Environ. Sci. Technol. 1989, 23, 699-707
Sterling, T. D.; Dimich, H.; Kobayashi, D. J. Air Pollut. Control Assoc. 1982,32, 250-259.
Ingebrethsen, B. J.; Heavner, D. L.; Angel, A. L.; Conner, J. M.; Steichen, T. J.; Green, C. R. J. Air Pollut. Control ASSOC. 1988,38, 413-417. Keith, C. H.; Derrick, J. C. J.Colloid Sci. 1960,15,340-356. Okada, T.; Ishizu, Y.; Matsunuma, K. Beitr. Tabakforsch. 1977,9,153-160. Ingebrethsen, B. J. Aerosol Studies of Cigarette Smoke. TCRC, Knoxville, TN, October 13-16, 1986. Hiller, F.C.; McCusker, K. T.; Mazumder, M. K.; Wilson, J. D.; Bone, R. C. Am. Rev. Respir. Dis. 1982,125,406-408. McMurry, P.H., personal communication to C. L. Benner, 1987. McMurry, P. H.; Rader, D. J. Aerosol Sci. Technol. 1985, 19, 1176-1182.
(33) Badre, R.; Guillerm, R.; Abran, N.; Bourdin, M.; Dumas, C. Ann. Pharm. Fr. 1978,36,443-452. (34) Klus, H.; Kuhn, H. Beitr. Tabakforsch. Znt. 1982, 11, 229-265. (35) Hoffmann, D.; Adams, J. D.; Brunnemann,K. D. Toxicol. Lett. 1987,35, 1-8. (36) Taves, D.R. Talunta 1968,15,969-974. (37) Stevens, R. K.Environ. Znt. 1985,11, 271-283. (38) Leaderer, B.P.,personal communication to D. J. Eatough on results being prepared for publication, 1987.
Received for review June 13,1988. Revised manuscript received December 2,1988. Accepted January 24, 1989. This research was supported by R. J. Reynolds Tobacco Co. through a grant to Hart Scientific, Znc.
Bioaccumulation Model of Organic Chemical Distribution in Aquatic Food Chains Robert V. Thomann
Manhattan College, Bronx, New York 10471 A model is developed for calculating the concentration of organic chemicals in a simple generic aquatic food chain. Chemical uptake efficiency from water, excretion rate, and chemical assimilation efficiency are variable as a function of the octanol-water partition coefficient, KO,. The model indicates the significance of the growth rate and variable efficiency of uptake in the calculation of the bioconcentration factor (BCF) under field conditions. Growth rate effects result in a reduction of simple lipid-based partitioning by a factor of 2-5. Food chain effects are not significant up to log KO, of - 5 . For log KO,of 5-7, calculated and observed field concentration factors in top predators indicate significant (1-2 orders of magnitude) elevations above calculated field BCF values. Above log KO! =. 7, food chain effects are sensitive to the chemical assimilation efficiency and phytoplankton BCF.
Introduction The possible transfer of chemicals from lower levels of the food chain to top predators and subsequently to humans has been a matter of considerable investigation and controversy. The degree to which chemical accumulation occurs, or indeed whether it occurs at all, is of significance in assessing the impact of the discharge of potentially toxic chemicals into the aquatic ecosystem. A need exists to synthesize available data with contemporary understanding of the mechanisms of uptake and transfer and to develop a generalized framework for evaluating the bioaccumulation potential of chemicals. The purpose of this paper is to extend a previously developed steady-state model of the distribution of chemicals as a function of trophic level in the ecosystem (1). The extension includes additional mechanistic details for organic chemicals. The octanol-water partition coefficient (K0J is used as the ordering parameter for the tendency of the chemicals to partition into the lipid compartments of the organism. Other factors, e.g., protein, non-lipid organic matter, may also influence the partitioning, but the effect of these factors may be presumed to be small. The chemical concentration on a lipid basis in the organism is used as the state variable for two reasons: (a) lipophilic chemicals partition more readily into the lipid 0013-936X/89/0923-0699$01.50/0
pool of an organism and hence observed whole body concentrations would tend to vary with lipid content, and (b) simple partitioning of an organic chemical assumes that the lipid-based concentration normalized by the water concentration would be equal to KO,. It is recognized that not all of the variability in organism chemical concentration is explained by a lipid-based concentration. Nevertheless, as a normalization in a generic food chain, a lipid-based concentration-state variable would eliminate a potential major source of the variability in whole body concentrations. Food chain accumulation is the increase in the organism chemical concentration over that which would be expected from exposure to the chemical in the water phase only. The bioaccumulation factor (BAF) is the equilibrium ratio of the organism chemical concentration resulting from the water and food routes to the water concentration, i.e.,
N = v/c
(1)
where N is the lipid-based BAF [pg/kg(lp) i pg/L; kg(1p) = kg of lipid], Y is the organism concentration of the chemical on a lipid basis [pg/kg(lp)], and c is the available (dissolved) water concentration (pg/L). The BAF is to be contrasted to the bioconcentration factor (BCF), which is the equilibrium ratio of the organism concentration to the water concentration where exposure is only to the chemical in the water. Thus,
N , = v,/c
(2)
where N , is the lipid-based BCF [pg/kg(lp) + pg/L] and v, is the chemical concentration in the organism due to water only. The ratio of N / N , is a measure of the tendency for the chemical to accumulate in the organism from both food and water exposure over the concentration expected from the water only. If an equilibrium (steady state) has been reached, then N / N , > 1 indicates that food chain accumulation has occurred. Data from both the laboratory and field have been reported by various researchers indicating some bioaccumulation of chemicals depending on the nature of the chemical (1-6).
