### An evaluation of hexabromocyclododeca ne (HBCD) for Persistent Organic Pollutant (POP) properties and the pot ential for adverse effects in the

environment

Submitted to:

European Brominated Flame Retardant Industry Panel (EBFRIP)

Prepared by:

Jon Arnot, Ph.D. University of Toronto Scarborough

Lynn McCarty, Ph.D. LS McCarty Scientific Research & Consulting

James Armitage, Ph.D. Stockholm University

Liisa Toose-Reid, M.Sc.

Environmental Consultant

Frank Wania, Ph.D. University of Toronto Scarborough

Ian Cousins, Ph.D. Stockholm University

May 26, 2009Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation ii EXECUTIVE SUMMARY

This document is an environmental ri sk assessment of hexabromocyclododecane (HBCD). HBCD is also assessed with regards to persistent organi c pollutant (POP) and persistent, bioaccumulation and toxicity (P BT) categorization criteria. Mass balance model simulations are used to compare HB CD with benchmark chemicals including listed POPs, non-POPs, and candidate POPs. Fate and exposure model predictions are compared with available monitoring data in “source” and “remote” regions to corroborate likely patterns for emissions and mode-of-entry to the environment. Body/tissue-based effects and no effects thresholds (PNECs) and total daily intake (TDI) rates associated with no observed adverse effects levels (NOA ELs) are combined with exposure data for screening level assessments of risk in “source” and “remote” environments.

The findings in the present st udy show that HBCD fulfils so me, but not all, of the UNECE and UNEP Stockholm Convention POP cr iteria. For example, based on UN-ECE POP criteria, HBCD shows the potential for long range transboundary atmospheric transport (1a), and bioaccumulation (1d); howev er, the available environmental half-life data suggest an uncertain categorization for persistence (1c), and current monitoring and toxicity data indicate that HBCD levels in re mote regions are below those associated with the likelihood of adversely affecting human health and/or th e environment (1b). Furthermore, available monito ring and modelling data sugg est long range transboundary atmospheric transportation potential (2a); how ever, based on current data, HBCD is not likely to have adverse human health and/or e nvironmental effects as a result of its long range transboundary atmospheric transport (2b).

Commercial (technical) t-HBCD is a chemical mixture, primarily consisting of the three

diastereomers α -, β -, and γ -HBCD. Mixtures and chiral substances need to be evaluated

on an individual basis because each structure has unique properties. The present assessment includes a comprehensive collection and critical review of available physicalchemical property and transformation data , monitoring data, novel model simulations, and toxicity data for t-HBCD and α -, β -, and γ -HBCD. Individual diastereomers were evaluated to the greatest extent possible base d on the available data. Due to the limited availability of data for the diastereomers, pa rticularly with regards to transformation and isomerization, it was ultimately necessary to evaluate HBCD based on representative properties selected for t-HBCD. In some cas es, model predictions for the diastereomers were compared with t-HBCD predictions.

Physical-chemical property measurements and estimates were critically reviewed and thermodynamically consistent property values were calculated (Final Adjusted Values, FAVs). Measurements and model estimates fo r environmental degradation and metabolic biotransformation half-lives were critically evaluated. Median half-lives and the expected range of plausible values (lower and upper bound estimates) were selected for air, water, soil, sediment, fish, birds and mammals. Effo rts were made to reduce uncertainty in the estimate of these key properties, while recogn izing that uncertainty in these estimates cannot be eliminated entirely. Thus, many mass balance calculations also consider uncertainty for the required model input parameters.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation iii Available monitoring data were separated in to general categories of “local/near-point source”, “source” and “remote” regional scales based on distances, corresponding with production and industrial facilities, urban/rura l areas, and Arctic regions, respectively. HBCD is detected in each of these regi ons indicating the poten tial for long range transport (i.e. 1a and 2a of the UN-ECE PO P criteria). However, spatial trends show decreasing concentrations with increased distance from known point sources. Furthermore, remote regions are shown to ha ve environmental concentrations that are orders of magnitude lower than those near -point sources. Tem poral trends in the monitoring data were explored but no consis tent trends could be identified when considering all of the available studies. Ther e appear to be slight increases in certain compartments in the past 15-20 years; however, other compartments show no noticeable trends. The slight increase is primarily observed in compartments that require longer times to reach steady state and may reflect an approach to steady state levels.

Mass balance model simulations compare HBCD to classified POPs, substances that are not considered to be POPs (non-POPs), and s ubstances that are pres ently under review as candidate POPs (candi-POPs) in a variety of benchmarking exercises. HBCD is shown to have some potential for long range transport and overa ll persistence (P OV ); however, other non-POP chemicals also show these pr operties. These properties for HBCD are generally found to be lower than POPs and candidate POPs, particularly when median and lower bound degradation half-lives fo r HBCD are considered The benchmark comparisons do not provide clear evidence for assigning HBCD as a “POP” or a “nonPOP” largely because of the unc ertainties in the half-life data and the wide range of LRT

and P OV values for POPs and non-POPs. Uncertai nty for HBCD mode-of-entry to the environment is also a factor. These findings and comparisons of the plausible range of half-life values for HBCD with media specific half-life persistence criteria result in an ambiguous persistence categorization (i.e . 1c of the UN-ECE and UN Stockholm Convention criteria).

Realistic emissions estimates were used to mo del steady state concentr ations in a range of representative species of vary ing trophic position including fi sh, birds, marine mammals, and humans in a regional environment. These predictions were compared with monitoring data representative of regions in Northern Europe (Sweden, Norway). Reasonable agreement of the predicted concentr ations with the available monitoring data (i.e. typically within a factor of 3) suggests that HBCD is near steady state conditions in

the environment and that the selected model input parameters for HBCD are reasonable. The model predicted upper trophic level organisms, particularly species that consume fish such as marine mammals and piscivorous bird s, to have the highest exposure potential. The model predictions are corroborated by th e available monitoring data. These findings support selected estimates for slow biot ransformation rates in fish and the bioaccumulation potential categorization for HBCD (i.e. 1d of the UN-ECE criteria). A sensitivity analysis provides recommenda tions to reduce uncertainty in model predictions. For exposures to marine mammals biotransformation half-lives in biota are the most sensitive parameters followed by degradation half-lives in water and sediment.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation iv This largely reflects the primary route of dietary exposure to upper trophic level organisms from the consumption of fish.

Global scale mass balance model predictions using spatially resolved estimates for

realistic emission rates and assumed mode-of- entry scenarios were also found to be in reasonable agreement with available monitoring data from different regions. The model predicts lower concentrations in remote regions than in source regions also reflecting the observed spatial trends in the monitoring data.

Regional scale dynamic mass balance calculatio ns were used to estimate response times for HBCD in the environment. Response times in most environmental media are predicted to range from a few days (air) to about 5 years (soil). In comparison, listed POPs such as PCB 180 have much longer response times in the environment, of the order of years to decades. Thus, the model results i ndicate that concentrations of HBCD in the environment will decline faster in response to reduced emissions than those of many listed POPs. The relatively short response times for HBCD also partly explain why steady state model predictions are in good ag reement with the monitoring data.

A toxicological evaluation of HBCD was carried out with resp ect to a PBT classification based on European Commission “T” criteria and for the basis of conducting a risk assessment using available monitoring data. We first discuss the findings for the “T” categorization followed below by the findings for the risk assessment. The aquatic exposure-based ecotoxicity data available for HBCD are generally confounded by either the use of a cosolvent or a generator column. OECD testing guidance for difficult substances and mixtures (HBCD is both) spec ifically recommends that such practices be

avoided because of the uncertainties associated with interpreting such exposure-based test results. Therefore, these a quatic exposure-based results that were used in previous

assessments are considered herein to be uncertain and of ques tionable reliability, particularly for comparison to the aq uatic exposure-based “T” criteria.

The predominant isomer in t-HBCD is γ -HBCD, which has a water solubility limit of about 2 to 3 μg·L -1 . For exposure concentrations of t-HBCD greater than about 3 μg·L -1
precipitated γ -isomer in the water column make it impossible to produce a purely dissolved aqueous t-HBCD exposure. The low water solubility of t-HBCD brings into question the validity of using the European Commission NOEC “T” criterion of 10 μg·L -1
for toxicity assessments, or for that matter, other aquatic exposure-ba sed criteria greater than a substance’s water solubility limit. It is likely that this “T” criterion is inappropriate

and unsuitable for the PBT classification of HBCD and other substances with very low water solubility. Importantly, whereas some of the aquatic toxicity data are above the water solubility limit for γ -HBCD, those data that are at, or just below, the water solubility limit show no effects. Thus, the positive “T” assessment conclusion in the previous risk assessment by the European Commission is not supported in the present assessment. The present detailed interpretati on and evaluation indicate s that available key aquatic toxicity data for HBCD show low toxicity.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation v The present risk assessment was used to a ssess HBCD with respect to POP evaluation criteria (e.g. UN-ECE 1b and 2b, the potential for sign ificant adverse effe cts as a result of long range atmospheric transport). To conduct this assessment reliable toxicity endpoints for possible effects and no eff ects are needed. Aquatic exposur e-based toxicity endpoints, such as those used in previous risk assessments, are not readily applicable for multimedia-based exposures to HBCD in the environment, particularly for higher trophic level organisms that are exposed to HBCD by the consumption of food. Alternative methods for toxicity and risk evaluations th at are not affected by the confounding issues related to the aqueous-exposure toxicity e ndpoints are the body/tissue-residue and total daily intake (TDI) approaches. Illustrative ex amples of these alternative approaches for HBCD risk evaluation were prepared to address the UN-ECE POPs evaluation (i.e. 1b and 2b). For the body/tissue-residue approach , dose-response data based on measured amounts of HBCD in the organism are used to estab lish thresholds that can be directly compared to organism monitoring data. As discussed below, the present study adopts two residue-based PNECs; one for baseline narcos is and one for an assumed and unidentified more specific mode of action (“worst-case” ). For the TDI approach, HBCD exposure dose levels associated with laboratory NOAE Ls are compared with estimated HBCD

intake rates in the environment. These two general approaches ar e considered viable methods to estimate the likelihood of significa nt adverse effects in upper trophic level organisms in source and remote regions.

Available residue-based toxicity data suggest that HBCD toxicity to freshwater and

marine fish and earthworms is due to baseli ne narcosis, the least toxic mode of action exhibited by organic chemicals. A residue-based PNEC was established for this mode of

action. Some recent work with rodents exposed to t-HBCD includes residue-effect estimates for several response endpoints. It is un certain if some of the observations in the rat data (i.e. detectable physical and/or biochemical changes but no organism level survival, growth, or reproduction effects) are due to effects at the extreme tail of baseline narcosis toxicity or that a separate, more specific, yet unknown mode of toxic action is operational. As a “worst case” risk assessmen t exercise, another residue-based PNEC was

assumed at a tissue level 100 times lower th e baseline narcosis PNEC to address the

uncertainties associated with the mammalian to xicity data. This “worst case” toxicity PNEC is considered to be a more stringen t and conservative threshold for the present screening assessment; however, it is unclear wh ether this “worst case” PNEC is truly indicative of a significant adverse effect.

Available monitoring data were separated in to general categories of “local/near-point source”, “source” and “remote” regional scales and subdivided into organism groupings.

For the baseline narcosis mode of toxic actio n residue-based PNEC th ere is no indication of potential adverse effects based on any of the available monitoring data for invertebrates, fish, marine mammals, or birds. For the more stringent “worst case” PNEC there was no indication of exposures exceeding this threshold for any of these organism groups at “remote” locations. For “source” regi ons, the upper end of the marine mammal monitoring concentration distribution exceeds th e “worst case” PNEC threshold while the upper end of the bird monitoring data enters the “worst-case” PNEC threshold. For “local/near point source” re gions (i.e. sites near known point source emissions ofArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation vi HBCD), the majority of the marine ma mmal and fish monitoring concentration

distributions exceed the “worst case” PNEC threshold and the upper end of the bird monitoring data enters the “worst-case” PNEC threshold. Based on the “worst case” body/tissue-residue PNEC threshold, present HBCD concentrations in biota at local/source regions approach levels warranti ng a more comprehensive risk assessment as is typically conducted for non-POP substances.

The potential for significant adverse eff ects was also evaluated by comparing TDI exposure estimates of HBCD for upper trophic level specie s in the environment (i.e. marine mammals) against a TDI NOAEL of 10 mg·(kg-bw·d) -1 derived from laboratory testing data. This NOAEL was recommended in the European Commission Draft Risk Assessment Report. The TDI required to obt ain a steady state concentration of HBCD corresponding with the highest measured conc entration in a marine mammal in a remote region is about 5 orders of magnitude below the NOAEL. The TDI of HBCD corresponding to a steady state concentratio n at the 95th percentile of measured

concentrations in marine mammals in “s ource” and “local/near point source” regions ranges from about 2 to 3 orders of magnitude below the NOAEL. Based on this endpoint for risk characterization, TDIs for piscivor ous marine mammals in all three geographic regions were well below the NOAEL suggest ing no potential for significant adverse effects (i.e. 1b and 2b of the UN-ECE POP criteria).

In summary, simply because a substance m eets screening level hazard categorization criteria and can be detected in remote environments, does not constitute sufficient justification for concluding th at there is a likelihood of si gnificant adverse effects in remote environments. The final judgement on whether a candidate substance such as HBCD should be categorized as a POP requi res a more detailed and thorough risk-based assessment. A risk-based approach using or ganism-based dose metrics is suggested to evaluate exposure levels in the environment by comparing them with levels associated

with effects and no effects.

Three evaluations using these methods were illustrated in the present study using

available data. These evaluations do not s upport the classification of HBCD as a POP since there are no indications that significan t adverse effects are likely to occur in organisms living in remote areas distant fr om known point-source emissions of HBCD. The present assessment does not include a thorough uncertainty analysis; however,

conservative values have been selecte d. A comprehensive risk assessment fully characterizing the uncertainty in the present findings is not possible at this time due to isomer specific data limitations for HBCD, in particular for the diastereromers (i.e. isomerization rates, isomer specific degradation rates, and potential differences in isomer toxicity) Recommendations are provided in the report to reduce uncertainty in further

risk-based evaluations of HBCD.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation vii TABLE OF CONTENTS

Executive Summary............................................................................................................ ii

Table of Contents.............................................................................................................. vii

List of Tables ................................................................................................................. ..... x List of Figures................................................................................................................ ..xiii

1.0 Introduction............................................................................................................... .... 1
1.1 Background............................................................................................................... 1
1.2 Report Objectives...................................................................................................... 2

2.0 Substance Information .................................................................................................. 3
2.1 Identity and Stereochemistry .................................................................................... 3
2.2 Analytical Methods and Isomerization ..................................................................... 5
2.3 Physical-Chemical Properties ................................................................................... 6
2.3.1 Addressing Isomerization .................................................................................. 6
2.3.2 Selection of Reliable Estimates for Solubility and Partitioning ........................ 7
2.3.3 Thermodynamic Consistency Calcula tions for Chemical Properties ................ 8
2.3.4 Uncertainty in FAVs........................................................................................ 10
2.3.5 Physical-Chemical Properties for Benchmarking............................................ 11
2.4 Degradation, Biotransformation and Half-Life Data.............................................. 13
2.4.1 Environmental Degrad ation Half-lives............................................................ 13

2.4.2 Biotransformation Half-lives ........................................................................... 20
2.4.3 Halogen Bond Strength and Persistence.......................................................... 26
2.4.4 Half-Lives for Benchmarking.......................................................................... 26
3.0 Exposure Characterization.......................................................................................... 28

3.1 Production and Use................................................................................................. 28
3.2 Emissions and Mode-of-Entry ................................................................................ 28
3.3 Monitoring Data...................................................................................................... 29

3.3.1 Air .................................................................................................................... 30

3.3.2 Sediment .......................................................................................................... 32

3.3.3 Water and Soil.................................................................................................. 37

3.3.4 Plants................................................................................................................ 37

3.3.5 Invertebrates..................................................................................................... 38

3.3.6 Fish................................................................................................................... 38

3.3.7 Birds and Mammals ......................................................................................... 39

3.3.8 Humans ............................................................................................................ 42

3.4 Time Trends of Environmental Concentrations...................................................... 46
3.4.1 Abiotic Compartments..................................................................................... 46
3.4.2 Biota: Source Regions...................................................................................... 47
3.4.3 Biota: Remote Regions .................................................................................... 51
3.4.4 Key Considerations.......................................................................................... 52Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation viii 3.5 Fate and Exposure Modelling ................................................................................. 52
3.5.1 Benchmarking.................................................................................................. 54

3.5.1.1 The Tool.................................................................................................... 54

3.5.1.2 RAIDAR ................................................................................................... 58

3.5.1.3 The Tool and RAIDAR............................................................................. 68
3.5.1.4 GloboPOP: Arctic Contamination Potential (eACP 10 ) ............................. 71

3.5.1.5 AC-BAP.................................................................................................... 76
3.5.2. Model Comparisons with Monitoring Data .................................................... 78
3.5.2.1 RAIDAR and Real World Regional Scale Monitoring Data.................... 78

3.5.2.2 BETR-World............................................................................................. 85
3.5.3 Time Trends and Response Times................................................................... 89

3.5.3.1 CoZMoMAN ............................................................................................ 89

4.0 Effects Characterization.............................................................................................. 95
4.1 Data Compilation and Review................................................................................ 95

4.1.1 Terrestrial Data ................................................................................................ 96
4.1.2 Aquatic Data .................................................................................................... 98
4.1.2.1 Data Quality Considerations..................................................................... 98
4.1.2.2 Aquatic Toxicity Data Review.................................................................. 99

4.2 Effect Values for PBT or POPs Cl assification and Risk Assessment .................. 102

4.2.1 PBT and POPs "T" Classification.................................................................. 102
4.2.2 Previous Approaches for Effect Characterization.......................................... 107
4.2.3 Current Approaches for Effect Characterization ........................................... 108
5.0 Hazard Characterization (PBT/POP) ........................................................................ 113
5.1 Screening Criteria ................................................................................................. 113
5.2 Assessment of Persistence and Long Range Transport ........................................ 116

5.2.1 Persistence...................................................................................................... 116
5.2.2 Long Range Transport Potential .................................................................... 117

5.3 Assessment of Bioaccumulation........................................................................... 118
5.4 Assessment of Toxicity (Hazard).......................................................................... 120

6.0 Risk Characterization................................................................................................ 121
6.1 Ecological Risk Assessment ................................................................................. 121
6.1.1 Body/Tissue Residue Based Risk Assessment .............................................. 121
6.1.2 Total Daily Intake Based Risk Assessment ................................................... 128

7.0 Conclusions............................................................................................................... 1 29
7.1 Ecological Risk Assessment ................................................................................. 129

7.2 POP Assessment ................................................................................................... 129
7.2.1 Persistence and Long Range Transport Potential .......................................... 129

7.2.2 Bioaccumulation ............................................................................................ 130
7.2.3 Toxicity and Potential for Adverse Effects.................................................... 130
7.3 Commentary on Previous Assessments ................................................................ 131

7.4 Recommendations................................................................................................. 133Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation ix 8.0 References................................................................................................................. 135

9.0 APPENDIX............................................................................................................... 151
9.1 Physical Chemical Properties ............................................................................... 151
9.2 Degradation, Biotransformation and Half-lives.................................................... 161
9.3 Monitoring Data.................................................................................................... 162

9.4 Mass Balance Models ........................................................................................... 190
9.4.1 General Background ...................................................................................... 190
9.4.2 Supplementary Data for Globo-POP Modelling............................................ 191
9.4.3 Supplementary Data for BETR-World Modelling......................................... 192
9.4.4 Supplementary Data for RAIDAR Modelling ............................................... 193

9.5 Detailed Comments on HB CD Toxicity Data and Previous PBT Assessments... 193
9.5.1 Water Solubility, Bioavailability and Cosolvents.......................................... 193
9.5.2 Aquatic Toxicity Testing with Di fficult Substances and Mixtures ............... 195

9.5.3 Critical Evaluation of PBT Assessments ....................................................... 196
9.5.4 Further Commentary on POP Assessments ................................................... 199Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation x LIST OF TABLES

Table 2.1. Selected values for physi cal-chemical properties (at 25 o C) and relative variance values used as inputs for th ermodynamic consistency calculations..................... 9

Table 2.2. Final adjusted values (FAVs at 25 o C), relative variance values, percent adjustments, and assigned confidence factor s used in the present study for mass balance modelling and data analyses obtained from the thermodynamic consistency calculations (Schenker et al. 2005). .................................................................................. 10

Table 2.3. Selected physical-chemical pr operties for the model benchmarking simulations. Data are from (Cowan-Ellsbe rry et al. 2009) except for Aldrin and Heptachlor which are from (Schenker et al. 2005; Mackay et al. 2006). ......................... 12

Table 2.4. Degradation half-lives for tota l HBCD proposed in the European Risk Assessment Report (RAR) (EC 2008). ............................................................................. 16

Table 2.5. Total degradation half-lives (d ) assumed for all model simulations ............... 19

Table 2.6. Molar mass balance for diastereoi somer bioisomerization and actual total elimination (fecal egestion, gill respir ation and biotransformation)................................. 21

Table 2.7. Mass balance model summary fo r estimating biotransformation and

bioisomerization rate constant s from a dietary feeding study in rainbow trout (Law et al. 2006b). .................................................................................................................... ..... 23
Table 2.8. Selected properties of fluorine, chlorine, bromine and iodine atoms where X is the halogen (Lide 2000; Neilson 2003)......................................................................... 26

Table 2.9. Selected primary transformati on half-lives (HL; days) for the model benchmarking simulations. Data are from (C owan-Ellsberry et al . 2009) except for *,

in which environmental HLs were selected based on (Arnot et al . 2005; Mackay et al. 2006; U.S. EPA 2009) and fish HLs are fr om (Arnot et al. 2009; U.S. EPA 2009). Avian and mammalian HLs (Av/Mam) for * were assumed to be 1/3 estimates for fish. Transformation half-lives ( HL N ) in biota are listed normalized to 1 kg body size and scaled to mass ( Mi ; kg) specific values ( HLi ) for different species in the models as HLi

= HL N x ( Mi /1) -0.25 ............................................................................................................. 27
Table 3.1. Total estimated European Union (EU-Releases) of HBCD by emission compartment and emission source category (p oint sources versus diffuse sources). Adapted from TemaNord (2008). ..................................................................................... 29

Table 3.2. Average water, suspended sedime nt and soil samples at point and urban sources in Europe.............................................................................................................. 37

Table 3.3. Summary of monitoring data collected near known point sources including the range of reported values and selected median values. ................................................ 43

Table 3.4. Summary of monitoring data collected in “s ource” regional environments including the range of re ported values and selected median values. ................................ 44

Table 3.5. Summary of monitoring da ta collected in “remote” regional environments including the range of re ported values and selected median values. ................................ 45

Table 3.6. Calculated doubling times (years) from sediment cores in Europe and Japan. ......................................................................................................................... ....... 47Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation xi Table 3.7. RAIDAR V2.0 Level II maximum e xposure assessment factors (EAF), relative rankings and the organism of c oncern for HBCD and benchmark chemicals. EAF values listed are base 10 logarithms......................................................................... 61

Table 3.8. RAIDAR V2.0 Level III (100% emissions to air) maximum exposure assessment factors (EAF), relative rankings and the organism of concern for HBCD

and benchmark chemicals. Values li sted are base 10 logarithms. .................................... 62

Table 3.9. RAIDAR V2.0 Level III (100% emissi ons to water) maximum exposure assessment factors (EAF), relative rankings and the organism of concern for HBCD

and benchmark chemicals. Values li sted are base 10 logarithms. .................................... 63

Table 3.10. RAIDAR V2.0 Level III (50% emissions to air and 50% emissions to water) maximum exposure assessment fact ors (EAF), relative rankings and the organism of concern for HBCD and benchmar k chemicals. Values listed are base 10 logarithms. .................................................................................................................... .... 64

Table 3.11. RAIDAR V2.0 overa ll persistence (P OV ; d) estimates using Level II and

Level III fate calculations. ................................................................................................ 66

Table 3.12. Summary of RAIDAR model sensitivity for input parameters on estimated total body burdens in the a quatic/marine mammal base d on a unit emission rate (TBB U ; ng/g) assuming equal emissions of HBCD to air and water................................ 68

Table 3.13. Selected compounds, OH radical reactiv ity and eACP 10 calculated using

GloboPOP model. ............................................................................................................. 75

Table 3.14. Average concentrations in air, herring and humans during the second decade of simulation in CoZMoMAN using continuous emissions at a rate of 1 t ·yr -1
per 1Mio km 2 for different assumption concerni ng the mode of emission and the degradation half-lives in wa ter, soil and sediment............................................................ 92

Table 3.15. Maximum concentration of H BCD (using either median or upper bound estimates for half-lives in water and soil) and three benchmark POPs predicted during the hypothetical 40 year simulati ons displayed in Figure 3.31......................................... 95

Table 4.1. Summary of key data on the terre strial environmenta l effects of HBCD:

Studies considered valid in previous EU assessments 1 ..................................................... 97
Table 4.2. Summary of key data on the a quatic environmental effects of HBCD:

Studies considered valid in previous EU assessments 1 ................................................... 101

Table 4.3. Summary of critical toxicity data and PNEC estimates for environmental

effects of HBCD from Eu ropean Commission RAR (2008) 1 ........................................ 108
Table 5.1. PBT and vPvB screening criteria according to European Commission TGD (EC 2003)...................................................................................................................... .. 114
Table 5.2. POP screening criteria accordi ng to the Stockholm Convention (UNEP 2001). ......................................................................................................................... ..... 115

Table 9.1. Summary of water solubility data for HBCD considered reliable in the EU RAR (reproduced from (EC 2008)). ............................................................................... 153

Table 9.2. Model estimates for the octa nol-water partition coefficient (K OW )............... 158

Table 9.3. Selected values for physical-chemical properties and relative variance values used as inputs for therm odynamic consistency calculations. .............................. 159Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation xii Table 9.4. Final adjusted values (FAVs at 25 o C), relative variance values, percent adjustments, and assigned confidence factor s used in the present study for mass balance modelling and data analyses obtained from the thermodynamic consistency calculations (Schenker et al. 2005). ................................................................................ 160

Table 9.5. BIOWIN TM v4.10 model output for HBCD ( not isomer specific) (US EPA 2009). ......................................................................................................................... ..... 161

Table 9.6. General equations used to estimate aerobic environmental biodegradation

half-lives from BIOWIN TM model output and corresponding values calculated for HBCD (Arnot et al. 2005; US EPA 2009)...................................................................... 161Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation xiii LIST OF FIGURES

Figure 2.1. The three main diastereoisome rs (pairs of enan tiomers) in t-HBCD (recreated from (EC 2008); data from (Heeb et al. 2005)). ................................................ 4

Figure 2.2. Illustration of analytical methods comparison (data from (Petersen et al. 2004). ......................................................................................................................... ......... 5
Figure 3.1. Air concentrations in Sweden and Finland in 2000-2001. *Aspvreten is 80 km NE of the XPS facility and may be influenced by those emissions, although is

termed “remote” in the literature. ..................................................................................... 31

Figure 3.2. Isomeric distribution in gaseous and particulate air samples in Guangzhou, China (Yu et al., 2008)...................................................................................................... 32

Figure 3.3. Isomeric distribu tion in particulate air samp les from Central USA (Hoh and Hites, 2005)............................................................................................................... . 32
Figure 3.4. Downstream trend for mean se diment concentrations reported from Europe, Asia and North America. Error bars represent minimum and maximum values for each type of sediment (Sellström et al. 1998; Eljarrat et al. 2004; Morris et al. 2004; Remberger et al. 2004; Schlabach et al. 2004a; Schlabach et al. 2004b; Klamer et al. 2005; Verslycke et al. 2005; Minh et al. 2007)........................................... 33
Figure 3.5. Surface sediment concentrations in two European river systems. The River Haggan is a tributary of the River Viskan. La ke Skaresjon is an unimpacted lake that is nearby the River Viskan. Dashed line repr esents relative location of initial HBCD

release site. Location data are not to s cale. Data are from Sellström et al. 1998 (Sweden) and Schlabach et al. 2004 (Nor way), Eljarrat et al., 2004 (Spain). .................. 34

Figure 3.6. Proportion of isomers in Drammen River and Fjord sediment samples and

concentration of Σ HBCD at each location. Only site 7-Vikersund is non-impacted.

Locations of sources of HBCD were not included in the study (Schlabach et al. 2004a). ........................................................................................................................ ...... 35
Figure 3.7. Estuarine concentratio ns and isomeric breakdown. ....................................... 36

Figure 3.8. Spatial Trends: HBCD in Tokyo Bay surface sediments from Minh et al. (2007)......................................................................................................................... ....... 36
Figure 3.9. Geometric mean to tal HBCD concentrations ( 95% CIs indicated by error bars) in harbour porpoise blubber ( μ g·kg -1 lw) from various lo cations over the period 2000–2003 (Zegers et al. 2005). ....................................................................................... 41

Figure 3.10. Time trend for HBCD in urban sediments ................................................... 46

Figure 3.11. Temporal trends in geometri c mean total HBCD concentrations ( μ g·kg -1
lw) in blubber of harbour por poises stranded or bycaught in UK coastal waters for the period 1994–2006 (Law et al. 2006 c; Law et al. 2008).................................................... 47

Figure 3.12. Geometric mean to tal HBCD concentrations ( μ g·kg -1 lw) in herring muscle sampled at various locations in the Baltic Sea (Naturhistoriska riksmuseet 2008). ......................................................................................................................... ....... 49
Figure 3.13. Mean total HBCD concentration ( μ g·kg -1 lw) in guillemot eggs (Sellström et al. 2003). Data from (N aturhistoriska riksmuseet 2008) not included here.................. 49Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation xiv Figure 3.14. Mean total HBCD concentration ( μ g·kg -1 lw) in human breast milk samples (Fangstrom et al. 2008). ...................................................................................... 50

Figure 3.15. Mean total HBCD concentrations ( μ g·kg -1 lw) in eggs of herring gull,

Atlantic Puffin and Kittiwake from two remo te island in the north of Norway, Røst (left panel) and Hornøya (right panel) (Knudsen et al. 2005)........................................... 51

Figure 3.16. Reported concentrations of to tal HBCD in peregrine falcon eggs ( μ g·kg -1
lw) inhabiting Greenland (V orkamp et al. 2005).............................................................. 51

Figure 3.17. Estimated gas-particle partitioni ng behaviour of HBCD as a function of atmospheric temperature................................................................................................... 53

Figure 3.18. Long range transport (LRT) estim ates (characteristic travel distance, CTD; transfer efficiency, TE) and overall persistence (P OV ) estimates for the selected substances using The Tool V2.0 model. ........................................................................... 57

Figure 3.19. A conceptual overview of the RAIDAR model (modified from Arnot et al. 2006). ..................................................................................................................... ...... 59
Figure 3.20. Total body burdens (TBB; mmol ) estimated in “sour ce” (upper figure) and “remote” (lower figure) regional envir onments based on an assumed unit emission rate (1 kg·h -1 ). Red bars – POPs; White bars – no n-POPs; Black bars – Candi-POPs..... 70

Figure 3.21. Zonal subdivision of the GloboP OP model, based on latitudinal climatic

bands. ......................................................................................................................... ....... 72
Figure 3.22. Arctic Contamination Potentia l after 10 years of constant emissions (eACP 10 ) of POP, non-POP, Candi-POPs and t- HBCD (under different assumptions) as a function of reactivity with OH radicals (k OH , cm 3 ·molecule -1 ·s -1 ). Unless noted otherwise, emissions assumed to be 100% to air and reactions with OH radicals assumed to occur only in the gas phase. Note: The eACP 10 of CCl 4 is shown on the yaxis because it was assumed to have a negligible k OH. ..................................................... 73
Figure 3.23. Chemical space diagrams identifying partitioning properties favoring accumulation of a globally emitted, persistent chemical in an Inuit woman as quantified with the Arctic Contamination and Bioaccumu lation Potential (AC-BAP) (Figures from Czub et al., 2008). The circle in the lower right of each panel represents the approximate partitioning properties of H BCD (mixture). In the panel to the right,

the blue line encircles ar eas of elevated AC-BAP 10 (10 years of emissions) whereas the red line represents an elevated AC-BAP 70 (70 years of emissions). HBCD, when persistent, has an elevated ACP (> 10 % of the maximum). The partitioning properties of other organic chemicals are indicated for comparison. ................................................ 77

Figure 3.24. RAIDAR model predictions (×) an d monitoring data (o) for HBCD based

on an assumed actual emission rate of 9.942 t ·yr -1 (EU region) x 1/40 (regional scale and unit emission differences), 95% of which is released to water and 5% to air and using median environmental half-lives. The me dian value for birds is from muscle and the range reflects data for eggs. The gray bars are BETR-World model estimates for the European region. ......................................................................................................... 81

Figure 3.25. RAIDAR model predictions (×) an d monitoring data (o) for HBCD based

on an assumed actual emission rate of 9.942 t ·yr -1 (EU region) x 1/40 (regional scale and unit emission differences), 70% of which is released to water and 30% to air and

median environmental half-lives. The median value for birds is from muscle and theArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation xv range reflects data for eggs. The gray ba rs are BETR-World model estimates for the European region................................................................................................................ 83
Figure 3.26. RAIDAR model predictions (×) an d monitoring data (o) for HBCD based

on an assumed actual emission rate of 9.942 t ·yr -1 (EU region) x 1/40 (regional scale and unit emission differences), 70% of which is released to water and 30% to air and

95 th percentile values for environmental half-l ives. The median value for birds is from muscle and the range reflects data for e ggs. The gray bars are BETR-World model estimates for the European region. The yellow triangles are estimates assuming

metabolic biotransformation rates in birds and mammals are equal to the estimate for

fish........................................................................................................................... .......... 84
Figure 3.27. Comparison of BETR -World model results and with available monitoring data. Solid points compare monitoring data to results using the default half-lives physical-chemical parameters and open poi nts compare to results using the 95 th
percentile half-lives.......................................................................................................... . 87
Figure 3.28. Atmospheric concentrations pr edicted by BETR-World model using the upper boundary half-lives. ................................................................................................ 88

Figure 3.29. Freshwater sediment concentr ations predicted by BETR-World model using the upper boundary half-lives.................................................................................. 88

Figure 3.30. Time variant concentrations of HBCD in several media as calculated by CoZMoMAN assuming 20 years of steady emi ssions to air (30%) and water (70%), followed by 20 years of no emissions............................................................................... 90

Figure 3.31. Comparison of simulated time tren ds of HBCD (long ha lf-lives in surface media and three benchmark POPs, normalized to the average over the entire simulation period. ............................................................................................................. 94
Figure 4.1. Key aquatic toxicity exposure data for PBT or POPs assessment................ 104

Figure 4.2. Residue-based organism toxi city and residue-based PNECs. ...................... 110
Figure 6.1. Residue-based risk assessment w ith summary monitoring data from the EU RAR (EC 2008)............................................................................................................... 123

Figure 6.2. Residue-based risk assessment w ith available “remote” region monitoring

data........................................................................................................................... ....... 125
Figure 6.3. Residue-based risk assessment w ith available “source” region monitoring

data........................................................................................................................... ....... 126
Figure 6.4. Residue-based risk assessment w ith available “local /near point source” monitoring data. .............................................................................................................. 1 27

Figure 9.1. Comparison of eACP 10 values for t-, α -, β -, γ -HBCD.................................. 191Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 1 1.0 INTRODUCTION

1.1 B ACKGROUND

Chemical assessment methods and criteria ha ve been developed over the last 40 years and are now being applied broadly in regul atory programs such as the United Nations Stockholm Convention on Persistent Organic Pollutants (POPs) (UNEP 2001). Programs

such as these often screen intrinsic chemi cal properties against selected criteria or “trigger values” in specified and separate categories such as persistence (P), bioaccumulation (B), and toxic ity (T). Often few experiment al and monitoring data are available for current chemical regulatory assessment needs, necessitating the use of Quantitative Structure-Activity Relationships (QSARs) to obtain chemical property estimates for mass balance models to enable environmental fate and exposure prediction (Muir and Howard 2006).

Hexabromocyclododecane (HBCD) is used as a flame retardant primarily in building insulation composed of extruded or expanded pol ystyrene foam but also in textiles and other products. Technical (commercial) t-HBCD is actually a mixture of primarily three

diastereomers: α -, β -, and γ -HBCD (Heeb et al. 2005; Heeb et al. 2008). In the present study t-HBCD refers explicitly to the technical mixture, α -, β -, and γ -HBCD refer to the three diastereomers, and HBCD refers to cases in which the referen ce has been unclear in previous reports or distinc tions are not considered rele vant for the discussion. The Swedish Chemicals Agency has conducted a risk assessment for t-HBCD for the European Commission (EC 2008) and the Nordic Council of Ministers has completed a report in which information on environmenta l properties for t-HBCD were compared with the criteria for POP categorization (TemaNord 2008). The TemaNord document is being used to recommend adding t-HBCD to Annex A of the United Nations Stockholm

Convention on POPs and Annex 1 of the United Nations Economic Commission for Europe (UN-ECE) Protocol on POPs under the Convention on Long range Transboundary Air Pollution.

The European Commission Draft Risk Asse ssment Report (RAR) for t-HBCD was

carried out in accordance with Council Re gulation (EEC) 793/93 on the evaluation and control of the risks of existing substan ces using a method supported by a Technical Guidance Document (TGD) (EC 2003; EC 20 08). The RAR concludes that t-HBCD meets PBT criteria as outlined in the TGD; however, as noted in the draft report, the

information contained therein does not necessa rily provide a sufficient basis for decisionmaking regarding the hazards, exposures or th e potential risks associated with t-HBCD (EC 2008). In the RAR, t-HBCD was considered a T-substance largely because of the 21- day Daphnia No Observed Effect Concentration (NOEC) of 3.1 μg·L -1 (EC criterion for NOEC <10 μg·L -1 ). The RAR indicated an absence of persistence based on degradation half-lives of 1.5 and 7 days for anaerobic se diments and soils, respectively; however, tHBCD was considered to be “P” because it could be found in biota of remote regions such as the Arctic. In the TemaNord report (2008), t-HBCD was classified as “P” based on temperature-adjusted degradation half-lives of α - and γ -HBCD in aerobic sedimentsArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 2 (12 o C) and negligible degradation in soil from one particular study (Davis et al. 2004).

Half-life data documented in the RAR and Te maNord reports are largely contradictory

and uncertain.

There are uncertainties with the available data (e.g. degradation half-lives) and the interpretation of these data such that the conclusions in the previous assessments may be inappropriate. In particular, as to whether or not HBCD is likely to have significant

adverse human health and/or environmenta l effects as a result of its long range transboundary atmospheric transport. H BCD is very hydrophobic and complications related to conducting water-based tests and uncertainties interpreting the data for hydrophobic substances are well recognized. Th e PBT hazard categories are intended to identify chemicals that may pose potential risks to humans and the environment. These screening level methods and cr iteria may not effectively iden tify and prioritize chemicals of concern and complementary holistic me thods are recommended (Arnot and Mackay 2008). Wu and colleagues (2008) and Leonards and colleagues (2008) have also argued that more comprehensive POP assessments should compare threshold body burdens associated with measured or expected concentrations in the environment. The combination of sophisticated environmental fate and expos ure models with organismbased toxicity levels has actually been advo cated for a number of years (McCarty and Mackay 1993). Combining monitoring data and mass balance models maximizes theoretical and empirical know ledge for comprehensive chem ical assessments (McKone et al. 2007; Cowan-Ells berry et al. 2009).

1.2 R EPORT O BJECTIVES

The primary objective of the present study is to provide a scientif ically defensible assessment of t-HBCD, and the three dias tereomers where possible, using physicalchemical property data, emission estimates, multimedia fate, bioaccumulation and exposure models, toxicity and effects info rmation, monitoring data, and spatial and temporal trends of environmental concentrations. This assessment includes the collection and critical evaluation of Final Adjusted Values (FAVs) for physical-chemical properties used throughout the assessment and the colle ction and critical review of available monitoring and testing data. The study uses st ate of the science mass balance models and QSARs to compare HBCD with selected PO Ps, candidate POPs currently under risk review, and non-POPs. Mass balance models seek to corroborate emissions estimates, half-life degradation data and monitoring da ta including estimated response times ot

HBCD emission reductions in various environmental compartments. The modelmonitoring comparisons can also provide justification for the selected degradation halflives. The present assessment also serves as a critical appraisal of the TemaNord (2008) recommendation to categorize HBCD as a POP and provides a more comprehensive perspective in relation to the proposed POP categorization of HBCD according to the UN

Stockholm Convention and UN-EC E POP criteria. The present study also highlights key

issues related to the RAR.

The focus of the present study is on an ecological risk assessment; however, human farfield exposures to POPs, non-POPs, and ca ndidate POPs are also considered whenArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 3 comparing model predictions with monitori ng data. There are uncer tainties associated with indoor (nearfield) exposures; therefor e, a comprehensive human health risk assessment is not considered in the pres ent study. The RAR found no need for further

information or for risk reduction measures be yond those, which are being applied already for humans exposed via the environment (EC 2008).

2.0 SUBSTANCE INFORMATION

Details regarding t-HBCD and the different isomers that comprise the mixture can be found elsewhere (Heeb et al. 2005; Koppen et al. 2008) and in the RAR (EC 2008). The general objective in this section is to r ecognize there are differences between t-HBCD (mixture) and the diastereomers and to revi ew and select physical-chemical properties and half-life data for applications in the present assessment.

2.1 I DENTITY AND S TEREOCHEMISTRY

The CAS Registry contains two numbers representing the undefined mixtures of tHBCD: 25637-99-4 (without numbering for the position of the bromine substitution pattern) and 3194-55-6 for 1,2,5,6,9,10-HBCD (B echer 2005). It is noted that the

differences in the SMILES notation for th e two CAS numbers result in different representative structures and can therefore lead to different estimates in chemical properties. For example, the SMILES for CAS number 3194-55-6 in EPI Suite  (U.S. EPA 2009) is consistent with the structures in Figure 1- 1 of the RAR; whereas the

SMILES in EPI Suite  for the CAS number 25637-99-4 shows a substitution pattern of 1,3,5,7,9,11-HBCD which is inconsistent with Figure 1-1 of the RAR. Thus, for the present study CAS number 3194-55-6 is cons idered to represent the appropriate substitution pattern. The CAS numbers reflect undefined mixtures of isomers each with differences in chemical properties; therefore, certain properties cannot be reliably defined

for t-HBCD.

