Influence of the Energy Relationships of Organic Compounds on Toxicity to the CladoceranDaphnia magnaand the FishPimephales promelas

ECOTOXICOLOGY AND ENVIRONMENTAL SAFETY ARTICLE NO. ES961481

36, 27–37 (1997)

Influence of the Energy Relationships of Organic Compounds on Toxicity to the Cladoceran Daphnia magna and the Fish Pimephales promelas GIULIO P. GENONI Department of Hydrobiology and Limnology, Swiss Federal Institute for Environmental Science and Technology (EAWAG), CH-8600 Duebendorf, Switzerland Received December 27, 1995

INTRODUCTION A concern of ecotoxicology is to predict the toxicity of substances to living systems. Existing models of structure–activity relationships (SARs) are statistical and applicable within classes of substances only. Predictive models derived from first principles are wanting. Transformity, a measure of the relative amount of energy required to generate a component or a flow in a transformation process, may help predict toxicity. This notion derives from two concepts. First, common substances are more likely to be processed by the biosphere than are rare substances. Second, transformity expresses energy relationships between parts of a system. Substances that require more energy to form are also the more unusual and the more difficult to process. A correlation was hypothesized to occur between the rarity and complexity of a substance, and thus its transformity, and its toxicity. To search for general patterns that transcend individual studies, this hypothesis was tested by using data available for 70 compounds, including simple and chlorinated alkanes, alkenes, alcohols, benzenes, phenols, biphenyls, organic acids, and acetaldehyde. Published data on their Gibbs energy of formation were used as an estimate of transformity. These were compared to published data on their acute toxicity to the cladoceran Daphnia magna and the fish Pimephales promelas (measured as the 48- and 96-hr LC50 values, respectively). For both species there were significant positive correlations across compound classes between transformity and toxicity. In contrast, some correlations within classes were negative, with chemical reactivity and physicochemical properties presumably exerting the prevailing influences within these classes. This suggests that the general (across-classes) and smaller-scale (within-class) patterns are complementary perspectives. The functional relationship across classes was a monotonous increase in toxicity followed by a plateau, though the exact form could not be established with certainty. Gibbs energy of formation has limitations as an estimate of transformity and therefore these observations await confirmation. The correlation between transformity and toxicity may be an important generalization in ecotoxicology, because it may contribute a conceptual framework for making a cross-class comparisons of toxicity. Moreover, transformity may provide a unifying approach to the study of bioaccumulation, toxicity, and specificity. © 1997 Academic Press

A concern of ecotoxicology is to predict the toxicity of compounds to living systems. Models of structure–activity relationships developed so far have been derived statistically and are applicable within classes of substances only. Predictive models derived from first principles are wanting (Forbes and Forbes, 1990). Macroscopic concepts may help predict the toxicity of a substance. One such concept is transformity, i.e., the relative amount of energy required to generate a component or a flow in a transformation process. Transformity has been hypothesized to correlate positively with the bioaccumulation tendency of substances (Odum, 1991; Genoni and Montague, 1995) and with toxicity (Knight, 1981; Odum, 1991). This notion derives from the concepts that the living part of the biosphere and its physical environment coevolve (Pomeroy, 1970) and that transformity expresses energy relationships between different parts of a system (Odum, 1983). Is it possible to integrate these two concepts to express toxicity in terms of energy relationships? The purpose of this paper is to examine this question, following a brief description of the two concepts. The biosphere and the physical environment interact and coevolve. The availability of materials to living systems (cells, organisms, populations, ecosystems) depends on the chemical composition of the physical environment. Reciprocally, the processing of these materials by living systems affects the composition of the physical environment (Pomeroy, 1970). Some materials are common and living systems have evolved a capability of processing them by metabolizing them or by incorporating them into their tissues as building blocks. Other materials have required more energy for their formation, because they have been concentrated or derived from the common and simple ones. These materials are rare and unusual, chemically complex, and effective and specific in their site and mode of action (Odum, 1983). Many such rare and effective substances have beneficial roles, developed over evolutionary time, at naturally occurring concentrations (e.g., hormones, pheromones, trace elements). If such substances are novel to a 27 0147-6513/97 $25.00 Copyright © 1997 by Academic Press All rights of reproduction in any form reserved.