@ 1989 American Chemical Society
Environ. Sci. Technol., Vol. 23, No. 6, 1989 699
L E V E L 12
L E V E L Yl
200-
OtiYTO-
PLANKTON
----3
PLANKTON
-
L E V E L 63
L E V E L Y4
SHALL
TDP
FISH
------)
PREGATOR
G = (dwip/dt)/wip = (dwp/dt)/wp
(6)
For constant lipid fraction over time; a simple growth equation is given by dw/dti = (C - r)w (7) so that
G=aC-r (~ISSOLVEC
WATER CONCENTAATIG~
Flgure 1. A simple four-level food chain used for model.
For example, Tanabe et al. (5) reported data from the western North Pacific ecosystem for hexachlorocyclohexane isomers (CHCH), PCBs, and CDDT. The data indicated a progressive increase of N for PCBs and CDDT from zooplankton to squid, an upper trophic level predator. Such an increase was not observed for EHCH, a less lipophilic group of chemicals. Connolly and Pedersen ( 4 ) have compiled data on chemicals over a range of K,, values and have shown that measured field BAF values in the upper levels of the food chain are above that expected from a lipid partitioning under exposure to the water only. They hypothesize that one of the reasons for such elevated concentrations is the generation of a chemical gradient in the gut of the animal from preferential lipid breakdown. Theory Consider a simple four-level food chain as shown in Figure 1. An equation for the uptake of a chemical from the water and from food for the ith level (i = 2, 3, 4) is given by
where r is the organism respiration rate (day-’) and a is the food assimilation efficiency. The food consumption is then estimated from C = (G r)/a (8)
+
Across a general food chain, the growth rate is given approximately by (7, 8)
G = 6w-8 (9) where for w [in g(w)], 6 is -0.002 a t 10 “C and fi varies from 0.2-0.3 (I, 8). The respiration rate is approximately (6)
r = dw-Y
(10)
where for w [in g(w)], 4 varies from 0.014 to 0.05 and y from 0.2 to 0.3 for routine metabolism (7). The ranges in the coefficients are functions of the specific organism and ecosystem conditions. Assume that the organism is exposed to the chemical from the water route only. Then eq 5 at steady state gives the BCF as
where umi and U,J-~ are the chemical whole body burden (pg/organism) of the predator (i) and prey (i - l), respectively, k L is the uptake rate of the chemical from the water [L/day-kg(w); kg(w) = kg wet wt], w is the wet weight of the organism [kg(w)], is the chemical assimilation efficiency (pg of chemical absorbed/pg of chemical ingested), C’+l is the specific consumption rate [kg(w) of prey/kg(w) of predator-day], and Ki is the chemical excretion rate (day-l). The concentration of the chemical on a lipid basis in the predator, ui [pg/kg(lp)] is vi = vmi/Wip,i (4)
The BCF is therefore the ratio of the uptake rate to the excretion rate plus the organism growth rate. The latter term reflects the “growth dilution” in the concentration of the chemical as a result of an increase in the lipid weight of the organism. For many laboratory BCF experiments, the fish are exposed under a maintenance diet that does not result in growth, although this is not always true. In the field, however, the fish may be undergoing continuous growth, so that eq 11 must be used to estimate the concentration of the chemical in the organism due to water uptake only. Steady-StateFood Chain. Returning now to eq 5 and a simple four-level food chain as shown in Figure 1, the solution to each level of the food chain at steady state (in the concentration) is given by the following equations ( I ) : = NwlC (124
where the lipid weight, wlp,i(kg(lp))is
and
Wlp,i
= PiWi
(44
for pi as the fraction lipid weight [kg(lp)/kg(w)]. From eq 3 and 4,the concentration equation is
= uai
for kui
= k‘ui/Pi
(54
and Ci,i-l = C’i,i-l(Pi-1/Pi)
(5b)
The latter two quantities are the lipid-normalized uptake and food consumption rates, respectively. G is the net growth rate (day-’) of the lipid weight (wlp) of the organism given by 700
Envlron. Sci. Technol., Vol. 23, No. 6, 1989
+ u+
i = 2, 3 , 4
(12c)
where uwi is the concentration due to water uptake only and u, is the concentration due to food chain transfer. Considering this latter contribution and eq 7a, the relationship between u, and ui-l can be written as ub = (aC/[K + (aC - r ) ] ) ~ ~ - ~ (13)
-
-
The concentration in the predator becomes greater than that in the prey when K 0 and CY 1. Then, under those conditions, ub/ui-l = C / ( a C - r) (14) In general, C > aC - r, i.e., 1 g/day of food input results
in less than 1 g/day of growth in the predator (see also ref 9). Therefore, the concentration in the predator due to food intake would be expected to be greater than that in the prey under low K and high CY. This elevated concentration is a function of the energetics of the organism (food conversion efficiency, respiration, growth) and the characteristics of the chemical (assimilation efficiency and exretion rate). See also ref 4. In terms of the BAF, eq 12b can be written as (154 N2 = Nw2 + f21Nwl (15b) N 3 = Nw3 + f32Nw2 + f32f21Nwl = Nw4 + f43Nw3 + f43f32Nw2 + f43f32f21Nwl where
