Environ. Sci. Technol. 2011, 45, 111–117
A Framework for Evaluating the Contribution of Transformation Products to Chemical Persistence in the Environment† C A R L A A . N G , ‡ M A R T I N S C H E R I N G E R , * ,‡ K A T H R I N F E N N E R , §,⊥ A N D KONRAD HUNGERBUHLER‡ Institute for Chemical and Bioengineering, ETH Zu ¨ rich, 8093 Zu ¨ rich, Switzerland, Eawag, Swiss Federal Institute for Aquatic Science and Technology, 8600 Du ¨ bendorf, Switzerland, and Institute of Biogeochemistry and Pollution Dynamics (IBP), ETH Zu ¨ rich, 8092 Zu ¨ rich, Switzerland
Received March 31, 2010. Revised manuscript received August 25, 2010. Accepted September 3, 2010.
The REACH legislation of the EU requires that transformation products be included in chemicals assessment for chemicals produced or imported in amounts exceeding 100 tonnes/year. However, including transformation products in assessments could be considered an intractable problem, particularly given the paucity of available data and the difficulty of predicting the most likely transformation route from the many possible products of a complex parent chemical (the so-called “combinatorial explosion” problem). Here, we present a scheme for identifying transformation products that substantially contribute to the joint persistence of a parent chemical and its substance family. Our scheme integrates methods for the prediction of biodegradation products, the estimation of physicochemical properties and degradation half-lives, and the calculation of a persistence metric, the joint persistence. We compare results from our scheme to 22 test cases with known transformation products. Our results highlight that the “combinatorial explosion” problem can be managed but that there is a serious need for better data for environmental halflives of chemicals.
Introduction In the EU, the REACH legislation (Registration, Evaluation, Authorisation and restriction of CHemicals) has been established to accelerate the process of risk assessment (1). However, this process is seriously impeded by a lack of data concerning chemical properties and toxic effects of a large number of industrial chemicals (2-4). The cost and effort of collecting such data remain major challenges. Here, we evaluate whether, and how, effective chemical screening for persistence can be performed given these limitations. In particular, we focus on the problem of transformation products and their contribution to persistence in the † This manuscript is part of the Environmental Policy: Past, Present, and Future Special Issue. * Corresponding author e-mail:
[email protected]; phone: +41-44-632 3062; fax: +41-44-632 1189. ‡ Institute for Chemical and Bioengineering, ETH Zu ¨ rich. § Eawag, Swiss Federal Institute for Aquatic Science and Technology. ⊥ Institute of Biogeochemistry and Pollution Dynamics (IBP), ETH Zu ¨ rich.
10.1021/es1010237
2011 American Chemical Society
Published on Web 09/21/2010
environment. Persistence is an important hazard metric that also constitutes a complex assessment problem, as degradation pathways and half-lives of chemicals in the environment are highly variable and poorly characterized. Evidence indicates that transformation products can be as or even more persistent than their parent compounds and thus must be included in chemical assessment (5-11). Accordingly, REACH requires that registration documents for chemicals produced or imported in excess of 100 tonnes/ year include information about transformation products (TPs) (1). However, it is unclear whether persistent TPs are a concern for many chemicals. Given the cost of measuring chemical properties for single chemicals, a viable method to screen for potentially important TPs is needed (12). Such a method should simplify the task considerably, because identification and evaluation of all possibly relevant TPs is a formidable problem that cannot be solved routinely for thousands of chemicals. Here, we present a method to estimate the contribution of TPs to the joint persistence of an entire substance family (a parent compound, PC, and its TPs), the core of which is a scheme that systematically reduces the problem to a level that can be handled with estimation tools currently available, namely EPISuite (13) and the University of Minnesota Pathway Prediction System, UM-PPS (14).
