- Email: [email protected]
Fragment constant method for prediction of fish bioconcentration factors of non-polar chemicals
Chemosphere 41 (2000) 1563±1568
Fragment constant method for prediction of ®sh bioconcentration factors of non-polar chemicals q Shu Tao *, Haiying Hu, Xiaoxia Lu, R.W. Dawson, Fuliu Xu Department of Urban and Environmental Sciences, Peking University, Beijing 100871, People's Republic of China Received 16 March 1999; accepted 23 January 2000
Abstract A fragment constant method for prediction of ®sh bioconcentration factor (BCF) was established based on experimental BCF values for 80 non-polar chemicals from nine classes. The model was evaluated using coecients of determination and mean residuals, which are 0.995 and 0.1836, respectively. Jackknife tests were applied to examine the robustness of the prediction model on a class-by-class basis. Ó 2000 Elsevier Science Ltd. All rights reserved. Keywords: Bioconcentration factor; Fish; Fragment constant
1. Introduction Fate modeling is a technique with the potential for predicting expected chemical distributions in various sectors of the environment. Among the many properties available for describing distributions, the biological concentration factor (BCF) has proven very important as far as the behavior and fate of water-born chemicals in the aquatic environment is concerned (Barron, 1990). Of the various aquatic species, ®sh typically serve as targets for BCF assessments because of their importance as a human food source along with the availability of standardized testing protocols (Barron, 1990). Bioconcentration factors can be measured experimentally. However, the measurement of BCF values for all the kinds of chemicals that organisms can potentially be exposed to is very dicult, if not impossible. For this reason, the prediction of BCF is an essential component
q
Funding was provided by The National Scienti®c Foundation of China [49632060] and [49525102]. * Corresponding author. Tel.: +86-10-627-51938; fax: +8610-627-51938. E-mail address: [email protected] (S. Tao).
in understanding the environmental behavior and toxicity of chemicals. Studies on BCF prediction modeling began in the mid-1970s. One approach was to estimate a chemical's BCF based on the experimental relationship between BCF and other physiochemical parameters such as the octanol/water partitioning coecient (Kow ), water solubility or the soil adsorption coecient (Koc ) (Devillers et al., 1996). Another approach based on molecular connectivity indices has also been extensively reported in the literature (Sabljic and Protic, 1982; Koch, 1983). The relationship between BCF and ®ve molecular connectivity indices along with a number of correction factors based on the experimental BCFs of 80 non-polar organic pollutants from the same database was studied (Lu and Tao, 1999). In addition to molecular connectivity indices, the fragment constant approach may also prove to be valid for predicting BCFs. Relying on HammettÕs postulation that free-energy based parameters are additive (Hammett, 1970), Rekker (1977) proposed a generalized approach for predicting Kow in which each part, or fragment, of a chemical was given a value so that their sum would yield the log Kow . The only input necessary for this approach is the chemical structure. Leo (1975, 1985), using a similar approach, established a set of rules
0045-6535/00/$ - see front matter Ó 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 5 - 6 5 3 5 ( 0 0 ) 0 0 0 4 9 - 7
1564
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
for fragmenting chemicals and developed a computer algorithm with a coded program capable of calculating the Kow values of organic compounds using the fragment method. The fragment constant method has also been tried in the prediction of Koc (Tao et al., 1999). In addition to the contributions of fragments, the sorption property of a chemical is further in¯uenced by the moleculeÕs structure. Structural eects can, however, be adequately described by using structural correction factors which take into account molecular ¯exibility, unsaturation, multiple halogenation, branching, interactions with H-polar fragments and so forth (Leo, 1975). The fragment approach, however, has not been tried for the prediction of BCF, although its application in the case of Kow and Koc has been considered successful. The objective of this study was to develop a statistical model capable of predicting the BCF values of non-polar organic pollutants based on fragment constant and the structural correction factors. The robustness of the model was tested using the modi®ed Jackknife tests.
either aliphatic chain or aromatic ring, ±Br, ±C(H)¸, and ±C¸). The in¯uence of molecular structural features on BCFs was also taken into consideration for the modeling in this study. Speci®cally, molecular ¯exibility, unsaturation, multiple halogenation, branching, and interactions with H-polar fragments were represented by 16 structural factors listed in Table 1. A stepwise regression was performed during a preliminary analysis and the results con®rmed the independent contribution of each factor selected. 2.3. Model development and validation A linear multivariate regression was conducted based on the experimental BCF values collected and the number of individual fragments and structural features of each chemical using the following equation: log BCF
a b X X ni fi m j Fj ; i1
2. Methodology 2.1. Collection of experimental BCFs Experimental BCFs in ®sh for 80 non-polar organic compounds, including halogenated hydrocarbons, alkyl and alkenyl benzenes, polycyclic aromatic hydrocarbons, hexachlorocyclohexanes, etc. were collected from the literature (Veith et al., 1980; Donald, 1982; Garten and Trabalka, 1983; Oliver and Niimi, 1983; Oliver and Niimi, 1984; Freitag et al., 1985; Opperhuizen et al., 1985; Isnard and Lambert, 1988; Gobas et al., 1989; Mackay et al., 1992; Nendza, 1993). The ®sh used as test species included rainbow trout, guppies, fathead minnows, bluegill sun®sh, golden ide and other freshwater species. All the data were measured under ¯owing water conditions and presented based on the whole weight of the ®sh. The resulting experimental BCFs were found to range from 0.30 to 5.36 log-units. In those instances where there were more than one experimental BCF available in the database for a chemical, a median value was derived for purposes of regression modeling.
