Environ. Sci. Technol. 2006, 40, 109-119
PCB Congeners and Dechlorination in Sediments of Lake Hartwell, South Carolina, Determined from Cores Collected in 1987 and 1998 PHILIP A. BZDUSEK,† E R I K R . C H R I S T E N S E N , * ,† CINDY M. LEE,‡ U S A R A T P A K D E E S U S U K , ‡,§ A N D DAVID L. FREEDMAN‡ Department of Civil Engineering and Mechanics, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin 53201, and Department of Environmental Engineering and Science, Clemson University, Clemson, South Carolina 29634-0919
Four sediment cores were collected from Lake Hartwell, SC, in 1987 and 1998 and analyzed for polychlorinated biphenyl (PCB) congeners. Total PCBs ranged from ∼0 to 58 µg/ g. Positive matrix factorization (PMF) was applied to the data sets to determine PCB source profiles. Two factors were determined for each data set. One factor resembled the original estimated PCB mixture of 80% Aroclor 1016 and 20% Aroclor 1254 and the other factor was a dechlorinated version of the mixture. Evidence of a dechlorination plateau is apparent from the PMF loading solutions because the dechlorinated congener profiles do not change from 1987 to 1998, but the contribution to the profile from the dechlorinated factor increases from 73% (1987) to 87% (1998). PMF source contributions and plots of PCB concentration versus congener for individual samples provide evidence of enhanced dechlorination at high concentrations. After source apportionment an anaerobic dechlorination model was applied to the dechlorinated source profiles to quantify possible dechlorination pathways. It was found that dechlorination process M, extended to target biphenyl rings with up to six chlorines, provided the best fit for an individual process, and M + Q provides the best fit for combined processes, although M + LP also provides a similar fit. Process LP targets the higher chlorinated congeners and appears to dechlorinate PCBs in the sediments initially.
Introduction The Savannah District U.S. Army Corps of Engineers constructed Lake Hartwell between 1955 and 1963 by damming the Savannah, Seneca, and Tugaloo Rivers (1). Located on the Georgia-South Carolina border, the reservoir covers ∼22 660 ha. In February 1990, Lake Hartwell was included in the Sangamo Weston, Inc./Twelve Mile Creek/Lake Hartwell Superfund site on the National Priorities List due to extensive contamination with polychlorinated biphenyls (PCBs) (2). * Corresponding author phone: (414) 229-4968; fax: (414) 2296958; e-mail:
[email protected]. † University of Wisconsin-Milwaukee. ‡ Clemson University. § Present address: King Mongkut’s Institute of Technology Ladkrabang, Thailand. 10.1021/es050194o CCC: $33.50 Published on Web 11/24/2005
2006 American Chemical Society
The Sangamo Weston facility was built in 1955 for the manufacturing of electrolytic capacitors, with expansions in 1956 and 1961 to include the manufacture of mica capacitors, power factor capacitors, and potentiometers (2). Sangamo Weston used PCBs as the dielectric fluid for the power factor capacitors. The PCBs used for this application were primarily Aroclors 1242, 1254, and 1016 (1). Although the use of PCBs by Sangamo Weston was terminated in 1977, the EPA estimates 180 000 kg of PCBs were discharged into Town Creek up to that time (1). The estimated percent contribution to sediment PCBs by Aroclor is 80 ( 2% 1016 and 20 ( 2% 1254, based on sediment surveys (3), modeling of weathering of PCB congeners (4), and sales records (5). The record of decision (ROD) for the Superfund site specifies continued fish advisories, public education, monitoring of the sediments and biota, and regulation of impoundments to facilitate burial of the contaminated sediments (1). This monitored natural attenuation can benefit from weathering of the PCBs through biological and physicochemical processes. Modeling of key weathering processes such as anaerobic dechlorination of PCBs, which has been documented for several contaminated sites, such as the Sheboygan River, Fox River, Hudson River, Ashtabula River, Silver Lake, and Woods Pond (6-11), can improve our understanding of the critical mechanisms for decreasing risk. Anaerobic dechlorination is an important process for removing predominantly meta or para chlorines from higher chlorinated congeners to create more di-, tri-, and