Envlron. Scl. Technol. 1907, 21, 72-76
Phys. Chem. 1981,85,2262-2269. Nicovich, J. M.; Thompson, R. L.; Ravishankara, A. R. J. Phys. Chem. 1981,85, 2913-2916. Perry, R. A.; Atkinson, R.; Pitts, J. N., Jr. J. Phys. Chem. 1977,81, 1607-1611. Witte, F.; Urbanik, E.; Zetzsch, C. J. Phys. Chem. 1986,90, 3251-3259. Tully, F. P., Sandia National Laboratories, Livermore, CA, private communication, 1985. Weast, R. C., Ed. Handbook of Chemistry and Physics, 54th ed.; CRC Press: Cleveland, OH, 1973-1974; pp D126D127. Paquette, L. A. Principles of Modern Heterocyclic Chemistry; Benjamin: New York, 1968; p 309. ROSS,S. D. Inorganic Infrared and Raman Spectra; McGraw-Hill: London, UK, 1972; pp 154-157. Bellmy, L. J. The Infrared Spectra of Complex Molecules,
2nd ed.; Wiley: New York, 1958; p 346. (26) The Coblentz Society Evaluated Infrared Reference Spectra; Sadtler Research Laboratories, Inc.: Philadelphia, PA, 1959-1979; Vol. 1-11, see, for example, spectrum numbers 2058,4349. and 9678. (27) Singh, H. B.; Ludwig,'F. L.; Johnson, W. B. Atmos. Environ. 1978. 12. 2185-2196. (28) Crutzen,'P. J. In Atmospheric Chemistry;Goldberg, E. D., Ed.; Springer-Verlag: Berlin, West Germany, 1982; pp 313-328. I
Received for review April 7,1986. Accepted August 4,1986. We gratefully acknowledge the California Air Resources Board for financial support through Contract A3-126-32 (Project Monitor, Jack K. Suder).
Are Polychlorinated Biphenyl Residues Adequately Described by Aroclor Mixture Equivalents? Isomer-Specific Principal Components Analysis of Such Residues in Fish and Turtles Ted R. Schwartz" and David L. Stalling National Fisheries Contaminant Research Center, US. Fish and Wildlife Service, Columbia, Missouri 6520 1
Cynthia L. Rice
U S . Fish and Wildlife Service, State College, Pennsylvania 16801 Polychlorinated biphenyl (PCB) residues from fish and turtles were analyzed with SIMCA (Soft Independent Modeling of Class Analogy), a principal components analysis technique. A series of technical Aroclors were also analyzed to provide a reference data set for pattern recognition. Environmental PCB residues are often expressed in terms of relative Aroclor composition. In this work, we assessed the similarity of Aroclors to class models derived for fish and turtles to ascertain if the PCB residues in the samples could be described by an Aroclor or Aroclor mixture. Using PCA, we found that these samples could not be described by an Aroclor or Aroclor mixture and that it would be inappropriate to report these samples as such.
