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Prediction of chemicals biodegradation in soils: a tentative of modeling
Phys. Chem. Earth (B), Vol. 24, No. 6, pp. 495-499,
1999 0 1999 Elsevier Science Ltd All rights reserved 1464-1909/99/$ - see front matter
Pergamon
PII: S1464-1909(99)00036-2
Prediction of Chemicals Biodegradation in Soils: a Tentative of Modeling S. Pass’, T.M. Vogel’, H. Vaudrey3, F. Baud-Grasset3 and J.C. Block’ ‘UMR Universitk - CNRS 7564, LSE/LCPE, FacultC de Pharmacie, 5 rue Albert Lebrun, BP 403, F54001 Nancy, France. E-mail: [email protected] 2Rhodia Eco Services /A.T.E., 17, rue du PCrigord, F-69330 Meyzieu, France 3Rhbne-Poulenc Industrialisation, 24 avenue Jean Jam-&, F-69 153 DBcines-Charpieu, France Received 17 July 199%; accepted 9 February I999
Abstract. Natural attenuation of organic pollutants in the environment is controlled mainly by three groups of parameters: (i) environmental characteristics (e.g., soil characteristics (e.g., organic matter), (ii) chemical hydrophobicity of the pollutant, structure etc.) sod (iii) nature and density of biodegraders. parameters describing environmental Potential characteristics and chemical characteristics were collected from the literature and stored into a data base. LJsing multiple linear regressions, we have expressed the observed half-life of chemicals as a function of environmental rod characteristics. Models demonstrate that chemical environment influences the prediction of the persistence time of molecules that have a predicted half-life of less than 150 days. Conversely, for molecules that are predicted to be less rapidly biodegradable (i.e.. t,,z > 150 days) environmental parameter variations only slightly modify (about 10%) then persistence times. 0 1999 Elsevier Science Ltd. All rights reserved
1. Introduction The persistence of organic chemicals in the environment depends on at least 3 groups of factors: (i) the activity of the biodegrading microorganisms (Allard and Neilson, 1997; Benoit and Ban&o, 1997; Bolan and Baskaran, 19%; Holden and Firestone, 1997; Masaphy aul Mandelbauma, 1997, etc.) ; (ii) the physicochemical for example porosity, characteristics of the soil, humidity (Patil et al., 1988; Willems et al., 19%), sod organic matter concentration (Barriuso et al., 1997; Harms andZehnder, 1995; Ogmm et al., 1985, etc.) ; and (iii) the chemical structure (Cowan et al., 1996; Damborsky, 19%; Damborsky ef al., 1996; Lange&erg et al., 1996; Okey and Stensel, 19%; Peijnenburg, 1994, etc.).
predicting their biodegradability has been relatively well biodegradability structure studied for quantitative relationships (QSBR), where the structure of the chemicals is directly linked with their biodegradability(Boethling, 1986; Boethling, 1997; Bossert and Bartha, 1986; Cambon and Devillers, 1993; Dearden and Nicholson, 1986; Okey and Stensel, 1996; Vaishnav et al., 1987). The effect of certain environmental factors on the biodegradation kinetics is well known, for example the effect of temperature ard humidity (Patil et al., 1988; Thinmarayanan et al., 1985; Walker and Brown, 1983), mainly by controlling the activity of microorganisms. However, there has been no study linking chemical structure and environmental characteristics to compound biodegradation kinetics (expressed for example by half-life). Models able to predict the biodegradation kinetics of organic compounds (giving for example half-lives) in soils would be useful for (i) deciding if an accidental pollution could be “naturally” rcsorhed rapidly, or if specific treatment was needed, (ii) designing new chemicals which present less risk to the environment, or (iii) limiting the number of biodegradation tests for existing molecules. To our knowledge, no model predicting the natural biodegradability of chemicals in unsaturated soils has been described. The development of a database using new experiments is unrealistic. One solution is to collect biodegradation data for organic compounds in soils by a critical analysis of the scientific literature, but in this case there are at least 3 difficulties to be overcome: (i) on the one hand, the biodegraders, and more specifically their activity and then density, are only very rarely described, except by certain authors (Gonsior and West, 1995; Grecr and Shelton, 1992; Sabourin et al., 19%); (ii) on the other hand, the description of soils is insufficient to provide a mechanistic picture of biodegradation: for instance, it is unfortunate that only the organic matter (or organic carbon) concentration is described, since the nature of this organic matter (i.e.,
Correspondence to: Pr. J.C. Block
495
496
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils
humic and fulvic acids, humin, polysac&arides) or its polarity is certainly an important parameter affecting pollutant availability (Bolan and Baskamn, 1996; Corseuil and Alvarez, 1996; C&aye et al., 1997; Knaebel et al., 1996; Shuttleworth and Cemiglia, 1995); (iii) finally, there is considerable heterogeneity in the published data due to different laboratories using different expressions for chemical biodegradation: primary or final degradation, or compound half-life using the equation t,,2 = Ln2/k, (k is the first-order biodegradation constant), or even using “residence time” for the molecule (after a 50% reduction in its concentration). Another cxamplc of data limitations is the extrapolation (or interpolation) of experiments carritd out under environmental conditions other thau in soils (in water, with activated sludges, etc.) (Howard rl al., 1991). Despite these difficulties, this article describes a modeling attempt to predict biodegradability in soils from the knowledge of the chemical structure and of the soil composition. This study is based on published data collected through a litterature review.