Methods Evaluating Data Needs. The residence time of a chemical in an environmental system is calculated as the ratio of its steady-state mass to its emission rate (8). This definition assumes any degraded chemical mass is lost from the system. In reality, the mass is typically transformed through transformation reactions into one or more TPs that could also represent a substantial mass of chemicalsand a possible hazardsin the environment. A persistence metric including TPs is the joint persistence (JP), which integrates the primary persistence (PP) of the PC with the persistence of all relevant TPs (7, 8). To calculate JP, we use a multispecies multimedia model (8) that includes terms in the mass balance equations describing the formation of TPs as the PC degrades (see Supporting Information, SI). The model thus allows for the simultaneous calculation of PC and TP mass in each environmental compartment to calculate JP. While this model is easily implemented even with many TPs, data requirements are greatly increased. For each PC, emission rates, physicochemical propertiess the octanol-water partition coefficient (KOW), Henry’s Law coefficient (H), and organic carbon-water partition coefficient (KOC)sand media-specific degradation half-lives are required. For soil and water, we focus on biodegradation as the dominant transformation process, and in air consider reaction with hydroxyl radicals. Thus, a biodegradation scheme is also needed to identify the TPs formed, and their proportion. Finally, partitioning properties and half-lives must be included for each TP. Selection of Test Cases. To develop and assess our persistence evaluation scheme, we selected 22 PCs with known biodegradation pathways (8, 10, 11): bromoxynil octanoate, 2,4-D, R-HCH, heptachlor, DDT, aldrin, atrazine, MTBE, perchloroethylene, amidosulfuron, alachlor, chlorpyrifos, dicamba, diuron, fluoroglycofen-ethyl, kresoximmethyl, glyphosate, mecoprop-P, mesotrione, orbencarb, sulcotrione, and TCPN. These chemicals are mainly currentuse pesticides (see SI for details, including primary uses and biodegradation pathways). VOL. 45, NO. 1, 2011 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
111
Pesticides are among the best-studied manufactured chemicals, particularly regarding the identity of TPs, and are thus well suited to the development of a system to study the importance of TPs. However, because TPs are not usually commercial chemicals, their properties are largely unknown. Even for our 22 test cases a full empirical data setsincluding measured values for all physicochemical properties, fractions of formation, and biodegradation half-lives (but not the degradation rate constant in air)swas only available for atrazine. For alachlor, which was the compound with the second most measured values, those were available for more than 50% of these properties. Accordingly, the main task in building our persistence-screening scheme was the evaluation of methods to estimate biodegradation half-lives, biodegradation schemes, and partition coefficients. For each parameter, we chose an estimation method and surveyed the literature to determine the uncertainty associated with each method, which we then propagated through the model to derive uncertainty ranges of JP. Throughout our analysis we used empirical property data for atrazine and alachlor to gauge the performance of our screening scheme. Uncertainty Associated with Property Estimation. We used the Estimation Program Interface Suite (EPISuite) to estimate physicochemical properties (KOW, KOC, and H), the pseudo-first-order hydroxyl radical reaction rate constant in air (kOH), and biodegradation half-lives in soil and water (thalf). The different modules of EPISuite have been well studied and compare favorably with other estimation tools in terms of predictive power (see SI). In addition, EPISuite is freely available and easy to use in batch mode, which is best suited to the screening approach we have taken here; even for only the 22 test cases considered in our study, we needed to estimate all the properties listed above for several hundred compounds (parent chemicals plus possible biodegradation products). There are additional property estimation tools that can outperform EPISuite to varying degrees depending on the property considered, notably, SPARC (15), ClogP (16) and CosmoTherm (17). However, application of these tools requires substantial additional preprocessing of chemical structures, or does not support batch processing of multiple chemicals, or does not produce a sufficient benefit in terms of performance to justify employing an additional tool (see Supporting Information). Published reports of uncertainties associated with each EPISuite method were used to construct probability distributions for each property (see SI): KOW estimated using KOWWIN (18), KOC estimated using KOCWIN (KOW-based method) (19), H estimated using HENRYWIN (20), kOH estimated using AOPWIN (21, 22) with an assumed hydroxyl radical concentration of 2.0 × 106 molecules/cm3 (22), and thalf in soil and water estimated using BIOWIN3 with the regression from ref 23 that translates the BIOWIN scores to half-lives (2). The module with the most performance issues is BIOWIN3, EPISuite’s