Persistent, Bioaccumulative, and Toxic Chemicals I - ACS Publications

Chapter 11

Methylmercury Bioaccumulation Dependence on Northern Pike Age and Size in 20 Minnesota Lakes 2

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Gary E . Glass1,2. John A. Sorensen , and George R. Rapp, Jr.2 1National Health and Environmental Effects Research Lab., Mid-Continent Ecology Division, U.S. Environmental Protection Agency, Duluth, M N 55804

(218-726-8909; [email protected]) 2Archaeometry Lab., University of Minnesota at Duluth, Duluth, M N 55812

Mercury accumulation in northern pike muscle tissue (fillets) was found to be directly related to fish age and size. Measurements were made on 173 individual northern pike specimens from twenty lakes across Minnesota. Best fit regressions of mercury fillet concentration (wet wt.) vs. length, weight and age were found using power (log-log transforms) as compared with exponential or linear forms. The resulting equations (after averaging the individual equations for the 20 cases) are: [Hg], ppb fillet = 0.50•(length, cm)2.17 = 7.3•(weight, g)0.68 = 110•(age, yr)1.00, with r2 = 0.76, 0.72, and 0.76, respectively. Lengths, weights, and ages ranged over 22-110 cm, 60-7,500 g, and 0.3-11 yr, respectively, and [Hg] ranged from 50 to 2,000 ppb. Inter-lake comparisons were made using computed mercury levels for standard length, weight and fish age for each of the 20 lakes studied. Age based comparisons were found to correlate significantly with methylmercury concentrations in lake water, and with total mercury concentrations in lake plankton. Methylmercury bioaccumulation factors (BAFs, total Hg in fillets divided by total methylmercury in lake water) ranged from 0.3 - 14 x10 for individual specimens. Methylmercury B A F s showed a similar dependency on fish size and age as [Hg] i n fillet, i.e., methylmercury B A F s = 3,030•(length, cm)2.17 = 47,100•(weight, g)0.68 = 752,000•(age, yr) , where values represent the means of twenty different lake equations. Fish mercury levels indicate that each lake differs i n its o w n characteristics controlling bioaccumulation processes and transport through the aquatic food chain. Assuming no size or age dependency of fish mercury concentrations and BAF's may result i n impact overestimates for younger fish and underestimates for older fish from mercury contamination i n aquatic ecosystems. 6

1.00

150

© 2001 American Chemical Society

Lipnick et al.; Persistent, Bioaccumulative, and Toxic Chemicals I ACS Symposium Series; American Chemical Society: Washington, DC, 2000.


151 Growing levels of mercury in the atmosphere from fossil fuel combustion, and more recently, from solid waste incineration, are the leading causes of anthropogenic sources and increases in environmental exposure. A recent comprehensive study by the U S E P A (1) resulted in a large volume of information documenting the well known and less well known features of the mercury environmental and public health problem. One of the main exposure routes of mercury to man and wildlife is through consumption of freshwater fish. Mercury concentrations in fish are the leading cause for restricting fish consumption due to exceedence of toxic levels by human and wildlife consumers (2-4). It is generally recognized that mercury levels in fish are related to fish size (5-7), and are usually expressed as concentration at a given size of specimen Comparisons between different waterbodies are usually made by computing the mercury level expected to be present in fish of the same size (8). Plots of mercury muscle concentrations versus length are generally curvilinear, and log-log transformations are necessary to observe linear functionality with a normal distribution of data. A n average sized 3 yr old fish is commonly chosen for making comparisons across sites and waterbodies. For northern pike and walleye, these lengths are approximately 55 and 39 cm, respectively (8), and 1 kg weight for northern pike (9). Length is generally assumed to represent fish age in making comparisons, and standard sizes of fish have been used in comparing fish across different water bodies to "control" for differences in age and, therefore, exposure. However, this assumption is seldom tested as fish age data usually lag the reporting and interpreting of fish mercury levels for a given survey. The focus of this report is to: 1) review the assumption that northern pike size is a reasonable indicator of age, 2) test i f fish size or age is best related to mercury exposure, and 3) define the relationships between mercury levels and Β AFs in muscle tissue versus fish size and age.

