Pre-publication Working Paper – do not cite or circulate. © the authors, 2011 Systems Thinking Applied to Bioaccumulation and Persistence: New Science for Chemical Assessment Akos Kokai, Megan Schwarzman, Michael Wilson UC Berkeley, Centers for Occupational and Environmental Health

Frameworks for chemical assessment often aim to identify substances that persist in ecosystems and bioaccumulate in organisms, because these tendencies increase a chemical’s potential for long-term harm to human and environmental health. Since the late 1990s, numerous chemical assessment frameworks worldwide—both regulatory and non-regulatory—have implemented comparable methods for evaluating the bioaccumulative potential of organic chemicals. While the scientific understanding underpinning these methods has advanced significantly since their initial development, conventional methods have not kept pace with the science. As a result, current practices risk widespread misidentification of chemicals of concern to human and environmental health.

New tools and criteria based on both experimental and computational methods have been proposed, but have not yet been implemented on the scale of conventional methods. Chemical safety assessment has critical applications in informing public policy, green chemistry research, and sustainable design and development. In order for such efforts to be most health-protective, contemporary methods for the evaluation of bioaccumulative potential must be implemented and continually improved.

The system makes the pollutant Bioaccumulation and persistence are outcomes of systems that comprise chemicals, the physical environment and ecosystems. Chemical safety assessment frameworks face the challenge of evaluating complex system outcomes using limited existing data or generating new information that is not readily obtainable. The most frequently used metrics—a compound’s intrinsic physicochemical properties (e.g. partition coefficients and reaction rate constants)—are important determinants of bioaccumulation potential and environmental persistence (Mackay 2001), but they are not the only factors and are not necessarily the best metrics for evaluating and classifying chemicals. Instead, approaches informed by determinants of persistence and bioaccumulation on multiple levels of system organization—molecular, organismal, ecological and environmental—while necessarily more complex, can ultimately improve the validity and utility of chemical assessment and decision making. Bioaccumulation Several metrics of bioaccumulative potential are listed in Table 1. All major regulatory programs and assessment frameworks categorize the bioaccumulative potential of chemicals based on their values of BCF, BAF or KOW relative to established thresholds (see Table 2). The vast majority of chemicals in commerce 1|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

lack experimental measurements of either BAF or BCF (Arnot 2006), so the majority of assessments ultimately rely on the strongly correlated parameter KOW. However, these regulatory criteria were formulated without defining bioaccumulation as an ecological or toxicological endpoint, and thresholds were set using the quantitative metrics predominantly available at the time (Mackay & Fraser 2000). As a result, these metrics now mischaracterize many chemicals relative to how they would be characterized using contemporary measures and a system-based approach. For chemical evaluation methods to accurately inform chemical regulation and the design of safer substances, we need to explicitly answer the question of what specific endpoint is addressed in the identification of “bioaccumulative” substances. Table 1. Several quantities associated with the evaluation of bioaccumulativity. Quantities are unitless unless otherwise indicated. Quantity

Name

TMF

Trophic magnification factor

BAF

Bioaccumulation factor (L kg–1)

BMF

Biomagnification factor

BCF

Bioconcentration factor (L kg–1)

KOA KAW

Octanol-air partition coefficient Air-water partition coefficient

KOW

mmBAF

Octanol-water partition coefficient Multimedia bioaccumulation factor (m2 organism–1)

What it measures Trend in normalized chemical concentration between several trophic levels Ratio of normalized chemical concentration between two trophic levels (predator/prey) Steady-state distribution of chemical between aquatic organism and aquatic environment, including dietary intake Steady-state distribution of chemical between aquatic organism and water, with no dietary intake Equilibrium partitioning between octanol and water Equilibrium partitioning between octanol and air Equilibrium partitioning between air and water Fraction of total amount of chemical in the environment (per m2) that is accumulated in one organism (McLachlan 2010)

Table 2. Representative numeric criteria for evaluating bioaccumulative potential Regulatory program

TSCA New Chemicals Program PBT Policy (1999) Canadian Environmental Protection Act (1999) UNEP Stockholm Convention (2001) Washington State Department of Ecology (2004) REACH (2007)

California EPA Hazard Traits (public draft, 2010)

Categorization

May be bioaccumulative Bioaccumulative Bioaccumulative Bioaccumulative Bioaccumulative