28

GIULIO P. GENONI

living system being exposed to them, this system may be less capable of processing them. In any process, a fraction of the initial energy is lost by heat dissipation at each transfer or transformation step. The energy remaining after transformation may be said to have been “concentrated” from the initial (“dilute”) energy into a component or flow of the process. The initial energy directly and indirectly required for production of the energy of interest has been called emergy (Scienceman, 1987) (“embodied energy”; Odum, 1983). Emergy is expressed in “emjoules,” where the prefix “em” merely serves to emphasize that energy relationships are being considered (Odum, 1986). For comparing energy requirements of two different components or flows of energy, one may use the ratio of the emergy involved to the actual energy contained in the components or flows. This ratio, termed transformity, is the amount of energy of one kind required to generate one unit of energy of another kind (Odum, 1986) (“energy transformation ratio”; Odum, 1983). The transformity of a flow (such as in a chemical reaction or in a trophic transfer between populations) is expressed in emjoules per joule. Alternatively, the transformity of a component (such as a quantity of a substance or the biomass of an organism) may be expressed in emjoules per gram or emjoules per mole. These concepts are illustrated in Fig. 1 by a hypothetical chain of transformations, which might be a trophic chain or the formation of a substance. The “currency” is the energy that enters the process. Among organisms, those in the higher trophic levels have a high transformity and they are few because they require a concentration of materials and energy from the lower trophic levels (Colinvaux, 1978). Among compounds, some have a high transformity because of the large amounts of energy used in the geological and biological processes that have generated and concentrated them from precursor states (elements and simple molecules). This differentiation generates patterns of commonness and rarity (Odum, 1986). To calculate transformities of components or processes, the energy transfers between components must be known. In practice, this is done from data on energy flows in environmental networks and in industrial and economic networks; however, such data are seldom available. As an alternative approach, the Gibbs energy of formation of a compound may be used as an estimate of its transformity, since it describes the total energy involved in the transformation steps from elements to simple

and finally to complex molecules (Odum, 1983; Genoni and Montague, 1995). Gibbs energy of formation is a commonly used tool for the thermodynamic analysis of biotransformations (Mavrovouniotis, 1991). Thus, in first analysis the rather novel concept of transformity can be compared with the more traditional concept of Gibbs energy of formation. The hypothesis tested here is that a correlation occurs between the rarity, complexity, and energy required for forming a substance, and thus its transformity, and its effectiveness. There is much circumstantial evidence for this: organisms and the chemical industry invest energy to produce molecules that are unusual, chemically complex, effective, and specific in their mode of action (e.g., Bryson et al., 1995). More direct evidence lies in the correlation between molecular volume (which often contributes to molecular complexity) and toxicity (Hall and Kier, 1984; McGowan and Mellors, 1986). When a new theory emerges, one can look at known data under a new point of view and derive new insights (Brown 1995). This approach was followed to test the hypothesis stated above, for a set of organic compounds, with published data on their Gibbs energy of formation and on their acute toxicity to the cladoceran Daphnia magna (water flea) and the fish Pimephales promelas (fathead minnow). This might allow detection of general patterns that transcend individual studies. Furthermore, the functional relationship between transformity and toxicity was investigated. Increasingly complex substances require increasingly specific (and energetically costly) modes of defense. Highly unusual and complex substances may not encounter any mechanism of defense, and there may be a point beyond which toxicity is maximal and any increment in transformity does not lead to any increment in toxicity. Therefore, with increasing transformity, a monotonous increase in toxicity followed by a plateau might be predicted. METHODS

Published data on 70 compounds, including simple and chlorinated alkanes, alkenes, alcohols, benzenes, phenols, biphenyls, organic acids, and acetaldehyde, were used. For each compound, 48-hr EC50 values from immobilization tests as well as 48-hr LC50 values from mortality tests were used as a measure of acute toxicity to the cladoceran D. magna Straus; both were used because immobilization and death are often

FIG. 1. Comparison of the concepts of energy, emergy, and transformity in a hypothetical chain of transformations, as, for example, a trophic chain or a chain of formation of complex compounds from simpler ones (adapted from Odum, 1983). Arrows represent flows of energy.