Introduction The Tinicum National Environmental Center (TNEC), located in Philadelphia and Delaware counties, Pa, includes Tinicum Marsh, the largest freshwater tidal marsh remaining in Pennsylvania. The area is used extensively by thousands of migratory waterfowl and shorebirds, and nine species of waterfowl nest there. The marshes and waterways of Tinicum also provide habitat for many species of fish, amphibians, and reptiles, including two turtle species listed as endangered by the Commonwealth of Pennsylvania. Also located just up stream from TNEC is the Clearview Landfill, and located within TNEC's borders is the Folcroft Landfill. Both of these sites were used to dispose of municipal wastes, and the dumping of hazardous and industrial wastes was suspected. An initial contaminant survey by EPA in 1983 revealed low-level contamination of Darby creek (located within TNEC) with polychlorinated biphenyls (PCB), chlordane, and polycyclic aromatic hydrocarbons (PAH). Because of concern about the possible effects of these landfills on the fish and wildlife resources of the TNEC, the National Fisheries Contaminant Research Center carried out analyses to determine the relative composition 72
Environ. Sci. Technol., Vol. 21, No. 1, 1987
Not subject to
of PCBs as Aroclors in the tissue of fish and turtles. This was accomplished by the recently reported technique of isomer-specific PCB analysis and chemometric examination of the resulting data (1-3). The resulb of the residue analyses for PAH's, pesticides, toxaphene, and heavy metals, as well as pathology observations, have been reported on these samples ( 4 ) . Because 209 congeners of PCBs are theoretically possible, most commercial mixtures and environmental samples display many peaks when analyzed by capillary gas chromatography (GC) (1 - 3, 5, 6). Most quantitative analyses ultimately involve a comparison of the GC chromatographic pattern of a sample with one or several prominant peaks of a commercial product. This approach may give a realistic value for total PCB concentration, provided that the PCB pattern in the sample is similar to that found in the commercial PCB standard. Values for PCB concentration based on this method must be regarded as only approximate, however, because the complexity of chromatograms from biological samples makes it impossible to verify the assumption of identical or even similar PCB patterns. Several mathematical methods are applicable to the problem of determining PCB residues as Aroclor-derived mixtures, among which are the linear learning machine, linear discriminant analyses, K nearest neighbors (KNN), solution by simultaneous linear equations, and many others. Solution by simultaneous linear equations and K nearest neighbors have been used to express PCB residues as mixtures of Aroclors (1, 7). These methods all have common problems: little information about the internal class structure, no assessment of relevance of the individual variables, no information about outliers or deviation systems, no opportunity provided for pattern analysis. Because these methods are based on the assumptions of similarity, they will calculate a value for PCB residues. However, these methods do not provide a means of defining dissimilarity. The underlying, and often unstated, U S . Copyright. Published 1986 by the American Chemical Society
Table I. Matrix Representation of Sample Analysis for Three Classes, p Peaks, and R Samples (Chromatography Data Matrix)
sample no. (objects) class 1
peak no. (variables) 12 3 L . p
1 2
;2
ID no. 1 2
3 4
3
class 2
Table 11. Sample Designation and Total Concentration of PCB Residue (Summation of 105 Measured Isomers)
5
6 7 hi
8 9
10 class 3 n
assumption in these statistical methods is that the samples are similar to Aroclors and that the degree of similarity is sufficient to warrant their being quantified and reported in terms of Aroclors equivalences. As judged by our analytical experience, this assumption is often not valid. Once the Aroclor is exposed to environmental degradation (physicalor photochemical, or by partitioning, metabolism, etc.), its composition changes markedly (5). The problem of demonstrating that PCB residues can be accurately represented as Aroclor mixtures requires a more comprehensive quantitative approach than these methods provide. Special care must therefore be exercised in evaluating environmental PCB residues and expressing these residues in terms of Aroclor equivalences. Pattern Recognition in Characterization of PCB Residues. The method of Principal Components Analysis (PCA), to which SIMCA (Soft Independent Modeling of Class Analogy) belongs, makes no a priori assumptions of similarity to Aroclors. The SIMCA pattern recognition technique, developed by Wold and co-workers, has been described in detail elsewhere (8-12); hence, only