Statistical Method To Evaluate the Occurrence of PCB Transformations

Statistical Method To Evaluate the Occurrence of PCB Transformations in River Sediments with Application to Hudson River Data. Sandra C. Karcher, Mitc...
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Environ. Sci. Technol. 2004, 38, 6760-6766

Statistical Method To Evaluate the Occurrence of PCB Transformations in River Sediments with Application to Hudson River Data SANDRA C. KARCHER, MITCHELL J. SMALL,* AND JEANNE M. VANBRIESEN Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3890

Polychlorinated biphenyls (PCBs) were produced in the mid 1900s for industrial use. The term PCBs refers to 209 theoretically possible chlorinated compounds of the biphenyl molecule (congeners). The number and location of the chlorines govern both the environmental fate and toxicity of each congener. Changes in the distribution of congeners in river sediments can result from congener transformation and/or preferential congener transport. This study exploits the distribution of PCB congeners, specifically congeners that maintain a constant ratio relationship in the commercially manufactured PCB mixtures (Aroclors), to quantify the likelihood of congener distribution shifts in river sediment. By using relative abundances, the influence of total PCB bias is eliminated. Correlated congeners (tracker pairs) maintain a constant relative proportion in sequentially morehighly chlorinated Aroclors, thus there is no need to know the source contaminating Aroclors a priori. Using the Frame et al. database of Aroclor congener distributions, 276 pairs of correlated congeners, constructed from 95 individual congeners, are identified. A comparison study of Aroclors and Hudson River sediments included 218 of the 276 tracker pairs. Conclusive evidence of a shift in the congener proportions is found in 120 of the 218 cases, a much greater number than expected if no change in congener distribution had occurred.

Introduction Polychlorinated biphenyls (PCBs) were commercially produced from the 1930s to the 1970s as complex mixtures for a variety of uses. During this period commercial sales of PCBs totaled nearly 570 × 106 kg from domestic sources and about 1.4 × 106 kg from imports (1, 2). The chemical and physical stability of PCBs contributed to their widespread use as dielectric fluids in capacitors and transformers and additionally to their use in printing inks, paints, dedusting agents, pesticides, carbonless copy paper, and other applications (3). The properties making PCBs desirable in industrial applications also make them difficult to degrade in the environment. They are persistent, lipophilic, and bioaccumulative. PCBs have been shown to be ubiquitous pollutants and have been found in most animal and human adipose samples, milk, sediment, and numerous other * Corresponding author phone: (412)268-8782; fax: (412)268-7813; e-mail: [email protected]. 6760

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matrices (3). Since river systems have historically been in close proximity to industrial facilities and the recipients of anthropogenic waste, river sediments have become a significant sink for PCB contamination. The bulk of the PCB contamination in the United States traces back to PCB mixtures produced by the Monsanto Industrial Chemicals Company (3). PCB mixtures manufactured by Monsanto were designated with the Aroclor trademark (3). These Aroclor mixtures were synthesized in a batch process by heating biphenyl and adding anhydrous chlorine in the presence of ferric chloride. The average degree of chlorination of the batch was controlled by the reaction time to yield the desired physical and chemical properties. The total percent weight chlorine is indicated in the last two digits of the Aroclor designation, except in the case of Aroclor 1016 (41% chlorine by weight) (1). The term PCBs refers to 209 theoretically possible discrete chemical compounds called congeners. Each PCB congener consists of two benzene rings carrying 1-10 substituted chlorine atoms and is named based on the number of attached chlorines and their attachment locations. Congeners with the same number of substituted chlorines, regardless of location, are referred to as isomers and are considered members of the same homologue group. The locations for the substituted chlorines are termed ortho, meta, and para; the ortho-position corresponding to 2 and 6 on both the A and A′ ring, the meta-position to 3 and 5 on both the A and A′ ring, and the para-position to 4 on both the A and A′ ring, as shown in Figure 1. Historically PCBs found in sediment samples have been reported as total PCBs, as percentages of commercial Aroclors, as the commercial Aroclor with the closest chlorine mass, or with congener shortlists. These methods are typically based on pattern matching, a means by which an environmental sample and a solution of known concentration are run through a similar measurement system and the output either visually or mathematically compared. Pattern matching is a subjective quantification process which is highly dependent on the analyst’s selection of the most important criteria (i.e. peak selection) used for comparison. Another common method used to quantify total PCBs is to select target congeners for analysis and weight the results according to a predetermined function (4). The problems associated with pattern matching and other historical methods of PCB quantification are well documented (4-8). As such, caution must be applied when using historical data for analysis as results can be systematically biased. The dechlorination process for PCBs has been the subject of much research over the past several decades. Dechlorination is the process by which one or more of the chlorines on the PCB molecule are removed. Understanding if, and to what extent, dechlorination is occurring at a site can have important implications for remedial design strategies. Due to historical PCB reporting methods, characterizing source Aroclor contaminants and recognizing PCB transformation patterns in field sediments has been difficult. Biologically mediated dechlorination is thought to be dependent on the structural arrangement of substituted chlorines (9), the degrading microorganisms (10), physical and chemical characteristics of the river system (11), and PCB concentration (12, 13). It has been further suggested that a threshold concentration exists below which no biological dechlorination takes place (12) and that there is a toxic effect, inhibiting the dechlorination reaction, at high concentrations of PCBs (13). It is currently not clearly understood whether these constraints are defined by indi10.1021/es0494149 CCC: $27.50