ENERGY RELATIONSHIPS AND TOXICITY OF ORGANIC COMPOUNDS

used interchangeably in the literature. In addition, 96-hr LC50 values from mortality tests were used as a measure of acute toxicity to the fish P. promelas Rafinesque. Since compounds express their toxicity as molecules, these toxicity endpoints were expressed in units of micromoles per liter rather than micrograms per liter. Results of tests on embryo–larval life stages were not included in the analysis, because these are often significantly more sensitive than later stages. For each compound, the average of the available toxicity values was used, to avoid the bias from compounds for which more data are available. For each compound, the Gibbs energy of formation in solution (Dolfing and Harrison, 1992; Holmes et al. 1993; Dolfing and Janssen, 1994) was used as an estimate of transformity. The units may be said to be emjoules per mole, to indicate that energy relationships are being considered (Odum, 1986). The resulting data sets included the transformity for a compound and its toxicity to Daphnia (65 pairs) and Pimephales (49 pairs). For each set the Spearman rank correlation coefficient (Hollander and Wolfe, 1973) was calculated with the StatView statistical package (Feldman et al., 1987). This test is distribution-free and is therefore not sensitive to outliers, nor does it assume linearity. The functional relationship of toxicity versus transformity was investigated by comparing the fit of linear and polynomial regressions with StatView. Moreover, for some classes of compounds examined individually, a pattern differing from the general pattern became apparent. Thus, the Spearman rank correlation coefficient was calculated for each class of compounds as well. RESULTS

The Gibbs energy of formation and toxicity of the compounds, with the references used, are given in Table 1. The entire data sets are displayed in Figs. 2 and 3. For both species, the comparison between the Gibbs energy of formation and the LC50 reveals a significant negative rank correlation (Daphnia: r 4 0.49, P < 0.0001, N 4 65; Pimephales: r 4 −0.59, P < 0.001, N 4 49). This indicates that as transformity increases, toxicity increases for the entire data sets. When classes of compounds are examined individually, a different pattern emerges. For certain classes of compounds (alkanes and alcohols), as transformity increases, toxicity tends to increase (alkanes—Daphnia: r 4 −0.03, P < 0.90, N 4 17; Pimephales: r 4 −0.23, P < 0.47, N 4 11; alcohols— Daphnia: r 4 −0.48, P < 0.13, N 4 11; Pimephales: r 4 −0.44, P < 0.17, N 4 11). For alkenes, benzenes, phenols, and biphenyls in the case of Daphnia, as transformity increases, toxicity tends to decrease (alkenes—Daphnia: r 4 0.60, P < 0.30, N 4 4; Pimephales: r 4 0.50, P < 0.48, N 4 3; benzenes—Daphnia: r 4 0.67, P < 0.02, N 4 13; Pimephales: r 4 0.75, P < 0.02, N 4 11; phenols—Daphnia: r 4 0.97, P < 0.002, N 4 11; Pimephales: r 4 0.81, P < 0.03, N 4 8; biphenyls—Daphnia: r 4 0.89, P < 0.05, N 4 6; Pimephales:

29

r 4 −1.00, P < 0.32, N 4 2). These correlations are not significant for all classes, but essentially they are significant for those where toxicity decreases; these are the benzenes, phenols, and biphenyls, i.e., the ones in which only the number and position of chlorine atoms, and not the skeleton of the molecule, vary. As to the functional relationship of toxicity versus transformity across classes, third-order polynomial regressions gave the best fit (Daphnia: F 4 3.91, P < 0.01, N 4 65; Pimephales: F 4 1.77, P < 0.17, N 4 49), followed by secondorder polynomial and linear regressions. DISCUSSION

