 2004). A global distillationeffect has been proposed as explanation, involving the
5migration of pollutants from warm areas to cold places,5where they are trapped (Muir et al., 1990; Simonich and5Hites, 1995; Wania and Mackay, 1995, 1996). This effect has5also been observed in the context of mountain areas, leading5to higher concentrations of organochlorine compounds at5higher elevation (Grimalt et al., 2001). Daly and Wania (2005)5emphasized that examination of altitudinal trends of organo-5chlorine compound concentrations in mountains requires the6study of sites that are located at comparable distances from6the polluting sources. However, this is not always possible, e.g.6in the European topography.
niversity of Lleida, Av. Alcalde Rovira Roure 177, E-25198, Lleida,
Felipe-Sotelo).
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the environmental and physiological processes determining.., Sci Total Environ (2008), doi:10.1016/j.scitotenv.2008.06.020
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Please cite this article as: Felipe-Sotelo M, et al, Assessment of the environmental and physiological processes determiningthe accumulation of organochlorine compounds in European m..., Sci Total Environ (2008), doi:10.1016/j.scitotenv.2008.06.020
a Composite samples of muscle and liver, corresponding to thesame specimen. These samples were used for the joint analysis ofliver and muscle tissues by unfolding-PCA (augmented matrixDaug). t1:29
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In this context, fish have been used as sentinel organismsfor the assessment of long-range transport of organochlorinecompounds tomountain lakes, as they are the top predators inthese environments (Grimalt et al., 2001; Vives et al., 2004).However, accumulation of organochlorine compounds in fishtissues can also be dependent on many other biologicalfactors. Consequently, both environmental and physiologicalparameters must be considered to unravel the distributionpatterns of these compounds.
Chemometric methods allow valuable information to beextracted from multivariate data arrays, which are difficult tohandle using classical univariate statistical methods. Envir-onmental monitoring studies produce large amounts of data,containing multiple parameters whose interpretation is farfrom simple. Chemometric methods provide tools for findingrelationships between groups of analyzed analysed samplesand/or related variables or parameters (Massart et al., 1988).
The aim of the present study was to determine thedependences between the accumulation of organochlorinecompounds in fish tissues (muscle and liver) and differentparameters, including both geographical (altitude, longitude,latitude and average temperature) and physiological variables(age, weight, size and lipid content, see Grimalt et al., 2001 andVives et al., 2004 for further details of the physiologicalparameters used in this research). This paper addresses anew approach to this problem, using chemometric methods(principal components analysis) to unravel these depen-dences. The data are handled as a whole, in a fast andstraightforward way. Moreover, we attempt to determine therelative importance of such parameters in the accumulation oforganochlorine compounds by means of partial least squares(PLS) models.
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Experimental data in this work correspond to the concentra-tion of the following 12 organochlorine compounds: hexa-chlorobenzene (HCB), hexachlorocyclohexanes (α- andγ-HCH),polychlorobiphenyls (PCBs #28, 52, 101, 118, 138, 153 and 180),dichlorodiphenyltrichloroethane (DDT) and dichlorodiphenyl-dichloroethane (DDE). Fish were captured in 23 mountain lakesin Europe, whose locations are shown in Fig. 1. Table 1 indicatesthe total number of samples collected at each mountain lake.There were a total of 191muscle samples (note that the numberof samples isnot evenlydistributedbetweenthedifferent lakes).Seventy-eight liver tissuesampleswerealso takenandanalysedfrom some of the captured specimens. Full details of thesampling procedure and analysis are described elsewhere(Grimalt et al., 2001). Several additional parameters, includingboth physiological (age, weight, size and lipid content) andgeographical variables (altitude, longitude, latitude and annualaverage temperature) were included in the multivariate dataanalysis. They were obtained from previous studies (Grimaltet al., 2001; Vives et al., 2004). The altitude of lakes correspondsto the number of meters above sea level and the averagetemperature is the mean air temperature, provided by theDepartment of Geology and Geography of the University ofEdinburgh (UK). The experimental data were transferred intoMATLAB 6.5 (the Mathworks, Natick, USA) and PLS 3.5 Toolbox
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
1software (Eigenvector Research Ltd. Manson, WA, USA) was1used for the multivariate analyses.