 2004 American Chemical Society Published on Web 10/21/2004

FIGURE 1. Biphenyl molecule. vidual congener concentrations, by the relative abundances of congeners, or by the total mass of PCBs present in a sample. Much of the confusion stems from not knowing the specific Aroclor (such as A1242) or combination of Aroclors (such as A1242 and A1260) that represent the original source contamination at a site of study. Knowledge of the source contaminating Aroclors, in addition to providing a starting point for evaluating potential in situ PCB transformations, can be important for identifying potentially responsible parties. Many authors have developed methods that attempt to identify original source Aroclor contamination in environmental field samples. These Aroclor identification methods take very diverse approaches. For example, Sather et al. (14) use a least mean squares approach in which they calculate a residual between the amount of each congener found in the sample, and the expected amount based on the congener distributions in three commercial Aroclors. They determined that this method works well for nonweathered samples. Furthermore they claim that when the source Aroclor is known, the method can be used to determine compositional alteration from Aroclor patterns. Imamoglu and Christensen (15) employ a derivative of a principal component analysis model, with some additional consideration given to nonnegative constraints. This model yielded results indicating that the source contaminant in the Fox River, Wisconsin was Aroclor 1242. Newman and colleagues propose a characteristic ratio approach (16). The ratio method asserts that there are certain persistent congeners that are present in only some of the commercial Aroclors. Using these ratios, the source Aroclor can be identified. Newman et al. conclude that for the three Aroclors dominant in the California marine environment, the congener ratios can be used to detect, speciate, and quantify combinations of Aroclors. The characteristic ratio approach used by Newman et al. is similar in concept to the methodology presented here; however, this work focuses on congeners that maintain a constant ratio in the commercial Aroclors in an attempt to identify PCB transformation, rather than on congeners with notably different ratios being used to identify a specific source Aroclor. Commercial Aroclors have frequently been used as laboratory instrument calibration standards. Knowing which Aroclors are present in an environmental sample enables the laboratory to select the most appropriate Aroclors for calibration. Samples containing unweathered Aroclors require calibration for up to 150 congeners (1). Additional calibration may be required for environmental samples where transformation has occurred, resulting in daughter products not calibrated as part of the original 150. The individual quantification of all 209 congeners was found by Frame and colleagues to be too slow and difficult for routine analysis. Thus, they proposed a mathematical process for converting the peaks of a one pass DB-1 gas chromatograph and an electron capture detector (GC/ECD) system into 209 individual congeners (17). Caution must be used when applying this method because the fraction of mass assigned to each congener in a coeluting peak is based on the weight percent distributions of congeners in the commercial Aroclors selected for calibration. Thus, quantification is biased by the expected Aroclor distribution.

