PCA statistical method for interpretation of levels and distribution of PCB, DDT and HCH in fish and marine food samples from Adriatik Sea

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1 PCA statistical method for interpretation of levels and distribution of PCB, DDT and HCH in fish and marine food samples from Adriatik Sea Aurel Nuro, Pranvera Lazo, Koste Koci, Elda Marku Tirana University, Faculty of Natural Sciences, Chemistry Department co.uk Abstract Organochlorinated compounds such as polychlorinated biphenyls (PCBs) and organochlorinated pesticides are widely distributed contaminants that can induce various toxic responses including immunotoxicity, carcinogenicity, and adverse effects on reproduction, development, and endocrine functions. From the Vietnam War in 1970s to the Belgium crisis in 1999, a great concern has been raised about contaminating of food with PCBs. Synthetic pesticides have been used since early to mid-twentieth century when insecticidal properties of DDT were recognized. DDT was first introduced on a large scale to fight fleas, lice, flies and mosquitoes and reduce the spread of insect borne diseased such as malaria and yellow fever. Organochlorine pesticides are the first class compounds introduced in agricultural and civil uses to counteract noxious insects and insect-born disease. In general organchlorinated compounds are lipophilic with noticeable chemical and environmental stability. Food is generally recognized as the main source of human intake by organchlorinated compounds. More than 90% of the total daily intake of these contaminants is derived from food. The study included the analyses of fish, mussels and marine foods collected between December 2004 and March 2006 in Albanian market and producers with origin from Adriatik Sea. The organchlorine pesticides detected were HCHs (a-, b-, y- and d-isomers) and the DDT-related chemicals (o,p-dde, p,p-dde, p,p-ddd, p,p-ddt). Analysis of PCBs was based on the determination of the seven PCB markers (IUPAC Nr. 28, 52, 101, 118, 138, 153 and 180) measured by gas chromatography electron capture detection. These compounds were measured on the muscle tissues and all results were expressed on a fresh weight basis. The results of surveillance on indicators polychlorinated biphenyls are study with PCA statistical method. All samples had PCBs and pesticides chlorinated content far below the EC Regulation (2375/2001/EC) limits. Keywords: Organoclorine pesticides, PCBs; Albanian food; PCA, Chemical analysis Introduction The term pesticide is used to indicate any substance, preparation or organism used for destroying pests. Synthetic pesticides have been used since in the early to mid twentieth century. Over 800 pesticide active ingredients are formulated in about different commercial products. The modern history of pesticides dates back to World War II when for the first time the insecticidal properties of DDT were recognized. DDT was first introduced on a large scale to fight fleas, lice, flies and mosquitoes and reduce the spread of insect borne diseased such as malaria and yellow fever. Many public health benefits have been realized by the use of pesticides, but their potential impact on the environment is substantial too (Di Muccio, 1996). Organoclorine pesticides (OCP) are the first class of compounds of synthetic pesticides introduced in agricultural and civil uses to counteract noxious insects and insect-born disease. In BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