N4
(154
0.9
4
1
I
2
4
6
LOG OCTPNOL WPTER PPRTITIQN COEF
= Qi,i-l[Ci,i-l/(Ki+ GJ1 (15d) The cumulative effect of the transfer of chemical from the lower levels of the food chain to the top predator can now be seen. The principle parameters in the above formulations are k,, K , and C Y . Each are now examined. Uptake and Chemical Transfer Efficiency. Following Neely (IO),the uptake rate, k, can be expressed as k, = V E / W ~ , (16) fi,i-1
where V is the ventilation volume (L/day) and E is the efficiency of transfer of the chemical. Since V = r’wlp/c, (17) for r’ as the respiration rate on an oxygen basis [g of 02/day.kg(lp)]and c, as the oxygen concentration (kg/L), then k , = r%/co (18) Relating r’ to r (day-’) through r’ = (a,a,/a,dp)r (19) for aW as the oxygen to carbon ratio, a, as the carbon/dry weight ratio, and awd as the wet/dry weight ratio, gives k , = (a,a,rE/a,dpc,) (20) For a, = 2.67, a, = 0.45, and awd = 5, and c, = 8.5 mg/L, and r given by eq 10 with 4 = 0.036 (7), a convenient expression for k , is
k , = 103(w-7/p)E
1
(21)
for w [in g(w)]. It has been suggested for some time (11, 12) that the transfer efficiency across the gill membranes of fish would depend on such chemical properties as the lipid partition coefficient, steric properties, and molecular weight (12-16). McKim et al. (14) reviewed the suggested mechanisms. Briefly, at low log KO, values, the transfer across the lipoprotein gill membrane is rapid through aqueous diffusion layers, but is hindered by the lipid membrane because of low fat solubility. At increasing KO,,.,the resistance from the membrane is reduced and the transport is porportional to the log KO,. A plateau is then reached where the aqueous diffusion layer controls the transport, and at very high log KO,, water solubility of the chemical limits transport and the efficiency decreases. Also, the uptake efficiency has been observed to be a function of the ventilation volume, which in turn is related to the respiration rate of the organism. At higher ventilation volume (respiration),the efficiency is reduced, which has been ascribed to increased diffusion dead space and a shunting away of inspired water from respiratory surfaces (17).Thus in eq 20, E is considered as a function of the respiration.
8
10
KO*
Flgure 2. Observed and assumed efflciency of chemical uptake. Squares from ref 14,750 g(w); crosses from ref 17,30 dw); diamonds from ref 18, 0.6 g(w).
Using the weight of the organism as the measure of the respiration rate (eq lo), Figure 2 shows data from ref 14, 17, and 18 on the uptake efficiency for a range of fish weights. As seen Figure 2, the efficiency increases with increasing log K , to a plateau, with a subsequent decrease at higher log KO, (>6-7). The data in Figure 2 indicate an apparent increase in E at log KO,of 4-6 for the smaller organisms. No ready explanation is available for this increase and the data are simply represented as two empirically guided functions. The data in Figure 2 are thus represented by the following log linear equations: log E = -2.6 + 0.5 log KO, for log KO, = 2-5 (22a)
E = 0.8 for log KO, = 5-6 log E = 2.9 - 0.5 log KO, for log KO, = 6-10 for organisms of order 10-100 g(w). These equations are used in calculating the uptake rate, k, in eq 21. Excretion Rate. The relationship between the excretion rate and log KO,has been examined and a variety of empirical equations have been suggested (16,19).If the variable uptake efficiency previously discussed is utilized, a more mechanistic relationship can be derived for the excretion rate as a function of the log KO,. The basic assumption is that the lipid-normalized BCF is equal to the KO, at zero growth and at equilibrium. This assumption has been examined at length (20)where it is concluded that when the time to equilibrium conditions is properly accounted for, the chemical BCF is given by an equilibrium partitioning into the lipid, such partitioning being represented adequately by the KO,. Thus, for N , = KO,in eq 11and by using zero growth, the excretion rate becomes kll(W,~$(KOW)) K= (24) KO,
where k , is given by eq 21-23. Mechanistically, this equation implies the same mechanisms that hinder or enhance transport into the organism are operative in the transport out of lipid pools across lipoprotein membranes and into the excretory systems. Figure 3 (top) shows a comparison between eq 24 and reported literature data on excretion rates. As shown, the Environ. Sci. Technol., Vol. 23, No. 6, 1989 701
-?'\ 2-
,
14
.