Lake Selection and Sampling Methods The 20 Minnesota lakes (Figure 1) selected for this analysis are from various regions of the state and were part of a larger set of 80 lakes studied to determine trends i n mercury deposition and lake quality (10). These lakes are valued for their fishery resource and have been surveyed previously for mercury contamination levels (as early as 1975). The 20 lakes selected for this analysis were sampled for additional measurements of lake water chemistry (total methylmercury concentrations) to assess fish mercury bioaccumulation.

Lake Selection Study lakes were selected based on the following considerations: 1) presence of northern pike fish populations, fish population surveys, and fish mercury residue data (primarily available for N E Minnesota); 2) existence of past water quality data; and 3) to be of significant resource value and representative (e.g. no point sources or extraordinary anthropogenic inputs) of other lakes in the region



152

Figure 1. Minnesota bioaccumulation study.

map

of twenty lakes selected for

methylmercury

Sampling and Sample Processing Fish Sampling A total of 173 northern pike were sampled from 20 lakes across Minnesota by staff of the Department of Natural Resources (MDNR) and Voyageurs National Park (VNP) during May - Sept. of 1995 and 1996. Fish were collected using gill and/or trap nets (11), tagged with an identification number, placed in a plastic bag, and kept i n a cooler until transferred to the M D N R or V N P local area headquarters where they were frozen. Fish were then sent by courier or 1-day parcel delivery to the University of Minnesota - Duluth ( U M D ) Archaeometry Laboratory where they were received frozen, logged in, and stored frozen until processing. The goal of the fish sampling was to collect at least 10 individual fish of lengths that were evenly distributed across the available pool of fish lengths sampled. Fish Processing Fish weights and lengths and sex were determined i n the field and in the laboratory during processing. This provided a cross-check of fish identification. Laboratory processing included: scaling the fish (saving scale samples and cliethra for aging); removing a fillet, slicing it i n strips, and refreezing; grinding while frozen, and separately storing the bulk ground sample and a small aliquot (5-10 g) for analyses i n zip-loc bags.


153


Water Sampling Water sampling for the study lakes was accomplished i n the fall (when lakes were mixed) of 1996 by U M D personnel. Some lakes were sampled twice (spring and fall of 1996) in order to investigate variabilities associated with dates of sampling. Field measurements and sampling were conducted i n a deep portion (not necessarily the deepest) of the lake, according to methods described elsewhere (8,10). Plankton Sampling Plankton was sampled using a Wisconsin style net (Wildco Wildlife Supply Co.) and a Minnesota plankton bucket with check valve (J. Shapiro, U . of Minnesota, Mpls, M N ) according to methods described elsewhere (8,10).

Methods of Analyses Mercury Analyses Total mercury was analyzed in fish fillets, plankton, and unfiltered water samples using cold vapor atomic absorption spectrometry (EPA Method 245 (12, 13), Perkin Elmer model 403 spectrometer) using methods previously reported (8, 14). Although the analyses were conducted for total mercury, it is generally accepted that the form of mercury i n freshwater fish muscle (fillet) tissue is 95+% that of monomethylmercury(II) (15) and for plankton, 10-30% methylmercury (16). A l l fish were analyzed as individuals rather than composites. A subset of water samples was analyzed for methylmercury using phase ethylation (17) followed by fluorescence detection (EPA Method 1631 [18]). Fish and plankton mercury levels are reported on a wet weight and dry weight basis, respectively. Accuracy of all mercury measurements was checked using spikes of known concentrations and NIST (National Institutes of Science and Technology) certified tissue samples. Laboratory precision was checked by analyzing at least 10% of all samples i n duplicate. Fish A g i n g Fish ages were determined by the various M D N R area offices and V N P staff personnel that collected the fish. Annular lines on scales was the primary method used, however, V N P personnel used both scales and cliethra. Numerous cross checks with experienced personnel were employed to assure quality of this data set. Regression and Statistical Analyses Relationships between variables were quantified using linear regressions and Pearson correlation coefficients. Variable transformations (log, exponential, or untransformed) are indicated for each result presented. Statistical analyses were made with Macintosh computers using Excel and Systat software.