1
j1
where a and b are the total numbers of the atomic or group fragments and structural features de®ned by the model; ni and mj the numbers of the ith fragment and the jth structural factors of the chemical; fi is the fragment constant for the ith fragment and Fj is the structural correction factor for the jth structural feature. SPSS was applied for the regression. The coecient of determination for the multiple regression (R2 ) and the adjusted coecient of determination (R2adj ) were taken into consideration in testing the quality of the regression. Additionally, the studentized residual was used as an additional statistic in testing the quality of the regression. A modi®ed Jackknife test was used for regression validation. The test was then applied to successive chemical groupings in which one of the nine classes of chemicals was removed at each iteration (Lu and Tao, 1999). A multiple linear regression was performed after each deletion to calculate the Jackknife coecient of determination and then compared with the same parameter derived prior to deletion of a chemical group for purposes of robustness evaluation.
2.2. Identi®cation of the fragment and correction factors The fragments were identi®ed according to LeoÕs de®nition which refers to a fragment as an atom, or atoms, whose exterior bonds are to isolating carbon atoms (Leo, 1975). As the contribution of a fragment to the sorption behavior of a chemical depends on the type of isolating carbon atom to which the fragment is attached, each of the fragments was treated as dierent fragments. There were nine total fragments identi®ed from the 80 chemicals (±H, ±C±, ±C6 H5 , ±Cl attached to
3. Results and discussion 3.1. Model development A linear multiple regression was conducted on the experimental BCFs obtained from the database. The equation and the coecient of determination derived as a result of using the nine atomic or group fragments and the 16 structural correction factors is
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
1565
Table 1 Structural correction factors No.
Symbol
Factors
Explanation
1 2 3 4 5 6 7 8 9 10 11 12 13
FC FCH FCH2 U FCH 3 Fb Fb FrCl F¸ F¸U F¸U=2 FmhG1 FmhG2 FO
Number of quaternary carbon Number of triple carbon Number of secondary carbon Number of primary carbon attached to aromatic ring Number of aliphatic chain bond ± 1 Number of aliphatic ring bond ± 1 Number of branching on a ring cluster Number of aliphatic chain double bond Number of double bond attached to aromatic ring Number of vicinal double bonds Number of carbon bonds to two halogens Number of carbon bonds to three halogens Number of vicinal carbons bonds to multiple halogens
14 15 16
Fm Fa Fa; c
Quaternary carbon factor Triple carbon factor Secondary carbon factor Primary carbon (aromatic) factor Aliphatic chain bond factor Aliphatic ring bond factor Ring cluster branching factor Aliphatic double bond factor Aromatic double bond factor Vicinal double bond factor Double halogenation factor Triple halogenation factor Vicinal multiple halogenation factor Meta multiple carbon factor PCB a chlorine factor PAH a±c factor
log BCF
a b X X ni fi mj Fj ; i1
R2 0:995;
2
j1
where ni , mj , fi , and Fj are the same as those in Eq. (1). R2 is the coecient of determination. Statistically, the regression model is able to account for as much as 99.5% of the variation in the experimental BCFs corresponding to the 80 chemicals. The fragment constants and the structural correction factors derived as regression coecients are listed in Tables 2 and 3. A scatter plot of the 80 measured BCF values against the predicted ones is presented in Fig. 1. The plot demonstrates that the residuals of the estimation are acceptable in terms of its predictive certainty, especially when the errors imbedded in the measured data are taken into consideration.
Number of meta-carbons bonds to multiple halogens Number of chlorine attaches to a carbon of PCB Number of halogen attaches to a or c carbon of PAH
Table 2 Derived fragment constantsa Fragment
f
±H ±C(±) ±C6 H5 ±Cl ±Br ±C(H)¸ ±C¸
)0.032 )0.615 1.241 0.423
fU
f U=2
0.816 0.797
0.430
a
f AR
0.286 0.097
Superscripts on the fragment constant which denote the type of attachment are /: attached to aromatic ring, //2: attached to double bond, AR: fused in aromatic ring.
Table 3 Derived structural correction factors
3.2. Error evaluation
Factor
F
The mean residual for the predicted BCF based on the regression testing was 0.1836 log-units. Of the 80 chemicals studied, the individual residuals for 78.8% of them were less than 0.3 log-units. For 92.5% of the 80 chemicals studied, the individual residuals were less than 0.5 log-units. The relative errors in the model for individual chemicals studied were examined using the studentized residuals compared with the calculated log BCF values. The results are plotted in Fig. 2. As can be seen in Fig. 2, the studentized residuals are symmetrically distributed around zero with no speci®c trend. It appears that for most of the chemicals studied, their residuals are relatively small. The four chemicals with the highest residuals are benzene, hexabromoben-
FC FCH FCH2 U FCH 3 Fb Fb FrCl F¸ F¸U F¸U=2 FmhG1 FmhG2 FO Fm Fa Fa;c
1.524 0.508 0.688 1.072 )0.369 0.197 )4.886 0.922 0.781 1.875 0.221 0.123 )0.264 )0.153 )0.435 )0.127
1566
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
Fig. 3. Mean residuals of various chemical classes. Fig. 1. Relationship between the calculated and measured log BCF.