tool for predicting ultimate biodegradation. It was developed not on the basis of measured values but rather on expert judgment (the results of a scoring survey) and thus the uncertainties associated with its performance are inherently different from those surrounding a collection of empirical values. In addition, the empirical values available are themselves highly variable, complicating attempts to assess BIOWIN prediction reliability (21). Biodegradation Pathways. The prediction of biodegradation pathways and associated TPs for a given PC was particularly challenging. We used the University of Minnesota’s Pathway Prediction System (UM-PPS), an online tool (14) that predicts aerobic biodegradation products for a given molecule (entered as a SMILES string). Predictions are based on data in the UM-BBD (Biocatalysis/Biodegradation Database), which contains observed biodegradation pathways collected from the literature. Observed pathways 112
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 45, NO. 1, 2011
are encoded as a set of biotransformation rules (“btrules”), which work on specific structural elements. As this is a structure-based method, a complex PC structure may provide multiple sites for a single type of reaction. UM-PPS will return all products of this type of reaction, even when this leads to the creation of multiple pathways with similar end products (hereafter referred to as analogous products). Thus, over several generations a complex PC can, in a “combinatorial explosion”, lead to a large number of predicted TPs (on the order of one hundred over three to four generations). However, these do not represent the observed biodegradation scheme but rather many possible products from similar routes of biodegradation. Although the program does provide some ranking of reactions (e.g., as “Likely”, “Neutral”, “Unlikely”), multiple applications of a single rule will have the same likelihood, and most reactions are “Neutral”. These likelihoods are not sophisticated enough to enable ranking of most likely pathways or to produce quantitative fractions of formation (ff) for predicted TPs. As the central part of our practical screening method, we developed three simplification rules to limit the number of TPs considered. First, we identified multiple application of the same btrule within a single generation leading to the emergence of analogous products as discussed above. When this occurred, we chose only a single application of the rule to represent such products, simplifying the analysis of persistence by reducing the number of possible TPs. In other words, when analogous products emerged for a chemical, only the first was kept (according to order listed in UM-PPS output). This procedure did not affect the JP estimate (see Discussion). Second, for application of different btrules, when UM-PPS provided different likelihoods only the most likely products were kept. Finally, we evaluated the possibility of limiting the output to a minimum number of generations. To this end, we compared JP estimates based on different numbers of generations, as detailed in the Results section. The simplification scheme is illustrated for mecoprop-P in Figure 1. The simplification of UM-PPS output for mecoprop-P reduced the number of TPs from 39 to 7. The known pathway has only one TP, 4-chloro-2-methylphenol (TP-1A in the UM-PPS scheme). Since UM-PPS provides no quantitative probabilities for predicted TPs, a way to determine the ff for each product is needed. For the simplified pathway this is defined as the inverse of the number of reactions (or btrules) applied to each degrading chemical. This assumes that each reaction occurs with equal probability. Thus for TP-1A, to which three btrules are applied, ff for its three TPs is 0.33. For mecopropP, which also has three immediate products, only two btrules are applied; the first two products are the result of a single reaction. Thus for these products, on a mole basis, the ff is assumed to be 0.5 for each. Construction of Screening Method. Our screening method for TP contribution to chemical persistence has five components: EPISuite provides physicochemical properties; UMPPS provides probable biodegradation products; our pathway simplification scheme reduces UM-PPS output to a limited set of TPs with associated ff values; the multispecies multimedia model uses the outputs from EPISuite and the simplification scheme to calculate PP and JP in a fourcompartment environment; finally, results are compared to persistence criteria, such as those used within the REACH legislation (Figure 2). This comparison, which incorporates the uncertainty in JP propagated from property estimation, allows us to evaluate whether our screening scheme can identify cases where inclusion of TPs leads to a change in classification from not-P to P.
FIGURE 1. Simplification scheme applied to mecoprop-P. Three elimination rules are used to reduce the full output from UM-PPS (four generations) from 39 possible products to 7, in comparison to the single transformation product in the known pathway. For the simplified pathway, the fraction of formation (ff) for any compound is calculated as the inverse of the number of reactions (nr) applied to its parent compound.