The stereochemistry of HBCD is complex in cluding 16 stereoisomers (Law et al. 2005) and HBCD is subject to isom erization during product synthesis and in the environment. These elements and the general lack of measured data provide a challenge for robust chemical assessments. CAS numbers have been assigned to the three main diastereoisomers comprising the majo rity of t-HBCD as 134237-50-6, 134237-51-7,

134237-52-8 for α - β - and γ -HBCD, respectively (Janák et al. 2004). As discussed below,

these three diastereomers have different physical-chemical properties (MacGregor and Nixon 2004; Goss et al. 2008) and will therefor e display distinct fate and transport characteristics in the environment. Figure 2.1 illustrates the three main diastereoisomers

α -, β - and γ -HBCD found in t-HBCD. Thes e three diastereoisomers are chiral and exist as pairs of enantiomers in t-HBCD for a total of six stereoisomers (+/-) α -, (+/-) β - and (+/-) γ

HBCD. α - and γ -HBCD have a C2 axis of symmetry, but β -HBCD does not (Smith et al. 2005). Studies from thermal rearrangement indicate that α -HBCD is the most thermodynamically stable isomer of the three (Smith et al. 2005). There are two achiral

diastereoisomers that can be found in t-HBCD at levels usually less than 1% each, termed

δ - and ε -HBCD (Law et al. 2005; EC 2008).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 4 Br

Br

Br Br

Br Br

α -HBCD (CAS 134237-50-6), the line indicates a mirror plane

Br

Br

Br Br

Br Br

β -HBCD (134237-51-7), the line indicates a mirror plane

Br

Br

Br Br

Br Br

γ -HBCD (134237-52-8), the line indicates a mirror plane

Figure 2.1. The three main diastereoisomers (pai rs of enantiomers) in t-HBCD (recreated from (EC 2008); data from (Heeb et al. 2005)).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 5 2.2 A NALYTICAL M ETHODS AND I SOMERIZATION

The challenges of analyzing brominated flame retardants are well recognized (Law et al. 2005; de Boer and Wells 2006). Analysis and modelling of HBCD are further complicated by the possibility of interconversio n of the different isomers (isomerization) (Zegers et al. 2005; Law et al. 2006b; Koppen et al. 2008). Thermal isomerization has resulted in LC-MS methods being a dvocated over GC-MS methods whenever diastereomeric composition has to be preserved during analysis. Sensitivities of the methods are different; however, as illustrate d in Figure 2.2, quantific ation of total HBCD by the two methods is compar able (Petersen et al. 2004).

Figure 2.2. Illustration of analy tical methods comparison (data from Petersen et al. 2004).

The complex competing interconversion pathways of α -, β - and γ -HBCD at increased temperatures illustrated by Koppen et al. (2008) may also occur at lower temperatures, but at slower rates and possibly following different isomerization mechanisms (e.g., enzymatic conversions, photoisomerization). Heeb et al. (2008) have elucidated kinetics

and mechanisms for isomerization and noted th e possibility of isomer ization occurring at lower temperatures. This study also noted th at the highly symmetrical conformation of α

HBCD is thermodynamically favored and adopts optimized and less reactive conformations compared to β - and γ -HBCD. Both α -HBCD enantiomers thermally degraded rapidly with no detectable isomeri zation reactions. Heeb et al. (2008) also show

the stereoselective formation of α -HBCD from γ -HBCD. Heeb et al. (2008) advocate the

need to elucidate the fate of HBCDs in th e environment as individual stereoisomers.

Two recent studies suggest further uncertain ties in HBCD measurements and data analysis. Kajiwara and colleagues (2009) have shown that soxhlet extraction methods can result in isomerization during sample preparation (predominantly γ -HBCD to α -HBCD). This was also postulated by Heeb et al. ( 2008). The Kajiwara study also noted changes in isomeric patterns from t-HBCD to those in textiles. In studies of house dust comparing isomeric patterns in the presence and absenc e of light Harrad and colleagues (2009) haveArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 6 shown that HBCD isomers are subject to photoisomerization. This study suffers somewhat from an incomplete account of the HBCD mass balance; ho wever, if verified, photoisomerization may also occur in various environmental compartments (atmosphere, soils, surfaces). The isomeri zation of HBCD at lower temp eratures than previously considered (e.g. solvent extr action) and in the presen ce of light suggests that isomerization could be occurring during sa mpling and analysis and perhaps while in storage. These issues have implications for the QA/QC of HBCD data that have not been fully considered in previous assessments, and while recognized he re, these issues can neither be fully rectif ied in the present study.

2.3 P HYSICAL -C HEMICAL P ROPERTIES

Measured and modelled estimates of physical -chemical properties for t-HBCD and the three main diastereoisomers ( α -, β - and γ -HBCD) relevant for the present study were compiled and critically reviewed as detailed in Section 9.1. Based on the critical review, physical-chemical property values were select ed. The physical-chemical properties in the present study include molar mass (M; g·mol -1 ), melting point (T M ; o C or K), water solubility (S; mol·L -1 ), vapor pressure (P; Pa), Henry’s law constant (H; Pa·m 3 ·mol -1 ), and the dimensionless equilibrium partiti on coefficients between air and water (K AW ), octanol and water (K OW ), and octanol and air (K OA ). For some parameters, such as the

K OW , there are many measurements and estimates available, whereas for other parameters, such as the K OA , the number of studies reportin g measurements/estimates is limited. Methods to seek thermodynamically consistent and thus more accurate solubility and partitioning properties are applied to obt ain Final Adjusted Values (FAVs) for the evaluation of testing and mass balance modelling.

2.3.1 Addressing Isomerization

Initial efforts were made to collect, review and select m onitoring data and model input parameters for diastereomer specific analyses . Through the course of the research it was recognized that diastereomer data analyses and modelling w ould be limited largely as a result of unknown isomerization rates and proc esses that may occur in most, if not all, environmental compartments. Many of the repor ted measured properties and tests are for t-HBCD, which is a mixture of isomers, ea ch with unique prop erties. The poorly characterized, and seemingly co mpeting, isomerization proce sses and rates occurring in the natural environment and during analytical procedures complicates the interpretation of monitoring and testing data and the applic ation of mass balance models. For example, when t-HBCD is exposed to an organism in a test the different isomers may be subject to different rates of interconversion during e xposure and within the organism and the different isomers (and enantiomers) may also have different biological activities. It

therefore often remains uncertain which isomers (and enantiomers) are actually responsible for the perceived biological activ ities. Previous studies have used mass

balance models to evaluate t-HBCD as a mixt ure; therefore, due to a general lack of information to assess the isomers specifica lly and to compare model estimates with limited monitoring and testing data reported as Σ HBCD we include representative properties selected for t-HBCD r ecognizing that this is not ideal.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 7 The uncertainties associated with isomerization, in pa rticular unknown isomerization rates in the multiple compartments of the environment, not only limit our ability to interpret the time trend and monitoring data, bu t also our capacity to model the behaviour of specific isomers. Furthermore, existing fate and bioaccumulation models are not capable of accounting for isomerization. If th e required input data for isomerization rates and pathways were available, models used in the present study could be revised to improve fate and exposure assessments.

2.3.2 Selection of Reliable Estimate s for Solubility and Partitioning

Reliable estimates of solubility and partitioning for commercial HBCD and the three diastereoisomers are required to improve the in terpretation of test data and to input into mass balance models. One objective of the pr esent study is to obtain thermodynamically consistent physical-chemical properties as s uggested by various aut hors (Cole et al. 2000; Beyer et al. 2002; Schenker et al. 2005). These FAVs were obtained from the selection of critically evaluated property estimates and th e application of the least squares adjustment procedure for property harmonization suggested by Schenker et al. (2 005). The fugacity ratio F (unitless) is required to correct for the influence of intermolecular interactions of the solid phase, which are not relevant to chemical partitioning when dissolved in solutions or phases. The solid state property data (subscript S) were corrected to the subcooled liquid state valu es (subscript L) using

P L = P S /F (2.1)

S L = S S /F (2.2)

where F is estimated to be 0.023 based on a T M of 190 o C for commercial (technical) HBCD and the assumption that the entropy of fusion is 56.5 J·K -1 ·mol -1 (Mackay 2001). F is expected to be different for the diastere oisomers due to differe nt melting points and different entropies of fusion; however, for the screening level calculations in the present study the same value of F is also applied to the diastereoisomers. F cancels out in the calculation of partition coefficients.

Some of the physical-chemical property estimat es selected for the calculation of FAVs

are different from the values selected in the RAR. These discrepa ncies are detailed in

Section 9.1 and summarized below.

The RAR discusses the selection of a solid state water solubility S S for t-HBCD of 66

μ g·L -1 (EC 2008). When there is 65.6 μ g.L -1 of dissolved t-HBCD in pure water, the total

content of HBCD in the water is 610 μ g.L -1 including 544.4 μ g.L -1 of non-dissolved γ and β -HBCD diastereoisomers (EC 2008). The pr edominant diastereoisomer in t-HBCD

is γ -HBCD which has a water solubility about 20 times lower than t-HBCD. This is an important issue with regards to the interpreta tion of toxicity information derived from in experiments with water-based exposures; th erefore, for consistency in the report we

selected a value of 3.0 μ g.L -1 for S S to represent t-HBCD.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 8 The RAR discusses the selection of a solid state vapor pressure P S of 6.3×10 -5 Pa at 21°C for t-HBCD from two reported measurements (EC 2008). According to OECD guidelines

both of the methods are not recommended for s ubstances with vapor pressures as low as the values measured for HBCD (OECD 2002) . Wania (2003a) determined a subcooled liquid state vapor pressure P L of 2.41×10 -5 Pa at 25 o C for t-HBCD relying on the gas chromatographic retention time me thod by Bidleman (1984) and using p , p ’-DDT as the reference compound and a suite of organoc hlorine pesticides as the calibration compounds. This value was selected for the present study.

The RAR calculates a Henry’s law constant of 0.75 Pa·m 3 ·mol -1 for t-HBCD as the ratio

of their selected values for P S and S S (EC 2008). The different values for P S and S S selected in the present study re sult in an estimate of 0.120 Pa·m 3 ·mol -1 . The EPI Suite  bond method estimate (U.S. EPA 2009) of 0.174 Pa·m 3 ·mol -1 provides an independent

and consistent estimate for inclusion in the least squares adjustment calculations.

2.3.3 Thermodynamic Consistency Calcul ations for Chemical Properties

The methods of Schenker et al. (2005) were used to obtain thermodynamically consistent estimates for the solubility and partitioni ng properties of t-HBCD and the three

diastereoisomers. The data selected as i nput for the calculation of thermodynamically

consistent FAVs are listed in Table 2.1. An effort was made to maximize input parameters, but to ensure that data sources were reliable and independent. In this context “independent” means that physical-chemical properties used in the harmonization calculations were obtained from distinct measurements or estimates. For example, K AW estimates derived from ratios of P S and S S were not included in the calculations because

the selected estimates of P S and S S were already used as input . Reliability was considered high if multiple data sources pr ovided similar values. Model estimates were considered as

well as measured data. The harmonization method also requires and provides relative variance values for chemical properties. Thes e values provide guidanc e as to the relative uncertainty of the different pr operties. The relative variance value inputs were selected based on professional judgement of the general reliability of the various parameters.

The issue of the mutual solubility of water and octanol can be important when seeking to obtain thermodynamic consistency in solubility and partitioning parameters (Cole et al. 2000; Beyer et al. 2002). Typical K OW measurement techniques, such as the shake-flask and slow stir methods, result in the mixing of water and octanol phases, which results in the presence of some octanol in the water pha se and of some water in the octanol phase. Measurements of the octanol-air partition coefficient (K OA ) and of the solubility in octanol, on the other hand, are obtained using “d ry” octanol, i.e. octanol that has not been saturated with water. Similarly, measurements of water solubility and air-water partition coefficients are typically performed with pure water. To account for the possible influence of the mutual solubility of water and octanol on the phase partitioning of highly hydrophobic substances, relationships have b een proposed for converting measured K OW values to ratios of concentrations in pure octanol and pure water C O /C W . For the thermodynamic consistency analysis, the equa tion recommended by Beyer et al. (2002)

was used to convert K OW to C O /C W prior to adjustment and to convert the adjusted C O /C W back to K OW after adjustment.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 9 Table 2.2 lists the thermodynamically consistent FAVs for the relevant solubility and partitioning properties. The percentages of adju stments were modest for changes from the selected input values to the calculated FAVs for most prope rties suggesting that the data are generally consistent and reasonable. Notably, all of the K OW values were slightly increased and the solubility in octanol decreased in all cases except for α -HCBD for which it increased (largest percentage adjustme nt). Notably, the adjustment suggests that

α -HCBD is about 4 times more soluble in octanol than γ -HCBD. This may be partially explained by the high symmetry of the α -isomer (Heeb et al. 2008) and may partially explain some of the observed increases in bioaccumulation patterns in the environment

for α -HCBD. The relative ranking of the FAVs for K OW are consistent with the rankings

of the measured water solubi lities and the independent K OW estimates by Hayward et al. (2006) that were not used in this procedure. The FAVs for K OA are also comparable to the estimates that were not included in the consistency calculations.

The method was applied to t-HBCD in the current study; however, a thermodynamic consistency calculation should gene rally not be used for mixtur es, but rather for discrete substances with distinct properties. Th e method was applied for consistency with diastereomer methods and for comparing m odel estimates for t-HBCD and individual diastereomers with monitoring data for t-HBCD . Considering the uncertainty in the FAVs for t-HBCD in the context of the greater uncer tainties associated with actual emissions, mode-of-entry, isomerization and degradatio n in the environment, the errors in calculating “representative” FAVs for t-HBCD are considered to be comparatively low.

Table 2.1. Selected values for physical-chemical properties (at 25 o C) and relative variance values used as inputs for thermodynamic consistency calculations.

Substance P L , Pa S L , mol · m -3 S OL , mol · m -3 Log K AW Log K OW a Log K OA

t-HBCD 2.41×10 -5 2.01×10 -4 698 -4.15 5.63 10.71 b

α -HBCD 2.93×10 -4 3.27×10 -3 698 -4.35 5.59 9.62 b

β -HBCD 3.86×10 -5 9.86×10 -4 698 -4.71 5.44 10.50 b

γ -HBCD 2.00×10 -5 1.41×10 -4 698 -4.15 5.53 10.79 b

Relative variance

t-HBCD 3 2 4 4 3

α -HBCD 5 1 4 5 4

β -HBCD 5 1 4 5 4

γ -HBCD 5 1 4 5 4

a log K OW values corresponding with log (Co/Cw) values of 6.02, 5.97, 5.76, and 5.89 for t-, α -, β -, and γ

HBCD, respectively

b values not used as inputs for calculation of FAVs because they are not “independent” values, i.e., they were derived from vapor pressure and octanol solub ility estimates (see Appendix). The values are included for comparisons with FAVsArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 10 Table 2.2. Final adjusted values (FAVs at 25 o C), relative variance values, percent adjustments, and assigned conf idence factors used in the present study for mass balance modelling and data analyses obtained from the thermodynamic consistency calculations (Schenker et al. 2005).

Substance P L , Pa S L , mol · m -3 S OL , mol · m -3 Log K AW Log K OW a Log K OA

t-HBCD 3.03×10 -5 2.33×10 -4 380 -4.28 5.77 10.46

α -HBCD 3.00×10 -4 2.77×10 -3 1340 -4.36 5.38 9.96

β -HBCD 4.29×10 -5 9.89×10 -4 630 -4.76 5.47 10.47

γ -HBCD 2.42×10 -5 1.68×10 -4 300 -4.23 5.80 10.40

Relative variance

t-HBCD 1.95 1.27 2.13 2.13 1.95 3.45

α -HBCD 2.70 0.82 2.20 2.70 2.20 4.50

β -HBCD 2.70 0.82 2.20 2.70 2.20 4.50

γ -HBCD 2.70 0.82 2.20 2.70 2.20 4.50

Percent adjusted

t-HBCD 26% 16% -45% -26% 39% 26%

α -HBCD 2% -15% 92% -2% -38% 2%

β -HBCD 11% 0% -9% -10% 7% 11%

γ -HBCD 21% 19% -57% -18% 87% 21%

Confidence factor ( Cf )

t-HBCD 3 2 3 3 3 3

α -HBCD 3 1.5 3 3 3 3

β -HBCD 3 1.5 3 3 3 3

γ -HBCD 3 1.5 3 3 3 3

a log K OW values corresponding with log (Co/Cw) values of 6.22, 5.68, 5.81, 6.25 for t-, α -, β -, and γ

HBCD, respectively

2.3.4 Uncertainty in FAVs

The FAVs have been carefully selected to reduce uncertainty; however, it is recognized that uncertainty in model input parameter values cannot be completely eliminated. Uncertainty in model input pa rameters such as chemical properties and half-lives propagate uncertainty in environmental fa te and exposure model predictions. Monte Carlo analysis and analytical methods can be used to assess the sensitivity and uncertainty of model input parameters on model output (MacLe od et al. 2002). The analytical method uses a confidence factor Cf (or more appropriately an uncertainty factor) to express the degree to which parame ter X may deviate from the median M in a lognormal distribution. For example, a Cf of 2 suggests that 95% of all of the values in

the distribution are within 2 and 0.5 times M. A 95% probability can be expressed as:Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 11 0.95 y probabilit = M Cf < X < Cf M   

  

⋅ (2.3)

Thus, the variance in the lognormal distri bution increases with an increase in Cf . Values of confidence can be derived empirically or subjectively or in combination. There is a degree of co-dependence between the three part ition coefficients, but the three solubility parameters are independent. It is recognized that certain propertie s are often used as surrogates in the models (e.g., K OW for lipid-water partitioning and organic carbon-water partitioning, K OA for lipid-air partitioning) and that variable conditions occur in the environment. Based on these considerations, th e data review, the relative variance values calculated by the harmonization proce dure, and professional judgement, Cf s were selected for the FAVs as listed in Table 2.2.

No estimates for energies of phase transitio n are available for technical HBCD or the

diastereoisomers.

2.3.5 Physical-Chemical Properties for Benchmarking

The chemicals selected for this preliminar y benchmarking exercise representing POPs were as follows: HCB, PCB28, PCB101, PCB180, Aldrin and Heptachlor. Non-POPs include biphenyl, p-cresol, at razine and tetrachloromethane and candidate POPs (CandiPOPs) include α -, β -, γ -HCH, and PBDE-99. These substa nces represent a range of physical-chemical properties. Most of these subs tances have been used in previous POPs benchmarking assessments (Cowan-Ellsberry et al. 2009). Aldrin and heptachlor were

selected because these POPs have shorter half-l ives in air than most POPs and these halflife estimates are comparable to those for HBCD.12Table 2.3. Selected physical-chemical properties for the model benchmarking simulations. Data arefrom (Cowan-Ellsberry et al.2009) except for Aldrin and Heptachlor which are from (Schenker et al. 2005; Mackay et al. 2006).Status Name Abbrev MW (g.mol-1) log KOWlog KAWlog KOAPOP Hexachlorobenzene (HCB) HCB 284.8 5.61 -1.51 7.12POP 2,4,4'-trichlorobiphenyl (PCB 28) PCB28 257.6 5.66 -1.93 7.86POP 2,2',4,5,5'-pentachlorobiphenyl (PCB 101) PCB101 326.4 6.38 -2.08 8.83POP 2,2',3,4,4',5,5'-hexachlorobiphenyl (PCB 180) PCB180 395.3 7.15 -2.51 10.17POP Aldrin Ald 364.9 6.25 -2.01 8.26POP Heptachlor Hept 373.4 5.96 -1.78 7.74NOP Biphenyl Biphenyl 154.2 4.06 -1.96 6.02NOPp-cresolp-cresol 108.1 1.97 -4.26 6.23NOP Atrazine Atrazine 215.7 2.73 -6.84 9.57NOP Tetrachloromethane CCl4 153.8 2.83 0.19 2.64Candi-POP alpha-hexachlorocyclohexaneα-HCH 290.8 3.88 -3.59 7.48Candi-POP beta-hexachlorocyclohexaneβ-HCH 290.8 3.91 -4.83 8.74Candi-POP gamma-hexachlorocyclohexaneγ-HCH 290.8 3.76 -3.96 7.72Candi-POP 2,2',4,4',5-pentabromodiphenyl ether PBDE99 564.7 6.76 -3.67 11.26Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 13 2.4 D EGRADATION , B IOTRANSFORMATION AND H ALF -L IFE D ATA

Reliable estimates for environmental and biol ogical compartment half-lives are required

for mass balance model calculations and enviro nmental half-lives are used for screening level hazard categorizations of chemical Pers istence (P). The primary objective of this

section is to describe the compilation, review and selectio n of primary transformation half-life (HL) estimates for the bulk physical compartments of the environment (atmosphere, water, soil and sediment) and fo r biological species (f ish, mammals). This

HL estimate is defined as the time it takes for half of the chemi cal amount present to undergo a change in structure. Distinctions between transformation from HBCD to a degradation product and isomeri zation will be discussed (e.g. biotransformation resulting in elimination from an organism and bioiso merization that does not result in chemical elimination from an organism). Measurements and model estimates are considered.

2.4.1 Environmental Degradation Half-lives

Degradation half-lives in physical compar tments of the environment need to be distinguished from experiment ally-derived dissipation or disappearance times since the latter two include losses attributable to pro cesses other than transf ormation of the parent compound e.g. adsorption to surface of glasswar e, volatilization out of test system. In practice, distinguishing between degrada tion and dissipation is often difficult.

Data characterizing the degradation potentia l of HBCD has been compiled and reviewed in the RAR (EC 2008) and the TemaNord repor t (2008). Experimental data are available for an aqueous test system, sediments and soils (aerobic, anaerobi c) and sewage sludge. Here we briefly discuss the available data and then present the values selected for the current model simulations along with estimates of the associated uncer tainties. Data from sewage sludge are not discussed here since they are the least relevant for the model simulations conducted for this study.

Atmosphere (air)

For most semi-volatile organic compounds, re actions in the gas phase with OH radicals are considered to be the most important degr adation pathways in the atmosphere. Hence, a typical approach to estimate atmospheric degradation half-life ( HL air , s or d) is to use a hydroxyl radical reaction rate constant ( k OH , cm 3 ·molecule -1 ·s -1 ) and an assumed OH radical concentration ([OH], molecules·cm -3 ) according to the following pseudo-first order equation:

[OH] k = HL

OH air ln2 (2.4)

k

OH may be derived from experiment s or field observations but due to lack of data is more frequently calculated using estimation software. For example, the k OH of HBCD estimated using AOPWIN v1.92 (U.S . EPA 2009) is 6.12×10 -12 cm 3 ·molecule -1 ·s -1 . Based on the reported model performance, this estimate is ex pected to be accurate within a factor of 3Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 14 in either direction (U.S. EPA 2009). Sin ce AOPWIN v1.92 does not distinguish between

α -, β - and γ -HBCD, we assume the same value for all isomers. In EPI Suite v4.0, the [OH] is assumed to be 1.5×10 6 molecules·cm -3 whereas a value of 5×10 5 molecules·cm -3
was assumed in the TemaNord report (2008). The value of 1.5×10 6 molecules·cm -3 is meant to represent a 12-hour (daylight) average whereas the value of 5×10 5

molecules·cm -3 represents a 24-hour av erage. When converting HL air from seconds into days, it is therefore important to select a value of [OH] that is consistent with the assumptions regarding the time period over which the reactions occur.

Bahm and Khalil (2004) derived a 24 hour global annual average [OH] of 9.2×10 5

molecules·cm -3 , with a value of 9.8×10 5 molecules·cm -3 for the northern hemisphere and

8.5×10 5 molecules·cm -3 for the southern hemisphere. These values are consistent with (Prinn et al. 1995) and (Montzka et al. 2000 ) who deduced [OH] from atmospheric measurements of methyl chloroform, reporti ng 24 hour global annual average values of 9.7(±0.6)×10 5 and 1.1(±0.2)×10 6 molecules·cm -3 respectively. Using the 24-hour average [OH] value calculated by Bahm and Khalil ( 2004) for the northern hemisphere and the

default k OH , the estimated atmospheric half-life is ~ 1.3 days. Using the 12 hour (daylight) [OH] assumed in AOPWIN v1.92 and assuming a night time [OH] of 0 the estimated atmospheric half-life is ~ 1.8 days. Considering the uncertainty in model estimates of k OH , the range is 0.4 to 4 days and 0.6 to 5.4 days respectively, meaning th at it is not possible to unambiguously conclude that HBCD exceeds (or does not exceed) the atmospheric half-life criterion for persistence in air (> 2 days). With respect to POP benchmarking it is important to standardize the relevant a ssumptions for all chemicals considered, particularly if HL air values rather than k OH rates are being compared.

Water

Biodegradation of HBCD in an aqueous test system was investigated by Schaefer and Haberlein (1996), who conducted a Closed Bottle Test (OECD Guideline 301D, (OECD 1992)) to assess ready biodegradab ility under aerobic conditions. The results of these data were used to conclude that no biodegrada tion of HBCD occurred over a 28-day period (EC 2008; TemaNord 2008). However, accordi ng to Shaefer and Haberlein (1996), the

concentration of HBCD in th e test solution was 7.7 mg·L -1 , which is at least three orders of magnitude greater than the estimated wa ter solubility of th is compound (~3 μg·L -1 ). This discrepancy introduces a bias in the inte rpretation of the experimental results since

the concentration of the test substance is used to es timate biochemical oxygen demand (BOD, mg O 2 ·mg test substance -1 ). BOD is then compared to theoretical oxygen demand (ThOD) to calculate % degradation. While it is true that OECD Guidelines for the 301D

test recommend that the test s ubstance be present at concentra tions in the range of 2 to 10 mg·L -1 (OECD 1992), this recommendation implies that the 301D test is inappropriate for HBCD since this compound has a low water sol ubility. Therefore, in contrast to the RAR (EC 2008) and TemaNord report (2008), we do not consider the Sch aefer and Haberlein (1996) results to be reliable and instead char acterize them as inc onclusive. Note that concerns about the validity of this test have been previous ly raised (Davis et al. 2005).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 15 Model output characterizing ready biode gradability generated using BIOWIN TM (EPI Suite v4.0) was reported in (TemaNord 2008) and is summarized in Section 9.2. HBCD is classified as not readily biodegradable under aerobic conditions according to the criteria developed for the model. Based on BIOWIN 4 (Expert Survey, Primary Biodegradation Model), the expected time frame for primary de gradation to an initial metabolite is on the order of weeks. However, as noted in the Te maNord report (2008), the cyclic structure of HBCD is not explicitly accounted for by the fragment approach applied in the BIOWIN4

model.

Potential abiotic degradation pathways for HBCD were pres ented in the RAR (EC 2008). Abiotic degradation of HBCD in surface waters is also possible via the indirect photolysis mechanisms, which result from the generati on of photooxidants including OH radicals in the water column. However, in addition to knowledge of HBCD’s 2 nd -order rate constant for reaction with OH radicals in aqueous solu tion, a representative daylight or 24-hour average [OH] in water would also have to be estimated. HBCD is not expected to be subject to hydrolysis re actions in the water.

In summary, there are no reliable empirical data characterizing the degr adation kinetics of HBCD in water. Methods to estimate degrad ation half-lives from QSAR model output are

discussed below.

Sediment

Based on the sediment degradation studies cited in the RAR (EC 2008), it can be concluded that i) degradation of HBCD proceeds at a faster rate under anaerobic conditions than under aerobic conditions ii) ba sed on experimental data from sterilized test systems, abiotic reaction pathways suppor t a degree of baseline degradation in the environment but iii) degradation of HBCD in the presence of a viable microbial community proceeds at a faster rate than in the absence of microbes (i.e. sterilized test system)

Table 2.4 lists two sets of degradation ha lf-lives that were used for total HBCD (Simulation Study 1 and 2) in the RAR (EC 2008) . These values include the extrapolation

of measured half-lives at 20 o C to 12 o C (EC 2008). The adopted approach for scaling degradation half-lives for POP and PBT scre ening level assessments is fundamentally wrong however because it is only applicable to abio tic extrapolations. This method is not recommended by recognized experts in the field due to a lack of a scientific justification (Boethling et al. 2009). Furthermore, the presumed adjustments do not consider the adaptive nature of microbial popula tions in different environments.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 16 Table 2.4. Degradation half-lives for to tal HBCD proposed in the European Risk Assessment Report (RAR) (EC 2008).

Test System Simulation Study 1 Simulation Study 2

Aerobic freshwater sediments

20 o C

12 o C 11 days

21 days 101 days

191 days

Anaerobic freshwater sediments

20 o C

12 o C 1.5 days

2.8 days 66 days

125 days

Data used to derive estimates for “Simul ation Study 1” were ta ken from Davis et al. (2003a). These data are now published in the peer-reviewed literatur e as Davis et al. (2005)). Data used to derive estimates for “S imulation Study 2” were taken from Davis et al. (2004), now published as Davi s et al. (2006). Note that the degradation half-lives calculated directly from the rate constant s presented in the Supporting Information of Davis et al. (2006) are somewhat different than the half -lives presented in the RAR (EC 2008). For example, the average degradation half-life of the α -, β - and γ -HBCD isomers is 94 days whereas a value of 101 days wa s reported in the RAR (European Commission 2008) These discrepancies are presumably re lated to different statistical treatment of the underlying data.

In the studies conducted in Davis et al. (2006), the apparent half-life of α -HBCD tended to be longer than that of β - and γ -HBCD. For example, in aerobic freshwater sediments (20 to 22 o C), the estimated degradation half-life of α -HBCD was 128 days versus 72 and 92 days for β - and γ -HBCD respectively (Davis et al. 2006). These differences were not

found to be statisti cally significant ( p > 0.05), in part due to the high level of variance in replicate observations. In digester sludge, the degrada tion half-life of β -HBCD was significantly shorter than that of α - and γ -HBCD but no statistically significant difference

was found between the latter two isomers. In contrast, Gerecke et al. (2006) reported slower degradation kinetics for α -HBCD (~ 50%) in digest er sludge but no difference

between β - and γ -HBCD. Hence while there is some evidence to support the claim that α

HBCD is more recalcitrant than other isomers, the uncertainty in the available data does

not allow a definitive conclusion to be drawn.

While differences in susceptibility to degr adation among HBCD isomers is an important consideration, the more relevant issue to be addressed is the apparent discrepancy in the magnitude of the degradation rate consta nts between Simulation Study 1 (Davis 2003a; Davis et al. 2005) and 2 (Davis et al. 2004; Davis et al. 2006). Berg and Nyholm (1996)Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 17 discussed the fact that industr ial chemicals are typically pr esent in the environment at such low concentrations that they can be expected to be degraded as ‘secondary substrates’ (i.e. marginal contribution to en ergy flow in system) as opposed to primary substrates. This mode of degradation is ch aracterized by first or der kinetics with a constant rate parameter and it is therefore impor tant that chemical concentrations used in test systems be low enough to ensure that th e ‘secondary substrate’ degradation regime predominates (Berg and Nyholm 1996). Note that this does not necessar ily mean that test

concentrations must be similar to environm entally-relevant con centrations to yield representative results, only th at concentrations should not be elevated to the point that the ‘secondary substrate’ degradation regime no longer dominates. Davis et al. (2003a; 2005) conducted the degradation studies using nominal HBCD concentrations in the range of 25 to 90 μg·kg -1 dw whereas the degradation studies in Davis et al. (2006) had nominal concentrations in the range of ~ 3 to 5 mg·kg -1 dw. Davis et al. (2006 ) suggested that the apparent degradation rates from their studies could be biased lo w due to a shift from firstto zero-order degradation kinetics at the high concentr ations used. At elevated concentrations, degradation kinetics may b ecome limited by processes such as mass

transfer into the microbes rath er than transformation of th e parent compound itself. While these considerations are important, it is also worth noting that the experimental data in all test systems (sludge, aerobic and anaerobic sediments) were desc ribed reasonably well assuming a first-order decay model (Davis et al. 2006).

With respect to mass balance modelling and exposures to air-resp iring organisms, the most relevant degradation half-lives are those derived from test s in viable aerobic sediments, since these sediments are most re presentative of the “active sediment layer” included in the model domain (upper 0 to 5 cm ). Aerobic/oxic conditions may or may not prevail in this upper layer, largely depe nding on the condition of the overlying water column. Sediment burial is included in fate models as a mechanism of removal, transporting substances to deeper sediment la yers in which their subsequent fate is no longer explicitly considered, i. e., they are “lost” from the system. Selecting appropriate half-life values for model purposes is hindered by the recognized uncertainties in the data and variability in the natural environment. Th e reported degradation half-lives in viable aerobic sediments (20 o C), ranging from 11 to 128 d represent the range of measured values that are considered to be genera lly reliable There are three reported aerobic sediment degradation half-lives for commer cial HBCD, 11, 32, and 94 d (or 11, 32 and 101 d, if the values in Table 2. 4 are preferred). The median va lue in both cases is 32 and the two other data points are within approxima tely a factor of 3. This distribution is consistent with a log normal distribution for de gradation half-lives measured in test data and expected in the environment. Different sediments in the natural environment are expected to have a range of genetic compet ence for degrading HBCD (variable microbial communities) and it is plausible that some reduced oxygen conditions may occur. Thus data from the laboratory studies were extra polated to environmental conditions using a confidence (uncertainty) factor of 6 in the present study. Base d on a median value of ~ 32 d, 95% of the expected values fall between 5.3 and 194 d. This uppe r bound level is also consistent with abiotic half-lives (sterile aerobic sediment environments). The lower bound can be considered more representati ve of aquatic systems where anaerobic

conditions tend to dominate.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 18 Soil

Degradation studies in soils were reported in Davis et al. (2003b) and Davis et al. (2004). Data from Davis et al. (2003b) were published in the peer-review ed literature as Davis et al. (2005). Note that the degr adation studies presented in this study was conducted at more environmentally-relevant c oncentrations and hen ce, in principal, are expected to be more representative, all other factors bei ng equal (Davis 2003b; Davis et al. 2005). Similar to the sediment studies, there was a substantial difference in the degradation

kinetics between the tests conducted at more environmentally-relevant concentrations (μg·kg -1 dw) (Davis 2003b) and high er concentrations (mg·kg -1 dw) (Davis et al. 2004). Hence the issues regarding elevated concen trations and apparent degradation kinetics discussed above and in Davis et al. (2006) al so apply to the soil studies presented in Davis et al. (2004).

For the EUSES model simulations conducted in the RAR (EC 2008), two scenarios were again considered. One scenario assumed an aer obic (viable) soil degr adation half-life of 63 days (20 o C, based on Davis et al. (2003b)) wh ile the other assumed negligible degradation (i.e. infinite half-life, base d on (Davis et al. 2004)). The assumption of negligible degradation in aerobic soils seems overly conservative or at least atypical, given the range of values considered for aerobic sediments. For example, estimated degradation half-lives in property ha ndbooks and screening level QSAR model predictions for both persistent and non-persis tent chemicals generally follow the pattern

HL water ≤ HL soil ≤ HL sed (Howard et al. 1991; Mackay et al. 2006; U. S. EPA 2009). Sinkkonen and Paasivirta (2000) also proposed that, as a first appr oximation, the same empirically derived first-order biodegradation rate constant (a naerobic, aerobic) could be

assumed for soils and sediments when estimat ing degradation half- lives for PCBs and PCDD/Fs. Hence there are precedents in the scientific literature for interrelating degradation half-lives in different environmental media although no definite conclusions can be made for HBCD. Overall, since ther e is no conclusive evidence upon which to

exclude the data from Davis et al. (2004), half-lives in ex cess of those estimated from Davis et al., (2003b; 2005)) will also be considered in the model simulations for HBCD.

Model Estimated Aerobic Biodegradation Half-lives

The standard BIOWIN TM model output provides a qua litative assessment of biodegradability (bins based on numerical output) rather th an quantitative estimates of degradation half-lives (U.S. EPA 2009). For example, bins include “ready” or “not ready” for general degradability and “day s-weeks”, “weeks-months” for primary and ultimate degradation times. Arnot et al. (2005) proposed a method to derive environmental biodegradation half-lives for screening level assessments from numerical

BIOWIN TM model output. The appr oach calibrated BIOWIN TM numerical model output to empirical biodegradation half-lives through linear regression. Thes e linear regression equations along with the necessary BIOWIN TM input are presented in the Appendix (Table 9.6). Based on this appr oach, an aerobic biode gradation half-life in the range of 85 to 130 days is estimated for HBCD (not is omer specific). Aronson et al. (2006) alsoArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 19 utilized similar correlation approaches to estimate biode gradation half-lives from

BIOWIN TM model output. For example, based on BIOWIN3 model output for HBCD (ultimate biodegradation surv ey model), the recommended de fault degradation half-life (primary degradation) in the water column is 60 to 120 days (Aronson et al. 2006). However, it should be noted that the range of experimentally deri ved degradation halflives correlated to this category of BIOWIN 3 model output is 1 to 1420 days, indicative of the high degree of variability and un certainty in the experimental data.

We recommend that further studies be conducted for biodegradation half-life

estimation in soil and se diment. Studies on the at mospheric fate of HBCD, particularly with respect to reactions with OH radicals, would also be valuable since the efficiency of this degradation pa thway can have a major influence on atmospheric long range transport potential

Selected Degradation Half-lives for Model Simulations

The total degradation half-lives ( HL ; d) assumed for the main bulk environmental media are presented in Table 2.5. These values are based on the available data and consideration of BIOWIN model output. Confidence factors ( Cf ) were also selected based on a review of the available data and professional judgment.

Table 2.5. Total degradation half-lives (d) assumed for all model simulations

Media Default HL (d) Cf 95% Range

Air* 1.3 3 0.4 to 4.0

Water (fresh, marine) 85 10 8.5 to 850

Sediments (fresh, marine) 35 6 6.0 to 210

Soil 85 10 8.5 to 850

* refers to gas-phase reactions only

Air

The default value for models requiring HL air as model input of 1.3 d is based on AOPWIN v1.92 model output for the hydroxyl radical rate constant ( k OH ) and the 24 hour average [OH] suggested by (Bahm and Kha lil 2004) for the northern hemisphere. The Cf reflects the expected uncertainty in the hydroxy l radical rate constant only. For models requiring k OH only, the default value of 6.12×10 -12 cm 3 ·molecule -1 ·s -1 is used instead (same Cf ).

Water

The default value selected of 85 d for HL water is based on aerobic degradation half-lives

derived from BIOWIN TM model output following Arnot et al. (2005) and Aronson et al. (2006). The larger Cf reflects uncertainty in the methods applied to arrive at the default value and the absence of empirical measurements.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 20 Sediment

The default selected value of 35 d for HL sed is justifiable as either the median degradation half-life based on available empirical data fo r total HBCD or the geometric mean of the range (~ 10 to 130 d). The Cf was selected to arrive at lower and upper bounds consistent with the RAR (EC 2008) and the available data.

Soil

The default selected value of 85 d for HL soil was based on consideration of data presented in Davis (2003b) and BIOWIN TM model-derived estimates of aerobic biodegradation

half-lives. The selected Cf allows the longer degradation ha lf-lives implie d by data in Davis et al. (2004) to be addre ssed in the model simulations.

In contrast to typical default extrapolation va lues for half-lives (e.g. water:soil:sediment; 1:2:9 (Mackay et al. 2006; U. S. EPA 2009)), the half-lives selected in the present study follow the pattern HL water = HL soil ≤ HL sed . Thus, the trend in selected degradation halflives diverges from the usual pattern; however, it is more consistent with the available

data.

2.4.2 Biotransformation Half-lives

The RAIDAR and CoZMoMan models require the whole body primary biotransformation half-life ( HL ; d) as an input parameter; otherwise biotransformation is assumed negligible in fish, birds and mamma ls. These biotransformation half-lives are assumed to be first order processes and thus relate to whole body biotransformation rate constants (d -1 ) as HL = ln2/ k M . Biotransformation half-lives and rate constants are dependent on body mass and to a lesser de gree temperature (Hu and Hayton 2001; Nichols et al. 2007). Biotransformation half -lives for organisms of specific body sizes and temperatures ( HL X ) need to be calculated from the selected normalized half-life ( HL N ) as (Arnot et al. 2008b; Cowan-Ellsberry et al. 2009)

HL X = HL N (W X /W N ) -0.25 (2.5)

where W X is the specific mass of the organism (kg) in the model and W N is the selected normalized mass of the organism (e.g., 1 kg fish ). Temperature differences are considered negligible in the present study. Five sets of data, including in v itro and in vivo studies,

were reviewed to select estim ates for whole body biotransformation rate constants in fish

and mammals.

Fish Biotransformation Law et al. (2006b) conducted a dietary uptake (feeding) and depuration study. Fish (~0.25 kg) were fed diastereomer specific diets and first-order “depuration” rate constants

of 0.0044 and 0.0048 d -1 with respective ha lf-lives of 157 d ( ± 71 SE) and 144 d ( ± 60 SE) were reported for β - and γ -HBCD. Estimates for α -HBCD could not be determined

because “elimination” did not follow first- order kinetics. The st udy shows the potentialArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 21 for bioisomerization of the different isom ers whereby the isomers are apparently

converted from one isomeric form to another. Some of the HBCD is not being eliminated or depurated from the organism but merely transformed into a nother diastereomer;

therefore these “elimination” rates do not n ecessarily account for th e loss of HBCD from the organism via transformation to a metabolite.

Table 2.6 summarizes the molar elimination an d formation of the diastereoisomers during the “elimination” phase based on the data repor ted in Table 2 of Law et al. (see also Figure 1 from that study) (2006b). The data suggest that γ -HBCD is predominantly

converted to α -HBCD (70%) and only about 30% of th e total elimination half-life is actually attributable to “elim ination of HBCD” from the or ganism through the formation of a metabolite. The data suggest that β -HBCD is predominantly converted to α -HBCD; however, the molar change during the depuration period actually sugge sts a net “gain” of total HBCD. It is not possible for the fish to create HBCD; therefore, based on the general uncertainty of the reported measurements it can be assumed that there is no apparent “loss of HBCD” from the organism for β -HBCD. The data also suggest that α

HBCD is predominantly eliminated w ith minimal conversion to the other

diastereoisomers. Based on the data for γ -HBCD (high conversion to α -HBCD), some of

the γ -HBCD isomer being formed from α -HBCD in the α -HBCD exposure study is expected to be converted back to α -HBCD during the experiment. It would seem that γ

HBCD is converted to α -HBCD faster than α -HBCD is converted to γ -HBCD, which would support the overall obser vation for the attenuation of γ -HBCD isomers in food webs. Furthermore, if relative bioisomerization rates approximate the relative rates of thermal isomerization, γ -HBCD would be converted to α -HBCD at rates approximately

10 times faster than α -HBCD is converted to γ -HBCD (Koppen et al. 2008).

