The prediction of bioconcentration in fish

Water Res. Vol. 18. No. 10, pp. 1253-1262, 1984 Printed in Great Britain

0043-135..1. 8 4 5 3 . 0 0 + 0 . 0 0 Pergamon Press Lid

THE P R E D I C T I O N OF BIOCONCENTRATION IN FISH R. P. DAVIES and A. J. Donas Department of the Environment, Building Research Establishment. Princes Risborough Laboratory,, Princes Risborough, Aylesbury, Bucks HPI7 9PX, England (Receit'ed April 1983)

Abstract--Fish bioconcentration factors (BCF) are used lbr the prediction of the environmental effects of new chemicals and some studies have proposed that they can be predicted from physico-chemical properties of the chemical. Values have been taken from the literature for octanol:water partition coeffacients (P) and water solubilities (WS) of chemicals and their correlations with BCF assessed. The values were only accepted for analysis if they satisfied certain experimental criteria intended to ensure strict comparability. The BCF correlations with both P and WS were highly significant statistically and similar to those obtained in other studies; however, the range of chemical types was limited, most being organochlorines of relatively low molecular weight. It is concluded that further data of comparable quality are needed before the predictive power of the correlations can be properly assessed. Methods of measuring BCF and factors influencing the measured values are discussed. Comparisons of laboratory and field BCF values are made and although the data are very limited, the values are in reasonable agreement.

INTRODUCTION

The European Economic Community (EEC) has recently introduced a New Chemicals Notification Scheme (EEC, 1979). This requires that all chemicals newly marketed within the EEC must first be assessed for potential environmental hazard by government authorities of one of the member states. Test methods for assessment schemes have also been proposed by the Organisation for Economic Co-operation and Development (OECD, 1981a, b, c). Among the potential hazards is that of bioconcentration, which is of concern for two reasons. First, an organism can build up residues of chemical(s) without being harmed by them but, subsequently, predators upon that organism may suffer toxic effects. This possibility has been demonstrated with a number of chemicals and species--the classic example being mortality in fish-eating birds in Clear Lake, California, caused by DDD (Hunt and Bischoff, 1960); more recently, a similar effect has been shown for organophosphates (Hall and Kolbe, 1980). Second, some stages in the fish life-cycle take up and tolerate chemicals which later either become toxic during periods of stress or which are passed on to produce toxic effects in more susceptible stages. For example spawning in fish can lead to toxic effects in the parent fish (Mayer et al., 1975), and transfer of toxicant to the ova (Mayer et al., 1975; Parrish et aL, 1977, 1978). Fish bioconcentration is assessed from the bioconcentration factor (BCF), defined as the ratio of the concentration of the chemical in whole fish based on wet weight (Cf) to that in water (Cw) at steady state. ,© Crown Copyright 1984.

This study is concerned initially with establishing the reliability of relationships between physicochemical properties and the extent of accumulation in fish tissue. It then discusses some of the factors involved in bioconcentration measurement and interpretation. The value of a relationship between bioconcentration and physico-chemical properties, octanol:water partition coefficient (P) and aqueous solubility (WS), lies in predicting results of the expensive and time-consuming BCF test from those of relatively cheap and simple determinations to provide a preliminary environmental safety assessment of a chemical. Several attempts have been made to establish such a relationship by correlating bioconcentration data for various test organisms with physico-chemical properties of the chemicals; the correlations derived using fish are given in Table 1. The shortcomings of some of the data used in those studies prompted the present critical evaluation of the correlations using data obtained under closely defined and comparable experimental conditions. The partition coefficient between water and a lipid solvent is a simple model for prediction of bioconcentration. Many lipophilic compounds are known to accumulate in tissue lipids and chemicals must pass a biological membrane partly composed of phospholipids during uptake. Octanol has been used most widely in such studies (Hansch and Leo, 1979) and P has been correlated empirically with a wide range of types of biological activity besides bioconcentration (Hansch, 1980). Water solubility has been shown to be related inversely to P (Chiou et al., 1977; Kenega and Goring, 1980; Banerjee et al., 1980) although for solids an additional factor to take account of the difference between the aqueous solubility of the solid 1253

R. P. D,~t~ and A+ J. DOBBS

1254

Table 1. Published correlations between BCF a n d P. W S E q u a t i o n (log B C F =

)

Conditions

Reference. Notes

W a t e r Solubility 3.995 - 0 . 3 8 9 (log W S )