I
Table I. Data Used for Figure 3 Excretion Rate
I
A 2 OAPHNI& a 3
fig plot no.
-8
-
-9
2
!
4
;+
LOG OCTANOL MATER P A R T I T I O N COEF
-6
-7 -E
10 KO*
-
-
1
- , 2
b
6
E
10
LOG OCTANOL WATER P A A T I T I O N COEF. KO*
Flgure 3. Top: Observed and computed chemical excretion rates using eq 24. Bottom: Including growth, eq 25. See Table I for key to references.
agreement is good and the changing slopes of K with KO, reflect the varying efficiency of release across membranes. In some instances, the excretion experiment may have included some growth of the organism, thereby confounding the estimate of K. The effect of growth is discussed below. The high values of K for Daphnia (21) at 0.0006 g can be noted. The results also show that one would expect the excretion rate to be lower for the larger fish due to decreased respiration and in general a greater ratio of volume to membrane area. Such difference may be expected to be about an order of magnitude decrease in K i n the region of log KO, of 4-6. Excretion data do not exist for the larger fish (ca. 1000 g) because of obvious difficulties of measurement. Under field conditions, the relationships shown in Figure 3 (top) for the excretion rate would not be expected because of growth rate effects. This is shown in Figure 3 (bottom), where log K + G is plotted as a function of log KO,using eq 9 for G with 4 = 0.01, p = 0.2, and K given by eq 24. That is, K + G = (ku/Kow)+ O . O ~ W - ~ . * As shown in Figure 3 (bottom), a plateau is reached reflecting the effect of growth rate dominating the "loss" of chemical due not to excretion mechanisms but to the increase in the mass of the lipid pool. Chemical Assimilation Efficiency. The assimilation of the chemical through ingestion of food is also a lipoprotein membrane transport mechanism across the gut wall of the organism and into the blood stream. Some of the factors governing that transport would be expected to be similar to the factors affecting the efficiency of transport across the gill membrane. Other factors such as respiratory effects on gill transfer would not be expected to influence 702
Environ. Sci. Technol., Vol. 23, No. 6, 1989
chemical
isoquinoline acridine benz[a]acridine tetrachloroethane carbon tetrachloride malathion diazinon naphthalene naphthalene 1,4-dichlorobenzene 11 1,4-dichlorobenzene 12 kepone 13 lindane 14 1,2,4-trichlorobenzene 15 1,2,3-trichlorobenzene 16 biphenyl 17 1,3,5-trichlorobenzene 18 1,2,4-trichlorobenzene 19 diphenyl ether 20 anthracene 21 anthracene 22 anthracene 23 1,2,3,5-tetrachlorobenzene 24 2-biphenyl phenyl ether 25 pentachlorobenzene 26 pentachlorobenzene 27 pentachlorophenol 28 2,5-dichlorobiphenyl 29 2,5-dichlorobiphenyl 30 heptachlor 31 heptachlor 32 dieldrin 33 hexachlorobenzene 34 2,2',5-trichlorobiphenyl 35 2-ethylhexyl diphenyl phosphate 36 2,4',5-trichlorobiphenyl 37 DDT 38 DDT 39 2,2',5,5'-tetrachlorobiphenyl 40 Tetrachlorobiphenyl 41 PCB-1248 42 2,3',4',5-tetrachlorobiphenyl 43 TCDD 44 PCB-1260 45 mirex 46 mirex 1
2 3 4 5 6 I 8 9 10
fish log K KO, day-' log
1.82 3.30 4.45 2.60 2.64 2.9 2.9 3.3 3.3 3.37 3.37 3.8 3.85 4.2 4.11 4.09 4.15 4.2 4.21 4.31 4.3 4.3 4.48 4.55 4.94 4.94 5.0 5.3 5.3 5.38 5.38 5.48 5.50 5.59 5.7 5.77 6.0 6.0 6.09 6.1 6.1 6.23 6.6 6.9 7.5f 7.5f
2.63" 1.72" 1.14" 0.3 0.78 0.1 -0.1
-0.7 -1.7 0.0 -0.2 -1.82 0 -1.85d -0.34 -0.43 -0.4 0.23c -0.17 0.2 -0.96e -0.01 -0.583 -0.45 -0.96 -0.82 -0.82 -1.17 -0.96 -1.48 -1.09 -1.0 -1.24 -1.32 0.0 -1.68 -1.0 -2.4 -1.82 -2.10 -1.92 -2.0 -1.92 -2.40 -1.72 -4.0s
ref 21
21 21 22, 23' 22, 23' 24 25 26 26 18' 22, 23b 27 28 29 18b
22, 23' 18' 29 22, 23' 21
30 31 18' 22, 23' 18'
37' 33 32' 37' 33 35 28 22, 23' 32' 36 32' 28 28 32, 37b 38 39 32' 40 39 41 42
"For Daphnia puler. 'As cited in ref 19. c"Slown portion of biphasic excretion. "Fast" portion of biphasic excretion. 'For ovster. fFrom ref 14. SUutake from food.