12.1. Principal components analysis (PCA) and matrix1augmentation
1PCA transforms amultivariate data array into a new data set in1which the new variables are orthonormal and explain max-1imum variance (Jolliffe, 2002). It is based on the bilinear decom-1position of the original data set: D(I× J)=U(I×N)VT
(N× J)+E(I×J ), where1D is the original data array, with I rows (observations) and J1columns (variables);U is thematrixof scores of dimensions I×N,1whereN is the reduced number of components;VT is thematrix1of loadingswith dimensionsN× J and E is thematrix of residuals1that are notmodelled by theN principal components (PC). Thus,1a reduced set of components are found that synthesize all the1information from the original matrix. These new variables can1be expressed as linear combinations of the original ones. PCA1provides the analyst with useful tools for data exploration, like1the scores U (sample-related) and the loadings VT (variable-1related). The loadings VT quantify how much of each of the1original variables are used to define each PC. Thus, the variables1that best describe the sources of variance in the data can be1identified. The scores U are the projections of the original data1onto the new vector space, defined by the loadings. Their plots1reveal the sample clusters and groups (Massart et al., 1988).
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The aim of the present study was to establish andinvestigate the possible relationships between the accumula-tion of organochlorine compounds and several physiologicaland geographical parameters. For this purpose, two data setswere built and analysed by PCA. The first one corresponded tofish muscle samples Dmuscle (191×20) and the second one toliver samples Dliver (78×20). In these two matrices, the rowscorresponded to the different samples and the columns to theorganochlorine compounds and physiological and geographi-cal variables. A discussion about the loadings revealed therelationships between the variables and the parameters.
Before carrying out the analysis, values below the detectionlimit were set to one half of the limit value, as recommendedby Farnham et al. (2002). Missing values were imputed by PCAusing the function implemented in PLS Toolbox 3.5. Pre-treatment of the data sets was autoscaling (mean centeringand scaling to variance equal to 1).
Additionally, PCA can be applied straightforwardly andsimultaneously to multiple data sets by means of data matrixaugmentation (Smilde et al., 2004). This procedurewas appliedto the joint analysis of liver and muscle tissues, to determinewhether any of the organochlorine compounds were accu-mulated preferentially in either of the two analyzed analysedtissues. In this case, only the organochlorine compoundconcentrations were considered. Consequently, the numberof variables was reduced from 20 to 12. A new subset wasextracted from the matrix Dmuscle, corresponding to the 71specimens for which a liver sample was also available. Thisresulted in the new matrix Dmuscle2 (71×12). Concatenatedwith thismatrix (column-wise datamatrix augmentation), theDliver2 (71×12) data matrix was set down on the Dmuscle2
(71×12) matrix, giving the new augmented matrix Daug, ofdimensions 142×12. The resulting matrix Daug was thenautoscaled before the calculation of the joint PCA model.
2.2. Partial least squares
Themain purpose of partial least squares (PLS) regression is tobuild a multilinear regression model, y=Xb+e, where y is thepredicted response vector, X is the predictor matrix, b is theregression vector, and e is the residuals vector (Martens andNaes, 1991). PLSmodels produce latent variables (LV)whicharelinear combinations of the original predictor X variables,covarying optimally with the sought predicted values in y.Thus, there is no correlation between the new latent variables(or factors) on the one hand and maximum correlationbetween X and y variables on the other hand. PLS modelssearch for the latent variables that maximize the covariancebetween X and y blocks. In order to optimally interpret thecorrelation structurebetweenXvariables and y variables in PLSmodels, it is better to look at the PLSweight vectors (w) insteadof looking directly at the variable coefficients in the final bregression vector. In the regression vector, the variousphenomena that correlate X and y are intermixed, combinedand averaged. This confuses interpretation when looking forthe main correlation sources between X and y. In contrast, theweights provide information separately for each latent vari-able (LV). This information can be interpreted separately asnon-directly measured hidden phenomena expressing caus-ality relationships. In particular, weight vector w1 for the first
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
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PLS latent variable gives precise information about how the Xvariables combine to form the first score vector of matrix X,which optimally correlates with the y variable.X variables thatare highly correlated with the y variable will give high positiveor negative weightsw1, depending on whether they are highlypositively or negatively correlatedwith y. Consequently, in thiswork PLS was considered a tool for unravelling the relativeimportance of the geographical, temperature and physiologi-cal (X) variables affecting the accumulation of organochlorinecompounds in fish tissues (y). In the present study, PLS wasused basically as an explanatory tool, since an optimalprediction would probably require more complete data sets,including additional X variables. Data autoscaling pretreat-ment was applied to both X predictor matrices and y predictedvectors, and cross-validation by the leave-one-outmethodwasused to select the appropriate number of latent variables ineach case and to avoid overfitting. See references for a moredetailed explanation of the PLS method (Massart et al., 1988;Martens and Naes, 1991; Vandeginste et al., 1998).