FIGURE 2. Tracker selection process with selection criteria. This study explores a statistical method for detecting in situ PCB transformation without knowledge of the source contaminating Aroclors by focusing on congeners that maintain a constant relative proportion in sequentially morehighly chlorinated commercial Aroclors. These correlated congeners, referred to herein as trackers, are not affected by the problems associated with pattern matching or bias in total PCB measurements. The method exploits the fixed ratio relationship of tracker pairs by comparing the relative abundances found in the commercial Aroclors to those found in environmental samples. The study is presented in two parts. Part one details the identification of the correlated congener pairs. The data and methods used in the tracker identification process are presented, followed by the results and a brief discussion. Part two describes how the method can be used to determine whether shifts in tracker pair relationships have occurred. Application of the method is demonstrated using environmental field samples from the Hudson River. The results of this analysis are presented followed by a discussion of the broader implications of the study.

Data and Methods of Aroclor Tracker Pair Identification The identification of tracker pairs in commercial Aroclors is made using data published by Frame and colleagues (herein referred to as the Frame Aroclor Congener Distribution Data (FACDD)) (1). The FACDD consists of characterized congener distributions in multiple lots (totaling 17) of eight different commercial Aroclors (A1221, A1232, A1016, A1242, A1248, A1254, A1260, and A1268). Identifying tracker pairs is a multistep, computationally intensive procedure. Figure 2 details the selection process, indicating the number of congeners, or the number of tracker pairs meeting the stated criterion at each step. The results are presented in the following section.

Results of the Aroclor Tracker Pair Identification The tracker selection process first considers adequate representation of each congener in the FACDD. Congeners detected in seven or more lots (out of the 17 characterized by Frame et al.) are considered acceptably represented. To confirm that the selected congeners represent varying degrees of chlorination, not one level only, congeners present at three or more of the eight Aroclor chlorination levels are identified. Congeners that are adequately represented are than examined as they relate to each other. For visualization and analysis, one congener is assigned to the x-axis (called CAx) and the other to the y-axis (called CAy). The superscript A in all variable names indicates that the parameter is calculated from Aroclors. The percent weight of the relative abundance of the congener data for the CAx congener is denoted as xA and for the CAy congener as yA. In computing the correlation coefficient (details are provided in the Supporting InformaVOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 3. Congener tracker pairs in the FACDD (276). tion, Part S1), one-half of the minimum percent detected for each congener is used to replace nondetected relative abundances. To ensure that the correlation coefficient between a tracker pair candidate is not inflated by the occurrence of too many nondetections, congener pairs are required to have seven or more overlapping detections (those where xA and yA are detected in the same Aroclor lot) to be selected as a tracker pair. The procedure was implemented with a correlation coefficient criterion of 0.97. As shown in Figure 2, 276 pairs of correlated congeners, constructed using 95 individual congeners, have been identified as trackers. The 276 tracker pairs are shown in Figure 3 (the classification of the points is discussed in the following section). Figure 3 consists of 552 points, 276 where each tracker pair is graphed as (CAx, CAy) and 276 where each tracker pair is graphed as (CAy, CAx). The dashed lines throughout the graph divide the space into isomer groups with the homologue number appearing on the top and right side of the chart. This figure shows, with few exceptions, that identified tracker pairs consist of congeners with the same number of substituted chlorines. Based on the properties of biphenyl, as a biphenyl molecule is chlorinated and transformed into PCB congeners, some of the PCB congeners accept chlorine at a predictable rate, fixing their relationship with respect to other congeners. It is expected that the physical/chemical properties governing the acceptance of a chlorine molecule would be similar in cases where the congeners are isomers. Finding this to be the case in most of the tracker pairs identified supports the underlying premise of the method. Figure 3 also elucidates relationships between groups of congeners, rather than focusing only on congener pairs. 6762

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Considering the magnified square in the bottom right of the figure, it can be seen that congener 27 participates in five tracker pair relationships. Congener 27 is in tracker pair relationships with congeners 16, 17, 19, 20, and 26. Further investigation reveals that all of these congeners participate in tracker pair relationships with each other (for example, congeners 19 and 17 are a tracker pair). Thus, as would be expected based the PCB manufacturing process, correlated congeners can occur in clusters. A considerable benefit of working with relative proportions of congeners is that the linearity of the correlation relationship between the congeners is not affected by bias in the estimated total amount of PCBs. A systematic high bias in total PCBs reduces the relative amounts of both congeners in the tracker pair, whereas the converse is true for a low bias.