2 general they are lipophilic compounds with noticeable chemical and environmental stability. OCP are used extensively as insecticides sterilizes, and herbicides. In particular, those used as insecticides are extremely toxic to living bodies. Most OCPs have been progressively restricted and then banned in the 1970s in most industrialized countries a widespread environmental pollution has resulted from their use in agriculture and civil uses. Polychlorinated biphenyls (PCBs) are a class of chemical compounds in which 1 10 chlorine atoms are attached to a biphenyl backbone. Commercial PCB mixtures were used in a wide variety of applications and mainly as dielectric fluids in capacitors and transformers, and as heat exchange fluids. They are chemically highly stable, lipophilic compounds and resist microbial, photochemical, chemical and thermal degradation. Unfortunately, the same properties which make PCBs interesting for industrial use cause them to accumulate in biota (Erickson, 2001). The PCB concentrations were shown to increase through the different trophic levels of the food chains (Norstrom, 2002). PCBs cause adverse effects on reproduction, development, and endocrine function including thyroid hormone homeostasis and estrogen-responsive tissues (Gray et al., 2002; Hansen, 1998; Morse et al., 1994; Safe, 1994). The toxic effects of PCBs depend on their degree of chlorination and pattern of chlorine substitution. For dioxins and dioxin-like PCBs, the existence of a common mechanism has led to the development of the toxic equivalency concept. (Van den Berg et al., 1998). For non-dioxin-like PCBs, the sum of the seven markers is also a rather reliable index of the health risks since it integrates some of the most persistent PCBs in humans. The sum of the seven PCB markers represents only 10 to 30% of the total congeners of commercial PCBs, depending on their degree of chlorination. This means that the pattern of these seven markers represents also a small proportion of the original fingerprint. This principle was based on the assumption that the ratio would be constant over time, i.e. kinetics for the non-planar PCBs and dioxins in animals were assumed to be identical. In 11/01/2004 it was decided by the EU to accept the analysis of the 7 indicator PCBs as an indicator for dioxins, assuming a ratio of between the sum of concentrations for the socalled indicator PCBs (PCBs 28, 53, 101, 118, 138, 153 and 180) and dioxins (expressed in TEQs). Based on this, PCB limits of 200 ng/g fat for animal and egg fat and 100 ng/g fat for milk fat were established. Human exposure to organochlorinated pollutants primarily occurs through food contamination. Fish, meat and diary products are the most important dietary sources of OCP and PCBs for humans and it is widely accepted that these pollutants will be present in food for many years to come (Battershill, 1994). 2. Principal Components Analysis Principal components analysis (PCA) is a technique for simplifying a data set, by reducing multidimensional data sets to lower dimensions for analysis. Technically speaking, PCA is an orthogonal linear transformation that transforms the data to a new coordinate system such that the greatest variance by any projection of the data comes to lie on the first coordinate (called the first principal component), the second greatest variance on the second coordinate, and so on (Lindsay I Smith, 2002; M.A. Sharaf, et al. 1986). PCA can be used for dimensionality reduction in a data set while retaining those characteristics of the data set that contribute most to its variance, by keeping lower-order principal components and ignoring higher-order ones. Such low-order components often contain the "most important" aspects of the data. PCA take data points, and rotates it such that the maximum variability is visible. Another way of saying this is that it identifies your most important gradients. A regression line can then be fitted that represents the "best" summary of the linear relationship between the variables. If we could define a variable that would approximate the regression line in such a plot, then that variable would capture most of the "essence" of the two items. Subjects' single scores on that new factor, represented by the regression line, could then be used in future BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

3 data analyses to represent that essence of the two items. In a sense we have reduced the two variables to one factor. Note that the new factor is actually a linear combination of the two variables. Combining two correlated variables into one factor, illustrates the basic idea of factor analysis, or of principal components analysis to be precise. If we extend the two-variable example to multiple variables, then the computations become more involved, but the basic principle of expressing two or more variables by a single factor remains the same. The extraction of principal components amounts to a variance maximizing (varimax) rotation of the original variable space. This type of rotation is called variance maximizing because the criterion for (goal of) the rotation is to maximize the variance (variability) of the "new" variable (factor), while minimizing the variance around the new variable. When are more than two variables, can think of them as defining a "space," just as two variables defined a plane. Thus, when we have three variables, we could plot a three- dimensional scatterplot, and, again we could fit a plane through the data. With more than three variables it becomes impossible to illustrate the points in a scatterplot, however, the logic of rotating the axes so as to maximize the variance of the new factor remains the same. After we have found the line on which the variance is maximal, there remains some variability around this line. In principal components analysis, after the first factor has been extracted, that is, after the first line has been drawn through the data, we continue and define another line that maximizes the remaining variability, and so on. In this manner, consecutive factors are extracted. Because each consecutive factor is defined to maximize the variability that is not captured by the preceding factor, consecutive factors are independent of each other. Put another way, consecutive factors are uncorrelated or orthogonal to each other. The variances extracted by the factors are called the eigenvalues. This name derives from the computational issues involved. Eigenvalues is a measure of how many factors to retain. As mentioned earlier, by its nature this is an arbitrary decision. However, there are some guidelines that are commonly used, and that, in practice, seem to yield the best results. First, retain only factors with eigenvalues greater than 1. In essence this is unless a factor extracts at least as much as the equivalent of one original variable, we drop it. This criterion was proposed by Kaiser, and is probably the one most widely used. A graphical method is the scree test first proposed by Cattell. We can plot the eigenvalues shown above in a simple line plot. Both criteria have been studied in detail (E.R. Malinowski and D.G. Howery, 1980; K. Varmuza, 1980; D.D. Wolff and M.L. Parsons, 1983; Fukunaga, Keinosuke, 1990; Pearson, K., 1901). Theoretically, one can evaluate those criteria by generating random data based on a particular number of factors. One can then see whether the number of factors is accurately detected by those criteria. Using this general technique, the first method (Kaiser criterion) sometimes retains too many factors, while the second technique (scree test) sometimes retains too few; however, both do quite well under normal conditions, that is, when there are relatively few factors and many cases. In practice, an additional important aspect is the extent to which a solution is interpretable. 3. Materials and Methods The study included the analyses of fish, mussels and marine foods in a total number of 96 cases. Samples were collected between December 2004 and March 2006 in Albanian market and producers with origin from Adriatic Sea. Sampling was made according to the European Commission Recommendation on the monitoring of background levels of dioxins and dioxin-like compounds (SANCO/4546/01). 3.1 Preparation of samples for PCB analysis The method used was based on EN 1258/1/2/3/4 for determination of OCP and PCBs in food samples. The samples were transferred to an aluminum container and stored at -10 C, until they were homogenized with anhydrous sodium sulfate (Merck, Darmstadt, Germany). The sample was extracted by water bath assisted extraction (1 g fresh weight (fw) of organism tissue with BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