gut transfer. Two hypotheses are therefore investigated: and (a) the assimilation efficiency is a constant for all KOw, (b) a is defined by the efficiency formulation used for the transfer across the gill (eq 22). Figure 4 (top) shows data obtained from laboratory experiments on the assimilation efficiency of chemicals. As seen, the data show both a constant a with KO,for some chemicals and an apparent decline of a with log KO, above -6.0 for other chemicals. In order to evaluate the response of the food chain model, two formulations for a = f(K,,,) were used: a = 0.5, independent of KO,,and a declining function of a with KO, using eq 22 as a guide. A further examination of a can be made indirectly by using laboratory data relating the concentration in the predator to that in the food. Using the efficiency formulations of eq 22 in eq 15d for a and comparing to reported laboratory data yields the results shown in Figure 4 (bottom). The data shown in Figure 4 (bottom) are from laboratory feeding experiments where f is measured. Again, there appears to be a general increase in the labo-
'1
0.9
LOG OCTANOL W A T E R
/L
8
6
4
LOG OCTINOL WATER P A R T I T I W COEF. KO*
PARTITION C O E F
10
KOY
Figure 5. Phytoplankton BCF as function of Kow. Squares from ref P
a
I
0 . 5 throughout
51; crosses from ref 52; diamonds from ref 48.
Table 11. Parameters Used in Model Calculations
param d
P
c
Y
P
0.60.4
a
-
w2 w3
2
4
6
B
WA
descriptn
value
in eq 9: G = 6w-8
0.01
in eq 1 0 r = ~
0.036
0.20 W Y
fractn lipid, levels 2-4 food assim effic, levels 2-4 wt of level 2 w t of level 3 wt of level 4
LOG OCTANOL WATER PIRTITIW COEF. KO"
Figure 4. Top: Chemlcal food assimilation efficiency data from laboratory compared to eq 15. Number key: 1 from ref 32, 2 from ref 43, 3 from ref 44, 4 from ref 42, 5 from ref 45, and 6 from ref 46. Bottom: Redatcdprey ratio from laboratory compared to eq 24a using eq 15 for a. Number key: 1 from ref 32, 2 from ref 47, 3 from ref 44, 4 from ref 42, and 5 from ref 41.
under the more realistic conditions of field growth. The field BCF from eq 11given the preceding expressions for k, (eq 21-23) and K (eq 24) is then
ratory predator/prey ratio up to a log K, of -5.5 and then a decrease at higher log KO,values.
where constant parameters are used across the food chain. From eq 9,21, and 23, and using the parameter values in Table 11, eq 26 is
Model Calibration Equations 12a and 15 can now be applied to a generic field situation through utilization of eq 21-24, which incorporate the hypothesized functional relationships with Kow. Equation 12a requires specification of the phytoplankton BCF and eq 15 requires calculation of the field BCF, both as functions of KO,. BCF for Phytoplankton. The above discussion applies to the food chain levels above the phytoplankton. The uptake and release of chemicals by the base of the food chain and the resulting BCF must be examined before proceeding to the incorporation of food transfer into the I model. Figure 5 plots reported phytoplankton laboratory BCF as a function of KO,using a lipid fraction of 0.01 (48). As shown, the phytoplankton BCF on a lipid basis is approximately equal to the KO,up to log KO,of -6. At log KO,greater than -6, the BCF appears to plateau. This may reflect varying cell densities (49,50). Lederman and Rhee (48) reported an observed decrease of the phytoplankton BCF with increasing population cell density. In the food chain model below, two functional relationships for the phytoplankton lipid-based BCF is used: (I) N,, = K , and (2) N,] = KO,for 2 I log KO,I 6, then constant at log N,, = 6.5. Field BCF. The mechanisms of uptake, excretion, and growth can be combined in a generalized first-order approximation to the expected field BCF, i.e., the BCF for an organism exposed only to the chemical in the water
[ "21
N , = KO, 1 + -
b
J