Bioaccumulative (B) Very bioaccumulative (vB) Bioaccumulative

Numeric criteria Fish BCF or BAF ≥ 1000 L kg–1, or log KOW > 4.2 Fish BCF or BAF ≥ 5000 L kg–1, or log KOW > 5.0 BAF or BCF ≥ 5000 L kg–1, or log KOW ≥ 5.0 BCF or BAF > 5000 L kg–1, or log Kow > 5.0 BCF or BAF > 5000 L kg–1, or log KOW > 5 BCF > 2000 L kg–1 BCF > 5000 L kg–1 BAF > 2000 , L kg–1, or log Kow ≥ 4

2|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011 Scientific consensus now supports the definition of a bioaccumulative substance as a substance that biomagnifies in food chains (SETAC 2009). Pollutants can concentrate in biota by non-dietary uptake from environmental media (bioconcentration) as well as by ingestion of pollutants. Digestive uptake of hydrophobic organic compounds from food can be highly efficient, with the result that predator-prey relationships in food webs can significantly increase pollutant fugacities (or normalized concentrations) in species with higher trophic position. The degree of trophic magnification (biomagnification) of a chemical is determined by the characteristics of each food web as well as by the characteristics of the chemical (Gobas 2007). Trophic magnification is known to be a major pathway of wildlife contamination in some terrestrial and marine ecosystems, such as in the arctic (Thomas 1992 - terrestrial).

Measurement, indicators and metrics The conventional metrics for bioaccumulation were not designed to evaluate trophic magnification potential, especially not in terrestrial species. BCF and BAF are measures of bioaccumulation specific to aquatic organisms (see Table 1), and their correlation with KOW reflects the important role of lipid-water partitioning in bioaccumulation for these species. Recent studies combining wildlife monitoring with modeling have shown that KOW is a salient indicator of biomagnification potential in aquatic piscivorous food webs. However, KOW and BCF are not accurate predictors of biomagnification potential in terrestrial or marine mammalian food webs, which include air-breathing species (such as humans). For these types of food webs, modeling studies demonstrate that biomagnification potential is more appropriately described as a function of multiple partition coefficients, i.e. both KOW and KOA (or KAW). 1 The KOW of many synthetic organic chemicals fall below common regulatory thresholds, despite having phase partitioning properties known to produce biomagnification in terrestrial ecosystems (roughly, 2 > log KOW > 11 and log KOA > 5)refs (Gobas QSARCombSci 2003). Examples of such “miscategorized” bioaccumulative chemicals have been identified in wildlife and human biomonitoring (Gobas 2007). This has critical implications for chemicals policies that routinely identify potentially bioaccumulative compounds based on measures of KOW alone.

Equally important is the need to account for chemical metabolism in assessing bioaccumulation potential. Partition coefficients cannot describe the contribution of metabolic biotransformation to the bioaccumulation outcome. Regardless of their partitioning properties, chemicals do not bioaccumulate if they are metabolized and subsequently eliminated by the organism more rapidly than they are taken up. 1

KOA = KOW/KAW

3|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

Biotransformation rates can have a vast effect on the outcome (Mackay 2010, McLachlan 2010). Rate constants for biotransformation are species-dependent, and are not well known, representing a key data gap for bioaccumulation assessment. Chemical partitioning properties, while tremendously useful in bioaccumulation assessment, must be recognized as incomplete indicators.

Experiments designed to measure biomagnification provide quantitative metrics that are more robust than partition coefficients, BCF or BAF. The trophic magnification factor (TMF) is a quantity derived from the fugacities (normalized concentrations) of chemicals in the species of a particular food web, and from the trophic positions of those species as determined from 15N/14N isotopic ratios (Gobas). Extensive field data are required to calculate TMF, and measurement is only possible for chemicals already present in the environment. However, TMF is the best measure of a chemical’s ecosystem-level bioaccumulative potential (SETAC 2009), because TMF describes the overall change in chemical fugacity over several trophic levels. A value of 1 represents no change in pollutant fugacity with trophic level; TMF > 1 indicates biomagnification and TMF < 1 indicates trophic dilution. The predator/prey biomagnification factor (BMF) is the ratio of chemical fugacities in an animal and its diet (prey), and thus provides similar information as TMF, but for a narrow trophic relationship. BMF can be measured in laboratory experiments or in the field.