Results Uncertainties Associated with Property Estimation. We used the probability distributions of the EPISuite estimates to propagate uncertainty from KOW, H, KOC, thalf, and kOH to estimated PP and JP for our 22 test cases. We first considered the known biodegradation pathways so that uncertainties would derive solely from property estimation. Using the estimated property values, we find that in six cases the inclusion of TPs has no effect on PC classification when
compared to a threshold persistence value of 60 days: for MTBE, glyphosate, and perchloroethylene both PP and JP are below the threshold value; for DDT, aldrin, and heptachlor the PC persistence alone is well above the threshold. For another eight compounds, classification would not change if based on geometric means only: for mecoprop-P mean values of both PP and JP are below the threshold, while for mesotrione, sulcotrione, R-HCH, bromoxynil, chlorpyrifos, TCPN, and fluoroglycofen both geometric means exceed 60 VOL. 45, NO. 1, 2011 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
113
FIGURE 2. Screening method for estimating the contribution of transformation products to the joint persistence of a substance family. days. However, in all of these cases the 95% confidence intervals of PP, JP, or both cross the 60-day threshold (see SI). Finally, for eight casess2,4-D, kresoxim-methyl, dicamba, orbencarb, alachlor, diuron, amidosulfuron, and atrazinesthe geometric mean for the PP is below the threshold but for JP exceeds the threshold; thus, by consideration of the PC alone the chemical would be classified as “not-P”, but when TPs are considered the classification would change to “P”. Therefore, inclusion of TP persistence is important for these eight compounds. However, the confidence intervals about these estimates are on average between 1 and 2 orders of 114
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 45, NO. 1, 2011
magnitude around the geometric mean (see SI for details). Thus, correct classification of these compounds relative to persistence thresholds would require substantial reduction in the uncertainties around both PC and TP properties. To assess the origins of this substantial uncertainty and determine for which properties reduction of uncertainty could be most beneficial, we have ranked PC and TP properties according to their contribution to variance in the JP. For all 22 cases, thalf of either the PC or one of its TPs is within the top three most important input parameters. After thalf, the most important sources of uncertainty are KOC and
H. Finally, PC properties contribute most to uncertaintysin 16 of 22 cases a PC property is the top contributor to uncertainty in JP (see SI). As observed in previous analyses of uncertainty in JP (24), there is a clear need for better halflife values for reliable assessment of persistence, and this applies to PCs as much as to TPs. When comparing these results to the two cases where empirical property data are availablesalachlor and atrazineswe find the estimate-based PP and JP predictions are well within the confidence bounds of those based on empirical data. For alachlor the geometric means of PP and JP based on experimental data are lower than those derived from estimated property data, but by a factor less than half the width of the confidence interval. In the data-based case the JP value is below the threshold while for the estimatebased case JP exceeds 60 days. For atrazine, for which most empirical property values were available, predictions based on measured and estimated properties are very similar: PP is below the threshold and JP is above it in both cases. Construction of Simplified Biodegradation Pathways. We focus our pathway construction analysis on the eight cases (2,4-D, kresoxim-me, dicamba, orbencarb, alachlor, diuron, amidosulfuron, and atrazine) for which accurate estimates of JP would be essential for correct persistence classification. From the UM-PPS output over four generations, the “combinatorial explosion” is obvious, particularly for compounds such as alachlor, orbencarb, and atrazine, for which the full output contains 300, 111, and 97 possible TPs, respectively, whereas the known pathways include 4, 5, and 3 TPs. However, this output can be substantially reduced by our simplification scheme. Following application of the first two simplification rules (removing analogous products; choosing most likely products), the number of TPs is reduced to 68, 34, and 15. This has little effect on the predicted JP (the average absolute difference between the simplified and unsimplified 4-generation pathways is 10%). Thus Rules 1 and 2 (see Figure 1), which seek to remove analogous products and those identified by UM-PPS as less likely to occur, perform as desired. It should be noted that for Rule 1 this is a consequence not only of true similarity among predicted products, but also of the fact that BIOWIN may not be able to determine differences in biodegradation half-lives among structurally similar compounds. The next important task in the simplification of UM-PPS output is determination of the minimum number of generations required to predict JP (Rule 3, see Figure 1), since for each generation considered the total number of TPs and thus the complexity of the analysis increase substantially. To this end, we comparedsafter simplification using Rules 1 and 2sestimated JP values based on 4, 3, and 2 generations of UM-PPS output to JP values derived from the known biodegradation pathway (see SI for a comparison of both pathways for each chemical). We find that for 7 out of these 8 cases our scheme produces JP estimates most similar to those using only known products when two generations are included (average error in JP: 22%). Only for diuron would inclusion of a third generation lead to a better estimate of JP (2 generations under-predict JP by 49% whereas 3 generations overpredict it by 41%). However, given that the JP of diuron is well over 200 days even based on 2 generations of predicted products, this does not result in an undesirable false negative persistence classification. We thus chose two generations as the optimum number to define our simplification Rule 3. If we also consider the remaining 14 test cases, i.e., those for which inclusion of TPs did not affect classification as P or not-P, the choice of two generations for Rule 3 works for all substance families (with an average error of 28%) except fluoroglycofen-ethyl, for which JP predictions were substantially lower than the JP of the known pathway unless four generations were included (see SI for details).