Data and Methods for Determining Whether Shifts in Tracker Pair Proportions Have Occurred Shifts away from the fixed ratio relationships of the Aroclor tracker pairs are indicative of sediment weathering, which likely includes both congener transformation and preferential congener transport. Statistically significant shifts in the proportions of tracker pair congeners are identified by comparing the relative abundances of congeners in environmental samples to those observed in Aroclors. The field data used in this study were collected from Hudson River sediment. As part of the continued monitoring of the Hudson River, the U.S. Environmental Protection Agency, TAMS Consultants, Inc., and Gradient Corporation conducted Phase 2 of the Hudson River PCBs Reassessment

Remedial Investigation/Feasibility Study (RI/FS). In 1994, as part of Phase 2, a Low-Resolution Sediment Coring Program (LRSCP) was conducted. The LRSCP is an investigation of PCB levels in selected hot spot areas of the Upper Hudson, river mile 197.3 to 156 (18). As part of the LRSCP, total PCB results are reported for 371 samples from 170 locations. One location lacked coordinates in the database leaving 169 locations and 369 samples. Each result is given in two ways, as Value1 and Value2. The Value1 field represents the validated data with nondetect levels as reported by the laboratory and confirmed by validation. The Value2 field contained the validated congener detections, along with a modified value when no result could be determined above the detection limit (18). As a means of working with the most reliable data, only data where Value1 ) Value2 are used in this study. Of the 369 samples, 23 field duplicates are reported in a separate database. The LRSCP database includes 145 individually quantified congeners for each field sample, plus a total PCBs result. All Value2 data results were converted to a percent weight relative abundance by dividing each individual congener result by the total PCBs and multiplying by 100. Also as part of the LRSCP, reported in a separate database, are the calibration standards for two lots of seven Aroclors (A1221, A1232, A1016, A1242, A1248, A1254, and A1260). Results are given in a Value field (not as Value1 and Value2). These data are converted to relative percent weight abundance just as the Value2 results of the LRSCP. To prepare both the FACDD and the LRSCP standards Aroclor data for comparison to the environmental field data, the relative abundances are converted to log space. The log relative abundances of the CAx congener are denoted as XAi where XAi ) log10(xAi); similarly for the CAy congener, YAi where YAi ) log10(yAi). The variable i from 1 to 31 represents each Aroclor lot (17 from the FACDD and 14 from the LRSCP calibration standards). The superscript A in all variable names indicates that the data are from the Aroclors. Next, a regression of YA verses XA is performed using least mean squares, yielding estimates of a slope, mA, and intercept, bA. Once the equation for the linear relationship is characterized, the predicted YAi value (denoted as Yˆ iA) at each observed XAi value is found using the standard form of a line Yˆ iA ) mAXiA + bA. This is done for each tracker pair in each Aroclor lot. The residual of the observed YAi value and the predicted Yˆ iA value can then be computed (RiA ) YiA - Yˆ iA), followed by the mean, standard deviation, and variance of the residual array (σRiA2). Formulas for computing these parameters are provided in the Supporting Information, Part S1 and S2. To prepare the LRSCP field data for comparison to the Aroclor data, the relative abundances were converted to log space. The log relative abundances of the CPx congener are denoted as XPi where XPi ) log10(xPi); similarly for the CPy congener, YPi ) log10(yPi). The variable i runs from 1 to n, where n is the number of field samples where both congeners in the tracker pair have results with Value1 ) Value2. The superscript P in all variable names indicates that the data are from field data of the LRSCP. By tracker pair, using the equation for the linear relationship found previously with the Aroclor data, the predicted YPi value (noted as Yˆ iP) at each observed XPi is found using the standard form of a line Yˆ iP ) mAXiP + bA. The residual of the observed YPi value and the predicted Yˆ iP value is then computed (RiP ) YiP - Yˆ iP), followed by the variance of the residual array (σRiP2). As with the Aroclors, a regression of YP verses XP is performed, yielding estimates of a slope, mP, and intercept, bP of the linear relationship for each tracker pair. Additionally, the variance associated with the field measurement error of each congener is determined using the LRSCP field duplicate data (σYP2 and σXP2). The development of these variances is detailed in the