A separate PLS model was considered to investigate thechanges in concentration of each organochlorine compoundin the different samples, taking as predictor variables thecorresponding geographical and physiological parameters(altitude, longitude, latitude, temperature, age, weight, sizeand lipids). Only muscle tissue data were considered here.Initially, the PLS models were developed for all the individualmuscle samples, regardless of their origin. This gave apredictor matrix X of dimensions 191×8 (common to all themodels) and 12 predicted vectors y 191×1, each one corre-sponding to one of the organochlorine compounds. Subse-quently, new PLS models were developed, which includedlake-averaged organochlorine compound concentrations andparameters. This resulted in a new predictor matrix X ofdimensions 22×8 and 12 new predicted vectors y of dimen-sions 22×1. This latter approach provided similar conclusionswith better fits. These results are discussed in more detail inthe following section. In order to interpret the correlationbetween X and y variables, the set of PLS weight vectorsw1 forthe first PLS component was examined in detail in this work.
3. Results and discussion
The calculation of pair-wise correlations revealed some of themost important relationships between the 20 variables andwas used as an exploratory tool. The two data sets, frommuscle and liver samples, were considered separately. Theresults are shown in Tables 2 and 3 respectively, as are thevariation ranges and basic statistics for all the parametersstudied in this work. In general terms, the average concentra-tions of organochlorine compounds were higher in liver thanin muscle tissue, with the exception of DDT and DDE inparticular. The dispersion of these values was also slightlyhigher for the concentrations of organochlorine compounds inliver than inmuscle, and wasmore noticeable for PCBs of highmolecular weight.
In the case of muscle samples (Table 2), high positivecorrelations were observed between the concentrations of theheaviest PCBs (larger than 0.6, in bold in Table 2). Moreover,the percentage of lipids in muscle correlated positively with
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Table 2t2:1 – Basic statistics and binary correlationsa between the variables for the muscle samples (all lakes considered)t2:2t2:3 Parameter Range Mean SD Correlation coefficients
HCB α-HCH
γ-HCH
DDE DDT PCB#28 PCB#52 PCB#101 PCB#118 PCB#138 PCB#153 PCB#180 Lipids Weight Size Age Altitude Temperature Latitude Longitude
Table 3t3:1 – Basic statistics and binary correlationsa between the variables for the liver samples (all lakes considered)t3:2t3:3 Parameter Range Mean SD Correlation coefficients
HCB α-HCH
γ-HCH
DDE DDT PCB#28 PCB#52 PCB#101 PCB#118 PCB#138 PCB#153 PCB#180 Lipids Weight Size Age Altitude Temperature Latitude Longitude
Fig. 2 –Loadings of the first two principal components of the PCAmodels ofmuscle tissue (a) PC1; (b) PC2 and liver (c) PC1; (d) PC2data sets.
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RRthe concentration of HCB (0.69). For PCBs, this correlation wasrather poor. The rest of the compounds correlated moderatelyand always positively with the lipids (0.34–0.4). PCBs of highmolecular weight also exhibited moderate positive correla-tions with the age of the captured specimens. Regarding thegeographical variables, temperature and latitude correlatednegatively with the concentrations of all the organochlorinecompounds. This tendency was more marked for the highermolecular weight PCBs and for DDE and DDT.
In the liver samples (Table 3), good correlations betweenthe heavy PCBs were observed, as in the muscle samples. Thecorrelations between the percentage of lipids (in muscletissue) and the concentration of organochlorine compoundsin liver were always negative in this case, in contrast to theobserved tendencies in muscles. The concentrations oforganochlorine compounds in liver were positively correlatedwith the age of the specimens. This tendency was clearer thanin muscle tissue. However, poor correlations were obtainedbetween the weight and size of the captured specimens andorganochlorine compound concentrations. Correlations oftemperature with organochlorine compound concentrations
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
2in liver were in general moderate. The main accumulation2tendency was not as clear for liver as it was for muscle2samples, since both positive and negative correlation coeffi-2cients were observed for liver samples.