4 hexane/dichloromethane (4/1), (v/v) (Fluka, Germany, pesticide grade). The internal standard PCB 29 was added to the sample prior to extraction. The extract was purified by shaking with 15g silica gel with 45% sulfuric acid. The organic and acid phases were separated. After filtration, the extract was concentrated in a Kuderna Danish to 5 ml volume and a second purification on a column of florisil with 5% water (particle size 0.063±0.2 mm; Merck, Darmstadt,Germany) was performed. The organ chlorines were eluted with 15 ml of a mixture of hexane/dichloromethane (5/1), (v/v). The extract was concentrated and transferred to hexane (Spectroscopy grade; Fluka, Germany), and analysed by GC-ECD (Koci, K.,1997; Rene G. van der Hoff, 1999; Schantz, M. M., et al., 1993) Apparatus and chromatography Gas chromatographic analyses were performed with an HP 6890 Series II gas chromatograph equipped with a 63 Ni electron-capture detector and a split/splitless injector. The column used was a HP-5 [low/mid polarity, 5% (phenyl methyl siloxane)] (25 m x 0.33 mm I.D., 0.25µm film). The split/splitless injector and detector temperatures were set at C and C, respectively. Carrier gas was He at 19.8ml/min and make-up gas was nitrogen 10,2ml/min. The initial oven temperature was kept at 60 0 C for 4min, which was increased, to C at 20 0 C/min, held for 7min, and then increased to C at 4 0 C/min for 20min. The temperature was finally increased to C, at 10 0 C/min, held for 7min. Injection volume was 2μl, when splitless injections were made. PCB and OCP quantification was performed by internal standard method. Results and discussion Data collected from this study about concentrations of PCB markers and OCP in marine samples from Adriatic coast are interpreted from PCA statistical method in two directions. One direction was interpretation based on distribution of different pollutants (PCB markers, DDTs and HCHs) in marine samples. Results of gas chromatographic analyze and PCA method interpretation was based also on total of PCB markers, ΣDDTs and ΣHCHs for four groups of different type of marine foods. These groups had the same number of a kind and included samples of fish, mussels, mollusk and sepia in a total number of 96 cases Distribution of PCB markers Statistical data of the seven PCB markers are presented in table 1. All markers were present in marine samples. Elevated concentrations were for volatile PCB. In this first group PCB28 is the first factor with mean value ng/g f.w. Eigenvalue and it s contributed were respectively 2.96 and 42.3% (Tab. 2). PCB52 is another indicator of volatile PCBs. It was second factor with mean value 8.94 ng/g f.w.; Eigenvalue 1.72 and percent of contribute 24.5%. Volatile PCB indicators reflect the fact that PCBs in our country have atmospheric origin. The second group was PCB 101; PCB118; PCB 153 and PCB138 that are most favorable in seven PCB markers for accumulated in biota. The third factor was PCB 101. The fourth, the fifth and the sixth factor was respectively PCB153; PCB 118 and PCB138. This is depended from degree of accumulation of individual marker and also from other factors such as their origin, other sources of pollution, degree of chlorination, structure, ect, PCB188 is an indicator of non-volatile PCBs. It presence was in low values. In Pearson correlation matrix are evident these facts. These were very clearly for the markers of second group that had the highest positive correlation values. Volatile indicators had also a positive correlation between each others. BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