Several interesting points can be noted from this equation. The calculated field log BCF reaches a maximum of 5.5 at a log KO,of 6. At higher log KO,values, the field BCF decreases due to decreased efficiency of transfer (eq 27) and increasing effect of organism growth (eq 26). Equation 26 with actual growth rates for a given ecosystem is therefore suggested as the field BCF from which the concentration of chemical in an organism due only to water uptake can be estimated. Equation 26 indicates that at log K , greater than -6, the use of the KO,for the BCF is not correct and can lead to a significant underestimation of the difference between an observed concentration in the field and that expected from equilibrium partitioning into the lipid pool. Comparison to Field BAF Data. As noted above, two of the principal inputs to the model where there is uncertainty about the behavior of N with KO, are a,the chemical assimilation efficiency, and the phytoplankton BCF, Nlw.Accordingly, the comparison of the model to observed data is through a sensitivity of the model calculations to a and Nlw. In addition, a sensitivity to growth rate of the top predator is also presented. Using the parameters indicated in Table I1 and eq 15, the BAF for level 4, the top predator level, is compared to some reported field BAF values in Figure 6 by using Environ. Sci. Technol., Vol. 23, No. 6, 1989 703
Table 111. Data Used for Field Bioaccumulation Factors As Shown in Figure 6 (All Data Are for Fish Assumed As Level 4 of Food Chain)
fig 6 plot no. 1
2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41
*
location
organism
chemical
Lake Ontario Lake Ontario Lake Ontario Lake Ontario WN Pacific Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario WN Pacific Lake Ontario Lake Ontario Lake Ontario Lake Ontario Lake Ontario Oxbow Lakes, LA Hudson River Hudson River Hudson River Hudson River Lake Superior WN Pacific Lake Michigan Lake Michigan Lake Michigan Lake Michigan Lake Michigan Lake Michigan Lake Ontario Lake Ontario Siskiwit Lake, Ise Royal, MI
rainbow trout rainbow trout rainbow trout rainbow trout squid rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout squid rainbow trout rainbow trout rainbow trout rainbow trout rainbow trout largemouth bass largemouth bass largemouth bass largemouth bass largemouth bass lake trout PCB lake trout lake trout lake trout chinook, coho salmon lake trout lake trout rainbow trout rainbow trout lake trout
dichlorobenzene hexachloroethane lindane a-BHC ZHCH 1,2,4-trichlorobenzene 1,2,3,4-trichlorobenzene 1,2,4,5-trichlorobenzene methoxychlor hexachlorobutadiene pentachlorobenzene pentachlorophenol heptachlor epoxide hexachlorobenzene 2,5,2'-trichlorobiphenyl p,p'-DDE 2,5,2',5'-tetrachlorobiphenyl 2,3,2',3'-tetrachlorobiphenyl DDT y-chlordane 2DDT a-chlordane 2,4,5,2',5'-pentachlorobiphenyl 2,3,4,5,6-pentachlorotoluene dieldrin octachlorostyrene PCBs PCB (1254) PCB (1254) PCB (1254) PCB (1254) PCB (1254) 6.5 PCBs PCBs PCBs PCBs PCBs PCBs 2,3,6,2',4',6'-hexachlorobiphenyl mirex 26 organic compounds
log KO, 3.4 3.58 3.7 3.8 3.8 4 4.5 4.52 4.68 4.78 4.94 5.08 5.4 5.5 5.6 5.7 5.8 5.8 5.98 6
6 6
6.1 6.2 6.2 6.2 6.5 6.5 6.5 6.5 6.5 6.5 7.02 6.5 6.5 6.5 6.5 6.5 6.5 6.7 7.5 3.8-9.0
log BAF, L/kg(lp)
ref
2 53 54 54 5 54 54 53 2 53 53 2 2 53 54 54 54 54 2 54 5 54 54 54 2 54 57 55 55 55 55 56
3.18' 4.57' 4.1° 3.940 4.36 4.18O 4.98' 4.8" 5.40 4.70 5.330 4.1" 5.550 6.84" 6.87' 8.35' 7.38O 6.48" 7.8° 5.98' 6.82' 7.24' 8.02" 5.46" 6.57O 7.24 6.3* 6.9 6.51 6.67 6.8O 7.21e 5 7.lf 7.4c 6.95d 7.48 7.1d 7.65O 8.0P 8.27'a 4.3-8.8
4 4 4 4 4 4 54 54 58
'Using p = 0.08. bAt c = 50% total water concentration. cThe 90th percentile. dThe 10th percentile. eFor c = 0.8ng/L. fFor c = 5 na/L, median. #For c = 10 na/L, median. hloa K,, from ref 14. Table IV. Data Used for Field Predator/Prey Ratios As Shown in Figure 8
fig 8 plot no.