23.1. Principal components analysis (PCA) and matrix2augmentation
2Three data pretreatment methods were assayed: logarithmic2transformation (log scaling), column scaling and column2autoscaling. Log scaling is sometimes recommended when2either the range of values for a given variable is large, covering2several orders of magnitude, or when the data distributions2are very asymmetric (this is common in environmental data,2where some samples exhibit values that are too high/low)2(Massart et al., 2005). Previously, a constant value was added2to each individual value, to avoid negative values after log2calculation (xnew=log (K+xold)). In the column scaling pre-2treatment, each individual value was divided by the standard2deviation of its corresponding column. In autoscaling, the3mean of the corresponding column was first subtracted and
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then column scaling was applied. After preliminary studies,autoscaling was finally chosen. Column scaling and logarith-mic pretreatments gave PCA models in which data variancewas mostly assigned to the latitude and altitude of lakes(which were the variables with the highest values, in absoluteterms). Hence, they did not provide any valuable informationabout the dependence of organochlorine compounds on othergeographical and physiological parameters. Autoscaling wasused throughout the study, since it provided more interpre-table models.
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Fig. 3 –Representation of PC1 loadings versus PC2 to show the decontent (— line) and relations between temperature, latitude andand (b) liver data sets.
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
PCA models developed for the muscle and liver tissuesexplained 65% and 63% of total variance respectively, when 4principal components were considered for each model.
In the PCA model for the muscle data set, PC1 (35%explained variance, Fig. 2a) gave high positive loadings forDDE, DDT and for the PCBs of higher molecular weight (PCBs #101, 118, 138, 153 and 180), followed by the remaining organo-chlorine compounds for altitude, age and lipid percentage.Temperature and latitude showed relatively high negativeloadings. PC2 (13% explained variance, Fig. 2b) clearly
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pendence of organochlorine compound accumulation on lipidaltitude and the accumulation of OCs (---- line) for (a) muscle,
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associated concentrations of HCB, α- and γ-HCH with the lipidcontent, and, to a lesser extent, with temperature, fish weightand length—all with high positive loadings. In contrast,length, age, altitude and concentration of PCBs gave negativeloadings. PC3 (9% explained variance) was mostly positivelyloaded by latitude, as opposed to altitude and temperature, aswell as to PCB#28 and 52 to a lesser extent. Finally, PC4 (8%explained variance) explained the particular behaviour ofPCB#28 and 52, with high positive loadings in this PC, as wellas in longitude.When the PCAmodel for the liver sampleswasevaluated, similar loading profiles were observed (see PC1 andPC2 loadings for liver in Fig. 2c and d respectively). Acomparison of Fig. 2a and c shows that the main differencebetween both PC1 loading profiles for muscle and liver is thechange in the sign of the correlation for lipids. In musclesamples, lipids were positively correlated with organochlorinecompounds. In contrast, in liver samples this correlation wasnegative. This could be due to the fact that a reduction of lipidsin the muscle would tend to decrease the concentration oforganochlorine compounds in this tissue and increase theiraccumulation in the liver. In addition, the age of the specimenseemed to play a more important role in organochlorinecompound concentrations in liver than in muscle (see thehigher loading of age in PC1 for liver than for muscle, Fig. 2cand a respectively). However, in the comparison betweenFig. 2b (muscle) and d (liver), the HCB loadings differ. WhereasHCB loadings for PC2 were high and positive for musclesamples, they were around zero for liver samples. The mainconclusion is that although liver and muscle tissues followedthe same trends, there were differences in some of thepatterns for specific compounds. The representation of PC1loadings versus PC2 in Fig. 3 gives a better view of therelationships between the variables. Regardingmuscle data, inFig. 3a it can be seen that concentrations of HCB, α- and γ-HCHare clearly grouped and associated with the lipid content inmuscle tissue, whereas PCB#28 and #52 showed negativecorrelations with lipids. The remaining compounds showintermediate behaviour in terms of lipids. In the samerepresentation (Fig. 3a), relationships among organochlorinecompounds and temperature, altitude and latitude are high-lighted. It can be observed that temperature opposes altitude.Latitude is in an intermediate situation. When the latitudeincreases, the altitude of the lakes tends to decrease. All the