5 Tabela 1. Statistical data about distribution of PCB markers PCB28 PCB52 PCB101 PCB118 PCB188 PCB153 PCB138 N OF CASES MINIMUM MAXIMUM MEAN ST.DEV Tabela 2. PCA data about distribution of HCHs LATENT ROOTS (EIGENVALUES) EXPLAINED BY COMPONENTS PERCENT OF TOTAL EXPLAINED PCB28 PCB52 PCB101 PCB118 PCB PCB153 PCB PEARSON CORRELATION MATRIX PCB28 PCB52 PCB101 PCB118 PCB188 PCB153 PCB138 PCB PCB PCB PCB PCB PCB PCB Total of PCB markers in marine samples Statistical data of ΣPCB markers in different marine samples are shown in table 3. Most polluted samples were fishes with mean value 60.5 ng/g f.w. The second factor were mussels samples with mean value 45.9 ng/g f.w. Sepia samples were the third factor with mean value 36.2 ng/g f.w. and the fourth factor with low level were mollusks. PCA data of ΣPCB markers are shown in table4. In Pearson correlation matrix fishes, mussels, mollusks and sepia samples have positive correlation values between each others. Pollution levels and correlation values are connected between each others because of their biology and habitat. Mussels have the low positive correlation values with others groups. BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

6 Tabela 3. Statistical data about total of PCB markers in different marine samples Fish mussel mollusk sepia N OF CASES MINIMUM MAXIMUM MEAN STANDARD DEV Tabela 4. PCA data of ΣPCB markers in different marine samples LATENT ROOTS (EIGENVALUES) EXPLAINED BY COMPONENTS PERCENT OF TOTAL EXPLAINED Fish mussel sepia mollusk PEARSON CORRELATION MATRIX Fish mussel mollusk sepia Fish mussel mollusk sepia Distribution of DDT in marine samples Statistical data about distribution of DDTs are shown in table 5. DDE were the enormous contaminant of DDTs. Mean value for 39 cases of marine samples were 36.6 ng/g f.w. (fresh weight). It were the first important factor with Eigenvalue 1,95 and It s contribute were 48.7% of DDTs (Tab. 6). The second factor were DDD with mean value 20.5 ng/g f.w. Eigenvalue for DDD were 1,02 and 25.4% it s contribute. p,p-ddt were the third factor with mean value and Eigenvalue respectively 13.6 ng/g f.w. and Fourth factor were o,p-ddt because of it s low level in DDT formulation and low stability. Don t use of DDT after 90 and it metabolism are both factor that explain fact that p,p.-dde was in concentration elevated confront with DDT. These factors are the same about DDD confront DDT. Note that p,p.-dde has a great potential for magnification in the food chain and it is very persistent between other breakdown products of DDT. Pearson correlation matrix confirms this interpretation. p,p-dde and p,p-ddd has the high positive value 0,59 and also p,p-ddd and p,p-ddt has a positive correlation with respective value BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

7 Tabela 5. Statistical data about distribution of DDTs o,p-ddt p,p-dde p,p-ddd p,p-ddt N OF CASES MINIMUM MAXIMUM MEAN STANDARD DEV Tabela 6. PCA data about distribution of DDTs LATENT ROOTS (EIGENVALUES) EXPLAINED BY COMPONENTS PERCENT OF TOTAL EXPLAINED p,p-dde p,p-ddd p,p-ddt o,p-ddt PEARSON CORRELATION MATRIX o,p-ddt p,p-dde p,p-ddd p,p-ddt o,p-ddt p,p-dde p,p-ddd p,p-ddt ΣDDT in marine samples In table 7 are shown statistical data of ΣDDT in different marine samples. Most polluted from DDTs and the first factor were fishes with mean value 75.7 ng/g f.w. The second factor were mussels samples with mean value 75.1 ng/g f.w. Sepia samples were the third factor with mean value 47.4 ng/g f.w. and the fourth factor with low level were mollusks samples. Table 8 shown PCA data of ΣDDT in different marine samples. In Pearson correlation matrix mussels, mollusks and sepia samples have very lower positive and negative correlation values between each others. This fact is very interested because of different ways of pollution for fourth groups. Main ways of their pollution of DDTs are habitat, bioconcentration and bioaccumulation process, ect. In different species these factors and for sure other factors have different impact and different levels of pollution. BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