Results support the main hypothesis of this work, that transformity may be positively correlated with toxicity across compound classes. The correlation is positive also for the subsets of alkanes and alcohols, but negative for alkenes, benzenes, and phenols. Though the small sample sizes within classes allow only prudent conclusions (yet most of the negative correlations, notably for benzenes and phenols, were significant), it is interesting to speculate about this difference within versus across classes. It may be an illustration of the often stated principle that the larger-scale pattern (in this case, across classes) and the smaller scale pattern (in this case, within classes) are complementary perspectives and that they may be different. Here the broader pattern may be dominated by factors such as rarity and complexity, whereas factors such as the chemical reactivity (steric crowding of chlorine atoms) and the physicochemical properties (tendency to sorb to lipids) (Balaban et al., 1980) may prevail within a class of compounds and modify the relationship between transformity and toxicity. Earlier studies have pointed to a similar conclusion. For instance, the toxic effect of nonsubstituted alcohols and hydrocarbons can be predicted from a single physicochemical parameter (the octanol–water partition coefficient); in contrast, for substituted alcohols as well as for halogenated hydrocarbons, a combination of several parameters is required, with more parameters needed as compounds become more complex and specific in their mode of action (Hutchinson et al., 1980; Hermens, 1990; Hermens and Opperhuizen, 1991). The correlation with transformity, however, does apply over a broad array of compounds, including specific and nonspecific ones. One must hasten to add that the Gibbs energy of formation has limitations as an estimate of transformity and that consequently these observations await confirmation. Gibbs energy of formation may underestimate the transformity of compounds, because it does not account for the activation energy involved in each transformation step from simple to complex molecules. Moreover, consider the case of two enantiomers that would differ in their abundance: whereas their transformity is not the same, their Gibbs energy of formation is. As to other parameters that one might consider for estimating transformity, enthalpy may capture the heat energy that is used in a transfor-

30

GIULIO P. GENONI

TABLE 1 Transformity (Measured as the Gibbs Energy of Formation in Solution, DfG0) and Acute Toxicity to Daphnia magna and Pimephales promela˜s of Various Organic Compounds D. magna

P. promelas

DfG0 (kemj/mol)

Average 48-hr LC50 or 48-hr EC50 (mmol/liter)

Na

References

Alkanes Dichloromethane

−66.11

4,856.82

6

Trichloromethane

−66.50

1,920.04

6

Tetrachloromethane

−45.10

152.73

4

1,2-Dichloroethane

−72.93

3,602.31

12

1,1,2-Trichloroethane

−77.64

671.76

7

1,1,1-Trichloroethane

−69.04

3,607.74

4

1,1,1,2-Tetrachloroethane 1,1,2,2-Tetrachloroethane

−77.75 −88.92

142.99 222.10

1 10

1,1,1,2,2-Pentachloroethane

−68.26

78.87

6

Hexachloroethane

−49.62

17.26

10

1,2-Dichloropropane

−79.88

439.51

3

1,3-Dichloropropane

−80.38

1325.99

3

−100.53 −31.81

239.88 16,502.07

1 2

Pentane n-Hexane

9.24 18.50

135.00 5,824.14

1 2

Octane

35.91

3.31

1

LeBlanc, 1980; Hermens et al., 1984a; Abernethy et al., 1986; Ku¨hn et al., 1989; Rippen, 1993 LeBlanc, 1980; Qureshi et al., 1982; Hermens et al., 1984a; Abernethy et al., 1986; Gersich et al., 1986; Cowgill and Milazzo, 1991 LeBlanc, 1980; Rippen, 1993; Abernethy et al., 1988 LeBlanc, 1980; Qureshi et al., 1982; Hermens et al., 1984a; WHO, 1987; Moore et al., 1991b; Rippen, 1993 Buikema and Benfield 1980; Richter et al., 1983; Walbridge et al., 1983; Hermens et al., 1984a; Rippen, 1993 LeBlanc, 1980; Abernethy et al., 1986; Rippen, 1993 LeBlanc, 1980 LeBlanc, 1980; Richter et al., 1983; Moore et al., 1991b; Rippen, 1993 LeBlanc, 1980; Richter et al., 1983; Hermens et al., 1984a LeBlanc, 1980; Richter et al., 1983; Mount and Norberg, 1984; Thurston et al., 1985; Elnabarawy et al., 1986; Streit, 1991 LeBlanc, 1980; Streit, 1991; Rippen, 1993 Buikema and Benfield, 1980; LeBlanc, 1980; Hermens et al., 1984a Hermens et al., 1984a Hermens et al., 1984a; Ku¨hn et al., 1989 Abernethy et al., 1986 Abernethy et al., 1986; WHO, 1990a Abernethy et al., 1986

