Environ. Sci. Technol. 2005, 39, 7020-7026
Concentrations and Fluxes of Salmon-Derived Polychlorinated Biphenyls (PCBs) in Lake Sediments EVA M. KRU ¨ M M E L , * ,† IRENE GREGORY-EAVES,§ ROBIE W. MACDONALD,‡ LYNDA E. KIMPE,† MARC J. DEMERS,† JOHN P. SMOL,| BRUCE FINNEY,⊥ AND JULES M. BLAIS† Department of Biology, University of Ottawa, 150 Louis Pasteur, Ottawa, Ontario K1N 6N5, Canada, Department of Fisheries and Oceans, Institute of Ocean Sciences, Sidney, British Columbia V8L 4B2, Canada, Department of Biology, McGill University, Montreal, Quebec H3A 1B1, Canada, Paleoecological Environmental Assessment and Research Laboratory, Department of Biology, Queen’s University, Kingston, Ontario K7L 3N6, Canada, and Institute of Marine Science, University of Alaska Fairbanks, Fairbanks, Alaska 99775
Fourteen sediment cores were collected from 10 lakes spanning a large gradient of sockeye salmon returns (040 000 spawners km-2) in Alaska and British Columbia in 1995-98 and 2002/03. The cores were analyzed for 210Pb to determine sedimentation rates and focusing factors. Polychlorinated biphenyl (PCBs) concentrations in the surface sediments (0-2 cm) were highly correlated with the number of sockeye salmon returns to each nursery lake. For 2002/03, the correlation between PCB concentration and number of salmon spawners was best with no correction factors applied, but decreased considerably when corrected for sedimentation rates, and was improved again by correcting for sediment focusing. Although ΣPCB concentrations were similar in 1995-98 and 2002/03, the congener patterns varied. Because salmon are the dominant source of PCBs for most of these lakes, variation in sediment congener pattern likely derives from variation in congener patterns carried by the salmon. Overall, total PCB input by salmon has remained relatively constant since 1995. Unlike temperate Great Lakes contaminant studies, the North American west-coast lakes dominated by salmon bio-transport showed no sign of recent decrease in PCBs.
Introduction Although PCBs are efficiently transported to remote regions (1-3), their concentrations are much lower when compared to source regions such as the Great Lakes (4). Nevertheless, lower concentrations in physical media of remote regions can still lead to remarkably high concentrations in top * Corresponding author phone: (613)562-5800 ext. 6668; e-mail:
[email protected]. † University of Ottawa. ‡ Institute of Ocean Sciences. § McGill University. | Queen’s University. ⊥ University of Alaska Fairbanks. 7020
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predators due to factors such as slow growth rates, high lipid concentrations, and biomagnification (5). Currently, PCB concentrations 60 40 530 1251
a
Information was taken from refs 18 and 28-32.
b
There were no depth data available for this lake.
from VWR (Mississauga, ON). Reference sediment (National Institute of Standards and Technology 1944 River Sediment) and method blanks were routinely analyzed with every sample batch. Method detection limits for ΣPCBs were 6.1 ng/g dry weight, based on a typical sample weight of 3.5 g. Blanks contained 2.8 ng/g dry weight based on a typical sample weight of 3.5 g. Recoveries based on all surface sediments were 64% ( 20% SD for PCB 30 and 80% ( 27% SD for PCB 204. All samples were blank-corrected. PCB Extraction: Fish. Approximately 8 g of salmon white muscle with skin was extracted in the ASE. Lipids were
removed by automated gel permeation chromatography (GPC Autoprep 1002A, Analytical Bio-Chemistry Laboratories, Inc.). Cleanup and fractionation was accomplished using a method similar to that described for the sediments. All extracts were evaporated to 500 µL in isooctane, and Mirex was added as an internal standard. Method blanks and a certified reference material (National Institute of Standards and Technology 2978 Mussel Tissue) were analyzed with each batch of samples. Method blanks contained 171.8 pg ΣPCB/g based on a typical sample weight of 8 g. All samples were blank-corrected. Recoveries based on all adult muscle tissue VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Total 210Pb profiles for all cores dated in (A) 2002/03 and (B) 1995-98. All 210Pb profiles were fitted using the equation y ) p2*e-xp1 + p3*(1 - e-xp1). p3 equaled supported 210Pb concentrations where background was reached. Background 210Pb values were not reached and had to be inferred from 226Ra concentrations for the following cores: (A) Frazer, Karluk, Red, and Upper Olga. The sedimentation rate was calculated by multiplying p1 with λ (λ ) 210Pb constant, 0.0311 yr-1); the 210Pb inventory was obtained by multiplying p1 with p2. Values for sedimentation rates and 210Pb inventories can be found in Table 2. were 63.7% ( 7.8% SD for PCB 30 and 74.1% ( 7.7% SD for PCB 204. To determine lipid content, 3 g of muscle tissue with skin was extracted using the ASE. The extract was evaporated to complete dryness, and the lipids were calculated gravimetrically. PCBs were analyzed on a Hewlett-Packard 6890 series II gas chromatograph with a 63Ni micro electron-capture detector, using splitless injection with an inlet temperature of 250 °C. One microliter of extract was separated on a 30 m × 0.25 mm (0.25 µm film) DB-5MS column (J&W Scientific) using helium carrier gas at 3.1 mL/min. Sediment Dating. Sedimentary profiles of 210Pb, 226Ra, and 137Cs were produced using a digital high purity germanium well detector (DSPec, Ortec), following methods described in ref 15. Sedimentation rates were calculated from total 210Pb activities and cumulative dry mass by fitting an exponential curve through the data (Figure 2) using the following equation:
Ctot(t) ) Cunse-λt + Csup(1 - e-λt)
(1)
where Ctot is the total 210Pb activity, λ is the radioactive decay constant for 210Pb, Csup is the 210Pb activity supported by the parent radioisotope 226Ra, and Cuns is the unsupported 210Pb activity (slightly modified from ref 15). The activity of unsupported 210Pb, which is used for the calculation of sediment dates, sedimentation rates, and focusing factors, is easily obtained by subtracting supported activity from the total activity. Sediment focusing factors were estimated by dividing 210Pb inventories of a given sediment core by the expected atmospheric 210Pb flux (1042 Bq cm-2 yr-1, value based on ref 16).
Results and Discussion 210Pb
Inventories and Sedimentation Rates. Only 4 of the 12 dated cores were of insufficient length to reach background 210Pb concentrations; for these, we inferred the supported 210Pb from the 226Ra concentrations by taking the average of the 226Ra found in the core (15) (Figure 2A,B, Table 2). This procedure was validated from two cores where background concentrations of 210Pb were reached and 226Ra data were available. In these two cases, the inferred 210Pb background 7022
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values corresponded precisely to the determined 226Ra concentrations. Decay curves could be fitted with an R2 value >0.9 with the exception of Frazer (0.83) and Meziadin cores (0.74) collected in 2002/03. The high correlations, indicating a uniform accumulation of sediments for all cores, allow for good estimates of 210Pb inventories, sedimentation rates, and focusing factors (Table 2). The observed wide variation in 210Pb inventories (1800-11 500 Bq m-2) may be attributed to variations within and between lakes due to sediment focusing (17). Sedimentation rates are within the expected range of subarctic lakes ranging from 0.008 g cm-2 yr-1 for Iliamna to 0.27 g cm-2 yr-1 for Meziadin. Most lakes had sedimentation rates within the range of 0.01-0.04 g cm-2 yr-1. Iliamna is an Alaskan mainland lake with a large surface area (2662 km2) and a very large watershed (16 888 km2), which is not glacierfed and receives low organic input from the scarcely vegetated catchment. The high sedimentation rate of Meziadin Lake is most likely caused by glacial flour input. Gilbert and Butler (18) reported sedimentation rates between 0.04 and 7.3 g cm-2 yr-1 for Meziadin Lake with higher values near the glacial inflow. For Spiridon Lake, only a very short, angled core could be obtained, which provided a poor basis for 210Pb dating. For Upper Olga (1998), only surface sediments were available. The sedimentation rate for Spiridon Lake (Table 2) was determined using an ash layer at a depth of 4-5 cm, which was ∼1 cm thick and corresponds to the eruption of the volcano Novarupta-Katmai in 1912 (19). The ash layer was validated by microscopic analysis, which confirmed the presence of volcanic glass. PCB Concentrations in Surface Sediments for All Years Combined. ΣPCBs sediment concentrations across all lakes ranged from 0.6 to 20.8 ng g-1 dry weight (Table 3). ΣPCB concentration is correlated with salmon spawner density, as are many individual congeners (Figure 3). The congeners presented in Figure 3 include congeners particularly elevated in salmon and provide a broad overview of the ∼100 congeners analyzed. For PCB 6, there seems to be a slight increase with increasing salmon spawners, but in several sediment samples collected from lakes with high spawning density PCB 6 is not detectable. Overall, the relationship between PCB concentration and salmon density shows the highest correlation for PCB 101, followed by coeluting
TABLE 2. Escapement Numbers (i.e., Number of Salmon Returning to the Nursery Lake), 210Pb Inventory, 226Ra Average Values Found in the Cores, Sedimentation Rates, and Focusing Factors for the Lakes Sampled in 2002/03 and 1995-98 lakes
210Pb
226Ra
inventory (Bq m-2)
escapement (spawners km-2)a
Spiridon Kinaskin Meziadin Frazer Upper Olga Karluk Red
0 0 5966 11 662 17 897 19 207 34 731
Cored 2002/03 NA 1943 ( 238 11 528 ( 3088 4759 ( 799 2900 ( 375 5113 ( 363 6321 ( 139
Becharof Iliamna Upper Ugashik Frazer Upper Olga Karluk Red
1827 2147 6442 11 864 21 274 21 366 38 799
Cored 1995-98 3607 ( 287 1792 ( 100 9245 ( 100 6396 ( 311 NA 2519 ( 117 4552 ( 219
a Escapements are based on 10-year averages. eruption in 1912. NA, data were not available.