a short presentation of pertinent features will be given here. This pattern recognition technique is based on derivation of disjoint principal component models for classification of objects (samples). The primary objective of PCA is to get an overview of similarity among samples represented by data tables. The data tables discussed here have the matrix format shown in Table I. In this matrix, the notation x denotes a data point, index n denotes an object upon wgch a chemical measurement has been made (a sample), and index p denotes a measured variable (a PCB isomer). Therefore the element xp? represents the value of variable p on object n. The matrix containing the data is called X in matrix notation. Analyses of PCBs can sometimes create large data tables that are difficult to interpret. In this study, for example, the matrix comprises 19 objects and 105 isomers, and thus contains 1995 elements. Chemometricmethods can greatly improve the analyst’s ability to describe sample similarity in large data matrices. The utility of principal components modeling of multivariate data like those encountered in complex mixtures originates from graphical presentations of sample similarity, as well as from statistical results. Sample data are treated as points in higher dimensional space, and projections of these data are made in two- or three-dimensional space in a way that preserves most of the existing relations among objects and variables (8). Geometrically, a data table with p variables can be interpreted as a p-dimensional space with each object represented as a point. This feature is especially helpful in visualizing data having more than three dimensions. In this study, projections were made from 105-dimensional
11 12
13 14 15 16 17 18
19
designation Aroclor (1:ll:1:1) Aroclor 1248 Aroclor 1254 Aroclor 1242 Aroclor 1260 Aroclor (1:l:l:l) turtle, A turtle, B turtle, C turtle, D fish, A fish, B turtle, E turtle, D turtle, D fish, A fish, B fish, A fish, B
replicate no. 1 1 1 1 1 2 1 1 1 1 1 1 1 2
3 2 2 3
3
total PCB, PP/P
14.8 15.7 11.6 16.3 2.5 3.2 27.3 13.3 13.8 2.4 2.3 2.2
2.7
space into two-dimensional space. The SIMCA class models are bilinear projection models obtained by decomposing the class data matrix X into a score matrix T (n X F),a loading matrix P (F X p), and a residual matrix E. The calculations involved in principal components are summarized in the following equation:
X = 1.X + TP + E
(1)
The objective was to derive a model of the data set presented in Table I1 through a data matrix, X, having n objects (19 samples) and p variables (105 isomers) in which the concentration value of the PCB isomer, xpn, could be calculated. The diagonal matrix ;is the mean of variables x(p) in all samples. The n X F score matrix, T, describes the projection of the n sample points onto the F-dimensional hyperplane defined by the F X p loading matrix. If the residuals, E (or unexplained part of the measurement not modeled), are small when compared with the variation in X, then the model is a good representation of X. Once the class models are calculated, objects are classified by fitting their data to the various class models. A standard deviation for each model is calculated on the basis of the residuals. This represents a class tolerance level around the principal component model in measurement space. The standard deviations for the objects are calculated from the residuals, and the objects are classified on the basis of their distance from the class models. This technique was applied to fish and turtle samples discussed above to determine if the residues of PCBs could be described as an Aroclor or some mixture of Aroclors. Experimental Section Samples of fish and fat from turtles were submitted to the Columbia National Fisheries Research Laboratory for isomer-specificPCB analysis. The fish samples consisted of two species (five fish per composite sample), brown bullheads (Ictalurus nebulosus) and white suckers (Catostomus commersonii). The turtle samples were from snapping turtles (Chelydru serpentinu) collected at TNEC by a commercial turtle harvester who is licensed by the Fish and Wildlife Service to harvest them. The fish samples were homogenized and stored frozen until analysis. The fish and turtle samples were later extracted by column chromatography and gel permeation enrichment techniques (14). PCB residues were isolated by adsorption column chromatography on silica gel/ Environ. Sci. Technol., Vol. 21, No. 1, 1987
73
101
44
il
.-
0 120°
10
20
CI
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ICI,
41
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Figure 1. Capillary gas chromatogram of an Aroclor standard mixture consisting of Aroclors 1242, 1248, 1254,and 1260 (1:l:l:lw/w/w/w/). Peak numbers are based upon the IUPAC system, ref 5.