Compound

1,2,3-Trichloropropane 1-Chlorobutane

Average 96-hr LC50 (mmol/liter)

N

3,406.33

5

Rippen, 1993

927.01

3

Mayes et al., 1983; Phipps et al., 1984

153.10

2

Rippen, 1993; Phipps et al., 1984

918.55

4

Moore et al., 1991b; Rippen, 1993

649.50

2

Walbridge et al., 1983; Rippen, 1993

548.31

6

Moore et al., 1991b; Rippen, 1993

121.09

4

36.23

3

5.93

5

1,260.83

5

1,153.77

2

Walbridge et al., 1983; Moore et al., 1991b; Rippen, 1993 Veith 1983a; Walbridge et al., 1983; McCarthy et al., 1985 Veith et al., 1983a; Walbridge et al., 1983; Call et al., 1985; Phipps and Holcombe, 1985; Thurston et al., 1985 Walbridge et al., 1983; Call et al., 1985; Rippen, 1993 Walbridge et al., 1983; Call et al., 1985

References

31

ENERGY RELATIONSHIPS AND TOXICITY OF ORGANIC COMPOUNDS

TABLE 1—Continued D. magna

Compound

DfG0 (kemj/mol)

Average 48-hr LC50 or 48-hr EC50 (mmol/liter)

P. promelas

Na

References LeBlanc, 1980 LeBlanc, 1980; Streit, 1991; Rippen, 1993 Canton and Adema, 1978; Buikema and Benfield, 1980; LeBlanc, 1980; Sloof et al., 1983; Abernethy et al., 1986; Moore et al., 1991a; Rippen, 1993 LeBlanc, 1980; Richter et al., 1983; Rippen, 1993

Average 96-hr LC50 (mmol/liter)

N

References

1,273.98

5

WHO, 1990a; Rippen, 1993

360.15

5

Veith et al., 1983a; WHO, 1985; Rippen, 1993

101.22

7

Veith et al., 1983b; Walbridge et al., 1983; Rippen, 1993 Veith et al., 1983a,b; Poirier et al., 1986; McCarty, 1987; Rippen, 1993 Brungs et al., 1977; Chang et al., 1981; Veith et al., 1983a,b; McCarty, 1987; Rippen, 1993 Phipps and Holcombe, 1985; Thurston et al., 1985

Alkenes trans-1,2-Dichloroethylene 1,1-Dichloroethylene

32.06 32.23

2,269.45 674.64

1 4

1,1,2-Trichloroethylene

25.41

395.60

10

Tetrachloroethylene

27.59

79.68

6

Alcohols Methanol

−175.39

312,109.86

1

Ku¨hn et al., 1989

896,537.83

5

Ethanol

−181.75

227,476.06

7

309,384.27

6

2-Chloroethanol

−204.00

3,313.70

4

485.15

2

2,2,2-Trichloroethanol

−217.40

1,463.62

3

1,718.16

3

Veith et al., 1983a,b; Thurston et al., 1985

n-Propanol

−175.81

106,125.90

6

81,897.34

2

McCarty, 1987; WHO, 1990b

2-Propanol

−185.94

129,649.98

2

171,256.24

4

n-Butanol

−171.84

26,761.13

1

Ziegenfuss et al., 1986; Takahashi et al., 1987; Cowgill and Milazzo, 1991; Rippen, 1993 Mount and Norberg, 1984; Thurston et al., 1985; Elnabarawy et al., 1986; Ku¨hn et al., 1989 Thurston et al., 1985; Elnabarawy et al., 1986; Ku¨hn et al., 1989 Canton and Adema, 1978; Sloof et al., 1983; Ku¨hn et al., 1989; WHO, 1990b Veith et al., 1983b; Rippen, 1993 Ku¨hn et al., 1989