154

Results and Discussion N. Pike Growth - Age Relationships The growth relationships of northern pike length, weight and age data are presented in Figure 2. The relationship between fish length and weight is very tight with a loglog regression r of 0.98 for the combined 20 lakes. The same value (0.98) is obtained when the r results for the individual lake regressions are averaged. Similar length and weight vs. age regression results show significantly weaker relationships with combined lake r values of 0.69 and 0.65, respectively. The regressions (loglog) for individual lakes show higher r , with average values o f 0.83 and 0.82, respectively. For selected ages between 1 and 7 yr (Fig. 2, center), the range of fish lengths between different lakes can vary by a factor of about two. This indicates that fish length alone is not necessarily a good predictor of fish age. The relationship between fish weight and age is more variable than fish length. For ages between 1 and 7 yr the range of observed weights is greater than a factor of two and may be as high as five for lakes of extreme productivity. Estimates of fish age based on weight are clearly less reliable than fish length (Fig. 2, bottom). O f the mathematical models considered, the best at fitting the fish length vs. weight relationship was found to be the power regression (linear regression of log transformed variables). The r statistic indicating the "best fit" averaged 0.98, 0.97, and 0.93, respectively, for power, exponential, and linear regressions. 2


2

2

2

2

Fillet Mercury Concentration vs. N. Pike Growth, Age The regression equation form that best describes the mathematical relationship between mercury fillet concentrations vs. fish length, weight, and age was found to be the power regression. This is indicated by the r statistic, the "best fits" for each of the individual lakes, using three mathematical models tested, summarized in Table I. O f the three models tested, the power format gives a slightly better overall performance, and is better in a majority of the individual cases, than both the exponent and linear formats. The equations for each of the mathematical models showing the relationships between mercury concentrations and northern pike size and age are given in Table I and plotted for the individual lakes in Figure 3. These results show the different fish mercury concentration vs. fish growth responses that result from mercury contamination of the food chain. The rates of mercury uptake or bioaccumulation by northern pike are different across the 20 lakes studied. The differences in slope for mercury concentration vs. age (log-log) shown in Table I vary by a factor of 5 from the lowest to the highest. The causal mechanisms explaining the differences in response and bioaccumulation are due to differences in watershed mercury loading rates, terrestrial and aquatic mercury methylation rates and factors controlling them, and food chain/lake productivity which may tend to dilute the exposure for a given production of methylmercury. A study of controlling mechanisms was a major objective of the eighty lakes study (10) and an in-depth analysis is currently underway (19). 2


155

-Bearhead Ο Big +Blackduck

•|f 4000 -f

• Crane Δ Harriet


Ο Hendricks XJuUa XKabetogama

20

40

60 Length, cm

80

100

1 2 0

#Kjostad φ Lt. Vermilion AMazaska ©Moose HMukooda Ο Pelican

Δ Rainy 5i Sand Point 1 Stevens SRTooth φ Washburn HWirth

Age, yr Figure 2. Northern pike growth measures: length, weight, and age relationships for 20 Minnesota lakes. Individual lake trend lines (log-log) are shown.



r2

0.038 0.034 0.016 0.022 0.083

16.1 -509 10.6 -381 476 13.7 3.2 -1770 61.7 8

mean median stdev min max Linear:

mean median stdev min max

0.50 0.08

0.76 0.81

0.19 0.97

1.82 129.90 fHg] vs length

1

1

|

0.28 0.17 0.44 0.05 2.11

0.00068 0.00038 0.00065 0.00021 0.00270

0.68 0.69 0.26 0.29 1.24

intcpt

iffntcpt

0.13 0.94

0.04 31.86

0.06 0.99

0.69 0.73

0.11 0.91

26.9 410.4 [Hgl vs weight 136 126 124 -51 362

0.66 0.66

192.1 157.0

kogfHglYS weight

0.72 0.78

7.34 4.65

Log[Hg] YS. Logfweight)

slope

1

1

\

intcpt itfntcpt

115 88 79 27 292

0.29 0.27 0.14 0.10 0.73

1.00 1.06 0.31 0.30 1.48

-13 -27 109 -187 282

27.3 290.0 (Hgl vs age

137.1 132.0

LoglHglYsage

26.3 230.7

109.8 86.4

Log[Hg] YS, Logfage)

slope

Abbreviations and units: η = number of fish; intcpt = intercept; [Hg], ng Hg/g; length, cm; weight, g; age, yr.