Fig. 2. Plot of studentized residual against calculated log BCF.
zene, 2,20 ,4,4-PCB, and 2,3-PCB. One possible explanation for this result could be the very long half-life associated with these chemicals. It is recognized that relatively larger errors can be generated during BCF measurements of those long-life chemicals (Oliver and Niimi, 1985). The calculated CookÕs distance for all cases of the chemicals examined are all less than 0.42, supporting the reliability of the linear model. In order to investigate the dierence among the various types of chemicals, the chemicals in the study were divided into nine classes as follows: chlorinated aliphatic hydrocarbons (CAH), monocyclic aromatic hydrocarbons (MAH), chlorinated benzene (CB), brominated benzene (BB), polychlorinated biphenyls (PCB), polybrominated biphenyls (PBB), chlorinated naphthalenes (CN), polycyclic aromatic hydrocarbons (PAH), and others (OTH, including chlordane, hexachlorocyclohexanes, heptachlor, DDT and DDE). The dierence among the various classes in terms of the modeling error was found to be signi®cant as shown in Fig. 3, where the mean residuals of the nine classes of chemicals are illustrated by means of bar chart. Of the nine classes, the mean residuals of CB (0.31), PBB (0.28), and CN (0.27) were above the overall av-
erage (0.1836 log-units, indicated by a broken line in the ®gure) of the groups as a whole. PBBs and CN have relatively complicated molecular structures, which serve as the primary source of errors. The 16 structural factors selected for the modeling exercise were still not sucient for a full explanation of all of the structural features of these chemicals. Moreover, the measured BCF values for the chemicals in these classes may also bear relatively higher as an experimental error source than the others. Error in the prediction model may be generated from a number of sources. One such source follows from potential inaccuracies in the measured data itself. According to Lu et al. (1999), the mean variation among 592 duplicate experimental BCF values for the 120 chemicals reported in the literature is 0.362 log-units. This suggests that a signi®cant portion of the modeled residual was contributed from the error contained in the experimental BCF value. However, only 80 chemicals among the 120 were involved in determining these residuals. As such, it is expected that the portion of error contributed from the measurement should be less than 0.362. 3.3. Robustness of the model Considering the relatively small number of collected experimental BCFs in the study, the database was not divided into modeling and testing sets for purposes of model validation. Instead, an alternative approach using a modi®ed Jackknife test was applied. The regression analysis was repeatedly performed on subsets of the database with various classes of chemicals removed one at a time. Not only can the model be repeatedly validated by such a procedure, but the resulting information provides some indication relative to the modelÕs robustness as well. The adjusted coecients of determination and the mean residuals for the Jackknife tests conducted with successive deletion of the nine classes (CAH, MAH, CB, BB, PCB, PBB, CN, PAH, and OTH) one at a time are presented in Fig. 4.
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
1567
Fig. 4. The adjusted coecient of determination and the mean residuals of the Jackknife test with deletion of various chemical classes one by one (the corresponding values without deletion were shown as the broken lines).
A brief examination of Fig. 4 indicates no unusually high variations. The adjusted R2 s and mean residuals generated by deletion of various classes were either identical to or only slightly dierent from the original values derived without deletion (shown by the broken lines in the ®gure). The maximum variations were found to be 0.001 and )0.03 log-units for the adjusted coecients of determination and the mean residuals respectively, indicating a relatively high degree of robustness for the model in general. As expected, the class-by-class deletion caused either no or only slight increases in the R2 s or decreases in the residuals with few exceptions. It is not surprising to see the relatively high variations in the Jackknife R2 s and residuals for CB, PBB, PAH, and CN, in view of the relatively high mean residuals observed in these classes (Fig. 3). On the other hand, the Jackknife residual for CAH (the only aliphatic class among all the chemicals studied) is higher than in the original case. Although there were only 13 chemicals in the sample for this class, their similar components and structure were described fairly clearly by the fragments and correction factors selected during model development. Furthermore, the variation in BCF values for the CAH chemicals was between 0.3 and 3.76 log-units, well within the range suggesting a perfect linear relationship between BCF and the thermodynamic parameters (Muir et al., 1985; Fox et al., 1994). Considering the variation among the 80 chemicals comprising the data set and the fact that the experimental BCF values varied across a range exceeding ®ve log-units, the non-class speci®c correlation model seems to allow estimation of BCF values for a large variety of organic compounds within the constraints of experimental uncertainties.
4. Conclusion BCF values for non-polar chemicals can be estimated using the fragment constant approach when supplemented by structural correction factors. A linear model is suitable in terms of prediction certainty and model robustness.