23,220.57

5

n-Pentanol n-Hexanol

−161.00 −148.53

8,095.24 401.00

1 1

Ku¨hn et al., 1989 Veith et al., 1983b

6,920.00 1,003.98

1 4

n-Heptanol

−133.92

633.39

5

320.00

1

n-Octanol

−126.60

180.00

1

Canton and Adema, 1978; Sloof et al., 1983 Veith et al., 1983b

Veith et al., 1983a,b; WHO, 1990c; Rippen, 1993 Veith et al., 1983a,b; McCarty, 1978; WHO, 1987 Veith et al., 1983b Veith et al., 1983a,b; Broderius and Kahl, 1985; McCarty, 1987 Veith et al., 1983b

104.22

6

Veith et al., 1983a,b; Broderius and Kahl, 1985; McCarty, 1987; Pickering et al., 1989

133.92

4,255.88

10

414.77

6

Pickering and Henderson, 1966; Chang et al., 1981; De Graeve et al., 1982; Hall et al., 1984; Rippen, 1993

Benzenes Benzene

Canton and Adema, 1978; Buikema and Benfield, 1980; Sloof et al., 1983; Abernethy et al., 1986; Streit, 1991; Rippen, 1993

32

GIULIO P. GENONI

TABLE 1—Continued D. magna

Compound Chlorobenzene

DfG0 (kemj/mol)

Average 48-hr LC50 or 48-hr EC50 (mmol/liter)

P. promelas Average 96-hr LC50 (mmol/liter)

N

References Pickering and Henderson, 1966; Mayes et al., 1983; Hall et al., 1984; Rippen, 1993

Na

References LeBlanc, 1980; Bobra et al., 1985; Abernethy et al., 1986; Gersich et al., 1986; Cowgill and Milazzo, 1991; Rippen, 1993 LeBlanc, 1980; Hermens et al., 1984a; Bobra et al., 1985; Abernethy et al., 1986; Rippen, 1993 LeBlanc, 1980; Calamari et al., 1983; Richter et al., 1983; Hermens et al., 1984a,b; Bobra et al., 1985; Abernethy et al., 1988 LeBlanc, 1980; Gersich et al., 1986; Rippen, 1993 Hermens et al., 1984; Bobra et al., 1985; Abernethy et al., 1986 LeBlanc, 1980; Bobra et al., 1985; Holcombe et al., 1987; Abernethy et al., 1988; Rippen, 1993 Hermens et al., 1984a; Abernethy et al., 1988 Hermens et al., 1984a; Abernethy et al., 1988 LeBlanc, 1980; Bobra et al., 1983; Hermens et al., 1984a; Abernethy et al., 1986; Streit, 1991 LeBlanc, 1980; Hermens et al., 1984a Buikema and Benfield, 1980; Hermens et al., 1984a; Bobra et al., 1983, 1985; Abernethy et al., 1986 Abernethy et al., 1986

325.12

11

133.07

4

Hall et al., 1984; Rippen, 1993

55.07

3

Veith et al., 1983a; Hall et al., 1984; Broderius and Kahl, 1985

90.04

9

5.55

1

Mayes et al., 1983; Hall et al., 1984; Rippen, 1993 Hall and Kier, 1986

16.71

7

Veith et al., 1983a; Hall and Kier, 1986; Holcombe et al., 1987; Rippen, 1993

18.20

1

Hall and Kier, 1986

4.41

2

McCarthy et al., 1985; Hall and Kier, 1986

1.41

1

Hall and Kier, 1986

77.25

1

Rippen, 1993

Dowden and Bennett, 1965; LeBlanc, 1980; Qureshi et al., 1982; Veith et al., 1983b; Hermens et al., 1984a,b; Millemann et al., 1984; Phipps et al., 1984; Environment Canada, 1985; Keen and Baillod, 1985; Gersich et al., 1986; Holcombe et al., 1987; Pilli et al., 1988; Ku¨hn et al., 1989; Cowgill and Milazzo, 1991; Streit, 1991; Rippen, 1993