0.14 0.92

0.74 0.78

0.76 0.78

47.47 35.67

0.00 0.20 0.95 4.18 LogfHgl YS length

2.17 2.15 0.76 0.94 3.88

α

jffntcpt

LogfHgl vs. Logflength»

mean 9 median 10 stdev 2 min 4 max 10 Exponential:

Power;

η slope intcpt

£

0.15 0.99

0.75 0.79

0.18 0.99

0.74 0.79

0.20 0.98

0.76 0.80

Table L Fillet Mercury Concentration Dependence on N. Pike Growth Measures: Results are Averages Across Regressions for Individual Lakes Using Equations with Power, Exponential, and Linear Forms"



157

Ο

2000

4000

6000

8000

Weight, g

Age, yr Figure 3. Northern pike fillet mercury contamination concentrations (ppb ww) vs. fish growth measures for 20 Minnesota lakes. Individual lake trend lines (log-log) are shown. t


158


Definition of Standard Fish Sizes for Contamination Comparisons Once the best model for describing the relationship between mercury concentrations in fillets as a function of fish size and age measurements has been selected (log-log), then the question of how best to describe or represent the status of mercury contamination i n a given lake and across lakes may be asked. Should mercury contamination concentrations be compared for the same length, same weight, or same age fish across lakes or other water bodies? A n example of each case for two standard size selections are presented in Table II for the 20 lakes studied. The ages 3 and 6 yr were selected to be most representative of the data set, and computed the length and weight of fish (power regression) for each lake having these ages, and averaged their values. These average length and weight values were then used with the individual lake's regression equation to compute the mercury content for a fish of that "standard" size. The results of the calculations are given in Table II and show significant variations between each of the three growth characteristics.

Table II. Calculated Mercury Concentrations (ng Hg/g, ww, Fillet) and Methylmercury BAFs for N. Pike Using Power Regression Equations [Hg] statistics (20 regressions for 20 lakes) for standard size or age (ng/g) min max coef. of average median sldev var. (%)

regression description

standard size or age

[Hg] vs length [Hg] vs length

53.9 cm 73.6 cm

359 796

233 456

353 1140

93 209

1710 5490

98 143

B A F vs length B A F vs length

53.9 cm 73.6 cm

2340 5136

2120 3783

1960 6511

616 1109

9990 32095

84 127

[Hg] vs weight [Hg] vs weight

986 g 2688 g

440 1070

252 527

609 2160

100 212

2930 10100

138 202

B A F vs weight B A F vs weight

986 g 2688 g

2840 6840

2220 3870

3480 12500

660 1130

17100 59300

123 183

[Hg] vs age [Hg] vs age

3yr 6 yr

321 666

257 505

202 471

107 191

862 2020

63 71

B A F vs age B A F vs age

3yr 6 yr

2140 4380

1990 3570

1030 2480

569 1020

5040 11800

48 57

A popular choice i n the past has been to select a standard length (8, 20) to represent the basis of comparison between different sites or different times. However, the results of this study indicate that age may be the better choice. A correlation analysis among calculated mercury concentrations i n standard size fish based on length, weight, and age shows that all three indices are related and show the same general trends with correlation coefficients ranging from 0.78 to 0.92. A better test,



