Environ. Sci. Technol. 1995, 29,2893-2897
Great Lakes Herring Gull Egg PCB Concentrations Indicate Approximate SteadpState CRAIG A. STOW* Center for Limnology, University of Wisconsin, Madison, Wisconsin 53706
PCB concentrations in Great Lakes herring gull eggs exhibited decreases following the imposition of regulations banning PCB manufacture. However, gull egg concentrations in Lakes Superior, Michigan, Huron, and Ontario now appear to have stabilized or slightly increased. Concentrations in Lake Erie gull eggs still appear t o be decreasing, though the variability in these data may limit the ability to differentiate trend changes until more data are available. This pattern is consistent with previously reported findings of stabilizing PCB concentrations in Great Lakes fishes and suggests that future improvements will be slow and difficult to discern.
Introduction The proposed Great Lakes Water Quality Guidance (1)is prefaced with the argument that Great Lakes contaminant concentrations, PCBs in particular, are no longer rapidly declining as they were following the imposition of regulations in the 1970s ( 2 , 3 ) . Because this contention is used to justify increased industryregulation,the notion that PCBs may have stabilized has met with opposition (4). Empirical support regarding the issue is mixed, though some of the published data extend only into the 1980s. Lake Ontario lake trout (Salvelinus namaycush) PCBs generally decreased through 1988,and DDE concentrations remained approximately constant following an initial decline (5). PCBs have displayed no apparent trend in Lake Ontario smelt (Osmerusmordax) and slimy sculpin (Cottus cognatus) through 1988,but sculpin DDE concentrations have decreased (6'). Suns et al. (7)found PCB and DDT levels in spottail shiners (Notropis hudsonius) to be declining in some Great Lakes locations and stabilizing in others. Jeremiasonet al. (8)reported continuing decreases in Lake Superiorwater column PCB concentrations through 1992. Stow et al. (9) examined seven species of Lake Michigan fishes and found PCB concentrations in five species to have stabilized while two species appeared to exhibit slight increases since the mid-1980s. The absence of continuing PCB declines in salmonid sportfishes is of particular concern from a human health perspective because concentrations have stabilized near the U.S. Food * Telephone: 608-263-3146;e-mail address:
[email protected]; faX
608-265-2340.
0013-936~95/0929-2893509.00/0
Q 1995 American Chemical Society
and Drug Administration 2 mglkg action level and are far from the International Joint Commission target levels of 0.1 mg/kg (10). To further explore whether contaminant levels in the Great Lakes Basin continue to decline, I examined PCB concentration trends in herring gull eggs from each of the five lakes. I fit the data from each lake to three alternative models: an exponential decay model
an exponential decay model with a non-zero asymptote
a double exponential decay model
PCB, = ~
~
e
+
-~~ ~ e 4- ~ ~ 5 '
where PCBt is the PCB concentration at time t, and the Cs and k's are unknown parameters determined from the model fit. In the exponential decay model, C1 is a somewhat arbitrary initial concentration and kl is a first-order rate constant. This model describes dynamics in which one effective contaminant pool is continuously decreasing, at a decreasingrate, toward azero asymptote. The exponential decay model has been used by most previous investigators to characterizepostregulation contaminant dynamics and, early on, appeared to describe many trends fairly well. The non-zero asymptote model describes contaminant decline in a system with two effective pools, one that is decreasing and one that is essentially stable. C3 is the asymptotic concentration maintained by the stable contaminant reservoir. This model would be expected to provide a good fit to the data if the initial PCB decline followingregulation was followed by a period of essentially unchanging concentrations. In the double exponential model, two effective contaminant pools decrease at different rates to produce the observed decline. The initial decline, characterized by C4 and k4, is relatively rapid, and the secondary decline, characterized by C5and k5, is relatively slow. The double exponential model would fit the data well if PCB concentrations were still decreasing, but the underlying mechanisms were different from those that controlled decreases immediately following regulation.
Methods The data were obtained from reports published by the Canadian Wildlife Service, and a detailed description of collection and analytical methods can be found therein (11, 12). An array of organic contaminants and metals in several species of Great Lakes fish-eating bird eggs have been collected since 1970 and provide one of the few longterm basin-wide data sets available. PCB data are expressed as a 1:l mixture of Aroclor 1254:1260.