Table 2.6. Molar mass balan ce for diastereoisomer bioiso merization and actual total elimination (fecal egestion, gill re spiration and biotransformation).

Elimination phase Other isomers formed (+) or lost (-) during this time period Isomer

fed to

fish Day 56 Day 168 Parent

"lost" during this time period gamma beta alpha Net loss

of
parent

isomer % actual

elimination of parent isomer

alpha 1.4 0.5 0.9 -0.23 -0.1 N/A 0.9 ~100

beta 0.4 0.3 0.1 0 N/A +0.2 -0.1 ~0

gamma 0.4 0.17 0.23 N/A -0.01 +0.16 0.07 ~30.4

Table 2.6 suggests that α -HBCD is biotransformed primarily to a metabolite, whereas γ

HBCD is predominantly biotransformed to α -HBCD. β -HBCD does not appear to be biotransformed to a metabolite. Our interpretati on of the data is somewhat different from the interpretation of Law et al . (2006b) who suggested that α -HBCD is recalcitrant to

bioisomerization. Non-first order e limination kinetics were reported for α -HBCD. Indeed non-first order kinetics may be the result of the relatively faster ra tes of conversion of γ

HBCD back to α -HBCD. A “pseudo-elimination rate constant” based on the molar quantity at the start of the elimination (Day 56) and the molar quantity at the end of the elimination phase (Day 168) is estimated as 0.009 d -1 for a 0.25 kg fish. From the net lossArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 22 it appears that most of α -HBCD is actually biotransformed to a metabolite and eliminated from the fish, corresponding to an el imination half-life of about 75 d for α -HBCD.

Table 2.7 summarizes the para meters required to estimate biotransformation and bioisomerization rates using a mass balance estimation method for fish when applied to the Law et al. dataset for Rainbow trout (A rnot et al. 2008a). There are three possible biotransformation rate constant ( k M ) estimates that could be considered. The first is “loss of parent” diastereomer. This includes bioisomerization fr om one diastereomer to a different diastereomer (and s ubsequent bioisomerization reac tions) and the formation of metabolites (elimination). This would be calcula ted based on the “total depuration” rate constant ( k T ) reported by Law et al. (2006b). The s econd is biotransformation to a metabolite that is no longer HBCD. This value is based on the reported total depuration rate constant (or estimated depuration rate constant for α -HBCD) and a correction for the actual loss of parent diastereom er (Table 2.6), i.e., 30% of the reported total depuration

rate constant for γ -HBCD. The third transformation ra te constant is the difference

between the two estimated rate constants which is assumed to be the result of

bioisomerization to another isomeric form of HBCD but without elimination from the fish, i.e., 70% of the reported to tal depuration rate constant for γ -HBCD. It must be recognized that the measured data are uncer tain and that possibl e interconversion back and forth between the different isomeric forms at competing, and presumably different rates, further confounds the interpretation of the data and the ability to accurately

estimate bioisomerization and biotransformatio n (elimination) rate constants. Since the bioaccumulation models used in the present study do not currently account for bioisomerization only the second rate constant (loss of HBCD from the organism) is considered relevant for exposure modelling at this time.

The estimated total elimination rate is so slow for β -HBCD, that no estimate for biotransformation is possible using th e mass balance method. The estimated

biotransformation rate constants for Rainbow trout (~0.25 kg) were 1.7×10 -3 and 3.0×10 -4

d -1 for α -, and γ -HBCD, respectively. The estimati on method provides screening level information regarding confidence in the estimated k M values (Arnot et al. 2008a; Arnot et al. 2008b). Although an estimate for γ -HBCD is calculated, the percentage of positive

values is less than 25% co rresponding to a low confidence category assignment for the

estimated k M value (Arnot et al. 2008b) . Although an estimate for α -HBCD is calculated, the output confidence factor is greater than 10.3 correspon ding to a “low” confidence category assignment for the estimated k M value (Arnot et al. 2008b). Using a rearrangement of Equation 2.5, the correspond ing primary biotransformation half-lives

normalized HL N to a 1 kg fish are 3330 and 720 d for α -, and γ -HBCD, respectively. Thus, the analysis from the measured data from Law et al. (2006b) suggest “very slow” (Arnot et al. 2009) and low confidence pr imary biotransformation half-lives for α -, and γ

HBCD.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 23 Table 2.7. Mass balance model summary for estimating biotransformation and

bioisomerization rate constants from a dietary feeding study in rainbow trout (Law et al. 2006b).

Diastereomer

Parameter α -HBCD β -HBCD γ -HBCD

Value C ƒ Value C ƒ Value C ƒ

W

(kg) 0.25 1.5 0.25 1.5 0.25 1.5

L F (kg/kg) 0.045 1.5 0.045 1.5 0.045 1.5

T

(°C) 11.5 1.05 11.5 1.05 11.5 1.05

G D (kg/d) 0.005 W 2 0.005 W 2 0.005 W 2

C

OX (mg/L) 9.9 1.25 9.9 1.25 9.9 1.25

E

D N/R a N/A 0.41 2 0.41 2

K

OW 2.40x10 5 3 2.95x10 5 3 6.31x10 5 3

k

T (d -1 ) b 9.0x10 -3 4.4 c 4.41x10 -4 4.6 d 1.44x10 -3 4.2 d

k

M (d -1 ) 1.7x10 -3 13 No estimate possible 3.0x10 -4 7

Cf

: confidence factor; k T : total elimination rate constant; W : whole body weight of the fish; L F : whole body lipid fraction of the fish; T : water temperature; G D : feeding rate; C OX : dissolved oxygen concentration; E D : chemical transfer efficiency between the gastrointestinal tract and the organism; K OW : octanol-water partition coefficient; k M : primary biotransformation half-life (loss of NA: not applicable; NR: not reported.

a Model default calculation

b Reported or calculated elimination rate cons tants for “net loss of parent isomer”. For α -HBCD, the rate constant described in the text is assumed first-order, for β -HBCD and γ -HBCD, the reported rate constants are corrected for the % of actual elimination of the pare nt isomer as reported in Table 2.6. Since the value

for β -HBCD is apparently “no net loss” a value of 10% of the reported value was assumed

c Assumed

d Calculated from the coefficient of variation from the empirical elimination half-life estimates following (Slob 1994).

There are complicating factors with these data other than bioisomerization. The Law et al. (2006b) data are sampled from muscle ti ssue and not whole body elimination rate constant estimates. The fish were being dos ed via the food and H BCD would be subject to first-pass at the liver, wh ich could be the predominant site for bioisomerization as well

as biotransformation of the HBCD isomer s to metabolites. Whole body distribution (toxicokinetics) is expect ed to be slow and changes in the liver are expected to be faster than changes in the muscle tissue. The preferential solubility in storage lipids (solubility in octanol) for α -HBCD may be another complicating fact or in the analysis. Furthermore, the study recognized that stea dy state was not reached.

Drottar and Krueger (2000) conducted a flow through bioconcentrati on test for t-HBCD at two different exposure concentrations for Rainbow trout (0.035 kg). The measured average concentrations were 0.18 and 1.8 μg·L -1 , for the low and high levels, respectively. The whole body bioconcentration fa ctors (BCFs) were 13,000 (not at steadyArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 24 state) and 9,000 L·kg -1 , respectively. This study reports to tal elimination rate constants of

0.0228 and 0.0359 d -1 for t-HBCD for the low and high levels, respectively. It is noted that the sponsor of the study did not want th e first two sample times during the depuration phase (days 1 and 3) analyzed and thus these time points were not included in the elimination rate constant estimates. The imp lications are unclear. The total uptake and elimination rate constants were calculated by BIOFAC and were used as inputs into the mass balance calculations for estimating k M . The mass balance calculations for primary

biotransformation of t-HB CD to metabolites provide k M estimates of 0.0027 d -1 ( Cf = 7.4)

and 0.012 d -1 ( Cf = 6.7) for the low and high leve ls, respectively. According to the screening level information regarding confidence in the estimated k M values, both estimates are considered “high” (Arnot et al. 2008b). The study documents some experimental difficulties maintaining a constant water concentration for the low level exposure test. These difficulties may result in errors in the BIOFAC rate constant estimates which are derived by fitting a model to the chemical concentration time trends.

The biotransformation rate constants from both exposures correspond to primary

biotransformation half-lives of 260 and 56 d for the mass of the fish in the study (0.035 kg). Using a rearrangement of Equation 2.5, the corresponding primary biotransformation

half-lives normalized HL N to a 1 kg fish are 1080 and 235 d for t-HBCD for the low and high levels, respectively. These biotransform ation rates are consid ered “very slow” and “slow”, respectively (A rnot et al. 2009).

Nyholm et al. (2009) evaluated the dietary ex posure and biotransformation of various brominated flame retardants, including HBCD, in Zebrafish. There was low recovery for HBCD in this study and only relative concen trations could be determined. Thermal degradation during the analysis is considered to be a plausible explanation for the low recovery. A “high bioaccumulation potential ” was suggested by the authors, although bioaccumulation parameters such as elimination rate constants were not reported.

A QSAR for predicting screeni ng level primary biotransformation rates in fish estimates

a k M of 0.0071 d -1 for a 1 kg fish (Arnot et al. 2009; U.S. EPA 2009). This rate constant corresponds to a HL N of about 100 d.

In consideration of all of the factors for th e biotransformation data available for fish a

HL N of 250 d is selected for mass balance modelling with a Cf of 7. Thus, 95% range of the generated estimates for HL N will be between 35 and 1750 d.

Mammalian Biotransformation Geyer et al. (2004) reported tota l elimination half-life estimat es for rats and humans for tHBCD. The data for humans were derived from body burdens and daily intake rates. It is

unclear how the data for rats were determin ed. The reported total elimination half-lives are 64 d (reported range of 23 to 219) and 8 d for humans and rats, respectively. The total elimination half-life includes elimination pro cesses such as respiration, fecal egestion, urinary excretion and biotransformation. Th e mass balance method for fish was refined for application to mammalian half-life data assuming 70 kg for the human and 0.25 kg for

the rat. The estimated HL N (normalized to 1 kg) are 25 d ( Cf = 3.4) and 13 d ( Cf = 3.2) from the reported human and rat total elimination half-life data. A HL N value of 20 d ( CfArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 25 = 5) was selected for the mass balance e xposure modelling for t-HBCD. The 95% range of the generated estimates for HL N will be between 4 and 100 d. This HL N value is about 1 order of magnitude faster than the HL N value selected for fish.

In vitro biotransformation studies have been conducted for laborator y rats and a harbour seal (sampled shortly after death) using mi crosomal liver preparations (Zegers et al. 2005). Significant reductions in LC-MS chromat ogram peaks in the in vitro assays for rats (incubations of 1:1: 1 isomeric mixture of α -, β - and γ -HBCD) showed that β - and γ

HBCD diastereromers were biotrans formed; however, the peak of the α -HBCD did not decrease “significantly”. The incubations of a 1:1:1 mixture of the HBCD isomers with the harbour seal microsomes showed average decreases of the pare nt isomers by 69, 60,

and 17% for the β -, γ -, and α -HBCD isomers, respectively. β - and γ -HBCD were also tested separately and new peaks of bromin ated compounds (indicative of metabolites) were only formed when NADPH was added confirming a cytochrome P-450 mediated biotransformation (Zegers et al. 2005). Unfortunately, it does not appear that α -HBCD was analyzed for the possibility of forming p eaks in the same isolated manner. Therefore the possible biotransformation of α -HBCD by cytochrome P-450 cannot be confirmed or refuted. Although α -HBCD was reduced to a lesser degree some formation of α -HBCD

from γ -HBCD (bioisomerization) may have been possible during the simultaneous

incubations.

Invertebrate Biotransformation For earthworms, estimated biota-soil accumula tion factors (wet weight-soil/wet weightworm) ranged between 0.03 and 0.08 based on th e total concentration of HBCD in worm tissue and estimates for the wet weight concentr ation in soil after 28 day of exposures to a range of soil concentrations (EC 2008). The diastereomer specific biota-soil accumulation

factor for α -HBCD is more than one order of magnitude higher than the value for γ

HBCD (EC 2008). The study suggests a degree of bioisomerization of γ -HBCD to α

HBCD or preferential biotransformation of γ -HBCD. It is recognized that this is not a valid BCF or BSAF study.

Biotransformation Rate Summary

The selected half-life values are based on a cr itical review of the limited available data and thus require a degree of expert judgement. It is noteworthy that the primary transformation half-lives in water, soil, and sediment are shorter than the half-lives in fish. While this may be possible, it is counter intuitive, especially with regards to the degree in which the values di ffer (e.g. aerobic sediment – 35 d; 1 kg fish – 250 d). The data for fish are not definitive, but are suppor ted by bioaccumulation measurements in the environment showing biomagnification in food webs (low biotransformation rates). This suggests that the environmental half-lives ma y be underestimates. Therefore, some of the mass balance model simulations will explore the possibility of half-lives in water, soil and sediment that are grea ter than median values.

In summary, there is conflicting evidence for diastereomer-specific biotransformation and bioisomerization rates and pathways based on in vitro, laboratory and monitoring data. These data are for different species under diffe rent conditions and na tural variability isArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 26 expected among and between individuals and species. It is recognized that α -HBCD is usually the predominant diaste reoisomer in monitoring data particularly for upper trophic level species (e.g. marine mammals, birds), but the precise reason for this cannot be elucidated from the data. It may be a re sult of preferential biotransformation to

metabolites of the β - and γ -HBCD isomers and a limited cap acity for biotransformation

for α -HBCD in the food webs. It could also be that bioisomerization rates for γ -HBCD to

α -HBCD are relatively faster th an biotransformation rates for α -HBCD resulting in the preferential accumulation of α -HBCD. Different relative sol ubilities for the diastereomers in storage lipids may also be a factor. The uncertainty associated with bioisomerization and biotransformation limits the ability to accurately model isomer-specific HBCD bioaccumulation and subsequent exposures to organisms in the environment.

It is recommended that more comprehensive studies on bioisomerization and biotransformation in various species be conducted.

2.4.3 Halogen Bond Strength and Persistence

Table 2.8 compares general prope rties of different halogens (F , Cl, Br, I), in particular with regards to halogen-carbon bonds. With respect to reactivity, the relative bond strength is an important consideration as is electronegativity. The order of bond strength

is C-F > C-Cl > C-Br > C-I a nd the order of electronegativity is F > Cl > Br > I. These general trends support the hypothesis th at organobrominated compounds are less persistent than chlorinated and fluorin ated analogues (Neils on 2003), although steric hindrance and halogen substitution patterns are also important considerations.

Table 2.8. Selected properties of fluorine, chlorine, bromine and iodine atoms where X is the halogen (Lide 2000; Neilson 2003).

Property Fluorine

(F) Chlorine

(Cl) Bromine

(Br) Iodine

(I)

Atomic Mass 18.998 35.443 79.904 126.904

Ionic radius (nm) 0.133 0.181 0.196 0.198

Bond length in CX 4 (nm) 0.132 0.177 0.194 0.213

C-X bond energy (kJ mol -1 ) 552 397±29 280±21 209±21

Electronegativity 4.0 3.0 2.8 2.7

2.4.4 Half-Lives for Benchmarking

Table 2.9 lists half-life values selected for the benchmark chemicals.27Table 2.9. Selected primary transformation half-lives (HL; days) for the model benchmarking simulations. Data are from (CowanEllsberry et al. 2009) except for *, in which environmental HLs were selected basedon (Arnot et al. 2005; Mackay et al. 2006;U.S.EPA 2009) and fish HLs are from (Arnot etal. 2009; U.S. EPA 2009). Avian and mammalian HLs (Av/Mam) for * were assumed tobe 1/3 estimates for fish. Transformation half-lives (HLN) in biota are listed normalized to 1 kg body size and scaled to mass (Mi; kg)specific values (HLi) for different species in the models asHLi=HLNx (Mi/1)-0.25.Status Abbrev HL_Air HL_Water HL_Soil HL_Sediment HL_Fish (1kg) HL_Av/Mam (1 kg)POP HCB 650 1000 1500 9000 860 290POP PCB28 6 300 450 2700 190 8.3POP PCB101 20 1200 1800 10800 370 44POP PCB180 80 2200 3300 19800 1100 29000POP Ald * 0.2 1000 1000 2000 75 25POP Hept * 1.5 25 50 150 90 30NOP Biphenyl 3 15 22.5 135 5.2 0.2NOPp-cresol 0.3 5 7.5 45 0.35 0.2NOP Atrazine 0.4 50 75 450 0.3 0.2NOP CCl4 900 150 225 1350 0.2 0.2Candi-POPα-HCH 50 440 660 3960 55 20Candi-POPβ-HCH 50 900 1350 8100 90 30Candi-POPγ-HCH 50 400 600 3600 120 40Candi-POP PBDE99 20 850 1275 7650 285 95Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 28 3.0 EXPOSURE CH ARACTERIZATION

3.1 P RODUCTION AND U SE

HBCD has been on the world market since the 1960s but its use in the production of flame-retarded polystyrene materials only began in the 1980s (EC 2008). The estimated total production volume of HBCD in 2001 was 16 700 t with approximately 57% of global distribution occurring in Europe, 23% in Asia-Pacific region, 17% in North America (5% in other regions) (see (Tem aNord 2008)). Estimated HBCD production in

2003 was 21 900 t , suggesting that global production of th is substance is s till increasing.

HBCD is used primarily as an additive flame retardant (EC 2008). The four main products in which HBCD is used are:

i) Expandable polystyrene (EPS) ii) Extruded polystyrene (XPS) iii) High Impact Polystyrene (HIPS) iv) Polymer dispersion for textiles

End-product uses include insula tion and packing materials (EPS, XPS), electrical and electronic parts (HIPS) and te xtile coating agents (polymer dispersions). According to industry information, the main use (90%) of HBCD is for flame-retarded polystyrenes (EC 2008), predominantly EPS and XPS. While the production of polymer dispersions for textiles may be more limited, this category is important to consider since textile backcoating (industrial) was estimated to be th e dominant release category to waste- and surface waters (EC 2008).

3.2 E MISSIONS AND M ODE

- OF -E NTRY

Several considerations are required for the ev aluation of chemical c oncentration trends in environmental and biological compartments. Pr edicted concentration tr ends and values of P and LRT are dependent on mode of entry of substances into the environment and the parameters of the modelled environment. Furt her, the emission histor y and the location of emissions (source area) can determine if ps eudo-steady state conditions are expected. This is particularly important for substances with high P OV , since they will take a long time to reach steady state, especially in soil and sediment compartments where degradation is usually slower. Since P OV , B, T and LRTP are estimated as intrinsic properties independent on the amount of s ubstance released, an ‘indirect’ model

evaluation can be conducted us ing relative concentrations.

Table 3.1 summarizes initial estimates of emi ssions and mode of entry to the environment for HBCD (EC 2008). Alternative plausibl e emission regimes were explored by corroborating mass balance mode l estimates with available monitoring data. Mode of entry information was used to determine the ra tio of emissions occurring to air, water and soil (EC 2008). These assumptions were consid ered a starting point but variations wereArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 29 also explored using model simulations and co mparisons to available monitoring data. For HBCD simple assumptions are necessary based on the uncertain use and expected release

scenarios.

Table 3.1. Total estimated European Un ion (EU-Releases) of HBCD by emission compartment and emission source category ( point sources versus diffuse sources). Adapted from TemaNord (2008).

Compartment Estimated Release (kg·yr -1 ) (% of total) Attributed to

Point Sources Attributed to

Diffuse Sources

Air 508 (6%) 53.6% 46.4% Wastewater (to STP)

From STP to Recipient water* 6251 (72%)

1250 98.1% 1.9%

Surface Water (direct) 1933 (22%) 89.2% 10.8%

* ~ 20% of incoming HBCD was estimated to be released from STP, ~ 80% is associated with sludge (EC 2008). Total degradation rate constant is assumed to be zero however.

The total estimated European Union emissions presented in Table 3.1 were based on an analysis of current producti on sites and do not include emi ssion estimates from a major HBCD production facility in the UK whic h closed at the end of 2003 (EC 2008). According to estimates, this production facility used to be the largest point source of HBCD emissions in Europe. Another production facility, located in Germany, closed in

1997 but no emission data are available from this site.

Emission estimates from the UK production site were assessed in two different surveys (EC 2008). In the first survey, emissions to ai r and surface water were estimated to be approximately 600 and 1335 kg·yr -1 respectively for the year 2000. In the second survey, performed in 2001, emissions to ai r were estimated to be 3400 kg·yr -1 whereas emissions to wastewater (following onsite sewage treatment) were estimated to be 2000 kg·yr -1

. These data suggest that i) emissions to ai r were proportionally higher for the EU region than indicated by Table 3.1, and ii) total emissions for the EU region were higher in absolute terms in the past.

HBCD is an additive BFR (physically mi xed into polymeric products) and is not covalently bonded to the produc ts as some other BFRs. HBCD is therefore expected to have higher leaching and emissions rates duri ng the product life cycle compared to other BFRs that are chemically integrated (Law et al. 2005; Nyholm et al. 2009).

3.3 M ONITORING D ATA

Monitoring data from industrialized source regions and remote (Arctic) regions have been used as an indicator of LRT potential; however, the mere presence of a substance in a remote area is not evidence for a high LR T potential in itself. Modern analyticalArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 30 methods can measure certain substances at c oncentrations of femtograms per cubic meter (10 -15 g·m -3 ). An important consideration is pot ential concentration gradients from sources in which the transport potential relati ve to other chemicals can be determined.

There are limited monitoring data available, particularly for the abiotic environment. The uncertainties associated with isomerization pr ocesses and rates in the environment further complicate any potential for isomeric -specific analysis at this time.

3.3.1 Air

Monitoring of HBCD in air ha s taken place in both remote and source regions of Sweden,

the United States and China since the ear ly 1990s. Early atmospheric measurements made in 1990-1991 at Ammarnäs and Hoburgen, Sweden showed that HBCD was present in ambient air in remote locations at picogram per cubic metre concentrations (Bergander et al. 1995). Measurements later reported at Rörvik, Sweden and Pallas, Finland, in 2000 and 2001 showed that rural Sc andinavian locations were still exhibiting similar levels 10 years later, in fact Aspvreten, south of Stockholm exhibited concentrations up to 100 times the other background sites (25 and 280 pg·m -3 ) (Remberger et al. 2004). Aspvreten is 80 km NE of the XPS facility and may be influenced by those emissions, although is term ed “remote” in the l iterature. This study also reported HBCD levels in air in urban areas (76 and 610 pg·m -3 ) as well as by point sources (19 to 1,070 pg·m -3 ). Figure 3.1 shows that urban areas have similar atmospheric levels of HBCD as textile processing facilities and demolition landfill sites.

HBCD is present in air predominantly in th e particulate phase. Yu et al. (2008) show concentrations in Guangzhou in Southern China to range from 0.7 to 3.1 pg·m -3 total, with 85 to 95% associated with particulates. This study also shows the importance of the urban environment with respect to atmospheri c concentrations of HBCD as the highest

level recorded was at an urban site. The ma terials used in the construction/renovation of

urban areas as well as the treated cons umer products utilized by the population are probable sources of HBCD to the urban atmosphere.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 31 Figure 3.1. Air concentrations in Sweden and Finland in 200 0-2001. *Aspvreten is 80 km NE of the XPS facility and may be influe nced by those emissions, although is termed

“remote” in the literature.

Hoh and Hites (2005) show the influence of urban areas as well as the influence of production facilities on atmosphe ric concentrations of BFRs in the atmosphere in the

United States. Levels at the Arkansas si te, approximately 150 km from Great Lakes Chemical (El Dorado, AR) and Albemarle (Magnolia, AR) plants where BFRs are manufactured were about 10 pg·m -3 , similar to those measured in Chicago (Hoh and Hites 2005). The background regional sites in north ern Michigan and southern Louisiana

showed concentrations between 0.16 and 8 pg·m -3 , similar to the industrialized and urbanized sites. The similarity of the backgr ound sites to “near sour ce” regions suggests that HBCD is emitted, presumably from non-point sources (diffuse), in sufficient quantities to have become detectable in ma ny populated areas of th e global environment.

It is interesting to note is th at at each of the sites in Gua ngzhou, the isomeric distribution

is at least 50% α -HBCD (Figure 3.2). The similar prof iles are in contrast to what was observed by Hoh and Hites (2005 ) in the US where the isomer ic distribution is dissimilar across space and time (Figure 3.3). The spatial and temporal proximity of the Chinese samples to one another may explain their si milarities. Atmospheric degradation is an important loss process and photoi somerization may also occur, therefore, differences in

latitude and cloud-cover could play a role in the variati on in the American data.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 32 Figure 3.2. Isomeric distribution in gaseous and particulate air samples in Guangzhou, China (Yu et al., 2008).

Figure 3.3. Isomeric distribu tion in particulate air sample s from Central USA (Hoh and Hites 2005).

3.3.2 Sediment

Sediment is the most frequently monito red abiotic environmental compartment for HBCD. Sediment is a sink for hydrophobic chemicals such as HBCD. Due to the relatively high levels of organic matter present in the sediments and emissions to water, it is expected that HBCD will be present in sediments near source regions. A complication arises as aerobic degradation of HBCD is apparently much slower than anaerobic degradation, thus sediment analysis require s information on the aerobic status. MarineArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 33 sediments are reducing environments covered by an oxic surface layer of variable depth. For example, oxygen penetration depth in sediments from productive shallow coastal waters may only reach a few millimeters compar ed with cm or dm scales in oceanic sediments underlying a deep oligotrophic water column (Kristensen 2000). Estuarine and coastal sediments may also be anaerobi c due to high organic input and pronounced

stratification due to freshwater inflows (Drever 1997). Studies of fresh and estuarine sediments up and downstream from point source s such as textile plants and waste water treatment plants show the influence of thes e point sources to water systems. Studies involving point sources in fluvial and estuar ine systems in Scandinavia, Switzerland, the Netherlands, Belgium and the UK, as we ll Tokyo Bay in Japan and the highly

industrialized Detroit River in the USA/Canada show spat ial sediment contamination trends. Figure 3.4 shows the downstream trend in average reported sediment

concentrations.

Figure 3.4. Downstream trend for mean sedi ment concentrations reported from Europe, Asia and North America. Error bars repr esent minimum and maximum values for each type of sediment (Sellström et al. 1998; Eljarrat et al. 2004; Morris et al. 2004; Remberger et al. 2004; Schlab ach et al. 2004a; Schl abach et al. 2004b; Klamer et al. 2005; Verslycke et al. 2005; Minh et al. 2007).

Contaminant levels in isolat ed sediments in remote loca tions can implicate long range transport potential. Christensen et al. (Chris tensen et al. 2004) examined sediment cores from Lake Ellasjøen on Bear Island (Bjør nøya), a remote island in the Arctic Ocean (Latitude: 74.517 N, Longitude: 19.017 E). Lake Ellasjøen, which is on the southern end of the island, is highly productive due to input s of seabird guano and it receives relatively high rates of precipitation for the island. Re latively high levels of contaminants areArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 34 observed due to bio-transport and precipitati on (Christensen et al . 2004). Sediment cores reveal that HBCD was also detected (4.34 μ g·kg -1 dw) in a subsurface layer dating from

1973 to 1987 with isom er ratios at ~10% α -HBCD, ~1% β -HBCD and ~89% γ -HBCD. This sediment core suggests a historical us e pattern for HBCD and an isomeric pattern

similar to t-HBCD.

Surface sediment monitoring data show th at releases to river systems migrate downstream. Eljarrat et al. (2004), Sellström et al. ( 1998) and Schlabach et al. (2004b) studied European rivers in wh ich there is a mix of possibl e industrial and urban sources. All studies used monitoring st ations upstream of the impacted areas and then at sites downstream. Figure 3.5 shows that although ther e is variation in the concentration of HBCD in sediments along the watercourses, th ere is a general increase as the sampling proceeds downstream. It should also be noted that the levels observed in the upstream portions of these rivers (first point on each series) were below the detection limits of the

studies and that HBCD levels increased between 1 and 4 orders of magnitude downstream. Figure 3.6 expands on the Dramme n River and Fjord data and shows the

concentrations and the isomeric br eakdown at each site. Note that β -HBCD is reported below detection limits in most samples (MDL = 0.08 μg·kg -1 dw).

Figure 3.5. Surface sediment concentrations in two European river systems. The River Haggan is a tributary of the Ri ver Viskan. Lake Skaresjon is an unimpacted lake that is nearby the River Viskan. Dashed line represents relative location of initial HBCD release

site. Location data are not to scale. Data are from Sellstr öm et al. 1998 (Sweden) and Schlabach et al. 2004 (Norway), Eljarrat et al., 2004 (Spain).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 35 Figure 3.6. Proportion of isomers in Dramme n River and Fjord sediment samples and

concentration of Σ HBCD at each location. Only si te 7-Vikersund is non-impacted.

Locations of sources of HBCD were not incl uded in the study (Schlabach et al. 2004a).

Estuarine environments are susceptible to c ontamination due to high sedimentation rates and current and historical industrial activity. Several studies have documented the levels of HBCD in sediments in the Scheldt Estuary (NL, BE), the Humber Estuary (UK), Dublin Bay (UK), Drammenfjord (NO) a nd Tokyo Bay (JP) (Morris et al. 2004; Schlabach et al. 2004a; Verslycke et al. 2005; Minh et al. 2007). Of these locations, the Scheldt has the highest average leve l of HBCD: 41 (range 0.7-99) μg·kg -1 dw and Tokyo Bay has consistently the lowest average: 0.76 (range 0.056 – 2.1) μg·kg -1 dw. Figure 3.7 shows the concentrations reported in these studies and the isomeric breakdown at each site. In Tokyo Bay, Minh et al. (2007) show the spatial variation of contaminant within the bay, ranging from 2.1 μg·kg -1 dw at the highly urban and industrial end of the bay to significantly lower concentrations nearing the mouth of the bay (Figure 3.8). This suggests that contaminant reaching the bay th rough riverine and dir ect inputs is being transported in small but detectable quantities to the oceans. Schlabach et al. (2004a) show similar results in Drammensfjord, NO.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 36 Figure 3.7. Estuarine concentra tions and isomeric breakdown.

Figure 3.8. Spatial Trends: HBCD in T okyo Bay surface sediments from Minh et al. (2007).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 37 In the North Sea off the Netherlands and Germany, both nearshore and offshore

measurements of surface sediment concentra tions were made (Klamer et al. 2005; Lepom et al. 2007). The nearshore sediments ranged between 3.4 to 6.9 μg·kg -1 and the offshore sediments ranged from 0.2 (detection limit) to 1 μg·kg -1 dw. These offshore

measurements in the North Sea show that H BCD sediment concentrations are detectable; however, at lower concentrations th an those nearer to point sources.

3.3.3 Water and Soil

Remote measurements in soil and the wa ter phases for HBCD are scarce. Studies reporting on these media are either centered in urban areas (Morris et al. 2004; Marvin et al. 2006) for water, or close to point source industrial facilities (P etersen et al. 2004; Remberger et al. 2004) for wate r and soil. These samples are often taken on the premises

or a few hundred metres from the facilities. A summary of these data is presented in

Table 3.2.

Table 3.2. Average water, suspended sedi ment and soil samples at point and urban sources in Europe.

Compartment Point Urban n Ref.

Water 141 8 Petersen et al. 2004

μg·L -1 0.006 2 Remberger et al. 2004

Susp Sed 1.6 4 Remberger et al. 2004

μg·kg -1 dw 1.3 8 Marvin et al. 2006

Soil 813 3 Remberger et al. 2004

μg·kg -1 dw 265 40 Petersen et al. 2004

It is recommended that more monitoring data be obtained, particularly in regional (non-point source) environments. Si nce air and water are the receiving compartments, these environments should be considered a priority.

3.3.4 Plants

Only one study documenting measured concentra tions in HBCD in plants was cited in (EC 2008). The study reported measurements in stair-step moss ( Hylocomium splendens )

from locations in southern and northern Norway (Schlabach 2002), ranging from below the limit of detection (3 μ g·kg -1 ww) to 11 mg·kg -1 ww for total HBCD. The most interesting aspect of this st udy was the spatial trend in repo rted concentrations; higher concentrations were generally reported for th e more southern locati ons in Norway, falling

below detection limits at the more northern si tes. No additional studies reporting HBCD concentrations in plants have been located in the available literature.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 38 3.3.5 Invertebrates

Monitoring data are available for both freshw ater and marine invertebrates and include samples “near point sources”, “regional source” and “remote” regions. Reported

concentrations in marine inve rtebrates range over four or ders of magnitude (EC 2008), indicative of strong spa tial gradients in exposur e related to proximity to point sources. For example, (Fjeld et al. 2005) reported concentrations of total HBCD in blue mussels ( Mytilus edulis ) from Åsnefjord, Norway, site of an EPS bead production facility, range from ~3,125 to 17,335 μ g·kg -1 lw. Concentrations in blue mussels sampled along the Norwegian coast were substantially lower, ranging from ~50 to 400 μ g·kg -1 lw. These latter concentrations are similar to those repo rted for the Kattegatt (Swedish Baltic coast) which ranged from 20 to 300 μ g·kg -1 lw. In this case, the highest value was detected near a sewage treatment plant (Göransson et al. 2004); concentrations from the other sites ranged from 20 to 70 μ g·kg -1 lw. Reported concentrations in tissues from other invertebrate species (e.g. hermit crab, lobs ter, oysters, prawns, scallops, starfish) harvested in EU and Norwegian waters th at are not near known point sources for

emissions tend to fall into a si milar range range, i.e., 10 to 100 μ g·kg -1 lw (EC 2008). However, it is difficult to compare concentrations across species since habitat use (benthic vs. pelagic), trophic position, a nd possibly biotransformation capacity all influence exposure and corresponding body burde ns of hydrophobic substances such as

HBCD.

Studies reporting concentrations of HBCD in invertebrates inhabiting remote regions are scarce. Tomy et al. (2008) re ported concentrations in zoopl ankton (mixed, n = 5), shrimp ( Pandalus borealis and Hymenodora glacialis , n = 5) and clams ( Mya truncate and Serripes groenlandica , n = 5) from the Eastern Arc tic sampled in 2000/2001; geometric mean values were 1.1, 1.9 and 1.4 μ g·kg -1 lw total HBCD respectively. These relatively low values are consistent with the studies conducted by Sormo et al. (2006) and Morris (2007) which sampled invertebrates from the Norwegian Arctic and Barrow Strait (Canada) but reported that concentra tions did not exceed detection limits.

3.3.6 Fish

Monitoring data are available for both freshw ater and marine fish and also include samples from industrialized and remote regions (marine only). As with invertebrates, the range of reported values is high, spanning 5 to 6 orders of magnitude (EC 2008). This variability is again related to proximity to sources but may also be influenced more by trophic position of sampled species in comparison to invertebrates.

The highest reported concentrations of total H BCD in freshwater fish were reported for Brown trout ( Salmo trutta ) and eel ( Anguilla anguilla ) sampled in close proximity to known sources in the UK, Belgium and Sweden . In these studies, concentrations in muscle tissue as high as 160 mg·kg -1 lw were reported (dow nstream HBCD production plant) and values typically ranged from ~ 1 to 50 mg·kg -1 lw (EC 2008). Substantially lower muscle tissue concentrations ( ≤ 35 μ g·kg -1 lw) were reported in e.g. Brown trout inhabiting lakes in Switzerland (Schmid et al. 2004), perch ( Perca fluviatilis ) from lakes in Sweden ( ≤ 25 μ g·kg -1 lw) (Sternbeck et al. 2004) and pike ( Esox lucius ) from freshwater bodies in Finland (< 5 μ g·kg -1 lw, (Peltola unpublished )). Concentrations inArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 39 fish sampled upstream of known sources are al so substantially lower (on average) than downstream (EC 2008). These observations are broadly consistent with the emission mode of entry scenario presented in the (E C 2008); emissions to wastewater and surface water (confined to downstream areas of partic ular drainage basins , given the low log K AW estimated for HBCD) dominate over atmospheric emissions (which tend to disperse more rapidly and lead to reduced spatial grad ients in exposure). Comparisons between freshwater fish inhabiting i ndustrialized areas and fish in habiting remote regions (i.e. Arctic) are not possible due to lack of data.

Reported concentrations in marine fish in cluded in (EC 2008) are lower on average and also span a narrower range of values in comp arison to freshwater fish. As expected, fish species inhabiting marine environments impacted by point source discharges such as the Western Scheldt estuary tend to have highe r reported concentrations. For example, measured conconcentrations of HBCD in sole ( Solea vulgaris ) and whiting ( Merlangius merlangus ) muscle tissue ranged from approximately 100 to 1110 μ g·kg -1 lw and 20 to

275 μ g·kg -1 lw respectively (de Boer et al. 2002). In comparison, the reported concentration of total HBCD in whiting analy zed as part of a UK market survey (pooled sample, n = 60) was 28 μ g·kg -1 lw (EC 2008). Reported concen trations of eel (muscle) sampled in the Western Scheldt estuary ranged from 10 to 310 μ g·kg -1 lw (de Boer et al. 2002; Janák et al. 2005), far below levels repor ted in more impacted freshwater regions (see above). An extensive database is available for Baltic herring ( Clupea harengus ) muscle tissue, covering the years 1999–2007 (N aturhistoriska riksmuseet 2008). These data are noteworthy because the Baltic Sea e nvironment is often more seriously impacted by anthropogenic pollution due to the slow tur nover of these waters and extensive drainage basin (Swedish Environmental Protection Agency 2009). Geometric mean

concentrations in these fish over this tim e period range from approximately 5 to 25 μ g·kg 1 lw. These concentrations are similar to leve ls reported for herring (muscle) available on the UK market (9.5 μ g·kg -1 lw), assumed to be caught in UK waters (EC 2008).

Limited data are available for marine fish i nhabiting the waters of remote regions. Mean reported concentrations in polar cod ( Boreogadus saida ) and redfish ( Sebastes mentella ) muscle sampled in the Eastern Arctic (D avis Strait) in 2000/2001 were 0.4 and 2 μ g·kg -1
lw respectively (Tomy et al. 2008). These m easured concentrations in polar cod are substantially lower than valu es reported by (Jenssen et al . 2004) for the Svalbard area (range 5 to 25 μ g·kg -1 lw, liver tissue) and the Nort h-East Atlantic (range 7 to 23 μ g·kg -1
lw, liver tissue) (Bytingsvik 2004). This a pparent discrepancy could reflect spatial patterns in exposure (Svalbard is much closer to sources in Europe) although the fact that different tissues were analyzed in different laboratories could al so be an important

consideration.

3.3.7 Birds and Mammals

Tissue concentrations of HBCD have been reported for bird species (terrestrial and marine) and marine mammals from industrializ ed and remote regions. Concentrations in polar bears from various regions of the Arctic are also available.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 40 Measured concentrations of HBCD have been measured in Peregrine falcon ( Falco
peregrinus ) eggs sampled in Sweden, the UK and Greenland (EC 2008). Reported

concentrations of total HB CD in eggs sampled from wild populations ranged from approximately 50 to 2400 μ g·kg -1 lw in the southern population and 35 to 500 μ g·kg -1 lw in the northern population (Sellström et al. 2001; Lindberg et al. 2004). HBCD was detected above detection limits in only 12 of 51 eggs sampled in the UK (Leslie et al. 2004). However, detection limits were unusua lly high in this study; when above the limits of detection, the reported concentrations were up to 780 μ g·kg -1 lw, i.e. similar to concentrations reported for Sweden. Reporte d concentrations in eggs sampled in Greenland over a similar time period (1995–2 000) were generally much lower, ranging

from < 0.1 to 30 μ g·kg -1 lw (Vorkamp et al. 2005).

Sellström et al. (2003) pr esented a series of meas urements in guillemot ( Uria aalge ) eggs collected over the period 1969–2001. More rece nt measurements extending this dataset are presented in (Naturhistor iska riksmuseet 2008). The in creasing tempor al trend in

these data is discussed in more detail in Se ction 3.4. Reported concentrations in eggs over the period 1995–2000 (pooled samples, n = 10) range from 110 to 170 μ g·kg -1 lw, broadly similar to peregrine falcons. Concentr ations of total HBCD in guillemot muscle issue sampled in the same area in 2000 are generally lower, ra nging from 25 to 148

μ g·kg -1 lw (geometric mean = 65 μ g·kg -1 lw) (Lundstedt-Enke l et al. 2006). No comparisons to guillemot populations inhabiting other regions can be made at this time

due to lack of data.

Yolk sac from newly hatched European shag ( Phalacrocorax carbo ) inhabiting the midNorwegian coast was also analyzed recently, wi th total HBCD concentrations reported at

levels of 417±208 μ g·kg -1 lw (Murvoll et al. 2006). Measur ed concentrations of HBCD in birds inhabiting more remote regions (e.g. Svalbard, Bjørnøya) have been reported for several species includ ing glaucous gulls ( Larus hyperboreus ; plasma, eggs, dead/dying birds), herring gulls ( Larus hyperboreus ; plasma, eggs, dead/dying birds), Atlantic puffin ( Fratercula artica ; eggs), kittiwake ( Rissa tridactyla ; eggs) and Northern Fulmar ( Fulmarus glacialis ; liver). The highest concentrations in birds inhabiting these areas were presented in Knudsen et al. (2007) for gl aucous gulls. Here conc entrations in brain

and liver tissue were re ported as 98.9±136 and 3026±4322 μ g·kg -1 lw α -HBCD respectively. However, of the 21 birds sampled, nearly 50% were classified as completely or severely emaciated (total or near-com plete lack of abdominal fat) and 30% as emaciated (substantial loss of abdominal fat) (Knudsen et al. 2007). In these animals, hydrophobic compounds accumulated over the life-time of the animal have been redistributed into remaining body lipids as abdominal fat was consumed to meet metabolic demands. Since faecal egestion is limited during starvation episodes, elimination of hydrophobic compounds is extrem ely inefficient and it is therefore not surprising that elevated lipid-normalized con centrations are reported for brain and liver

tissues in these animals. These levels cannot be considered representative however and should be disregarded in the context of risk or hazard assessment. Reported concentrations in glaucous gu lls sampled for other studies are far lower. For example, Verrault et al. (2004) reported mean total HBCD concentrations of approximately 20

μ g·kg -1 lw in plasma samples from glaucous gu lls from Svalbard for the year 2004. TheseArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 41 concentrations are similar to the mean tota l HBCD values reported in Verrault et al. (2004) for plasma samples from glauc ous gulls inhabiting Bjørnøya (35 to 50 μ g·kg -1 lw). Higher concentrations were reported in glauc ous gull egg samples from the same island (140 μ g·kg -1 lw). These concentrations are comparab le to levels reported in eggs of herring gulls, Atlantic Puffins and Kittiwake from different remote locations in 2003 (Knudsen et al. 2005), which ranged from 45 to 175 μ g·kg -1 lw total HBCD.