Both BMF and TMF directly quantify biomagnification endpoints, and are applicable to a wide range of species; they have been proposed as alternative metrics for bioaccumulation assessment (SETAC 2009). The relevance and utility of these data merit their inclusion in in vivo testing efforts, and would be strengthened by the standardization of testing methods.

Modeling and predictive methods—old and new Given the difficulty and cost of obtaining in vivo test data, chemical safety assessment methods are increasingly looking to computational methods for help in closing data gaps and assisting decision making (cite NRC report). By synthesizing available knowledge and placing data in an analytical and predictive framework, computational models can provide valuable information. Yet the models are only as good as their inputs. In the assessment of bioaccumulation potential, computational methods to estimate BCF or BAF values using only KOW are well established and widely used. However, these methods should be revised and new decision making tools should be developed based on the advances in bioaccumulation science outlined above. Sophisticated models have been developed to study bioaccumulation and to compute estimates for a range of endpoints. Mechanistic models at the single organism level can estimate bioconcentration and dietary bioaccumulation. Predictive models for fish BCF have evolved to the level of being able to estimate biotransformation rates (Howard, USEPA). At the ecosystem level, food web models 4|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

can estimate BMF and TMF, using trophic relationships among a diversity of organisms (e.g., plants, fish and mammals). Linking food web bioaccumulation models with physical environmental fate models allows the environmental partitioning of pollutants, and the exposures to each species from all environmental media (e.g., air, water, soil, sediment), to be calculated using fugacity-based methods (Mackay). (Gobas 2004, 2007, Czub 2004, McLachlan 2010)

Such a holistic approach allows quantification of bioaccumulation in relation to environmental contamination or pollutant emission, without the limitations of media-specific metrics like the aquatic BCF and BAF. Rather, integrative multimedia metrics of bioaccumulation potential are available as model outputs. For instance, McLachlan et al. (2010) have defined the multimedia bioaccumulation factor, mmBAF (m2 organism–1), as the ratio of chemical quantity in one organism of interest to the quantity of chemical in the multimedia environment per 1 m2 unit. This ratio can be interpreted as the fraction of chemical in the total environment that is accumulated in one organism. As a numeric metric, mmBAF is well suited for use in chemical assessment because it accounts for bioaccumulation from the total environment and from food webs, it can in principle be calculated for any species of interest using a reference set of global environmental parameters, and it quantifies the same bioaccumulation endpoint commensurably in all species.

The informative and predictive capabilities of models are subject to uncertainty due to model design, and due to uncertainty and natural variability in the empirical parameters that determine model outcomes. Characterization of overall uncertainties is important to support the use of models in chemical safety assessment, especially as a basis for decision making. The applicability of models based on quantitative structure-activity relationships is limited by the nature of the chemicals whose known properties were used to construct the modeled relationship. For example, models that estimate biological and environmental partitioning behavior based on KOW, KOA and KAW are typically based on neutral, nonpolar organic compounds and, as a result, do not account for specific binding interactions. Food web bioaccumulation models require detailed consideration of many biological, environmental and physicochemical factors, many of which are not currently known. For instance, biotransformation rate constants are generally not well described for all species and chemicals. Yet modeling studies suggest that biotransformation rates as significant determinants of bioaccumulation potential. To be most useful, research efforts should account for the limitations and data requirements of bioaccumulation models.

Relevance to policy objectives and green chemistry goals For most substances, persistence and bioaccumulativity increase exposure potential, proportionally increasing the risk of long-term harm to human and environmental health. For this reason, chemical policies typically prioritize persistent and bioaccumulative substances for detailed evaluation or for precautionary action. Yet in applying this principle, even new chemicals policies 5|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

such as the European Union’s REACH regulation, have adopted decades-old metrics for evaluating persistence and bioaccumulativity (Schwarzman and Wilson, 2009). These new chemical regulations still primarily rely on BCF, BAF and KOW, metrics that have formed the de facto definition of bioaccumulativity, to the exclusion of more scientifically valid measures such as BMF and TMF (SETAC, 2009). As a result, many chemicals policies are formulated to overlook the best empirical evidence of bioaccumulation potential.