2. Data selection Data were collected from the literature published over a 30 year period to describe organic molecule bidgradation experiments following the criteria listed below: the degradation was caused by microorganisms (i.e., biodegrAa&~ or in other words, with controls proving the absence of physical, chemical, etc. degradation) and the soil was described as completely as possible (in a certain number of publications no information on soil characteristics was supplied) and hence these publications were not used and the compound described must be an organic molecule alone and of known chemical structure and not a mixture of several compounds with a more or less well determined chemical structure (e.g., rubber, oils). Over 600 articles were analysed of which 75 of them were taken into account following the criteria decribed above. This bibliographic study made it possible to constitute a data base that currently contains 600 entries out of which 150 possess all the environmental variables described. Consequently, statistical treatment of environmental data, molecular data and half-life applies only to these 150 entries (one entry corresponds to the biodegradation time of one molecule in a given environment), out of which polyaromatic hy droca&ms (PAH) are represented 42 times and herbicides 78 times (Table 1). This data base is biased for at least two reasons: the molecules represented are more often resistant than not resistant to biodegradation and they are not equally represeated (24 data relative to atine sod only one to 2-amino naphthalene, leading to representative frequencies of 16 and 0.7%. respectively) (Table 1).
Table 1. Description of statistical analyses: ““me are represented. PAH anthracene benzene chrysene coronene fluoranthene benao[b]fluroaothene benzo[k]ll”roanthene fluorene naphthalene 1.ami”o,naphthalene 2.amino,naphthalene be”zo[ghi]petylene phenanthrene pyrene be”zo[a]pyre”e
the data base (150 entries) used for the and number of times that these molecules Herbicides 2.4-D atrazine chlorsulfuron isoprduron linwo” metolachlor
(3) (2) (3) (3) (3) (3) (3) (3) (3) I;;
Others benzidine DCPA Cnttrophenol p-toluidine triflurali”
(3)
(5) wj (9) (3) (16) (21)
(3) (18) (2) :5;
‘(:; (3)
Tabk 2. Reporting frequency of description, scale and average of descriptors of soil(s) (Temperature: “C; Water content: %; f&: (fraction of organic matter): %; sand, silt and clay: %: CEC (catton exchanee cata&+: mEa for 100 e of soil) out of a total of 600 pieces of data. Character&c Reporting Range Average frequency PH 97% 2.10 8.25 6.7 Temperature 94 % 5.0 - 40.0 23.1 water content 87 % 2.520000 423 f 86% 0.12- 100 6.6
.
3. Mean soil
_,
.
-
characteristics
Soils are traditionally described by the variables: pH, organic matter concentration (f&, cationic exchange capacity (CEC), particule size distribution (sand, silt, clay). These descriptors are those most commonly found in the scientific literature to describe soils during organic molecule biodegradation experiments, plus two environmental variables: temperature and water content. The parameter most reported is pH (frequency: 97%). followed by temperature (94%). water content (87%), organic matter (86%), particle size distribution: sand, silt, clay (60% each) and lastly CEC (46%) Fable 2). Reported pH values vary over a large range of 6 pH units, although 90% of these values fall within the 4.6 - 8.0 interval. Temperature ranges 5 to 40°C although 90% of the values are between 10 and 3O”C, which is a temperature rauge compatible with the survival and growth of many species of bacteria in the environment. Water content presents au extremely wide range of variation, since in some experiments, the authors work with natural soils whereas in others they add a few grams of soil to a liter of water. However, 90% of the water content values fall between 10 and lOO%, or 10 g to 100 g of water per 100 g of dry soil. Organic matter varies from 0.1 to near 100 g per 100 g of dry soil, with 90% of the values ranging from 0.9 to 16%. The organic matter values of 0.1% (i.e., very low concentrations) correspond to “coarse textured” soils (99.8% sand). Particle size distribution is defined by sieving: the 50
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils Table 3. Variables used for describing molecule structures, adapted from (Boethling et al., 1994; Okey and Stensel. 1996; Peiinenburp, _ _ 1994; T&k a
ym to 2 mm fraction corresponds to sands (coarse from 2 mm to 200 pm, fine from 200 pm to 50 pm), the 2 to 50 pm fraction to silts (coarse from 50 ym to 20 pm, fine from 20 pm to 2 pm) and the fraction lower than 2pm to clays (Bonneau and Souchier, 1994). Finally, the CEC ranges from 0.9 to 52.0 mEQ for 100 g of dry soil, 90% of these values being between 2.0 and 40 mE$.