tetrachlorobiphenyls, which are generally less toxic and aerobically degradable (11-15). Physicochemical processes can also influence PCB concentrations (16-18). Several modeling approaches have been investigated for understanding anaerobic dechlorination of PCBs. Previously an eigenvalue based factor analysis (FA) model was applied to determine PCB source profiles for input into an anaerobic dechlorination model (6, 7). Polytopic vector analysis (PVA) has also recently been used to characterize PCB congener patterns (19). Another method, widely used for air pollution source apportionment (20-23), positive matrix factorization (PMF) (24, 25), can in contrast to FA and PVA take variable experimental errors into account, and is used here and in a companion paper (26) for the first time for sediment PCB source apportionment and dechlorination analysis. Inclusion of experimental errors allows both large and small congener concentrations to be reflected accurately. Thus original Aroclor patterns, if present, can be resolved as a factor. The anaerobic dechlorination model that was used in conjunction with the FA studies considered random sequences of dechlorination processes with no preference for reaction type, e.g., doubly flanked meta, doubly flanked para, etc. (27). However, preferential sequences are probably more realistic and may improve the fit of the model to experimental data. Two key questions that this modeling can address are evidence for the progress of dechlorination and the relationship between concentration and dechlorination activity. The modeling can provide support for observations from laboratory and field studies that indicate dechlorination in several areas within Lake Hartwell is stalled (28, 29). The establishment of a dechlorination plateau has been shown clearly in laboratory studies by others (30). A dechlorination plateau is a stalled dechlorination process; that is, the rate of reductive dechlorination has slowed to such an extent that no chlorine removal is observed over a long time period (months to years). In the laboratory microcosm study of Lake Hartwell sediment, the time period when the chlorines per biphenyl stayed the VOL. 40, NO. 1, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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same (2.9-3.0 Cl/biphenyl for about 6 weeks) was considered the dechlorination plateau (28). In the field observations, the equivalent depths that had the same Cl/biphenyl from 1987 to 1998 were considered as a plateau (29). Knowledge of a dechlorination plateau is useful for evaluating remediation efforts, such as the sediment capping specified by the ROD for Lake Hartwell. Management of the impoundments upstream of Lake Hartwell can potentially be changed to increase sedimentation in areas experiencing a dechlorination plateau. The concentration and dechlorination relationship has been addressed by Sokol and colleagues (31, 32) who assessed the in situ PCB reductive dechlorination in St. Lawrence River sediments. The extent of dechlorination varied widely from site to site and there was no correlation between sediment PCB concentrations (2-8000 ppm) and the extent of dechlorination. However, some concentration dependence that was congener specific in the range of 4-35 ppm was identified (32, 33). The first objective of this study was to evaluate if a dechlorination plateau has been reached in Lake Hartwell sediments. This was done by examining PMF source profiles (also referred to as loadings) that were used as input to a model incorporating preferential anaerobic dechlorination pathways. Interpretable source profiles include a near original Aroclor distribution profile, which is a linear combination of multiple Aroclor profile(s), and dechlorinated version(s) of Aroclor profiles. The model was applied to two data sets from Lake Hartwell obtained in 1987 and 1998. A portion of the data (i.e., PCB concentration versus sediment depth) was previously published by Pakdeesusuk et al. (29) and Farley et al. (4). The second objective was to investigate the effect of initial PCB concentration on the rate and extent of anaerobic dechlorination. This study represents a unique application of PCB source apportionment because it compares PCB data sets from approximately the same location at two different time periods.