WS (ppb). r = -0.923.

n = 11. t = 7.20

Lu

and Metcalf ( 1 9 7 8 )

static water model ecosystem 3.*t - 0 5 0 8 ( l o g W S ) 2.791-0.564

(log W S )

W S ( # m o l l - ~ ) , r ~ - 0 . 9 6 4 . n = 8. t = 9 . 0 4 flowing water W S ( p p m ) , r = - 0 . 7 2 . n = 36+ t = 6 0 5

Chiou et al. ( 1 9 7 7 ) ( B C F data from Neely et al.. 1974) Kenaga and Goring (1980)

flowing water 2.183-0.629

(log WS)

W S ( p p m L r = 0 . 6 6 , n = 50, t = 6 . 0 9

Kenaga and G o r i n g ( 1 9 8 0 )

static water model ecosystem 3.71 - 0 . 3 1 6

(log W S )

WS (ppb), r = -0.565,

n = 25. t = 3.28

(Recalculation from Veith et

flowing water

al 1980)

5 . 0 9 - 0 . 8 5 (log W S )

WS (ppb), r = 0.87, n = II. t = 529

2.83-

W S ( p p m ) , n = 42,

0.55 (log WS)

static water model ecosystem flowing water

OctanoI-Water

Kobayashi (1981) by extrapolation from Fig. 2

Partition Coefficient

0 . 5 4 2 (log P ) + 0 . 1 2 4

r = 0 . 9 4 8 , n = 8, t = 7 . 3 0

0 . 9 3 5 (log P ) -

r = 0 . 8 7 , n = 26, t = 8 . 6 4

Neely et al. ( 1 9 7 4 ) - fish muscle only analysed Kenaga and Goring ( 1 9 8 0 )

flowing water 1.495

Metcalf et al. ( 1 9 7 3 )

flowing water 0 . 7 6 7 (log P ) - 0 . 9 7 3

r = 0 . 7 6 . n = 36, t = 6 . 8 2

Kenaga

and Goring ( 1 9 8 0 )

static water model ecosystem 0 . 8 5 (log P ) -

0.70

r =0.947.

Veith et al. ( 1 9 7 9 ) , P estimated chromatographically Veith et aL ( 1 9 8 0 )

n = 55, t = 2 1 . 4 6

flowing water 0 . 4 5 6 (log P) + 0 . 6 3 4

r = 0 . 6 3 4 . n = 25, t = 3+93

0 . 6 3 4 (tog P) + 0 . 7 2 9

r =0.788.

0 . 7 4 (log P ) -

static water model ecosystem n = 40, flowing water

flowing water 0.77

n = I1, t = 3 . 8 4

Lu

and Metcalf ( 1 9 7 5 )

Kobayashi (1981) by extrapolation from Fig. 3 r ~ correlation coefficient for the regression, n = number of data points, t = S t u d e n t ' s t for regression.

and supercooled liquid phases is required (Mackay et al., 1980). SELECTION

OF DATA

Data on BCF, P and WS were selected from the published literature according to the following criteria: (l) Only experimental determinations were used. (2) Data were included only from studies in which it appeared that a steady state had been reached. (3) BCF data were taken only from flow-through studies. (4) Only studies using freshwater fish were considered. (5) Data were included only from studies in which acclimatisation to test conditions had been carried OUt.

(6) BCF values obtained immediately after spawning were disregarded. (7) Studies using concentrations of chemicals which could be shown from the literature to be chemically toxic were not considered. (8) Data were included only from studies in which Cw was monitored directly by sampling and analysis. (9) Data were included only when P had been determined experimentally, either in a shake-flask or a sealed centrifuge tube, the latter being required for volatile compounds. (Despite this criterion there are still several sources of variation; for example the pH of the aqueous phase was seldom measured.)