With all three rules in place, the reduction in number of TPs for the 22 cases is between 50 and 95%, with a mean of 84%. Our simplification scheme is thus effective in managing the combinatorial explosion problem. Predicting the Importance of Biodegradation Products. Having established the uncertainties associated with chemical property estimation using EPISuite and an algorithm for selecting a biodegradation pathway based on UM-PPS output, we can now assess how our scheme can be used to determine the importance of TPs to chemical persistence. Focusing again on the eight critical compounds with mean PP values below 60 days but mean JP values exceeding this threshold, we plot the geometric means and 95% confidence intervals of (i) PP, (ii) JP of our simplified, 2-generation biodegradation pathway, (iii) JP of the known pathway, and (iv) JP for the known pathways using empirical property data for alachlor and atrazine (Figure 3A). The resulting 95% confidence intervals are so wide that they cross the persistence threshold in all cases. They are also symmetric about the geometric mean on a logarithmic scale, indicating that the joint persistence is close to lognormally distributed. One can extract from the mean and the width of the confidence interval the shape of the distribution and thus determine what fraction of JP values will be above or below the 60-day threshold for each chemical. We have illustrated this in Figure 3B, where we classified each chemical family relative to the threshold based on the geometric mean of a Monte Carlo sample (n ) 1000) and plotted the proportion of the estimates outside that classification (e.g., geometric mean JP below 60 days but 100 predictions in the sample above 60 days: 10% of sample is “misclassified”; Figure 3B). In all cases the difference between PP and JP is much larger than the difference between JP values for the known and UM-PPS-generated pathways. Thus, at least in terms of the geometric means, the cases including or discounting TPs are clearly distinguishable. Concerning the persistence classification based on JP estimates relative to the threshold value, the highest incidence of “misclassification” occurs when the mean persistence is close to the threshold value, as expected: nearly 50% of the sample would have a classification opposite the one indicated by the geometric mean. However, beyond the threshold to either side (higher/ lower), the proportion of misclassifications quickly decreases. At a mean JP value that is twice the threshold, less than 15% of the sample was classified differently from the mean. Our scheme is therefore able to determine when the persistence of TPs may play a crucial role in persistence classification according to a defined persistence threshold. Our results show a surprising range in the persistence of PCs that can result in JP values above critical thresholds. Although we cannot extrapolate in terms of fractions of the chemical universe that will have important contributions from TPs, we can say that there is a wide range of PC persistence that could lead to persistent TPs: when considering the 95% confidence intervals, anywhere from 1 day up to just below the persistence threshold, or well over an order of magnitude. In other words, the “TP problem” cannot be ignored.
Discussion Although the contribution of TPs to chemical persistence has been examined in some detail for pesticides, it is not yet known how significant their contribution will be to the wider realm of industrial chemicals. Here, we have presented a scheme that brings together all the components necessary to evaluate this problem in a general context: the estimation of properties for PCs and TPs, the prediction of biodegradation pathways, and the calculation of a persistence metric, JP. VOL. 45, NO. 1, 2011 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
115
FIGURE 3. (A) Comparison of joint persistence (JP) estimated using the simplified, UM-PPS-based and the known biodegradation pathways. Primary persistence (PP), which is based on the parent compound alone, is the same in either case. 95% confidence intervals are based on propagation of uncertainty from EPISuite property estimations (see SI for further discussion of uncertainty). (B) Proportion of JP estimates misclassified compared to classification based on geometric means. Estimates come from Monte Carlo sample used for uncertainty analysis (n ) 1000) and classification is based on whether geometric mean is above or below threshold value (60 days). Our scheme requires a number of assumptions and simplifications. In our comparison of JP between known and predicted pathways all properties were estimated using EPISuite, because even for “known” pathways data for TP properties are scarce (6). Rather than compare estimated properties (predicted pathway) to a mixture of predicted and measured properties (known pathway), we used estimated properties in all cases. For the estimation of media-specific half-lives, we assumed that aerobic biodegradation in water and soil and hydroxyl radical reaction in air were the most important routes of reaction, as previously suggested (25, 26). However, UM-PPS provides predictions of products only for aerobic biodegradation. We therefore assumed that TPs were formed only in the soil and water compartments. In our simplification scheme for UM-PPS output, the restriction of predicted pathways to two generations provides effective simplification while simultaneously improving our predictions of JP, and is an easily implemented “first cut” to be applied to UM-PPS output; we therefore recommend this 116
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 45, NO. 1, 2011
rule be applied first. We also found that Rule 1 substantially reduced UM-PPS output complexity while not affecting JP predictions. However, in this case part of the rule’s success was due to the inability of BIOWIN3 to distinguish among structurally similar compounds for half-life predictions. BIOWIN3 is thus a convenient tool for providing half-life estimates that reflect the current state of biodegradation predictions, but it may lack specificity. Thus, rules based on its predictions may need to be re-evaluated as better estimation tools become available. Our analysis leads to two overarching conclusions: (i) the problem of assessing transformation products and their effects on parent compound persistence is not intractable; (ii) the uncertainties associated with property estimation, particularly using quantitative structure-biodegradability relationships (QSBRs), remain unacceptably high.