4. Descriptor
497
Tabk 4. Analysis of variation on the relationships between the halflife (days) and (i) soil environment characteristics alone, (ii) the molecular characteristics alone and (iii) the characteristics of molecules and soils. d.f.: degrees of freedom, 150 data taken into account. I’ adjusted d.f. d.f. for d. f. model residual 8 Soil environment characteristics 0.15 141 Chemical characteristics 0.75 137 Chemical and soil characteristics 0.77 :: 129 lo”,
, * ,,..., . , ,,.,., , , ““.,
, * ““‘,
, , ‘“1
choice
In order to perform correlation studies and use mathematical models, a mathematical transformation of environmental variables and chemical structures was necessary to encode the information. Thus, for molecular structures, ckscriptive variables were chosen, mainly from the scientific literature 1996; (Boethling et al., 1994; Okey and Stensel, Peijnenburg, 1994; Tabak and Govind, 1993). The molecules were described using 10 structural descriptors (fable 3) and soil environment using the 8 variables described in the Table 2. Molecules are, thus, described by a 10 digit code and the environment by au 8 digit code aud c&d directly by the numerical value of the variable itself.
1
IO
100
1000
lo4
Ohserved half-life (days) Fi. 1. Correlation between the till observed and the tl,* predicted from molecular and environmental characteristics.
5. Modeling The object of the modeling was to predict the persistence time of organic molecules in soils using correlations developed from conceptual models and bibliographical data. The conceptual mode1 incorporates both chemical and environmental parameters, collected from the literature analysis. From this very heterogenous database containing a 18 digit code for each of the 150 data, multi-variate regressions were performed to examine potential relationships between compound environmental half-life and (i) soil characteristics alone; (ii) molecule characteristics alone; (iii) molecule and soil characteristics together. The simple multi-variate correlations calculated between compound half-life and compound characteristics (Table 3) or soil environment characteristics (Table 2) or both demonstrate the relative consistency of bibliographic data (Table 4). Concerns about the distribution of points znd
10
100 Observed t1/2 (days)
Fii. 2. Correlation between the t,/, observed (limited to 500 days) and the t,,* predicted from molecular and environmental characteristics.
their relative weights could further complicate (aud may be improve) any conclusions derived from these correlations (see Figures 1 and 2). In any case, it seems likely that the use of only environmental characteristics is significantly worse than the other “models”. This is not too surprinsing in that this biased data set is based on compounds of scientific or public policy interest, i.e., slow degrdng compounds. Our hypothesis has been that for difficult or slowly degraded compounds, tbe environment exhibits less control over rates.
498
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils
100
200 300 Observed half-life (davs)
‘MN
500
Fig. 3. Maximum variation of half-life predicted from molecular and environmental characteristics, as a function of observed t,,?.
If this simple correlation stands up to more rigorous examination, the apparent improvement of the correlation using both chemical and environmental characteristics at low half-lives would support our hypothesis (Figure 3). Indeed, the correlation using both chemical ad environmental characteristics produces variable half-life predictions for the same compound when environmental conditions change (Figure 4). This variability is the reason why in Figure 4 the predicted half-lives using chemical characteristics alone produces only 17 half-lives (as there am only 26 different compounds, and 9 had predicted half-lives about 500 days the limit used in Figure 4). Maybe of more interest are the potential reasons (and corresponding improvements) for the lack of better correlation coefficients such as those often observed with standardized biodegradation studies. Among the numerous possible hypotheses, four stand out as more important than the others: (i) the molecular descriptors used in these models do not make it possible to efficiently describe all the molecule classifications (substituted or non substituted aromatics, polyaromatics, aliphatics, etc.); (ii) the soil desaiptols in the scientific literature are not sufficiently precise and do not describe the interactions between pollutants and soil very well (no description of either the nature of the organic matter or the clays, no information on soil porosity or the biodegraders etc.); (iii) the data used comes from different laboratories where experimental methods, measurement methods, half-life expressions are extremely variable; (iv) no information is given about the biodegraders (e.g., their activity, their density) present in the soils.
6. Conclusions The ability to predict the biodegradation kinetics of organic chemicals in the environment is a key factor in evaluating soil pollution.~ Issues related to active soil remediation aud
0
100
200
303
403
500
Predicted tli2 from chemical alone (days) Fig. 4. t,,l predicted from molecular and environmental caracteristics as a function of tliz predicted from molecular characteristics alone.