362.00

27

102.30

227.83

8

1,2-Dichlorobenzene

84.30

24.00

6

1,3-Dichlorobenzene

81.80

50.30

11

1,4-Dichlorobenzene

78.30

57.92

4

1,2,3-Trichlorobenzene

71.70

10.24

4

1,2,4-Trichlorobenzene

60.50

42.28

9

1,3,5-Trichlorobenzene

56.80

8.33

2

1,2,3,4-Tetrachlorobenzene

55.80

3.76

2

1,2,3,5-Tetrachlorobenzene

49.20

20.70

5

1,2,4,5-Tetrachlorobenzene

53.50

1,228.25

2

Pentachlorobenzene

45.70

4.95

6

Hexachlorobenzene Phenols Phenol

46.00

0.02

1

−52.67

381.49

24

De Graeve et al., 1980; Chang et al., 1981; Phipps et al., 1981, 1984; Mayes et al., 1983; Hall and Kier, 1984; Environment Canada, 1985; Holcombe et al., 1987; Pilli et al., 1988; Rippen, 1993

33

ENERGY RELATIONSHIPS AND TOXICITY OF ORGANIC COMPOUNDS

TABLE 1—Continued D. magna

Compound

DfG0 (kemj/mol)

Average 48-hr LC50 or 48-hr EC50 (mmol/liter)

Na

P. promelas

References

2-Chlorophenol

−56.80

36.07

3

LeBlanc, 1980; Keen and Baillod, 1985; Rippen, 1993

3-Chlorophenol 4-Chlorophenol

−56.40 −53.10

40.91

5

2,3-Dichlorophenol 2,4-Dichlorophenol

−75.20 −84.80

19.02 17.18

1 6

2,6-Dichlorophenol 2,4,5-Trichlorophenol

−82.60 −97.20

20.86 9.12

1 2

2,4,6-Trichlorophenol

−104.60

11.67

8

LeBlanc, 1980; Cowgill and Milazzo, 1991; Rippen, 1993 Ku¨hn et al., 1989 LeBlanc, 1980; Ku¨hn et al., 1989; Rippen, 1993 Ku¨hn et al., 1989 LeBlanc, 1980; Rippen, 1993 LeBlanc, 1980; Hermens et al., 1984a; Kukkonen and Oikari, 1987; Rippen, 1993

2,3,4,5-Tetrachlorophenol

−110.00

2,3,4,6-Tetrachlorophenol

−114.30

1.50

2

2,3,5,6-Tetrachlorophenol Pentachlorophenol

−112.70 −112.30

2.46 2.18

1 19

275.20

13.24

4

259.60 249.00 250.00 225.90 185.90

21.20 2.28 2.23 2.00 0.10

1 1 1 1 1

LeBlanc, 1980; Dill et al., 1982; Bobra et al., 1983; Abernethy et al., 1986 Dill et al., 1982 Dill et al., 1982 Dill et al., 1982 Bobra et al., 1983 Dill et al., 1982

−140.00 −366.60 −385.80 −403.00 −359.50 −411.70

282,111.24 1,390.51 814.81

9 2 1

Pilli et al., 1988 Verschueren, 1983 Ku¨hn et al., 1989

674.95

1

Dowden and Bennett, 1965

Biphenyls Biphenyl

2-Chlorobiphenyl 3-Chlorobiphenyl 4-Chlorobiphenyl 4,49-Dichlorobiphenyl 2,29,4,49-Tetrachlorobiphenyl Others Acetaldehyde Acetate Chloroacetate Trichloroacetate Propionate 2,2-Dichloropropionate a

Number of toxicity data points averaged.

LeBlanc, 1980; Mount and Norberg, 1984 LeBlanc, 1980 Canton and Adema, 1978; LeBlanc, 1980; Adema and Vink, 1981; Sloof et al., 1983; Hermens et al., 1984a,b; Mount and Norberg, 1984; Thurston et al., 1985; Elnabarawy et al., 1986; Ku¨hn et al., 1989; Stephenson et al., 1991

Average 96-hr LC50 (mmol/liter)

N

References

87.69

10

31.89 34.23

2 2

Phipps et al., 1981; Hall et al., 1984; Pickering et al., 1989; Rippen, 1993 Rippen, 1993 Rippen, 1993