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I fit data from 1978 to 1992 to the three alternative models, using a least square criterion, to determine which model best tracks concentrations following the final implementation of PCB regulations. Because the data were all reported as sample means or were analyzed as multiegg composite samples, I weighted each observation by the number of eggs in the mean or composite (9).The data were fit under a log transformation of both sides of each equation to stabilize the conditional variance. I set time equal to zero in 1978 to make exponentiation possible. The criterion for selecting the best fit model was the extra sum of squares F test. This test provides a basis for comparing the fit of “nested” models. Models are nested if one model is a restrictedform of another. In this example, the exponential decay model is the same as the non-zero asymptote model with C, restricted to zero, and the nonzero asymptotemodel is the same as the double exponential modelwith ksrestricted to zero. For nonlinear models, the extra sum of squares test is better than using a t test to differentiate parameters from zero using the parameter standard error (13). For linear models, the extra sum of squares F test and standard error t test are equivalent. The F statistic is calculated as
ess, - ess,/df, - df,
mse,
-
Fdfr-dfuidfu
where ess, and ess, are the restricted and unrestricted error sums of squares, respectively, df, and df, are the restricted and unrestricted error degrees of freedom, respectively, and mse, is the mean squared error of the unrestricted model fit. This test is extremely general and was also used as a basis for separating or pooling sites within a lake. If plotted data from different sites appeared visually distinct, I fit the sites simultaneously and separately and compared model fits using the extra sum of squares test.
Results The data were all fit under a log transformation of both sides of the equation, and therefore the models, in the natural metric, are best interpreted as median trend lines. Plots of the best fit models, with uncertainty intervals of 2 standard errors on either side of the median, indicate that PCB trends in Great Lakes herring gull eggs are well determined (Figure1). Because the observations have been weighted by the number of eggs in each sample, R2values for the best fit models, ranging from 0.48 in Lake Erie to 0.78 in Lake Superior, are indicative of the signal to noise ratio of individual eggs about the overall trend. The best fit model for four of the five lakes indicates that PCB concentrations in herring gull eggs are no longer perceptibly decreasing (Figure 1, Tables 1 and 2). The exception is Lake Erie where the exponential decay model provides the best fit to the data. In addition to being the best fit model for Lake Erie, by the extra sum of squares criterion, the exponential decay model is the only model with an optimum in a realistic region of the parameter space. For example, -28.29, the asymptoticconcentration in the non-zero asymptote model (Table 11,has no physical interpretation. Optima with physically impossible values are substantially due to the high covariance among model parameters and relatively small data sets. In Lake Huron, the Channel Shelter Island site appears visually different from the bulk of the data (Figure IC),and Ftests for each model fit confirmthat this site has undergone 2894
ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29, NO. l l , 1995
a trend statistically discernible from the other Huron sites. Therefore, I treated this site separately from the rest of the Huron data. For Lakes Michiganand Ontario,the non-zero asymptote model fits the data best with estimated rate constants of -0.33 and -0.23 yr-l,respectively,and respectiveestimated asymptotes of 22.51 and 14.88 mglkg. The non-zero asymptote model also fits the Lake Huron Channel Shelter Island site best with a -0.37 yr-l estimated rate constant and a 42.32 mglkg asymptote. The best fit model for Lake Superior is the double exponential model with an initial estimated rate constant of -0.17 yr-l and a secondary constant of 0.24 y r l , suggesting a recent PCB increase. Similarly for Lake Huron, the best fit double exponential model has a positive secondary rate constant of 0.49 yr-l.