159 however, would be to have an independent measure of a parameter related to mercury exposure against which to test the calculated mercury concentrations i n standard size or age fish Measurements of other exposure levels of mercury contamination are concentrations i n water and in plankton Such measurements are quite independent of fish measurements, and were made on plankton as part of the eighty lake study (10) and on lake water for methylmercury as part of this analysis. The results (using untransformed data) show that standard fish mercury concentrations based on fish age (3, 6 yr) correlate stronger with both plankton mercury concentrations (r = 0.7, 0.6) and methylmercury water concentrations (r = 0.5,0.5) than do calculated fish mercury concentrations based on a standard fish length or weight. It is also interesting to note that the higher methylmercury correlations for plankton compared to water are consistent with ingestion as the primary exposure route for fish in these lakes. Mercury Bioaccumulation Dependency Early studies of waterborne toxic, bioaccumulative, persistent chemical pollutants focused on using the simplest model for representing a fish i n aquatic ecosystems. This "bag of o i l " fish model gave rise to numerous but similar approaches of considering all pollutant uptake as an "equilibrium partitioning" event, calculating "bioaccumulation factors" (BAF [dimensionless, and equivalent to ml/g or L/kg], the ratio of pollutant concentration in fish [ng/g] to that in water [ng/g] resulting from environmental exposure) and interpreting B A F s as constants related to partition coefficients between water and oil (hexane and octanol, most commonly). Little or no consideration has been given to pharmacological or metabolic mechanisms that actually control the uptake of methylmercury i n fish and incorporate substantial concentrations in the muscle tissue of fish (1). The B A F approach is typically used for organic pollutants, and has been recently extended to organic pollutants i n Great Lakes sediments (21). A more recent modeling concept involves the kinetic approach where trophic transfer of the contaminant is considered and bioaccumulation can be more accurately described (22). However, much work is needed to evaluate model coefficients. To evaluate the functional dependency of methylmercury bioaccumulation factors on fish size and age, methylmercury concentrations were measured in the lake water from which the fish were sampled. Since the most meaningful B A F for mercury would involve using the same chemical species present i n water as is present in fish muscle tissue (15), total methylmercury concentrations are the measurement of choice to be used to evaluate the nature of the B A F dependency with fish size and age. Methylmercury B A F s were calculated by dividing each fish mercury concentration by the lake water total methylmercury concentration. Sample results are presented in Table III for the standard sized fish. Comparisons between the two standard sizes immediately show the trend toward larger B A F s for older (larger) fish for each of the 20 study lakes. This observation is consistent with slow elimination rates of methylmercury yielding longer equilibration times for a kinetic model (22). In addition, it is also consistent with the idea that larger fish are exposed to larger doses of mercury because they consume larger and more contaminated prey. The B A F s for each lake were regressed (using log-log transformed data) against fish length, weight, and age. The resulting equations are presented in Table III. Since each of the fish concentration values within a given lake are divided by the



2.10 1.69 1.80 2.33 1.72 3.88 2.89 2.72 2.33 2.19 0.94 1.26 2.63 1.81 2.48 2.39 1.66 3.74 1.55 1.33 2.17 2.15 0.76 0.94 3.88

2.87 3.53 3.06 2.27 3.27 -0.74 1.07 1.08 2.32 2.63 4.37 4.22 1.66 3.32 2.06 2.31 3.48 0.53 3.53 3.57

0.72 0.81 0.70 0.86 0.71 0.75 0.82 0.91 0.90 0.86 0.87 0.20 0.95 0.75 0.57 0.83 0.78 0.80 0.88 0.62 0.76 0.81

0.2 0.20 23300 0.95

735 3390 1150 187 1880 0.2 12 12 210 424 23300 16600 46 2090 115 206 3040 3.4 3370 3720 3026 580

Log(PAF) vs Logdength) slope intcpt lQintcpt

Abbreviation: intcpt = intercept.

a

Bearhead Big Blackduck Crane Harriet Hendricks Julia Kabetogama Kjostad Lt.Vermilion Mazaska Moose Mukooda Pelican Rainy Sand Point Stevens Tooth Washburn Wirth average median stdev min max

Lake Name Log(BAF) vs Log(weight) slope intcpt lOintcpt t2 0.74 4.37 23180 0.66 4.82 0.56 66800 0.83 0.55 4.57 37300 0.63 0.67 0.87 4.33 21400 0.41 0.37 5.18 150000 294 1.20 2.47 0.80 0.96 3.21 1610 0.78 3.24 0.94 0.86 1750 0.82 0.77 4.08 12100 0.70 22800 4.36 0.86 0.29 5.11 130000 0.88 0.38 5.29 194000 0.13 0.91 3.46 2900 0.78 4.82 0.56 66300 0.68 0.78 4.08 12100 0.60 0.75 4.23 17000 0.79 4.94 0.48 87400 0.73 1.24 3.53 3380 0.69 0.51 4.71 51200 0.86 0.42 4.61 40700 0.63 0.72 0.68 47100 0.69 22960 0.78 0.26 294 0.29 0.13 1.24 0.94 194000