50.04

7

27.40

12

1.76

2

0.96

34

21.20 41.34

1 1

Dill et al., 1982 Dill et al., 1982

12,240.65

1

Spehar et al., 1980

2,028.40

1

Verschueren, 1983

Hall and Kier, 1986; BUA, 1989; Rippen, 1993

Phipps et al., 1981; Verschueren, 1983; Hall and Kier, 1986; Holcombe et al., 1984, 1987; Pickering et al., 1989; Rippen, 1993 Hall and Kier, 1984; Holcombe et al., 1984

Adelman et al., 1976; Brungs et al., 1977; Phipps et al., 1981; Cleveland et al., 1982; Hedtke and Arthur, 1985; Phipps and Holcombe, 1985; Spehar et al., 1985; Thurston et al., 1985; Ewell et al., 1986; Hall and Kier, 1986; Hedtke et al., 1986; Rippen, 1993

34

GIULIO P. GENONI

FIG. 2. Relationship between the transformity (measured as the Gibbs energy of formation in solution, DfG0) and acute toxicity to Daphnia magna of various organic compounds.

mation process, whereas entropy may capture the order and structure that arise in the process. In addition, the enthalpy of reaction may be related to detoxification processes, and entropy is the driving force for the partitioning of lipophilic compounds into the organism; however, this latter argument does not apply to nonlipophilic substances and has less generality than transformity. Gibbs energy of formation takes into ac-

count both enthalpy and entropy (indeed, it is the enthalpy minus the entropy). Even though, as noted above, it allows only somewhat crude estimates of transformity, it is still the best measure at present. Refined estimates of transformity may be possible in the future, as more knowledge becomes available on energy flows involved in the formation of compounds. What is the functional relationship between transformity and

FIG. 3. Relationship between the transformity (measured as the Gibbs energy of formation in solution, DfG0) and acute toxicity to Pimephales promelas of various organic compounds.

ENERGY RELATIONSHIPS AND TOXICITY OF ORGANIC COMPOUNDS

toxicity? That a third-order polynomial regression gives a better fit than linear ones suggests a nonlinear relationship. Toxicity increases more and more slowly with increases in transformity. Moreover, among the compounds used here, LC50’s are highest for short-chain alcohols and acetaldehyde, and decrease also toward lower transformities (organic acids). This may be due to between-class differences as discussed above and to the possibility that any biological system may be most efficient at using resources that have a transformity commensurate with its own transformity, as has been hypothesized by Knight (1981) and Odum (1991). Yet, in view of the fact that even the third-order polynomial regression was not significant in the case of Pimephales and that no data are available on the transformity of these organisms, this should be considered as speculative and as a suggestion for future research. For this reason, the regression equations may not represent meaningful relationships at this stage and are not reported here. Furthermore, it may be predicted that a correlation occurs also between the transformity of elements and their toxicity, since the energy required to incorporate them into molecules and to concentrate them into the biosphere is dependent on their atomic size and availability (Genoni and Montague, 1995). Finally, a corollary of this relationship is that a given substance may not be equally toxic to living systems differing in their transformity (e.g., organisms of various trophic levels). Thus, in an ecosystem, a high-transformity substance may be toxic to organisms of the lower as well as higher trophic levels. Its toxicity to the higher trophic levels may be compounded by its propensity for bioaccumulation (Genoni and Montague, 1995). In contrast, a low-transformity substance may affect only organisms of the lower trophic levels (Knight, 1981). Similar predictions may be made for the population level (sensitivity of life stages), for the organismic level (sensitivity of organs and tissues), and for the cellular level (sensitivity of organelles and metabolic pathways). CONCLUSION

The correlation between transformity and toxicity may prove to be an important generalization in ecotoxicology that contributes a conceptual framework for making across-class comparisons of the toxicity of elements or compounds. Moreover, the transformity concept may provide a unifying approach to the study of bioaccumulation (Genoni and Montague, 1995), toxicity (this article), and specificity (Genoni, 1997). ACKNOWLEDGMENTS L. R. Pomeroy’s and H. T. Odum’s works stimulated the conception of this study. The author thanks W. Angst, R. Behra, T. N. P. Bosma, D. Diem, J. Dolfing, H. Gu¨ttinger, C. L. Montague, and J. V. Ward for valuable discussion.

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