(10) Data on WS were included only if this property was measured by a method which excluded contributions from suspended particulates. For example the methods of Banerjee et al. (1980), Weil et al. (1974) and Biggar and Riggs (1974). A number of determinations of BCF, P and WS were included where liquid scintillation counting of a radiolabelled test compound was used without confirmatory analysis; potential sources of error with this approach include the presence of metabolites (in BCF tests) or of labelled impurities, particularly relatively water-soluble ones. Many papers and reports were studied but the above criteria were met, or could be confirmed, in relatively few. In these (Table 2) the range in BCF was from 3 to 100,000, that of P was from 8 to 5.56 x 106 and the WS varied between 1.7 x 10-6-208 gl -t. DISCUSSION

Relationship of BCF with P

or

WS

Despite the wide range of values displayed by BCF, P and WS there was not a correpsonding diversity of chemical structure; most of the chemicals were organochlorines and many of them were small molecules of relatively low molecular weight. It is disappointing that so many of the literature studies of bioconcentration were not able to satisfy our criteria and that the majority of the acceptable data came

The prediction of bioconcentration in fish

1255

Table 2. Bioconcentration factors and physico-chemical properties for ~etected chemicals

Chemical

Logt,) BCF*

Hexachlorobenzene Dieldrin Endrin Pentachlorobenzene

5.0. 4.27. 4.1. 3.70. 3.53.

56. 32, 32. 112. 28.

GC GC GC GC LS

6,36 5.56 4.32

Butyl benzyl phthalate Pentachlorophenol Di(2-ethylhexyl) phthalate 2-Methyl naphthalene Acenaphthene

2.89. 2.89, 2.76. 2.61, 2.59,

2l. 32. 56, 26. 28.

LS GC LS. GC LS LS

405 5.01 5.11 4.11 3.92

Acrolein 2-Chlorophenol •/-Hexachlorocyclohexane 2,4-Dimethyl phenol :c-Hexachlorocyclohexane

2.54. 2.33. 2.26, 2.18, 2.15,

28. 28, 32. 28, 4,

LS LS GC LS GC

0.90 2.16 3.66 2.42 3.89

Hexachloroethane Diethyl phthalate 1.2-Dichlorobenzene Pentachloroethane 1,3-Dichlorobenzene

2.14, 2.07. 1.95. 183, 1.82,

28, 21, 14. 14, 14,

LS LS LS LS LS

1,4-Dichlorobenzene Dimethyl phthalate Tetrachloroethylene Carbon tetrachloride Trichloroethylene

1.78, 1.76, 1.69, 1.48. 1.23,

14, 21, 21.

bis(2-Chloroethyl) ether 1,1, I-Trichloroethane I, [,2,2-Tetrachloroethane lsophorone Chloroform

1.04, 0.95. 0.90, 0.85, 0.78,

IBP (S-benzyl O,O-di-isopropyl pbosphorothioate) Atrazine 1,2-Dichloroethane

p.p'-DDT

Reference~ ............. BCF P

Logt, ) WS÷ Logt,) P (#g I ~)

WS

Fish species~ and temperature (:C)

2 21 2 2 I

FHM, 25 FHM, 25 G. 21 FHM, 25 BG. 16

5

BG. 16 FHM, 25 FHM. 25 BG, 12 BG. 16

023 099 1.34 1.38 3. I 1

6. I 1 I5, 19. 25 7, 23 12 26

4 20 13

3.46

26 22, 24. 25 17 18 26

26 10 20 14 I

8.32 7.06 3.96 6.90

26 26 3.25 26 3

26 1 13 I 27

28 1 21 I

BG, 16 BG. 16 FHM, 25 BG, 16 G, 24

3.93 1.40 3.71 2.89 3.44

4,43 6.85 5.19 5.89 5.13

26 26 26 26 26

26 26 I, 20 26 I

26 26 I 26 I

BG, BG, BG, BG. BF.

16 16 16 16 [6

14,

LS LS LS LS LS

3.37 1.61 2.53 2.73 3.30

4.87 6.63 5.69 5.88 6.17

26 26 26 26 26

1 26 I I 20

1 26 1 1 1

BG. BG, BG, BG, BG,

16 16 16 16 16

14, 28, 14, 14, 14,

LS LS LS LS LS

1.12 2.47 2.39 1.67 1.90

7.24 6.19 6.47 7.16 6.86

26 26 26 26 26

26 1 1 26 I

26 I 1 26 1

BG. BG. BG, BG, BG,

16 16 16 16 16

0.60,

14.

GC

3.21

13

13

0.48, 0.30,

8, 14.

GC LS

1.45

8 26

1

21,

4.94

4.40 3.59

4.70 6.90

I

16 16

P, 20 9 I

C. 12 BG, 16

*Figures refer respectively to the Log,0 BCF; time to equilibrium (days); analytical methods (LS = liquid scintillation counting. GC = gas chromatography). fLog,0 WS at 20 or 25~C. ++FHM = fathead minnow (Pimephales promelas); BG = bluegill sunfish (Lepomis macrochirus); G = guppy (Poecilia reticulata); P = Pseudorasbora parva; C = Corygonus fera.