natural bioremediation are currently difficult to address using mathematical models. Such models are generally non-accessible because of critical limitations: ?? the data published in the scientific literature am heterogeneous sod incomplete (absence of standardization between tests performed by different laboratories, variable methods for calculating half-lives, etc.); ?? compiling a database under completely standardized conditions (biodegradation test in soils, analytical method, calculation of half-life) is unrealistic, given the cost of such tests; ?? the density and activity of the biodeguklers in soils am rarely reported in the literature; ?? the soils are incompletely described (e.g., no information on the nature aud polarity of the organic matter, and thus on its ability to sorb organic molecules); ?? lastly, it is difficult to describe mathematically all the organic chemicals (HPA, pesticides, etc.) by a single coding system which is relevant and representative of the 2D sod 3D structures and of the physico-chemical properties of the molecules; ?? finally, the linear regressions allow to identify the key variables (environmental and chemical) in such models. Nevertheless, given the number of variables taken into account and their probable interaction (effect of pH, temperature, organic matter, etc. both on the activity of the bicdegradezs and on the behavior of the organic molecules), the majority of the phenomena considered would almost certainly be better described by non-linear equations. However, our modeling studies have enabled us to evaluate the effect of the environment on the biodegradation kinetics of organic molecules. Thus, variations in the environmental parameters markedly affected the half-lives of molecules which degrade fairly rapidly (i.e., in less than 120-180 days), but have less effect for more persistent molecules (i.e., t,,, > 120-180days). This could be explained by the fact that the persistent molecules rquim specific suitable biodegraders, probably fiquently absent
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils from soils. The dispersion of the biodegradation results linked to the probable absence in some tests of these biodegraders thus masks the effect of the other environmental variables. Several lines of research, not mutually exclusive, could be of interest to improve the predictive power of the models: (i) improving the coding system for the chemical molecules (e.g., inclusion of descriptors for quantic effects); (ii) use of non-linear regressions to predict the half-lives (e.g., neural networks); (iii) development of several models, depending on the class of the molecules. The ideal would be to be able to obtain more biodegradation data for organic chemicals from the scientific community, with as complete zstd exhaustive a description of the soils as possible, together with a description of the density and activity of the biodegraders present.
References Allard, 4.S. and Neilson, A.H., Bioremediation of organic waste sites: a critical review of microbioloeical Int. Biodet. Biodew& 39, _ aspects, . 253-285, 1997. Bartiuso. E.. Houot. S.. and Serra-Wit&e. C.. Influence of comoost addition &soil on the behaviour of herb&ides, Pestic. Sci., 49, 65175, 1997. Benoit, P. and Barriuso, E., Fate of C-lCring-labeled 2,4-D, 2,4 dichlorophenol and Cchlorophenol during straw composting, Biol. Fertil. Soils, 25, 53-59, 1997. Boethling, R.S., Application of molecular topology to quantitative structure-biodegradability relationships, Environ. Tax. Chem., 5, 797806,1986. Boethling, RS. 1997. Modeling to predict biodegradability applications in risk assessment and chemical design. In Eiotecbnology in the &u-t&able Environment. pp. 233-248 Environmental Science Research. Vol. 54 Boethling, R.S., Howard, P.H., Meylan, W., Stiteler, W., Beauman, J., and Tirado, N., Group contribution method for predicting probability and rate of aerobic biodegradation, Environ. Sci. Technof.. 28, 459-465, 1994. Bolan, N.S. and Baskaran, S., Biodegradation of 2,CD herbicide as affected by its adsorption-desotption behaviour and microbial activity of soils, Aust. J. SoilRes., 34, 1041-1053, 1996. 2. Constituanb et Bonueau, M. and Souchier, B., eds. 1994. P&logie. proprUt& du sol. Vol. Dew&me ddition. Paris: Masson. 665~ pp. Bossett, I.D. and Battha, R., Structure-biodegradability relationships of polycyclic aromatic hydrocarbons in soil, BUN. Environ. Contam. Toxicol., 37,490495, 1986. Cambon, B. and Devillers, J., New trends in structure-biodegradability relationships. Quant. &wt.-Act. R&t.. 12,49-56, 1993. Corseuil, H.X. and Alvarez. P.J.J.. Natural bioretnediation perspective for BTX-contaminated groundwater in Brazil: effect of ethanol, Water Sci. Tech., 34,3 11-3 18. 1996. Cowan. C.E., Federle, T.W., Larson, R.J., and Feijtel, T.C.J., Impact of biodegradation test methods on the development and applicability of biodegradation QSARs. SAR QSAR Environ. Res., 5,37-49. 1996.