Supporting Information, Part S3. Separate lists of the 95 congeners that contribute to the 276 tracker pairs as well as the pairs themselves are provided in the Supporting Information, Tables S1 and S2, respectively. Table S1 includes all relevant information that is specific to each congener (such as the number of FACDD Aroclor chlorination levels and FACDD Aroclor lots where the congener is present), while Table S2 includes information that pertains to each tracker pair (such as the slope and intercept of the Aroclor data and the number of Aroclor lots where the congeners are both present). To determine if the LRSCP data are different from Aroclor, the null hypothesis that the field data results were sampled from the same population as commercial Aroclors must be rejected based on a statistically determined level of significance. The testing of the null hypothesis for each tracker pair was conducted as follows: at a given level of significances Ho, the LRSCP data for the congener tracker pair are sampled from a population with relationships the same as those observed for the Aroclor data and H1, the LRSCP data for the congener tracker pair are sampled from a population with relationships that differ from those observed for the Aroclor data. As a result of the hypothesis testing, each tracker pair is categorized as being not Aroclor-like, Aroclor-like, or inconclusive. Two methods are used to categorize each tracker pair: a variance summation method and a slope and intercept method. The first method uses a summation of the variances of the relevant data distributions to identify tracker pairs that show a shift away from Aroclor-like relative abundances. When a shift away from Aroclor relative abundances is not identified, a slope and intercept test is applied to further differentiate the tracker pairs into those that appear similar to the Aroclors and those where no conclusion can be made regarding the pairs status. The two methods resulted from a preliminary study of congener variability performed on the LRSCP duplicate data. The study indicated that variability associated with measurement error is congener specific, thus the variance summation method incorporates these congenerspecific variances into the hypothesis testing. The congenerspecific field measurement variances for the LRSCP data, calculated as part of the preliminary study and used in the variance summation method, are provided in the Supporting Information, Table S1. Figure 4 shows graphs of hypothetical field data where the null hypothesis (that the field data are sampled from an Aroclor distribution) is rejected. The relationship shown with the solid line in Figure 4 (both parts a and b) is characterized by a slope (m.A) and an intercept (bA) calculated using the log of the Aroclor relative abundances and thus represents an Aroclor-like relationship between the congeners. The data shown in Figure 4a as open circles (o) indicate more of congener Cy than would be expected in Aroclors for a given amount of congener Cx. Conversely the data shown as x’s (x) indicate less of congener Cy than would be expected in Aroclors for a given amount of congener Cx. In both of these cases the tracker pair would be classified as not Aroclor-like. The data points displayed in Figure 4b are inside the banded interval and thus cannot be characterized as not Aroclor-like. It would, however, be inappropriate to suggest that these data are Aroclor-like. As shown in the flowchart provided in Figure 5, only pairs where the slope and intercept of the Aroclors and LRSCP data are similar are classified as Aroclor-like. The data shown in Figure 4b as dashes (-) indicate a different slope, and the data shown as pluses (+) a different intercept. Hypothesis testing for the slope and intercept method is performed using a t-distribution with a level of significance (LOS) of 0.01 (see the Supporting Information, Part S4). VOL. 38, NO. 24, 2004 / ENVIRONMENTAL SCIENCE & TECHNOLOGY

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FIGURE 4. Hypothetical field data demonstrating significant differences from Aroclor-like tracker pair relationships.