An entrenched reliance on KOW and BCF reduces the effectiveness of screening-level assessments of bioaccumulativity, i.e. the assessment of chemicals that lack detailed testing data, including new chemicals in design stage. Empirical BCF measurements are costly, time-intensive and, as a result, are available for few of the tens of thousands of chemicals in commerce (Gobas, 2006). Since these data are predominantly applicable to aquatic species, BCF measurements are most relevant in evaluating chemicals with potential aquatic toxicity. Major improvements to these methods are now available, and advances are likely to continue. One improvement on screening methods that could be immediately applied is to add KOA or KAW as a parameter along with KOW and measured or predicted BCF (Howard & Muir, 2010).

Equally importantly, chemical assessment frameworks—especially those used in regulation—must adopt emerging computational methods as screening tools. These methods enable the integration of multimedia and ecosystem-level analysis into meaningful and broadly applicable metrics, such as TMF and mmBAF (for persistence: POV, CTD & TE – see below), as well as others as they are devised. This and other advantages of computational methods cannot be realized if the only admissible data are values of KOW, BCF and BAF.

Ultimately, the most scientifically defensible assessments will be those performed using a weight of evidence approach that takes into account a variety of experimental and computed data—and their associated uncertainties (Wania, 2010). This approach would reduce the reliance on “bright line” numeric thresholds for individual parameters, allowing the evaluation of persistence and bioaccumulation potential to be linked insofar as they share common determinants, such as environmental fate (Arnot 2008). Finally, just as the quality of experimental data is subject to scrutiny in peer review, the integrity of computational models and predictive methods should be subject to the same open and transparent evaluation (NRC, 2007).

These principles are equally critical for informing fundamental chemical research, which is increasingly focused on improving the safety and sustainability of materials and manufacturing processes, according to the principles of green chemistry (Warner, Anastas, etc.). Structure-based predictive hazard assessment methods are poised to inform the design of new chemicals. The developers of these tools, and the synthetic chemists who will use them, must look beyond the prevalent, even 6|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

outmoded, metrics of persistence and bioaccumulative potential in order to make the most valid and scientifically defensible decisions. REFERENCES 1. 2.

3. 4. 5. 6.

7.

8.

9. 10.

11. 12. 13. 14.

15. 16. 17.

18. 19.

Arctic Monitoring and Assessment Programme. 2002. AMAP assessment 2002: Persistent organic pollutants in the arctic. Oslo, Norway. Armitage JM, Gobas FAPC. 2007. A terrestrial food-chain bioaccumulation model for POPs. Environ Sci Technol 41(11):4019-4025; doi: 10.1021/es0700597 ER. Arnot JA, Mackay D, Webster E, Southwood JM. 2006. Screening level risk assessment model for chemical fate and effects in the environment. Environ Sci Technol 40(7):2316-2323; doi: 10.1021/es0514085. Arnot JA, Gobas FAPC. 2006. A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Env Rev 14(4):257-297; doi: 10.1139/A06-005. Arnot JA, Mackay D. 2008. Policies for chemical hazard and risk priority setting: Can persistence, bioaccumulation, toxicity, and quantity information be combined? Environ Sci Technol 42(13):4648-4654; doi: 10.1021/es800106g. Arnot JA, Mackay D, Bonnell M. 2008. Estimating metabolic biotransformation rates in fish from laboratory data. Environmental Toxicology and Chemistry 27(2):341-351. Arnot JA, Mackay D, Parkerton TF, Bonnell M. 2008. A database of fish biotransformation rates for organic chemicals. Environmental Toxicology and Chemistry 27(11):2263-2270. Arnot JA, Mackay D, Parkerton TF, Zaleski RT, Warren CS. 2010. Multimedia modeling of human exposure to chemical substances: The roles of food web biomagnification and biotransformation. Environmental Toxicology and Chemistry 29(1):45-55; doi: 10.1002/etc.15. Arnot JA, Meylan W, Tunkel J, Howard PH, Mackay D, Bonnell M et al. 2009. A quantitative structure-activity relationship for predicting metabolic biotransformation rates for organic chemicals in fish. Environmental Toxicology and Chemistry 28(6):1168-1177. Beyer A, Mackay D, Matthies M, Wania F, Webster E. 2000. Assessing long-range transport potential of persistent organic pollutants. Environ Sci Technol 34(4):699-703. Boethling R, Fenner K, Howard P, Klecka G, Madsen T, Snape JR et al. 2009. Environmental persistence of organic pollutants: Guidance for development and review of POP risk profiles. Integrated Environmental Assessment and Management 5(4):539-556. Brown TN, Wania F. 2008. Screening chemicals for the potential to be persistent organic pollutants: A case study of arctic contaminants. Environ Sci Technol 42(14):5202-5209; doi: 10.1021/es8004514. Brown TN, Wania F. 2009. Development and exploration of an organic contaminant fate model using poly-parameter linear free energy relationships. Environ Sci Technol 43(17):6676-6683; doi: 10.1021/es901205j. Campfens J, MacKay D. 1997. Fugacity-based model of PCB bioaccumulation in complex aquatic food webs. Environ Sci Technol 31(2):577-583. Centers for Disease Control and Prevention. 2009. Fourth national report on human exposure to environmental chemicals:Centers for Disease Control and Prevention. Cowan-Ellsberry CE, McLachlan MS, Arnot JA, MacLeod M, McKone TE, Wania F. 2009. Modeling exposure to persistent chemicals in hazard and risk assessment. Integrated Environmental Assessment and Management 5(4):662-679. Czub G, McLachlan MS. 2004. Bioaccumulation potential of persistent organic chemicals in humans. Environ Sci Technol 38(8):2406-2412; doi: 10.1021/es034871v. Czub G, McLachlan MS. 2004. A food chain model to predict the levels of lipophilic organic contaminants in humans. Environmental Toxicology and Chemistry 23(10):2356-2366. Czub G, McLachlan MS. 2004. A food chain model to predict the levels of lipophilic organic contaminants in humans. Environmental Toxicology and Chemistry 23(10):2356-2366.