On the surface, these may seem to be conflicting conclusions, but they in fact refer to two very different aspects of TP assessment; the second refers to a serious, but at root practical, problem, while the first refers to the fundamental question of whether this is a field that can be fruitfully pursued. We believe that through construction of our scheme we have shown that when addressed systematically this is a problem that is not fundamentally unanswerable. By our selection, implementation, and evaluation of tools such as EPISuite and UM-PPS we have shed some light on important data gaps that still exist. UM-PPS is a very practical and useful tool that should be exploited further in the assessment of persistence. Further, we have shown that better estimates of thalf, KOC, and H are crucial to the assessment of persistenceswhether primary or joint. Due to these weaknesses, the problem of TP assessment cannot be solved by simple implementation or automation of existing tools. Considering the so-called “combinatorial explosion” problem: this phenomenon is well managed by our current simplification scheme, but an important question here is whether biodegradation half-lives of isomers or structurally very similar products are as similar as estimated by BIOWIN3. Thus, the “combinatorial explosion” problem cannot truly be solved until more sensitive methods are developed for estimating the relative biodegradation half-lives for isomers and structurally similar compounds. Better empirical data are sorely needed to provide a sounder basis for the construction of QSBRs. The call for better biodegradation data is not a new one; rather it is a need that has been identified again and again over the past two decades. Sixteen years ago Peijnenburg made the case for why QSBRs are needed and why their developmentsas of 1994swas seriously impeded: primarily, lack of good data (27). Now with the implementation of REACH we find ourselves more than ever in need of good data for biodegradation, particularly given the desire to reduce costs and labor intensity through development of reliable QSARs. Yet, the landscape of data reliability has not changed. This is the time to make it a priority: for better, more rigorous tests and more thorough documentation. The scheme we have developed can provide, via its integration of all aspects of the “transformation product problem”, a framework to guide further empirical research.
Acknowledgments We thank Sebastian Strempel for his assistance in the compilation of chemical property data, and the integrated EU project OSIRIS for funding (contract GOCE-ET-2007037017).
Supporting Information Available Further details regarding chemical properties, known and predicted biodegradation pathways, and the uncertainty analyses presented here. This material is available free of charge via the Internet at http://pubs.acs.org.
Literature Cited (1) EChA. About REACH. 2007. Available from http://echa.europa. eu/reach_en.asp. (2) Fenner, K.; Canonica, S.; Escher, B. I.; Gasser, L.; Spycher, S.; Tu ¨ lp, H. C. Developing methods to predict chemical fate and effect endpoints for use within REACH. Chimia 2006, 60, 683– 690. (3) Koch, L.; Ashford, N. A. Rethinking the role of information in chemicals policy: Implications for TSCA and REACH. J. Clean Prod. 2006, 14, 31–46.