The most extensive dataset avai lable for marine mammals in industrialized regions is for harbour porpoises ( Phocoena phocoena ) stranded or bycaught in the UK. Geometric

mean concentrations in blubber tissue sampled over the period 2000–2006 range from approximately 135 to 1705 μ g·kg -1 lw with peak values obs erved for the years 2001 and 2002 (Law et al. 2006c; Law et al . 2008). The temporal trends in these data are discussed in further detail in Section 3.4. Zegers et al. (2005) also repor ted measurements for harbour porpoises stranded over the peri od 2000–2003 which confirm the high levels reported by Law et al. (2006c ; 2008) for UK waters. Har bour porpoises stranded or bycaught in Galicia, Spai n were also included in this monitoring survery. As shown in Figure 3.9, the geometric mean total HBCD co ncentrations in blubber are much lower (a factor of 10 or more) in this region compared to locations in the UK. These findings have been attributed to point sour ces such as an HBCD productio n plant in NE England (Law et al. 2006c; Law et al. 2008).

1 10 100 1000 10000 100000

North, North
Sea (11) South, North
Sea (13) NW Scotland
(5) Irish Sea
(8) South Ireland
(3) Galicia,
Spain (3)ug kg-1lw

Figure 3.9. Geometric mean total HBCD c oncentrations (95% CIs indicated by error bars) in harbour porpoise blubber ( μ g·kg -1 lw) from various lo cations over the period 2000–2003 (Zegers et al. 2005).

Data from grey seals ( Halicoerus grypus ) inhabiting the Baltic S ea are also available (EC 2008). The reported mean (arithmetic) total HBCD concentration in blubber of these animals for the time period 2000–2002 is approximately 100 μ g·kg -1 lw (range 31 to 554

μ g·kg -1 lw), similar to harbour porpoises from Galicia, Sp ain (i.e. not indi cative of highlyArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 42 impacted environment). Total HBCD concentra tions in muscle tissue ranged from 30 to

87 μ g·kg -1 lw, somewhat lower than lipid-norma lized blubber concentrations. Reported

concentrations in marine mammals in indus trialized regions of North America also appear to be lower than from harbour porpoi ses inhabiting the wa ters of the UK. For example, reported α -HBCD concentrations in blubber of male white-sided dolphins from the US East Coast sampled over the period 2000–2004 range from approximately 35 to

350 μ g·kg -1 lw (Peck et al. 2008 ) while reported concentrations of to tal HBCD in blubber from California sea lions sampled over th e period 2000–2003 range from approximately

4 to 95 μ g·kg -1 lw with the majority of individuals below 20 μ g·kg -1 lw (Stapleton et al. 2006).

Concentrations of HBCD in mammals inhabiti ng the remote Arctic regions have been reported for walrus, beluga and narwhal (Tomy et al. 2008), ringed seal (Sormo et al. 2006) and polar bears (Gabriel sen et al. 2004; Jenssen et al. 2004; Muir et al. 2006; Sormo et al. 2006). Reported total HBCD concen trations in walrus, beluga and narwhal blubber inhabiting the eastern Ar ctic (e.g. Davis Strait) sampled in 1998 were 0.6, 1.4

and 3.4 μ g·kg -1 lw respectivel y (Tomy et al. 2008). These va lues are lowe r than those reported in mammals from Svalbard. For exam ple, the total mean HBCD concentrations in blubber of ringed seal sample in 2003 was 20 μ g·kg -1 lw whereas mean total HBCD concentrations in polar bears ra nged from approximately 12 to 45 μ g·kg -1 lw sampled in 2002–2003. Interestingly, mean total HBCD c oncentrations in polar bears inhabiting Greenland were similar to Svalbard (Muir et al. 2006) but reported concentrations in polar bears from the Bering Strait-Chukchi Sea area were substantiall y lower with a mean reported value of 0.4 μ g·kg -1 lw (range <0.1 to 35.1 μ g·kg -1 lw). If these concentrations are representative, it implies that i) emissions from Europe have a limited impact on the western Arctic and ii) HBCD inputs to the We stern Arctic, likely emanating from North American and Asia-Pacific sources, are substantially lower than inputs impacting Svalbard. Hypothesis ii) is at least consistent with available information on the distribution of global H BCD production and use.

3.3.8 Humans

HBCD levels have been reported in human br east milk (e.g. (Aune et al. 2002; Lignell et al. 2003; Kakimoto et al. 2008; Eljarrat et al . 2009)) and adipose tissue (Pulkrabová et al. 2009). Kakimoto et al. (2008) analyzed samp les in Japanese women (age 25–29) over the period 1973–2006; total mean HB CD concentrations over the period 2000 – 2006 ranged

from 1 to 4 μ g·kg -1 lw. These levels are higher than values reported for women in the Tromsø area (northern Norway); HBCD wa s detected in only 1/10 samples, at a

concentration of 0.13 μ g·kg -1 lw (Polder et al. 2008a). In contrast, substantially higher values were reported in a su rvey of women living in Northwestern Spain conducted in 2006/2007 (Eljarrat et al. 2009). In this loca tion, the geometric mean total HBCD

concentration was 27 μ g·kg -1 lw (range 3 to 190 μ g·kg -1 lw). It is also interesting to note

that the γ -HBCD isomer dominated in the majority of these samples, as opposed to the α

HBCD isomer. Considering a wider range of studies from different countries however (Aune et al. 2002; Lignell et al. 2003; Ryan et al. 2006; Antignac et al. 2008; Polder et al. 2008a), the typical range of total HBCD c oncentrations in human breast milk in populations inhabiting industrialized areas appears to be <1 to 5 μ g·kg -1 lw. No data onArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 43 concentrations of total HBCD in humans inhabiting the high Arctic are currently available for comparison.

HBCD has also been detected in human ad ipose tissue obtained by liposuction in the Czech Republic (Pulkrabová et al. 2009). Reporte d concentrations ranged from <0.5 to

7.5 μ g kg -1 lw. No other human adipose tissue da ta are available for comparison.

Table 3.3. Summary of monitoring data collected near known point sources including the range of reported values a nd selected median values.

Compartment Units Range Median ( Cf )

Air pg·m -3 10 - >1,000 100 (10)

Water μg·L -1 0.006 - 141 5 (30)

Soil μg·kg -1 -dw 265 - 813 300 (3)

Sediment μg·kg -1 -dw 1 – 1,000 10 (80)

Aquatic invertebrates μg·kg -1 -lw

Benthic invertebrates μg·kg -1 -lw 3,100 – 17,335 7,000 (2.5)

Fish - lower TL μg·kg -1 -lw

Fish – upper TL μg·kg -1 -lw 1,000 – 50,000 7,000 (7)

Birds μg·kg -1 -lw

Marine mammals μg·kg -1 -lw 130 - 1700 450 (4)

Terrestrial mammals μg·kg -1 -lw

Humans μg·kg -1 -lwArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 44 Table 3.4. Summary of monitoring data collected in “source” regional environments including the range of reported values and selected median values.

Compartment Units Range ( n ) Median ( Cf )

Air pg·m -3 0.1 - 600 10 (10)

Water μg·L -1

Soil μg·kg -1 -dw

Sediment μg·kg -1 -dw <0.1 - 30 0.5 (20)

Aquatic invertebrates μg·kg -1 -lw

Benthic invertebrates μg·kg -1 -lw 0.5 a - 100 30 (5)

Fish - lower TL μg·kg -1 -lw 5 a - 25 10 (3)

Fish – upper TL μg·kg -1 -lw 10 - 300 30 (3)

Birds (muscle tissue) μg·kg -1 -lw 25 - 150 65 (3)

Marine mammals μg·kg -1 -lw 30 - 750 150 (5)

Terrestrial mammals μg·kg -1 -lw

Humans μg·kg -1 -lw <DL - 190

a includes food basket survey dataArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 45 Table 3.5. Summary of monitoring data collected in “remote” regional environments including the range of reported values and selected median values.

Compartment Units Range ( n ) Median ( Cf )

Air pg·m -3 1 – 6.1 4 (3)

Water μg·L -1

Soil μg·kg -1 -dw

Sediment μg·kg -1 -dw <DL – 4.3 0.16 (25)

Aquatic invertebrates μg·kg -1 -lw <DL – 1.9 0.4 (5)

Benthic invertebrates μg·kg -1 -lw 1.4 1.4 (5)

Fish - lower TL μg·kg -1 -lw

Fish – upper TL μg·kg -1 -lw 0.4 – 2.0 1 (4)

Birds μg·kg -1 -lw 20 - 175 60 (5)

Marine mammals μg·kg -1 -lw <0.1 - 45 2 (20)

Terrestrial mammals μg·kg -1 -lw

Humans μg·kg -1 -lwArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 46 3.4 T IME T RENDS OF E NVIRONMENTAL C ONCENTRATIONS

3.4.1 Abiotic Compartments

Sediment cores from depositional zones w ithin study areas can be used to estimate historical levels of contamination. Typical ly, sediments are very slow to respond to changes in emission patterns and act as a re pository and can even be a source for certain

chemicals after emissions have been restri cted. The ages of the subsections of the sediment core are determined using 210 Pb dating. Several studies have determined historical profiles of HBCD contamination in Europe and in Japan (Christensen et al. 2004; Remberger et al. 2004; Minh et al. 2007; Kohler et al. 2008).

Figure 3.10. Time trend for HBCD in urban sediments

The Bear Island (Bjørnøya, NO) sediment core data were not included in this analysis as only one sample (1973-1982) was above the limit of detection (Christe nsen et al. 2004).

Of the sediment cores included in this anal ysis, the maximum concentration is 2.5 μg·kg -1
dw from 2001 in Lake Greifensee (CH) (Kohl er et al., 2008). Figur e 3.10 shows a clear

increase in concentrations with time. Usi ng simple linear regressi on, doubling times were calculated and are presented in Table 3.6. Although the absolute concentrations are near background levels, the Swiss and Japanese se diments show a dramatic increase, with doubling times of approximately 8 years for Gr eifensee and < 3 years for Tokyo Bay. It is recognized that a complication in the analysis of sediment core time trends is the uncertain degradation processes that may be occurring in de eper, older and possibly more

anaerobic sediments.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 47 Table 3.6. Calculated doubling times (years) from se diment cores in Europe and Japan.

Location Slope Intercept Doubling time, years

Tokyo Bay, JP 0.06 -113.61 2.9
Greifensee, CH 0.08 -155.52 8.3
Stockholm, SE 0.02 -32.97 156.4

Atmospheric data for HBCD are sparse, partic ularly for remote regions. There are some data from remote regions in Sweden; however, they were not sampled at the same time or

in a consistent manner.

3.4.2 Biota: Source Regions

The temporal trend in total HBCD concen trations in blubber of harbour porpoises stranded or bycaught in UK coastal waters over the period 1994–2006 was presented in (Law et al. 2006c; Law et al. 2008). These data are presented in Figure 3.11 as geometric mean HBCD concentrations (95% CI also shown).

Figure 3.11. Temporal trends in geomet ric mean total HBCD concentrations ( μ g·kg -1 lw) in blubber of harbour porpoises stranded or bycaught in UK coastal waters for the period 1994–2006 (Law et al. 2006c; Law et al. 2008).

Law et al. (2006) included data from 1994–2003 and interpreted the data as indicating, “evidence for an increase in recent years”. Additional data from animals sampled over the period 2003–2006 is presented in Law et al. (2008) and was interpreted as indicating, “a significant downturn in levels of hexabromocyclododecane in the blubber of harbour porpoises stranded or bycaught in the UK ”. According to Law et al. (2008), “investigation of time trends confirmed a statistically significant increase between 2000 and 2001 ( p <0.01) and a statistically signif icant decrease betw een 2003 and 2004 ( p < 0.05). Neither trend was confounded by age, se x, nutritional status, or location”. TheArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 48 significance of the apparent de creases in measured concentra tions (all samples) for 2005 and 2006 could not be confirmed by the statis tical methods applied. When examining the temporal trends by gender however, Law et al . (2008) report that “the downward trend

for adult females from 2003 was not statistically significant ( p = 0.072) but was from 2002 ( p = 0.019)” and that in adult males, “there is a decrease from 2003 to 2004 (although it is not quite statistically significant at the 5% level) but after 2004 levels increase again”.

The main reason for the high level of intere st in these data is that a major HBCD production site, located in NE England, ceased production at the end of 2003 (EC 2008). According to (EC 2008), this facility used to be the largest point source of HBCD emissions in Europe with releases to air in the range of 0.6 to 3.4 t ·yr -1 , 1.3 t ·yr -1 to surface

water and 2 t ·yr -1 to wastewater from the onsite se wage treatment plant in 2000/2001. Unfortunately, emission estimates and/or production volumes over the period 1994–2003

are not available. This information would be quite valuable since the statistically significant increase in measured concentrations observed in harbour porpoises over the period 2000–2001 is as interesting as the appa rent decline of HB CD over the period 2003–2004 with respect to understanding response times of the environment and food webs to changes in emissions of HBCD. Cons idering the outcomes of all trend analyses presented in Law et al. (2008), the limited nu mber of data points for the post-emissions phase for these organisms in addition to th e paucity of monitoring data for any other

relevant environmental media in the same re gion, it is difficult to conclude that the

decline in HBCD concentrations in harbour porpoises is re presentative of the post-2003 period. Consequently, no comparisons of th e response times of biota to emission

reductions between HBCD and other chemi cals are attempted using these data.

Roosens et al. (2008) reported a 3.5-fold decrease in mean concentrations of total HBCD in eel muscle sampled in 2006 (10 mg·kg -1 lw) for locations in the River Scheldt compared to 2000 (35.5 mg·kg -1 lw). In the same study, me an concentrations of PBDEs declined by a factor of n early 35, possibly reflecting th e restriction of penta-PBDE technical mixture in 2004. While it is po ssible that the apparently declining HBCD

concentrations in eel also reflect reduced emission/exposure, no corroborative monitoring

data of ambient environmental concentrations or food items was included in this study. Therefore, it is not possible to use these data to estimate e.g. relative response times in these organisms or arrive at meaningful c onclusions regarding the overall persis tence of

HBCD in the environment.

Monitoring data for herring muscle (1999–2007), guillemot eggs (1969–2007) and human breast milk (1980–2004) are available for locations in the Baltic Sea region. The temporal trends of these data are presented in Figures 3.12, 3.13 and 3.14 respectively.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 49 Figure 3.12. Geometric mean to tal HBCD concentrations ( μ g·kg -1 lw) in herring muscle sampled at various locations in the Balt ic Sea (Naturhistoriska riksmuseet 2008).

Figure 3.13. Mean total HBCD concentration ( μ g·kg -1 lw) in guillemot eggs (Sellström et al. 2003). Data from (Naturhistoriska riksmuseet 2008) not included here.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 50 Figure 3.14. Mean total HBCD concentration ( μ g·kg -1 lw) in human breast milk samples (Fangstrom et al. 2008).

Reported concentrations in guillemot eggs (S ellström et al. 2003) and human breast milk (Fangstrom et al. 2008) show slight in creases over the period 1980–1995. The overall increasing trends in reporte d concentrations over the time series are statistically significant in both cases as well. Interestingly, reported concentrations of total HBCD in herring muscle appear fairly constant over the period 1999–2007. The fact that concentrations in Baltic herring have been relatively stable while concentrations in higher trophic level consumers may still be increasing could reflect differences in uptake and

elimination kinetics. Hendriks et al. (2001) demonstrated that uptake and elimination kinetics are inversely proportiona l to body size. This fact imp lies that the time to reach near-steady state body burdens (assuming cons tant exposure levels) is also inversely proportional to body size. Whether emissions to the Baltic region have actually stabilized

cannot be confirmed however due to a lack of data on historical production, use and

releases of HBCD as well as a lack of temporal data for rele vant exposure media (e.g. air, water column).

Ismail et al. (2009) reported fairly constant concentrations in lake trout from Lake Ontario over the period 1979–2004. The highest mean concentrations in muscle tissue were observed for 1979. Not surprisingly then , when linear regression techniques were applied, a decreasing trend (statistically significant) in concentrations was observed. The

fact that concentrations in lake trout we re highest in 1979 is somewhat counterintuitive given the expectation that production/use (and hence emissi ons) has increased even in North America over the past two decades. Further information on production/use and emissions from this region could help clar ify the interpretation of these data.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 51 3.4.3 Biota: Remote Regions

Temporal trend data for biota inhabiting remote regions are available for several bird species (herring gull, Atlantic Puffin, Kittwake) inhabiting remote islands north of Norway (1983–2003) and Peregrine falcons inhabiting Greenland (1986–2003). These data are presented in Figure 3.15 and Figure 3.16.

Figure 3.15. Mean total HBCD concentrations ( μ g·kg -1 lw) in eggs of herring gull,

Atlantic Puffin and Kittiwake from two remote islands in the north of Norway, Røst (left panel) and Hornøya (right pa nel) (Knudsen et al. 2005).

Figure 3.16. Reported concentrations of total HBCD in peregrine falcon eggs ( μ g·kg -1 lw) inhabiting Greenland (Vorkamp et al. 2005).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 52 As shown in Figures 3.15 and 3.16, the tempor al trends from these two studies are not similar; a statistically significant increase is seen in herring gull, Atlantic Puffin and Kittiwake eggs whereas a non-sta tistically significant decrease is observed for Peregrine falcon eggs. (Braune et al. 2007) also docum ented declining concentrations of total HBCD in the eggs of ivory gulls ( Pagophila eburnea ) inhabiting Seymour Island (Canadian Arctic) based on levels in sa mples from 1976, 1987 and 2004. These results are consistent with the temporal trend of HBCD concentrations in Lake Ontario lake trout (Ismail et al. 2009). It is possible that these diverging temporal tre nds reflect different exposure sources (e.g. North Am erica vs Europe) and their re lative importance in various regions of the Arctic but there is no additiona l information to corroborate this assertion at

this time.

3.4.4 Key Considerations

Temporal trends in reported HBCD concentr ation in organisms from different locations do not show a uniform pattern. In some spec ies, concentrations of HBCD may have stabilized over the past decade or even be gun to decrease whereas there are indications

from other studies that con centrations are still increasi ng in other species, including

humans.

Analysis of temporal trend data in biota woul d be greatly facilitated by i) information on historical and current production/use of HB CD as well as the corresponding environment releases and ii) data characterizing the tempor al trends in relevant exposure media (e.g. air, water, sediment cores). These data gaps limit the abil ity to interpret the temporal

evolution of HBCD concentrations in the en vironment and compare them to the temporal evolution of other hydrophobic compounds which have been s ubject to restrictions or

bans on use.

3.5 F ATE AND E XPOSURE M ODELLING

Basic mass balance modelling concepts are described in the Appendix (Section 9.4.1). The models were used to (i) characterize environmental distribut ions and long range transport potential of HBCD using availa ble emissions and monitoring data, (ii)

corroborate concentration measurements with current emission estimates and predict response to reductions in emissions estimates , and (iii) compare HBCD degradation and long range transport properties with those of known POPs (e.g. PCBs), candidate POPs (e.g. PBDE 99), and recognized non-POPs (e.g. p -cresol) (Klasmeier et al. 2006).

HBCD Partitioning in the Atmosphere Before embarking on detailed modelling st udies, it is useful to first explore the partitioning behaviour of HBCD in the atmosphere. This important partitioningequilibrium determines the rate of atmospheric deposition and degradation of semivolatile organic chemicals (Lei et al. 2004). Th e gas-particle partitioning of HBCD as a function of temperature can be estimated using a simple K OA based equation and applying the log K OA of 10.46 for t-HBCD from Table 2.2 and a default energy of air-particle phase transfer of -80 kJ/mol. HBCD is predicted to change from a gas phase compound to a particle-associated chemical within the global environmental temperature range (+ 35 toArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 53 -35 °C) (Figure 3.17). Depending on the concen tration and composition of the particles,

that transition can occur with in a slightly different temper ature range. In a highly polluted atmosphere, a larger fraction of HBCD is e xpected to be partic le bound than in the remote atmosphere at the same temperatur e. This may explain the high particle-bound fraction measured in Guangzhou (see Section 3.3.1).

0% 25% 50% 75% 100%

-35 -25 -15 -5 5 15 25 35

T in °C% on particles curve shifts to the right for:
- higher particle concentration - higher organic matter fraction in particles volume fraction particles in air 10 11

volume fraction of organic carbon in particles: 0.1

log K OA at 25 ° C = 10.46

∆ H OA = -80 kJ/mol

Figure 3.17. Estimated gas-particle partitio ning behaviour of HBCD as a function of atmospheric temperature.

This suggests that HBCD will be subject to different atmospheric pr ocesses at different temperatures. At higher temperatures, a signif icant fraction of HBCD is present in the vapour phase, and may thus undergo gas phase reaction with OH radicals and gaseous deposition. At lower temperatures, the a ssociation with particles may prevent the oxidation of HBCD, but also increa se the rate of dry deposition.

Interestingly, the air-water equilibrium partition coefficient (K AW ) of HBCD also places it in a transition area with respect to the part itioning between the atmospheric gas phase and water droplets in the atmosphere (Lei and Wania 2004). HBCD is not very efficiently scavenged by warm rain, but could be quite efficiently scavenged by fairly cold precipitation. In this regard it does not matter whether cold temperatures drive the gaseous HBCD onto particles or water droplets , because particles te nd to be washed out efficiently by precipitation.

The long range transport of HBCD would pr esumably be most pronounced during cold, dry periods. Low temperature would cause HBCD to sorb to particles, where it is likelyArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 54 shielded from OH radical attack. In the ab sence of precipitation, small particles can remain airborne for extended periods of time and can therefore be transported long distances. Most simple long range transport a ssessment models (e.g. the Tool) would fail to recognize the potentia l for such transport, because they either only consider relatively high temperature conditions a nd/or rely on the assumption of continuous precipitation.

In summary, whereas in a warm (model) environment, precipitation scavenging is unlikely to be an important deposition pathwa y for HBCD, the situation may be quite a bit different in a cold environment. By ex tension, the continuous rain assumption of RAIDAR and the Tool is unproblematic as long as we only model warm environments.

3.5.1 Benchmarking

Models were used to compare HBCD prope rties with those of selected benchmark chemicals. The physical-chemical properties and half-lives are listed in Tables 2.2, 2.3, 2.5, and 2.9.

3.5.1.1 The Tool

The Tool V2.0 (Wegmann et al. 2009) is an OECD sanctioned evaluative model for estimates of long range transport potential and overall persistence in the environment.

The Tool was used to calculate the characte ristic travel distance (CTD, km), transfer efficiency (TE; %), and overall persistence (P OV , d) for t-HBCD and the benchmark

chemicals. CTD is an estimate of the distance at which the concentra tion of a chemical in a moving parcel of air that in teracts with the surface falls to (1/e) or 37% of its initial value (Bennett et al. 1999). TE is the percentage of emission flux that is deposited to the soil and water of a hypothetical region adjacen t to the region receiving the emissions (Wegmann et al. 2009). P OV is a metric of the overall reaction time in the defined environment and provides relative comparisons for chemical persistence as a result of degradation processes and intermedia partitioni ng. It includes half-life information for air, water, soil and sediment and the quantity of chemical in each compartment. It has been advocated as a more representative and integrated metric of chemical persistence rather than comparing estimated half-lives in individual environmental compartments to media specific criteria (Webster et al. 1998).

It is noted that a sediment compartment is not included in The Tool predictions and that this screening level model may not accurately capture gas-particle partitioning and related processes for all s ubstances under all environm ental conditions (see above).

Three calculations were made for t-HBCD. The first assume s selected median HLs for degradation in air, water a nd soil, the second assumes the lower bound degradation HLs in air, water and soil (t-HBCD, low), and the third assumes the upper bound degradation HLs in air, water and soil (t-HBCD, high) (Table 2.5).

For each chemical, i.e., “different HBCD s cenarios”, and the benchmark chemicals, The Tool makes three sets of calculations, one assuming 100% emissions to air, one assuming 100% emissions to water, and one assuming 100% emissions to soil. Results for each scenario are available; however, the default output for the model is the highest (mostArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 55 conservative, or ‘worst case’) value for each parameter (CTD, TE, and P OV ). Unless otherwise noted below, only the results for th e ‘worst case’ calculations are presented.

The Tool CTD Benchmarking Figure 3.18 compares the long range transport predictions of CTD and TE as well as P OV for the t-HBCD scenarios and the benchmark chemicals. The CTD predictions are approximately 200, 600 and 1,500 km for th e three different HBCD degradation scenarios (lower bound, median, and upper bound HL estimates, respectively). These

‘worst case’ results are for 100% emissions to air. When 100% of the emissions are assumed to water, the CTD predictions fo r HBCD are approximately 20, 200 and 1,300 km for the lower bound, median, and upper bound HL estimates, respectively. CTD is not

calculated when 100% emissions are to soil.

The ‘worst case’ CTD estimates for H BCD (100% emissions to air, upper bound degradation HLs) are near th e middle of the values for th e benchmark chemicals. The

lowest CTD value for the benchmark chemical s is for a non-POP: atrazine (125 km); however, the CTD for aldrin (a POP) is onl y about twice as high (230 km). The highest

CTD values are for a non-POP: tetrach loromethane (330,000 km), and a POP: hexachlorobenzene (200,000 km). Figure 3.18 shows that CTDs for POPs and non-POPs span about 3.5 orders of magnitude. The ‘w orst case” CTD values for the three HBCD

scenarios are closer to most of the non-POP CTD values than most of the POP CTD

values. All three HBCD scenarios show th at these CTD values are lower than CTD predictions for candidate POPs . HBCD CTD predictions are ge nerally closer to values

observed for “traditional non-POPs ”, regardless of degradat ion HL assumption. Clearly these CTD predictions for HBCD are much lo wer than values observed for “traditional

POPs” such as hexachlorobenzene and PCBs.

The Tool TE Benchmarking The TE predictions are approximately 0.05, 0. 42 and 2.7% for the three different HBCD degradation scenarios (lower bound, median, and upper bound HL estimates, respectively). These ‘worst case’ results ar e for 100% emissions to air. When 100% of the emissions are assumed to water, the TE predictions for HBCD are approximately <0.0001, 0.0007 and 0.16 % for the lower bound, median, and upper bound HL estimates, respectively. When 100% of the emissions are assumed to soil, the TE predictions for HBCD are approximately <<0.0001, <<0.0001 and 0.0001 % for the lower bound, median, and upper bound HL estimates, respec tively. The TE predictions for the lower bound and median HL assumptions are in the ra nge of the TE predictions for most of the non-POPs. The TE prediction for the upper bound HL assumption is about 1 order of magnitude lower than TE predictions for the candidate POPs and half of the benchmark POPs. The highest TE predictions are fo r hexachlorobenzene (a POP; 1,450%) and tetrachloromethane (a non-POP; 140%). The lowest TE values are for a POP: aldrin (0.001%), and a non-POP: p -cresol (0.02%).

The Tool P OV Benchmarking

The Tool ‘worst case’ P OV predictions range from about 10 days for p -cresol and the

HBCD lower bound HL scenario to 4,700 days for PCB 180. The P OV predictions areArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 56 approximately 12, 120 and 1,200 days for the three different HBCD degradation scenarios (lower bound, median, and upper bound HL estimates, respectively). These ‘worst case’ predictions occur when 100% of the HBCD emissions are assumed to soil. When 100% of the emissions are assumed to air, the P OV predictions for HBCD are approximately 1.3, 20 and 320 days for the lower bound, median, and upper bound HL estimates, respectively. When 100% of the emissions are assumed to water, the P OV predictions for HBCD are approximatel y 12, 120 and 970 days for the lower bound, median, and upper bound HL estimates, re spectively. The ‘worst case’ P OV predictions for HBCD (median and lower bound HLs) are similar to most of the ‘worst case’ P OV predictions for non-POPs ( ≤ 120 d) and separate from most POPs and candidate POPs ( ≥ 600 d). The upper bound degradation HL scenario results in P OV predictions that are comparable to many POP and candidate PO P benchmarks. The results highlight the uncertainty in environmental degradation half-life estimates on predictions of P OV for

HBCD. The benchmark simulations also shows that there is a POP (heptachlor; 70 d)

with a ‘worst case’ P OV comparable to most non-POPs and that there is a non-POP (tetrachloromethane; 1,270 d) with a ‘worst case’ P OV comparable to many POPs and

candidate POPs.

The mode-of-entry to the environment and th e rates of degradation are shown to have significant impacts on LRT potential and P OV predictions for HBCD. In particular, the mode-of-entry assumption influences LRT pot ential to a greater extent than the degradation HLs. The degradation HL assu mptions have a greater influence on the predictions for P OV . Due to the uncertainties in th ese two key parameters, there are uncertainties in comparisons with the benchmark chemicals. In general, however, HBCD shows a trend of having LRT and P OV predictions that are more consistent with values for

most of the benchmark non-POPs than valu es for most of the benchmark POPs and

candidate POPs.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 57 Figure 3.18. Long range transport (LRT) estimat es (characteristic tr avel distance, CTD; transfer efficiency, TE) and overall persistence (P OV ) estimates for the selected substances using The Tool V2.0 model. 10 100 1000 10000 100000 1000000

1 10 100 1000 10000CTD, km POPs

non-POPs

Candi-POPs

t-HBCD (median HLs)

t-HBCD (lower bound HLs)

t-HBCD (upper bound HLs)

0.0001

0.001

0.01

0.1

1 10 100 1000 10000

1 10 100 1000 10000



### The Tool P ov (d)TE, %Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 58 3.5.1.2 RAIDAR

Figure 3.19 provides a conceptual overview of the Risk Assessment IDentification And Ranking (RAIDAR) model (V2.0). Details are provided elsewhere (Arnot et al. 2006; Arnot and Mackay 2008). Briefl y, RAIDAR is a screening level steady state evaluative

model that combines information on chemical partitioning, degradation, environmental fate and transport, food web bioaccumulati on, exposure, effect endpoint and emission

rate in a coherent mass balance framew ork. RAIDAR includes a regional scale environment with an area of 100,000 km 2 (~90% land, ~10% wa ter with underlying sediment) and a variety of representative pl ant, invertebrate and vertebrate species including fish, wildlife, agricultural crops, livestock, and humans. Primary producers and

invertebrates bioconcentrate chemical from thei r ambient environment of air, water, soil, or sediment while all other species bio accumulate chemical from their ambient

environment and from their diet. The ma ss balance bioaccumulation models include major routes of chemical uptake and elimina tion. Only farfield e xposures are currently

considered for humans. The model does not in clude occupational or industrial exposures or indoor air and consumer product uses (e .g. personal care or household products) for exposures to humans. It is assumed that ther e are no losses or additions of chemical to food as a result of processing (e.g. animal husbandry) an d preparation (e.g. cooking, washing).

RAIDAR "active" soil depth is 20 cm and se diment depth is 5 cm. These compartments are assumed to be aerobic. Default calculations include chemical degradation on aerosols in the bulk air compartment and suspended particles in the bulk water compartment.

RAIDAR calculations are at an assume d constant temperature approximating temperatures associated with the physical-c hemical property estimates. Since substances such as HBCD can be transported between regions greater than the RAIDAR evaluative environmental scale (i.e. neighbouring source re gions), advective losses in air and water were considered negligible for all model ca lculations and benchmarking exercises in the

present assessment.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 59 Figure 3.19. A conceptual overview of the RAIDAR model (modified from Arnot et al. 2006).

Fundamentally, risk is a function of the rate, or quantity (Q), of chemical released to the environment; fate, transport and persistence (P ) in the physical environment (air, water, soil, sediment); bioaccumulation (B) in plants and animals; and toxicity (T) (Arnot and Mackay 2008). Elements of P and B determin e the potential for chemical exposure while QPB determine actual exposur es. RAIDAR combines P and B information into an exposure assessment factor (EAF) thus providi ng single values for transparent chemical comparisons and rankings based on exposur e assessment objectives . The RAIDAR model is used to predict regional scale chemical concentrations in the environment and representative species. The model is not parameterized to a particular regional

environment. The model uses an assumed un it emission rate for comparing HBCD with

benchmark chemicals. Estimates of actual emi ssions can be used to scale unit emissions for comparisons of RAIDAR pred ictions with monitoring data.

RAIDAR EAF Benchmarking

RAIDAR V2.0 was used to calculate maximu m exposure assessment factors (EAF) in the regional environment for HBCD and the benc hmark chemicals. Essentially this is a hazard-based exposure metric identifying the representative organism in the regional environment that is expected to have the highest level of chemical exposure. The EAF combines information on chemical emission, fate and transport, degradation, food web bioaccumulation for all representative spec ies in the RAIDAR environment. EAF is similar to the human intake fraction; however, it also includes absorption efficiency. It is calculated using a uni t emission rate (E U ) of 1 kg·h -1 for each chemica l; therefore, the Air

Sediment Soil

WaterArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 60 maximum exposure potentials predicted for a set of chemicals can be compared and

ranked.

The individual diastereomers and t-HBCD were modelled for comparison. In addition, the lower and upper bound HLs were also co nsidered as t-HBCD (low) and t-HBCD (high), respectively, to assess the uncertainty of these parameters on the EAF and

RAIDAR P OV estimates. Level II and Level III fate model calculations were considered.

For the Level II fate calculations mode-of- entry information is not required because the model assumes equilibrium between the physical compartments of the environment (air, water, soil and sediment). The Level III fate calculations require mode-of-entry information because intermedia transport processes (e.g. precipitation) are considered, which can result in non-equilibrium cond itions between the physical environmental compartments. For the Level III fate calcula tions, three scenarios were considered for mode-of-entry. The first assumes 100% of th e unit emissions are to air, the second

assumes that 100% of the emissions are to wa ter and the third assu mes that 50% of the

emissions are to air and 50% are to water.

Table 3.7 lists the organisms identified as having the highest poten tial for exposure, the associated EAF estimates, i.e., maximum expo sures, and the relati ve rankings of the

chemicals based on the Level II fate calcu lations. The re lative exposure potential for tHBCD and the three diastereomers (median HL s) are in the middle of the rankings (10- 13). t-HBCD (low bound HLs) shows a ranking equivalent to the non-POPs and t-HBCD (high bound HLs) shows a ranking equivalent to the POPs and the candidate POPs. Aquatic (marine) mammals are identified as the organisms with the highest exposure potential for all HBCD scenarios. This is a result of high lipid contents for these organisms and high dietary intake of HBCD through the consumption of fish. HBCD is not highly biotransformed in fish, resulting in high dietary sources for the aquatic/marine

mammals. Piscivorous birds ar e the representative organisms that show the second highest exposure potenti al (data not shown).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 61 Table 3.7. RAIDAR V2.0 Level II maximum exposure assessment factors (EAF), relative rankings and the organism of concern for HB CD and benchmark chemicals. EAF values listed are base 10 logarithms.

Status Abbrev Rank Log EAF Organism of concern

POP HCB 2 -2.08 Aquatic mammal

POP PCB28 14 -4.45 Aquatic mammal

POP PCB101 5 -2.71 Aquatic mammal

POP PCB180 1 -1.48 Aquatic mammal

POP Aldrin 16 -4.92 Terrestrial carnivore

POP Heptachlor 15 -4.84 Terrestrial carnivore

NOP Biphenyl 19 -7.26 Benthic invertebrate

NOP p -cresol 21 -8.33 Upper trophic level fish

NOP Atrazine 18 -6.64 Benthic invertebrate

NOP CCl4 20 -7.45 Aquatic mammal

Candi-POP α -HCH 9 -3.88 Aquatic mammal

Candi-POP β -HCH 6 -3.29 Aquatic mammal

Candi-POP γ -HCH 8 -3.57 Terrestrial carnivore

Candi-POP PBDE99 4 -2.66 Aquatic mammal

t-HBCD 11 -4.38 Aquatic mammal

α -HBCD 13 -4.40 Aquatic mammal

β -HBCD 12 -4.39 Aquatic mammal

γ -HBCD 10 -4.38 Aquatic mammal

t-HBCD (low) 17 -5.37 Aquatic mammal

t-HBCD (high) 7 -3.41 Aquatic mammalArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 62 Table 3.8 lists the organisms identified as having the highest poten tial for exposure, the associated EAF estimates (i.e. maximum EA Fs), and the relative rankings of the chemicals based on the Level III (100% emissi ons to air) fate calculations. The relative exposure potential for t-HBCD and the three diastereomers (median HLs) are in the middle of the rankings (10-13). t-HBCD (low bound HLs) shows a rank ing equivalent to the non-POPs and t-HBCD (high bound HLs) shows a ranking equivalent to the POPs

and candidate POPs. These relative results are similar to the Level II results except that now the terrestrial carnivore (e.g. wolf) is identified as the organism with the highest exposure potential for all HBCD scenarios. This is a result of the higher trophic level of

this animal and the fate and distribution of HBCD under this release scenario, i.e., HBCD is predominantly distributed to soil.

Table 3.8. RAIDAR V2.0 Level III (100% em issions to air) maximum exposure assessment factors (EAF), relative rankings an d the organism of c oncern for HBCD and benchmark chemicals. Values listed are base 10 logarithms.

Status Abbrev Rank Log EAF Organism of concern

POP HCB 2 -1.15 Aquatic mammal

POP PCB28 14 -4.08 Aquatic mammal

POP PCB101 4 -2.15 Aquatic mammal

POP PCB180 1 -0.72 Aquatic mammal

POP Aldrin 17 -4.90 Terrestrial carnivore

POP Heptachlor 15 -4.30 Terrestrial carnivore

NOP Biphenyl 20 -7.71 Foliage vegetation

NOP p -cresol 21 -8.34 Foliage vegetation

NOP Atrazine 18 -6.23 Foliage vegetation

NOP CCl4 19 -7.45 Aquatic mammal

Candi-POP α -HCH 9 -3.60 Terrestrial carnivore

Candi-POP β -HCH 7 -3.21 Aquatic mammal

Candi-POP γ -HCH 6 -3.12 Terrestrial carnivore

Candi-POP PBDE99 3 -1.90 Aquatic mammal

t-HBCD 11 -3.86 Terrestrial carnivore

α -HBCD 12 -3.87 Terrestrial carnivore

β -HBCD 13 -3.92 Terrestrial carnivore

γ -HBCD 10 -3.86 Terrestrial carnivore

t-HBCD (low) 16 -4.33 Terrestrial carnivore

t-HBCD (high) 8 -3.47 Terrestrial carnivoreArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 63 Table 3.9 lists the organisms identified as having the highest poten tial for exposure, the associated EAF estimates (i.e. maximum EA Fs), and the relative rankings of the chemicals based on the Level III (100% emi ssions to water) fate calculations. These

relative results are similar to the Level II results.

Table 3.9. RAIDAR V2.0 Level III (100% emissi ons to water) maximum exposure assessment factors (EAF), relative rankings an d the organism of c oncern for HBCD and benchmark chemicals. Values listed are base 10 logarithms.

Status Abbrev Rank Log EAF Organism of concern

POP HCB 2 -0.97 Aquatic mammal

POP PCB28 9 -2.74 Aquatic mammal

POP PCB101 4 -1.35 Aquatic mammal

POP PCB180 1 -0.31 Aquatic mammal

POP Aldrin 7 -2.38 Aquatic mammal

POP Heptachlor 15 -3.09 Aquatic mammal

NOP Biphenyl 18 -6.15 Benthic invertebrate

NOP p -cresol 21 -8.15 Upper trophic level fish

NOP Atrazine 19 -6.51 Benthic invertebrate

NOP CCl4 20 -6.99 Benthic invertebrate

Candi-POP α -HCH 16 -3.28 Aquatic mammal

Candi-POP β -HCH 8 -2.63 Aquatic mammal

Candi-POP γ -HCH 14 -3.02 Aquatic mammal

Candi-POP PBDE99 3 -1.14 Aquatic mammal

t-HBCD 11 -2.76 Aquatic mammal

α -HBCD 13 -2.79 Aquatic mammal

β -HBCD 12 -2.78 Aquatic mammal

γ -HBCD 10 -2.76 Aquatic mammal

t-HBCD (low) 17 -3.32 Aquatic mammal

t-HBCD (high) 6 -2.36 Aquatic mammalArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 64 Table 3.10 lists the organisms identified as having the highest potential for exposure, the associated EAF estimates (i.e. maximum EA Fs), and the relative rankings of the chemicals based on the Level III (50% emissions to air and 50% emissions to water) fate

calculations. These results are similar to the Level II results and the Level III results assuming 100% emissions to water. This highl ights the low air-water partition coefficient (K AW ) of HBCD and the moderately high K OW (i.e. significant transport to sediments when entering the water).

Table 3.10. RAIDAR V2.0 Level III (50% emissions to air and 50% emissions to water) maximum exposure assessment factors (EAF), relative rankings and the organism of

concern for HBCD and benchmark chemicals. Values listed are base 10 logarithms.