b
average (Bq kg-1)
sedimentation rate (g cm-2 yr-1)
focusing factor
NA 22.3 ( 1.7 40.8 ( 2.0 22.4 ( 4.6 13.6 ( 6.7 31.9 ( 6.7 30.2 ( 11
0.03b 0.0252 ( 0.004 0.2675 ( 0.0806 0.0173 ( 0.0042 0.0103 ( 0.0020 0.0196 ( 0.0024 0.0236 ( 0.0012
NA 1.9 11.1 4.6 2.8 4.9 6.1
0.0379 ( 0.0040 0.0079 ( 0.0007 0.0697 ( 0.0116 0.0244 ( 0.0029 NA 0.0112 ( 0.0010 0.0159 ( 0.0011
3.5 1.7 8.9 6.1 NA 2.4 4.4
NA NA NA NA NA NA NA
Sedimentation rate is based on an ash layer corresponding to the Novarupta-Katmai volcanic
TABLE 3. ΣPCB Concentrations (ng g-1 Dry Weight) and ΣPCB Focus-Corrected Accumulation Data (ng m-2 yr-1) for the Lakes Examined in This Study, as Well as Comparison Data from Other Studies
lakes
ΣPCB concentration (ng g-1 dry weight)
ΣPCB foc-cor. accumulation (ng m-2 yr-1)
Spiridon Kinaskin Meziadin Frazer Upper Olga Karluk Red
Cored 02/03 0.6 0.8 0.8 8.4 9.4 14.3 20.8
NAa 125.7 194.6 309.5 337.1 585.6 816.9
Becharof Iliamna Upper Ugashik Frazer Upper Olga Karluk Red
Cored 95-98 2.1 6.3 5.4 14.6 15.1 14.3 17.5
228.4 287.9 426.2 579.4 NAa 662.1 635.6
Muir et al. (4) Gubala et al. (27) Rawn et al. (31)
Other Studies 2.42-38.5 0.2-30.7 2.19-33.5
110-4250 6.3-241.2 1350-11 500
a 210Pb
data were not available to calculate focus-corrected accumu-
lation.