8 Tabela 7. Statistical data about total of DDTs in different marine samples Fish mussel mollusk sepia N OF CASES MINIMUM MAXIMUM MEAN STANDARD DEV Tabela 8. PCA data of ΣDDT in different marine samples LATENT ROOTS (EIGENVALUES) EXPLAINED BY COMPONENTS PERCENT OF TOTAL EXPLAINED Fish mussel sepia mollusk PEARSON CORRELATION MATRIX Fish mussel mollusk sepia Fish mussel mollusk sepia Distribution of HCHs In table 9 are shown statistical data about distribution of HCHs. Technical HCH isn t in use in our country but the present concentrations are being influenced by past use of technical HCH. It s interesting to note that lindane were not the first isomer in total HCH contribution. β-hch concentration was higher than the other isomers concentrations because of its higher tendencies to accumulate in fatty tissue. It was the first important factor with contribute of 41.1% and Eigenvalue 1.64 (Tab. 10). Mean value for beta isomer were 11,5 ng/g f.w. Concentrations of other lindane isomer were also interesting for the fact that alfa isomer were highest than lindane and delta isomer in comparable value. The second factor were alfa isomer with mean value 5.45 ng/g f.w.; Eigenvalue 1.02 and with contribute of 24.5%. The third factor was lindane. Mean value were 5.04 ng/g f.w.; Eigenvalue 0.76 and 18.9% it s contribute. Fourth factor was delta isomer with mean value 3.96 ng/g f.w. The α/γ HCH ratio, an indicator of current technical HCH application was around 1. This value doesn t reflect recent use of HCH formulations or that present concentrations are being BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

9 influenced by past use of technical HCH. Factors connected with HCHs profile is because other lindane isomers were waste of lindane fabric in Porto-Romano, Durres or possibly ongoing releases of HCH isomers from waste repositories. Pearson correlation matrix confirms this interpretation. β-hch with δ-hch has the high positive value 0,36 and also positive correlation have α-hch with β-hch and α-hch with δ-hch with respective value 0.26 and All these isomers had positive correlation between each others and a low positive correlation with lindane because of their same origin. Tabela 9. Statistical data about distribution of HCHs α-hch β-hch γ-hch δ-hch N OF CASES MINIMUM MAXIMUM MEAN STANDARD DEV Tabela 10. PCA data about distribution of HCHs LATENT ROOTS (EIGENVALUES) EXPLAINED BY COMPONENTS PERCENT OF TOTAL EXPLAINED β-hch α-hch γ-hch δ-hch PEARSON CORRELATION MATRIX α-hch β-hch γ-hch δ-hch α-hch β-hch γ-hch δ-hch ΣHCHs in marine samples The most pollutet from HCHs and also the first factor were fishes with mean value 25.1 ng/g f.w. (Tab.11). The second factor were mussels samples with mean value 23.8 ng/g f.w. Sepia samples were the third factor with mean value 21.2 ng/g f.w. and the fourth factor with low level were mollusks samples (18.9 ng/g f.w.). Table 12 shown PCA data of ΣHCH in different marine samples. In Pearson correlation matrix mussels, mollusks and sepia samples have very lower positive and negative correlation values between each others. It is very interested the higher negative value between fishes and mussels. Both were most polluted species but are evident different ways of pollution for both groups. Main ways of their pollution of marine species from BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