Damborsky, J., A mechanistic approach to deriving quantitative structure-activity relationship models for microbial degradation of organic compounds, SAR QSAR Environ. Res., 5,27-36. 1996. Damborsky. J., Manova, K., and Luty, M. 1996. A mechanirtic approach to deriving quantitative structiue biodegradation relationships. A case study : dehalogenation of haloaliphatic compounds Kluwer academic Publishers. 75-92. Dearden. J.C. aad Nicholson. R.M., The prediction of biodegradability by the use of quantitative structure-activity relationships : correlatioh of biological oxygen demand with atomic charge difference, Pestic. Sci.. 17,305.3 10, 1986 Ganaye, V.A., Keiding, K., Vogel, T.M., Viriol, M.L., and Block, J.C., Evaluation of soil organic matter polarity by pyrene fluorescence spectrum variations, Environ. Sci. Teehnol.. 31,2701-2706, 1997. Gonsior, S.J. and West, R.J., Biodegradation of glycol ethers in soil, Environ. Ton. Chem.. 14. 1273.1279. 1995. Grew, L.E. and Shelton, D.R., Effect of inoculant strain and organic matter content on kinetics of 2.4.dichlorophenoxyacetic -acid deeradation in soil. At&. Environ. Microbial.. 58. 1459-1465. 1992. Hart&., H. and Z&ttder, A.J.B., Bioavailability of sorbed 3cholorodibenzofuran, Appl. Environ. Microbial.. 61, 27-33, 1995. Holden, P.A. and Firestone, M.K., Soil microorganisms in soil cleanup: how can we improve our understanding?, J. Environ. Qual.. 26, 32.40, 1997. Howard, P.H., Bwthling, R.S.. Jarvis, W.F Meylan, W.M., and E.M., M., eds. 1991. Handbook of environmental degradation rates. Chelsea, Michigan: Lewis Publishers, Inc. 725~. Knaebel. D.B., Federle, T.W., Mcavoy, D.C., and Vestal, J.R., Microbial mineralization of organic compounds in an acidic agricultural soil: effects of preadsotption to various soil constihlents, Environ. Ton. Chem.. 15, 1865-1875, 19%. Langenberg, J.H.. Peijnenburg. W.J.G.M., and Rotije, E.. On the usefulness aad reliability of existing QSBRs for risk asSess.ement and priority setting, SAR QSAR Environ. Res., 5, 1-16, 1996. Masaphy, S. and Mandelbauma, R.T., Atrazine mineralization in slunies from soils irrigated with treated waste water, Applied Soil Ecology, 6, 283-291, 1997. Ogram, .4.V., Jessup, R.E., Ou, L.T., and Rae, P.S.C.. Effets of sorption on biological degradation rates of (2,4-dichlorophenoxy) acetic acid in soils, Appl. Environ. Microbic& 49, 582-587, 1985. Okey, R.W. and Stensel, H.D., A QSAR-based biodegradability model A QSBR, Wat. Res.. 30,2206-2214, 1996. Patil, S.G., Nicholls, P.H., Chamberlain. K., Btiggs, G.G.. and Bromilow, R.H., Degradation rates in soil of I-benzyltriazoles and two triazoles fungicides, Pestic. Sci., 22,333.342, 1988 Peijneuburg, W., Structure-activity relationships for biodegradation : a crilical review, Pure Appl. Chem., 66, 1931-1941, 1994. Saboutin, C.L., Carpenter, J.C., Leib, T.K.. and Spivack, J.L., Biodegradation of dimethylsilanediol in soils, Appl. Environ. Microbial., 62,4352-4360, 19%. Shuttlewotth, K.L. and Cemiglia, C.E., Environmental aspects of PAH biodegradation. Appl. Biochem. Biotechnol., 54, 291-302, 1995. Tabak, H.H. and Govind, R., Prediction of biodegradation kinetics using a nonlinear group contribution method, Environ. Tax. Chem., 12. 251. 260,1993. Thitttnarayanan, K., Zimdhal, R.L., and Smika, D.E., Chlorsulfuron adsorption and degradation in soil, Weed Sci., 33, 558563, 1985. Vaishnav, D.D.. Boethling, R.S., and Babe”, L., Quantitative structurebiodegradability relationships for alcohols, ketones and alicyclic 1987. cornponds, Chemosphere. 16,695703, Walker, A. and Brown, P.A., Measurement and prediction of chlorsulfuroa persistence in soil, Bull. Environ. Contam. Toxicol., 30. 3653?2,1983. Willems, H.P.L., Lewis, K.J., Dyson, J.S.. and Lewis, F.J., Mineralization of 2,CD and atrazine in the unsaturated zone of a sandy loam soil, Sol E&l. Biachem., 28,989-996, 19%.
1999 0 1999 Elsevier Science Ltd All rights reserved 1464-1909/99/$ - see front matter
Pergamon
PII: S1464-1909(99)00036-2
Prediction of Chemicals Biodegradation in Soils: a Tentative of Modeling S. Pass’, T.M. Vogel’, H. Vaudrey3, F. Baud-Grasset3 and J.C. Block’ ‘UMR Universitk - CNRS 7564, LSE/LCPE, FacultC de Pharmacie, 5 rue Albert Lebrun, BP 403, F54001 Nancy, France. E-mail: [email protected] 2Rhodia Eco Services /A.T.E., 17, rue du PCrigord, F-69330 Meyzieu, France 3Rhbne-Poulenc Industrialisation, 24 avenue Jean Jam-&, F-69 153 DBcines-Charpieu, France Received 17 July 199%; accepted 9 February I999
Abstract. Natural attenuation of organic pollutants in the environment is controlled mainly by three groups of parameters: (i) environmental characteristics (e.g., soil characteristics (e.g., organic matter), (ii) chemical hydrophobicity of the pollutant, structure etc.) sod (iii) nature and density of biodegraders. parameters describing environmental Potential characteristics and chemical characteristics were collected from the literature and stored into a data base. LJsing multiple linear regressions, we have expressed the observed half-life of chemicals as a function of environmental rod characteristics. Models demonstrate that chemical environment influences the prediction of the persistence time of molecules that have a predicted half-life of less than 150 days. Conversely, for molecules that are predicted to be less rapidly biodegradable (i.e.. t,,z > 150 days) environmental parameter variations only slightly modify (about 10%) then persistence times. 0 1999 Elsevier Science Ltd. All rights reserved
1. Introduction The persistence of organic chemicals in the environment depends on at least 3 groups of factors: (i) the activity of the biodegrading microorganisms (Allard and Neilson, 1997; Benoit and Ban&o, 1997; Bolan and Baskaran, 19%; Holden and Firestone, 1997; Masaphy aul Mandelbauma, 1997, etc.) ; (ii) the physicochemical for example porosity, characteristics of the soil, humidity (Patil et al., 1988; Willems et al., 19%), sod organic matter concentration (Barriuso et al., 1997; Harms andZehnder, 1995; Ogmm et al., 1985, etc.) ; and (iii) the chemical structure (Cowan et al., 1996; Damborsky, 19%; Damborsky ef al., 1996; Lange&erg et al., 1996; Okey and Stensel, 19%; Peijnenburg, 1994, etc.).