§*Banerjee et al. (1980); 'Biggar and Riggs (1974); 3Canton et al. (1975); 4Chiou et al. (1982); SGledhill et al. (1980); ~Grzenda et aL (1970); SGrzenda et al. (1972); ~Gunkel and Streit (1980); OGunther et al. (1968); *°Hansch and Leo (1979); lIJarvinen et al. (1977); *-"Jarvinen and Tyo (1978); a3Kanazawa (1981); 14Karickhoffet aL (1979); ISK6nemann and van Leeuwen (1980); *6Mackay and Shiu (1977); *TMayer (1976); I~Melancon and Lech (1979); IL'~'4iimiand Cho (19811; mOECD (1981a); 'tOECD (1981b): Z-'Parrish et aL (1978); ~Reinert (1972); Z'*Trujillo et aL (1982); -"sVeith et al. (1979); Z6Veith et al. (1980); 'TWilliams (1982); Z~Worthing (1979).

from a single study (Veith et al., 1980; Banerjee et al., 1980). The data from Table 2 are plotted in Figs I and 2 together with the least mean squares regression lines and the 95% confidence limits of the regression. The regression equations are as follows: WS vs BCF log10 BCF = 4.358 - 0.444 [log~0 WS (/ag 1-t)] r = -0.803 n = 2 9 , t =7.00 P vs BCF logl0 BCF = 0.597 (Iogt0 P) + 0.188 r = 0.748 n = 31, t = 6.07 WS (nM) vs BCF (not plotted) logl0 BCF = 3.052-0.356 [log10 WS (/~ M)] r = -0.824 n = 2 9 , t =7.56.

Although all the above regressions are highly significant statistically it is possible to obtain more reliable correlations (Fig. 3) by restricting the range of chemicals to the hydrocarbons and chlorohydrocarbons included in Table 2. When the regressions between BCF, P and WS for the remaining (non-hydrocarbon or chlorohydrocarbon) chemicals were examined these were not found to be statistically significant at the 95% level, which suggests that the significant correlations noted above may not be generally applicable. The data in Table 2 give a markedly better correlation between BCF and WS than between BCF and P, particularly when WS is quoted in molar units (this applies whether all the data or only the hydrocarbon/chlorohydrocarbon data are used). With such a small data base and a relatively large spread of data it is not possible to determine the significance of the better correlation with water solubility quoted in molar units.

1256

R.P. DAv[~ and A. J. DOBBS ,a

i

\ \

L

\\

0 ! ~-

"[

i 2

I 3

O0

L 4

I 6

3 0 "oN 7

[ 8

I 9

L o g , e W S (H-g L-')

2

4 Log

6

8

P

Fig. 3. Hydrocarbon and chlorohydrocarbon data taken from Table 2. Regression analysis: Log BCF = - 1.30 + 0.98 log P; r = 0.898: n = 20; t = 8.66.

Fig. I. BCF plotted against water solubility (WS).

The regression lines obtained above are comparable with those derived from other non-static studies (see Table 1) particularly at high log P and low log WS. However the agreement may be deceptive because there is a general lack of reliable determinations in this region and the different studies tend to use the same data. Clearly more work is needed before the predictive power of P and WS can be judged critically, but at present the evidence suggests that upper limits to BCF may be predicted from P or WS since observed BCF values rarely fall outside an order of magnitude greater than the predicted value. Therefore if P or WS were used to screen chemicals the appropriate approximate values of P and WS for given BCF values would be as follows:

P WS

BCF < 102

BCF < 103

<20 >10,000ppm

< 10~ >200ppm

Deviations f r o m the B C F / P relationship There is some evidence that chemicals that are extremely hydrophobic do not bioaccumulate as much as would be expected based on the correlations available in this study and elsewhere (Table 1); a study of the accumulation of halogenated biphenyls

6

S

0

4

b

0 --

0

0

0

4

5

_J 2

1

1

2

3

6

7

Lo%oP

Fig. 2. BCF plotted against octanol/water partition coefficient (P).