Discussion While the models fit the data reasonably well, one should realize that the plotted data understate visually the true amount of noise present, because most points on the plots are summary statistics for several (typically10)eggs. Prior to 1985,analyseswere conducted on individualeggs rather than on composite samples. Reported sample standard deviations from that period illustrate the relatively high uncertainty in each plotted datum (11). For example, the 1982data in Lakes Superiorand Michigan appear somewhat above the trend lines and might be interpreted as a consistent anomaly in the overall pattern. Calculated standard errors, however indicate reasonable uncertainty intervals (k2 standard errors) of24.2-55.2 for the high 1982 Superior datum (Figure la) and 52.11-81.67 for the high Michigan datum (Figure lb). Contaminant data are often highly variable (141, and failure to accommodate this variability by appropriate weighting techniques can lead to overly certain or erroneous conclusions. The predominant message from t h i s analysis is that PCB concentration declines that occurred following the implementation of regulations restricting the manufacture and use of PCBs have slowed through most of the Great Lakes region. In Lakes Superiorand Huron,recent slight increases appear to have occurred, as indicated by positive rate constants in the best fit double exponentialmodels. I make this assessment with caution however because model parameters are highly correlated and impossible to determine with precision individually. The apparent increases are plausibly the result of sampling error or random variation and show up because they occur chronologically in the last few samples in systems where little year to year change is occurring. I do not interpret these apparent increases to be indicative of increasing trends suggesting sustained upward trajectories. I do interpret the increases to mean that PCBs are either no longerdecliningor declining so slowly that other signals or noise are visible. A continued PCB decrease in Lake Erie gull eggs may be related to Erie’s short hydraulic retention time, approximately 2.6 yr. This is the shortest retention time of all the lakes,and a more rapid flushing by less contaminated water may be sustaining the net PCB decline. Additionally, 2.6 yr is short relative to the lifespan of many fishes, and this may cause the pool of contaminants in the gull prey population to turn over more continuously. Alternatively, the exponential decay model may fit Lake Erie best because the relatively low signal to noise ratio (R2 = 0.48) and small sample size (n = 33) do not provide enough information to make the alternative models sta-
78
80
82
84
86
88
90
92
70
80
82
84
86
00
90
92
75
50 25
1251
1
f
All 5 Lakes
m
0
I I
I
t
0 70
0 80
82
84
86
08
90
92
-
. 0 78
0
80
82
84
86
88
90
92
Year FIGURE 1. PCB concentration data with best fit model and an uncertainty interval of k2 standard errors (a-e). From 1978 to 1985, each datum is a sample mean, usually consisting of 10 eggs. From 1986 to 1992. each datum is a composite sample, usually consisting of 10 eggs. The sampling locations and numbers of samples, (given in parentheses)are denoted as follows: (a) Superior: Agawa Rock (15) (+); Granite Island (14) (OkChene Island (2) (0);Gull Island (2) (*I; Knife Island (2) (heart); Leadman Islands (2) (4); Lake Linden (2) (0);Huron Island, Little Traverse Island, Marathon, Mammainse Harbor, Mutton Island, Papoose Island, and west of Almos Shoal (1 each) (A). (b) Michigan: Big Sister Island (14) (+); Gull Island (13) (0); Gravel Island (2) (0); Bellows Island, Fish Island, Hat Island, Spider Island, and Trout Island (1 each) (A]. (c) Huron: Channel Shelter Island (13) (O), best fit model is depicted separately; Chantry Island (15) (+I; Double Island (15) (0);Pumpkin Point (3) (heart); Manitoba Reef (2) (0);Black River, Castle Rock, Little Charity Island, Little Saddlebag Island, Nottawasaga Island, Snake Island, St. Martin Shoal, Turtle Rock, and West Mary Island (1 each) (A). (d) Erie: Middle Island (16) (+);Port Colborne (15) (0);Middle Sister Island and Sandusky Turning Point (1 each) (A). (e) Ontario: Toronto Harbor (16) (heart); Snake Island (15) (0);Niagara River (13) (0);Hamilton Harbor (8) (0);Strachen Island (6) (a); Pigeon Island (4) (0); Gull Island (3) (+); Scotch Bonnet Island (2) asterisk. (f) Best fit model from each lake, From top to bottom on left they are as follows: Ontario, Michigan, Erie, Superior, and Huron.