Status Abbrev Rank Log EAF Organism of concern

POP HCB 2 -1.05 Aquatic mammal

POP PCB28 9 -3.02 Aquatic mammal

POP PCB101 4 -1.58 Aquatic mammal

POP PCB180 1 -0.47 Aquatic mammal

POP Aldrin 7 -2.68 Aquatic mammal

POP Heptachlor 15 -3.38 Aquatic mammal

NOP Biphenyl 18 -6.44 Benthic invertebrate

NOP p -cresol 21 -8.45 Upper trophic level fish

NOP Atrazine 19 -6.53 Foliage vegetation

NOP CCl4 20 -7.25 Benthic invertebrate

Candi-POP α -HCH 16 -3.44 Aquatic mammal

Candi-POP β -HCH 8 -2.83 Aquatic mammal

Candi-POP γ -HCH 14 -3.19 Aquatic mammal

Candi-POP PBDE99 3 -1.37 Aquatic mammal

t-HBCD 11 -3.04 Aquatic mammal

α -HBCD 13 -3.08 Aquatic mammal

β -HBCD 12 -3.06 Aquatic mammal

γ -HBCD 10 -3.04 Aquatic mammal

t-HBCD (low) 17 -3.62 Aquatic mammal

t-HBCD (high) 6 -2.63 Aquatic mammalArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 65 γ -HBCD has a consistently higher EAF ranking than α -HBCD; however, the differences between isomers are relatively insignificant in the context of th e current study. The general similarities in the results for t-H BCD and the diastereomers suggests that the representative properties for t-HBCD are lik ely sufficient to characterize HBCD for the modelling exercises in the context of the pr esent study. Notably all diastereomers and tHBCD currently share the same environmenta l degradation and biotransformation halflives as a result of substance specific data limitations and the available estimates for the partitioning properties are comparable. In gene ral, the differences between the EAFs for t-HBCD (median HLs for degradation), t-HBCD (lower bound or fast HLs for degradation), and t-HBCD (upper bound or sl ow HLs for degrada tion), are slight; however, the rankings relative to the benchmark chemicals can be affected significantly. Clearly, these preliminary results show that assumptions concerning mode-of-entry and environmental HLs are critically important for the benchmark assessment of HBCD.

RAIDAR P OV Bencmarking Table 3.11 compares the predictions for P OV based on the different assumptions used in

the RAIDAR model simulations. For HBCD , Level III simulations show lower P OV predictions than the Level II calculations. There are also slight differences for the
different diastereomers, with α - and β -HBCD showing the longest overall environmental persistence. This is a reflection of differences in the distribution of the diastereomers in

the environment as a result of the differences in partitioning properties and not a result of degradation half-lives, which are assumed th e same for all forms of HBCD in these

simulations. In most cases the P OV predictions for HBCD are more similar to P OV values

for the non-POP benchmark chemicals than to P OV values for the POP and candidate POP benchmark chemicals. These general comparisons are most notable when the median or lower bound HL estimates are assumed; however, even when the upper bound HL

estimates are assumed P OV predictions of HBCD typically below, or well below, P OV predictions for benchmark POP and candidate POP chemicals. The P OV predictions for tHBCD are higher using The Tool than usi ng RAIDAR, particularly for emissions to water, because The Tool does not co nsider degradation in sediment.

The Level III simulations show that mode-of-entry is important for P OV predictions for most, if not all, substances. For HBCD, P OV predictions are lower when emissions are to

air than when emissions are to water a ssuming either the median or lower bound HL estimates. However, when the upper bound HL estimates are selected there is little

difference between the P OV predictions based on the mode-of-entry assumption.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 66 Table 3.11. RAIDAR V2.0 overall persistence (P OV ; d) estimates using Level II and

Level III fate calculations.

Status Abbrev Level II Level III (Air) Level III (Water) Level III (A&W)

POP HCB 2028 1467 1542 1505

POP PCB28 296 34 491 263

POP PCB101 2149 317 1455 886

POP PCB180 4588 2097 1954 2025

POP Aldrin 77 2 861 432

POP Heptachlor 53 3 90 47

NOP Biphenyl 10 5 16 10

NOP p -cresol 5 1 7 4

NOP Atrazine 80 26 73 49

NOP CCl4 1287 1288 1211 1249

Candi-POP α -HCH 739 489 620 554

Candi-POP β -HCH 1776 1677 1414 1546

Candi-POP γ -HCH 726 564 623 594

Candi-POP PBDE99 1834 1159 1740 1449

t-HBCD 118 15 66 41

α -HBCD 118 16 77 46
β -HBCD 119 30 74 52

γ -HBCD 118 14 65 40

t-HBCD (low) 12 1 11 6

t-HBCD (high) 1124 314 295 304

RAIDAR Sensitivity and Uncertainty Analysis A sensitivity analysis was done for t-HBCD us ing the Level III 50% emissions to water, 50% emissions to air mode-of-entry scenario . The endpoint selected for the sensitivity analysis was the total body burden base d on the assumed unit emission rate (TBB U ; ng) for the organism with the highest potential for exposure, i.e., the aquatic (marine) mammal. The justification for the selection of this representative organism is supported by the monitoring data. The sensitivity analys is indicates the infl uence of model input parameters (physical chemical properties a nd primary transformation half-lives) on the model output. Based on the sensitivity of the parameter ( Sens ) and the uncertainty of the parameter (e.g., Cf listed in Tables 2.2 and 2.3), the relative contributi on of variance ( CV ) for each parameter on t-HBCD exposure to the representative marine mammal can beArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 67 determined. For example, the sensitivity ( Sens ) of an input parameter ( P i ) on TBB calculations (model output) can be approxima tely quantified as (MacLeod et al. 2002)

Sens = ( ∆ TBB/TBB)/( ∆ P

/ P i ) (3.1)

where ∆ TBB is the change in the TBB quantity and ∆ P

is a fixed change to a selected input parameter (e.g., 0.1%). The contribution to variance (uncertainty) of the substance input parameters ( CV i ) on the TBB calculations are ev aluated as a function of the variance (uncertainty) σ 2 i and sensitivity ( Sens i ) of the individual input parameters as described by (MacLeod et al. 2002)



## ∑

= =

n

k P P P P

i

k k i i

S S CV

1 2 2 2 2

σ σ (3.2)

Table 3.12 shows that the most sensitive parameters to the TBB U calculations are the primary biotransformation HLs in the marine mammal and in fish (its primary dietary item). The degradation HLs in water and sedime nt are also sensitive input parameters, but

to a lesser extent than the biotransformati on HLs. The biotransformation rates obviously

influence the level in the marine mammal b ecause it largely determ ines the residence time in the body. Biotransformation is partic ularly important for chemicals with high bioaccumulation potential in air breathing organisms (high K OW and high K OA ). Due to these partitioning properties and the high lipid content of the organism, other routes of chemical elimination (respiration, urinati on) are very slow. T hus an increase in biotransformation half-life results in an increase in the TBB. The uncertainty in the biotransformation half-life is less than the assumed uncertainties in water and sediment; however, because of the sensit ivity of the model to this parameter it has the highest contribution to variance in the predicti on. Approximately 94% of the uncertainty

associated with TBB U for t-HBCD is a function of the biotransformation HLs in fish and

the marine mammal.

It is emphasized that this analysis is depe ndent on the structure of the model (assumed feeding relationships, organic carbon content in sediment) and the selected endpoint. The selection of other endpoints (e.g . exposure to fish or inverteb rates, or concentrations in different compartments) will result in diffe rent sensitivities on the output. Since the marine mammal is identified as the organism with the highest EAF according to LIII fate

calculations that include emissions to wate r and marine mammals show some of the highest concentrations in the environment this selected endpoint for the sensitivity analysis is justifiable.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 68 Table 3.12. Summary of RAIDAR model sensitivit y for input parameters on estimated total body burdens in the aquatic/marine ma mmal based on a unit emission rate (TBB U ; ng/g) assuming equal emissions of HBCD to air and water.

Parameter Sensitivity Contribution to Variance

Vapor Pressure -0.01 0.00

Water Solubility 0.01 0.00

K OW 0.03 0.00

HL - Air 0.02 0.00

HL - Water 0.23 0.05

HL - Soil 0.00 0.00

HL - Sediment 0.21 0.03

Biotrans HL - Fish 0.82 0.50

Biotrans HL - Av/Mam 0.91 0.42

3.5.1.3 The Tool and RAIDAR

Two models were combined to compare HBCD against the benchmark chemicals by estimating screening level exposure potential in a “source region” and a “remote region”. Details of this method are provided elsewh ere (Cowan-Ellsberry et al. 2009). RAIDAR V2.0 is used to assess fate, partitioning, degradation and food web bioaccumulation to predict unit total body burdens (TBB U ; mmol) in a “representative” marine mammal based on a unit emission rate of 1 kg·h -1 (Arnot et al. 2006; Arnot and Mackay 2008). The OECD Tool (Wegmann et al. 2009) is used to calculate the characteristic travel distance (CTD, km) of each substance in air and scale estimated body burdens in the RAIDAR “source region” environment to estimates of body burdens in a “remote region” environment. The potential for exposure in a “remote region” is a ssessed using the CTD

to estimate an effective emission rate into the remote region as a result of long range

transport in air from a source region 2,500 km away.

For the present simulations only atmospheric tr ansport to the remote region is considered and not transport in oceans or river water. The estima ted body burden in the “remote region” is calculated by scaling the body burde n in the “source region” by the fraction of the chemical that is transported to the “r emote region”. For this screening assessment

identical environmental conditions (e.g. temperature, surface c overage of water, soil and vegetation) and exposure pathways (e.g. diet ary selection) descri be both the source and remote regions. This screening level approa ch also may not accurately capture gasparticle partitioning and related processes for all substances under all environmental conditions (see above). Level III RAIDAR fate calculations were assumed 100%

emissions to air and no advective losses from the source region were assumed for calculating exposures in the source region. Median environmental HLs were selected.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 69 Figure 3.20 compares the TBB U predictions for the various substances in source and

remote evaluative environments. For the source region simulation, the TBB U predictions span 9 orders of magnitude from a POP (PCB 180) to a non-POP ( p -cresol). HBCD “source region” TBB U prediction is lower than most POPs and candidate POPs; however, it is comparable to some existing POPs (h eptachlor, aldrin). For the “remote” region benchmarking scenario, the TBB U predictions span >20 orders of magnitude from a POP (PCB 180) to a non-POP (atrazine); however , certain non-POPs have higher TBB U predictions in remote regions than listed POPs. The HBCD TBB U prediction is acomparable to certain POPs (heptahclor), bu t several orders of magnitude lower than other POPs (HCB, PCB 180). The HBCD TBB U prediction is also about 3 orders of magnitude lower than the TBB U predictions for the candidate POPs. It is noteworthy that The Tool long range transport predictions a ssume 100% of the emissions in the source region are to air. As shown above, the actual mode-of-entry to the source region environment can greatly influence the CTD calculation for most substances, including

HBCD.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 70 Figure 3.20. Total body burdens (TBB; mmol) estimated in “source” (upper figure) and “remote” (lower figure) regional environments based on an assumed unit emission rate (1 kg·h -1 ). Red bars – POPs; White bars – non-POPs; Black bars – Candi-POPs. -12

-8 -4 0 4HCBPCB28PCB101PCB180AldrinHeptachlorBiphenylp-cresolAtrazineCCl4a-HCHb-HCHg-HCHPBDE99t-HBCDlog TBB / mmol -8 -6 -4 -2 0 2 4HCBPCB28PCB101PCB180AldrinHeptachlorBiphenylp-cresolAtrazineCCl4a-HCHb-HCHg-HCHPBDE99t-HBCDlog TBB / mmol10-1310-1410-19Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 71 3.5.1.4 GloboPOP: Arctic Contamination Potential (eACP 10 )

Wania (2003b; 2006) introduced th e Arctic Contamination Potential (ACP) as a metric of long range transport efficiency, a model-cal culated value which is independent of

emission rate. There are two different formul ations of the ACP metric, mACP and eACP, both of which are calculated using the GloboPOP model (Wania and Mackay 1995). mACP represents the relative enrichment of contaminants in the northernmost climatic zone (N-polar, see Figure 3.21) and is calculated as

%) 100 ( 1 1

TG A T M M M mACP −

= (3.3)

where M T 1 is the total mass of contaminant in the N-polar zone (all compartments), M A1 is the total mass of contaminant in the atmosphere of the N-polar zone and M TG

is the total mass of contaminant in the global environment (all compartments). eACP represents the potential for absolute contamination of the nor thernmost climatic zone and is calculated

as

%) 100 ( 1 1

TG A T E M M eACP −

= (3.4)

where E TG is the total mass of contaminant emitted into global environment over the model simulation period (defau lt is 10 years). mACP and eA CP tend to be similar for perfectly persistent compounds (Wania 2006) but can vary substantially for degradable

substances such as those considered here. We use eACP here because it is a targetoriented indicator of long range transport (L RT) potential that has been compared to other model-derived indicators of LRT potentia l in the past (Fenner et al. 2005). Both

ACPs are sensitive to the assumed emissi on mode of entry (i.e. compartment that chemical is emitted into) with lower values expected as the fraction of contaminant released to the atmosphere is reduced (W ania 2003b). The geographical distribution of

emissions can also influence the results. The typical approach applied in the past for ranking exercises was to assume that emissions occur 100% to the atmosphere for all chemicals, all of which are distributed according to the proportion of the global population living in each climatic zone. To be comparable with previous studies and also consistent with the publicly available versi on of the model, the de fault distribution of emissions was assumed here (based on population in each zone). The length of the simulation was also left at 10 years, hence the metric we present is eACP 10 (note that

eACP values increase as a function of time (Gouin and Wania 2007) but this is not relevant for this benchmarking exercise).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 72 Figure 3.21. Zonal subdivision of the GloboP OP model, based on latitudinal climatic

bands.

Note that GloboPOP differs from some of the other m odel tools applied in this assessment with regards to the treatmen t of atmospheric degradation. Here, k OH is used as model input and the degradati on half-life is calculated as a function of latitudinallyspecific OH radical concentration and temperature.

We first conducted eACP 10 simulations using physical-chemical property values for α -, β -, and γ -HBCD as well as values characterizi ng the technical mixture (t-HBCD). The results of these preliminary simulations are presented in the Appendix (Figure 9.1). Since

no substantial differences in eACP 10 were observed, results presented below are for tHBCD only. As with the other benchmarking exercises, a series of simulations were conducted to generate results across the rang e of degradation half -lives presented in Table 2.5 (lower bound, median and upper bound estimates).

The calculated eACP 10 for the chemicals included in this benchmarking exercise are presented in Figure 3.22 as a function of the reactivity with OH radicals in the atmosphere (k OH , cm 3 ·molecule -1 ·s -1 ). Note that this figure should not be interpreted to

mean that OH radical reactivit y is the sole dete rminant of long range transport (LRT) potential. Atmospheric deposition and air-surf ace exchange are also important processes influencing environmental fate, hence degrada tion half-lives in other media also affect model output. As a general trend however, subs tances which are more recalcitrant to OH radical reactions have longer degradati on half-lives in ot her media. Hence k OH can broadly serve as a proxy fo r overall persistence. The k OH and eACP 10 values are also presented in Table 3.13.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 73 Figure 3.22. Arctic Contamination Potentia l after 10 years of constant emissions (eACP 10 ) of POP, non-POP, Candi-POPs and t-H BCD (under different assumptions) as a function of reactivity with OH radicals (k OH , cm 3 ·molecule -1 ·s -1 ). Unless noted otherwise,

emissions assumed to be 100% to air and r eactions with OH radicals assumed to occur only in the gas phase. Note: The eACP 10 of CCl 4 is shown on the y-axis because it was assumed to have a negligible k OH.

The eACP 10 values of the POP compounds range from 0.004–1.6%. Aldrin and heptachlor had the lowest eACP 10 values (0.04 and 0.004% respectively), substantially lower than the other POPs. This model output is related to the elevated reactivity towards

OH radicals and lower half-life values in other environmental media of Aldrin and particularly Heptachlor in comparison to the other POPs. For example, according to AOPWIN v1.92 (US EPA 2009), Heptachlor and Al drin are roughly 1 to 3 orders of magnitude more susceptible to OH radical attack compared to the other POPs. HCB had the highest eACP 10 (1.61%) while the PCBs ranged from 0.17% (PCB28) to 0.65% (PCB180).

The eACP 10 values for the non-POPs included in the benchmarking exercise ranged from

0.004–0.18%. The lowest eACP 10 values were calculated for biphenyl and p-cresol (0.004%) followed by atrazine (0.016%). Th ese three compounds are characterized by low degradation half-lives in all bulk environmental compartments. In contrast, CCl 4 had a relatively high eACP 10 due to its persistence in the at mosphere and longe r half-lives in water, soil and sediments compared to the other non-POPs.

The eACP 10 values for the candidate POPs included in the benchmarking exercise ranged

from 0.37–0.55%. eACP 10 values of the HCH isomers we re similar (0.50–0.55%) while a lower value was calculated for PBDE99 (0.37%). These calculated eACP 10 values fallArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 74 within the same range as the PCBs indica ting that these candidate POPs are broadly similar to other POPs with respect to long range transport potential.

eACP 10 output for t-HBCD is presented for four different scenarios i) lower bound HL estimates (i.e. lowest degradation half-lives in all compartments ii) t-HBCD (median half-life values, all comp artments) iii) upper bound HL estimates and iv) Reduced emissions to Air (median half-life values, 30% of emissions to air, 70% to surface water). Assuming 100% emissions to air i.e. Scenarios i) – iii), the eACP 10 of t-HBCD ranges from 0.005% (lower bound HLs) to 0.4% (upper bound HLs); the eACP 10 assuming the

default scenario values is 0.05%. With reduced emissions to air (and median HLs), the

eACP 10 is reduced to 0.02% demonstrating the in fluence of mode of entry on this metric of long range transport potential. The fact that the range of estimated eACP 10 for t-HBCD spans two-orders of magnitude indicates the sensitivity of the model output to assumptions regarding degradation half-lives. Interestingly, eACP 10 is least sensitive to

the assumed half-life in fres hwater sediments, largely due to the fact that this compartment is only a small fraction of the global environment. Assumed half-lives in soil and water (includes ocean surface wate r) have a greater influence since these compartments have comparatively large volum es. Therefore, if experimental studies demonstrate the degradation half-lives in th ese compartments are substantially longer than the maximum values assumed here, it is no longer reasonable to assume that the

maximum eACP 10 calculated here is representative.

The overall interpretation of these results is complicated by the range of eACP 10 values exhibited by the POP compounds. Assumi ng median parameter values, the eACP 10 of tHBCD is lower than PCBs, HCB and the othe r candidate POPs but higher than Aldrin and Heptachlor. In comparison to t-HBCD (median k OH value), Heptachlor and Aldrin are roughly 1 order of magnit ude more susceptible to OH radical attack. The eACP 10 of Aldrin is roughly the same as t-HBCD however because it is more persistent in all other compartments. Heptachlor, on the other hand, has lower eACP 10 primarily because it is degraded in other compartments with si milar HL as t-HBCD (median values). Partitioning properties (i.e. distribution) also influence overall persistence and eACP 10

. For example, Aldrin and Heptachlor have log K AW values 2 orders of magnitude higher. Strictly speaking, this benchmarking exercise could be interpreted to mean that t-HBCD has a long range trans port potential similar to other POPs. However, the long range transport potential of Aldrin and Heptachlor is clearly different fr om other POPs (and Candidate POPs) included in the benchmarki ng exercise. For example, the results imply that the presence of Aldrin and Heptaclor in remote regions necessarily requires higher absolute emissions in comparison to other PO Ps in order for a similar mass of chemical

to reach this environment. Under default assu mptions regarding degradation half-life, the same argument could be made for t-HBCD based on its eACP 10 . It is also worth noting

that the eACP 10 of all non-POPs falls within the ove rall range of POPs as well (0.004 – 1.61%). In conclusion, while these simulations provide some insight into the relative long range transport potential of t-HBCD, absolute emissions a nd mode of entry of these

emissions must also be considered.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 75 Two additional eACP 10 simulations were conducted to further elucidate the fate and transport of t-HBCD in the global environmen t, one assuming perfect persistence in all compartments (i.e. negligible degradation) and one assuming no atmospheric degradation (default degradation half-lives in all othe r compartments). The model output is also

included in Table 3.13. The eACP 10 assuming perfect pers istence is 0.93%, approximately 20 times higher than the default value. In this simulation, the main processes limiting long range transport ar e atmospheric deposition and other sink processes (e.g. sediment burial, transport to deep ocean via particle settling). The eACP 10 value assuming no degradation in the atmosphe re (and default half-lives in all other compartments) is 0.11%. This model output conf irms the fact the i) degradation in other compartments has an influence on the long ra nge transport potential of t-HBCD and ii) air-surface exchange processes (e.g. wet/dry de position) transfer significant quantities of t-HBCD from the atmosphere to other compartments in source regions.

Table 3.13. Selected compounds, OH radical reactivity and eACP 10 calculated using

GloboPOP model.

Status Chemical Scenario k OH eACP 10

POP HCB Default HLs 1.69e-14 1.61%

POP PCB28 Default HLs 1.19e-12 0.17%

POP PCB101 Default HLs 3.35e-13 0.53%

POP PCB180 Default HLs 1.05e-13 0.65%

POP Aldrin Default HLs 6.46e-11 0.04% POP Heptachlor Default HLs 6.11e-11 0.004%

Non-POP Biphenyl Default HLs 6.77e-12 0.004%

Non-POP p -cresol Default HLs 4.11e-11 0.004%

Non-POP Atrazine Default HLs 2.73e-11 0.016%

Non-POP CCl 4 Default HLs - 0.18%

Candi-POP α -HCH Default HLs 5.73e-13 0.55%

Candi-POP β -HCH Default HLs 5.73e-13 0.53%

Candi-POP γ -HCH Default HLs 5.73e-13 0.50%

Candi-POP PBDE99 Default HLs 5.50e-13 0.37%

- t-HBCD (low) Lower bound HLs 1.84e-11 0.005% - t-HBCD (median) Default HLs 6.12e-12 0.05% - t-HBCD (high) Higher bound HLs 2.04e-12 0.40% - t-HBCD (median) Reduced E to Air 6.12e-12 0.02% - t-HBCD (median) Perfect Persistence - 0.93% - t-HBCD (median) No Atmospheric Deg - 0.11%Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 76 3.5.1.5 AC-BAP

The chemical screening methodology for POPs introduced by Brown and Wania (2008) will be used for chemical comparisons in this section. This method aims to identify the domain of physical-chemical properties that result in LRT to the physical Arctic

environment and accumulation in the Arctic human food chain. For example, properties of HBCD will be compared with the chemical space maps for Arctic ContaminationBioaccumulation Potential (AC-BAP) without the actual applicati on of the combined

Globo-POP and ACC-Human m odels (Czub et al. 2008).

The Arctic contamination and bioaccumula tion potential (AC-BAP) provides a metric relating global chemical emission to the body burden and thus the internal exposure of a human living in the remote Arctic (Czub et al. 2008). The AC-BAP combines the zonally averaged global transport m odel Globo-POP, which has been used in previous ACP calculations (Wania 2003b; Wania 2006), with the human food-chain bioaccumulation model ACC-HUMAN (Czub and McLachlan 2004) , which has been used to estimate

bioaccumulation in Inuit. The AC-BAP is defined as the ratio of human body burden m H (g·person -1 ) and the cumulative emissions of a chemical to the global environment e TG (g):

AC

− BAP= m H

e TG (3.3)

AC-BAP has a unit of person − 1 and represents the fraction of the cumulative global emissions stored in a single Arctic resident . A high value stands for a high potential of a

chemical to reach the remote Arctic and to bioaccumulate in humans based on the

assumed food chain and evaluative environments.

In an effort to understand the influence of partitioning on the AC-BAP, Czub et al. (2008) calculated AC-BAP for a range of hypothetical chemicals that were assumed to be perfectly persistent and to be emitted at a constant rate into the global atmosphere for a period of 10 or 70 years 1 . The resulting AC-BAP-values were plotted as a function of the octanol-air and air-water part ition coefficient, yielding a chemical space diagram which identifies partitioning property combinati ons that favour accumulation of a globally emitted, persistent chemical in Ar ctic residents (Czub et al. 2008).

The partitioning properties of HBCD (FAVs) in Table 2.2 were compared with the “chemical space” diagrams for AC-BAP developed by Czub et al. (2008). Figure 3.23 shows the chemical space diagram for AC-BAP developed by Czub et al. (2008) and the estimated location of HBCD. Assuming co mplete persistence of HBCD in the environment and in food webs (“persistent H BCD”) and using the results of the AC-BAP model predictions for the chemical space the following inferences can be made. “Persistent HBCD” is expected to have a pproximately 20-30% of the maximum AC-BAP

1 The zonal distribution of global emissions is assumed to follow the latitudinal distribution of the global population.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 77 estimate assuming 10 years of emissions fo r all chemicals. “Persistent HBCD” is expected to have approximately 15-25% of the maximum AC-BAP estimate assuming 70 years of emissions for all chem icals. In contrast to the study by Brown and Wania (2008) relying on EPI Suite  predictions (U.S. EPA 2009), the partitioning properties recommended here, place “persistent HBCD” in to the part of the chemical space with elevated AC-BAP (>10 % of the maximum). This suggests that HBCD does have partitioning properties that favour tran sport to the Arctic environment and

bioaccumulation in the Arctic human food chain.

HBCD HBCD

HBCD

Figure 3.23. Chemical space diagrams identi fying partitioning properties favoring accumulation of a globally emitted, persistent chemical in an Inuit woman as quantified

with the Arctic Contamination and Bioaccumu lation Potential (AC-BAP) (Figures from Czub et al., 2008). The circle in the lower right of each pane l represents the approximate partitioning properties of HBCD (mixture). In the panel to the right, the blue line

encircles areas of elevated AC-BAP 10 (10 years of emissions) whereas the red line represents an elevated AC-BAP 70 (70 years of emissions). HBCD, when persistent, has an elevated ACP (> 10 % of the maximum). Th e partitioning proper ties of other organic chemicals are indicated for comparison.

The AC-BAP of “persistent H BCD” is somewhat higher than that of persistent PBDE 47 and persistent 2,3,7,8-tetrachlorodibenzo-p-di oxin. This suggests that if degradation and

biotranformation of HBCD are similar to th ese two chemicals and emissions patterns are similar (mode of entry, quantity, and spatial- temporal aspects) than exposure potential in the Arctic environment may also be comparable.

It should be mentioned that both 1,3, 5,7,9,11-hexabromocyclododecane (CAS 25637-99- 4) and 1,2,5,6,9,10-hexabromocyclododecane (CAS 3194-55-6) had been ear-marked as potential Arctic contaminants in the st udy by Brown and Wania (see Table 1 in Brown and Wania, 2008). In both cases this was a result of a so-called high POP Score, indicating high structural resembla nce with a selec tion of known POPs 2 , rather than partitioning properties that would yield an el evated AC-BAP, if HBCD were perfectly

2 Structural attributes of known POPs were a high degree of halogenation and internal connectivity,

and an intermediate molecular size.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 78 persistent. Brown and Wania (2008) used partitioning propertie s supplied by EPI Suite  predictions (U.S. EPA 2009), so a revisiting of this analysis using the partitioning properties recommended here is appropriate.

3.5.2. Model Comparisons with Monitoring Data

“Realistic” simulations of HBCD using em issions estimates allow for comparisons between model predictions and available m onitoring data. These comparisons can be

used to corroborate emission estimates and selected properties for HBCD.

3.5.2.1 RAIDAR and Real World Re gional Scale Monitoring Data

The RAIDAR model was used to predict concen trations in general source regions of the European environment (e.g. regional area s in Sweden) and these estimates were compared to available monitoring data re presentative of regiona l non-point sources (Table 3.4). This comparison seeks to corr oborate actual emission rate estimates and uncertainties in persistence estimates (e.g. degradation half-lives in water). This is done by considering various assumptions for em issions in a regional area (100,000 km 2 ) and different mode-of-entry scenarios and compar ing model predictions with the monitoring

data. The various estimates for actual emissions can be used to scale the results based on the unit emission rate to the environment (E U ) of 1 kg·h -1 . For example, if the actual estimated emission rate (E A ) is 0.5 kg·h -1 the unit output from the model is scaled lower by a factor of 2. Therefore if the uni t concentration in a marine mammal, C U (ng·g -1 lw) is 100, based on the actual emission rate estimate the corresponding actual concentration predicted by the model C A (ng·g -1 lw) is 50. This approach e xploits the linear calculations

of the model.

It is emphasized that the model predicts st eady state chemical concentrations in the environment. The application of a steady st ate model for comparisons with monitoring data is not appropriate if chemical concentrations in the environment have not approached steady state. Whether or not a ch emical can achieve steady state (or nearness to steady state) depends on the relationship between the rate at which a chemical can respond to changes in emissions (i.e. response ti me; see section 3.5.3.1) and the extent to which emissions have changed with time. There is insufficient evidence to determine if

HBCD emissions have remained constant in recent years; tempora lly-resolved emission

data are not available and time- trend data, while showing a sl ight long-term increase over 20-30 years (Section 3.4), are inconclusive for recent years. The less persistent a chemical is the less important it is that emissions remain constant over time when undertaking steady state modelling because the time response of concentrations to changing emissions can be relatively fast. Leve ls of HBCD in the environment appear to be near steady state as a resu lt of long term produc tion and use in regional environments in Europe. The apparent pseudo-steady state conditions are further supported by predicted response times and the time required to steady state (see Section 3.5.3.1 below). This evidence supports the applicab ility of a steady state model for comparisons to monitoring

data.

For model comparisons with monitoring data the general model structure must be reasonably representative of th e regional environments in which the monitoring data wereArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 79 obtained. This approach may work well in spite of changing emissions as long as the emission estimates and monitoring data used for comparison are from the same general time period. Similar comparisons of RAIDAR predictions for PBDE-99 with monitoring data have shown that this relatively si mple approach can reasonably reproduce environmental observations (Cowan-Ellsberry et al. 2009). For simplicity in this analysis, and due to data limitations, only t-HBCD pred ictions will be considered for comparisons

with Σ HBCD monitoring data.

The degree of fit between model predictions and observations can be quantified providing an indication of Model Bias (MB = C A / C O ). If the predicted concentration C A is greater

than the observed concentration C O then MB is greater than 1. If the predicted

concentration C A is lower than the observed concentration C O then MB is less than 1. Model Bias can be calculated as the average MB for different compartments and provides insights into model fit with the observed da ta. Assuming the structure of the model is representative of the environment and the i nput parameters are reasonable and that the system is near steady state, the estimates fo r the actual emission rate , mode-of-entry, and half-lives can be expl ored by minimizing MB.

An initial estimate for the actual emission rate was determined as follows. According to

the RAR ~9.942 tonnes of HBCD are released to the European Union (EU) environment annually (See Table 3.1) (EC 2008). The area of the EU member states is ~4,500,000

km 2 . The EU region is approximately 45 times greater than the RAIDAR regional environment. The annual unit emission rate in RAIDAR is 1 kg·h -1 or 8.76 t ·yr -1 . The

default RAIDAR unit emission rate is about 40 times greater than the actual estimated emission rate after adjusting for differences in scale (1/45). The unit emission rate in

RAIDAR was therefore scaled as 1/40 to approximate the actual emissions to a representative 100,000 km 2 regional scale European environment. A confidence factor of 5 was assigned to the actual emissions es timate. According to the RAR, HBCD is

released to the environment as follows: 6% to air, 72% to wastewater and 22% to surface water. Therefore, the initial assumption was that approximately 95% of the emissions are to water and 5% are to air. The available regional scale monitoring data (Table 3.4) and

food basket studies from Norw ay (Knutsen et al. 2008) and Sweden were selected to represent European regions for model comparisons.

Figure 3.24 illustrates the initial comparison of RAIDAR predictions and monitoring data

based on the RAR emissions and mode-of-entr y assumptions and the median half-life values in the environment for HBCD (Table 2.5). There is reasonable agreement between model predictions and observed data for most compartments, particularly considering the uncertainty in the monitoring and modelling estimates. Many of the estimates for biota

are within a factor of 3 and the overall MB for biota is 0.97 (underestimating by ~3%). Average MB estimates can cancel each other out, so they must be interpreted cautiously. There is a tendency toward a systemic under prediction for certain compartments. Measured regional scale abiotic concentrations are particularly lacking, except for air. The monitoring data for air reflect available regional scale measurements in Sweden and the model underestimates these measurements according to the current scenario. Data for sediment is for a regional non-point sour ce (upstream estimates). There are noArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 80 representative regional monitoring data for soil. All soil measurements have been near production facilities. Estimates for RAIDAR regional scale wa ter and soil concentrations were compared with estimates from BETR-World (see below).

The data for aquatic biota are reasonably good; however, predicted concentrations in food sources originating in the terrestrial envir onment are too low. The underestimation of measured levels in air and terrestrial organi sms (eating mostly crop materials) suggests that emissions to air may be higher than pres ently estimated. This is further supported by monitoring data for dust in i ndoor environments and region al air concentrations from other global regions with lower use patter ns than Europe. Assuming the model is representative of the conditions , this slight underestimation s uggests that either the halflife estimates are too short, the emissions estimates are too low, the mode-of-entry proportions are different, or some co mbination of these three factors.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 81 Figure 3.24. RAIDAR model pred ictions (×) and monitoring da ta (o) for HBCD based on

an assumed actual emission rate of 9.942 t ·yr -1 (EU region) x 1/40 (regional scale and unit emission differences), 95% of which is re leased to water and 5% to air and using

median environmental half-lives. The median value for birds is from muscle and the range reflects data for eggs. The gray bars are BETR-World model estimates for the European region. -4 -3 -2 -1 0 1 2 3

log concentration Air (pg/m 3 )

Water (pg/L)

Soil (pg/g dwt)

Sediment (ng/g dwt)

-6 -4 -2 0 2 4

log concentration (ng/g lwt) Humans

Piscivorous birds & eggs Marine mammals

Chickens

Fish - Upper TL

Shellfish Dairy products

Meat products

Fish - Lower TLArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 82 RAIDAR does not consider wastewater treatmen t. It is estimated that approximately 20%

of emissions to treated wast ewater are prevented from en tering the natural environment (EC 2008). This would suggest that the proport ional mode-of-entry for total emissions is therefore higher to air than 5% and total emissions may be lo wer than the initial scaled

estimate.

As an alternative to the initial assumption, the proportion of emissions to air were

increased from 5% to 30% and the emissions to water were decreased accordingly (70%). Figure 3.25 illustrates the results of these adju stments showing better agreement in the air and terrestrial organisms with some minimal “loss” in predictive accuracy for other compartments. Model Bias for the biotic compartments is lower under this scenario at 0.79 (average underprediction of 21%).

There are a number of different scenarios that could be explored, in particular with regards to the uncertain degradation half-liv es. Another alternative scenario considered was to use the original emissions scaling f actor, the 30:70 ratio fo r air : water mode-ofentry and the upper bound estimates for environm ental degradation half-lives (Table 2.5). Figure 3.26 illustrates the re sults of these assumptions in comparison to the source regional scale monitoring data. There is still reasonable ag reement in many compartments; however, there is now a greate r MB in predictions for biota with an overall average MB for biological compartments of 2.23.

Figure 3.26 also includes model predicti ons for humans, marine mammals and birds assuming the biotransformation half-lives fo r these organisms are equivalent to the estimates for fish on a body weight basis. Wh en this conservative assumption is applied the model predictions are much greater than the available monitoring data. This suggests

that the median biotransformation half-lives selected in the present study are reasonable and that HBCD (or at least some of the diastereomers) are bi otransformed at faster rates

in birds and mammals than in fish.

In summary, the steady state model predicti ons are in good agreemen t with monitoring data for a wide range of media. Levels of HBCD in the environment appear to be near steady state in all environmental media as a result of long term production and use in regional environments in Europe. Steady state model predictions for persistent chemicals have not provided as good predictions for slow responding compartments such as soils and sediments based on comparisons with monitoring data for certain PAHs and hexachlorobenzene (Armitage et al. 2007). This evidence suggests that HBCD is not as persistent as the PAHs and hexachlorobenzene.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 83 Figure 3.25. RAIDAR model pred ictions (×) and monitoring da ta (o) for HBCD based on

an assumed actual emission rate of 9.942 t ·yr -1 (EU region) x 1/40 (regional scale and unit emission differences), 70% of which is re leased to water and 30% to air and median

environmental half-lives. The median value for birds is from muscle and the range reflects data for eggs. The gray bars are B ETR-World model estimates for the European region. -4 -3 -2 -1 0 1 2 3

log concentration Air (pg/m 3 )

Water (pg/L)

Soil (pg/g dwt)

Sediment (ng/g dwt)

-6 -4 -2 0 2 4

log concentration (ng/g lwt) Humans

Piscivorous birds & eggs Marine mammals

Chickens

Fish - Upper TL

Shellfish Dairy products

Meat products

Fish - Lower TLArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 84 Figure 3.26. RAIDAR model pred ictions (×) and monitoring da ta (o) for HBCD based on

an assumed actual emission rate of 9.942 t ·yr -1 (EU region) x 1/40 (regional scale and unit emission differences), 70% of which is released to water and 30% to air and 95 th
percentile values for environmental half-lives. The median value for birds is from muscle and the range reflects data for eggs. The gray bars are BETR-World model estimates for the European region. The yellow triangl es are estimates assuming metabolic

biotransformation rates in birds and mamm als are equal to the estimate for fish. -4 -3 -2 -1 0 1 2 3

log concentration Air (pg/m 3 )

Water (pg/L)

Soil (pg/g dwt)

Sediment (ng/g dwt)

-6 -4 -2 0 2 4

log concentration (ng/g lwt) Humans

Piscivorous birds & eggs Marine mammals

Chickens

Fish - Upper TL

Shellfish Dairy products

Meat products

Fish - Lower TLArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 85 The predicted concentrations in humans ar e somewhat higher than the median of the measured value; however, many uncertainti es affect the comparisons of human predictions with monitoring data for humans. RAIDAR calculates concentrations for adult male humans; whereas the available m onitoring data are predominantly for nursing mothers. In general, reproductive and lactat ional losses are recogni zed as lowering lipid

normalized concentrations of hydrophobic chemicals in repr oductive females compared to males. Thus, predictions for males in RA IDAR may over predic t monitoring data for females. Humans are included in this analysis because the general human diet is composed of a range of items including m eat and dairy products, fish, vegetables and fruits which represent a range of possible s ources of exposure. In addition, due to the limited monitoring data presently available the human data provide a valuable source of reference. On the other hand, due to differen ces in diet (omnivorous, vegetarian), and the import of foods from various regions, dietar y levels can vary widely for the sample human population. This generally results in a high degree of variance in human data. Finally, the model does not consider potential ne ar-field exposures to humans such as the

indoor environment.

The model-measurement comparison can be affected by detection limits. Typically, when summary statistics are calculat ed from monitoring data wi th a significant number of nondetects (e.g. soils, water, humans), those non-detects are often repleaced with 0.5 the

detection limit value or some other correction. This results in uncertain values for the lower bound estimates (because they cannot be detected) and a general bias towards overestimated medians. Datasets with few non-detects, such as for marine mammals, upper trophic level fish, pisci vorous birds and their eggs, ar e considered to be more accurate compared to samples with frequent non-detects.

There are a number of different scenarios that can be considered; however, since there are uncertainties in the actual emissions, degrada tion half-lives, physical-cemical properties, mode-of-entry and the model, it is difficult to as certain all parameters at the same time. In general, the model – monitoring data compar ison suggests that current parameterization

estimates are reasonable. If current emission a nd scaling estimates are accurate, than this would suggest that a ratio of 30:70 (air:water ) for mode-of-entry and half-lives that are between the median and upper bound estimates are appropriate starting points for subsequent modelling. It must be recognized th at there is an invers e relationship between

emissions estimates and de gradation half-lives.

3.5.2.2 BETR-World

The BETR-World model is a global scale, geographically explicit model that allows the

user to both assess the trans port potential of chemicals from source regions and create a mass-balance of these chemicals through the defi nition of historical or estimated regional loadings. The model consists of 25 predominan tly terrestrial or ocean ic regions that are linked through quantified atmospheric, fluvi al and oceanic advective pathways. Each region consists of a maximum of seven environmental media, upper atmosphere, lower atmosphere, freshwater, freshwater sediments, soil, vegetation and co astal and/or oceanic water. The terrestrial regions are delineated to incorporate latitudinal climatic changes asArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 86 well as political boundaries. This marriage of environmental and political consideration effectively captures both the conditions aff ecting long range atmospheric transport of chemicals as well as providing a framework to support regulatory de cisions (Toose et al. 2004). The model accommodates both dynamic and steady state conditions and the efficiency of transport of chemical emitted fr om each region to a ta rget region, such as the Arctic, can be determined (Reid and Mackay 2008).

For the BETR-World model calculations emi ssion estimates (ca. 2001) were derived from various sources; but mainly those de scribed in the TemaNord (2008) document (Table 3.1) and those sourced from BSEF as cited from the Arctic Monitoring and Assessment Program (AMAP 2005) (see Table 9.9 in the Appendix). Based on Tables 3.1 and 9.7 emissions were scaled for the propor tion releases per unit demand in Europe. Australia has regulated the use of HBCD and the annua l use for 2001 was about 40 metric tonnes (NICNAS Australian Governme nt Department of Health and Ageing 2005). Emissions were assigned at low levels to regions for which there were no direct data as monitoring data show that HBCD is found in locations ar ound the world including South Africa (Polder et al. 2008b). The assume d emissions for the BETR-World regions

based on these data are summarized in the Appendix (Table 9.9). For mode-of-entry it

was assumed that emissions were 70% to the lower air compartment and 30% to freshwater for every region. The FAVs for t-HBCD were used (Table 2.2). The model was run twice; once for the “default” envir onmental half-lives and a second time for “upper 95 th percentile” environmental half-lives as described in Section 2 (Table 2.5). Half-lives in vegetation and coastal waters were assumed equal to the half-lives for water.

Figure 3.27 shows that the model predicts European concentrations, except the lone freshwater measurement, to within a factor of 4.2. The lone “Arctic” measurement from freshwater sediments on Bear Island, NO (3.87 ng·g -1 ) is underpredicted by 5 and 3 orders of magnitude for the median (4.8×10 -5 ng·g -1 ) and the upper bound HL (1.1×10 -3

ng·g -1 ) simulations. The literature indicates that this monitoring location is a relative “hotspot” for contaminants. This remote lake is located in a very barren region; however, it is highly productive due to the inputs of guano from seabirds (Christensen et al. 2004). In addition to the “biotransport” of contaminants to the lake via migratory seabirds, it is a location with unusually high prec ipitation for the high arctic due to the topography of the region, creating yet another unusual vector by which contamination of this remote lake

can occur. At the scale of the BETR-World model, where the smallest regions are still several hundred thousand square kilometers, hot spots such as these cannot be adequately captured. The other very low value shown on Figure 3.27 is from monitoring of the Detroit River suspended sediments; also a hi ghly industrial and urbani zed area that is not well predicted by this large global-scale lo w-resolution model. The BETR-World model predicts background concentrations in the glob al environment and is not of sufficiently high spatial resolution to predict concentra tion “hot-spots”. Predic tions using the upperboundary half-lives underpredict the averag e concentrations by 3x in air and 10x in freshwater sediments. The predictions appr oach lower values of measured sediment concentrations in Tokyo Bay (0.056 to1.3 ng·g -1 in the middle to outer bay, respectively (Minh et al. 2007)).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 87 Figure 3.27. Comparison of BETR-World mode l results and with available monitoring data. Solid points compare mo nitoring data to results using the default half-lives physical-chemical parameters and open points compare to results using the 95 th percentile

half-lives.