congeners 138-163 and 153-132-105. We are therefore confident that the key factor explaining the relationships between PCB congener concentrations in sediments and spawner numbers in our study is the congener abundance in salmon (the source), which will be discussed in more detail below. PCB concentrations in the sediments vary not only between congeners and lakes but also among different years. The highest congener concentrations were found for PCBs 101, 153-132-105, and 138-163 in Red Lake sediments from 2002 (Figure 3; 40 000 spawners), with concentrations up to 1.7 ng g-1 dry weight (8.2% of the sum PCBs for this lake). The concentrations in Red Lake sediments cored in 1998 are, for the most part, less than one-half of those from 2002. Although this concentration discrepancy is true for some of the congeners presented in Figure 3, it is not consistently
FIGURE 3. PCB concentrations of surface sediments from the years 1995-98 and 2002/03 combined, plotted against the number of spawners returning to the lakes. PCBs (pg g-1 dw) are log[concentration + 1] transformed. Surface sediments included 0-2 cm representing 5.3 ( 3.5 years. Therefore, the escapement data were averaged over a 10-year period. PCB 6 was fitted with a straight line; for the other congeners and ΣPCBs, the equation y ) p3 + p2*(1 - e(-x/p1)) was used. true for all of the remaining congeners examined. Thus, ΣPCB concentrations in Red Lake are similar for both years and reach 17.5 ng g-1dry weight in 1998 and 20.8 in 2002 (Table 3). Lake sediment contaminant concentrations reported in other northern climates exhibit ΣPCB concentrations similar to those found here (Table 3). Muir et al. (3) obtained concentrations (sum of 90 congeners) between 2.4 and 38.5 ng g-1dry weight from lake sediments spanning the Canadian Arctic and Subarctic. Rawn et al. (20) found similar concentrations (sum of 104 PCB congeners) of 2.2-33.5 ng g-1 dry weight in Yukon lake sediments. Gubala et al. (21) compared two Alaskan lakes and reported ∑PCB concentrations (61 congeners) of 0.2 and 30.7 ng g-1dry weight. VOL. 39, NO. 18, 2005 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 4. Surface sediment PCB loadings for the years 2002/03. Plotted are (A) PCB concentrations, (B) PCB deposition, and (C) focuscorrected PCB deposition against density of spawners. The PCB data are not transformed, and all plots are fitted with a straight line. PCB Concentration, Deposition, and Focus-Corrected Deposition in Cores from 2002/03. Although the delivery of PCBs from ocean to lakes by salmon is clear and can be fairly easily quantified (escapement (kg yr-1) × concentration (ng kg-1)), the mechanisms and efficiencies of transfer to food webs and sediments within lakes, which are more difficult to quantify, remain to be done. Feeding on salmon eggs and on salmon carcasses in the watershed promotes direct entry of PCBs to aquatic food webs (8), but a portion of the contaminant load will be directly released into the lake, and partition into the organic matter of particles. The particles sink to the bottom of the lake, will be partly resuspended, and finally focused into depositional zones of the lake. To consider the potential effect of sedimentation rate and focusing on PCBs in sediments, we limited the database to the 2002/03 cores where PCB concentrations are normally distributed and thus may be plotted without transformation. The 2002/03 data show a linear positive relationship between PCB concentrations and numbers of spawners for all of the congeners (Figure 4A). PCB 6 shows a very weak correlation between concentration in sediment and escapement, which is expected because it has the lowest abundance in salmon. The remaining congeners in Figure 4A, however, display strong relationships between concentration and escapement. The strength of these correlations appears to be robust considering that no allowance has been made for diluting or concentrating effects due to varying sedimentation rates. 7024
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Deposition flux of PCBs to surface sediments, estimated from sedimentation rate, does not correlate as strongly with the number of spawners (Figure 4B) as does the simple correlation with surface PCB concentration (Figure 4A). A possible explanation for the reduced correlation coefficient is that the total sedimentation, which includes organic and inorganic components, does not account for dilution or concentration of contaminants within the sedimenting material. For example, a lake with higher inputs of inorganic materials with low affinity for organochlorine compounds such as PCBs would exhibit a lower than expected rate of contaminant deposition. Wong et al. (17) compared contaminants in sediment cores from Lake Ontario and found that the variability in contaminant accumulation rates is greatly reduced when sedimentation rates are corrected for focusing. Focus correction for our data, applied by dividing PCB deposition by the focusing factor, results in noticeable improvement of the relationship between PCB deposition and escapement for ΣPCBs and most of the congeners, with the exception of PCB 6 (Figure 4C). The improvement in correlation evident between focus-normalized PCB deposition and escapement implies that PCB inputs from salmon are thoroughly mixed and distributed within each lake. A localized input of salmon-derived PCBs could lead to a much higher loading of contaminants in a core taken close to the source, or a lower loading when the core is taken far away from the localized input. In cases where PCB variability is