References Barron, M.G., 1990. Bioconcentration. Environ. Sci. Technol. 24, 1612±1618. Devillers, J., Bintein, S., Domine, D., 1996. Comparison of BCF model based on log P. Chemosphere 33, 1047±1065. Donald, M., 1982. Correlation of bioconcentration factors. Environ. Sci. Technol. 16, 274±278. Fox, K., Zauke, G.P., Butte, W., 1994. Kinetics of bioconcentration and clearance of 28 polychlorinated biphenyl congeners in zebra®sh brachydeanio rerio. Ecotox. Environ. Safety 28, 99±109. Freitag, D., Ballhorn, L., Geyer, H., Korte, F., 1985. Environmental hazard pro®le of organic chemicals: an experimental method for the assessment of the behaviour of organic chemicals in the ecosphere by means of simple laboratory tests with 14C labelled chemicals. Chemosphere 14, 1589±1616. Garten, C.T., Trabalka, J.K., 1983. Evaluation of models for predicting terrestrial food chain behavior of xenobiotics. Environ. Sci. Technol. 17, 590±595. Gobas, F.A.P.C., Clark, K.E., Shiu, W.Y., Mackay, D., 1989. Bioconcentration of polybrominated benzenes and biphenyls and related superhydrophobic chemicals in ®sh: role of bioavailability and elimination into the feces. Environ. Toxicol. Chem. 8, 231±245. Hammett, L., 1970. Physical Organic Chemistry: Reaction Rates Equilibria and Mechanisms, second ed. McGrawHill, New York. Isnard, P., Lambert, S., 1988. Estimating bioconcentration factors from octanol/water partition coecient and aqueous solubility. Chemosphere 17, 21±34. Koch, R., 1983. Molecular connectivity index for assessing ecotoxicological behaviour of organic compounds. Toxicol. Environ. Chem. 6, 87±96. Leo, A.J., 1975. Symposium on structure-activity correlations in studies of toxicity and bioconcentration with aquatic organisms, Great Lakes Research Advisory Board, Burlington, Ontario, pp. 151. Leo, A.J., 1985. Computer calculation of octanol/water partition coecients of organic solutes from structure. In: Seydel, J.K. (Ed.), QSAR and Strategies in the Design of Bioactive Compounds. VCH, Weinheim, pp. 294±298. Lu, X.X., Tao, S., 1999. Prediction of ®sh bioconcentration factors of non-polar organic pollutants based on molecular connectivity indices. Chemosphere 39, 987±999. Lu, X.X., Tao, S., Hu, H.Y., Ye, S.F., in press. Prediction of bioconcentration factor of organic compounds in ®sh by
1568
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
molecular connectivity indices and function correction factors. Chinese J. Appl. Ecol. Mackay, D., Shiu, W.Y., Ma, K.C., 1992. Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals. Lewis Publishers, Michigan. Muir, D.C.G., Marshall, W.K., Webster, G.R.B., 1985. Bioconcentration of PCDD's by ®sh: eects of molecular structure and water chemistry. Chemosphere 14, 829±833. Nendza, M., 1993. QSAR of bioconcentration: validity assessment of log Pow /log BCF correlations. In: Nagel, R., Loskill, R. (Eds.), Bioaccumulation in Aquatic Systems. VCH, Weinheim, pp. 43±66. Oliver, B.G., Niimi, A.J., 1983. Bioconcentration of chlorobenzenes from water by rainbow trout: correlations with partition coecients and environmental residues. Environ. Sci. Technol. 17, 287±291. Oliver, B.G., Niimi, A.J., 1984. Rainbow trout bioconcentration of some halogenated aromatics from water at environmental concentrations. Environ. Toxicol. Chem. 3, 271±277. Oliver, B.G., Niimi, A.J., 1985. Bioconcentration factors of some halogenated organics for rainbow trout: limitations in their use for prediction of environmental residues. Environ. Sci. Technol. 19, 842±849. Opperhuizen, A., van der Velde, E.W., Gobas, F.A.P.C., van der Steen, J.M.D., 1985. Relationship between bioconcen-
tration in ®sh and steric factors of hydrophobic chemicals. Chemosphere 14, 1871±1896. Rekker, R., 1977. The Hydrophobic Fragmental Constant. Elsevier, Amsterdam. Sabljic, A., Protic, M., 1982. Molecular connectivity: a novel method for prediction of bioconcentration factor of hazardous chemicals. Chem. Biol. Interaction 42, 301±310. Tao, S., Piao, H.S., Dawson, R., Lu, X.X., Hu, H.Y. Estimation of organic carbon normalized sorption coecient (koc ) for soils using the fragment constant method. Environ. Sci. Technol. 33, 2719±2725. Veith, G.D., Macek, K.J., Petrocelli, S.R., Carroll, J., 1980. An evaluation of using partition coecients and water solubility to estimate bioconcentration factors for organic chemicals in ®sh. In: Eaton, J.G., Parrish, P.R., Hendricks, A.C. (Eds.), Aquatic Toxicology. ASTM STP 707, pp.116±129. Dr. Shu Tao received his Ph.D. degree in environmental science at the University of Kansas in 1984. He is now a full professor in Peking University. His interests include aquatic geochemistry of naturally occurring organic matter, heavy metal contamination of agricultural soil, qualitative relationship of structure and property of organics, and bioavailability of trace elements.