in the killifish (Oryzias latipes) suggested that BCF is proportional to P below log,0 P of 6, but above this level BCF does not increase proportionately and may decrease (Sugiura et al., 1978). However these BCF values were not obtained at "steady state" and the P data were estimated from chromatographic (HPLC) retention times and there is no convincing evidence that HPLC gives realistic values for log,0 P > 6. Another study, of a range of types of dyestuffs with measured log,0 P between - 3.0 and + 5.1 and calculated log,0 P up to 9.5 showed no relation of BCF to P. None of the chemicals had a BCF > 100 (Anliker et al., 1981). Dyestuffs tend to be large molecules and the dispersed (non-ionised) dyestuffs used in the study had molecular weights in the range 450--550; it is possible that this limited the movement of the chemicals into the fish. Compounds are metabolised to different extents in fish and many chemicals may be more readily metabolised than those included in this study. Organochlorines are generally recognised as being resistant to biodegradation and the occurrence of biotransformation may be the principal reason for the non hydrocarbon and chlorohydrocarbon chemicals failing to give a significant correlation of BCF with P or WS. The range of transformation reactions demonstrated in vivo in fish includes hydrolysis, oxidation, acetylation, dealkylation and conjugation (Lech and Bend, 1980). Estimates of the quantitative effect of such metabolism on bioaccumulation are extremely difficult because in addition to other factors the activities of the enzymes involved have been found to vary widely within and between species, with season and with ambient temperature (Pyysalo et al., 1981; Neff, 1979; James et al., 1977). Bioaccumulation studies have demonstrated in vivo conversion to varying extents: approx. 50% of heptachlor residues were in the form of heptachior epoxide after 24-day exposure of spot (Schimmel et al., 1976); in the study using di(2-ethylhexyl) phthalate quoted in Table 2, from 20 to 70% (increasing with Cw) of the residues were in the form of metabolites after 56 days exposure; and approx. 90% of the residues in fathead minnows after 7 days exposure to benz(a)acridine were in the form of(unidentified) metabolites (South-

The prediction of bioconcentration in fish worth et al.. 1981). An additional complication is that activities of metabolic enzymes can be increased ("induced") by exposure to a range of environmental contaminants, e.g. petroleum hydrocarbons (Gruger et al., 1977), PCB's (Lidman et al., 1976). This effect has been shown in the field; for example, fish exposed to oil spills showed increased levels of enzyme activity (Kurelec et al.. 1977). The practical implication of this is that additional information may be obtained if efforts are made to detect metabolites in BCF testing. The B C F f a c t o r and its measurement

In evaluating BCF as a predictive datum for environmental behaviour it is necessary to take two considerations into account: How reproducible BCF measurements are; whether they are repeatable and what experimental factors affect the value? How do laboratory BCF values relate to uptake and retention of the same chemicals present in natural waters?

1257

fish often increase in weight during a long trial, so that although net uptake of a chemical may be continuous, and no steady state reached, the tissue concentration levels off with time as a result of "'growth dilution" rather than elimination. For this reason it is important that any weight changes of the test fish are measured and reported. In an attempt to reduce the duration and expense of BCF testing, a more rapid "'kinetic" method has been devised (Neely et al., 1974; Branson et al., 1975)*. This method depends on the assumption that an estimate of equilibrium BCF can be derived from kinetic data. Fish and water are envisaged as two compartments and uptake and clearance of a chemical as a reversible reaction: Assuming first order kinetics, the change with time of Cf is dCf dt

- =

where k~ and kz are constants at steady state when dCf --=0 dt

Repeatability

There is a paucity of information on the repeatability of the BCF test. The accumulation of the PCB Aroclor 1016 in adult fathead minnows (Veith et al., 1979) has been reported in one thrice repeated experiment where BCF ranged from 30,200 to 50,000 and in a further pair of experiments (using fish from a different source) the results obtained were 37,200 and 61,500. However it is not possible to tell from the published data whether a steady state condition had been reached. Variation within replicated experiments has been reported only rarely. Three examples are given in Table 2 where the standard error as a percentage of the mean ranges from 7 to 28~. Variability within the test population tends to increase with duration of exposure (Mayer, 1976; Melancon and Lech, 1979; Reinert, 1972; Buckler et al., 1981) giving increased variability for compounds with high BCF, which require longer test periods to reach steady state.

kt Cw-kzCf

k~ k,

-

Cf Cw

= BCF.