tistically discernible. Lake Erie has the highest mean squared error of the five lakes (1.23) and the lowest decay coefficient (-0.065). This combination may cause any slowing of the PCB decline to be imperceptible until more data have accumulated. It seems somewhat implausible that PCB concentration declines in eggs from lakes that drain into (Huron) and receive drainage from (Ontario) Erie, respectively,would have slowed while concentrations in Erie gull eggs continue to decrease. Perhaps Lake Erie declines will level off as Erie gull egg PCB levels approach
levels in Huron and Ontario eggs (Figure 10. PCB data from the Channel Shelter Island site in Lake Huron are discernible from and notably higher than, data from the rest of Lake Huron (Figure IC). This is likely because Channel Shelter Island, near the mouth of the Saginaw River, is in a heavily impacted bay, one of the designated Areas of Concern (15). The existence of longterm contaminant data from this area will be extremely valuable to help assess the effectiveness of remedial action in Saginaw Bay once the clean up is complete. VOL. 29, NO. 11, 1995 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 1
Estimated Models and Summary Statistics" Superior sample size
mean squared error
w
mean squared error ff
mean squared error
w a
Michigan
Huron
46
34
36.94e-0,083t 0.78 0.66
Exponential Decay 71.52e-O.Oga 29.78e-0,077t 0.95 1.38 0.66 0.49
44
37.97e-0,29t +12.10 0.56 0.76
Non-Zero Asymptote *7 1.85e-0.33t 25.88e-0,24t +22.51 f10.60 0.69 1.26 0.76 0.54
*47.45e-0.l7t +0.33e-0.24t 0.52 0.78
Double Exponential 83.69e-0,24t *35.59e-0,1n +8.35e0.069t +0.01e0.49t 0.70 1.13 0.76 0.60
Erie
Ontario
33
69 83.59e-0.12t 0.95 0.74 "85.58-0.23t f14.88 0.85 0.77
83.39e-O.O4' -28.29 1.27 0.48 55.46e-O.OSt -2.15 x 10-12e1.ggt 1.31 0.49
Asterisk denotes best fit model based on the extra sum of squares criterion.
TABLE 2
Model Comparison f Statistics model comparison
Superior
Michigan
Huron
Erie
Ontario
exponential/non-zero asymptote exponentiaVdouble exponential non-zero asymptote/double exponential
= 18.04 = 12.17 F1,42 = 4.85
= 12.94 = 6.54 F1.30 = 0.40
F1.41 = 4.79 Fz.40= 5.63 F1.40 = 5.93
F1.30 = 0.09
F1,SS = 8.51 Fz,B~ = 5.14 F1,~5 = 1.67
6.43
6.42
It is interesting to note that the best fit initial decay coefficient for Lake Superior (-0.17 yrl) is near the water column coefficient (-0.20 yr-l) estimated by Jeremiason et al. (8) though it is not clear if alternative models were considered in that analysis. The best fit coefficient for Lake Michigan (-0.33 yr-l) is also near PCB decay coefficients (-0.32, -0.28, -0.30, -0.33, -0.32, and -0.33 yr-') estimated for six of seven fish species in the lake (9). The similarity of the PCB decay coefficients for different components of a given ecosystem suggests that processes determining PCB concentrations are closely linked. In this study, the estimated rate constants from the best fit models are not all statisticallydiscernible among lakes; however, the optima are quite different from one another, and aggregate model fits to the data are discernible. These differences among lakes suggest that, initially, individual lake characteristicsdetermined PCB levels and the response that occurred when PCBs were regulated. Many factors were probably responsible for initial differencesincluding overall contamination in the watershed, physical characteristics, and food-web differences. Despite initial differences, the PCB concentration trajectories appear to be converging (Figure 10, implying that conditions common to the lakes may be increasinglyimportant. One possibility is that water column PCB concentrations in each lake are approaching approximate steady state with atmospheric inputs, resulting in similar levels and comparable present behavior. Steady state is the time invariant state of an open system. The perception of steady-state conditions is related to temporal scale and resolution. Smith ( 4 ) has argued that it is unlikely that PCBs in the Great Lakes exist in a timeinvariant state. His assertion is probably correct;net burial in sediments and degradation processes likely occur, albeit slowly. From a management perspective,however, waiting for further improvements in PCB levels can no longer be 2896 U ENVIRONMENTAL SCIENCE &TECHNOLOGY / VOL. 29. NO. 11,1995
6,31
F2.30
h.29
= 0.11 = 0.14
considered part of a practical strategy. The 40-80% fish PCB concentration decreases that occurred in the first ~ 2 0 yr following regulation are unlikely to be repeated in the next 20 yr. Changes that large would be discerniblebut are not currently evident (9). Human PCB exposure can be controlled somewhat by consumption advisories and manipulation of fish stocks (16). However, options to further lower background levels are limited. Further effluent limitationswill not affect legacy compounds like PCBs. Remedial actions may work well to relieve local contamination problems, but it is uncertain whether these cleanups will result in detectable improvements basin-wide. Accurately assessing future improvements will require large samples to detect small changes and could best be accomplished with a coordinated sustained monitoring effort. Great Lakes managers and the public may have to cope with PCB concentrations near current levels for many years. However, it is fortunate that management actions taken in the 1970s to reduce PCBs and other contaminants have resulted in substantial decreases. What would an appropriate management response be, at this point, if organochlorine contaminants were persistingat concentrations sufficient to cause serious ecosystem harm and blatant human health effects?