Figures 3.28 and 3.29 show the air and freshw ater sediment concentrations predicted by the model based on the upper bound half-lives . The model aims to predict average concentrations over large regions. Figure 3. 28 shows that lower air (0 to 1 km)

concentrations are less than detectable limits in the southern hemisphere, over the North

American Pacific Ocean and in the middle A tlantic Ocean. Other locations, notably the Arctic and the Orient, are pr edicted to be above detectab le levels at about 0.5 pg·m -3

. Levels reported in the air in China are between 0.08 to 3 pg·m -3 (Yu et al, 2008). Levels in the United States are predicte d to be between 0.5 and 1 pg·m -3 which is supported by the data of remote monitoring stations there (0.6 to 8 pg·m -3 , Hoh and Hites 2005).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 88 Figure 3.28. Atmospheric concentrations predicted by BETR-World model using the upper boundary half-lives.

Figure 3.29. Freshwater sediment concentra tions predicted by BETR-World model using the upper boundary half-lives.

Figure 3.29 shows the predicted levels of HBCD in freshwater sediment. Regions without local emissions are predicted to have less than 1 pg·g -1 dw (0.001 μg·kg -1 dw). Northern Canada (and Alaska), central South America, Africa, the Middle East, India and Australia are predicted to have between 1-10 pg·g -1 dw. Reported detection limits for HBCD in sediments vary between 0.01 to 1.2 μg·kg -1 dw; the top predicted level is in Europe (0.04 to 0.78 μg·kg -1 dw), which may be below some studies’ detection limits. This estimated range for regional scale, non-poi nt source sediment concentr ations agrees well with measured background (upstream) concentrat ions (River Cinca, ES: 0.1 μg·kg -1 dw (Eljarrat et al. 2004) Lake Marsjon, SE: 0.279 μg·kg -1 dw (Sellström et al. 1998). Urban

measurements of freshwater sediments in St ockholm, SE range between 0.8 to 1.5 μg·kg -Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 89 1 dw (Remberger et al. 2004) and ones from a lake near Zurich, CH range between 1.8 to

2.5 in near-surface and surface se diments (Kohler et al. 2008).

BETR-World estimates for transport pathways of HBCD to the Arctic using the upper

bound environmental half-lives are summarized in Table 9.9. Seventy-two per cent of the chemical arriving in the Arctic is being transported there via the air (60% in the upper atmosphere and 12% in the lower atmosphere); 0.2% arrives from riverine outflows to Arctic water; the remaining 27.5% enters via marine water inflows, including 15.66% from European Marine water and 9% from the North Atlantic. Total advective inflow of chemical is 1655 kg·yr -1 . Total advective outflow is 1155 kg·yr -1 ; 330 kg·yr -1 advection permanently from the system (prima rily to deep oceans) and 169 kg·yr -1 chemical degradation losses.

3.5.3 Time Trends and Response Times

3.5.3.1 CoZMoMAN

The CoZMoMAN model (Breivik et al.) is an amalgam of the contaminant fate model CoZMo-POP (Wania et al. 2006) and the human food chain bioaccumulation model ACC-Human (Czub and McLachlan 2004). As such, it calculates - based on a mechanistic description of th e underlying processes - the enti re chain of events linking a

chemical’s emissions to the environment to the residue levels it may establish in the human body. In particular, it does so dynamically , i.e. it allows for temporal changes in emissions and in environmental parameters (such as temperature or the OH radical concentration in the atmosphere). One of the key pieces of information that such a model can provide is the time scale of concentr ation changes in res ponse to changes in emissions. Specifically, it can estimate the tim e it takes for a chemical to reach a steady state situation, during which concentrations in a medium stay constant because the inputs and outputs to that medium ar e balancing each other. If that time is short, it can lend credibility to the use of steady state models such as RAIDAR. CoZMoMAN can also

assess the time it takes for concentrations to decline, after emission have been reduced or eliminated altogether.

CoZMoMAN was used here to estimate the time to steady state and the extent of contamination reversibility for HBCD (i.e. how long will it take the environment to clean up if emissions were reduced to zero?). For this purpose, no realistic emission scenario was used but instead a hypothetical scenario that consists of 20 years of continuous emissions at a rate of 1 ton per 1 million km 2 and year (1 t ·yr -1 per 1Mio km 2 ), followed by 20 years without any emissions. In the defa ult scenario, it was assumed that 30 % of the emissions take place to the atmosphere, and 70 % to the fresh water environment. Additional calculations were performed assuming release either entirely to the atmosphere or entirely to water. The defa ult parameterization for the environment and

human food chain of the Wester n Baltic Proper drainage basin was used (Breivik et al.). The FAVs for the partitioning properties of t-HBCD from Table 2.2 and the degradation

half-lives from Table 2.5 were used in th ese simulations. Additi onally, a second set of simulations with the half-lives in water, se diment and soil compartments at the upperArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 90 bound ( HL soil = HL water = 850 days, HL sediment = 210 days) was conducted to assess the impact of the considerable uncertainty in the degradation rates on the model results.

Figure 3.30 displays the calculated concentrati ons in air, soil, water, sediment, herring

and humans. The water concentrations are fo r the estuarine compartment, which does not

receive direct emissions of HBCD, but is downstream from the fresh water compartment which does receive the emissions. The herring concentrations are the average of 10 different age classes, although the variability in concentrations betw een age classes is relatively limited. The human concentration is for a female 20 years of age at the onset of

the simulation.

Figure 3.30. Time variant concentrations of HBCD in several media as calculated by CoZMoMAN assuming 20 years of steady emi ssions to air (30%) and water (70%), followed by 20 years of no emissions. Air

0 0.2 0.4 0.6 0.8

1 1.2

0 5 10 15 20 25 30 35 40concentraion in pg/m3 Soil

0 50 100 150 200 250

0 5 10 15 20 25 30 35 40concentration in pg/g OC

Water

0 2 4 6 8 10 12 14 16

0 5 10 15 20 25 30 35 40concentration in pg/L Sediment

0 5 10 15 20

0 5 10 15 20 25 30 35 40concentration in ng/g OC

Herring

0 10 20 30 40 50 60 70 80

0 5 10 15 20 25 30 35 40concentration in ng/g lipid Human

0 0.1 0.2 0.3 0.4 0.5

0 5 10 15 20 25 30 35 40concentration in ng/g lipid



### HBCD assuming HL soil/water 

### = 85 days, HL sediment 

### = 35 days

HBCD assuming HL soil/water 

### = 850 days, HL sediment 

### = 210 days constant emissions
at 1 ton/year/1Mio km 2 (30 % air, 70 % water) no emissions

Air

0 0.2 0.4 0.6 0.8

1 1.2

0 5 10 15 20 25 30 35 40concentraion in pg/m3 Soil

0 50 100 150 200 250

0 5 10 15 20 25 30 35 40concentration in pg/g OC

Water

0 2 4 6 8 10 12 14 16

0 5 10 15 20 25 30 35 40concentration in pg/L Sediment

0 5 10 15 20

0 5 10 15 20 25 30 35 40concentration in ng/g OC

Herring

0 10 20 30 40 50 60 70 80

0 5 10 15 20 25 30 35 40concentration in ng/g lipid Human

0 0.1 0.2 0.3 0.4 0.5

0 5 10 15 20 25 30 35 40concentration in ng/g lipid



### HBCD assuming HL soil/water 

### = 85 days, HL sediment 

### = 35 days

HBCD assuming HL soil/water 

### = 850 days, HL sediment 

### = 210 days constant emissions
at 1 ton/year/1Mio km 2 (30 % air, 70 % water) no emissionsArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 91 Time to Steady State Using the default degradation half-lives HBCD rapidly reaches a steady state distribution in all model compartments (pink line in Figure 3.30), i.e., concentrations remain constant from year to year after only a few year s of continuous emissions. With the longer degradation half lives (upper bound estimates fo r soil, water and sediment), the time to steady state is longer, yet stil l less than 10 years in most compartments (blue line in Figure 3.30). The exception is the soil compartment, which is still accumulating HBCD after 20 years of continuous emissions (upper right panel in Figure 3.30). We may infer that a steady state modelling approach is generally accep table for HBCD, as long as emission rates remain fairly constant on the ti me scale of a decade a nd as long as soil is not the primary compartment of interest.

Contamination Reversibility The situation is mirrored in the time it takes for contaminant levels to drop after

emissions ceased. If the default half-lives ar e realistic, most of the HBCD will disappear from the environment and from organisms within a time frame of a few years (pink line in Figure 3.30). If the longer half-lives are mo re suitable, the time for recovery is on the time scale of a decade (blue line in Figure 3.30), again with the exception of soil concentrations, which will require multiple decades to decline.

Seasonal Fluctuations Figure 3.30 and Table 3.14 reveal s that the extent of air con centration variability during a year depends on the degradation half-lives an d the mode of emission. It is larger for shorter half-lives and with a decreasing fraction of HBCD being emitted to air. It is particularly pronounced if there are no emissions to air. The model results for seasonal air fluctuations are supported by the limite d seasonal monitoring data for air.

Absolute Concentration Values Figure 3.30 also reveals that the absolute co ncentration levels in most media would be considerably higher if HBCD is more persis tent in the surface media than the default degradation half-lives suggest. The difference is media de pendent, ranging from less than

a factor of two for air concen trations to an order of ma gnitude for soil and sediment

concentrations.

In addition to the degradation half lives, the absolute concentration values are further highly dependent on the mode of emission. Ta ble 3.14 lists the average concentration in various media during years 10 to 20 of the simulation, when HBCD reaches steady state in most compartments. Either default or slow degradation half-lives were used in these

simulations.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 92 Table 3.14. Average concentrations in air, herring and humans during the second decade of simulation in CoZMoMAN using con tinuous emissions at a rate of 1 t ·yr -1 per 1Mio

km 2 for different assumption concerning the mo de of emission and the degradation halflives in water, soil and sediment.

100 % to air 30 % air, 70 % water 100 % to water

Half lives Default Long Default Long Default Long

Air in pg/m 3

Average 2.01 2.79 0.63 0.91 0.04 0.10

Max 3.03 3.54 0.91 1.08 0.08 0.19

Min 1.09 2.04 0.38 0.743 0.004 0.02

Max/min 2.78 1.74 2.39 1.45 19.95 11.07

Herring in ng/g lipid

Average 28 110 18 64 13 45

Max 31 115 19 67 14 46

Min 26 105 16 61 12 43

Human in ng/g lipid

Average 0.19 0.53 0.09 0.27 0.05 0.16

Max 0.22 0.62 0.10 0.32 0.06 0.17

Min 0.17 0.47 0.08 0.24 0.04 0.14

Although the focus of the simulations here is on the time response and not on the absolute concentration values, it should be noted that the CoZMoMAN m odel is linear with respect to the emissions. This implies that th e results can be scaled for emissions rates

other than 1 t ·yr -1 ·per 1Mio km 2 by simple multiplication. For example, if the real

emission rate is 5 t ·yr -1 ·per 1Mio km 2 all of the concentrations in Figure 3.30 and Table 3.14 have to be multiplied by a factor of 5. Following the data in the RAR and the same approach described above for RAIDAR for estimates of actual (i.e. 9.942 t ·yr -1 ·per 4.5Mio km 2 ), the unit emissions would be multip lied by a factor of about 2.2.

Whereas it is not surprising that air concen trations are much higher when emission occur exclusively into the atmosphere, it is less intuitive, why also herring and human concentrations should be higher, if a hi gher fraction of HBCD is emitted to the atmosphere rather than the water compartment. The explanation lies in the fact that the concentrations are for fish being exposed to the concentrations in the estuarine water compartment downstream from the fresh wa ter compartment receiving the emissions. Apparently, the atmosphere is more efficien t in delivering contaminant to the estuarineArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 93 water compartment than water advection – becau se most of the emitte d HBCD is retained in the sediments of the receiving water compartment.

Overall, it is clear that re producing absolute concentration values with the models will only be feasible, if the rate and mode of emission is well established and if the degradation half-lives in the surface media are better constrai ned. The model in fact can give some indication of how this may be achieved:

• The simulated air concentrations are fairly insensitive to the degradation half-lives in surface media (Figure 3.30), yet highly dependent on the assumed mode and rate of emission (Table 3.14). This implies that the emission rate to the atmosphere may be estimated fairly accurately, if reliable atmospheric background concentrations of HBCD become available.

• Furthermore the extent of seasonal air c oncentration variability may provide some clues as to the likely mode of emissi on. Table 3.14 suggests that much higher

summer-winter differences in air concentratio ns can be expected, if direct releases to the atmosphere are very limited and emission occur mostly to the water

compartment.

• Once the rate and mode of emission is established, the likely magnitude of degradation rates may be estimated by c hoosing values for the half-lives that

reconcile modelled and measured concentr ations and time trends in soils and

sediments.

To place into context the time to steady st ate and the extent of reversibility of contamination of HBCD, we performed additional CoZMoMAN calculations for three benchmark POPs (polychlorinated biphenyl congeners 101 and 180, and hexachlorobenzene). The emission scenario for these substances was assumed to be

identical to the one used in the HBCD simu lations, namely 20 years of steady emissions into air (30 %) and fresh water (70%), followed by 20 years without emissions. The time trends in Figure 3.31 are nor malized to the average over th e 40 year simulation period to

focus on the differences in trends rather th an the absolute concentration levels being predicted. The time trend for HBCD in Figur e 3.31 refers to the simulations using the upper bound half-lives in surface media (water a nd soil). In all media, HBCD displays a shorter time to steady state than the thr ee benchmark POPs. It also shows faster

concentration declines in all media than the benchmark POPs. In summary, we would expect HBCD concentrations in the physical environment and in the organisms making up the human food chain to respond much faster to changes in emissi ons than categorized

POPs.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 94 Air

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

0 5 10 15 20 25 30 35 40concentraion in pg/m3

PCB-180 HCB PCB-101

HBCD Soil

0.0 0.5 1.0 1.5 2.0 2.5

0 5 10 15 20 25 30 35 40concentrati on i n pg/g OC

PCB-180

HCB P CB-101

HBCD

Water

0 .0 0 .5 1 .0 1 .5 2 .0 2 .5

0 5 10 15 20 25 30 35 40concentrati on in pg/ L

PCB-180

HCB

PCB-101 HBCD Sediment

0.0 0.5 1.0 1.5 2.0 2.5

0 5 10 15 20 25 30 35 40concentrati on i n ng/g OC

PCB-180 HCB

PCB-101 HBCD

Herring

0.0 0.5 1.0 1.5 2.0 2.5

0 5 10 15 20 25 30 35 40concentration in ng/g l ipid

PCB-180

HCB PCB-101

HBCD Human

0.0 0.5 1.0 1.5 2.0 2.5

0 5 10 15 20 25 30 35 40concentrati on i n ng/g lipid

P CB-180

HCB

PCB-101 HBCD

Figure 3.31. Comparison of simulated time tr ends of HBCD (long half-lives in surface media and three benchmark POPs, normalized to the average over the entire simulation period.

Table 3.15 compares the maximum predicted co ncentration during the 40 year simulation period for HBCD and the three benchmark POPs. The lower persistence of HBCD

assures that the maximum concentrations it can achieve in air, soil, and sediment are lower than those of POPs, if they were emitted in an identical fashion. On the other hand, if the longer surface half-lives are us ed, CoZMoMAN predicts higher maximum

concentrations for HBCD in water and he rring compared to PCB-101 and PCB-180. Also, the predicted maximum concentration of HBCD in humans is very similar to that predicted for HCB. This highlight s that a chemical that is ge nerally less persistent than categorized POPs, could potentially bioaccumula te to higher concentrations in organisms than those POPs, if they were emitted in similar fashion and in comparable amounts.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 95 Table 3.15. Maximum concentration of H BCD (using either median or upper bound estimates for half-lives in water and soil) and three benchmark POPs predicted during the hypothetical 40 year simulations displayed in Figure 3.31.

HBCD

(median HL) HBCD

(upper bound HL) HCB PCB-101 PCB-180

Air in pg/m 3 0.9 1.1 194 14 2.5

Soil in pg/g OC 21 208 1700 500 261

Water in ng/m 3 4.4 15 24 13 7.1

Sediment in ng/g OC 1.2 17 21 48 95

Herring in ng/g lipid 19 67 34 34 38

Human in ng/g lipid 0.2 0.5 0.6 2.0 4.1

4.0 EFFECTS CHARACTERIZATION

Often bright line exposure based criteria are us ed to screen “chemi cals of concern” for more comprehensive evaluations (e.g. PBT screening assessments). Simply because a substance does not “pass” a screening level bright line criterion for toxicity, does not mean it necessarily causes adverse effects in the environment (Arnot and Mackay 2008). Estimating the likelihood of significant advers e effects or risk, which is a function of magnitude and duration of exposure that induces a given adverse effect response, is the issue regulatory programs ultimately seek to ad dress. The objective of this section is to compile and critically review the availabl e ecological and mammalia n toxicity data for

the selection of adverse effects threshold values for comparison with measured and modelled exposure levels in the environment.

Two key issues need to be considered for th e effects characterizati on of HBCD. The first is whether or not available ecotoxicity test data can be used for screening (PBT) and/or more comprehensive risk (POP) assessment. Most ecotoxicity endpoints are based on concentrations in exposure media (water and soil) assuming that this surrogate measurement can be directly compared to criteria for effects or no effects. This assumption can be confounded for many reasons , particularly for chemicals with low water solubility. The second issue relates to how best to use available laboratory toxicity testing information to evaluate the potential for significant adverse effects in real world environments. Both issues are discussed by in cluding a review of th e current scientific

literature.

4.1 D ATA C OMPILATION AND R EVIEW

The data available for the toxicity of HBCD has been complied and is well presented in the European Commission RAR (EC 2008). Unless noted otherwise the material reported below is cited as reported in that document. This information is presented to illustrate theArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 96 data that was used to form the basis of the toxicity component of the PBT evaluation for HBCD by the European Commission (EC 2008) and a proposed POPs categorization by TemaNord (2008). This information was also used by the European Commission to develop risk-based environmental regulator y evaluation targets - predicted no effect concentrations (PNECs). From the European Commission (2008) report, it can be seen that there are other valid toxicity data that were reviewed but do not appear in the tables below as they demonstrate cases where HBCD showed less, or much less, toxicity.

4.1.1 Terrestrial Data

Table 4.1 summarizes the key terre strial toxicity data selected for review in the present study from the European Commission RAR (E C 2008). Other availabl e laboratory studies

with mammals indicate that HBCD is not carcinogenic, mutagenic, or toxic to reproduction.

The soil micro-organism and vascular plant da ta are from good quality tests; however, the results are of limited use for establishing HBCD toxicity as they ar e reported as greater than a no observed effect concentration ( > NOEC). These results provide no indication of an exposure where toxic effects might be expected and can therefore only mean that

HBCD does not appear to be toxic to these orga nisms under these conditions. The soil invertebrate test produced both a NOEC and a lowest observed effect concentration (LOEC). These two observations are about a factor of 2 apart; therefore, it is reasonable

to use the NOEC as a conservative estimate of soil invertebrate chronic effect.

The rat testing data from van der Ven et al. (2006) is based on a modelled dose-effect interpretation where the Benchmark Do se Lower-confidence Limit (BMD-L) is considered equivalent to a chronic no obs erved adverse effect level (NOAEL). The corresponding critical effect dos e (CED) for this effect endpoi nt of liver weight increase in females is 22.9 mg·(kg-bw·d) -1 . The 2-generation rat repr oduction study of Ema et al. (2008) used relatively widely spaced exposure levels with the lowe r exposure of 10 to 14 mg·(kg-bw·d) -1 considered as a very conservative NOAEL and the next dose level of 101 to 141 mg·(kg-bw·d) -1 considered as an effect level. The toxicity to mice (Erickson et al. 2006) is based on a single oral gavage dose 10 days after birth. Some behavioral effects were reported at 0.9 mg·kg -1 and many behavioral effects we re reported at the higher dose of 13.5 mg·kg -1 . However, it is difficult to accurately convert this value to a comparable continuous daily dosage, and the pr oposal that it be used to asse ss risk in bird eggs is an assumption not supported by a detailed justification in the RAR (EC 2008).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 97 Table 4.1. Summary of key data on the te rrestrial environmental effects of HBCD: Studies considered valid in previous EU assessments 1

.

Compartment & Species Method Results Remark and reference

TERRESTRIAL COMPARTMENT

BIRDS & MAMMALS

Rat 28-day NOAEL/MBD-L of 22.9 mg/kg bw/day. (van der Ven et al. 2006)

Rat 2-generation

NOAEC/NOAEL of 150 ppm dry weight in food (10 mg/kg bw/day) (Ema et al. 2008)

Mouse Single dose oral gavage of 0.9 mg/kg body weight produced behaviourial

disturbances. (Erickson et al. 2006). Internal dose estimate of 0.9 mg/kg wet weight calculated by assuming 100% absorption of oral dose. Mouse data was suggested to be

used for risk evaluation with comparisons to bird egg monitoring data.

INVERTEBRATES

Earthworms (Eisenia fetida/andrei) OECD 207, OECD Proposed Earthworm reproduction test, USEPA 850-6200, GLP. No effect on 28-day survival with exposure up to a mean

measured concentration as 4,190 mg/kg dry soil. EC50 and NOEC are both greater than 4,190 mg/kg dry soil.

LOEC of 235 mg/kg dry soil and a NOEC of 128 mg/kg dry soil. in 56-day reproductive study. (Aufderheide et al. 2003). Adjusted NOEC of 59 mg/kg dry soil based on a organic

matter correction from 7.4% to

the 3.4% content of standard

soils.

PLANTS

Corn, cucumber, onion, ryegrass, soybean, and

tomato OECD 308 (proposed revision), USEPA 850- 4100, 850-4225 (public drafts), GLP. No statistically significant changes to mass or growth were observed after a 12 day exposure to a 3-isomer

mixture of HBCD.

NOEC > the highest measured exposure level of 6,200 mg/kg dry soil. (Porch et al. 2002)

MICRO-ORGANISMS

Soil micro-organisms Nitrogen transformation,

OECD 216 NOEC ≥ 750 mg/kg dry weight (Förster 2007)

1 Extracted from Tables 3-173 and Sections 3.3.4.1 to 3.3.4.3 in European Commission (2008)Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 98 4.1.2 Aquatic Data

4.1.2.1 Data Quality Considerations

Available aquatic toxicity data have been used for PBT (EC 2008) and POP (TemaNord 2008) assessments. HBCD is poorly soluble in water (hydrophobic). Chemicals with low water solubility can be notoriously difficult to assess for toxicity using external exposure metrics (e.g., Maeder et al. 2004; Mayer and Reichenberg 2006; Meador 2006; McCarty and Arnot 2008). Therefore, before reviewing th e aquatic toxicity data, key issues related to the quality of these data and their applicability for PBT and POP assessments are first presented.

Most of the available aquatic toxicity data are of uncertain reliability for toxicity

assessment and effects characterization fo r risk assessment and POP categorization because either (i) test organisms were expos ed to water concentrations that exceed the water solubility limits for γ -HBCD (effectively t-HBCD for toxicity testing purposes when t-HBCD is tested), (ii) bioavailabil ity was not quantified (the truly dissolved,

bioavailable chemical concentration was not measured), (iii) cosolvents were used without quantifying the influence of the coso lvent on bioavailability and partitioning between the bulk water compartment and the te st organism, (iv) organisms were exposed to a mixture of isomers simultaneouly, or (v) some combination of the aforementioned issues. Further details of these issues and how they relate to previous assessments of HBCD are presented in Section 9.5 and briefly summarized below. Despite the

uncertainties described below in the availa ble aquatic toxicity data some general conclusions are also presented.

The water solubility of α -, β -, and γ -HBCD are 41, 15, and 2.5 μg·L -1 , respectively (see Section 2.3.2 and Appendix 9.1). A water solub ility estimate for t-HBCD (the sum of all isomers) is 58 μg·L -1 ; however, the γ -HBCD isomer makes up about 80% of t-HBCD. Thus, when the solubility limits for all components of the mixt ure are considered (i.e. 58 μg·L -1 ), there are undissolve d precipitates of the β - and γ -HBCD isomers in the water column as they are substantially above th eir water solubility limits. The issue of β - and γ

HBCD precipitates forming at the water solub ility limit of t-HBCD (i.e. sum of water solubilities for each isomer) is recognized in the European Commission RAR in the section for chemical properties, but seemingl y ignored in the evalua tion of the aquatic toxicity data (EC 2008). Precipitates in the water column may exert effects on the test organism as a result of physical interactions with the precipitates and not as a result of the actual toxicity of the chemical. Only by keep ing the t-HBCD concentration near the water solubility limit for γ -HBCD is it possible for all of the HBCD present during the toxicity

test to remain dissolved in the water. T hus a water solubility of about 2 to 3 μg·L -1 (the approximate water solubility for γ -HBCD) represents a concentration at which all isomers of HBCD are truly dissolved and no significant precipates are expected.

For substances with low water solubil ity (i.e. hydrophobic), such as HBCD, some

fraction of the total water column concentrat ion will not be bioavailable to the test organism because it partitions into or onto or ganic phases in the bulk water compartment. Typical measurements of the bulk exposure water concentration us e solvent extractionArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 99 methods that measure both the truly dissolved and the bound, or sorbed, non-bioavailable fraction of chemical. This confounds the quantif ication of the concen tration in the water that is actually being absorbed by th e organism during the toxicity test.

There can be complications in terpreting toxicity data for hydrophobic substances when

cosolvents are used. The use of cosolvents a ffects the bioavailability of a substance in aqueous exposure media. The degree to whic h the altered bioavailability occurs is speculative without a greater quantitative unders tanding of the influence of the cosolvent on toxicity endpoints in comparison to “pur e” water phase exposures. Contrary to guidance from the OECD for testing with the use of cosolvents (OECD 2000), key quantitative parameters necessary to make these corrections and interpretations were not reported for the HBCD studies. The standard pr actice of including a solvent control in the design does not address this issue. The solv ent control establishes whether the solvent itself is causing any adverse effects. It doe s not provide any information about how the cosolvent may be affecting the test substanc e or test organisms (e.g. bulk phase water solubility, bioavailablity) a nd thereby altering toxicity.

Evaluating the toxicity of mixtures is a chal lenge in terms of a theoretical foundation and in practical application (McCarty and Bo rgert 2006b; McCarty and Borgert 2006a). The general challenge is identifying which substance is responsible for any effect and if that

substance acts in isolation or if there are possible additive, synerg istic or antagonistic interactions. In addition to the broad gene ral challenge, the OECD guidance document on testing of difficult substances and mixtures ( 2000) specifically advises against the use of cosolvents in cases such as t-HBCD (mixture of isomers). Similarly, the use of generator systems such as generator columns is specific ally contraindicated for toxicity testing of difficult substances and mixtures such as HBCD. Generator systems are not considered appropriate for substances which differ in th eir water solubility since these differences will result in selective deplet ion of the more water solubl e components from the column

or disk matrix and thus influence their rela tive concentrations in the water phase (OECD 2000).

4.1.2.2 Aquatic Toxicity Data Review

Table 4.2 summarizes the key a quatic toxicity data selected for review in the present study from the European Commission RAR (EC 2008). The available sedi ment toxicity is

not addressed because these data are not readily translated to a water-based exposure and there were no body/tissue residue-effect data th at could be used in the present risk

assessment.

Plants, Algae and Microorganisms There are no toxicity data for freshwater or marine vascular plan ts but there are some results for freshwater and marine algae. In a ddition to the usual chal lenges of interpreting algae test data (e.g. the use of growth media with various and variable levels of mineral, vitamins, and nutrients, rather than plain wate r, and measuring effect s at in a specific exponential growth phase) almost all of the reported results were obtained with

cosolvents. As different cosolvents were us ed at different concentrations, effects on bioavailability and toxicity are unquantified. However, since many of these tests wereArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 100 near or in excess of water solubility levels, th e results when cosolvents were used must be considered uncertain and likely overestimates of the actual aquati c toxicity. In other words, if the test had been conducted without cosolvent any toxicity that may have been reported would have occurred at a hi gher exposure concentration. Although the microorganism test with activated sludge did not use a cosolvent, the reported value is greatly in excess of HBCD water solubility and is thus of little utility for PBT/POPs classification or general risk assessment.

Invertebrates There are acute and chronic toxicity data fo r the aquatic invertebra te Daphnia. The acute

test is of little util ity as no effects were a ttributed to the HBCD exposure and the results indicate that the 48-hr EC50 is greater than the highest measured exposure level of 3.2 μg·L -1 . The 21-day chronic test i ndicated a NOEC of 3.1 μg·L -1 and a LOEC of 5.6 μg·L -1
based on reduced mean length. A cosolvent was used in both cases. Therefore these data are considered unreliable since they are most likely overestimates of the actual aquatic toxicity under these conditions.

Fish

There are acute and chronic t oxicity data for the rainbow trout. The 96-hour acute test shows no effects at all at the highest measured concentration of 2.5 μg·L -1 . Similarly, the 88-day chronic study reported that no effects were observed at all for several endpoints (hatching, survival, growth) and that the NOE Cs was greater than the highest exposure level of 3.7 μg·L -1 . Neither of these studies found a ny effects due to HBCD exposure and the aquatic exposure levels associated with adverse effects is at some unknown higher level. Despite the lack of effects, due to the use of cosolvent, these data are likely overestimates of the actual aquatic toxicity under th ese conditions.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 101 Table 4.2. Summary of key data on the aqua tic environmental effects of HBCD: Studies considered valid in previous EU assessments 1

.

Compartment & Species Method Results Remark and

Reference Cosolvent Used

AQUATIC COMPARTMENT

FISH

Acute toxicity

Onchorhyncus mykiss OECD 203 and

TSCA 40/797/1400,

and ASTM

Standard E729-88a No mortalities or other

effects around 2.5 μ g/l. (Graves and Swigert 1997b) dimethylformamide

Chronic toxicity

Rainbow trout ( Oncorhynchus mykiss ) Flow-through

OECD 210 and

OPPTS 850.1400 NOEC μ g/l Hatching success ≥ 3.7 Swim-up ≥ 3.7 Larvae & fry survival

≥ 3.7

Growth ≥ 3.7 (Drottar et al. 2001) acetone

INVERTEBRATES

Acute toxicity

Daphnia magna OECD 202. Static immobilisation test,

and TSCA 40/797/1300, and

ASTM Standard

E729-88a 48 h EC 50 >3.2 μ g/l (Graves and Swigert 1997a) dimethylformamide

Chronic toxicity

Daphnia magna TSCA , OECD Flow through 21 day test. NOEC 3.1 μ g/l LOEC length 5.6 μ g/l (Drottar and Krueger 1998) dimethylformamide

ALGAE

Selenastrum
capricornutum OECD 201 and

TSCA40/797/1050 72 h EC 50 and LOEC

>2.5 μ g/l. NOEC could

not be estimated. Study 2 (Roberts and Swigert 1997) dimethylformamide

Skeletonema

costatum

Thallassiosira pseudonana

Chlorella sp. Marine algal bioassay method,

different marine growth media 72 h EC 50 = 9 μ g/l (lowest value)

72 h EC 50 = 40 μ g/l (lowest value)

96h EC 50 >water solubility Study 3 (Walsh et al. 1987a) Not according to guidelines, results only used as supportive acetone

Skeletonema

costatum OECD 201, ISO

10253:1995 and EU

Directive

92/69/EEC –

Method C.3 NOEC <40.6 μ g/l

EC 50 >40.6 μ g/l Study 4

(Desjardins et al. 2004) No cosolvent, generator column used for single exposure concentration

Skeletonema

costatum OECD 201 NOEC >10 μg/l

EC 50 52 μ g/l Study 5 (Desjardins et al. 2005) NOEC: dimethylformamide EC50: no cosolvent, generator column used for single exposure

concentrationArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 102 Compartment & Species Method Results Remark and

Reference Cosolvent Used

SEWAGE TREATMENT PLANT

MICROORGANISMS

Activated sludge Respiration

inhibition

OECD 209 EC 50 15 mg/l Limit test with one test concentration,

EC 50 is estimated. (Schaefer and Siddiqui 2003) No cosolvent

1 Extracted from Table 3-173 in European Commission (2008). Cosolvent information extracted from the

associated text and added.

4.2 E FFECT V ALUES FOR PBT OR POP S C LASSIFICATION AND R ISK A SSESSMENT

The European Commission RAR (2008) employs t oxicity data in two distinct ways. The first is the PBT classification process - does HBCD meet the criteria for classification as a PBT - while the second is the generation of predicted no-effect concentrations (PNEC) for use in risk assessment. TemaNord (2008) used the same toxi city data in their screening level POPs classifica tion process. In the present assessment we first critically

evaluate the values selected in the aforem entioned assessments for screening HBCD as a potential PBT chemical (hazard) and for asse ssing the potential of significant adverse effects from exposure to HBCD in the environm ent (risk). Then, based on our analysis of the available data, alternative toxicity e ndpoints are proposed for assessing the potential of significant adverse effects from exposur e to HBCD in the environment (risk).

4.2.1 PBT and POPs "T" Classification

The key toxicity data consid ered valid and diagnostic in the European Commission RAR (2008) and TemaNord POP proposal (2008) are the aquatic toxicity data. These data included the reproduction NOE C for Daphnia of 3.1 μg·L -1 (Drottar and Krueger 1998), the chronic ELS NOEC for rainbow trout of 3.7 μg·L -1 (Drottar et al. 2001) and the growth EC50 marine algae test for Skeletonema of 52 μg·L -1 (Walsh et al. 1987b).

Figure 4.1 illustrates the aquatic toxicity data listed in Table 4.2 and the water solubility

limit for γ -HBCD (about 2 to 3 μg·L -1 ). This is effectively the water solubility limit for tHBCD for aquatic toxicity testing purposes since γ -HBCD will precipitate at concentrations greater than its water solub ility limit. The testing data appear to demonstrate some adverse effects (e.g. LOEC) above the wate r solubility of γ -HBCD; however, these “effect data” are highly uncer tain due to the unquantified influence of undissolved HBCD (precipitate) a nd the presence of cosolvent.

Given the low water solubility of HBCD, it is quite likely that a "toxicity cutoff" is being observed (Veith et al. 1983). Mayer and Reic henberg (2006) also discuss a melting point cut-off for baseline toxicity. The melting poi nt value they propose is ~220 °C which is near the melting point for γ -HBCD. In essence, this cutoff occurs because it is not possible to sustain a concentr ation in the water at a level high enough to cause a toxic effect during the test exposure. Since HBCD is not showing any significant adverse toxicArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 103 effects at the water solubility limit, this and other observations suggests that it may possess only a baseline narcotic mode of toxi c action (the least potent mode of action).

For the majority of the key data points used in the previous HBCD evaluations no effects of any kind at any of the exposure levels were observed (i.e. NOEC). Most of these

“NOEC values” have been derived when th ere are no effects observed at the highest exposure concentration. Alt hough reporting a NOEC value in th is manner is correct it can be misleading, particularly if there are no reported concentrations of an effect concentration (i.e. LOEC). A NOEC clearly in dicates that no effects were found at the specified exposure; however, for the majority of the reported NOEC va lues there were no indications of an exposure concentration wh ere some effects might be expected (i.e. LOEC). Thus, an actual toxic exposure concen tration might be close to the NOEC level, or perhaps 3 to 5 times higher, or perhaps 10 to 1000 times higher. As detailed in Section 9.5.3, without an LOEC value there is no way of knowing where the transition threshold

from “no effect” to “effect” occurs. This issue (i.e. NOEC without LOEC) limits the applicability of the NOEC data for chemical assessments. Importantly, as can be seen in Figure 4.1 all of the available NOECs at or below the effective water solubility limit for t-HBCD (2 to 3 μg·L -1 ), indicate the same basic info rmation: no adverse toxic effects

were observed. In such circumstances the us e of “no effects” data to indicate possible

“toxic effects” for chemical assessments is de emed to be of poor sc ientific defensibility, particularly when there are other viable alternatives to assess the likelihood for significant

adverse effects in the environment. Remain ing mindful of these issues, the available aquatic toxicity data are examined in further detail below.

Fish The key fish data from Table 4.2 and Figure 4. 1 indicate no effects for either acute or chronic exposure to rainbow trout at 2.5 and 3.7 μg·L -1 , respectively, in tests employing a cosolvent. In particular, the chronic ELS NOE C for rainbow trout (Dro ttar et al. 2001) is actually reported as > 3.7 μg·L -1 as no effects for several en dpoints were observed at any exposure concentration tested. In both of thes e fish studies the meas ured exposure levels were only about 1/3 to 1/2 of the nomin al levels, suggesting substantial water solubility/bioavailability problems, despite the use of cosolvent. Other fish toxicity data reviewed in the European Commission RAR (2008) indicate acute toxicity test results at approximately 100 and 10,000 mg·L -1 , where cosolvent was used, and no effects were reported. Based on this complete lack of obs erved adverse effects in exposed fish it is clear that HBCD does not appear to be toxic to fish via water-borne exposure, even with the enhanced bioavailability due to the use of cosolvent.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 104 Figure 4.1. Key aquatic toxicity exposure data for PBT or POPs assessment.

Invertebrates The key aquatic invertebrate data from Table 4.2 and Figure 4.1 indi cate no effects in a 48-hour exposure of Daphnia at the highest exposure level of 3.2 μg·L -1 and 21-day exposure of Daphnia producing a NOEC of 3.1 μg·L -1 and a growth LOEC of 5.6 μg·L -1

. Since cosolvent was used in both studies, th e reported values are most likely confounded and overestimate toxicity such that studies carried out w ithout cosolvent would likely generate toxicity estimate with a higher num erical value showing HBCD to be less toxic. Furthermore, the LOEC is above the water solubility limit for γ -HBCD (effectively tHBCD for toxicity testing purposes since γ -HBCD will precipitate at concentrations greater than its water solubility limit).

In addition to the cosolvent and water solubi lity issues, a detailed examination of the results from the 21-day studies (Drottar an d Krueger 1998) indicates some problems in the latter part of this test, as can be seen in Table 3-157 in Eur opean Commission (2008). Mortality began to be observed in the negativ e control exposure with 1 death at 14 days and 2 at 21 days. Additionally, there was 1 death in the solvent control at 21 days. Also,

non-monotonic distribution of deat hs began to be seen at 14 days with 1, 1 and 2 deaths in the control, 1.6, and 11 μg·L -1 exposure levels, respectively, but not at the 0.87, 3.1 and 5.6 μg·L -1 exposures. Although some additional deaths occur at other exposure levels at 21 days, the distribution is still non-monotonic as no mortality occurred at the 3.1 μg·L -1

level. The non-monotonic distribution of deaths and the total of 3 d eaths in the controls versus only 5 deaths in the highest exposure level of 11 μg·L -1 at 21 days suggest that

some other factor other than HBCD was i nvolved in causing the mortality. This alsoArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 105 raises uncertainty about the validity of the growth and reproductive effects reported, since a general increase in some other stressor suffici ent to cause general increases in mortality will also affect non-lethal endpoints.

The available Daphnia data do not clearly and unequivocally establis h that HBCD causes adverse effects to Daphnia at exposure levels at or below than the water solubility limit

for γ -HBCD (or effectively, t-HBCD). As we ll, available data do not unequivocally establish that the chronic toxicity of HBCD to aquatic invertebrates occurs below the PBT “T” criterion of 10 μg·L -1

.

Algae The key algae data from Table 4.2 and Figure 4.1 indicate a combina tion of no-effect and

effects data and most obtained with the us e of cosolvents. In the first study for a freshwater alga (Roberts and Swigert 1997) lit tle information is reported on effects. An

NOEC could not be estimated and the EC50 a nd LOEC were both reported as greater than 2.5 μg·L -1 . A cosolvent was employed in this test. In the sec ond study with three marine algae (Walsh et al. 1987b) several gr owth EC50s were obtai ned that varied both within and between species and no NOEC values were calculated. A cosolvent was also employed in this study. Since this work de viated from standard testing methodology the European Commission (2008) judge d that this information shoul d only be used in support

of other work obtained with standard met hods. Thus, it should not be used as primary

evidence in either PBT or POPs "T" evaluation.

The third study with marine alga (Desjardins et al. 2004) did not use a co-solvent but was complicated by a non-standard methodology in which only 1 exposure concentration was employed. A growth inhibiti on of approximately 10% was observed at 72-hours at an exposure concentration of 40.6 μg·L -1 , obtained with the use of an HBCD-saturated generator column. The 72-hour EC50 for grow th is reported as greater than 40.6 μg·L -1
and the NOEC is reported as less than 40.6 μg·L -1 . These data were obtained in a study where a generator column was used to produc e the exposure media. Problems with this methodology are discussed elsewhere (Sectio n 9.5), and the OECD (2000) testing guidance specifically recommends against this practice. Thus, these data cannot be considered as reliable for comparisons with the PBT acute "T" criterion of 100 μg·L -1 , the PBT chronic "T" criterion of 10 μg·L -1 , or in a POPs screening assessment.

The fourth study with marine al ga (Desjardins et al. 2005) c ontains information from two studies, one with co-solvent and one without. The study without cosolvent was complicated by by a non-standard methodology in which only 1 exposure concentration was employed. A growth inhibition of 51% was observed at 72-hours at an exposure concentration of 52.6 μg·L -1 , obtained with the use of an HBCD-saturated generator column. Thus, the 72-hour EC50 for growth is reported as 52 μg·L -1 . A NOEC could not

be estimated. These data were also obtai ned using a generator column to produce the exposure media (see Section 9.5 and (OECD 2000)) The study with co solvent reported no adverse effects after 72 -hours of exposure in any of the test concentrations employed. The NOEC was reported as be ing greater than 10 μg·L -1 , which was the hi ghest exposureArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 106 concentration used (i.e. there was no LOEC reported) and at a concentration above the water solubility limit for γ -HBCD.