10 HCHs are the same with PCBs and DDTs as is noted above. About lindane and its isomers are possibly ongoing releases of HCH isomers from waste repositories. Tabela 11. Statistical data about total of HCHs in different marine samples Fish mussel mollusk sepia N OF CASES MINIMUM MAXIMUM MEAN STANDARD DEV Tabela 12. PCA data of ΣHCH in different marine samples LATENT ROOTS (EIGENVALUES) EXPLAINED BY COMPONENTS PERCENT OF TOTAL EXPLAINED Fish mussel sepia mollusk PEARSON CORRELATION MATRIX Fish mussel mollusk sepia Fish mussel mollusk sepia Conclusion Using PCA statistical method for interpretation simplified data set of gas chromatographic analyze of marine samples. PCA method was used for interpretations on distribution of organochlorinated pollutants (PCB markers, DDTs and HCHs) in these samples and also for interpretation of total for PCB markers, DDTs and HCHs. Second interpretation was done for four groups of different species of marine foods with the same number of kind included samples of fish, mussels, mollusk and sepia. All markers were present in marine samples. Elevated concentrations were for PCB 28 and PCB52. These markers reflect the fact that PCBs in our country have atmospheric origin. The most favorable PCB markers for accumulated in biota were second contributors and also had the highest positive correlation values. Non-volatile PCBs was in low values. Most polluted samples of ΣPCB markers were fishes. In Pearson correlation matrix fishes, mussels, mollusks and sepia samples have positive correlation values between each others. Correlation values are connected with the same origin of PCBs. PCB levels for seven markers were lower than EU normative for marine food products. BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

11 DDE and DDD were the enormous contaminant of DDTs. Don t use of DDT and it metabolism explain fact that p,p.-dde and DDD was in concentration elevated confront with DDT. p,p.-dde has a great potential for magnification in the food chain and it is very persistent between other breakdown products of DDT for these reason DDE has around 50% contribute. p,p-dde and p,p- DDD has the high positive correlation value. Most polluted marine samples from DDTs were fishes and mussel samples with low difference between respective mean values. Fishes, mussels, mollusks and sepia samples have very low positive and negative correlation values between each others because of different ways of pollution for fourth groups. Main ways of DDTs pollutions for marine species are; habitat, bioconcentration and bioaccumulation process, ect. Levels of DDTs were from 7 to 15 times lower than normative for marine food products. Lindane was not the first isomer in total HCH contribution. β-hch concentration was higher than the other isomers concentrations because of its higher tendencies to accumulate in fatty tissue. Concentrations of other lindane isomer were also interesting for the fact that alfa isomer were highest than lindane and delta isomer in comparable value. This value doesn t reflect recent use of HCH formulations or that present concentrations are being influenced by past use of technical HCH. Factors connected with HCHs profile is because other lindane isomers were waste of lindane fabric in Porto-Romano, Durres or possibly ongoing releases of HCH isomers from waste repositories. α-hch, β-hch and δ-hch isomers had positive correlation between each others and a low positive correlation with lindane because of their the same origin. Marine samples had far away content of HCHs. It is very interested the negative value between fishes and mussels. Both were most polluted species but are evident different ways of pollution for both groups. References: Battershill, J.M., Review of the safety assessment of PCBs with particular reference to reproductive toxicity. Human Exp. Toxicol. 13, COUNCIL REGULATION (EC) No 2375/2001. Amending Commission Regulation (EC) No 466/2001 setting maximum levels for certain contaminants in foodstuffs. November EN Official Journal of the European Communities L 321/1 Di Muccio, Organoclorine, Pyrethrin and Pyrethroid Insecticides: Single Class, Multiresidue Analysis of. Pesticides. Pesticides EN (2000) Part 1: Fatty Food-Determination of Pesticides and polychlorinated biphenyls [PCBs] EN (2000) Part 2: Extraction of fat, Pesticides and polychlorinated biphenyls [PCBs and - Determination of Fat Content EN (2000). Part 3: Clean-up methods EN (2000) Part 4: Determination, Confirmatory tests, miscellaneous Erickson, M.D., Introduction: PCB properties, uses, occurrence, and regulatory history. In: Robertson, L.W., Hansen, L.G. (Eds.), PCBs: Recent Advances in Environmental Toxicology and Health Effects. The University Press of Kentucky, Lexington, Kentucky, pp E.R. Malinowski and D.G. Howery, Factor Analysis in Chemistry, (John Wiley & Sons, New York,). Gray JS., Biomagnification in marine systems: the perspective of an ecologist. Mar Pollut Bull;45: G.Rene van der Hoff, P. van Zoonen, Trace analysis of pesticides by gas chromatogaphy. Journal of Chromatogaphy A, Hansen, L.G., Stepping backward to improve assessment of PCB congener toxicities. Environ. Health Perspect. 106 (Suppl. 1), BALWOIS 2008 Ohrid, Republic of Macedonia 27, 31 May

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