Fragment constant method for prediction of ®sh bioconcentration factors of non-polar chemicals q Shu Tao *, Haiying Hu, Xiaoxia Lu, R.W. Dawson, Fuliu Xu Department of Urban and Environmental Sciences, Peking University, Beijing 100871, People's Republic of China Received 16 March 1999; accepted 23 January 2000
Abstract A fragment constant method for prediction of ®sh bioconcentration factor (BCF) was established based on experimental BCF values for 80 non-polar chemicals from nine classes. The model was evaluated using coecients of determination and mean residuals, which are 0.995 and 0.1836, respectively. Jackknife tests were applied to examine the robustness of the prediction model on a class-by-class basis. Ó 2000 Elsevier Science Ltd. All rights reserved. Keywords: Bioconcentration factor; Fish; Fragment constant
1. Introduction Fate modeling is a technique with the potential for predicting expected chemical distributions in various sectors of the environment. Among the many properties available for describing distributions, the biological concentration factor (BCF) has proven very important as far as the behavior and fate of water-born chemicals in the aquatic environment is concerned (Barron, 1990). Of the various aquatic species, ®sh typically serve as targets for BCF assessments because of their importance as a human food source along with the availability of standardized testing protocols (Barron, 1990). Bioconcentration factors can be measured experimentally. However, the measurement of BCF values for all the kinds of chemicals that organisms can potentially be exposed to is very dicult, if not impossible. For this reason, the prediction of BCF is an essential component
q
Funding was provided by The National Scienti®c Foundation of China [49632060] and [49525102]. * Corresponding author. Tel.: +86-10-627-51938; fax: +8610-627-51938. E-mail address: [email protected] (S. Tao).
in understanding the environmental behavior and toxicity of chemicals. Studies on BCF prediction modeling began in the mid-1970s. One approach was to estimate a chemical's BCF based on the experimental relationship between BCF and other physiochemical parameters such as the octanol/water partitioning coecient (Kow ), water solubility or the soil adsorption coecient (Koc ) (Devillers et al., 1996). Another approach based on molecular connectivity indices has also been extensively reported in the literature (Sabljic and Protic, 1982; Koch, 1983). The relationship between BCF and ®ve molecular connectivity indices along with a number of correction factors based on the experimental BCFs of 80 non-polar organic pollutants from the same database was studied (Lu and Tao, 1999). In addition to molecular connectivity indices, the fragment constant approach may also prove to be valid for predicting BCFs. Relying on HammettÕs postulation that free-energy based parameters are additive (Hammett, 1970), Rekker (1977) proposed a generalized approach for predicting Kow in which each part, or fragment, of a chemical was given a value so that their sum would yield the log Kow . The only input necessary for this approach is the chemical structure. Leo (1975, 1985), using a similar approach, established a set of rules
0045-6535/00/$ - see front matter Ó 2000 Elsevier Science Ltd. All rights reserved. PII: S 0 0 4 5 - 6 5 3 5 ( 0 0 ) 0 0 0 4 9 - 7
1564
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
for fragmenting chemicals and developed a computer algorithm with a coded program capable of calculating the Kow values of organic compounds using the fragment method. The fragment constant method has also been tried in the prediction of Koc (Tao et al., 1999). In addition to the contributions of fragments, the sorption property of a chemical is further in¯uenced by the moleculeÕs structure. Structural eects can, however, be adequately described by using structural correction factors which take into account molecular ¯exibility, unsaturation, multiple halogenation, branching, interactions with H-polar fragments and so forth (Leo, 1975). The fragment approach, however, has not been tried for the prediction of BCF, although its application in the case of Kow and Koc has been considered successful. The objective of this study was to develop a statistical model capable of predicting the BCF values of non-polar organic pollutants based on fragment constant and the structural correction factors. The robustness of the model was tested using the modi®ed Jackknife tests.
either aliphatic chain or aromatic ring, ±Br, ±C(H)¸, and ±C¸). The in¯uence of molecular structural features on BCFs was also taken into consideration for the modeling in this study. Speci®cally, molecular ¯exibility, unsaturation, multiple halogenation, branching, and interactions with H-polar fragments were represented by 16 structural factors listed in Table 1. A stepwise regression was performed during a preliminary analysis and the results con®rmed the independent contribution of each factor selected. 2.3. Model development and validation A linear multivariate regression was conducted based on the experimental BCF values collected and the number of individual fragments and structural features of each chemical using the following equation: log BCF
a b X X ni fi m j Fj ; i1
2. Methodology 2.1. Collection of experimental BCFs Experimental BCFs in ®sh for 80 non-polar organic compounds, including halogenated hydrocarbons, alkyl and alkenyl benzenes, polycyclic aromatic hydrocarbons, hexachlorocyclohexanes, etc. were collected from the literature (Veith et al., 1980; Donald, 1982; Garten and Trabalka, 1983; Oliver and Niimi, 1983; Oliver and Niimi, 1984; Freitag et al., 1985; Opperhuizen et al., 1985; Isnard and Lambert, 1988; Gobas et al., 1989; Mackay et al., 1992; Nendza, 1993). The ®sh used as test species included rainbow trout, guppies, fathead minnows, bluegill sun®sh, golden ide and other freshwater species. All the data were measured under ¯owing water conditions and presented based on the whole weight of the ®sh. The resulting experimental BCFs were found to range from 0.30 to 5.36 log-units. In those instances where there were more than one experimental BCF available in the database for a chemical, a median value was derived for purposes of regression modeling.