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Table 4 – Vapour pressure for the organochlorinecompounds analysed (Mackay et al., 1992)
Compound Vapour pressure (Pa)
γ-HCH 10−0.70
α-HCH 10−0.92
HCB 10−1.2
PCB#28 10−1.5
PCB#52 10−1.8
DDE 10−2.5
PCB#101 10−2.5
PCB#118 10−3.0
PCB#138 10−3.2
DDT 10−3.3
PCB#153 10−3.3
PCB#180 10−3.9
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
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3organochlorine compounds were negatively correlated to3temperature and positively associated with altitude, which is3in agreement with the global distillation theory. Thus, as the3temperature decreased, and/or the altitude increased, the3concentrations of organochlorine compounds increased and3accumulated. However, the same figure shows that this3relation was not exactly the same for all the organochlorine3compounds considered. In addition, some clustering was3observed, which can be explained in terms of the relative3volatility of the compounds. First, the less volatile compounds,3like DDT, DDE and PCBs # 101, 118, 138, 153 and 180, were3found at higher positive loadings of PC1 (lower temperatures)3(see vapour pressures in Table 4). In contrast, the remaining3compounds (HCB, α- and γ-HCH, PCB#28 and #52), which are3more volatile (see Table 4) appeared at somewhat lower3positive PC1 loadings (and higher temperatures). This is in3agreement with the effect of temperature on compounds of3different volatility. As temperature decreases, the concentra-3tion of less volatile organochlorine compounds increases, as3they are more easily trapped than those with higher vapour3pressures (more volatile). The explanation for altitude would3be exactly the opposite. These conclusions agree with3previous studies showing that soils (Ribes et al., 2002) and3snow (Blais et al., 1998) located at higher altitude exhibited3higher organochlorine concentrations than at lower sites.3Note that the main purpose of the present work was to reveal3dependences among variables and to try to give a simplified3interpretation of the relationships observed. The aim was not3to deliver a “cause and effect” conclusion. Moreover, multi-3variate approaches attribute the variation observed to some3(few and independent) new variables, which are combinations3of the individual parameters. Therefore, multivariate models,3like the ones presented here, provide a better overview of the3interdependences among variables than other studies that use3univariate linear regression to evaluate the dependence3between the pollutant variation and only one (so-called)4independent variable. When the representation of PC1 versus4PC2 loadings for liver samples was analyzed analysed (Fig. 3b),4it was found that the results for liver samples also agreed well4with the global distillation effect. In the case of liver samples,4all organochlorine compounds were negatively correlated4with temperature and latitude, and positively associated4with altitude. Moreover, organochlorine compounds in liver4were grouped according to their volatility, as was previously4highlighted for muscle samples. The main difference between4results obtained for liver and muscle samples involved the4relationship between the percentage of lipids and the4concentrations of organochlorine compounds (compare4Fig. 3a and b). For muscle samples, the increment in lipids4was clearly associated with higher concentrations of HCB, α-4HCH and γ-HCH. However, in liver samples, the percentage of4lipids was opposed to all the organochlorine compounds4considered. Moreover, in the case of liver tissue samples, the4effect of lipids on the concentration of organochlorine4compounds seemed to be more marked.4The representation of PC1 versus PC2 scores (Fig. 4) shows4that samples were clustered in different groups according4to their geographical origin. In general, samples located on the4right side of the plot (high positive PC1 scores) correspond4to muscle or liver samples of specimens that were more
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Fig. 4 –Plot of PC1 scores versus PC2, showing the grouping of samples according to their origin; data corresponding to(a) muscle and, (b) liver samples.