FIGURE 5. Flowchart for hypothesis testing to categorize each tracker pair. The arrow shown on both panels of Figure 4 represents the magnitude (U) of the relevant uncertainties. The characteristic uncertainty (U) is comprised of the uncertainties associated with the measurement and regression of the Aroclors and LRSCP data. The uncertainty can be visualized as a banded interval about the FACDD log-linear relationship (as shown in Figure 4, with dashed lines parallel to the solid line). The variance equation for the case where the null hypothesis is true is given as

σRiP2 ) σYP2 + (mA)2σXP2 + σRA2

(1)

The first term on the right of eq 1 is the variance due to field measurement error of YP (σYP2). The second term represents the variance from the field measurement error of XP (σXP2). The final term is the residual variance of the Aroclors. Using the field data replicates and applying a multiplier (mult), the revised variance equation is shown as eq 2

Is |RiP| > mult ×

x(σ

2 DYP /2)

+ (mA)2(σDXP2/2) + σRA2 (2)

where DXP ) (XiP1 - XiP2), DYP ) (YiP1 - YiP2), XiP1 and YiP1 are the log relative abundances for the CPx and CPy congener in the original sample, respectively, and XiP2 and YiP2 are the log relative abundances for the CPx and CPy congener in the duplicate sample. The full development of the magnitude of the uncertainty, including replicate variances and the corresponding variance equation, are described in the Supporting Information, Part S5. The discussion of the multiplier is provided in the Supporting Information, Part S6. A multiplier (mult) of one is used in this study, so that field sample congener pair data, when plotted as in Figure 4a, must have enough points lying either above or below a band of ( one standard deviation to be classified as not Aroclorlike. For each tracker pair, each data point from the field data is evaluated according to eq 2. If the field data residual (RiP) is positive and greater than the product to the right of the inequality, the point is considered as presenting above the band. If the field data residual is negative and its absolute 6764

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value is greater than the product to the right of the inequality, the point is considered as presenting below the band. The individual point results above the band, and below the band, are aggregated separately, and the number occurring in these groups is compared to the number that could occur by chance under the null hypothesis that the field data are sampled from a population that is Aroclor-like. This comparison is made based on the binomial distribution, with the probability that each point is outside of the band dependent on the value of the multiplier, using a level of significance (LOS) of 0.01 (details provided in the Supporting Information, Part S7).

Results of Field Data Analysis As a result of this field sediment data study, 218 of the 276 tracker pairs were categorized. The LRSCP data provided results for 145 (of the 209) individual congeners for each sample; of these, 88 overlapped with the 95 Aroclor tracker congeners. These 88 congeners formed 243 of the 276 tracker pairs. Of these tracker pairs, 25 were constructed of congeners where one (or both) of the participants had no field results where Value1 ) Value2, or where the data were not usable. Thus the number of tracker pairs with valid corresponding field data was reduced to 218 pairs, constructed from 83 individual congeners. These pairs are indicated in the Supporting Information, Table S2. For each of the 218 categorized tracker pairs, the slope and intercept of both the Aroclors (mA and bA) and the LRSCP (mP and bP) are provided, along with the general equations for their computation, in the Supporting Information, Table S2 and Part S1, respectively. The LRSCP field data variances are obtained from the 23 original and field duplicate data samples provided in the database. Only 95 congeners (out of a possible 145 individually quantified LRSCP congeners) had results where Value1 ) Value2 and Value2 > 0 in two or more of the 23 original and duplicate samples. Of those congeners, only 70 overlapped with congeners that participated in tracker pair relationships. In cases where no field measurement variance for a specific congener could be obtained directly from the LRSCP duplicate data, the maximum variance found for all 95 congeners with valid data is used. These values are also shown in the Supporting Information, Table S1. In summary, of the 218 categorized tracker pairs, 120 are categorized as not Aroclor-like, 23 are categorized as Aroclorlike, and 75 as inconclusive. These results clearly show that the congener pair relationships in the LRSCP data have changed compared to those expected for data sampled from an Aroclor distribution. At the level of significance used, it would have been expected that only three of the 218 tracker pairs would have been found to be not Aroclor-like, compared to the 120 determined as such. Even if some anticipated number of spurious trackers (congener pairs classified as

trackers in the original evaluation of the FACDD where no physical/chemical relationship existed) were included, only 5 tracker pairs would have been expected to reject the null hypothesis (Supporting Information, Part S8 describes spurious trackers).