1
j1
where a and b are the total numbers of the atomic or group fragments and structural features de®ned by the model; ni and mj the numbers of the ith fragment and the jth structural factors of the chemical; fi is the fragment constant for the ith fragment and Fj is the structural correction factor for the jth structural feature. SPSS was applied for the regression. The coecient of determination for the multiple regression (R2 ) and the adjusted coecient of determination (R2adj ) were taken into consideration in testing the quality of the regression. Additionally, the studentized residual was used as an additional statistic in testing the quality of the regression. A modi®ed Jackknife test was used for regression validation. The test was then applied to successive chemical groupings in which one of the nine classes of chemicals was removed at each iteration (Lu and Tao, 1999). A multiple linear regression was performed after each deletion to calculate the Jackknife coecient of determination and then compared with the same parameter derived prior to deletion of a chemical group for purposes of robustness evaluation.
2.2. Identi®cation of the fragment and correction factors The fragments were identi®ed according to LeoÕs de®nition which refers to a fragment as an atom, or atoms, whose exterior bonds are to isolating carbon atoms (Leo, 1975). As the contribution of a fragment to the sorption behavior of a chemical depends on the type of isolating carbon atom to which the fragment is attached, each of the fragments was treated as dierent fragments. There were nine total fragments identi®ed from the 80 chemicals (±H, ±C±, ±C6 H5 , ±Cl attached to
3. Results and discussion 3.1. Model development A linear multiple regression was conducted on the experimental BCFs obtained from the database. The equation and the coecient of determination derived as a result of using the nine atomic or group fragments and the 16 structural correction factors is
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
1565
Table 1 Structural correction factors No.
Symbol
Factors
Explanation
1 2 3 4 5 6 7 8 9 10 11 12 13
FC FCH FCH2 U FCH 3 Fb Fb FrCl F¸ F¸U F¸U=2 FmhG1 FmhG2 FO
Number of quaternary carbon Number of triple carbon Number of secondary carbon Number of primary carbon attached to aromatic ring Number of aliphatic chain bond ± 1 Number of aliphatic ring bond ± 1 Number of branching on a ring cluster Number of aliphatic chain double bond Number of double bond attached to aromatic ring Number of vicinal double bonds Number of carbon bonds to two halogens Number of carbon bonds to three halogens Number of vicinal carbons bonds to multiple halogens
14 15 16
Fm Fa Fa; c
Quaternary carbon factor Triple carbon factor Secondary carbon factor Primary carbon (aromatic) factor Aliphatic chain bond factor Aliphatic ring bond factor Ring cluster branching factor Aliphatic double bond factor Aromatic double bond factor Vicinal double bond factor Double halogenation factor Triple halogenation factor Vicinal multiple halogenation factor Meta multiple carbon factor PCB a chlorine factor PAH a±c factor
log BCF
a b X X ni fi mj Fj ; i1
R2 0:995;
2
j1
where ni , mj , fi , and Fj are the same as those in Eq. (1). R2 is the coecient of determination. Statistically, the regression model is able to account for as much as 99.5% of the variation in the experimental BCFs corresponding to the 80 chemicals. The fragment constants and the structural correction factors derived as regression coecients are listed in Tables 2 and 3. A scatter plot of the 80 measured BCF values against the predicted ones is presented in Fig. 1. The plot demonstrates that the residuals of the estimation are acceptable in terms of its predictive certainty, especially when the errors imbedded in the measured data are taken into consideration.
Number of meta-carbons bonds to multiple halogens Number of chlorine attaches to a carbon of PCB Number of halogen attaches to a or c carbon of PAH
Table 2 Derived fragment constantsa Fragment
f
±H ±C(±) ±C6 H5 ±Cl ±Br ±C(H)¸ ±C¸
)0.032 )0.615 1.241 0.423
fU
f U=2
0.816 0.797
0.430
a
f AR
0.286 0.097
Superscripts on the fragment constant which denote the type of attachment are /: attached to aromatic ring, //2: attached to double bond, AR: fused in aromatic ring.
Table 3 Derived structural correction factors
3.2. Error evaluation
Factor
F
The mean residual for the predicted BCF based on the regression testing was 0.1836 log-units. Of the 80 chemicals studied, the individual residuals for 78.8% of them were less than 0.3 log-units. For 92.5% of the 80 chemicals studied, the individual residuals were less than 0.5 log-units. The relative errors in the model for individual chemicals studied were examined using the studentized residuals compared with the calculated log BCF values. The results are plotted in Fig. 2. As can be seen in Fig. 2, the studentized residuals are symmetrically distributed around zero with no speci®c trend. It appears that for most of the chemicals studied, their residuals are relatively small. The four chemicals with the highest residuals are benzene, hexabromoben-
FC FCH FCH2 U FCH 3 Fb Fb FrCl F¸ F¸U F¸U=2 FmhG1 FmhG2 FO Fm Fa Fa;c
1.524 0.508 0.688 1.072 )0.369 0.197 )4.886 0.922 0.781 1.875 0.221 0.123 )0.264 )0.153 )0.435 )0.127
1566
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
Fig. 3. Mean residuals of various chemical classes. Fig. 1. Relationship between the calculated and measured log BCF.