Given the various issues discussed above, the available algae data cannot be considered to clearly and unequivocally esta blish that the chronic toxicity of HBCD to algae is below the European Commission PB T “T” criterion of 10 μg·L -1 . In fact, adverse effects to algae are only reported at e xposure levels in excess of the water solubility for the main γ

isomer. Perhaps the best algae data set (Desja rdins et al. 2004; Desj ardins et al. 2005), indicates that, despite the confounding issues, th e chronic toxicity of HBCD to algae is above the “T” criterion of 10 μg·L -1 and therefore it should notbe considered a PBT based on these studies. Specifically, both the solvent and no-solvent growth EC50 estimates of 40.6 and 52 μg·L -1 demonstrate chronic toxicity effects above the NOEC criterion of 10 μg·L -1 and the NOEC estimate of >10 μg·L -1 indicates that the NOEC is somewhere between the criterion value of 10 μg·L -1 and the EC50 estimates.

Conclusions The screening level PBT risk evaluation presented in the European Commission RAR (2008) and the TemaNord POPs classification proposal (2008) identif ied and selected key environmental toxicity data for HBCD. The pr esent review of the available data does not support the conclusion that HBCD meets the PBT "T" criterion nor the suggestion that these data are directly applicable for a PO P classification. Much of the HBCD data considered valid and applicab le in previous PBT and POP assessments is shown to contravene published international guidance on aquatic testing protocols (e.g. OECD 2000). Assessments must fully consider the imp lications of uncertain ty that exist when using data that are in violation of standardized testing protocols.

The water solubility of γ -HBCD (or effectively t-HBCD for toxicity testing purposes), is about 2 to 3 μg·L -1 , which is below the PBT NOEC criterion of 10 μg·L -1 . Therefore, it is not possible to have a sufficient amount of dissolved t-HBCD in the water column to

reach the NOEC criterion value to demonstrate that HBCD is “not T” for the purposes of screening level PBT classifica tion. Clearly, due to water solubility limitations, it is impossible to have a valid aquatic toxicity te st result for HBCD that can be adequately assessed using the existing PBT NOEC criteri on. The simplest and most informative statement on the aquatic toxicity testing da ta for HBCD is that no effects have been reported for exposure concentrations at or be low the estimated water solubility limit for

γ -HBCD.

In summary, using toxicity information based on water-based exposure to aquatic organisms does not appear to be a reliab le or appropriate methodology to establish

whether HBCD should be classified as a PBT or a POP on the ba sis of toxicity. A detailed interpretive review of available data does not support a conclusion that HBCD

meets the “T” criterion for PBT or POPs cl assification, as presented in European Commission (2008) and TemaNo rd (2008), respectively.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 107 4.2.2 Previous Approaches for Effect Characterization

Table 4.3 contains a summary of the crit ical toxicity data and the recommended

assessment factors considered for PNEC estimation by the European Commission RAR (2008). The TemaNord (2008) re port did not present a ge neral environmental risk assessment, but rather focused only on Stockholm Convention and UN-ECE “POPs” classification screening criteria . In general, these data are demonstrated to be confounded by various technical issues and are not reco mmended for comprehensive risk assessment. The Technical Guidance Document (EC 2003) used for the European Commission RAR (2008) also suggests that the lo west NOEC should be used in the PNEC estimation. Both a NOEC and the lowest effect exposure leve l (LOEC) are considered more appropriate for developing a PNEC since the threshold for the occurrence of adverse effects is somewhere between these two estimated endpoi nts. Without some reliable indication of exposure levels associated with an observed effect (i.e. LOEC), a NOEC is not considered appropriate for the estimation of a PNEC since in isolation it contains no reliable information on possible effect levels. Thus, the TGD is considered to represent a simplistic approach, acceptable perhaps for a screening evaluation, but not recommended for more comprehensive risk assessments. Ra ther than force a process beyond its domain of applicability, two alternative methodologies are proposed that are considered more appropriate for the assessment of potential significant adverse e ffects. The body/tissueresidue and the Total Daily Intake (TDI) approa ches are viable alternatives examined in

the next section.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 108 Table 4.3. Summary of critical toxicity data and PNEC estimates for environmental

effects of HBCD from Eu ropean Commission RAR (2008) 1

.

Compartment & Target EC Section Reference Critical Toxicity Data Predicted No-Effect

Concentration

TERRESTRIAL COMPARTMENT

Plants 3.3.2.1.1 No appropriate data NA

Invertebrates (soil) 3.3.2.2 59 mg/kg dry soil (Earthworm NOEC)

Assessment factor = 10 5.9 mg/kg dry soil

Mammals 3.3.4.2 150 ppm dry weight in food (10 mg/kg/d dose equivalent) (Rat reproduction NOAEL)

Assessment factor = 30 5 mg/kg in food

Bird egg 3.3.4.3 0.9 mg/kg wet weight (Mouse behavior LOAEL) NA

AQUATIC COMPARTMENT

Freshwater micro-organisms 3.3.1.5 15 mg/L (Activated sludge respiration inihibition)

Assesment factor = 100 150 μg/L

Freshwater organisms 3.3.2.1 3.1 μg/L ( Daphnia magna NOEC)

Assessment factor = 10 0.31 μg/L

Freshwater sediment organisms 3.3.1.7 8.6 mg/kg sediment dry weight (Worm NOEC)

Assessment factor = 10 0.86 mg/kg sediiment, dry weight

Saltwater organisms 3.3.5.2 3.1 μg/L ( Daphnia magna NOEC)

Assessment factor = 100 0.031 μg/L

Saltwater sediment organisms 3.3.5.4 8.6 mg/kg sediment dry weight (Worm NOEC)

Assessment factor = 50 0.17 mg/kg sediment, dry weight

1 Extracted from European Commission, 2008, for continuous exposures only.

4.2.3 Current Approaches for Effect Characterization

This section outlines the development of two general and related alternative approaches for deriving threshold values to characterize effects levels . These approaches are later used to assess HBCD for its potential to cau se significant adverse effects in different regions of the global environment. First th e body/tissue-residue approach is discussed followed by the Total Daily Intake (TDI) approach.

Body/tissue-residue Approach McCarty and Mackay (1993) examined some di sparate trends in environmental fate and environmental toxicity and suggested a cons olidated approach. It was suggested that routinely providing more detailed informati on for toxicity data, particularly some quantitative indication of the amount of substa nce in the body and/or tissues of exposed organisms that was associated with various types and degrees of adverse effects (the critical body residue approach), would pr ovide both an improved understanding of the toxicity of the substance and facilitate re gulatory use of that enhanced toxicity information. The facilitated regulatory use would occur in two areas. The first is by moreArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 109 direct comparison of laboratory-derived body/tissue residue dose-effect data with measured body/tissue concentrations of substa nces obtained from monitoring. The second is by enhancing both retrospective and prospe ctive use of increasi ngly sophisticated environmental distribution, fate, and exposure models to explain and predict toxicity. For hydrophobic substances, such as HBCD, a furthe r advantage is that an organism-based dose metric is largely free of the aforementio ned issues that pla gue aquatic exposurebased metrics (bioavailability, cosolvents). Toxicity information developed based on body/tissue-residues is more directly compar able with the monitoring data and model predictions for various organisms at various trophic levels in the environment. A recent study provides a good exam ple of using this approach to ev aluate potential risks of POPs to top predators (Leonards et al. 2008) and assessments of this type have also been advocated elsewhere (e.g. Wu et al. 2008).

A guide to developing residue -based regulatory guidance for risk assessment has been published (Meador 2006). The Society of E nvironmental Toxicology and Chemistry (SETAC) held a workshop on the tissue-residue approach in 2007 (Meador et al. 2008) and another on POPs and PBTs in 2008 (Sol omon et al. 2009). In these workshops and published proceedings the use of residue-based toxicity data was recommended. The U.S. EPA has also incorporated a residue-based approach in the proposed revisions to the Guidelines for Deriving Ambient Water Quality Criteria for the Prot ection of Aquatic and Aquatic-Dependent Wildlife document (U.S. EPA 2005). Th e U.S. EPA's Office of Pesticide Programs has adopted a residue-based approach for pestic ide registration and has published a white paper on the registratio n process for candidate pesticides with PBT-like properties (Anderson et al. 2008a). Re sidue-based risk assessments have been prepared for a candidate pesticide by the U. S. EPA (Anderson et al. 2008b) and on behalf of the proponent (McCarty and Arnot 2008).

The scientific and regulatory communities are in the process of either evaluating or formally adopting a critical body/tissue residue -based approach to better address the potential for significant adverse effects in the environment. This approach is not a panacea and many challenges exist, including how best to adequately address the target/nontarget lipid issue a nd the lack of a comprehensive, widely-applicable, generally accepted mode of toxic action classification scheme (McCarty and Borgert 2006a,b). In this latter regard for organic chemicals the ex tremes of the range of toxic potency appear to be defined by baseline narc osis and TCDD-like toxicity.

For HBCD a comprehensive review of all av ailable residue-based toxicity information

would take some time as the focus has been on exposure-based data and would require a

careful review of all of the or iginal publications. Some resi due-based data can be gleaned from the reviews provided in the European Commission ri sk assessment (2008). This, along with some other pertinent inform ation, is presented in Figure 4.2.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 110 Figure 4.2. Residue-based organism toxicity and residue-based PNECs.

On the left side of Figure 4.2 the approxi mate whole-body residue-effect ranges for the least toxic (baseline narcosis) and most t oxic modes of action (T CDD-like toxicity) for small aquatic organisms of the order of 5% lipid (McCarty and Mackay 1993). This provides perspective on the range of possi ble residue-based toxicity dose-response relationships.

The following aquatic and terrestrial toxicity data on HBCD were used to set the tentative

residue-based PNECs that are de veloped below and depicted in Figure 4.2. A marine fish no-observed effect residue (NOER) is ba sed on a 78-day sediment/diet exposure endocrine/reproductive study by (Kuiper et al. 2007). The highest average muscle concentration resulting from the various exposures was 274.2 μg·g -1 lipid with a reported maximum of 446 μg·g -1 lipid weight. It was reported that this residue "... did not affect any investigated parameters." The lipid co ntent of the muscle tissue was reported as 0.9%. The residue-based NOER was estimated as (446 μg·g -1 lw×0.009 = 4 mg·kg -1
ww/641.7 g·mol -1 =) 0.0063 mmol·kg -1 ww.

In a 32-day BCF test with freshwater fish (Veith et al. 1979) no adverse effects were observed. The mean exposure concentration was 6.2 μg·L -1 with an estimated BCF of 18,100 based on measured fish levels. Lipid leve ls for these fish are of the order of 5%. The residue-based NOEC was estimated as (6.2 μg·L -1 ×18100 = 112.22 mg·kg -1
ww/641.7 g·mol -1 =) 0.175 mmol·kg -1 ww.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 111 In a 35-day BCF exposure with freshwater fish no adverse effects were observed (Drottar and Krueger 2000). The highest measured whole-body fish concentration level was 16,154 μg·kg -1 ww and the lipid level was about 5-10%. This residue-based NOER is (16.2 mg·kg -1 ww/641.7 g·mol -1 =) 0.025 mmol·kg -1 ww.

In a 56-day soil exposure su rvival and reproduction study w ith earthworm (Aufderheide et al. 2003) at 28-days the highe st exposure level of 4,190 mg·kg -1 dry soil resulted in a maximum tissue concentration of 150 mg·kg -1 ww. This corresponds to a NOER of (150 mg·kg -1 ww/641.7 g·mol -1 =) 0.24 mmol·kg -1 ww in earthworms based on no-effects for survival. Effects on reproduction were found af ter 56 days at intermediate exposure

levels but no BCF or tissue residue informa tion was available. Earthworms have been shown to exhibit residue-effect relationships si milar to fish and aquatic invertebrates for a number of organic chemical s (Lanno and McCarty 1997).

There are three mammalian toxicity studies wi th t-HBCD where some residue data were collected. The 90-d rat study by Chengelis (2001 ) reported HBCD levels in adipose tissue which is largely a storage compartment for hydrophobic substances a nd is unlikely to be

a site where toxic action occurs. This informa tion was not used as it cannot be converted back to general tissue wet weight. Another group of researchers conducted both a 28-d (van der Ven et al. 2006) and a 1 generati on reproduction study (Lilienthal et al. 2009)

where some liver residue data was collected. In the 28-d st udy the lowest effect levels were reported for female rats which exhibite d thyroid and liver weight changes at BMDL levels of 1.6 and 22.9 mg·(kg-bw·d) -1 , respectively. The corresponding liver HBCD residue levels were 43 and 192 μg·g -1 lw, respectively. Corrected to wet weight from lipid weight, using a lipid conten t of 5%, corresponds to residues of 0.0034 and 0.015 mmol·kg -1 ww, respectively. The European Comm ission (2008) considered the BMD-L based on liver weight change to be the mo st robust. van der Ven et al. (2009) found a variety of effects due to HBCD exposur e over one generation. The most sensitive response they noted was modest behavior ch anges in rats (BMD-L based on changes in auditory thresholds and eff ects of catalepsy) at HBCD liver levels of 7 and 16-20 μ g·g -1
lipid weight, respectively (Lilienthal et al. 2009). The lipid content of the rat livers was approximately 5%. This corresponds to NOERs of (7 and 20 μg·g -1 lw/20 = 0.35 and 1 mg·kg -1 wet weight/641.7 g·mol -1 =) 0.00055 and 0.0015 mmol·kg -1 ww, respectively. A full regulatory assessment of the relevance of the findings of the 1 generation study has yet to be made, and the ecological significan ce of some of the detected changes is uncertain. However, in the interests of presenti ng an alternative to the baseline narcosis scenario, the lowest data for the modest be haviorial changes (more than an order of magnitude below the robust BMD-L for liver we ight change) will be used as a “worst case” exercise to develop a tentative re sidue-based specific toxicity PNEC.

The following is a preliminary interpretation based on available data, as presented in Figure 4.2, and should be considered as illust rative rather than definitive for development and use of a residue-based PNEC approach for HBCD. The NOER data for fish and the earthworm suggest that HBCD acts by the least toxic mode of action for organic chemicals, baseline narcosis, as the NOER da ta range up into the lower part of the chronic baseline narcosis residue range esta blished for fish and some invertebrates.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 112 Although there are no observed residue-based effect data for HBCD, the acute and

chronic mode of action information serve as the LOER and indicate where adverse effects are expected. As a baseline narcotic toxicant adverse effects will be largely determined by bioconcentration/bioaccumulati on potential. However, the very low water solubility of HBCD limits the amount of e xposure that any organism will encounter. The exposure-based toxicity cutoff that is enc ountered in aquatic exposures with highly hydrophobic, very low water solubility substances is operational here and this suggests that other exposure routes - primarily dietary ex posure - will be most important. Although there are no residue-based toxicity data for aquatic invertebrates or algae, the available exposure-based information suggests a toxicity cutoff due to water solubility limitations. Some residue information for these organism groups could provide confirmation of this

view.

The only bird or mammal residue-effect data available was that reported above for rats. However, as noted above, for the sake of generating specific-t oxicity PNEC, it is

assumed that the lowest residue-based dose-re sponse data represents the lower end of a range of toxic effects cause d by an unknown specific toxicity mode of action (i.e. something between baseline narcosis and TCDD-like toxicity). Also, for the sake of argument, it is assumed that this is also applicable to birds and bird eggs. Such assumptions, although not necessarily true in this case, are not unreasonable since organic chemicals may have different key modes of action in different organisms. Fish and invertebrates may exhibit only baseline narcos is since they do not have the biochemical pathway/receptor for the specific mode of action. On the other hand, higher organisms such as mammal and/or birds that may have the appropriate path way/receptor, exhibit effects due to the specific mode of action at a lower residue level th an baseline narcosis, thus preempting effects due to baseline narcosis.

Two residue-based PNEC development scenario s are plausible. In the first, HBCD is assumed to be a narcotic to all organisms. For this scenario a tentative residue-based

baseline narcosis PNEC is established star ting just below the lower end of the observed chronic toxicity residue range. In this case th e rat behavior data can be viewed as the

extreme lower tail of chronic narcotic effect s that are not routinely evaluated and are of uncertain and questionable relevance to th e key ecological response categories of population survival, growth, and reproduction employed in environmental risk

assessment.

In the second residue-based PNEC development scenario it is assumed that the rat residue data suggest that HBCD operates by a speci fic but unknown mode of toxic action that is further assumed to be operational for all or ganisms. The rat data, which are essentially threshold for modest behavioral responses, are assumed to be at the low end of the residue-based dose-effect relationship for th is unknown specific mode of action. Thus, a tentative residue-based specific toxicity PNEC is established just below the lowest rat

data.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 113 Total Daily Intake (TDI) Approach The other general alternative approach to char acterize the potential fo r adverse effects in

the environment is to utilize available diet ary exposure data as estimates for allowable Total Daily Intake (TDI). Dietary exposure tests of HBCD to laboratory models (rats)

have been conducted to de termine NOAEL and LOAEL based on exposure to t-HBCD dosed in food for a range of toxic endpoint s. The European Commission RAR includes a detailed summary of the test data available ca. 2007 – 200 8 (EC 2008). These data are primarily from 28 day exposure periods.

From studies by Chengelis (Chenge lis 2001) a LOAEL of 125 mg·(kg-bw·d) -1 for liver weight gain was considered reliable (EC 2008) . From data reported by van der Ven et al. (2006), a NOAEL/BMD-L of 22.9 mg·(kg-bw·d) -1 was selected for the risk characterization (EC 2008). For many of the en dpoints tested in th e van der Ven et al. study (2006), there were no significant effects re ported at the highest exposure level (200 mg·(kg-bw·d) -1 ), particularly in male s. These endpoints with no significant effects in males include thyroid weight increase, liver weight increase, pituitary weight increase, and bone density. Using the van der Ven et al. (2006) test data, Germer et al. (Germer et al. 2006) investigated enzyme induction. The only significantl y induced enzymatic

activities were detected for CYP 2B in ma les and CYP 3A4 in females from 10 mg·(kgbw·d) -1 (EC 2008). Other reported “very conservative” NOAEL values in the European risk assessment (i.e., Ema et al. 2008) were approximately 10 mg·(kg-bw·d) -1 (EC 2008). A NOAEL of 10 mg·(kg-bw·d) -1 was also deduced base d on a two-generation reproductive toxicity study in rats (EC 2008). Thus, for the purpose of risk characterization in the presen t study a NOAEL of 10 mg·(kg-bw·d) -1 was selected.

5.0 HAZARD CHARACTERIZATION (PBT/POP)

5.1 S CREENING C RITERIA

Table 5.1 presents the PBT and vPvB scr eening criteria proposed in the European Commission Technical Guidance Document (T GD) (EC 2003). Table 5.2 summarizes the criteria adopted in the United Nations Stockholm Convention for identifying potential POPs (UNEP 2001). Table 5.3 summarizes the screening criteria in the United Nations Economic Commission for Europe (UN-ECE) Protocol on POPs (UN-ECE 1998). The POP screening criteria are generally sim ilar with a few exceptions. In the UN-ECE Protocol a vapour pressure less than 1000 Pa is also included as a sc reening criterion for potential long range transboundary transport.

The UN-ECE criteria also include: Paragraph 2( a); monitoring or equivalent scientific information suggesting long range transboundary atmospheric transport, and Paragraph 2(b); whether sufficient information exists to suggest that th e substance is likely to have significant adverse human health and/or environm ental effects as a resu lt of its long range transboundary atmospheric transport (UN- ECE 1998). The Stockholm Convention also requests (Annex D, Paragraph 2), where possible, a comparison of toxicity or ecotoxicity data with detected or predicte d levels of a chemi cal resulting or anti cipated from its long range environmental transport (UNEP 2001).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 114 Table 5.1. PBT and vPvB screening criter ia according to European Commission TGD (EC 2003).

Category PBT-criteria vPvB-criteria

Persistence (P) Half-life > 60 d in marine water

Or > 40 d in freshwater

Or > 180 d in marine sediment

Or > 120 d in freshwater sediment Half-life > 60 d in marine or

freshwater

Or half-life > 180 d in

marine or freshwater

sediment Bioaccumulation (B) BCF a > 2000 BCF > 5000

Toxicity (T) Chronic NOEC < 0.01 mg/l

Or CMR b
Or Endocrine disrupting effects -

a Bioconcentration Factor

b Carcinogenic, Mutagenic and Toxic to ReproductionArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 115 Table 5.2. POP screening criteria accord ing to the Stockholm C onvention (UNEP 2001).

Category Criteria

1b. Persistence (P) Half-life in water > 2 months

Half-life in sediments > 6 months

Half-life in soils > 6 months a

1c. Bioaccumulation (B) Bioconcentration factor (BCF) > 5000 Bioaccumulaiton factor (BAF) > 5000 b
log K OW > 5

1e. Adverse Effects (“T”) (i) Evidence of adverse effects to human health or to the environment that justifies

consideration of the chemical within the scope of the Convention; or (ii) Toxicity or ecotoxicity data that indicate the potential for damage to human

health or to the environment. 1d. Long Range Transport Potential (LRT) (i ) Measured levels of the chemical in

locations distant from the sources of its release that are of potential concern; (ii) Monitoring data showing that long range environmental transport of the chemical, with the potential for transfer to a receiving environment, may have occurred via air, water or migratory species; or iii) Atmospheric half-life > 2 days for a chemical that migrates significantly through air.

a Or evidence that the substance is otherwise sufficiently persistent to be of concern within the scope of the

Convention

b Or evidence that a chemical presents other reasons fo r concern, such as the high bioaccumulation in other species, high toxicity or ecotoxicity; or monitoring data in biota indicating that the bioaccumulation potential of the chemical is sufficient to justify its consideration with the scope of the ConventionArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 116 Table 5.3. POP screening criteria accord ing to the UN-ECE Prot ocol (UN-ECE 1998).

Category Criteria

1c. Persistence (P) Half-life in water > 2 months

Half-life in sediments > 6 months

Half-life in soils > 6 months a

1d. Bioaccumulation (B) Bioconcentration factor (BCF) > 5000 Bioaccumulaiton factor (BAF) > 5000 b
log K OW > 5

1b. Toxicity (T) Potential to adversely affect human health

and/or the environment

1a. Potential Long range Atmospheric Transport (LRT) Vapour pressure < 1000 Pa Atmospheric half-life > 2 days c

a Or evidence that the substance is otherwise sufficiently persistent to be of concern within the scope of the protocol

b Or if the bioaccumulation potential is significantly lowe r than these criteria, other factors, such as the high toxicity of the substance, that make it of concern within the scope of the protocol

c Or monitoring data showing that the substance is found in remote regions

5.2 A SSESSMENT OF P ERSISTENCE AND L ONG R ANGE T RANSPORT

This section discusses the categorization of P and LRT. A caveat of the P categorization methodology is that a substance can be categori zed as P even if it does not cross brightline thresholds for degradation in specific environmental media if it is detected remote regions, as is the case for HBCD. We critically evaluate the selection of single compartment half-life P categorization reco mmendations in the EU RAR and compare model predictions of overall persistence (P OV ) for HBCD with benchmark chemicals.

5.2.1 Persistence

The selected degradation half-lives (HLs) fo r HBCD were presented in Table 2.5. The median HLs in sediments and soils, 35 and 85 days respectively, do not exceed any of the “P” criteria presented in Tables 5.1–5.3 whereas the median HL in water (85 days) does. The upper bound HL estimates for sediments and soils also exceed the “P” criteria. It

should be noted that median HL in water wa s derived from model estimates due to the lack of reliable data. Although there are uncertainties associ ated with the degradation studies showing elevated persistence in sedi ments and soils (e.g. (D avis et al. 2004)),

there is no definitive evidence demonstratin g that these data should be completely disregarded. Overall, based on available da ta, it can be concluded that HBCD likely fulfills some but not all of the bright-line cut-off “P” criteria presented aboveArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 117 However, it has been argued that overall persistence (P OV ) in the environment is a better indicator of chemical persistence rather than comparing multiple half-life estimates for air, water, soil and sediment against bright -line cut-off criteria in these individual compartments (Webster et al. 1998). Indeed m odels are necessary to assess “P” because it is not possible to trace a chemical in the act ual environment. There is no screening “P”

criterion for the P OV metric; therefore, P OV predictions for HBCD were compared to P OV predictions for POPs, non-POPs and candidate POPs (Figure 3.18 and Table 3.11). When default (median) HLs for HBCD were selected the P OV is usually at least 1 order of magnitude, or more, lower than five of th e six benchmark POPs. Notable exceptions are for benchmark comparisons with aldrin and heptachlor when emissions are assumed to

air. HBCD P OV predictions (median HLs) are also a bout 1 order of magnitude lower than

the four candidate POPs. Certai n non-POP benchmarks have P OV predictions greater than HBCD. When upper bound HL estimates were selected for HBCD and 100% emissions are to air, the P OV predictions are in a similar range as many benchmark POPs and the candidate POPs. When upper bound HL estimates were selected for HBCD and at least 50% of emissions are to water, the P OV predictions are generally much lower than many benchmark POPs and the candidate POPs (T able 3.11). When lower bound HL estimates

for HBCD were selected the P OV predictions are much lower than benchmark POPs (except for heptachlor) and the candidate POPs and mo re similar with most non-POPs. Without reduced uncertainty for the HL es timates it is difficult to arrive at an unambiguous conclusion regarding P OV benchmarking for HBCD.

Recommendations/Conclusions It is not possible to definitively concl ude whether or not HBCD fulfills the “P” degradation half-life crit eria based on the available experimental data. Model

assessments of P OV against benchmark chemicals do not provide any definitive categorization for persistence, largely due to a wide range of P OV values for listed

POPs. Additional studies on aerobic degradation in wat er, sediments and soils may help to reduce the uncertainty in the es timated half-lives for HBCD and the “P” criteria comparisons. Further data on a tmospheric chemistry of this compound would also be highly valu able for assessments of P OV . Regardless, in the context of POP assessments, HBCD detected in remote reg ions can always be cited as evidence that HBCD is a substance of concern, i.e ., the substance is otherwise sufficiently persistent to be of concern within the scope of the POPs criteria.

5.2.2 Long Range Transport Potential

The estimated sub-cooled liquid va pour pressures of HBCD (t-, α -, β -, γ -) were presented

in Table 2.2. All values are far below the va pour pressure criteri on presented in Table 5.3. The degradation half-life of HBCD in the atmosphere due to reactions with OH radicals was estimated to range from 0.4 to 4 days, considering the expected uncertainties

associated with reaction rates and radical conc entrations. While the median value (1.3 d) is below the criterion value of 2 days, th e uncertainty associated with the estimate

includes values that exceed the criterion.

The CTD, TE and eACP 10 of HBCD compared to POPs, non-POPs and candidate POPs are shown in Figures 3.18 and 3. 22, respectively. Similar to P OV , the CTD, TE andArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 118 eACP 10 predictions for HBCD are lower than some POPs (e.g. HCB, PCBs) included as benchmarks but higher than others (e.g. al drin, heptachlor). The non-POPs included in the study also exhibit a range of CTD, TE and eACP 10 values, some higher than HBCD and some lower. Hence, this element of the benchmarking exercise is also ambiguous. Overall, the mass balance modelling suggests that HBCD is subject to a low to moderate level of long range trans port potential (compared with existing POPs). Long range transport potential benchmark estimates also show that the potential is lower when emissions are greater to the wa ter compartment than to air.

Available monitoring data from remote regions indicate that HBCD has the potential to reach remote regions. While it is true that concentrations in biota appear to be much lower than in industrialized source regions , the studies documenting HBCD in various organisms in the Arctic provide sufficient ev idence of LRT potential according to criteria presented in Tables 5.2 and 5.3. It should be recognized that some sources of emissions are expected in “remote” regions as a result of historical and current human activities. For example, people in remote areas have been using products that are expected to contain HBCD (e.g. insulation, textiles). These “sourc e” emissions in “remote” regions may be exacerbated by the common practice of burning garbage (products containing HBCD) inNorthern communities. The potential emissions related to use and disposal of HBCDcontaining products in remote areas is unc lear. Thus, without improved knowledge of relative contributions of quanti ties released in remote regi ons compared with quantities released in source regions that are measured in remote regions it is difficult to ascertain the actual quantity in remote regions that is e xplicitly the result of long range transport.

HBCD seems to have a “low” to “mode rately-low” LRT potential based on model estimates; however, detectable levels in remote regions suggest that emissions have been sustained at high enough rates to allow for th ese findings. Use patterns, monitoring data and emissions estimates suggest that HBCD has been used for at least a few decades. Thus, continuous emissions combined low tr ansport efficiency can still result in detectable concentrations in remote regions.

Recommendations/Conclusions The uncertainty in the estimated long range transport potential could be reduced if

additional studies characteri zing the degradation half-lif e of HBCD in air, water, soil and sediment were available. In part icular, empirical data on atmospheric chemistry of this compound (e.g. OH radical reactivity) would be highly valuable. However, available monitoring data indi cate that HBCD is present in remote regions. This is considered sufficient evid ence that HBCD has long range transport potential as identified by the screening criteria.

5.3 A SSESSMENT OF B IOACCUMULATION

The bioconcentration studies cited in (EC 2008) and (Veith et al. 1979; Drottar and Kruger 2000; TemaNord 2008) had BCFs > 5,000 (i .e. “B” or “vB”). These studies used co-solvents to conduct the experiments, wh ich introduces some unc ertainty into the interpretation of the BCFs since guidelines recommend against the use of solvents forArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 119 bioconcentration tests (OECD 1996). A recent publication suggests that co-solvents and dispersants (surfactants) may not drastically change BCF estimates (Yakata et al. 2006), particularly in the context of the large uncer tainty generally associated with estimating BCFs for hydrophobic substances (Arnot and Gobas 2006). However, Yakata et al. (2006) tested six single co mpounds individually and only one was above its water solubility limit. Thus, it is difficult to establish the relevance of the information in Yakata et al. (2006) to the HBCD situation – a mixture of isomers with differing water solubilities. Overall, the BCF values deri ved from these studies are believed to be generally representative and exceed the “B” and “vB” screening criteria outlined in Tables 5.1, 5.2 and 5.3. Furt hermore, the estimated log K OW values (Final Adjusted Value, see Table 2.2) for the three major is omers are all > 5, in excess of the “B” and

“vB” criteria outlined in Tables 5.1, 5.2 and 5.3.

The majority of monitoring data also suppor t the bioaccumulation categorization using BCF data such that HBCD (or at least certa in isomers) are found to biomagnify (i.e. biomagnification factors, BMF, are > 1) and exceed BAF criteria in the environment (e.g. (Tomy et al. 2004; Law et al. 2006a; Sormo et al. 2006; Tomy et al. 2008)). Interpretations of the monitoring data are complicated by the observations that HBCD undergoes bioisomerization in biota and perhap s other compartments of the environment. Hence, it remains uncertain if all diastereom ers are bioaccumulative in the field. For example, Tomy et al. (2008) reported trophic magnification factors (TMF) of 2.1 and 0.5

for α - and γ -HBCD respectively in a study of an easte rn Arctic food web. There are other examples where different conclusions could be reached regarding bioaccumulation potential depending on what data are cons idered. Trophic level and lipid-normalized biomagnification factors (BMF TL s) for α -HBCD were greater than 1 for many predatorprey comparisons (e.g. narwhal : polar cod, beluga : polar cod) but not in all cases (e.g. beluga : redfish BMF TL < 1). Data from a food web study in the Western Canadian Arctic (Ismail et al. 2009) seem to contradict the pa tterns seen in Tomy et al. (2008). For example, the calculated BMF TL of α -HBCD for beluga : polar cod is 0.4 (although

BMF TL s greater than one were observe d in other cases, e.g., beluga : herring and beluga : Arctic cisco). There also appear to be discre pancies in field-derived BMFs for ringed seal

: polar cod feeding relationships among differ ent studies. For example, Sormo et al. (Sormo et al. 2006) reported a lipid-normalized BMF of approximately 11 (based on mean values, total HBCD) for ringed seals a nd polar cod sampled fr om Svalbard whereas data reported in (Tomy et al . 2008) yield a lipid- normalized BMF of 0.1 (mean values, total-HBCD), i.e., two orders of magnitude lower.

Recommendations/Conclusions Although there are uncertainties associated with the laboratory BCF data for fish and field data characterizing bioaccumulati on potential, the weight of evidence suggests that HBCD is “B” according to th e screening criteria presented in Tables 5.1, 5.2 and 5.3. It remains uncertain if all di astereomers are bioaccumulative in all species based on the uncertainty of the bioisomerization and biotransformation

rates for each isomer.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 120 5.4 A SSESSMENT OF T OXICITY (H AZARD )

The toxicity hazard evaluation of HBCD was ca rried out with respect to classification as a PBT based on European Commission “T” criterion and as a POP with respect to the descriptive hazard based criteria (Tables 5.2 an d 5.3). There is no available evidence to suggest that HBCD is carcinogenic, mutagenic, a reproductive toxica nt or an endocrine disrupting chemical. The exposure-based aquati c ecotoxicity data available for HBCD are

of limited value for screeni ng categorizations as all e xposure data are confounded by water solubility/bioavailability issues incl uding the use of a cosolvent or use of a generator column. In OECD testing guidanc e for difficult substances and mixtures (HBCD is both) there is a specific cauti on concerning the use of these practices.Notwithstanding thest problems, the ecotoxicity data show that no aquatic exposurebased toxicity effects occur at or below the water solubility limit for γ -HBCD (effectively, t-HBCD for toxicity testing purposes). The water solubility limit of γ -HBCD is about 2 to 3 μg·L -1 , which happens to be about 3 to 5 times lower than the chronic NOEC PBT criterion of <10 μg·L -1 . Since reliable aquatic e xposure-based toxicity data

cannot be obtained at concentrations that exc eed a chemical’s water solubility limit, it is impossible for HBCD to be above the EU PBT criterion. For the majority of chemicals with water solubility limits below “T” critieria (i.e. <10 μg·L -1 ), such criteria are generally not applicable. The concerns with using aquatic-based e xposure criteria for screening assessments are well documented in the scientific literature (McCarty and Mackay 1993; Gobas et al. 2001; Maeder et al. 2004). The available evidence for HBCD suggests it possesses a ba seline narcotic mode of toxic action, the least toxic mode of action for organic chemicals. Based on th e available ecotoxicity data, HBCD is considered to have “low toxicity”.

The POP assessment programs have no quantitative criteria for “T”. It has been suggested that criteria from the Dutch “PTB project” can be used as an assessment for “T” in UNECE POP assessments (Lerche et al. 20 02). Following this approach, available mammalian and ecotoxicity data for HBCD sugge st that HBCD is a Category 1 chemical (the least “toxic” category). The majority of the qualitative statements for screening potential toxicity and advers e effects in the UN Stockholm Convention (Table 5.2) and the UN-ECE POP Protocol (Table 5.3), respecti vely, seek to identify substances with the potential for toxic effects. All substances ha ve the “potential for adverse effects”, based

on a function of the dose and the duration of exposure. Thus, strictly speaking, these qualifying statements are ine ffective for screening poten tial POPs for “T”. More appropriately, both the UN-ECE Protocol and the UN Stockholm C onvention recognize

that substances should be evaluated for thei r potential adverse eff ects as a result of environmental exposures due to long range e nvironmental (or atmosphe ric) tranport, i.e., the risk of toxicity occurring in remote re gions. These more comprehensive assessments are beyond the hazard based screening criter ia, but are arguably more relevant for chemical management objectives. The follo wing section provides comprehensive risk

characterizations based on the likelihood of t oxic effects from HBCD in “local/near point source”, “source” region and “remote” regional scales.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 121 Recommendations/Conclusions A detailed evaluation of key exposure-ba sed aquatic toxicity studies for HBCD

indicates that there are a num ber of uncertainties. The da ta are largely unusable for a valid screening assessment of the toxici ty of HBCD for PBT/POPs categorization. The PBT and POP screening criteria are not effective for classifying very hydrophobic substances, especially mixtur es such as HBCD, for their toxicity potential. It is recommended that future t oxicity testing focus on improving data requirements for evaluating the potential for adverse effects in the environment. These data endpoints should focus on body/tissue-residues.

6.0 RISK CHARACTERIZATION

6.1 E COLOGICAL R ISK A SSESSMENT

It is critical to assess whether HBCD concen trations measured or modelled in different environmental compartments of remote areas and elsewhere are likely to cause adverse

effects. Adverse effects of HB CD are necessarily a function of actual dose estimates, i.e., exposure, and estimates for effects or no eff ects levels. In this section the critically

evaluated and selected metrics for effect and no effects levels based on the two alternative approaches - body/tis sue-residue and TDI - are co mpared with estimates of actual exposure in the environment. The pr imary focus is the likelihood of significant adverse effects in remote regions (i.e. the Arctic); however, evalua tions for the likelihood of adverse effects in source regions and n ear recognized point sources (impacted areas near industrial sites) of HBCD are also presented.

6.1.1 Body/Tissue Residue Based Risk Assessment

Figure 6.1 contains the tentative residue-b ased PNECs for the two proposed scenarios that were developed in Sectio n 4.2.3 and presented in Figure 4.2. The ranges of European monitoring data for invertebrates, freshwat er and marine fish, marine bird eggs, and marine mammals as summarized by the European Commission RAR (2008) are also

included. The limited data for freshwater in vertebrates are combined with the more abundant and broader range of ma rine invertebrate data in Figure 4.2. The lower section of each monitoring data range represents “r emote” regions, including Arctic sites, while the upper section represen ts mostly sampling data near kno wn industrial si tes (“local/near point source”), as evaluated in the European Commission RAR (2008).

In the first risk assessment scenario HBCD is assumed to be a baseline narcotic to all organisms. All of the monitoring data are be low this residue-based PNEC indicating that no adverse effects are expected due to exposures of organism s in the field, either in “remote” areas or in “source” regions” and “local/near point source” areas close to production and industrial locations.

The conclusions based on the second risk a ssessment scenario, where HBCD is assumed to operate by a specific mode of toxic action for all organisms, are more ambiguous. Most of the invertebrate monitoring data are below the specific-toxicity residue-based PNEC. The upper part of the i nvertebrate distribution at this PNEC is for musselsArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 122 sampled in a contaminated Norwegian Fjord. The remainder of the invertebrate

distribution starts more than an order of magnitude below the upper end of the range.Available monitoring data for marine fish are well below the specific-toxicity residuebased PNEC and hence no adverse effects are expected. However, for freshwater fish the upper third of the monitoring data exceeds the specific-toxcity residue-based PNEC. Similarly, for marine mammals, about 1/3 of the data are above the specific-toxicity residue-based PNEC range. The upp er limit of the the bird data also enters this range. These results suggest that fres hwater fish and marine mammals in contaminated regions near local point source emissions may exhibi t some adverse effect s from HBCD exposure but that those organisms in remote regions would not. The reported levels in birds from contaminated industrial regions/local sites are also near the threshold of where adverse effects from HBCD may occu r whereas levels from remote regions are not. A more detailed discussion of the potential environmental risks associated with the field monitoring data and the PNEC values is presented below.

The invertebrate monitoring data are not pe rsued further since the data are below the residue-based PNEC, except for mussels from a known contaminated site (“near/local point source). Rather than use the summary ranges presented in the European Commission (2008) report, the various monito ring data reviewed there were examined and specific information judged to be represen tative of “local” (nea r known point sources of HBCD release), “source” (in the general region where point sources are found), and “remote” (largely Arctic) observations were select ed. This is appropriate as the initial risk assessment analysis, presented above, indi cated that HCBD levels observed fish, mammals, and birds in remote areas, such as the Arctic and near-Arctic, did not appear to

have concentrations that are associated with possible adverse effects. As the likelihood of

adverse effects in biota in remote areas is a key issue in POPs classification, a more detailed evaluation is necessary to ensure the validity of this conclusion. Figures 6.2, 6.3

and 6.4 contain the risk characterization resu lts using monitoring data from local, source, and remote regions, respectively (Tables 3.3 to 3.5). For Figures 6.2, 6.3 and 6.4 both bird egg levels and body tissue levels are incl uded. Discussions on the suitability of using some of the bird monitoring data were presented in Section 3.3.7.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 123 Figure 6.1. Residue-based risk assessment w ith summary monitoring data from the EU RAR (EC 2008).Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 124 Inspection of Figure 6.2 (monitoring data fr om “remote” areas) indicates that adverse effects in fish, birds and marine mammals are unlikely due to the low measured tissue

levels of HBCD. This is true for both m ode of toxic action assu mptions, including the unidentified specific mode of toxic action that has a residue -based PNEC approximately

100 times lower than the baseline narcosis criterion. The marine mammal data has the largest range of th e three organism groups. The upper end of this range is also the closest of the three ranges to the unidentified residue -based specific mode of toxic action PNEC,

but does not enter it. Both of these observati ons for marine mammals are likely due to the wide ranging habits of some of these organi sms and their very high lipid content (related to the very substantial blubber content in th eir bodies). Lipid concentrations represent a challenge for interpretation due to a lack of knowledge of ta rget-to-storage lipid ratios; therefore, only wet weight concentrations are considered in the present assessment.

In terms of the potential for significant adverse effects from exposures to HBCD at different geographic scales, the information pr esented in Figures 6.1 to 6.4 suggest that there is potential for adverse effects occurring in organism s in areas near a known point source emission (Figure 6.4). However, adverse effects as a result of the long range transboundary atmospheric transport are unlikel y to occur in any organisms in remote regions including those occupying higher trophic levels (i.e. ma rine mammals). This brief and simple residue-based risk assessment exercise, despite its uncertainties and assumptions, provides a clear risk conclusion for HBCD.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 125 Figure 6.2. Residue-based risk assessment with available “remote” region monitoring

data.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 126 Figure 6.3. Residue-based risk assessment with available “source” region monitoring

data.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 127 Figure 6.4. Residue-based risk assessment with available “loca l/near point source” monitoring data.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 128 6.1.2 Total Daily Intake Based Risk Assessment

A second alternative approach for assessing the potential for adverse effects in the environment compares estimates of total daily intake rates for organisms in the environment with estimates of total daily inta kes from laboratory tests associated with NOAEL (10 mg·(kg-bw·d) -1 ). The highest reported t-HBCD concentrations in piscivorous marine mammals in remote regions (Svalbard) are for adult male ringe d seals (8 to 25 yrs old) (Sormo et al 2006). The average, me dian, and upper bound values are 19.6, 17.0 and 34.5 ng·g-lipid -1 , respectively. The unit emission rate in the RAIDAR model was

calibrated to obtain a con centration of 34.5 ng·g-lipid -1 in the representative piscivorous marine mammal. The corresponding total dail y dietary intake of t-HBCD to obtain a steady state concentration of 34.5 ng·g-lipid -1 in the marine mammal is about 8.1×10 -5
mg·(kg-bw·d) -1 . This suggests that current estimates of total daily diet ary intake of tHBCD in piscivorous marine mammals in remo te regions are approximately 5 orders of magnitude below available NOAEL data. There are some recognized uncertainties with this approach, in particular with regards to the actual steady state conditions in the environment (mammal and diet) and from th e laboratory studies; however, a similar general approach for characterizing potentia l risks to Arctic marine mammals using mammalian reference dose metrics from the laboratory for PFOS has been suggested (Kelly et al. 2009).