predicting their biodegradability has been relatively well biodegradability structure studied for quantitative relationships (QSBR), where the structure of the chemicals is directly linked with their biodegradability(Boethling, 1986; Boethling, 1997; Bossert and Bartha, 1986; Cambon and Devillers, 1993; Dearden and Nicholson, 1986; Okey and Stensel, 1996; Vaishnav et al., 1987). The effect of certain environmental factors on the biodegradation kinetics is well known, for example the effect of temperature ard humidity (Patil et al., 1988; Thinmarayanan et al., 1985; Walker and Brown, 1983), mainly by controlling the activity of microorganisms. However, there has been no study linking chemical structure and environmental characteristics to compound biodegradation kinetics (expressed for example by half-life). Models able to predict the biodegradation kinetics of organic compounds (giving for example half-lives) in soils would be useful for (i) deciding if an accidental pollution could be “naturally” rcsorhed rapidly, or if specific treatment was needed, (ii) designing new chemicals which present less risk to the environment, or (iii) limiting the number of biodegradation tests for existing molecules. To our knowledge, no model predicting the natural biodegradability of chemicals in unsaturated soils has been described. The development of a database using new experiments is unrealistic. One solution is to collect biodegradation data for organic compounds in soils by a critical analysis of the scientific literature, but in this case there are at least 3 difficulties to be overcome: (i) on the one hand, the biodegraders, and more specifically their activity and then density, are only very rarely described, except by certain authors (Gonsior and West, 1995; Grecr and Shelton, 1992; Sabourin et al., 19%); (ii) on the other hand, the description of soils is insufficient to provide a mechanistic picture of biodegradation: for instance, it is unfortunate that only the organic matter (or organic carbon) concentration is described, since the nature of this organic matter (i.e.,
Correspondence to: Pr. J.C. Block
495
496
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils
humic and fulvic acids, humin, polysac&arides) or its polarity is certainly an important parameter affecting pollutant availability (Bolan and Baskamn, 1996; Corseuil and Alvarez, 1996; C&aye et al., 1997; Knaebel et al., 1996; Shuttleworth and Cemiglia, 1995); (iii) finally, there is considerable heterogeneity in the published data due to different laboratories using different expressions for chemical biodegradation: primary or final degradation, or compound half-life using the equation t,,2 = Ln2/k, (k is the first-order biodegradation constant), or even using “residence time” for the molecule (after a 50% reduction in its concentration). Another cxamplc of data limitations is the extrapolation (or interpolation) of experiments carritd out under environmental conditions other thau in soils (in water, with activated sludges, etc.) (Howard rl al., 1991). Despite these difficulties, this article describes a modeling attempt to predict biodegradability in soils from the knowledge of the chemical structure and of the soil composition. This study is based on published data collected through a litterature review.
2. Data selection Data were collected from the literature published over a 30 year period to describe organic molecule bidgradation experiments following the criteria listed below: the degradation was caused by microorganisms (i.e., biodegrAa&~ or in other words, with controls proving the absence of physical, chemical, etc. degradation) and the soil was described as completely as possible (in a certain number of publications no information on soil characteristics was supplied) and hence these publications were not used and the compound described must be an organic molecule alone and of known chemical structure and not a mixture of several compounds with a more or less well determined chemical structure (e.g., rubber, oils). Over 600 articles were analysed of which 75 of them were taken into account following the criteria decribed above. This bibliographic study made it possible to constitute a data base that currently contains 600 entries out of which 150 possess all the environmental variables described. Consequently, statistical treatment of environmental data, molecular data and half-life applies only to these 150 entries (one entry corresponds to the biodegradation time of one molecule in a given environment), out of which polyaromatic hy droca&ms (PAH) are represented 42 times and herbicides 78 times (Table 1). This data base is biased for at least two reasons: the molecules represented are more often resistant than not resistant to biodegradation and they are not equally represeated (24 data relative to atine sod only one to 2-amino naphthalene, leading to representative frequencies of 16 and 0.7%. respectively) (Table 1).