sulfuric acid-silica gel (15). We separated PCB congeners by GC using a fused silica capillary chromatographic column (10 m X 0.1 mm i.d.) coated with C-87 hydrocarbon stationary phase (Chrompack Inc., Bridgewater, NJ); a 60-cm uncoated fused silica retention gap connected the injector to the analytical column. The data sampling and GC temperature program were controlled by a Varian autosampler Model 8000, which also delivered a calibrated amount of sample to the Varian 3700 gas chromatograph injection port. Chromatographic conditions were similar for all of the analyses: initial temperature, 70 OC, programmed at 3 OC/min to a final temperature of 275 "C; injector temperature (direct inject) 220 OC; detector temperature, 320 "C. An IBM 9000 microcomputer, which was interfaced with the GC, acquired data generated by the electron-capture detector. The data were preprocessed by a software package designed for laboratory data collection (Capillary Applications Program, IBM Instrument Division, Danbury, CT). The data were organized, by a Basic program, into a series of files on hard disk media and transferred off-line to a PDP-11/34 minicomputer (Digital Equipment Corp.). We then organized the data into tree-structured disk files, using our specialized laboratory data base management computer programs written in DMS-11 (Digital Standard MUMPS, Digital Equipment Corp.) for the PDP-11 family of computers. We selected 105 congeners of PCB for quantitation and effected calibration by using Aroclors 1242,1248,1254,and 1260 in a 1:l:l:l(w/w/w/w) mixture. A chromatogram of this mixed Aroclor standard 74
Environ. Sci. Technol., Vol. 21, No. 1, 1987
is shown in Figure 1. The method of peak identification was a retention index system in which n-alkyl trichloroacetates were used (16). Molar response factors were determined from a flame ionization detector by using the computer-based calculation methods described by Schwartz et al. (17). After the concentrations of individual isomers were determined, we retrieved the data from the MUMPS-based laboratory data base and transferred them to an IBM-AT (IBM Corp., Boco Raton, FL) microcomputer by way of an RS-232 link, using the computer program Cyber (Department of Linguistics, University of Illinois at Champaign-Urbana, Urbana, IL). For data evaluation by principal components analysis, we used SIMCA-3B for MS-DOS-based microcomputers (Principal Data Components, Columbia, MO). A series of Aroclors were analyzed by these techniques to provide a training data set for pattern recognition and to establish quality-control criteria for the data set. This training set included (1)replicate GC analyses, (2) mixtures (1:l w/w) of each Aroclor in combination with one other Aroclor, (3) varying ratios of Aroclors 1254 and 1260, (4) several 1:l:l:l mixtures of Aroclors, at varied concentrations, used in quantification of individual PCB isomers, and (5) tissue samples fortified with the 1:l:l:l mixture used in spike recovery determination. For the sake of clarity in the following discussion and data presentation, only individual Aroclors and the quantitative mixture will be presented. Table I1 shows the sample and training set data designation for this analysis.
~
Table 111. Accumulated Variance (%) Explained by Principal Components Models of Aroclor, Fish, and Turtle Samples (Measurements of 105 Isomers) principal component
1 2
A + 1260
class or sample group all samples fish turtles 71.4 86.5
50.7 61.4
A + 1254
FF
33.9 71.6
~
FF
T
H
FF
T
Results and Discussion Recoveries from fish tissue samples fortified with the 1:l:l:lAroclor mixture, determined at several fortification levels, averaged 106% f 18%. In addition, in three tissue samples fortified with radiolabeled 2,2/,4,4/,5,5’-hexachlorobiphenyl to obtain an independent estimate of sample recovery, average percent recovery was 95% f 4%. The total PCB concentration for each sample was determined by summation of individual PCB isomers within each sample (Table 11, column 4). Principal Components Analysis. The concentration data obtained for each GC peak in each sample analysis was then expressed as fractional parts of the total and normalized to sum equal to 100. This normalization minimizes the influence of total concentration and permits a comparison of compositional similarities among samples. The normalized data were examined by calculating principal components sample scores, T, and variable loading terms, P. After calculating two principal components for a class model, we prepared plots of sample similarity using the sample scores (TI vs. T2).The similarity of samples within the class can be assessed by the proximity of samples to each other in plots derived from principal components models. The statistical technique of cross-validation was used to determine that two principal components were statistically significant (18). The similarity concept for this study is based on two fundamentals and can be quantified in situations where two conditions are met: (1) measurements of the same type have been made on a number of systems (i.e., the same PCB isomers have been measured in all samples) and (2) the samples have been ordered into classes each of which contains only similar samples (i.e., the classes in our study were the Aroclor standards, fish samples, and turtle samples). The objective of classification is to get an appropriate description of the data structure within each class, in terms of a quantitative model. Class integrity is then tested by using PCA to determine whether objects (samples) have been assigned to the correct class. Assignment of the fish and turtle samples to classes was the primary goal in this data analysis. Class assignment in the analysis of PCB isomers is commonly approached by determining the relative Aroclor composition of an individual sample. In this work we assessed the similarity of Aroclors to the class models for fish and turtles. If there is a high degree of probability that Aroclors or mixtures of Aroclors can be classified as members of either or both of the class models for fish and turtles, the question becomes what mixture of Aroclors best describes the samples in the respective models. If the Aroclors or Aroclor mixtures cannot be classified as members of either class model within a reasonable degree of probability (1.5 SD), the question need not be posed because the samples are not judged to be Aroclors and cannot be described by any combination of Aroclors. It would be meaningless to report the PCBs in these samples in terms of relative Aroclors composition. The normalized data were statistically analyzed by using SIMCA, and two principal components were calculated.