Materials and Methods Sample Collection. Sediment cores were collected in 1987 as described by Farley et al. (4) and in 1998 as described by Pakdeesusuk et al. (29). Both expeditions used similar techniques. In brief, cores were collected with a gravity corer with 5-cm i.d. polycarbonate tubes. The cores were sectioned in 5-cm increments with only the sediment not in contact with the tubing used for extraction and analysis. Core sections were stored at 4 °C until extraction. PCB Extraction and Analysis. The 1987 cores were extracted according to the procedures described by Farley et al. (4). The 1998 cores were extracted in a similar manner with modifications described by Pakdeesusuk et al. (29). Both cores were extracted with a combination of acetone and isooctane using ultrasonication. Recovery standards showed the extraction efficiency to be 99 ((4.9) % in 1987 (4) and 103.4 (( 23.3) % in 1998 (29). Analyses for both sets of cores were conducted via gas chromatography with electron capture detection using similar conditions. The 1987 analysis used a Hewlett-Packard 5880A with a DB-5 (J&W Scientific) capillary column (30 m, 0.25 mm i.d., 0.25 µm film thickness) with aldrin as an internal standard (4). The 1998 analysis used a Hewlett-Packard 6890 with a ZB-5 (Phenomenex) capillary column with the same dimensions and internal standard as the 1987 analysis (31). Calibration standards consisted of a 4:1 mixture of Aroclor 1016/Aroclor 1254 in both cases. Detailed GC conditions can be found in Pakdeesusuk et al. (29) and Farley et al. (4). We assumed for both analyses that the biphenyl entity remained intact (31, 34, 35). Positive Matrix Factorization. The PMF model used for this study is based on the formulation given by Paatero (24) 110
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FIGURE 1. Flowchart outlining the PMF method with NNLS rotations. where the governing equation is
X)GF
(1)
The data matrix X (m × n) is factored into its components, the factor loading matrix G (m × p), representing source profiles, and the factor score matrix F (p × n), representing source contributions. Additionally, m, n, and p are the number of congeners, samples, and sources, respectively. A flowchart outlining the PMF model is shown in Figure 1. The objective function to be minimized is a weighted sum of squares Q of differences between the measured and calculated elements of the data matrix (Figure 1). The weights are 1/σij2 where σij is the uncertainty associated with element Xij of the data matrix X. E is an error matrix. Note that, for clarity, a prime and asterisks have been dropped in the expression for Q. Paatero (24) adds logarithmic penalty terms, to prevent negative elements in the factor loading and score matrices, and regularization terms. Our model considers only the weighted sum of squares and uses a rotation T (p × p) and its inverse T-1 based on the nonnegative least squares (NNLS) procedure developed by Lawson and Hanson (36), after the G′ and F matrixes have been determined by PMF, to eliminate negative elements. In addition, the data matrix X is initially average scaled by dividing the concentrations of congeners in each sediment layer by their respective average concentration for all layers and then back scaled (reverse average scaling, i.e., multiply by averages) after rotations. Columns of the loading matrix are normalized to 100%, G*, before application of the convergence criterion checking if changes in G* are smaller than a given small number , e.g., 0.01. Principal component analysis (PCA) involving eigenvalues and eigenvectors for the covariance matrix is used here only for diagnostic purposes. The elements of the diagnostics box will be discussed under diagnostics tools. The model was validated using Monte Carlo generated data sets, which will be discussed in the following section. The model was further validated with other data sets and the
results were comparable to those obtained using Paatero’s PMF2 program (24). Further details of the PMF model may be found in Bzdusek (37). The dimensions of the original data sets were 58 congener groups by 32 samples for 1987 and 58 by 44 for 1998. Some chromatographic peaks represented single congeners, but due to coelution, other peaks (e.g., PCB14 and PCB19) consisted of more than one congener. Many higher chlorinated congeners with low concentrations in Aroclors 1016, 1242, and 1254 had a significant number of nondetect values and were not considered for modeling. Also, samples near the sediment-water interface and deep in the sediment had many nondetect values. After elimination of samples and congeners with more than 15% nondetect values the dimensions of the 1987 data set were 37 congener groups by 23 samples. The same congeners were considered for the 1998 data set and 33 samples were retained for modeling. Validation of PMF Model. Artificial data sets were created using a Monte Carlo method to test the effectiveness of the PMF model (38). The artificial data sets represent a mixture of three unweathered PCB Aroclors. The Monte Carlo method is used to mix the unweathered Aroclors