Acknowledgments Steve Carpenter and Tara Stow provided helpful reviews. I would like to commend the Canadian Wildlife Service for establishing and maintaining an extremely valuable longterm data base. This work was funded by the University of Wisconsin Sea Grant Institute under grants from the National Sea Grant College Program, National Oceanic and Atmospheric Administration, U.S. Department of Commerce, and the State of Wisconsin: Federal Grant NA9OAAD-SG469, Project RIMW-41.
Literature Cied (1) Fed. Regist. 1993, 58, 20806-20809. (2) Devault, D. S.; Wilford, W. A.; Hesselberg, R. J,; Nortrupt, D. A.; Rundberg, E. G. S.; Alwan,A. K.; Bautista, C. Contaminant trends in lake trout (Salvelinus narnaycush) from the upper great lakes. Arch. Environ. Contarn. Toxicol. 1988, 15, 349-356. (3) Devault, D. S.; Clark, J. M.; Garet, L.; Weishaar, J. Contaminants and trends in fall run coho salmon. J. Great Lakes Res. 1988, 14, 23-33. (4) Smith, D. W. Are PCBs in the Great Lakes approaching a “new equilibrium”? Environ. Sci. Technol. 1995, 29, 42A-46A. (5) Borgmann, U.;Whittle, D. M. Contaminant concentration trends in Lake Ontario lake trout (Salvelinus narnaycush): 1977 to 1988. J. Great Lakes Res. 1991, 17, 368-381. (6) Borgmann, U.; Whittle, D. M. DDE, PCB, and mercury concenwation trends in Lake Ontario rainbow smelt (Osmerus rnordar) and slimy sculpin (Cottuscognatus): 1977 to 1988.J. GreatLakes Res. 1992, 18, 298-308. (7) Suns, K. R.; Hitchin, G. G.; Toner, D. Spatial and temporal trends of organochlorine contaminants in spottail shiners from selected sites in the Great Lakes (1975-1990). J. Great Lakes Res. 1993, 19, 703-714. (8) Jeremiason, J. D.; Hornbuckle, K. C.; Eisenreich, S. J. PCBs in Lake Superior, 1978-1992: Decreases in water concentrations reflect loss by volatilization. Environ. Sci. Technol. 1994,28,903914. (9) Stow, C. A.; Carpenter, S. R.; Eby, L.A.; Amrhein, J. F.; Hesselberg, R. J. Evidence that PCBs are approaching stable concentrations in Lake Michigan fishes. Ecol. Appl. 1995, 5, 248-260.
(10) International Joint Commission. Great Lakes Water Quality Agreement of 1978. (11) Bishop, C.A.; Weseloh,D.V.; Burgess, N. M.; Stmger, J.; Norstrom, R. J.; Turle, R.; Logan, K. A. A n Atlas of Contaminants in Eggs of
Fish-Eating Colonial Birds of the Great Lakes (1970-1988); Technical Report Series 152;Canadian Wildlife Service, Ontario Region: Ontario, 1992; Vol. I. (12) Pettit, K. A.; Bishop, C. A.; Weseloh, D. V.; Norstrom, R. J. An Atlas of Contaminants in Eggs of Fish-Eating Colonial Birds of the Great Lakes (1989-1992); Technical Report Series 193; Canadian Wildlife Service, Ontario Region: Ontario, 1994; Vol. I. (13) Bates, D. M.; Watts, D. G. Nonlinear RegTession Analysis and Its Applications; John Wiley & Sons: New York, 1988. (14) Madenjian, C. P.; Carpenter, S. R.; Rand, P. S. Why are the PCB concentrations of salmonine individuals from the same lake so highly variable? Can. 1.Fish. Aquat. Sci. 1994, 51, 800-807. (15) Hartig, J. H., Zarull, M. A., Eds. Under Raps; The University of Michigan Press: Ann Arbor, 1992. (16) Stow,C.A.; Carpenter, S. R.; Madenjian, C. P.; Eby, L.A.; Jackson, L. J. Fisheries management options to limit human contaminant exposure from Great Lakes fish consumption. Bioscience,in press.
Received for review April 21, 1995. Revised manuscript received July 10, 1995. Accepted July 10, 1995.@ ES950282G @Abstractpublished in Advance ACS Abstracts, August 15, 1995.
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