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RRcontaminated by organochlorine compounds (more positivePC1 loadings for both muscle and liver data sets). Musclesamples are illustrated in Fig. 4a. High contents of organo-chlorine compounds were found in most of the musclesamples from fish collected at Milchsee Lake and one samplefrom Schwarzsee Lake. These two lakes are at high altitudes(2500–2800 m) and middle latitudes (Central Europe, in theAlpine region). In contrast, one fishmuscle sample collected atRedò Lake and those collected at Aubé Lake (both in thePyrenees) had high positive PC2 scores, indicating a relativelyhigher concentration of HCB, α-HCH and γ-HCH (high positivePC2 loadings) and lower concentrations of the PCB family ofcompounds (negative PC2 loadings). In general, musclesamples collected at the highest latitudes (Northern locations)gave closer to zero PC1 and PC2 scores, indicating lower inputsof organochlorine compound contamination sources. Moresubtle differences among fish samples collected at differentlakes could have been found if samples with very highconcentrations of organochlorine compounds (especially forMilchsee Lake, which showed the highest scores of PC1) had
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
been eliminated and PCA calculations were rerun. In the caseof liver samples (scores in Fig. 4b), Øvre Neådalsvtan andNedre Neådalsvtan Lakes showed the lowest concentrationsof organochlorine compounds (negative scores of PC1, to theleft in Fig. 4b). Similar results were observed for musclesamples (Fig. 4a). Moreover, the highest concentrations oforganochlorine compounds in liver samples corresponded toone of the samples from Lake Redò, followed by the samplesfrom the Fallbekktjørna and Vel’ké Hincovo Lakes. Oncemore,Redò Lake showed the highest concentrations of α-HCH and γ-HCH (at the top of Fig. 4b, highest scores of PC2). However, thenumber of muscle samples was higher than the number ofliver samples (Table 1). Nevertheless, for the initial purposes ofthis study and for the sake of brevity, the plots in Fig. 4 areconsidered sufficiently illustrative of the main tendencies inthe effect of different contamination sources of organochlor-ine compounds in different mountain lakes in the Europeangeographical region under study.
Furthermore, in order to determine whether any of theanalysed organochlorine compounds tended to accumulate
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Fig. 5 –Biplots corresponding to the simultaneous analysis byPCA of the liver and muscle data (augmented matrix Daug),(a) PC1 versus PC2 and (b) PC2 versus PC3.
Table 5 t5:1– Characteristics of the PLS models developed foreach organochlorine compound
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RRpreferentially in any of the two fish tissues previouslyconsidered, the data corresponding to liver and muscle wereanalysed simultaneously, considering only the concentrationsof organochlorine compounds (neither the geographical norphysiological variables). The PCA model including the 3principal components for the augmented data matrix Daug
explained 78.1% of the total variance. In Fig. 5a, the biplotrepresentation (simultaneous plotting of loadings and scores)for PC1 versus PC2, shows that samples tended to groupaccording to their tissue nature, i.e. either muscle or liver.Liver samples tended to be associated with relatively higherconcentrations of PCBs, and appeared to be displaced towardslower PC2 scores. The biplot of PC2 versus PC3 (Fig. 5b) revealsthis association evenmore clearly. In both plots, liver samplesexhibited higher dispersion thanmuscle samples, which weremore homogeneous among different specimens. This resultagain confirms the different behaviour of the samplescollected for the two types of tissues, particularly in relationto the dependence between organochlorine compound con-centrations and the percentage of lipids.
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
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43.2. Partial least squares
4Partial least squares (PLS) models were developed to establish4the relative importance of the geographical and physiological4parameters in the accumulation of organochlorine com-4pounds in fish muscle. The aim was not to develop good4predictive models for the accumulation of organochlorine4compounds as a function of geographical and physiological4parameters, since this would require a higher number of4variables (not available in this work) and samples. Instead, the4goal was to establish which of the parameters had more4influence on the observed concentration of organochlorine4compounds in the fish tissues of the different mountain lakes.4In addition, we aimed to find potential systematic patterns or4trends in these dependencies. Therefore, the intention was4more qualitative than quantitative. PLS was chosen because it4is not affected by correlation among predictor X variables (in5this case the geographical and physiological parameters). In5fact, it improves precision inmodelling and prediction proper-5ties when this correlation occurs. PLS models were built5separately for each organochlorine compound. The concen-5tration of each compound was taken as the predicted variable5(y) and the set of 8 geographical and physiological parameters5as predictor variables (X). Several pretreatment procedures5were tested on both y and X (autoscaling, scaling to unit5variance and mean centering). Finally, autoscaling was5chosen, since it provided better models and largely explained5the variances of y variables. Some transformations of the5original data were also evaluated, in order to consider possible5non-linearities in the model. These transformations included5augmentation of the predictormatrix using quadratic terms of5the original variables and/or logarithmic transformation of y5variables. In some cases, these transformations slightly5improved the explained y variance for some of the organo-5chlorine compounds. However, these models were not con-5sidered significantly better, as they were more complex. This5complexity makes it more difficult to interpret the depen-5dences between the accumulation of organochlorine
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Fig. 6 –Representations of the weight vectors (w1w1) of the first latent variable, obtained for the PLS models developed for eachindividual organochlorine compound (organochlorine compounds were grouped for the representation according to theirsimilar behaviour).