Table 1. Description of statistical analyses: ““me are represented. PAH anthracene benzene chrysene coronene fluoranthene benao[b]fluroaothene benzo[k]ll”roanthene fluorene naphthalene 1.ami”o,naphthalene 2.amino,naphthalene be”zo[ghi]petylene phenanthrene pyrene be”zo[a]pyre”e
the data base (150 entries) used for the and number of times that these molecules Herbicides 2.4-D atrazine chlorsulfuron isoprduron linwo” metolachlor
(3) (2) (3) (3) (3) (3) (3) (3) (3) I;;
Others benzidine DCPA Cnttrophenol p-toluidine triflurali”
(3)
(5) wj (9) (3) (16) (21)
(3) (18) (2) :5;
‘(:; (3)
Tabk 2. Reporting frequency of description, scale and average of descriptors of soil(s) (Temperature: “C; Water content: %; f&: (fraction of organic matter): %; sand, silt and clay: %: CEC (catton exchanee cata&+: mEa for 100 e of soil) out of a total of 600 pieces of data. Character&c Reporting Range Average frequency PH 97% 2.10 8.25 6.7 Temperature 94 % 5.0 - 40.0 23.1 water content 87 % 2.520000 423 f 86% 0.12- 100 6.6
.
3. Mean soil
_,
.
-
characteristics
Soils are traditionally described by the variables: pH, organic matter concentration (f&, cationic exchange capacity (CEC), particule size distribution (sand, silt, clay). These descriptors are those most commonly found in the scientific literature to describe soils during organic molecule biodegradation experiments, plus two environmental variables: temperature and water content. The parameter most reported is pH (frequency: 97%). followed by temperature (94%). water content (87%), organic matter (86%), particle size distribution: sand, silt, clay (60% each) and lastly CEC (46%) Fable 2). Reported pH values vary over a large range of 6 pH units, although 90% of these values fall within the 4.6 - 8.0 interval. Temperature ranges 5 to 40°C although 90% of the values are between 10 and 3O”C, which is a temperature rauge compatible with the survival and growth of many species of bacteria in the environment. Water content presents au extremely wide range of variation, since in some experiments, the authors work with natural soils whereas in others they add a few grams of soil to a liter of water. However, 90% of the water content values fall between 10 and lOO%, or 10 g to 100 g of water per 100 g of dry soil. Organic matter varies from 0.1 to near 100 g per 100 g of dry soil, with 90% of the values ranging from 0.9 to 16%. The organic matter values of 0.1% (i.e., very low concentrations) correspond to “coarse textured” soils (99.8% sand). Particle size distribution is defined by sieving: the 50
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils Table 3. Variables used for describing molecule structures, adapted from (Boethling et al., 1994; Okey and Stensel. 1996; Peiinenburp, _ _ 1994; T&k a
ym to 2 mm fraction corresponds to sands (coarse from 2 mm to 200 pm, fine from 200 pm to 50 pm), the 2 to 50 pm fraction to silts (coarse from 50 ym to 20 pm, fine from 20 pm to 2 pm) and the fraction lower than 2pm to clays (Bonneau and Souchier, 1994). Finally, the CEC ranges from 0.9 to 52.0 mEQ for 100 g of dry soil, 90% of these values being between 2.0 and 40 mE$.
4. Descriptor
497
Tabk 4. Analysis of variation on the relationships between the halflife (days) and (i) soil environment characteristics alone, (ii) the molecular characteristics alone and (iii) the characteristics of molecules and soils. d.f.: degrees of freedom, 150 data taken into account. I’ adjusted d.f. d.f. for d. f. model residual 8 Soil environment characteristics 0.15 141 Chemical characteristics 0.75 137 Chemical and soil characteristics 0.77 :: 129 lo”,
, * ,,..., . , ,,.,., , , ““.,
, * ““‘,
, , ‘“1
choice
In order to perform correlation studies and use mathematical models, a mathematical transformation of environmental variables and chemical structures was necessary to encode the information. Thus, for molecular structures, ckscriptive variables were chosen, mainly from the scientific literature 1996; (Boethling et al., 1994; Okey and Stensel, Peijnenburg, 1994; Tabak and Govind, 1993). The molecules were described using 10 structural descriptors (fable 3) and soil environment using the 8 variables described in the Table 2. Molecules are, thus, described by a 10 digit code and the environment by au 8 digit code aud c&d directly by the numerical value of the variable itself.
1
IO
100
1000
lo4
Ohserved half-life (days) Fi. 1. Correlation between the till observed and the tl,* predicted from molecular and environmental characteristics.