so we can test the ability of the PMF model to unmix the combination of the Aroclors into its three original unweathered Aroclor profiles. The source profiles entered into the model were 40% Aroclor 1016, 40% Aroclor 1242, and 20% Aroclor 1254 (34). The PCB congeners considered were the same as those considered for the PMF modeling of the field samples with the exceptions of PCB54 (26-26) and PCB108 (2346-3), which are not present in the original Aroclor mixtures (40), but were measured in Lake Hartwell sediment samples. Thus, the resulting artificial data matrix had dimensions of 35 PCB congeners by 35 samples, which is comparable to the two data sets that were modeled (37 by 23 for the 1987 samples and 37 by 33 for the 1998 samples). Diagnostic Tools. Eigenvalues and cumulative variance are used for PCA. Their use for determining the quality of fit was discussed previously (38, 39). The coefficient of determination (COD), Exner function, and Q value, calculated from PMF, were the main tools used to evaluate the goodness of fit between the modeled data set and the actual data set, and to determine the number of significant factors. The number of factors was increased until a satisfactory fit between measured and reproduced data matrices was obtained. The COD gives congener specific information on the goodness of fit for each additional factor and equals 1.0 for a perfect fit (39). The congener based Exner function approaches the best fit at zero, and 0.1 is considered an excellent correlation (41). The weighted sum of squares of differences between calculated and measured data, Q, should approximately equal the number of degrees of freedom, df ) m × n - p × (n + m) for a good fit (37). The PMF model gives an overall approximation of the data set (X) using as few factors as possible. This approximation creates uncertainty as less significant factors, environmental variability, and analytical error are all apportioned into the model output. This error will be transferred into the dechlorination model through the source profiles (loadings) and impact the types and abundance of PCB congeners used in the dechlorination model analysis. The error is typically less than approximately 15% for successful fits, which is comparable to the experimental error and it is therefore quite acceptable. Anaerobic Dechlorination Model Considering Preferential Pathways. A new feature to consider preferential dechlorination pathways was implemented into the anaerobic dechlorination model developed by Imamoglu et al. (6, 7, 39). A flowchart outlining the dechlorination model is shown in Figure 2. The current model works on the assumption that a mass balance exists between congeners
FIGURE 2. Flowchart outlining the anaerobic dechlorination model. that lose chlorines and the ones that result from the reaction. Based on this assumption a least-squares method is used to alter an original Aroclor profile according to dechlorination processes H, M, H′, Q, P, N, and LP (42, 43) and combinations of these processes to resemble a source profile (i.e., loading) generated by PMF or FA. This method is described in detail below. A dechlorination process refers to a particular set of reactions that contain the same susceptible chlorines and chlorine homologue substrate range (43). For example, process M considers flanked (i.e., another chlorine is present in an adjacent position) and unflanked meta with a homologue substrate range of 2-4 chlorines (reactive chlorophenyl groups: 3, 23, 25, 34, 234, 236), whereas process H considers flanked para and doubly flanked meta chlorine of the 234chlorophenyl (reactive chlorophenyl groups: 34, 234, 245, 2345) with a homologue substrate range of 4-7 chlorines (43). Note that the underlined chlorines above, as well as in subsequent sections, are the ones that are removed. The objective function to be minimized for the anaerobic dechlorination model is m
S)
∑(yˆ - x ) j
2
(2)
j
j)1
where S is the sum of squares of differences between the given congener pattern (i.e., PMF loading) and the altered Aroclor profile determined from the model. Here, yˆj is the concentration of congener j (mol ‰) altered from the original Aroclor mixture (34), xj is the concentration of sample congener j (mol ‰)(i.e., value from PMF loading), and m is the number of marker congeners. The marker congeners are chosen to reduce the number of potential reactions and include only the most abundant and/or important congeners for dechlorination. Successful applications of the model to both laboratory and in situ anaerobic dechlorination data for the Fox River, WI, and the Astabula River, OH (6, 7, 39), have been documented. The model described above considers 100 different random reaction orders from which the average and standard deviation of the number of chlorines transferred per reaction are determined. A modified approach, which considers VOL. 40, NO. 1, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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of the complex environmental process, while providing some bias for experimentally determined reaction preferences.
