Aroclor mixtures

Huckins, J.N., D.L. Stalling, and J.D. Petty. 1980. Carbon-foam chromatographic separation of non-o,o -chlorine substituted PCBs from Aroclor mixtures. Jour. Assoc. Off. Anal. Chem. 63 750-755. 

The Aroclor mixtures that were commonly in commercial use are listed in Table 6, with PCB isomers, molecular weights, and percentages of chlorine in each [368,370 - 373]. Table 7 lists the specific isomers found in three of the major Aroclors used by the utility industry. This table also provides a listing of key environmental parameters used to evaluate the fate and transport of these PCBs. 

Figure 7.6 Principle components analysis (PCA) of PCB congener concentrations in technical Aroclor mixtures, contaminated water, caged brown trout, SPMDs, and hexane filled dialysis bags. The plot shows that 77% of the variance of samples within the 95% confidence ellipse is explained by PCI and PC2 and that caged fish and SPMDs are clustered together (PCA plot courtesy of Kathy Echols, USGS-CERC, Columbia, MO, USA).

A principal components multivariate statistical approach (SIMCA) was evaluated and applied to interpretation of isomer specific analysis of polychlorinated biphenyls (PCBs) using both a microcomputer and a main frame computer. Capillary column gas chromatography was employed for separation and detection of 69 individual PCB isomers. Computer programs were written in AMSII MUMPS to provide a laboratory data base for data manipulation. This data base greatly assisted the analysts in calculating isomer concentrations and data management. Applications of SIMCA for quality control, classification, and estimation of the composition of multi-Aroclor mixtures are described for characterization and study of complex environmental residues. 

Further complications are encountered in describing the residue profiles when more than one Aroclor mixture is encountered in an ecosystem. Thus, it is important to consider not only the total PCB concentration in a sample, but also to characterize the distribution of individual PCB isomers present in a sample. 

To illustrate the problems associated with evaluating such data, we conducted several studies with Aroclor standards and mixtures of these standards in an effort to determine what information could be readily obtained with the SIMCA method of pattern recognition (30-32). The following discussion illustrates some of the features of this approach and describes how the SIMCA method works when applied to Aroclor mixtures. 

Figure 5 illustrates the cases in which the data are represented by a point (A 0), line (A=l) or plane (A=2). A is the number of product terms in Equation 2. Samples clustered in a point represent replicate analyses of a single sample in which there is no variation other than measurement error, and the product term in Equation (2) is 0. In these last two situations, the data vary about the mean, m, and the position of each object on the line or plane given by the peak coordinates. An example of data that would form a line are those based on an analysis of a range of concentrations of a single Aroclor (A=l). Data that could be represented in a plane result from the analysis of the fractional composition of two (or more) Aroclor mixtures (A=2). In Fiqure 5, designates the class number of the these hypothetical samples. 

Figure 6. Fractional Composition Histograms from Four Replicate Analyses of Aroclor Mixture.

Figure 8. Plot of Variable Loadings (Betas) in Aroclor Mixtures.

Because many samples are analzyed in which the analyst is interested in determining which Aroclor mixtures are present, we applied the PLS method to the data obtained from the analysis of... 

Partial Summary of Data from the Gas Chromatographic Analysis of Aroclor, Aroclor Mixtures, and Transformer Oil analyses. 

The first results from the use of PLS were reported by Dunn fit al (6) who estimated the composition of PCB contaminated waste oil in terms of Aroclor mixtures. Stalling gt al (13), who reported on the characterization of PCB mixtures and the use of three-dimensional plots derived from principal components, demonstrated that the fractional composition of TCDD and other PCDD residues were related to their geographical origins. These two reports (6,13) described the application of an advanced chemometric tool in residue studies and illustrated the... 

In our present investigations, we encountered a pressing need for an objective, statistically based way of evaluating concentrations of as many as 105 individual PCB isomers in each sample analyzed by capillary GC. We summarize here some of the experience obtained in our laboratories from the use of SIMCA to characterize Aroclor mixtures and environmental PCB residues in a series of bird eggs. 

A series of Aroclors and known Aroclor mixtures were analyzed by these techniques to provide a training data set for SIMCA-3B. These standards Included replicate analyses, a 1 1 (w/w) mixture of each Aroclor in combination with one other Aroclor, and a 1 1 1 1 mixture of each Aroclor (Table I). 

The principal components model of the Aroclor seunples (Table i) preserves greater than 95% of the sample variance of the entire data set. From the 3-D seunple score plot (Figure 3) one can make these observations PCB mixtures of two Aroclors form a straight line three Aroclor mixtures form a plane and that possible mixtures of the four Aroclors are bounded by the intersection of the four planes. Samples not bounded by or inside the volume formed by the intersection of the four planes may... 

For the SIMCA analyses, the individual PCB isomer concentrations were normalized to sum 100. We examined the data by using the SIMCA-3B program to calculate principal components and to plot sample scores in a manner identical to that discussed for the Aroclor mixtures. The plot of sample data illustrates that the geographic locations have different residue profiles (Figure 5). 

Figure 4. Plots of the Fractional Composition of an Aroclor Mixture (A, Sample 9, Table 1) and the Modeling Power for a Three Component Model of the Samples in Table 1 PC-1 (B) PC-2 (C) and PC-3 (D).

Figure 2. Weight percent (peak mass I total mass X 100%) of each peak for an 80% 1016/20% 1254 standard Aroclor mixture and for the top section office Twelve Mile Creek—Lake Hartwell sediment cores. Sample location distances from the PCB source and weight-percent summaries for peaks 1-22 and 25-64 are given. Some peaks are not quantified because of chromatographic interferences (GC peaks 24, 27, and 64) or lack of analytical sensitivity (GC peaks 45, 48, 55, 56, 59, and 62). Peak 15 coelutes with peak 14. Peak 23 is the internal standard aldrin.

Pentachlorinated biphenyls and hexachlorinated biphenyls are the major PCB groups typically found in P. viridis. PCBs are sold commercially as technical mixtures, called Aroclors, each with a specific pattern of chlorination. Patterns have been determined for Aroclor mixtures 1221, 1232, 1242, 1248, 1254, 1260 and 1262 (Frame et al., 1996). Principal component analysis (PCA) was performed to compare the relative PCB congener profile of mussel tissues analysed in 2002 and the commercial Aroclor mixtures (Fig. 15.14). The closest match in the PCB data for P. viridis samples collected in Singapore from our study is the common Aroclor 1254. The slight discrepancy is due to the presence of PCB-149 in mussel tissue and a greater prominence of PCB-110 and -118 in Aroclor 1254. PCA analysis revealed that samples from the west Straits of Johore (Wl, W2 and W3) contain more penta-CBs and less hexa-CBs than samples from the east Straits (E6, E7 and E8). The sample collected in the south of Singapore (S4) has an intermediary pattern of PCB contamination. A similar match has been observed in marine crabs and fishes... 

Figure 16.2. Bi-plots showing the first two principal components of relative individual polychlorinated biphenyl (PCB) congener profiles in seafood in relation to congener profiles for Aroclor mixtures 1221, 1232, 1242, 1248, 1254, 1260, and 1262.

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