7|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

20. Czub G, Wania F, Mclachlan MS. 2008. Combining long-range transport and bioaccumulation considerations to identify potential arctic contaminants. Environ Sci Technol 42(10):3704-3709; doi: 10.1021/es7028679. 21. Debruyn AMH, Gobas FAPC. 2006. A bioenergetic biomagnification model for the animal kingdom. Environ Sci Technol 40(5):1581-1587; doi: 10.1021/es051800i ER. 22. European Chemicals Agency. 2008. Guidance on Information Requirements and Chemical Safety Assessment. Chapter R.11: PBT Assessment. . 23. Fatemi MH, Baher E. 2009. A novel quantitative structure-activity relationship model for prediction of biomagnification factor of some organochlorine pollutants. Mol Divers 13(3):343352; doi: 10.1007/s11030-009-9121-4. 24. Fenner K, Scheringer M, MacLeod M, Matthies M, McKone T, Stroebe M et al. 2005. Comparing estimates of persistence and long-range transport potential among multimedia models. Environ Sci Technol 39(7):1932-1942; doi: 10.1021/es048917b. 25. Fraser AJ, Burkow IC, Wolkers H, Mackay D. 2002. Modeling biomagnification and metabolism of contaminants in harp seals of the barents sea. Environmental Toxicology and Chemistry 21(1):55-61. 26. Gobas FAPC, Kelly BC, Arnot JA. 2003. Quantitative structure activity relationships for predicting the bioaccumulation of POPs in terrestrial food-webs. Qsar & Combinatorial Science 22(3):329336. 27. Gobas FAPC, de Wolf W, Burkhard LP, Verbruggen E, Plotzke K. 2009. Revisiting bioaccumulation criteria for POPs and PBT assessments. Integrated Environmental Assessment and Management 5(4):624-637. 28. Gouin T, Mackay D, Webster E, Wania F. 2000. Screening chemicals for persistence in the environment. Environ Sci Technol 34(5):881-884. 29. Gouin T. 2010. The precautionary principle and environmental persistence: Prioritizing the decision-making process. Environ Sci & Policy 13(3):175-184; doi: 10.1016/j.envsci.2010.01.005. 30. Harvey C, Mackay D, Webster E. 2007. Can the unit world model concept be applied to hazard assessment of both organic chemicals and metal ions? Environmental Toxicology and Chemistry 26(10):2129-2142. 31. Kelly BC, Gobas FAPC. 2003. An arctic terrestrial food-chain bioaccumulation model for persistent organic pollutants. Environ Sci Technol 37(13):2966-2974; doi: 10.1021/es021035x ER. 32. Kelly BC, Gobas FAPC, McLachlan MS. 2004. Intestinal absorption and biomagnification of organic contaminants in fish, wildlife, and humans. Environmental Toxicology and Chemistry 23(10):2324-2336. 33. Kelly BC, Gobas FAPC, McLachlan MS. 2004. Intestinal absorption and biomagnification of organic contaminants in fish, wildlife, and humans. Environmental Toxicology and Chemistry 23(10):2324-2336. 34. Kelly BC, Ikonomou MG, Blair JD, Morin AE, Gobas FAPC. 2007. Food web-specific biomagnification of persistent organic pollutants. Science 317(5835):236-239; doi: 10.1126/science.1138275. 35. Klecka GM, Muir DCG, Dohmen P, Eisenreich SJ, Gobas FAPC, Jones KC et al. 2009. Introduction to special series: Science-based guidance and framework for the evaluation and identification of PBTs and POPs. Integrated Environmental Assessment and Management 5(4):ISSN 15513777(print)|1551-3793(electronic). 36. Leo A. 2000. Octanol/Water partition coefficients. In: Handbook of Property Estimation Methods for Chemicals (Boethling RS, Mackay D, eds) . Boca Raton:CRC Press LLC, 5. 37. Mackay D. 1979. Finding fugacity feasible. Environ Sci Technol 13(10):1218-1223. 38. Mackay D. 1982. Correlation of bioconcentration factors. Environ Sci Technol 16(5):274-278. 39. Mackay D, McCarty LS, MacLeod M. 2001. On the validity of classifying chemicals for persistence, bioaccumulation, toxicity, and potential for long-range transport. Environmental Toxicology and Chemistry 20(7):1491-1498. 40. Mackay D, Paterson S. 1981. Calculating fugacity. Environ Sci Technol 15(9):1006-1014.