Thus the rate constants obtained during the early stages of accumulation and elimination in short term experiments are used to calculate BCF. The principal disadvantage with the method is that the uptake and elimination kinetics are more complex than the model implies. For example elimination rates have been shown to be time dependent (Kfnemann and Van Leeuwen, 1980; Trujillo et al., 1981; Melancon and Lech, 1979) and to be different during continued exposure from those obtained after exposure had ceased (Grzenda et al., 1972). The few data allowing direct comparison between "kinetic" and *'steady state" BCFs are listed in Table 3. The same trend in BCF values is evident in both sets of data. It is notable, however, that for corn-

Steady state and kinetic approaches to B C F

It is generally assumed that when a fish is exposed to a chemical a steady state can be reached where the uptake and elimination rates are equal. Tests using chemicals with BCF less than about 1000 tend to show that a plateau level of BCF is reached within about 30 days. However, with highly accumulative organochlorines, the plateau level may not be attained in a practical period, for example with the pesticide mirex no plateau occurred in a trial lasting 120 days (Buckler et al., 1981). In addition the test *After a short period of exposure, during which the uptake rate is determined, the fish are transferred to clean water and the elimination rate of the chemical from the fish is measured.

Table 3. "Kinetic" B C F values "'Kinetic" Steadystate Compound BCF* BCFt DDT

Hexachlorobenzene Tetradecylhepta

52,358.~ 7880 850**

100,000 18,600 700**

ethoxylate

Sodium dodecylbenzene

286~

20~

sulphonate

1,4-Dichlorobenzene

215

60

Diphenyloxide

190

470~r

Tetrachloroethylene Carbon

tetrachloride

39.6

49

17.7

30

*Values from Ncely et at. (1974) for analysed rainbow trout muscle tissue (except where noted). ")'From Table 2 (except where noted). **Values for bluegill sunfish, Bishop and Maki (1980). §Value for carp from Wakabayashi et al. (1978).

¶Value for rainbowtrout from Bransonet at. (1979).

R. P. Davx~ and A. J. DoBBs

1258

Table 4. Speciesdifferencesin BCF Species

Compound DDT Toxaphene Hexachlorobenzene 1.2,-1.-trichlo robenzene di(2-Ethylhexyl) phthalate

Fathead minnow

Bluegill sunfish

Green sunfish

Rainbow trout

Brook trout

( Pimephales promelas )

( Lepomis macrochirus )

( Lepomis c yaneUus )

I Salmo gairdneri )

(Salvelinus fontinalis )

100,000 (25 C) 69,450 (25 C ) t 18,500 {25-C) 2800 ( 15 ~C)~ 569 (25~C)

51.355 (15:C)" 5000 ( t 6 :C)~ 183 ( L6:C)'112 (16C) 'r

2 [ ,900 ( 15 C)~ 2300 ( 15 :C),]

5500 ( 15 :C}~ 890 ( 15 :C)~

*Reinert et al. (1974); tMayer et al. (1977)" ~.Mayer et al. (1975): §Veith et aL (1979'J; *'Barrows et aL (1980); other data from Table 2.

pounds with higher BCF. the kinetic values are less than the steady state values.

Experimental factors Aqueous chemical concentration, Cw. BCF can vary widely with Cw, though the pattern of variation differs with different chemicals. Where a decrease in BCF with increased Cw has been observed it is probable that, above a certain Cw, the compound taken up induces enzymes involved in its metabolism. This occurs for example with [~4C]di(2-ethylhexyl)phthalate, where a higher proportion of the total ~4C retained at higher Cw levels consisted of metabolites: BCF decreased from 569 to 91 with Cw increase from 2.5 to 62 p g 1-~ (Mayer, 1976). In contrast an increase in BCF with Cw has been found with toxaphene in brook trout (up from 5000 to 16,000 with an increase in Cw from 39 to 502 ng l -~ (Mayer et al., 1975) and with heptachlor in the estuarine fish spot (Leiostomus xanthurus), where BCF approximately doubles from 2430 to 5130 with an increase in Cw from 0.14 to 1.03#gl -~ (Schimmel et al., 1976). This could be associated with a toxic effect on the fish that increases uptake and is usually associated with a value of Cw which has been found to be chronically toxic e.g. reproductive impairment. Whatever the explanation behind these variations, the practical implication is that more than one Cw should be used in the BCF tests; this will improve the precision of the results and may provide information on metabolism or toxicity, valuble for prediction of environmental effects. Temperature. Measured BCFs have been shown to increase with temperature, for example the BCF for DDT in rainbow trout (Salmo gairdneri) increases from 4500 to 8000 in the range 5-15°C (Reinert et al., 1974). The effect may be due to an increase in the rate of chemical uptake by the fish. This has been shown to occur for a polychlorobiphenyl with perch (Perca fluviatilis) (Edgren et al., 1979) and DDT with perch and mosquito fish (Gambusia affinis) (Edgren et al., 1979; Murphy and Murphy, 1971). In both these studies, elimination rates were little affected by temperature. However the effect of temperature on rate of uptake is not uniformly observed. The BCF of Aroclor 1254 in rainbow trout decreases between 5 and 10~C but increases between 10 and 20~C. In