location
chemical
log KO,
predator
prey
u4/uB0
ref
1
Lake Superior WN Pacific Lake Ontario Finland Lakes Lake Ontario Finland Lakes Lake Huron Lake Ontario Lake Huron WN Pacific Lake Michigan Lake Michigan Lake Michigan Hudson River Sag. Bay Lake Ontario Finland Lakes WN Pacific Lake Huron Lake Ontario
lindane EHCH dieldrin HCB p,p'-DDE DDE DDE DDT DDT DDT PCB PCB PCB PCB PCB PCB PCB PCB PCB mirex
3.7 3.8 5.5 5.5 5.7 5.7 5.7 6 6 6 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 6.5 7.5
lake trout squid lake trout pike lake trout pike lake trout, walleye lake trout lake trout, walleye squid largemouth bass salmon lake trout largemouth bass largemouth bass lake trout pike squid lake trout, walleye lake trout
chub, sculpin myctophid smelt roach smelt roach sculpin, smelt smelt sculpin, smelt myctophid yellow perch yellow perch smelt yellow perch yellow perch smelt roach myctophid sculpin, smelt smelt
0.94 0.7 1.16 10.31 0.95 2.16 1.8 1.09 2.1 0.72 1.83 2.51 1.56 1.73 1.25 1.22 1.53 1.95 1.75 1.26
59 5 60 61 60 61 62
2 3 4 5 6 7 8 9
10 11 12 13 14 15 16 17 18 19 20
Concentration: rg/g(lp) in predator + rg/g(lp) in prey.
704
Environ. Sci. Technol., Vol. 23, No. 6, 1989
60
62 5 4 4
4 4 4 60 61 5 62 60
2y 2
I
I
I
I
I
6
4
I E
I
I 10
I 2
LOG OCTANOL WATER P A R T I T I O N C O E F . K O Y
Figure 6. Comparison of calculated BAF as function of KO, to data Using parameters of Table I1 and two assumptions on the chemical assimilatlon efficiency, a,and the phytoplanktonBCF. See Table I11 for key to references.
Figure 4 (top) for a. The data are largely from the Great Lakes and in some cases required assignment of lipid fraction or the fraction of the total water column concentration in dissolved form. These assignments are noted in Table 111. For all other cases, the reported tissue values and water concentrations were used to compute the BAF, or the reported BAF values were used directly. The BAF data are quite scattered and reflect the difficulty in measuring low water concentrations where small errors result in large changes in the BAF. Also, a steady state may not be operative in all cases, and sometimes a small number of samples may not fully represent a steady-state average. Nevertheless, the data clearly show a region of elevated BAF values in the region of log KO, of 5-7.5. The model calculations indicate that for level 4, the BAF reaches a maximum of 1order of magnitude higher than a simple no-growth lipid partitioning at a log KO, of -6.5 independent of the assumption on a. For log KO, above 6.5 and constant a = 0.5, the divergence between the calculated field BAF and field BCF increases. At log KO, of 8.0, the BAF is -4 orders of magnitude higher than the BCF, indicating that all of the chemical concentration in that region of KO, is due to food chain transfer. For the variable a case, the BAF decreases at log KO, > 6.5 and drops below the line N4= KO,. This is a direct consequence of the reduced chemical assimilation efficiency assumed for this region. To order of magnitude, the results of the model calculations provide a reasonable representation of the observed data from log KO, of -3.5-6.5, and such calculations do not depend significantlyon the functional form of a. Food chain accumulation becomes significant above log KO, of 5.0. Beyond log KO, of 6.5, the magnitude of the accumulation depends on the behavior of a as a function of K , But significant bioaccumulation is calculated in either case up to log KO, of 8. The sensitivity of the BAF for level 4 to the phytoplankton BCF is also shown in Figure 6. Again, up to log K , of -6.5, N4 is significantly above N4, regardless of the behavior of N1, and a. Therefore, bioaccumulation is calculated to be -2 orders of magnitude above an estimated field BCF. Beyond log KO, of 6.5, the various functional forms assumed for N,, and a significantly influence the BAF response. Thus, a constant a and constant phytoplankton BCF (curve B) is seen to result in a constant BAF for the top predators for log KO, > 6.5. On the other hand, a = f(Ko,) and N,, constant for log KO,
-
E
6
4
LOG OCTANOL WATER P A R T I T I O N C O E F .
10
KOY
Figure 7. Sensitlvtty of calculated BAF to growth rate of top predator.
1
4 l 3,5
-
4.110.31
I
Level t4/~eve1~3 P h y t n . ECF
Kow
Figure 8. Comparison of calculated predatorlprey ratio (v,lvg) to observed data a function of K, for two assumptions on the chemlcal assimilation efficiency, a. See Table IV.