A
T
AA
1
t
TT
1:1:1:1
I
T
T T T
A +
1248
A +
1242
THETA 2
Figure 2. Principal components plot derived from analysis of Aroclors, fish, and turtle samples: F, fish; T, turtle; A, technical Aroclors.
The accumualted variance explained by the principal component models is summarized in Table 111. In a plot vs. T2)derived from the first and of sample scores (TI second principal components (Figure 2), “A” represents Aroclors or Aroclor mixtures, “F”represent fish samples, and “T”represents turtle samples. This figure illustrates sample similarity on the basis of isomer distribution between Aroclors, fish, and turtle samples. Our data (Figure 2) indicate that the residue profile in the turtle samples differed from that in the fish and Aroclor samples and that the profile in fish samples appeared to be the more closely similar to that of Aroclor 1254. To ascertain if the PCB residues in fish and turtles could be dscribed by an Aroclor or Aroclor mixture, we calculated a two-component principal component model for each class of samples (fish and turtles); accumulated variance explained by the two principal component models is shown in Table 111. On the basis of the data for the objects ”known” to be of each class, one can test the two classes to determine their similarity. For the two sample classes, two models were obtained, each describing the data structure within its respective class. The Aroclors and Aroclor mixtures were then quantitatively categorized by determining the distance from each class to each Aroclor in terms of relative standard deviation from each respective class model. Using the relative standard deviation from the class model, one can assign the Aroclors to either the fish or turtle class, or to neither. Likewise, the fish can or cannot be assigned to the turtle class, and the turtles can or cannot be assigned to the fish class,. The class standard deviation (distributed as “F” statistics) for each group was calculated as well as the relative standard deviation of the Aroclors and Aroclor mixtures with respect to each class model. The resulting principal components plot is shown in Figure 3. The two vertical lines in Figure 3 are 1and 6 relative standard deviations from the turtle class model, and the two horizontal lines are 1and 4 relative standard deviations from the fish class model. This figure thus shows that both the fish and the turtle samples are well-defined in terms of their PCB isomer distribution and that neither model can accommodate samples within 2 standard deviations of the other. The composition of the fish and turtles differed substantially from that of the Aroclors. Such differences may be Environ. Sci. Technol., Vol. 21, No. 1, 1987
75
I
I
1242
TTTT I T
1248
4
-t
A
A
I
I I
1260
A
1254 A
Flgure 3. Coomans’ plot of class distances: vertical lines are located 1 and 6 standard deviation units from the turtle class model, and deviation units from the fish class model.
due to environmental or metabolic alterations that clearly effect major changes in the composition of the PCB isomers. Figure 3 demonstrates graphically and statistically that there is a high probability that these samples could not be described by any Aroclor or Aroclor mixture. It would be inappropriate to report these sample PCB residues as “Aroclor mixtures” or Aroclor equivalents; rather, they should be reported in terms of total PCB concentration.
Summary PCB residue patterns in environmental samples are often reported as Aroclor-derived mixtures or Aroclor equivalents. Such reports are often based on the subjective opinion of the analyst or on statistical techniques which assume that the PCB residues can be described by an Aroclor or Aroclor mixture and thus provide a‘report without addressing the validity of the assumptions. We have shown that if the data presented here were reported on the basis of the assumption of Aroclor similarity, it would result in an erroneous report. Acknowledgments Our appreciation is extended to Neil R. Meyer, who provided assistance in data formating for SIMCA analysis. Registry No. Aroclor 1248,12672-29-6;Aroclor 1254, 1109769-1; Aroclor 1242, 53469-21-9; Aroclor 1260, 11096-82-5.