(4) Scheringer, M.; Bo¨schen, S.; Hungerbu ¨ hler, K. Will we know more or less about chemicals under REACH? Chimia 2006, 60, 699–706. (5) Alexander, M. Biodegradation of chemicals of environmental concern. Science 1981, 211, 132–138. (6) Belfroid, A. C.; van Drunen, M.; Beek, M. A.; Schrap, S. M.; van Gestel, C. A. M.; van Hattum, B. Relative risks of transformation products of pesticides for aquatic ecosystems. Sci. Total Environ. 1998, 222, 167–183. (7) Fenner, K.; Schenker, U.; Scheringer, M. In Handbook of Environmental Chemistry; Vol. 2, Part P; Boxall, A., Ed.; SpringerVerlag: Berlin/Heidelberg, 2008; pp 121-149. (8) Fenner, K.; Scheringer, M.; Hungerbu ¨ hler, K. Persistence of parent compounds and transformation products in a Level IV multimedia model. Environ. Sci. Technol. 2000, 34, 3809–3817. (9) Garcia-Reyes, J. F.; Molina-Diaz, A.; Fernandes-Alba, R. Identification of pesticide transformation products in food by liquid chromatography/time of flight mass spectrometry via ‘fragmentation-degradation’ relationships. Anal. Chem. 2007, 79, 307–321. (10) Gasser, L.; Fenner, K.; Scheringer, M. Indicators for the exposure assessment of transformation products of micropollutants. Environ. Sci. Technol. 2007, 41, 2445–2451. (11) Schenker, U.; Scheringer, M.; Hungerbu ¨ hler, K. Including degradation products of persistent organic pollutants in a global multi-media box model. Environ. Sci. Pollut. Res. 2007, 14, 145– 152. (12) Jaworska, J. S.; Boethling, R.; Howard, P. H. Recent developments in boradly applicable structure-biodegradability relationships. Environ. Toxicol. Chem. 2003, 22, 1710–1723. (13) USEPA. Exposure Assessment Tools and Models: Estimation Program Interface (EPI) Suite, Version 3.2 (February 2007), 2000. (14) Ellis, L. B.; Gao, J.; Fenner, K.; Wackett, L. P. The University of Minnesota pathway prediction system: predicting metabolic logic. Nucleic Acids Res. 2008, 36, W427–432. (15) Carreira, L. A.; Hilal, S.; Karickhoff, S. W. In Quantitative Treatment of Solute/Solvent Interactions; Politzer, P., Murray, J. S., Eds.; Elsevier: Lausanne, 1994. (16) Mannhold, R.; Petrauskas, A. Substructure versus wholemolecule approaches for calculating logP. QSAR Comb. Sci. 2003, 22, 466–475. (17) Eckert, F.; Klamt, A. Fast solvent screening via quantum chemistry: COSMO-RS approach. AIChE J. 2002, 48, 369–385. (18) Wittekindt, C.; Goss, K.-U. Screening the partition behavior of a large number of chemicals with a quantum-chemical software. Chemosphere 2009, 76, 460–464. (19) Gramatica, P.; Giani, E.; Papa, E. Statistical external validation and consensus modeling: A QSPR case study for Koc prediction. J. Mol. Graph. Modell. 2007, 25, 755–766. (20) Dearden, J. F.; Schuurmann, G. Quantitative structure-property relationships for predicting Henry’s Law constant from molecular structure. Environ. Toxicol. Chem. 2003, 22, 1755–1770. (21) Gouin, T.; Cousins, I.; Mackay, D. Comparison of two methods for obtaining degradation half-lives. Chemosphere 2004, 56, 531– 535. (22) Atkinson, R.; Arey, J. Gas-phase tropospheric chemistry of biogenic volatile organic compounds: a review. Atmos. Environ. 2003, 197–219. (23) Arnot, J.; Gouin, T.; Mackay, D. Practical Methods for Estimating Environmental Biodegradation Rates, Report to Environment Canada; CEMN Report No. 200503; 2005. (24) Fenner, K.; Scheringer, M.; Hungerbu ¨ hler, K. Joint persistence of transformation products in chemicals assessment: case studies and uncertainty analysis. Risk Anal. 2003, 23, 35–53. (25) Aronson, D.; Boethling, R.; Howard, P.; Stiteler, W. Estimating biodegradation half-lives for use in chemical screening. Chemosphere 2006, 63, 1953–1960. (26) Sabljic, A.; Peijnenburg, W. Modeling lifetime and degradability of organic compounds in air, soil, and water systems (IUPAC Technical Report). Pure Appl. Chem. 2001, 73, 1331–1348. (27) Peijnenburg, W. J. G. M. Structure-activity relationships for biodegradation: a critical review. Pure Appl. Chem. 1994, 66, 1931–1941.
ES1010237
VOL. 45, NO. 1, 2011 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
117