Fig. 2. Plot of studentized residual against calculated log BCF.
zene, 2,20 ,4,4-PCB, and 2,3-PCB. One possible explanation for this result could be the very long half-life associated with these chemicals. It is recognized that relatively larger errors can be generated during BCF measurements of those long-life chemicals (Oliver and Niimi, 1985). The calculated CookÕs distance for all cases of the chemicals examined are all less than 0.42, supporting the reliability of the linear model. In order to investigate the dierence among the various types of chemicals, the chemicals in the study were divided into nine classes as follows: chlorinated aliphatic hydrocarbons (CAH), monocyclic aromatic hydrocarbons (MAH), chlorinated benzene (CB), brominated benzene (BB), polychlorinated biphenyls (PCB), polybrominated biphenyls (PBB), chlorinated naphthalenes (CN), polycyclic aromatic hydrocarbons (PAH), and others (OTH, including chlordane, hexachlorocyclohexanes, heptachlor, DDT and DDE). The dierence among the various classes in terms of the modeling error was found to be signi®cant as shown in Fig. 3, where the mean residuals of the nine classes of chemicals are illustrated by means of bar chart. Of the nine classes, the mean residuals of CB (0.31), PBB (0.28), and CN (0.27) were above the overall av-
erage (0.1836 log-units, indicated by a broken line in the ®gure) of the groups as a whole. PBBs and CN have relatively complicated molecular structures, which serve as the primary source of errors. The 16 structural factors selected for the modeling exercise were still not sucient for a full explanation of all of the structural features of these chemicals. Moreover, the measured BCF values for the chemicals in these classes may also bear relatively higher as an experimental error source than the others. Error in the prediction model may be generated from a number of sources. One such source follows from potential inaccuracies in the measured data itself. According to Lu et al. (1999), the mean variation among 592 duplicate experimental BCF values for the 120 chemicals reported in the literature is 0.362 log-units. This suggests that a signi®cant portion of the modeled residual was contributed from the error contained in the experimental BCF value. However, only 80 chemicals among the 120 were involved in determining these residuals. As such, it is expected that the portion of error contributed from the measurement should be less than 0.362. 3.3. Robustness of the model Considering the relatively small number of collected experimental BCFs in the study, the database was not divided into modeling and testing sets for purposes of model validation. Instead, an alternative approach using a modi®ed Jackknife test was applied. The regression analysis was repeatedly performed on subsets of the database with various classes of chemicals removed one at a time. Not only can the model be repeatedly validated by such a procedure, but the resulting information provides some indication relative to the modelÕs robustness as well. The adjusted coecients of determination and the mean residuals for the Jackknife tests conducted with successive deletion of the nine classes (CAH, MAH, CB, BB, PCB, PBB, CN, PAH, and OTH) one at a time are presented in Fig. 4.
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
1567
Fig. 4. The adjusted coecient of determination and the mean residuals of the Jackknife test with deletion of various chemical classes one by one (the corresponding values without deletion were shown as the broken lines).
A brief examination of Fig. 4 indicates no unusually high variations. The adjusted R2 s and mean residuals generated by deletion of various classes were either identical to or only slightly dierent from the original values derived without deletion (shown by the broken lines in the ®gure). The maximum variations were found to be 0.001 and )0.03 log-units for the adjusted coecients of determination and the mean residuals respectively, indicating a relatively high degree of robustness for the model in general. As expected, the class-by-class deletion caused either no or only slight increases in the R2 s or decreases in the residuals with few exceptions. It is not surprising to see the relatively high variations in the Jackknife R2 s and residuals for CB, PBB, PAH, and CN, in view of the relatively high mean residuals observed in these classes (Fig. 3). On the other hand, the Jackknife residual for CAH (the only aliphatic class among all the chemicals studied) is higher than in the original case. Although there were only 13 chemicals in the sample for this class, their similar components and structure were described fairly clearly by the fragments and correction factors selected during model development. Furthermore, the variation in BCF values for the CAH chemicals was between 0.3 and 3.76 log-units, well within the range suggesting a perfect linear relationship between BCF and the thermodynamic parameters (Muir et al., 1985; Fox et al., 1994). Considering the variation among the 80 chemicals comprising the data set and the fact that the experimental BCF values varied across a range exceeding ®ve log-units, the non-class speci®c correlation model seems to allow estimation of BCF values for a large variety of organic compounds within the constraints of experimental uncertainties.
4. Conclusion BCF values for non-polar chemicals can be estimated using the fragment constant approach when supplemented by structural correction factors. A linear model is suitable in terms of prediction certainty and model robustness.