Results and Discussion
FIGURE 3. Illustration of random numbers, uniformly distributed between 0 and 1, and mapped random numbers determining the sequence for dechlorination reactions. preferential pathways based on the literature (27), was considered for this study. The preferential order reflects the propensity for dechlorination to occur as follows: doubly flanked meta chlorine > doubly flanked para chlorine > singly flanked para chlorine > singly flanked meta chlorine > unflanked meta or para chlorine on di- or tri- substituted ring > isolated meta or para chlorine (27). Each category from the above chlorine reactivity sequence was assigned a preference value (λ) of 0.25, 0.40, 0.55, 0.70, 0.85, 1.0, respectively. This range of numbers provided sufficient bias for preferred reactions, but maintained some randomness. For λ ) 1 the reaction has no preference, and for λ approaching zero the reaction has high preference. The following equation was used to determine the preferential reaction order: 1/λ X1/λ 1 + X2 ) 1
(3)
where X1 is a random number, uniformly distributed between 0 and 1, and X2 is a mapped, i.e., calculated, random number. This equation is plotted in Figure 3 vs X1 and X2 for λ ) 1, 0.85, and 0.25. The initial reaction sequence is determined from X1 with the lowest random numbers representing the first reactions. The final reaction order is determined from X2 with the highest values of X2 representing the first reactions. Table 1 illustrates an example containing 23 of the 53 reactions involved in dechlorination process M. Each reaction was given an identification number (ID) and the corresponding λ value was determined as described above. Initial random numbers (X1) were generated for each reaction. The mapped random numbers (X2) were then determined using the corresponding λ value and random number X1 (Figure 3). The initial order based on random numbers and the λ values corresponding to each reaction are listed in Table 1. Reaction 12, PCB37 (34-4)fPCB15 (4-4), is the first reaction since its corresponding random number (X1) is the lowest at 0.002. In a similar manner reaction 12 is the first reaction in the final reaction sequence because its mapped random value (X2) is the highest (1.000). From Table 1 it is evident that reactions with low λ values, which have a high preference, shift to the top of the reaction sequence, i.e., reaction 14, PCB41 (234-2)fPCB17 (24-2), which removes a doubly flanked meta chlorine and is assigned a λ of 0.25. On the other hand, reactions with higher λ values, which have lower preference, shift toward the bottom of the sequence, i.e., reaction 21, PCB52 (25-25)fPCB18 (25-2), which removes an unflanked meta chlorine on a di-substituted ring and is assigned a λ of 0.85. This method maintains the randomness 112
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PMF Model Validation using Monte Carlo Generated Data Sets. The PMF model was validated with an artificial data set generated with a Monte Carlo model. Source profile results from a 35 by 35 data set generated using 40% Aroclor 1016, 40% Aroclor 1242, and 20% Aroclor 1254 as input into the Monte Carlo model are shown in the left panel of Figure 4. The right panel displays the literature source profiles (40) for Aroclors 1016, 1242, 1254, and a mixture of 80% 1016 and 20% 1254. It is apparent that the model reproduced each source profile well. Loading M1 is the Aroclor 1242 profile with some discrepancies for the higher chlorinated congeners. Loading M2 represents the higher chlorinated Aroclor 1254. Loading M3 has very low values for congeners greater than PCB74, thus it represents Aroclor 1016. Similar validations were carried out previously on additional artificial data sets with successful results (39). PCB Congener Profiles. Total PCB concentrations in Lake Hartwell sediments ranged from ∼0 to 58 mg/g. Average PCB concentrations for the congeners considered in the modeling analysis are shown in Figure 5. PCB4/10 (2-2/ 26-) with two ortho-substituted chlorines has the highest average concentration (∼16 nmol/g) for both the 1987 and 1998 data sets. The average congener profiles in Figure 5 are substantially different from the estimated PCB discharge of 80% Aroclor 1016 and 20% Aroclor 1254 (Figure 4) (1). In addition, the average congener profiles do not