4

0.67 0.55 1.25 1.06 0.30 1.42 1.48 0.95 1.15 1.06 0.82 0.45 1.06 1.14 1.28 1.19 1.08 1.23 1.02 0.84 1.00 1.06 0.31 0.30 1.48

6.00 6.17 5.46 5.95 6.22 5.60 5.39 5.53 5.65 5.93 5.66 6.15 5.94 5.74 5.83 5.99 5.72 6.12 5.67 5.35

0.61 0.93 0.78 0.81 0.59 0.98 0.91 0.91 0.36 0.95 0.87 0.20 0.97 0.59 0.76 0.91 0.75 0.79 0.91 0.67 0.76 0.80 226000 0.20 1660000 0.98

991000 1480000 288000 883000 1660000 397000 248000 341000 445000 858000 452000 1410000 866000 544000 678000 968000 523000 1310000 471000 226000 751800 611400

Log(BAF) vs Log(Age) slope intcpt IQintcpt r2

Table III. Methylmercury BAFs Dependence on N . Pike Growth: Length, Weight, and Age Relationships


161


2

methylmercury concentration for that lake, the slopes and r are the same as for regressions using the fillet mercury concentrations vs. fish growth measures. The individual plots of methylmercury B A F s vs. northern pike growth are presented i n Figure 4 where it is observed that the methylmercury B A F s increase with increasing fish growth measure and are more linear with fish age than with fish length or weight. It is interesting to compare coefficients of variations ( C V = std. dev./mean), given in Table II, for mercury concentrations and B A F s that have been normalized to standard sized fish. The first observation is that the lowest C V s (e.g. 63% for H g cone, and 48% for B A F at age 3) occur for the aged based normalizations. This further supports the evidence that age is a better choice than length or weight for modeling mercury accumulation i n fish. The second observation is that the coefficient of variation for methylmercury B A F s is lower than that for mercury concentration. This clearly indicates that methylmercury concentrations i n water have some explanatory power regarding the variability of mercury in fish tissue. The importance of fish mercury concentration and mercury B A F size and age relationships are illustrated by the following comparisons. The average mercury concentration was calculated using all sampled fish (n = 173) for the 20 study lakes by two methods: 1) a simple arithmetic average of the mercury concentrations [equivalent to assuming no size dependency], and 2) a mass weighted mean [equivalent to a composite sample of all fish; the proper method for the case of size dependency]. The results indicated that the simple arithmetic mean mercury concentration was 469 ng Hg/g, compared with the mass weighted mean concentration, 681 ng Hg/g, a 45% higher concentration and body burden i n the overall fish population. Methylmercury B A F s for 10 yr old fish are, on average, 10 times those for 1 yr old fish. Thus, the earlier concept of assuming no size or age dependency of fish mercury residues and B A F s (3, 4) may have resulted in impact overestimates for younger fish and underestimates for older fish from mercury contamination in aquatic ecosystems.

Acknowledgments The regional staff of the Minnesota Department of Natural Resources played a key role in the success of this project by their cooperation and application of their extensive experience in obtaining the necessary specimens required to build the foundation for this bench-mark study. We thank L . Kallemeyn of the V N P for help i n fish sampling and aging, K . Schmidt and T. Pierce for fish analyses; for water sampling and/or fish processing S. Cummings, R. Hedin, L . Heinis, C. Lippert, and R. Rude; J. Reed, D . Busick, and D . Sorensen for assistance in data base and manuscript preparation; and the U S E P A for salary (GEG), space, and laboratory equipment support for this project. Financial support was appropriated by the Minnesota Legislature, ( M L 95 Chp. 220, Sect. 19, Subd. 5(g) and M L 96 Chp. 407, Sec. 8, Subd. 9), from the Future Resources Fund and Great Lakes Protection Account, as recommended by the Legislative Commission on Minnesota Resources. The identification of commercial products is for descriptive and documentation purposes only and does not constitute endorsement. The contents of this manuscript do not necessarily reflect the official views of the U S E P A , and no official endorsement should be inferred.