5. Modeling The object of the modeling was to predict the persistence time of organic molecules in soils using correlations developed from conceptual models and bibliographical data. The conceptual mode1 incorporates both chemical and environmental parameters, collected from the literature analysis. From this very heterogenous database containing a 18 digit code for each of the 150 data, multi-variate regressions were performed to examine potential relationships between compound environmental half-life and (i) soil characteristics alone; (ii) molecule characteristics alone; (iii) molecule and soil characteristics together. The simple multi-variate correlations calculated between compound half-life and compound characteristics (Table 3) or soil environment characteristics (Table 2) or both demonstrate the relative consistency of bibliographic data (Table 4). Concerns about the distribution of points znd
10
100 Observed t1/2 (days)
Fii. 2. Correlation between the t,/, observed (limited to 500 days) and the t,,* predicted from molecular and environmental characteristics.
their relative weights could further complicate (aud may be improve) any conclusions derived from these correlations (see Figures 1 and 2). In any case, it seems likely that the use of only environmental characteristics is significantly worse than the other “models”. This is not too surprinsing in that this biased data set is based on compounds of scientific or public policy interest, i.e., slow degrdng compounds. Our hypothesis has been that for difficult or slowly degraded compounds, tbe environment exhibits less control over rates.
498
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils
100
200 300 Observed half-life (davs)
‘MN
500
Fig. 3. Maximum variation of half-life predicted from molecular and environmental characteristics, as a function of observed t,,?.
If this simple correlation stands up to more rigorous examination, the apparent improvement of the correlation using both chemical and environmental characteristics at low half-lives would support our hypothesis (Figure 3). Indeed, the correlation using both chemical ad environmental characteristics produces variable half-life predictions for the same compound when environmental conditions change (Figure 4). This variability is the reason why in Figure 4 the predicted half-lives using chemical characteristics alone produces only 17 half-lives (as there am only 26 different compounds, and 9 had predicted half-lives about 500 days the limit used in Figure 4). Maybe of more interest are the potential reasons (and corresponding improvements) for the lack of better correlation coefficients such as those often observed with standardized biodegradation studies. Among the numerous possible hypotheses, four stand out as more important than the others: (i) the molecular descriptors used in these models do not make it possible to efficiently describe all the molecule classifications (substituted or non substituted aromatics, polyaromatics, aliphatics, etc.); (ii) the soil desaiptols in the scientific literature are not sufficiently precise and do not describe the interactions between pollutants and soil very well (no description of either the nature of the organic matter or the clays, no information on soil porosity or the biodegraders etc.); (iii) the data used comes from different laboratories where experimental methods, measurement methods, half-life expressions are extremely variable; (iv) no information is given about the biodegraders (e.g., their activity, their density) present in the soils.
6. Conclusions The ability to predict the biodegradation kinetics of organic chemicals in the environment is a key factor in evaluating soil pollution.~ Issues related to active soil remediation aud
0
100
200
303
403
500
Predicted tli2 from chemical alone (days) Fig. 4. t,,l predicted from molecular and environmental caracteristics as a function of tliz predicted from molecular characteristics alone.
natural bioremediation are currently difficult to address using mathematical models. Such models are generally non-accessible because of critical limitations: ?? the data published in the scientific literature am heterogeneous sod incomplete (absence of standardization between tests performed by different laboratories, variable methods for calculating half-lives, etc.); ?? compiling a database under completely standardized conditions (biodegradation test in soils, analytical method, calculation of half-life) is unrealistic, given the cost of such tests; ?? the density and activity of the biodeguklers in soils am rarely reported in the literature; ?? the soils are incompletely described (e.g., no information on the nature aud polarity of the organic matter, and thus on its ability to sorb organic molecules); ?? lastly, it is difficult to describe mathematically all the organic chemicals (HPA, pesticides, etc.) by a single coding system which is relevant and representative of the 2D sod 3D structures and of the physico-chemical properties of the molecules; ?? finally, the linear regressions allow to identify the key variables (environmental and chemical) in such models. Nevertheless, given the number of variables taken into account and their probable interaction (effect of pH, temperature, organic matter, etc. both on the activity of the bicdegradezs and on the behavior of the organic molecules), the majority of the phenomena considered would almost certainly be better described by non-linear equations. However, our modeling studies have enabled us to evaluate the effect of the environment on the biodegradation kinetics of organic molecules. Thus, variations in the environmental parameters markedly affected the half-lives of molecules which degrade fairly rapidly (i.e., in less than 120-180 days), but have less effect for more persistent molecules (i.e., t,,, > 120-180days). This could be explained by the fact that the persistent molecules rquim specific suitable biodegraders, probably fiquently absent
S. Fass et al.: Prediction of Chemicals Biodegradation in Soils from soils. The dispersion of the biodegradation results linked to the probable absence in some tests of these biodegraders thus masks the effect of the other environmental variables. Several lines of research, not mutually exclusive, could be of interest to improve the predictive power of the models: (i) improving the coding system for the chemical molecules (e.g., inclusion of descriptors for quantic effects); (ii) use of non-linear regressions to predict the half-lives (e.g., neural networks); (iii) development of several models, depending on the class of the molecules. The ideal would be to be able to obtain more biodegradation data for organic chemicals from the scientific community, with as complete zstd exhaustive a description of the soils as possible, together with a description of the density and activity of the biodegraders present.
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