resemble any of the individual Aroclors. Both averages consist of a much higher amount of lower chlorinated congeners and a much lower amount of higher chlorinated congeners. On the basis of this observation substantial PCB dechlorination has occurred. In addition, PCB121/91/55 (246-35/236-24/2343) and PCB54 (26-26) are present, but are not a substantial part of the original Aroclor mixtures (Figure 4; 38). It should be noted that the final data sets for the 1987 and 1998 samples contained 5 and 12 samples, respectively, with total PCB concentrations under 25 nmol/g. Thus, the 1998 average is influenced by a higher number of low concentration samples, distorting a comparison of dechlorination between the two average PCB concentrations from 1987 and 1998. Instead, the mole % of PCBs from each data set can be compared for dechlorination analysis, which will be described with the anaerobic dechlorination model results. Diagnostic Tools. PMF was used to apportion PCB sources, i.e., original PCB profiles and dechlorinated profiles, and to determine source contributions. COD results indicated a substantial improvement from one-factor to two-factors, and a slight improvement from two-factors to three-factors for both data sets. The three-factor source profiles had one dechlorinated profile similar to loading L1 for the two-factor solution (Figure 6) and two profiles that were a split of the second loading from the two-factor solution and are not physically meaningful. Therefore, the two-factor solution was analyzed in this study. The two factors represent an original nearly unweathered Aroclor mix and a dechlorinated profile. These will only appear as two factors because the Aroclor mix comes from one source meaning that the congeners are subject to similar random environmental variations. By contrast, the Monte Carlo validation method (Figure 4) implied three separate sources, one for each Aroclor, that are subject to separate environmental variabilities. For the two-factor solution the 1987 and 1998 data sets had 6 and 5 COD values less than 0.5 and 9 and 16 data sets with COD values greater than 0.8, respectively. The Exner functions for the 1987 and 1998 data sets were 0.10 and 0.11, respectively. These COD and Exner
TABLE 1. Random Order Generated Considering a Bias toward Preferential Reaction Sequences following Williams (27) ID
reactiona
λb
X1c
X2 d
initiale order
λf
finalg order
λh
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
6 (2-3) f 1 (2-0) 16 (23-2) f 4 (2-2) 18 (25-2) f 4 (2-2) 20 (23-3) f 5 (23-0) 20 (23-3) f 6 (2-3) 22 (23-4) f 8 (2-4) 25 (24-3) f 7 (24-0) 26 (25-3) f 6 (2-3) 26 (25-3) f 9 (25-0) 31 (25-4) f 8 (2-4) 33 (34-2) f 8 (2-4) 37 (34-4) f 15 (4-4) 40 (23-23) f 16 (23-2) 41 (234-2) f 17 (24-2) 42 (23-24) f 17 (24-2) 44 (23-25) f 16 (23-2) 44 (23-25) f 18 (25-2) 45 (236-2) f 19 (26-2) 46 (23-26) f 19 (26-2) 49 (24-25) f 17 (24-2) 52 (25-25) f 18 (25-2) 53 (25-26) f 19 (26-2) 55 (234-3) f 25 (24-3)
1.0 0.7 0.85 1.0 0.7 0.7 1.0 0.85 1.0 0.85 0.7 0.7 0.7 0.25 0.7 0.85 0.7 0.7 0.7 0.85 0.85 0.85 0.25
0.877 0.626 0.212 0.916 0.134 0.968 0.620 0.641 0.014 0.790 0.900 0.002 0.516 0.624 0.265 0.399 0.978 0.304 0.567 0.216 0.135 0.979 0.214
0.123 0.605 0.861 0.084 0.960 0.115 0.380 0.466 0.986 0.300 0.253 1.000 0.708 0.960 0.892 0.703 0.089 0.869 0.663 0.858 0.919 0.044 0.999
12 9 5 21 3 23 20 15 18 16 13 19 7 14 2 8 10 1 11 4 6 17 22
0.7 1.0 0.7 0.85 0.85 0.25 0.85 0.7 0.7 0.85 0.7 0.7 1.0 0.25 0.7 0.85 0.85 1.0 0.7 1.0 0.7 0.7 0.85
12 23 9 5 14 21 15 18 3 20 13 16 19 2 8 7 10 11 1 6 17 4 22
0.7 0.25 1.0 0.7 0.25 0.85 0.7 0.7 0.85 0.85 0.7 0.85 0.7 0.7 0.85 1.0 0.85 0.7 1.0 0.7 0.7 1.0 0.85
a 23 of 53 reactions involved in process M. b λ values associated with each reaction numbered by the reaction ID. c Random numbers used to determine initial reaction sequence. d Mapped random value obtained from eq 3. e Random reaction sequence based on the random values associated with X1 (listed by reaction ID) top of the list is the first reaction (i.e., 12, 9, 5, etc.). f λ values associated with initial random reaction sequence. g Random reaction sequence based on the mapped random values associated with X2 (listed by reaction ID) top of the list is the first reaction (i.e., 12, 23, 9, etc.). h λ values associated with final bias random reaction sequence.