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compounds and the original variables. Twelve separatemodels were finally built using autoscaling pretreatmentand cross-validation by the leave-one-out method, to selectfor an appropriate number of latent variables. Explainedvariances as well as the number of latent variables for eachmodel are given in Table 5. Results shown in this table onlycorrespond to models obtained from individual fish values ofthe concentrations of each organochlorine compound in eachof the studied lakes. The explained concentration (y) variancesdiffered depending on the compound. Higher explainedvariances were found for HCB, DDT, DDE and most of thehigher molecular weight PCBs. In contrast, concentrations oflower molecular weight PCBs, #28 and #52, were more poorlyexplained by the geographical, temperature and physiologicalparameters investigated in this work. These two compoundsshowed clearly separate behaviour in the previous study usingPCA and their distinct behaviour is confirmed here. Com-pounds like α-HCH, γ-HCH and PCB#118 gave intermediateresults, with explained variances between 50 and 60%.
The discussion of the relative importance of the para-meters in terms of the accumulation of organochlorinecompounds can be made on the basis of the weight vectorfor the first score or latent variable, w1w1. Direct comparisonof the weight vectors can be made, as the autoscalingprocedure used to preprocess the data eliminated the scaledifferences between predictor variables or for differentpredicted (organochlorine compound concentration) vari-ables, since both y and X variables were autoscaled. Four
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
Tgroups of organochlorine compounds were considered in thediscussion, as they had similar structure and behaviour,which was also revealed by previous PCA studies. Fig. 6shows the representation of the first weight PLS vectors,w1w1,for these four groups of compounds. The larger the weightvalue (either positive or negative) for one particular X variable,the greater the effect (either positive or negative accordingly)this variable had in the explanation of the correlation with they variable (the observed concentration and accumulation ofthe considered organochlorine compound).
Fig. 6 shows that similar patterns were recognized for mostof the organochlorine compounds in all cases, with relativelyhigh positive weight values (positive correlation) for Xvariables such as lipid content, specimen age and lakealtitude, and negative weight values (negative correlation)for X variables like temperature and latitude. The other Xvariables, such as specimen weight and size and geographicallongitude of the lake, showed a random, lower contribution tow1w1 vectors. A first group of compounds was formed by HCB,α-HCH and γ-HCH. Fig. 6a shows that the concentration ofthese compounds positively correlated with the lipid contentin muscle and was also positively correlated with the age ofthe specimen and the latitude of the lake. Simultaneously, itwas inversely correlated with the temperature and latitude ofthe lake. The same occurred with the second group ofcompounds formed by DDE and DDT, which showed verysimilar patterns (Fig. 6b). Again, the main factors affecting theaccumulation of these compounds in the lipids of fish muscle
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were the specimen’s specimen's age, the lake altitude (bothwith high positive weights), the temperature and lake latitude(with negative weights). The concentrations of the group ofcompounds formed by PCBs of lower molecular weight,PCB#28 and #52, were not well explained by the investigatedvariables and were poorly correlated with the lipid content.Therefore, in this case, little can be inferred from the observedpositive and negative weight values and correlations. Finally,the fourth group embraces the remaining PCBs of highermolecular weight (PCB#101, 118, 138, 158 and 180). They againshowed quite a uniform pattern, as depicted in Fig. 6d. Again,the main factors affecting the concentration and accumula-tion of these PCBs in the muscle tissue were the percentage oflipids, the age of the fish specimen, the air temperature andlatitude (these two with negative correlations). This confirmsthat the lower the temperature (and the higher the altitude)the higher the concentration of these PCBs in the lipids of themuscle tissue of fish specimens. All these effects also agreewell with those previously obtained by PCA. It is thereforeconfirmed that the concentrations of most of the organo-chlorine compounds in the muscle tissue lipids of fishspecimens collected in different mountain lakes of Europeduring this study showed a clear tendency to be higher whenlake temperatures were lower (also at higher lake altitudesand lower lake latitudes). It is also clear that this accumulationwas also higher for the older studied specimens (higher age).