contrast, over the range 5-20°C, the BCF for fathead minnows and green sunfish has been observed to increase with a rise in temperature (Veith et al., 1979). Type and condition offish. Table 4 sets out the available comparative data on the influence of fish species on measured BCF. The data suggest that test species has a major influence on the BCF value obtained and as several species are specified in different OECD test guidelines (OECD, 1981a, b, c), this influence and the effect of genetic differences within the stock, clearly requires more detailed investigation. Some of the differences in Table 4 may be due more to influence of species weight or size than to species itself. Most laboratory studies tend to use small fish (wet weights 30 mg to 3 g) and more information is needed on the influence of fish size. The only study in this area relates to fathead minnows and hexachlorobenzene (Veith et al., 1979) covering the age range 30 days to "adult" (weight range 30mg to 1-2 g); the BCF values were very similar for all ages and within expected error limits. Short term studies show as expected that uptake rates per unit fish weight are larger for smaller fish (Murphy and Murphy, 1971; Matsumura, 1977; Murphy, 1971). Very young fry, tested before the yolk sac has been fully absorbed, show higher BCF than feeding fish (Veith et al., 1979; Mayer et al., 1975; Mac and Seelye, 1981). There is some evidence to indicate that differences in BCF between fish are smaller when Cf is calculated on a fat weight basis. This has been observed in both field (Veith, 1975) and laboratory studies (Lieb et aL, 1974). Total lipid content is variable within species (Jarvinen et al., 1977; Mayer et al., 1977; Veith, 1975; Leib et al., 1974) and would be valuable additional information in BCF tests. Bioconcentration in salt and fresh water. The available comparative data (Table 5) indicate that BCF values are generally lower in salt than in fresh water fish species. The exception is the BCF values for ~-HCH which in contrast to the other values were both obtained using the same fish species--the guppy. Since this is an isolated example it is not possible to comment on its general relevance. The reduced BCF values may be related to reduced uptake rates since two studies of the uptake of

The prediction of bioconcentration in fish

Compound Heptachlor Hexachlorobenzene Methoxychlor Pentachlorophenol :t-Hexachlorocyclohexane

Table 5. BCF values for fish in fresh and salt water BCF Fresh water Salt water Reference, comments 9500 2200 Veith et aL (1979), steady state not proven; Schimmel et aL (1976) 18.500 375 (Table 2); Cian et al. (1980) Veith et al. (1979), Steadystate not proven; 8300 264 Par'fish et aL (1977) 770 53 (Table 2); Trujilloet aL (1981) (Table 2): Canton et al. (1978) 140 464

organochlorines by fish have shown that the rate of uptake is decreased at increased salinity (Tulp et al., 1979; Murphy, 1970).

Relevance o f laboratory B F C measurements to f i e M conditions

Fish in their natural environment are exposed to pollutants in different ways from those used in the laboratory tests of BCF. Duration of exposure will be variable and may be continuous or seasonal/episodic. In addition, the aqueous concentrations of pollutants may vary widely and the pollutant may be partially adsorbed on particulate matter rather than in true solution. Furthermore, unlike the normal situation in BCF testing, all stages of the fish life cycle can be exposed to a chemical in the field. Fish in the wild may also eat contaminated food which might increase the residues they carry compared with fish exposed only to pollutants in the water. For example, when DDT is present at extremely low (ng 1-~) aqueous concentrations the main source of fish residues is the food they consume, because the rate of direct uptake from water is very low (Macek and Korn, 1970). Laboratory experiments using aqueous concentrations of chemicals in the/zg 1-) range indicate that residues can be accumulated from either contaminated diet or ambient water. In most cases, direct uptake from water will lead to a tissue residue concentration that is comparable to that expected where both water and food are contaminated (Canton et aL, 1975; Reinert, 1972; Jarvinen et al., 1977; Macek et al., 1979; Jarvinen and Tyo, 1978; Chadwick and Brocksen, 1969). The natural environment will be polluted by a number of different chemicals and interactions may occur whereby residues of one compound affect the uptake or retention of others; this has been shown for three chlorinated hydrocarbon insecticides (Mayer et aL, 1970). *A recent study (J. L. Schnoor "'Field Validation of Water Quality Criteria for hydrophobic pollutants", In Aquatic Toxicology and Hazard Assessment: Fifth Conference (Edited by Pearson J. G., Forster R. B. and