References Barron, M.G., 1990. Bioconcentration. Environ. Sci. Technol. 24, 1612±1618. Devillers, J., Bintein, S., Domine, D., 1996. Comparison of BCF model based on log P. Chemosphere 33, 1047±1065. Donald, M., 1982. Correlation of bioconcentration factors. Environ. Sci. Technol. 16, 274±278. Fox, K., Zauke, G.P., Butte, W., 1994. Kinetics of bioconcentration and clearance of 28 polychlorinated biphenyl congeners in zebra®sh brachydeanio rerio. Ecotox. Environ. Safety 28, 99±109. Freitag, D., Ballhorn, L., Geyer, H., Korte, F., 1985. Environmental hazard pro®le of organic chemicals: an experimental method for the assessment of the behaviour of organic chemicals in the ecosphere by means of simple laboratory tests with 14C labelled chemicals. Chemosphere 14, 1589±1616. Garten, C.T., Trabalka, J.K., 1983. Evaluation of models for predicting terrestrial food chain behavior of xenobiotics. Environ. Sci. Technol. 17, 590±595. Gobas, F.A.P.C., Clark, K.E., Shiu, W.Y., Mackay, D., 1989. Bioconcentration of polybrominated benzenes and biphenyls and related superhydrophobic chemicals in ®sh: role of bioavailability and elimination into the feces. Environ. Toxicol. Chem. 8, 231±245. Hammett, L., 1970. Physical Organic Chemistry: Reaction Rates Equilibria and Mechanisms, second ed. McGrawHill, New York. Isnard, P., Lambert, S., 1988. Estimating bioconcentration factors from octanol/water partition coecient and aqueous solubility. Chemosphere 17, 21±34. Koch, R., 1983. Molecular connectivity index for assessing ecotoxicological behaviour of organic compounds. Toxicol. Environ. Chem. 6, 87±96. Leo, A.J., 1975. Symposium on structure-activity correlations in studies of toxicity and bioconcentration with aquatic organisms, Great Lakes Research Advisory Board, Burlington, Ontario, pp. 151. Leo, A.J., 1985. Computer calculation of octanol/water partition coecients of organic solutes from structure. In: Seydel, J.K. (Ed.), QSAR and Strategies in the Design of Bioactive Compounds. VCH, Weinheim, pp. 294±298. Lu, X.X., Tao, S., 1999. Prediction of ®sh bioconcentration factors of non-polar organic pollutants based on molecular connectivity indices. Chemosphere 39, 987±999. Lu, X.X., Tao, S., Hu, H.Y., Ye, S.F., in press. Prediction of bioconcentration factor of organic compounds in ®sh by
1568
S. Tao et al. / Chemosphere 41 (2000) 1563±1568
molecular connectivity indices and function correction factors. Chinese J. Appl. Ecol. Mackay, D., Shiu, W.Y., Ma, K.C., 1992. Illustrated Handbook of Physical-Chemical Properties and Environmental Fate for Organic Chemicals. Lewis Publishers, Michigan. Muir, D.C.G., Marshall, W.K., Webster, G.R.B., 1985. Bioconcentration of PCDD's by ®sh: eects of molecular structure and water chemistry. Chemosphere 14, 829±833. Nendza, M., 1993. QSAR of bioconcentration: validity assessment of log Pow /log BCF correlations. In: Nagel, R., Loskill, R. (Eds.), Bioaccumulation in Aquatic Systems. VCH, Weinheim, pp. 43±66. Oliver, B.G., Niimi, A.J., 1983. Bioconcentration of chlorobenzenes from water by rainbow trout: correlations with partition coecients and environmental residues. Environ. Sci. Technol. 17, 287±291. Oliver, B.G., Niimi, A.J., 1984. Rainbow trout bioconcentration of some halogenated aromatics from water at environmental concentrations. Environ. Toxicol. Chem. 3, 271±277. Oliver, B.G., Niimi, A.J., 1985. Bioconcentration factors of some halogenated organics for rainbow trout: limitations in their use for prediction of environmental residues. Environ. Sci. Technol. 19, 842±849. Opperhuizen, A., van der Velde, E.W., Gobas, F.A.P.C., van der Steen, J.M.D., 1985. Relationship between bioconcen-
tration in ®sh and steric factors of hydrophobic chemicals. Chemosphere 14, 1871±1896. Rekker, R., 1977. The Hydrophobic Fragmental Constant. Elsevier, Amsterdam. Sabljic, A., Protic, M., 1982. Molecular connectivity: a novel method for prediction of bioconcentration factor of hazardous chemicals. Chem. Biol. Interaction 42, 301±310. Tao, S., Piao, H.S., Dawson, R., Lu, X.X., Hu, H.Y. Estimation of organic carbon normalized sorption coecient (koc ) for soils using the fragment constant method. Environ. Sci. Technol. 33, 2719±2725. Veith, G.D., Macek, K.J., Petrocelli, S.R., Carroll, J., 1980. An evaluation of using partition coecients and water solubility to estimate bioconcentration factors for organic chemicals in ®sh. In: Eaton, J.G., Parrish, P.R., Hendricks, A.C. (Eds.), Aquatic Toxicology. ASTM STP 707, pp.116±129. Dr. Shu Tao received his Ph.D. degree in environmental science at the University of Kansas in 1984. He is now a full professor in Peking University. His interests include aquatic geochemistry of naturally occurring organic matter, heavy metal contamination of agricultural soil, qualitative relationship of structure and property of organics, and bioavailability of trace elements.