Discussion The high number of tracker pairs that are not Aroclor-like indicates that congener distribution shifts have occurred in some of the Hudson River sediments. Figure 3 uses different symbols to show the categorization of each tracker pair. The figure indicates that Aroclor-like congener pairs, shown as red dots, are clustered toward the higher homologue groups, whereas the not Aroclor-like congener pairs, shown as blue pluses, occur more frequently in the lower chlorinated congeners. Additional information can be gleaned from the trichlorobiphenyl square, magnified in the lower right corner of the figure. Focusing on congener 19 (three orthosubstituted chlorines), the figure shows that congener 19 participates in 12 tracker pair relationships. Ten of these pairs indicated more congener 19 than would be expected in a sample from Aroclors; only congener 27 persisted/ accumulated more than 19 in the 12 pairs. Similar trends are found with other congeners, including congeners 4 and 10 (two ortho-substituted chlorines). The observation that orthochlorinated PCBs persist in Hudson River sediments has been previously reported (12, 19, 20). In the presentation of the results of the LRSCP (21), a molar dechlorination product ratio (MDPR) is used to indicate the degree of dechlorination at a particular location. The MDPR uses the molar sum of five congeners (four of which are exclusively ortho-substituted congeners) to normalize the field sample results. Using the MDPR analysis, varying degrees of dechlorination were demonstrated in upper Hudson sediments. The MDPR presupposes that no dechlorination or degradation is occurring to the five congeners summed for the normalization procedure. The findings from the MDPR method are similar to those presented in this study, lending support to the tracker pair methodology. One advantage to working with the tracker pairs is that the method is not dependent on presuming that certain congeners maintain a constant amount in river sediments. This allows for extension of the method beyond its use to determine if dechlorination has occurred. Research is currently underway to elucidate potential pathways of dechlorination based on the direction of shifts in proportions of the congeners. This method of exploiting tracker pairs can be adapted for different matrices and systems. Any number of field sample points (even just one) can be compared to the relative abundances of the tracker pairs to determine the probability of a PCB congener distribution shift. All the variables noted throughout with the superscript A were computed directly from Aroclors and are thus relevant to a broad range of sites. The slopes and intercepts of the relative abundances and the variance associated with the residuals of the Aroclor data (σRA2) are provided in the Supporting Information, Table S2. The magnitude of the uncertainty bands (U) calculated in this study can be applied to other sites, but caution is advised. The variances associated with the field measurement error of the individual congeners (σDXP2 and σDYP2) are computed with duplicate data from the LRSCP. These congener-specific values are probably good estimates for similar sampling programs and could be used to provide a preliminary estimate of the measurement error at a site (with similar sampling protocols) where no duplicate samples were taken or where no more applicable values can be found. However, site-specific determination of congener measurement errors (based on duplicate sampling) is advised.

Acknowledgments This work has been funded by the Packard Foundation as part of the Interdisciplinary Science Program and is being conducted under the project title “Effects of Sediment Biogeochemistry on the Environmental Fate and Persistence of Polychlorinated Biphenyls”. The work was conducted as part of an interdisciplinary team at Carnegie Mellon University. The authors thank the members of the team, David Dzombak, Ned Minkley, William Brown, Greg Lowry, Kathleen McDonough, Christine Wang, Matt Blough, Paul Murphy, and Jay Rao, for their contribution to this research. Additional assistance in this research was provided by Kevin Russell and John Connolly of Quantitative Environmental Analysis, LLC (QEA) and Northeast Analytical, Inc. (NEA).

Supporting Information Available Equations for the slope and intercept of tracker pairs using least mean squares and equations for the descriptive statistics of an array and the correlation coefficient; the development of residual variance equations for tracker pairs, the estimation of congener-specific measurement error variances, and more discussion on the various statistical methods; additional information in tabular form for the 95 individual congeners used to make the pairs (such as the congener-specific variance) and the 276 tracker pairs (such as the slopes and intercepts). This material is available free of charge via the Internet at http://pubs.acs.org.

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Received for review April 19, 2004. Revised manuscript received August 11, 2004. Accepted August 12, 2004. ES0494149