FIGURE 5. Slopes of the relationship of the PCB concentration in surface sediments from 2002/03 versus salmon spawners are plotted against the relative abundance of PCBs in sockeye salmon from 2002/03. The data are log-transformed and fitted with a straight line. produced by processes other than sediment redistribution, focus correction would not improve correlations (17). The PCB fluxes observed in lakes from this study are in the low range as compared to other lakes (Table 3). Rawn et al. (20) reported values of ΣPCBs between 1350 and 11 500 ng m-2 yr-1 for lakes in the Yukon. Gubala et al. (21) calculated fluxes of 6.3 and 241 ng m-2 yr-1 for their lakes in Alaska. Our highest value, ∼820 ng m-2 yr-1 for Red Lake, places our lakes closer within the range observed by Muir et al. (3), who found ΣPCB fluxes of 110-4250 ng m-2 yr-1 for lakes in the Northwest Territories. The Importance of PCB Congener Abundance in Salmon. As indicated earlier, the factor influencing the relationship between contaminant loading of the sediments and number of spawners will be mostly determined by the abundance of a particular congener in salmon. Studies looking at PCBs in fish often find elevated concentrations of congeners such as PCB 153, 101, 138, and 180 (22, 23). Because most of these congeners also show a tight relationship of sediment PCB load to spawner numbers in this study, relative abundances for PCB congeners in the sockeye salmon from all 2002/03 lakes were calculated. The data include 5 sockeye salmon each from Frazer, Upper Olga, Karluk, and Red Lake, and 3 sockeye salmon from Meziadin Lake. We indeed found highest abundances for PCB 153-132-105 (10.2%), followed by PCB 101 (9.8%) and PCB 138-163 (5.6%), whereas relative abundances for the other congeners shown were 1.5% for PCB 18 and 0.6% for PCB 6. The plotted congeners therefore suggest that the strength of the relationship between contaminant loading in the sediments and numbers of spawners is greatly influenced by the relative quantity of each particular congener transported in the salmon. All plots show consistently good fits for PCBs 101, 153-132-105, and 138-163. It is not surprising that the relationship for 153-132-105 is somewhat less strong than that for PCB 101, because the method did not discriminate congeners 132 and 105 from PCB 153. Only congener 153 is particularly known to accumulate in fish, and varying proportional loadings of congeners 132-105 will reduce the correlation. Another indicator for the strength of a linear relationship y ) ax + b between two variables is the slope, a, whereby a steeper slope indicates a higher significance of the dependence of y on x (24). Because the relationship between PCB loading in the sediments and escapement appears to depend on the relative abundance of the particular PCB congeners in the transporting salmon, more abundant congeners in salmon should show higher values of a in the regression equations. We therefore calculated slopes of the obtained regressions between PCB concentration and escapement for all of the congeners, and we plotted them against the relative abundance of the congeners in salmon (Figure 5). Only slopes from regressions with an R2 > 0.2
were included, and the data were log transformed. As expected, an increase of slope values is observed for congeners that are more abundant in sockeye salmon. It is important to note that this correlation was found only for sockeye salmon escapement and sediment data from the same year. Slopes derived for surface sediments from 1995 to 1998 yielded no relationship with escapement data for 2002/03. We mentioned earlier that PCB concentrations in the sediments varied between the different years for some congeners, but that similar ∑PCB concentrations have been delivered. One explanation for a shift in congener patterns in sediments could be that congener patterns in salmon vary at a scale similar to salmon oceanic lifetimes (∼3 years). Such a shift in PCB patterns would require a similar shift in the food foraged by salmon at sea, which itself would depend on spatial or temporal variation in PCB patterns in the migration range proposed by Beamish (33) (Figure 1). The SHEBA project collected PCB data that clearly show PCB pattern shifts between Arctic interior water-masses and water-masses originating in the Pacific Ocean or on the Arctic’s shelves (25). Fish data might provide further insight into variations in fish foraging patterns and PCB fingerprints. Unfortunately, there are no historical data of PCB concentrations in Alaskan salmon available, which would enable us to test whether temporal trends in salmon PCB loadings exist, and whether those trends can be recovered in sediments of salmon receiving lakes. However, changes of PCB congener patterns with depth in sediments have been reported previously (17, 26, 27) and could plausibly be attributed to a shift in PCB input patterns. Hence, there is circumstantial evidence that PCB congener patterns vary in the ocean leading to changes in the PCB pattern carried by salmon spawners from one year to the next and that these changes will be reflected in variation of congener patterns in the sediments. Further analysis will be needed to test this hypothesis.
Acknowledgments We thank Dave Paton (DFO) for his assistance with sediment sampling and Steven Cox-Rogers and crew (DFO) for the sampling of salmon. We also thank Dominique McMahon for her assistance with laboratory analyses. We are deeply indebted to Peter Appleby (University of Liverpool, UK) for efficiency calibration of the DSPec, allocation of his dating program, and consistent support. We are very grateful to Michael Scheer for his mathematical advice and programming work and Patricia Kimber (Tangodesign) for her map design. This work was supported by funding from the Environmental Sciences Strategic Research Fund (ESSRF) of the Department of Fisheries and Oceans, the ELJB Foundation, fellowships to E.M.K. and I.G.-E., and a research grant to J.M.B. from the Natural Sciences and Engineering Research Council of Canada. We also thank two anonymous reviewers whose comments improved the manuscript.
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Received for review April 6, 2005. Revised manuscript received June 16, 2005. Accepted July 7, 2005. ES050657Q