8|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

41. Mackay D, Paterson S. 1982. Fugacity revisited - the fugacity approach to environmental transport. Environ Sci Technol 16(12):A654-A660. 42. Mackintosh CE, Maldonado J, Jing HW, Hoover N, Chong A, Ikonomou MG et al. 2004. Distribution of phthalate esters in a marine aquatic food web: Comparison to polychlorinated biphenyls. Environ Sci Technol 38(7):2011-2020; doi: 10.1021/es034745r. 43. McLachlan MS, Czub G, Macleod M, Arnot JA. 2010. Bioccumulation of organic contaminants in humans: A multimedia perspective and the importance of biotransformation. Environ Sci Technol ASAP; doi: 10.1021/es101000w. 44. Meylan WM, Howard PH, Boethling RS, Aronson D, Printup H, Gouchie S. 1999. Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient. Environmental Toxicology and Chemistry 18(4):664-672. 45. Nichols JW, Bonnell M, Dimitrov SD, Escher BI, Han X, Kramer NI. 2009. Bioaccumulation assessment using predictive approaches. Integrated Environmental Assessment and Management 5(4):ISSN 1551-3777(print)|1551-3793(electronic). 46. Organisation for Economic Co-operation and Development. 1996. OECD Guidelines for Testing of Chemicals, Guideline 305: Bioconcentration: Flow-through Fish Test. . 47. Organisation for Economic Co-operation and Development. 2002. Report of the OECD/UNEP Workshop on the use of Multimedia Models for Estimating overall EnvironmentalPersistence and Long Range Transport in the Context of PBTS/POPS Assessment. Paris:OECD. 48. Papa E, Gramatica P. 2010. QSPR as a support for the EU REACH regulation and rational design of environmentally safer chemicals: PBT identification from molecular structure. Green Chem 12(5):836-843; doi: 10.1039/b923843c. 49. Powell A, Mackay D, Webster E, Arnot JA. 2009. Modeling bioaccumulation using characteristic times. Environmental Toxicology and Chemistry 28(2):272-278. 50. Rayne S, Forest K. 2010. D-ow and K-aw,K-eff vs. K-ow and K-aw(o): Acid/base ionization effects on partitioning properties and screening commercial chemicals for long-range transport and bioaccumulation potential. Journal of Environmental Science and Health Part A-Toxic/hazardous Substances & Environmental Engineering 45(12):1550-1594; doi: 10.1080/10934529.2010.506114. 51. Rodan BD, Pennington DW, Eckley N, Boethling RS. 1999. Screening for persistent organic pollutants: Techniques to provide a scientific basis for POPs criteria in international negotiations. Environ Sci Technol 33(20):3482-3488. 52. Scheringer M, Jones KC, Matthies M, Simonich S, van de Meent D. 2009. Multimedia partitioning, overall persistence, and long-range transport potential in the context of POPS and PBT chemical assessments. Integrated Environmental Assessment and Management 5(4):557-576. 53. Schwarzenbach RP, Gschwend PM, Imboden DM. 2003. Environmental Organic Chemistry. 2nd ed. Hoboken:Wiley. 54. Schwarzman MR, and Wilson MP. 2009. New Science for Chemicals Policy. Science November 21. 55. Sharpe S, Mackay D. 2000. A framework for evaluating bioaccumulation in food webs. Environ Sci Technol 34(12):2373-2379; doi: 10.1021/es9910208. 56. Slaveykova VI, Wilkinson KJ. 2005. Predicting the bioavailability of metals and metal complexes: Critical review of the biotic ligand model. Environmental Chemistry 2(1):9-24; doi: 10.1071/EN04076. 57. Solomon KR, Dohmen P, Fairbrother A, Marchand M, McCarty L. 2009. Use of (eco)toxicity data as screening criteria for the identification and classification of PBT/POP compounds. Integrated Environmental Assessment and Management 5(4):ISSN 1551-3777(print)|15513793(electronic). 58. Swackhamer DL, Needham LL, Powell DE, Muir DCG. 2009. Use of measurement data in evaluating exposure of humans and wildlife to POPs/PBTs. Integrated Environmental Assessment and Management 5(4):ISSN 1551-3777(print)|1551-3793(electronic). 59. United States Environmental Protection Agency. 1996. Ecological Effects Test Guidelines OPPTS 850.1710 - Oyster BCF. . 60. United States Environmental Protection Agency. 1996. Ecological Effects Test Guidelines OPPTS 850.1730 - Fish BCF. .