Bishops W. E.), pp. 302-315. ASTM STP 766, American Society for Testing and Materials, 1982) concludes that laboratory derived BCF values were within a factor of 1-4 of field measured values for a range of organochlorine chemicals. W.R. 18/IO---E

t259

There is a limited amount of data on chemicals found in fish and waters in the natural environment with which to compare laboratory BCF values. Data on DDT from Lake Michigan (Veith, 1975) and Big Creek, Ontario (Miles and Harris, 1971) indicate a range of "field BCF" over different species of 9 x 104-7 x 106 for Lake Michigan and 3.25-5.0 x i04 for the Ontario study. Laboratory BCF at ! x l05 falls into this range. Dieldrin levels in the Des Moines River, Iowa (Bulkley et al., 1981; Leung et al., 1981), indicate a "field BCF" range of 4 x 10J-l.6 x l04 (laboratory BCF 1.26 x 104). It is interesting to compare field data with laboratory predictions of BCF for hexachlorobutadiene, a compound for which no laboratory BCF test results have been published. It is a chlorinated hydrocarbon like many of the compounds used to establish the correlations in this study; using these correlations, estimated BCF values are 630 and 1050 based on WS and P (Banerjee et al., 1980) respectively; field BCF from the Ijsselmeer in Holland ranges from 400 to 1000 (Goldbach et al., 1976). No definitive conclusions can be drawn from these restricted data, but in these few cases the laboratory BCF reflects the bioaccumulation behaviour observed in polluted freshwater*. CONCLUSIONS The correlations achieved between physicochemical data and BCF are highly significant statistically but they are based on a restricted range of chemical types. For this reason they are limited in application, currently to relatively simple aliphatic and aromatic organochlorines. Since these correlations play an important part in the early stages of the environmental assessment of new chemicals there is an urgent need for further BCF test data of comparable quality, obtained using a much wider range of chemical types. There does not appear to be any consensus of opinion as to the degree of precision that BCF tests should achieve. In view of the considerable variations that can occur in the tests, and the comparable variations that can be expected in the environment, a high level of precision in the tests would be illusory. Nevertheless comparisons of chemical behaviour will have to be made and the discussion above indicates that for BCF tests to give results that are consistent

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and widely useful, further work is needed on the differences between fish species and strains, together with the appropriate sizes and temperatures to use. It would be desirable to test one c o m m o n European cyprinid. The Japanese already use carp so the common carp (Cyprinus carpio) would perhaps be a good species to start with. It is possible now to list some of the elements of "'good laboratory practice" in BCF testing (leaving aside details of apparatus construction, precautions concerning the well-being of the fish, etc). A suitable species of fish, held at an appropriate temperature and thoroughly acc[imatised to test conditions, and outside its reproductive season, should be used. Changes in weight and lipid content during the test should be recorded to eliminate "'growth dilution" effects. Exposure should be in "flow-through'" conditions. Aqueous concentrations in the exposure vessel should be measured, as should those in the whole fish samples, at intervals, preferably using a c o m p o u n d specific analytical method, and the variability of Cw and C f over time should be recorded. Analytical data indicative of the presence of metabolites should be reported. T w o or more aqueous concentrations should be used; all concentrations should be below the threshold of chronic toxicity. Duration of the test should be adequate for a "steady state" plateau level to be reached. Of the five BCF tests listed by the O E C D ( O E C D , 1981a, b, c) only two employ flow-through methods. One is, or is based on, the Japanese standard test, and the protocol for this seems broadly in line with the above proposals except that no provision is made for determination o f fish lipid content. The other is based on an American Society for Testing and Materials ( A S T M ) model which suffers from the weakness that steady state BCF is not required to be reached; instead, it is estimated by the kinetic methods discussed above. If a revised procedure were drawn up it would be desirable to test it widely using a few standard chemicals in order to assess its repeatability. REFERENCES

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