Finally, looking at Fig. 6 as a whole, it can be seen that,despite some specific differences, the shapes of w1w1 profileswere very similar for all the organochlorine compoundsstudied. The w1w1 vectors describe and recognize the domi-nant pattern or phenomenon that correlates organochlorinecompound concentration with the variables considered in thisstudy. This correlation is not random or stochastic, butsystematic for most of the investigated compounds. Most ofthe compounds follow very similar patterns, in whichtemperature (and consequently lake latitude and altitude)was always shown to play an important role in the accumula-tion of these compounds. Consequently, the results clearlyreinforce the phenomenological explanation provided by the“global distillation effect” theory and show that this is alsouseful for describing the altitudinal distribution of thesecompounds in mountain areas.
As stated above, some predictor variables in the presentstudy were highly correlated. For instance, it is well knownthat as the altitude of a lake increases, the average tempera-ture tends to decrease. In Europe, higher latitudes correspondto lakes of lower altitude. Collinearity between variables doesnot affect PLS models. In PLS models for each organochlorinecompound, the same conclusions were obtained when eitheraltitude or temperature was removed from the predictorvariables. In addition, the relative importance of the variableswas the same. This is not the case for classical multilinearregression (MLR) methods (regardless of whether or not astepwise selection of variables was used), due to the intrinsicdifficulties associated with these methods when collinearityamong variables is very high. The number and identity of theinitial parameters considered in the analysis can change thefinal regression model substantially. In this work, PLS modelswere preferred as they are more robust than MLR to thepresence of collinear variables.
Please cite this article as: Felipe-Sotelo M, et al, Assessment ofthe accumulation of organochlorine compounds in European m.
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64. Conclusions
6This paper analyses the dependence between the accumula-6tion of organochlorine compounds in fish muscle and liver6tissues and geographical and physiological parameters by6means of chemometric tools. It shows the ability of chemo-6metric tools to deal with complex environmental data sets and6problems. The multivariate data analysis methodologies used6here (PCA and PLS) were shown to be useful for investigating6and illustrating how rich environmental information can be6extracted in a relatively straightforward and fast way, by6simultaneously considering a large number of samples and6variables.6The results extracted from the PCA and applied to the6whole set of biological and environmental parameters are6in agreement with previous studies that propose a global6distillation effect to explain the presence of a higher concen-6tration of organochlorine compounds with increasing altitude6and decreasing temperatures. All the organochlorine com-6pounds considered in this work followed this general trend.6However, the trend seemed slightly more marked for organo-6chlorine compounds of lower volatility.6These conclusions were confirmed in both liver and6muscle samples. Nevertheless, there were differences be-6tween the muscle and liver in the effects of the percentage of6muscle lipids on the accumulation of organochlorine com-6pounds and the behaviour of HCB, which showed opposite6tendencies in both tissues. The joint PCA of both tissues6revealed that the PCBs had a slight tendency to accumulate6more in liver than in muscle. Moreover, the organochlorine6compound concentration range in liver was more scattered6than in muscle.6Geographical differences were found within samples.6PCA enabled samples to be clearly clustered according to6their origin. Whereas PC1 enabled us to differentiate between6lakes, PC2 allowed specimens to be individuated within each6lake.6The different PLS models developed in this study establish6the relative importance of the different parameters in relation6to the accumulation of organochlorine compounds in fish6tissues. The results reveal that the percentage of lipids, age,6altitude, latitude and temperature were the main factors6affecting the concentration and accumulation of the organo-6chlorine compounds in muscle fish samples. These results6also confirmed previous conclusions obtained by PCAmodels.6In both cases, accumulation of organochlorine compounds in6fish tissues in mountain lakes were shown to depend6significantly on altitude and latitude. Therefore, the results6support the global distillation theory and can be applied to a
description of the altitudinal profiles of these compounds in6mountain lakes.
6Acknowledgements
6M. F. S acknowledges the CSIC (Centro Superior de Investiga-6ciones Científicas) for an I3P fellowship in the IIQAB. Research6funds from Ministerio de Educación y Ciencia, Spain, research6project Nr. CTQ2006-15052 are also acknowledged.
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