9|Page

Pre-publication Working Paper – do not cite or circulate. © the authors, 2011

61. van Wijk D, Chenier R, Henry T, Hernando MD, Schulte C. 2009. Integrated approach to PBT and POP prioritization and risk assessment. Integrated Environmental Assessment and Management 5(4):697-711. 62. Veltman K, McKone TE, Huijbregts MAJ, Hendriks AJ. 2009. Bioaccumulation potential of air contaminants: Combining biological allometry, chemical equilibrium and mass-balances to predict accumulation of air pollutants in various mammals. Toxicol Appl Pharmacol 238(1):4755; doi: 10.1016/j.taap.2009.04.012. 63. Wania F. 2006. Potential of degradable organic chemicals for absolute and relative enrichment in the arctic. Environ Sci Technol 40(2):569-577; doi: 10.1021/es051406k. 64. Webster E, Mackay D, Wania F. 1998. Evaluating environmental persistence. Environmental Toxicology and Chemistry 17(11):2148-2158. 65. Weisbrod AV, Burkhard LP, Arnot J, Mekenyan O, Howard PH, Russom C et al. 2007. Workgroup report: Review of fish bioaccurnulation databases used to identify persistent, bioaccumulative, toxic substances. Environ Health Perspect 115(2):255-261; doi: 10.1289/ehp.9424. 66. Weisbrod AV, Woodburn KB, Koelmans AA, Parkerton TF, McElroy AE, Borga K. 2009. Evaluation of bioaccumulation using in vivo laboratory and field studies. Integrated Environmental Assessment and Management 5(4):ISSN 1551-3777(print)|1551-3793(electronic). 67. Zhang X, Brown TN, Wania F, Heimstad ES, Goss K. 2010. Assessment of chemical screening outcomes based on different partitioning property estimation methods. Environ Int 36(6):514520; doi: 10.1016/j.envint.2010.03.010.

Acknowledgements

The authors are grateful for support from the UC Berkeley European Union Center of Excellence, the Public health Trust, and the California Environmental Protection Agency’s Department of Toxic Substances Control.

10 | P a g e