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Literature Cited (1) Dunn, W. J., 111; Stalling, D. L.; Schwartz, T. R.; Hogan, J. W.; Petty, J. D. Anal. Chem. 1984,56, 1308-1313. (2) Stalling, D. L.; Dunn, W. J., 111; Schwartz, T. R.; Hogan, J. W.; Petty, J. Do;Johansson, E.; Wold, S. In Trace Residue Analysis, Chemometric Estimations of Sampling, Amount, and Error; Kurtz, D. A., Ed.; ACS Symposium Series 284; American Chemical Society: Washington, DC, 1985; pp 195-234. (3) Stalling, D. L.; Schwartz, T. R.; Dunn, W. J., 111; Petty, J. D. In Environmental Applications of Chemometrics;Breen, J. J.; Robinson, P. E., Eds.; ACS Symposium Series 292; American Chemical Society: Washington, DC, 1985; pp 1-15. (4) Rice, C. L.; Putman, D. J. “A Preliminary Survey of Contaminants in Fish and Wildlife at the Tinicum National Environmental Center, Philadelphia and Delaware Counties, Pennsylvania”; Resource Contaminant Assessment Report No. 84-2; U.S. Fish and Wildlife Service, State College Field Office: State College, PA, 1985. ( 5 ) Ballschmitter, K.; Zell, M. Fresenius’ 2. Anal. Chem. 1980, 302, 20-31. (6) Albro, P. W.; Corbett, J. T.; Schroeder, J. L. J. Chromatogr. 1981, 205, 103-111. (7) Schmitt, C. J.; Zajicek, J. L.; Ribick, M. A. Arch. Environ. Contam. Toxicol. 1985,14, 225-260. (8) Wold, S.; Sjostrom, M. In Chemometrics, Theory and Application; Kowalski, B. R., Ed.; ACS Symposium Series 52; American Chemical Society: Washington, DC, 1977; pp 243-282. (9) Wold, S.; Albano, C.; Dunn, W. J., III; Edlund, U.; Esbensen, K.; Geladi, P.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjostrom, M. In Chemometrics. Mathematics and Statistics in Chemistry;Kowalski, B. R., Ed.; Reidel: Boston, MA, 1984; pp 17-95. (10) Albano, C.; Dunn, W. J., 111; Edlund, U.; Johansson, E.; Norden, B.; Sjostrom, M.; Wold, S. Anal. Chim Acta Comput. Tech. Optim. 1978,103,429-443. (11) Derde, M. P.; Massart, D. L. Fresenius’ 2. Anal. Chem. 1982,313,484-495. (12) Wold, S.; Albano, C.; Dunn, W. J., III; Edlund, U.; Esbensen, K.; Hellberg, S.; Johansson, E.; Lindberg, W.; Sjostrom, M. Analusis 1984, 12(10), 477-485. (13) Kowalski, B. R.; Wold, S. In Handbook of Statistics; Krishnaiah, P. R.; Kanal, L. N.; Eds.; North-Holland: New York, 1984; Vol. 2, Chapter 31. (14) Stalling, D. L.; Tindle, R. C. J.Assoc. Off. Anal. Chem. 1972, 55, 32-38. (15) Schwartz, T. R.; Lehmann, R. G. Bull. Environ. Contam. Toxicol. 1982, 28, 723. (16) Schwartz, T. R.; Petty, J. D.; Kaiser, E. M. Anal. Chem. 1983,55, 1839. (17) Schwartz, T. R.; Campbell, R. D.; Stalling, D. L.; Little, R. L.; Petty, J. D.; Hogan, J. W.; Kaiser, E. M. Anal. Chem. 1984,56, 1303-1308. (18) Wold, S. Technometrics 1978, 20, 127. Received for review April 22, 1986. Accepted August 25, 1986. The mention of trade names or commercial products does not constitute endorsement or recommendation for use.