162

Ο

2

4

6

8

10

12

Age, yr Figure 4. Northern pike methylmercury bioaccumulation factors vs. fish growth measures for 20 Minnesota lakes. Individual lake trend lines (log-log) are shown.


163


Literature Cited 1. Mercury Study Report to Congress. Volume V I : A n ecological assessment for anthropogenic mercury emissions in the United States. Office of A i r Quality Planning and Standards/Office of Research and Development, U S E P A 1997, http://www.epa.gov/oar/mercury.html 2. Minnesota Fish Consumption Advisory. Minnesota Department of Health, Minneapolis, M N . 1999, 96 pp. 3. National Study of Chemical Residues in Fish. E P A 823-R-92-008a, b, Vols. I, II. U S E P A , Office Science and Technol. Washington, D.C. 20460 Sept. 1992 4. Final Water Quality Guidance for the Great Lakes System. U S E P A Final Rule, 40 C F R Pts. 9, 122, 123, 131, 132. [FLR-5173-7] R I N 2040-AC08 Fed. Reg. V o l . 60, No. 56 pgs. 15366-15425 Thurs. Mar. 23, 1995. 5. Fagerstrom, T., Asell, B . , Jernelov, A . Oikos 1974, 25, 14-20. 6. Olsson, M. Ambio 1976, 5, 73-76. 7. Barber, R. T.; Whaling, P. J. Environ. Sci. & Technol. 1984, 18, 552-555. 8. Sorensen, J. Α., Glass, G . E., Schmidt, K. W., Huber, J. K.; and Rapp, G . R., Jr. Environ. Sci. Technol. 1990, 24, 1716-1727. 9. Hakanson, L . Environ. Pollut. Ser. Β 1, 1980, 285-304. 10. Glass, G. E . , Sorensen, J. Α., Rapp, Jr., G. R. Mercury Deposition and Lake Quality Trends, Project I-11/I-15, Legislative Commission on Minn. Resources. St. Paul, MN 1998, pp 150. 11. Manual of Instructions for Lake Survey Special Publication No. 147. Minnesota Department of Natural Resources, St. Paul, MN 1993. 12. Determination of Metals in Environmental Samples EPA 600/4-91-010, U S E P A , Cincinnati, O H , 1991. 13. Methods for the Determination of Metals in Environmental Samples. EPA/600/R-94/111. Supplement I to E P A 600/4-91-010, U S E P A . Cincinnati, O H , 1994. 14. Glass, G. E . ; Sorensen, J. A ; Schmidt, K . W.; Rapp, Jr., G. R. Environ. Sci. & Technol. 1990, 24, 1059-1069. 15. Bloom, N. S. Can. J. Fish. Aquat. Sci. 1992, 49, 1010-1017. 16. Watras, C.J.; Bloom, N. S. Limnol. Oceanogr. 1992, 37, 1313-1318. 17. Liang, L . ; Horvat, M.; Bloom, N. S. Talanta, 1994, 41, 371-379. 18. Method 1631: Mercury in Water by Oxidation, Purge and Trap, and Cold Vapor Atomic Fluorescence Spectrometry. E P A 821-R-95-027. Office of Water, Engineering, and Analysis Div., U S E P A , Washington, D . C. 20460, 1995. 19. Glass, G . E . ; Sorensen, J. A.; Can. J. Fish. and Aquat. Sci. 1999, i n prep. 20. Parks, J.W.; Craig, P.C.; Ozburn, G.W. Can. J. Fish. and Aquat. Sci. 1994, 51, 2090-2104. 21. Cook, P. M.; Burkhard, L. P. Development of bioaccumulation factors for protection of fish and wildlife in the Great Lakes EPA 823-R-98-002 Office of Water, U S E P A , Washington, D C . 1998, 3, 19-27. 22. Reinfelder, J.; Fisher, N.; Luoma, S.; Nichols, J.; Wang, W . Science of the Total Environ. 1998, 219, 117-135.


Persistent, Bioaccumulative, and Toxic Chemicals I - ACS Publications

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