FIGURE 4. Results of Monte Carlo simulation (left column) and literature PCB source profiles (right column) (40). values represent a satisfactory reproduction of the measured data sets (39, 40). PMF Model PCB Source Profiles. The two-factor PMF solutions for the 1987 and 1998 data sets are displayed in Figure 6. Loading L1 for both data sets is a substantially dechlorinated profile of the original 80% Aroclor 1016 and 20% Aroclor 1254 mixture (Figure 2). PCB10/4 is the primary congener (∼25 mol %) for both dechlorinated profiles and is a moderate contributor in the original mixture (∼4 mol %). The majority of the higher chlorinated congeners present
in Aroclor 1254 (PCB66/95 to PCB180) have very small or no contributions in the dechlorinated loadings. The coeluting congeners PCB121/91/55 (246-35/236-24/234-3) have very small contributions in the original mixture and a 3-5 mol % contribution in the dechlorinated loadings. PCB14/19 (35-/26-2) also increased significantly (1% to 13%), while PCB15/17 (4-4/24-2) (6% to 5%), and PCB18 (25-2) (11% to 1%) decreased. The reason PCB15/17 and PCB18 are depleted compared to the original mixture is that they are both parent congeners for reactions involving PCB 4 (i.e., VOL. 40, NO. 1, 2006 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 5. Average PCB congener profiles in nmol/g for the 1987 data set and the 1998 data set for the samples considered for the modeling analysis. Error bars represent the standard deviation of the mean. Data from Pakdeesusuk (44). PCB17 (24-2)fPCB4 (2-2) and PCB18 (25-2)fPCB4 (22)). Loading L2 is the original PCB mixture with indications of slight dechlorination. Especially abnormal are the high concentrations of PCB121/91/55 (246-35/236-24/234-3), PCB43/49, and PCB47/75/48, and the low concentrations of PCB10/4, PCB18, and PCB33/20/53. The high concentrations for PCB43/49 (235-2/24-25) and PCB47/75/48 (24-24/2464/245-2) can be explained in part by dechlorination of PCB99 (245-24) to both PCB49 (24-25) and PCB48 (245-2) (process LP). The high concentration of PCB121/91/55 is primarily an artifact of the PMF model, since the COD values for this congener group are low, 0.46 and 0.31, for the 1987 and 1998 data sets, respectively. However, PCB121/91/55 can be produced from the reaction PCB132 (234-236)fPCB91 (23624). The low concentrations of PCB10/4 and PCB18 may be related to the higher volatility of the lower chlorinated congeners and subsequent losses during transport from the source to the sediments. For sediments from the St. Lawrence River ∼36% of the total PCBs lost was PCB10/4 in laboratory experiments (31). A study of Esthwaite Lake in the U.K. found PCB18 and PCB28 had the highest concentrations in sediment traps (16), meaning these PCBs are quite mobile. Assuming less dechlorination is occurring in loading L2, PCB10/4 and PCB18 would not be replenished. Given the fact that the exact amount of the original mixture is estimated and processes such as volatilization and solubilization are likely to have occurred during transport, loading L2 for both profiles is a fair approximation of a slightly dechlorinated original mixture. PMF Model PCB Source Contributions. PMF source contributions and total PCBs are displayed in Figure 7 for the 1998 data set. The x-axis shows the four different sampling locations and sediment layers, ordered by increasing depth of the sample. Score S1 represents the dechlorinated profile loading L1 and score S2 represents the original mixture loading L2. The source contributions for the 1987 data set follow similar trends and are not shown. The first observation is that score S1 (dechlorinated profile) follows the total PCBs profile closely. This indicates that the first sediments to dechlorinate PCBs have the highest initial PCB concentrations (>20 ppm), consistent with previous laboratory findings (33, 45, 46). Abramowicz et al. (45) observed the most rapid specific dechlorination activity in laboratory experiments was at the highest concentrations (>750 ppm), while a nearly linear relationship was noted between 0 and 250 ppm. The next observation is that the high contributions from score 114
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S2 are where the total PCBs are low, for example, samples G30-1 through G30-7 and G33-9 through G33-10. This indicates the original mixture is still present in the sediments at low PCB concentrations. Another important result of the PMF analysis is the percent contributions from the 1987 and 1998 data sets. The 1987 and 1998 percent contributions are 73% and 87% from loading L1 and 27% and 13% from loading L2 (Figure 6), respectively. The increase in percent contribution for the dechlorinated profile (loading L1) from 1987 to 1998 coupled with the similarity of the two source profiles L1 for 1987 and 1998 indicates that dechlorination has reached a plateau and cannot continue any further in dechlorinated sediments. However, sediments that have not been previously dechlorinated, perhaps lower PCB concentration sediments, are being dechlorinated to a plateau. This finding is consistent with results of laboratory experiments (28-30, 47). Note that little dechlorination activity is indicated when the total PCB concentration is