> 6.0 (curve D)show a marked decline of N4with KO,so that at log KO,of about 8-9, there is little or no bioaccumulation. Clearly this figure indicates an area for further investigation of the behavior of N,, and a in the region of log KO, > 6.5. Figure 7 shows the sensitivity of the calculation for level 4 to the growth rate of the top predator. The sensitivity to the growth rate is also significant and shows that for zero growth of the top predator the worst case BAF occurs, as would be expected, since under that condition the only mechanism for reduction of concentration is through excretion. An additional comparison can be made to observed field values of the predatorlprey ratios. The ratio of v4/v3 as calculated from eq 12 is shown in Figure 8 and compared to reported data. These data do not suffer from one drawback of the BAF data with respect to the uncertainty in the water concentration since that quantity does not have to be measured for the predatorlprey ratio. The observed field data for the ratio are generally only in the narrow log K , region of 5-7. In that region both the data and the model show food chain magnification ( v 4 / v 3 > 1) with a peak magnification at log KO,of -6-7. As shown in this figure, for a = f(Kow), v4/v3is calculated to be unity up to a log KO, of -5.5, indicating no food chain accumulation of the chemical. The ratio then increases to a peak value at log KO, of -6.0. At higher KO, levels, the ratio drops below unity and approaches zero. This reflects the “crossing over” of the food chain gradient at the higher KO,values. Constant a = 0.5 results in a plateau for log KO, > 7.0. Figure 9 shows the calculations for each of the trophic levels and provides an interesting projection of the food Environ. Scl. Technol., Vol. 23, No. 6, 1989 705
10
aOC 9
=no0 CHAIN
-EVEL
ni
awd C
C‘
E G K KO, k ’u N NW P r
V 2
W CY
P Y 6 V Vf
vm VW
4
oxygen/carbon ratio wet/dry weight ratio concentration of chemical in water (wg/L) specific food consumption rate [kg(w)/kg(w).day] efficiency of transfer of chemical net organism growth rate (day-’) chemical excretion rate (day-l) octanol-water partition coefficient chemical uptake rate [L/day.kg(w)] lipid-based bioaccumulation factor [L/kg(lp)] lipid-based bioconcentration factor [L/kg(lp)] fraction lipid weight [kg(lp)/kg(w)] organism respiration rate (day-’) ventilation volume (L/day) wet weight of organism [kg(w)] chemical assimilation efficiency exponent in growth rate eq 9 exponent in respiration rate eq 10 coefficient in growth rate eq 9 concentration of chemical in organism [pg/kg(lp)] organism chemical concentration due to food only [/.dkg(lp)l chemical whole body burden (pg/organism) chemical concentration in organism due to water only, [Pg/kg(lP)l coefficient in respiration rate eq 10
Acknowledgments Special thanks are given to colleagues John P. Connolly, Dominic M. Di Toro, and Donald J. O’Connor for their helpful criticism and discussion opportunities. The extended and insightful comments of an anonymous reviewer are most appreciated. Grateful acknowledgement is also offered to Eileen Lutomski for her dedicated typing of t h e manuscript. Registry No. DDT, 50-29-3; PCB-1248,12672-29-6;TCDD, 1746-01-6; PCB-1260, 11096-82-5; a-BHC, 319-84-6; p,p-DDE, 72-55-9;PCB-1254,11097-69-1;isoquinoline, 119-65-3;acridine, 260-94-6; benz[a]acridine, 225-11-6; tetrachloroethane, 25322-20-7; carbon tetrachloride, 56-23-5; malathion, 121-75-5; diazinon, 333-41-5; naphthalene, 91-20-3; 1,4-dichlorobenzene, 106-46-7; kepone, 143-50-0;lindane, 58-89-9; 1,2,4-trichlorobenzene,120-82-1; 1,2,3-trichlorobenzene, 87-61-6; biphenyl, 92-52-4; 1,3,5-trichlorobenzene, 108-70-3; diphenyl ether, 101-84-8; anthracene, 120-12-7; 1,2,3,5-tetrachlorobenzene, 634-90-2; 2-biphenyl phenyl ether, 6738-04-1;pentachlorobenzene,608-93-5; pentachlorophenol, 87-86-5; 2,5-dichlorobiphenyl, 34883-39-1; heptachlor, 76-44-8; dieldrin, 60-57-1; hexachlorobenzene, 118-74-1; 2,2’,5-trichlorobiphenyl, 37680-65-2; 2-ethylhexyl diphenyl phmphate, 1241-94-7; 2,4’,5-trichlorobiphenyl,16606-02-3;2,2’,5,5’-tetrachlorobiphenyl, 35693-99-3; tetrachlorobiphenyl, 26914-33-0; 2,3’,4’,5-tetrachlorobiphenyl, 32598-11-1;mirex, 2385-85-5; hexachloroethane, 67-72-1; 1,2,3,4-tetrachlorobenzene,634-66-2; 1,2,4,5-tetrachlorobenzene, 95-94-3; methoxychlor, 72-43-5; hexachlorobutadiene, 87-68-3; heptachlor epoxide, 1024-57-3; 2,5,2’-trichlorobiphenyl, 37680-65-2; 2,5,2‘,5’-tetrachlorobiphenyl,3569399-3; 2,3,2’,3’-tetrachlorobiphenyl,12789-03-6; a-chlordane, 5103-71-9;2,4,5,2’,5’-pentachlorobiphenyl, 37680-73-2;y-chlordane, 5566-34-7; 2,3,4,5,6-pentachlorotoluene,877-11-2; 2,3,6,2‘,4‘,6‘hexachlorobiphenyl, 68194-08-1.
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Received for review May 7, 1987. Revised manuscript received May 31, 1988. Accepted February 7, 1989. This work was partially supported by a grant (NSFIENG-87053) from the National Science Foundation to Manhattan College.
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