Estimates for the 95 th percentile of concentrations in piscivorous marine mammals from source region and local (near po int source) scale monitoring data were also selected for comparisons with the NOAEL TD I estimate of 10 mg·(kg-bw·d) -1 using the approach described above. The upper bound concentra tion estimates for regional source and local/near point source piscivorous marine mammals are about 750 and 6,300 ng·g-lipid -1

, respectively. These values correspond to RA IDAR total daily di etary intake rate

estimates of about 1.8×10 -3 and 1.5×10 -2 mg·(kg-bw·d) -1 for source region and local/near point source piscivorous marine mammals, re spectively. These estimated TDI rates are also well below the dietary consumption NOAEL.

The approach illustrated in the present evaluation could be further refined using a bioaccumulation model specific for the organi sms in which the data are reported (i.e. ringed seal) rather than the general paramete rs used to calculate bioaccumulation for the representative marine mamma l in RAIDAR. A more comprehensive assessment should also include TDI estimates for pups and calve s of marine mammals and an uncertainty analysis. For initial screening level purposes the present estimation is deemed sufficient

in the context of the difference between the estimated intake rates and the NOAEL. More comprehensive analysis should also be consid ered for other monitori ng data in remote regions, most notably for polar bears and for cer tain bird species. There are presently no

effects data available for risk characteri zation from bird speci es; therefore, the mammalian data would likely have to be considered.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 129 7.0 CONCLUSIONS

7.1 E COLOGICAL R ISK A SSESSMENT

Two versions of a body/tissue residue-based risk assessment were conducted. The first

was based on baseline narcosis and the ot her was based on an unidentified and assumed mode of toxic action associated with PNEC body/tissue residues 100 times lower than

baseline narcosis. Neither s upported classification of HBCD as a POP on the basis of toxicity as neither assessment indicated that adverse effects are e xpected in upper trophic level organisms living in remote areas di stant from known point-s ource emissions of

HBCD.

The likelihood of adverse e ffects to piscivorous marine mammals (recognized trophic position associated with high exposure levels ) was also evaluated by comparing estimates of total daily intakes of t-HBCD in the envi ronment with estimates of total daily intakes

associated with NOAELs from laboratory studies. The corr esponding total daily dietary

intake of t-HBCD to obtain a steady state concentration corresponding with the highest

measured concentration in a piscivorous marine mammal in a remote region is about 5 orders of magnitude below the NOAEL. The co rresponding total daily dietary intake of tHBCD to obtain a steady st ate concentration correspond ing with the upper bound of

measured concentrations in piscivorous marine mammal s in “source” and local scale (near point source) regional environments range s from about 2 to 3 orders of magnitude

below the NOAEL.

Simply because a substance meets screening le vel hazard categorizat ion criteria and can be detected in remote environments does not constitute a potential for adverse effects or

indicate there is a likelihood that adverse effects will occur. A more scientifically justifiable approach is to estimate the act ual risk for adverse effects by evaluating measured and/or modelled exposure levels in the environment with effects and/or noeffects levels. Following this standard risk-based approach current exposure data for HBCD in upper trophic level species in remo te regions show that exposure levels are

below current estimates associated with NOAELs. The analysis does show some cause for concern for HBCD in “local/near point s ource” regions. It is recommended that these instances be considered for more comprehens ive site-specific risk assessments conducted within regulatory framew orks typically employed for non-POP substances. These recommendations are further supported by the modelled and measured spatial trends in

the environment.

7.2 POP A SSESSMENT

7.2.1 Persistence and Long Range Transport Potential

Based on the assessment of degradation studi es and model output considered in this report, it cannot be definitively concluded th at HBCD does or does not meet the criteria for Persistence (P) and Long Range Transport (LRT) potential. Since there do not appear to be any analytical or methodological issues related to the majority of monitoring studies from remote regions, these data must be co nsidered reliable. Thus, according to the POPArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 130 screening criteria (see Tables 5.2 and 5.3) the presence of HBCD in remote regions can

be used to arrive at the conclusion that H BCD is persistent and exhibits sufficient LRT potential to warrant consideration as a pot ential POP. The model benchmarking results demonstrate no definitive conclusions regarding P and LRT assessments for HBCD.

Monitoring data support the previous assessment decisions for “P” and “LRT potential” as defined by the POP criteri a, i.e., a substance does not have to unequivocally fulfill the P- and LRT-criteria if it is present in wildlife distant from

areas of its use.

7.2.2 Bioaccumulation

Although there are uncerta inties associated with the labo ratory BCF data for fish and assessments of bioaccumulation potential base d on field biomonitoring data, the weight of evidence suggests that HBCD is bi oaccumulative according to existing POPs screening criteria (BCF > 5000). Al l isomers have estimated log K OW > 5 (See Table 2.2) which is also sufficient to fulfill this partic ular screening criteria. However, it remains uncertain if all diastereomers are bioaccumulative in the field, based on the uncertainty of

the bioisomerization and biotransformation rates for each isomer.

Available laboratory and field monitoring data and physical-chemical property data generally support the classi fication of HBCD as “B”.

7.2.3 Toxicity and Potentia l for Adverse Effects

There are no quantitative screening criteria fo r toxicity and adverse effects according to

the UN-ECE POP Protocol and UN Stockholm Convention (Tables 5.2 and 5.3). Available laboratory data show that HBCD is a substance of low toxicity, i.e., likely a

baseline narcotic mode of toxic action for aquatic organisms. Substances nominated for POP designation should be evaluated to determ ine if the substance is likely to have significant adverse human health and/or environm ental effects as a resu lt of its long range transboundary atmospheric transport (UN- ECE 1998) including, where possible, a comparison of toxicity or ecotoxi city data with detected or predicted levels of a chemical resulting or anticipated from its long range environmental transport (UNEP 2001). This approach is generally considered a risk characterization or risk assessment. Modelling and monitoring data show that upper trophic level organisms that consume fish have the highest exposure levels to HBCD. The pr esented residue-based and TDI-based risk

assessments indicate that no adverse effects ar e expected in upper tr ophic level organisms in regions remote from locations associ ated with the release of HBCD to the environment. Thus, HBCD does not meet th e general and vigorous component of the POPs “T” criterion relating to the likelihood of adverse effects in remote regions.

Neither a detailed interpretation of availabl e laboratory toxicity information, nor two separate risk assessment approaches employing available monitoring data for upper foodchain organisms in remote regions, supports the classification of HBCD as “T” according to POPs classification criteria.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 131 7.3 C OMMENTARY ON P REVIOUS A SSESSMENTS

Previous Assessments HBCD has been subject to a PBT assessmen t according to criteria presented in the European Commission Technical Guidance Document (TGD) (EC 2003; EC 2008). According to the screeni ng criteria of the UN Stockhol m Convention (UNEP 2001) and the UN-ECE Protocol, the TemaNord assess ment proposes a POP categorization for HBCD (TemaNord 2008). The major findings of these previous assessments are briefly

reviewed below with a focus on the proposed POP categorization.

It was concluded in the European RAR (EC 2008) that HBCD fulfills the “B” (and “vB”)

criterion as well as the “T” criterion (Chronic NOEC Daphnia survival, reproduction growth 3.1 μg·L -1 ; Chronic LOEC Daphnia reduced length 5.6 μg·L -1 ). According to the European Commission, HBCD does not unequi vocally fulfill the specific P-criterion, with some reliable studies indicating biodegr adation can occur (EC 2008). For example, when adjusted to 12 o C, some experimentally-derived degradation half-lives in aerobic

sediments are > 180 d but not others. The sc ientific validity of this temperature adjustment was not questioned (EC 2008). Additional evidence cited in favour of fulfillment of the P-criterion include the presence of HBCD in wildlife and abiotic samples remote from areas of use and the pr esence of HBCD in deep sediment layers. The final decision, that HBCD fulfills the PB T-criteria overall, was also justified based on the fact that the TGD specifically advises th at, “certain flexibility is required in the application of the criteria for instance in cases where one criterion is marginally not

fulfilled but the others are ex ceeded considerably” (EC 2003).

Similar data and arguments were presente d in the POP proposal (TemaNord 2008) to

arrive at a decision that HBCD fulfills the sc reening criteria presented in Tables 5.2 and

5.3. HBCD was concluded to fulfill the “P” criteria primarily based on i) temperature adjusted degradation half-lives in aerobi c sediments and ii) a study showing “no degradation observed” for soil. The presen ce of HBCD in remote regions and deep sediment layers was also discussed in the context of “P” screening criteria. The long range transport potential of HBCD was assessed through estimates of atmospheric halflife, application of fate and transport models and considerat ion of available monitoring data in remote regions. (TemaNord 2008) c oncluded that HBCD fulfilled the numerical criteria for atmospheric half-life and vapour pressure and showed similar LRT potential as mid-sized PCBs and PBDEs (based on m odel output). The strongest evidence of LRT potential presented in (TemaNord 2008) is th e summary of monitoring data from remote regions which demonstrate the HBCD is presen t in these areas. HBCD was classified as “B” in (TemaNord 2008) because it fulfills the numerical criteria for “B” based on experimental BCF data (range 8973 – 21940) and experimental log K OW (5.62).

The POP proposal (TemaNord 2008) concl uded that HBCD ha s “high aquatic ecotoxicity” based on tests with Daphnia magna showing a 28d-NOEC of 3.1 μg·L -1
(Drottar and Krueger 1998) and on a growth inhibition test with Skeletonema costatum (72h-EC50 of 52 μg·L -1 ; (Desjardins et al. 2005)). No criteria or justifications were presented to support the “high aquatic eco toxicity” proposal. Further, the confounding factors associated with these tests (e.g. use of cosolvent) were not thoroughly discussed.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 132 While available laboratory studies with mammals do not indicate that HBCD is carcinogenic, mutagenic or toxic to reproduc tion, effects in liver , thyroid gland and thyroid homeostasis were cited as additional reasons for concern. There was no clear assessment for the potential of HBCD to a dversely affect human health and/or the environment. Appendix 4 of the TemaNord POP proposal includes a summary table of listed sources concluded to cause poten tial risks as conducte d in the EU RAR (“conclusion iii”) (T emaNord 2008); however, it is notew orthy that all of the listed sources are specific or ge neric industrial sites a nd not remote locations.

Critical Appraisal of Previous Assessments Substances identified in category-based a ssessments may not alwa ys prove to require regulatory action when subjected to a more rigorous examination (Arnot and Mackay 2008; McCarty and Arnot 2008; Wu et al. 2008). In this section we critically evaluate the previous assessments, with a focus on the POP proposal.

The model – monitoring corroboration in the present study suggests that estimates for HBCD emissions to the environment proposed in the RAR (EC 2008) are, to a first approximation, reasonable. The modelling an alysis suggests th at the proportion of emissions to air may be greater than previ ously assumed and “actual” emissions may be different. Ultimately more reliable monitoring and half-life data can be used to further

evaluate HBCD emission rates and mode-of-entry to the envi ronment. The findings in the present study also support c onclusions from the previous assessments that there are

concerns for HBCD released to the environmen t at local scales near industrial sites.

HBCD was classified as “P” in (TemaN ord 2008) based primarily on temperatureadjusted degradation half-lives of α - and γ -HBCD in aerobic sediments (12 o C) and negligible degradation in soil from one pa rticular study. There is no consensus on the validity of applying a temperatur e-correction to adjust biodegr adation rate constants. In fact, Boethling et al. (2009) specifically recommend against such adjustments for screening level persistence categorization usi ng half-life criteria. However, according to

both Annex D of the Stockholm Conventio n (UNEP 2001) and the UN-ECE POPs protocol (UN-ECE 1998), a substan ce can be classified as “P” if there is evidence that the substance is, “otherwise suffici ently persistent to be of co ncern within the scope of the convention/protocol”. Since the presence of HB CD in remote regions can be interpreted as evidence that a compound is suffici ently persistent a nd the quality and representativeness of the monitoring data ar e not in dispute, the overall screening conclusions in TemaNord (2008) regarding “P ” are deemed reasonable. The monitoring data from remote regions also provide eviden ce of sufficient long range transport (LRT) potential to be considered unde r the POP protocol. Similarl y, based on the evaluation of available data, the screening assessment of bioaccumulation potential, “B”, in TemaNord (2008) is also deemed reasonable.

The previous conclusion that all key aquatic toxicity data are reliable for PBT assessment

and can also be considered for the POP cla ssification (TemaNord 2008) is not reasonable. As detailed in the present st udy, there are signifi cant reliability issues with regards to most of the previously selected aquatic -exposure based “T” endpoints. For the PBTArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 133 assessment, HBCD is essentially categorized as “T” because the substance is hydrophobic. There are no indicatio ns of toxic effects at or below the water solubility limit; however, the water solubility limit (~ 2 to 3 μg·L -1 ) is below the PBT “T” screening level criterion (i.e. “T” < 10 μg·L -1 ). Following this approach, all substances with water solubility limits < 10 μg·L -1 must be considered “T” for the EU PBT assessments. Clearly this policy needs further consideration for scientifically defensible assessments of

commercial chemicals.

With respect to POP categorization, the Tema Nord (2008) justification for toxicity and potential adverse effects is also considered to be unsatisfactory. Key concerns regarding the applicability of availabl e toxicity data are not thor oughly discussed. The POPs proposal relies heavily on the RAR ecotoxicity data and the “T” classification in the RAR, a conclusion which is not supported by our findings. Most importantly, the proposal considers the toxicity data in isolation without quantifying the potential for adverse effects as a result of actual exposures in the environment. Further deliberations on this topic can be found in Section 9.5.4.

In summary, there were no comparisons of toxi city or ecotoxicity data with detected or predicted levels of HBCD in the POP proposal (TemaNor d 2008) for either industrialized or remote regions despite th e abundance of available data. The present risk assessment

shows that based on available data there do not appear to be significant adverse health

and/or environmental effects as a result of long range transport.

The “P” and “B” classifications in prev ious assessments are deemed reasonable despite the discussed uncertainties; however, as discussed in the present study there are concerns regarding the previous asse ssments of toxicity and potential for adverse effects. The ecological risk a ssessment summarized in 7.1 provides an example of the type of analys is that should be considered.

7.4 R ECOMMENDATIONS

The risk characterizatio n in the present study is considered to be a more scientifically defensible approach for assessing the potenti al adverse effets for HBCD and its isomers on human health and the environment than pr evious assessments. Evaluations for remote regions show low (residue-based approaches ) to very low (total daily-intake-based approach) risk based on avai lable monitoring and toxicity data. Based on the current

estimates of low likelihood for adverse effects in remote regions, the relatively rapid response times of HBCD in many environm ental compartments to reduced emissions compared to categorized POPs, and spatial monitoring trends, it is recommended that efforts to reduce emissions at point sources (production and use facilities) continue. Certain “local/near point source” areas ma y require additional site-specific risk assessment work to establish appropriate regulatory activites.

The proposed methods and current results ca n be considered a starting point for any further assessments for HBCD, and more approp riately, the individual diastereomers. The present data limitations prevent a more co mprehensive quantifica tion of associatedArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 134 uncertainties with the risk assessment result s. There are a number of recommendations throughout the document that should be consid ered to reduce uncertainties in future assessments of HBCD. In particular, the model sensitivity analysis shows that metabolic

biotransformation rates are the chemical properties that contri bute the greatest variance to estimated exposure levels in higher trophic leve l organisms. Better information is needed

to characterize diastereoisomer conversion in the environment and food webs. Recent data suggest that isomerization may be occurring in many compartments of the environment and perhaps during sample storag e and analysis. Standardized methods for measuring HBCD and its isomers are necessary to reduce uncertainties in the monitoring data. The focus of future toxicity assessments should be for obtaining body/tissue residue

levels associated with eff ect or no effect endpoints.

Most monitoring data presently available ar e for “site-specific” areas near production sites. Monitoring data are needed for re gional areas, in particular for receiving environments (air and water) to reduce uncertainty in the present assessment. Monitoring studies should be continued and expanded in representative regional environments such as the Sweden/Baltic area. Other key environm ental compartments need to be measured to have a more complete ch aracterization of time trends and chemical distribution. Environmental compartments in remote re gions also need to be monitored more thoroughly to test the hypothese s proposed in the present ev aluation based on the models

and available measurements. As more monitori ng and test data become available, more modelling scenarios can also be considered.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 135 8.0 REFERENCES

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enrichment in the arctic. Environmental Science & Technology 40: 569-577.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 150 Wania F, Breivik K, Persson NJ, McLach lan MS. 2006. COZMO-POP 2 – a fugacitybased dynamic multi-compartmental mass balan ce model of the fate of persistent organic pollutants. Environmental Modelling and Software 21: 868-884. Wania F and Mackay D. 1995. A global dist ribution model for pe rsistent organicchemicals. Science of the Total Environment 161: 211-232. Warren CS, Mackay D, Webster E, Arnot JA. 2009. A cautionary note on implications of the well-mixed compartment assumption as applied to mass balance models of chemical fate in flowing systems. Environmental Toxicology and Chemistry Published on-line April 13, 2009: DOI: 10.1897/08-569.1. Webster E, Mackay D, Wania F. 1998. Ev aluating environmental persistence. Environmental Toxicology and Chemistry 17: 2148–2158. Webster E, Mackay D, Wania F, Arnot J, Gobas F, Gouin T, Hubbarde J, Bonnell M. 2005. Development and application of m odels of chemical fate in Canada: Modelling guidance document. Canadian Environmental Modell ing Centre, Trent University, Peterborough, ON. p. 129. Wegmann F, Cavin L, MacLeod M, Scheri nger M, Hungerbuehler K. 2009. The OECD software tool for screening chemicals for persistence and long-range transport potential. Environmental Mode lling and Software 24: 228-237. Wu RSS, Chan AKY, Richardson BJ, Au DW T, Fang JKH, Lam PKS, Giesy JP. 2008. Measuring and monitoring persistent orga nic pollutants in the context of risk

assessment. Marine Pollution Bulletin 57: 236-244. Xie WH, Shiu WY, Mackay D. 1997. A review of the effect of salts on the solubility of organic compounds in seawater. Marine Environmental Research 44: 429-444. Yakata N, Sudo Y, Tadokoro H. 2006. Influe nce of dispersants on bioconcentration factors of seven organic compounds with different lipophilicities and structures. Chemosphere 64: 1885-1891. Yu ZQ, Chen LG, Maw BX, Wu MH, Sheng GY, Fu J, Peng PA. 2008. Diastereoisomerand enantiomer-specific profiles of hexa bromocyclododecane in the atmosphere of an urban city in south China. Environmental Science & Technology 42: 3996-

4001. Zegers BN, Mets A, Van Brommel R, Minkenbe rg C, Hamers T, Kamstra JH, Pierce JG,

Boon JP. 2005. Levels of hexabromoc yclododecane in harbour porpoises and common dolphins from western European seas, with evidence of stereoisomerspecific biotransformation by cytochrome P450. Environmental Science & Technology 39: 2095-2100.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 151 9.0 APPENDIX

9.1 P HYSICAL C HEMICAL P ROPERTIES

The general objectives in this section are to obtain and re view measured and predicted estimates of physical-chemical propertie s relevant for modelling the fate and

bioaccumulation of HBCD in the environment. Based on this review, values were then selected to derive thermodynamically consiste nt estimates, final adju sted values (FAVs), which were then used for modelling and dataanalysis in the report. For the physicalchemical property consistency procedure, rela tive variance values were selected for the property estimates (Schenker et al. 2005). Di screpancies between the values in the present study and in the Draft EU Risk Assessment Report (RAR) are discussed.

Molar mass The molar mass (molecular weight) of HBCD is 641.7 g.mol -1 (U.S. EPA 2009).

Melting point The RAR lists and discusses a variety of melting point ranges for HBCD (EC 2008). The melting intervals for two commercial products are 175 to 183 °C and 187 to195 °C. EPI Suite estimates a melting point of 180 °C (453 K), which is a weighted value from three melting point submodels and is not specific for the diastereoisomers or commercial (technical) mixtures (U.S. EPA 2009). A meltin g point of 190 °C (463 K) was selected as an average value as input data for EUSES (EC 2008). For consistency with the RAR the same melting point T M (K) is selected for technica l HBCD for the present study; however, it is recognized that mixed isomer ranges lead to some uncertainty in the selection of this value. The RAR lists ranges of the melting point for the HBCD diastereoisomers as 179 to 181 °C, 170 to 172 °C and 207 to 209 °C for α -, β -, and γ

HBCD, respectively (EC 2008).

Fugacity ratio The fugacity ratio F (unitless) was not in cluded in the RAR and is necessary for estimating the properties of the subcooled liqui d which are used as a reference state for fugacity and activity calculations and fo r the thermodynamic consistency calculations. The fugacity ratio can be estimated as

F = exp(6.79(1 – T M /T)) (9.1)

where T is the system temperature (K) and the assumption that the entropy of fusion is

56.5 J·K -1 ·mol -1 (Mackay 2001). Thus, for technical HBCD (T M = 463 K), F is 0.023. The diastereoisomer specific F values are 0.029, 0.036, and 0.015 for α -, β -, and γ -HBCD, respectively. Considering ther e is greater uncerta inty for the entropy of fusion at the melting point, a value of 0.023 was used for F for all diastereoisomer specific

calculations.

It is recommended that the en tropy of fusion be measured.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 152 Water solubility

Table 9.1 lists the solid st ate water solubilities S S for HBCD from three studies using the generator column method, wh ich is considered reliable in the EU RAR (EC 2008). The first two studies were reporte d to be in general accordan ce with OECD guidelines and GLP compliant. The test substance in the firs t was a composite sample consisting of 8 %

α -, 5.37 % β - and 86.63 % γ -HBCD. The test substance in the second study was a composite sample consisting of 6.0 % of α -, 8.5 % of β -, and 79.1 % γ -HBCD. The RAR reports that in the second study only γ -HBCD could be quantified; however, a review of the test report indicates no di astereoisomer-specific analysis (Stenzel and Markley 1997). The third study reports solubilities of the th ree diastereoisomers in a salt-water medium used for an algal growth inhibition test. The so lubilities in salt-water are lower than those in fresh water and are generally consistent with the “salting out” effect (Xie et al. 1997).

The measurements considered reliable in the EU RAR are also consid ered reliable in the present study. The first study had fewer im purities and unknowns then the second study and all isomers could be quantifie d; therefore, these data are considered the most reliable for the present study. The estimate from the second study provides an approximation for the commercial mixture. Differences in temperature between some of the measured

values at 20 o C and the analyses in the present study (e.g. 25 o C) are considered negligible in consideration of the estimation uncertainty.

When technical HBCD is added to water using a generator a column all three

diastereoisomers are assumed to dissolve as discussed and il lustrated in Figure 1-2 of (EC 2008). That discussion states that based on the data from Study 1:

“ γ -HBCD will reach its maximum solubility at 2.1 μ g/l (with a total HBCD

dissolved concentration of 2.4 μ g/l), β -HBCD at 14.7 μ g/l (with a total HBCD

dissolved concentration of 39 μ g/l) and α -HBCD at 48.8 μ g/l (with a total

dissolved HBCD concentration of 65.6 μ g/l). At this level, 65.6 μ g/l dissolved HBCD, the total c ontent of HBCD in the water is 610 μ g/l, with 544.4 μ g/l representing non-dissolved γ - and β -HBCD.”

When using mass balance models and interpreting test data it must be recognized that if a chemical concentration in the “pure” water phase exceeds its water solubility it will precipitate out of solution. This is disc ussed in the EU RAR; however, a value of 66

μ g.L -1 was selected as a “worst case” estimat e for the technical pr oduct for input in EUSES. When a phase is “super-saturated” in this manner, then the mass balance equations must consider a “compartment” fo r the residual pool of precipitated chemical. The residual pool can then supplement the wate r phase as concentrations decrease in a manner analogous to the genera tor column. Most models, including EUSES, are not programmed to address “super- saturation” in the pure wate r phase. A concern with the

EUSES calculations is that the selection of a water solubility value about 20 times greater than the water solubility limit of the predominant γ -diastereoisomer in technical HBCD may result in some errors for calculations of intermedia transport (air-water partitioning, bioaccumulation) and for reactions in the wate r. As discussed in the EU RAR and above,Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 153 when a value of 66 μ g.L -1 is assumed for the water solubility, the concentration of total HBCD in the water (dissolved and precipitate) is approxima tely 300 times greater than

the value for γ -HBCD. It is recommended that, if possible, diastereoisomer-specific model simulations be conducted using data fo r each isomer. If HBCD is modelled as the

technical mixture the selected water solubility value should be near that of the lowest diastereoisomer, i.e., γ -HBCD.

Table 9.1. Summary of water solubility data for HBCD considered reliable in the EU RAR (reproduced from (EC 2008)).

Test substance Medium Water solubility (μg.L -1 )** Reference

α -HBCD 48.8±1.9 (CV = 2.8%)

β -HBCD 14.7±0.5 (CV = 3.4%)

γ -HBCD 2.1±0.2 (CV = 10.5%) Study 1

Σ HBCD (technical product) Water

65.6

α -HBCD 34.3

β -HBCD 10.2

γ -HBCD 1.76

Σ HBCD (technical product) Salt-water

46.3 Study 3

HBCD (technical product)* Water 3.4±2.3*** Study 2

* γ -HBCD was assumed in the RAR. **20 °C, ***25 °C; CV – Coefficient of Variation

Model estimates for the water solubility were also obtained and reviewed. These

estimates do not differentiate betw een diastereoisomers. When a log K OW value of 5.63 is used as input, EPI Suite Ver. 4.0 estimates S S values of 1.3 μ g/L and 3.0 μ g/L from the

WSKOWWIN Ver. 1.41 and WATERNT Ver. 1. 01 submodels, respectively (U.S. EPA 2009). These S S estimates are generally consistent with the measured values for γ -HBCD.

The SPARC model Ver. 4.2 (http://ibmlc2.c hem.uga.edu/sparc/) gene rates two slightly different sets of physical-chemical propert ies depending on the SMILES notation used.

The SMILES notation obtained for CAS# 256 37-99-4 shows a substitution pattern of 1,3,5,7,9,11-HBCD, which is inconsistent w ith the diastereoisomer configurations. The

SMILES notation obtained for CAS# 319 4-55-6 shows a substitution pattern of 1,2,5,6,9,10-HBCD, which is consistent with the diastereoisomer configurations (vicinal Bromine substitution. Of interest, the SMILES in the EINECS database for CAS# 25637- 99-4 shows a substitution patt ern that is also inconsis tent with the recognized diastereoisomer structures. Thus, the SMI LES notation for CAS# 3194-55-6 was used for

SPARC calculations. SPARC estimates an S S of 0.31 μ g/L and an activity coefficient in

water of 10 9.58 (T M =190 o C). The S S estimate is about 1 order of magnitude lower than the

measured values for γ -HBCD.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 154 There is general consistency be tween the different estimates for water solubility. For the present study the measured S S from Study 1 were the selected values for the individual

HBCD diastereoisomers. The S S (mol.m -3 ) estimates are 7.60×10 -5 , 2.29×10 -5 , 3.27×10 -6

mol.m -3 for α -, β -, γ -HBCD diastereoisomers, respectivel y. These estimates correspond to subcooled liquid water solubility values S L (mol.m -3 ) of 3.27×10 -3 , 9.86×10 -4 , 1.41×10 -4

mol.m -3 for α -, β -, γ -HBCD diastereoisomers, resp ectively. A value of 3.0 μ g/L (S S = 4.67×10 -6 mol.m -3 , S L = 2.01×10 -4 mol.m -3 ) is selected for te chnical (commercial) mixture HBCD for the thermodynamic cons istency calculations (see below). The coefficient of variation of 10.5% reported for γ -HBCD in Study 1 (EC 2008) is used as a general predictor of un certainty in the wate r solubility estimates for the thermodynamic consistency calculations. A re lative variance value of 1 was assigned to the water solubility estimates for the diastereoisomers and a relative variance value of 2 was assigned for the selected commercial mixture value.

Vapor pressure

The RAR discusses the selection of a solid state va por pressure P S for HBCD from two measurements (EC 2008). Measured values for P S of HBCD include 1.6×10 -5 Pa at 20 o C using the effusion method and 6.27×10 -5 Pa at 21 o C using the spinning rotor method. The OECD vapor pressure guidelines include th ree different effusion methods; however, only the isothermal thermogravimetry effusi on method is recommended by the OECD for substances with vapor pressures <10 -3 Pa (OECD 2002). Based on the evaluation in the RAR, one of the other two effusion methods was used. The spinning rotor method is not recommended for substances with vapor pressures <10 -4 Pa (OECD 2002). The spinning rotor gauge test was conducted according to Good Laboratory Practice (GLP) guidelines (EC 2008). The composition of HBCD in the effusion method is undetermined and the following percentages of diastereoisomers we re reported for the spinning rotor method:

α -HBCD 6.0; β -HBCD 8.5; and γ -HBCD 79.1. According to OE CD guidelines, both of these methods are not recommended for substances with vapor pressures as low as the values measured for HBCD; however, both me thods produced similar values. A value of

6.3×10 -5 Pa at 21°C for P S was used in the EU risk assessment.

Wania (2003a) reported a liquid state vapor pressure P L (subcooled) of 2.41×10 -5 Pa at

25 o C for t-HBCD. The value was obtained using the gas chromatographic-retention time method, which is suitable for substan ces with vapor pressures as low as 10 -5 Pa. This

value is selected for the FAV calculations.

EPI Suite Ver. 4.0 estimates a P S of 2.25×10 -6 Pa (U.S. EPA 2009) and SPARC Ver. 4.2

estimates a P S of 2.63×10 -9 Pa (http://ibmlc2.chem.uga.edu/sparc/). The EPI Suite estimate is about 3 times higher than th e measured estimate by Wania whereas the

SPARC estimate is 275 times lower.

At present there are no isomer-specific estim ates for vapor pressure. Goss et al. (2008) used the COSMOtherm model to calculate octanol-water (K OW ) and air-water (K AW ) partitioning of α -, β -, γ -HBCD diastereoisomers at 25 o C (see details below). From the isomer-specific quantum chemical software estimates of K AW and from isomer-specific estimates of water solubility S S it is possible to estimate P S for the three isomers asArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 155 P S = K AW .RT.S S (9.2)

where R is the gas constant (8.314 Pa.m 3 (mol.K) -1 ) and T is the temperature (298 K). This approach assumes that temperature differences (20 to 25 o C) are minimal. The resulting isomer-specific estimates for P S are 2.72×10 -10 Pa, 3.58×10 -11 Pa, and 1.86×10 -11

Pa for α -, β -, γ -HBCD, respectively. These m odel estimates are about 10 4 times lower than the measured values measured by Wania. Goss et al. (2008) note that “the absolute values of the calculated air-water partition coe fficients may be considerably off the real

values”. COSMOtherm also underestimates K AW for hexachlorocyclohexane (HCH) (Goss et al. 2008). Since the K OW estimates for HCH and HBCD are similar to measured estimates, this suggests that errors in COSMOtherm K AW estimates relate to errors in P. Thus, the COSMOtherm estimates for K AW and P S are not considered reliable; however,

in relative terms the P S results are similar to the measured values for S S such that values

for γ -HBCD are about 1 order of ma gnitude less than values for α -HBCD.

There are limited data and conflicting results fo r estimates of the vapor pressure. For the present study the measured P S estimate 7.23×10 -7 Pa (P L = 3.11×10 -5 Pa using an F of 0.023) using the gas saturation method is cons idered the most reliable estimate for the

commercial HBCD mixture and is selected as an input for thermodynamic consistency analyses (see below). Estimates for the di astereoisomers were derived by assuming P L for

γ -HBCD is approximately 2.0×10 -5 Pa and that the relative differences in values estimated by COSMOtherm are applicable such that P L for α - and β -HBCD are approximately 2.93×10 -4 Pa and 3.86×10 -5 Pa, respectively. Due to the limited data and uncertainty in estimating vapor pressure, greater uncertainty is considered for these values compared to the water solubility es timates. A relative variance value of 3 was assigned to the P L of the technical mixture and a value of 5 was assigned to the

diastereoisomers.

It is suggested that measured estimate s for isomer-specific vapor pressures be obtained using OECD recommended method s for low vapor pressure substances.

Solubility in Octanol There are no known reported measurements fo r the solubility of commercial HBCD or

the diastereoisomers in octanol. Estimates are provided below in the section on the octanol-water partition coefficient.

Henry’s law constant (Air-w ater partition coefficient) To our knowledge there are no measured estimates for Henry’s law constant H (Pa.m 3 .mol -1 ) currently available. The dimensionl ess air-water partit ion coefficient K AW is H/RT. EPI Suite Ver. 4.0 estimates (HEN RYWIN Ver. 3.2) for Henry’s law constants

are 0.174 and 6.52×10 -6 Pa.m 3 .mol -1 using the bond and group me thods, respectively (U.S. EPA 2009). The group method estimate is considered erroneous. The log K AW estimate corresponding to the bond method is -4. 15. H can also be estimated from vapor pressure and water solubility as P S /S S or P L /S L . In this manner the selected values of

2.41×10 -5 Pa and 2.01×10 -4 mol.m -3 correspond to an estimate for H of 0.120 Pa.m 3 .mol -1Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 156 (log K AW = -4.32). SPARC Ver. 4.2 estimates a value for H of 0.0055 Pa.m 3 .mol -1 (log

K

AW = -5.66) using the same approach, i.e., P L /S L (http://ibmlc2.chem.uga.edu/sparc/).

The estimates from EPI Suite bond method and th e relative solubilities of the selected values for vapor pressure and water solubility are consistent, indepe ndent estimates. The

EPI Suite bond method estimate is selected for thermodynamic consistency calculations for t-HBCD with an assigned relative variance value of 4.

Diastereoisomer estimates for K AW have been reported by Goss et al. using the COSMOtherm model (Goss et al. 2008). The log K AW values are -8.84, -9.20, and -8.64

for α -, β -, γ -HBCD, respectively. These values correspond to H values of 3.6x10 -6

, 1.6x10 -6 , 5.7x10 -6 Pa.m 3 .mol -1 for α -, β -, γ -HBCD, respectively. As discussed above and by the authors the absolute estimates are c onsidered to be erroneous, but the relative values are considered reasonable. Diastereoisomer estimates of log K AW from selected

values for P L /S L are -4.27, -4.63, and -4.07 for α -, β -, γ -HBCD, respectively. These estimates are dependent on the values for P L /S L , which have already been selected for thermodynamic consistency calculations. Indepe ndent estimates for the diastereoisomers were derived by assuming log K AW for γ -HBCD is equal to the EPI Suite estimate (-4.15)

and that the relative differences in values estimated by COSMOtherm are applicable such that log K AW for α - and β -HBCD are -4.35 and -4.71, respectively.

It is recommended that isomer-specifi c Henry’s law constants be measured.

Octanol-water partition coefficient The EU RAR reports a measured log K OW of HBCD of 5.625 at 25±0.05°C using the generator column method, which is also in accordance with GL P practices (EC 2008). The test sample was a composite containing 8.5 % β -, 6.0 % α - and 79.1 % γ -HBCD (total HBCD 93.6 %). The st udy was generally carried out according to the guidelines, although the concentration of the stock soluti on was approximately 0.2 % instead of 1 %.

As noted above in the discussion on wate r solubility and in the RAR (EC 2008), the generator column method for mixtures may re sult in excess, non-disso lved quantities of

diastereoisomers in the water column. MacG regor and Nixon (2004) report a mean water

concentration of 3.97 μ g-HBCD.L -1 (standard deviation 1.53 μ g-HBCD.L -1 ). The water

concentration is near th e water solubility limit for γ -HBCD.

The RAR also summarizes the diastereoisomer-specific log K OW values estimated for α -, β - and γ -HBCD using reversed-phase high-pe rformance liquid chromatography (RPHPLC) methods (Hayward et al . 2006). The estimated log K OW values for α -, β - and γ

HBCD were 5.07±0.09, 5.12±0.09 and 5. 47±0.10, respectively. The calibration compounds were chlorobenzenes and polychlor inated biphenyls, which as discussed by the authors, are not ideal because of th e structural dissimilarities between the organochlorines and HBCD (Hayward et al. 200 6). Assuming the solubility in octanol is similar for the diastereoisomers, the K OW estimates are relatively consistent with measurements of water solubility such that K OW increases with decreasing water solubility. The HPLC derived K OW values for the diastereoisomers are comparable but

lower than the measured K OW estimate for commercial HBCD using the generator

column method.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 157 The RAR includes a few other measured and modelled estimates for log K OW that were

considered unreliable. Th e RAR selected a log K OW of 5.625 for the commercial HBCD

mixture.

Table 9.2 lists 11 model estimates including EPI Suite Ver. 4.0, SPARC Ver 4.2 and COSMOfrag model estimates. The mean va lue of the model estimates for log K OW is 6.73

with a standard deviation of 0.76. These model estimates do not differentiate between

K OW values for the diastereoisomers. Goss et al. (2008) reported diastereoisomer log K OW estimates of 5.59, 5.44 and 5.53, for α -, β - and γ -HBCD, respectively, using the

COSMOtherm model. The COSMOtherm mode l estimates are of the same general magnitude as the ge nerator column K OW measurement for commercial HBCD and the

diastereoisomers determined from calibrati on using RP-HPLC. A notable difference is the relative ranking of hydrophobicity between the RP-HPLC and COSMOtherm

estimates.

There are uncertainties in the measured and estimated values for K OW for the commercial

mixture and the diastereoisomers. The solubili ties of the diastereoisomers in octanol are expected to be simila r and differences in K OW are expected to be the result of differences in water solubility. SPARC Ver. 4.2 calculates an activity coefficient in octanol γ O of 12.88 (unitless) and a solid stat e solubility in octanol S OS of 16.21 mol·m -3 (liquid state solubility in octanol S OL of 698 mol·m -3 ) for HBCD. The S OL value was selected as input for the thermodynamic consistency calculati ons for the commercial HBCD mixture and

the diastereoisomers. A relative variance value of 4 was assigned to this property

estimate.

The measured log K OW value of 5.63 was selected as input for the consistency

calculations for t-HBCD and a relative va riance value of 3 was assigned. For the diastereoisomers the estimates by Goss et al. were selected as input for the consistency

calculations and relative varian ce values of 4 were assign ed. Although these estimates were derived in silico they are close to m easured estimates and considering the potential underestimation of HBCD using the generato r column method and the lack of ideal

calibration substances for the RP-HPLC met hod, the selected valu es are considered reasonable for the thermodynamic consistency calculations.

It is recommended that isomer-specific oc tanol-water partition coefficients and solubility in octanol be measured.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 158 Table 9.2. Model estimates for the oc tanol-water partition coefficient (K OW ).

log K OW Model Reference

7.74 EPI Suite Ver. 4.0 (U.S. EPA 2009)

6.45 ALPOGPs (Tetko et al. 2005)

5.23 AC logP (Tetko et al. 2005)

6.75 AB/logP (Tetko et al. 2005)

6.14 miLogP (Tetko et al. 2005)

6.66 CosmoFrag (Tetko et al. 2005)

6.69 SPARC Ver. 4.2 http://ibmlc2.chem.uga.edu/sparc/

6.96 ALOGP (Tetko et al. 2005)

6.30 MLOGP (Tetko et al. 2005)

8.01 XLOGP2 (Tetko et al. 2005)

7.10 XLOGP3 (Tetko et al. 2005)

6.73 Average log K OW

0.76 Standard Deviation

Octanol-air partition coefficient There are no reported measurements for th e octanol-air partition coefficient (K OA , dimensionless); however, there are a few me thods for estimation. EPI Suite Ver. 4.0 estimates a log K OA of 10.47 with the KOAWIN program and a log K OA of 11.89 (based

on EPI estimates for K OW and K AW as log K OW - log K AW ). K OA can also be estimated from the relative fugacity capacities of octanol and air, Z O and Z A (mol·m -3 ·Pa -1 ), respectively as Z O / Z A . Z A is 1/RT and Z O can be estimated as 1/( ν O γ O P L ), where ν O is the molar volume of “dry” octanol (1.57×10 -4 m 3 ·mol -1 ). The predicted γ O of 12.88 (SPARC Ver. 4.2) and the selected P L values of 2.41×10 -5 , 2.93×10 -4

, 3.86×10 -5

, 2.00×10 -5 for t-, α

-

, β -, and γ -HBCD, respectively were used to estimate K OA , i.e., Z O / Z A . At 25 o C, log

K OA values are 10.71, 9.62, 10.50, and 10.79 for the t-HBCD and α -, β - and γ -HBCD diastereoisomers, respectively. Log K OA can also be estimated from the COSMOtherm

K

AW and K OW ; however, as discussed above the estimates for K AW are considered

erroneous.

No K OA values were selected as inputs for the thermodynami c consistency calculations

because the values for the diastereoisome rs are dependent on the estimates for the extrapolated estimates for P L ; however, the estimates provided above for K OA can be used for comparison with the final adjusted values (FAVs).

It is recommended that measured estimates for isomer-specific octanol-air partition

coefficients be obtained.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 159 Thermodynamic consistency fo r model input parameters The methods of Schenker et al. (Schenke r et al. 2005) were used to obtain thermodynamically consistent estimates for the solubility and partiti oning properties of tHBCD and the three diastereoisomers. The meth od was applied to t-HBCD in the current study; however, the thermodynamic consistency calculation should generally not be used for mixtures, but rather for di screte substances with distin ct properties. The method was applied for consistency with diastereomer methods and for comparing model estimates

for t-HBCD and individual di astereomers with monitoring da ta for t-HBCD. Considering the uncertainty in the FAVs for t-HBCD in the context of the greater uncertainties

associated with actual emi ssions, mode-of-entry, isomeriz ation and degradation in the environment, the errors in calculating “repre sentative” FAVs for t-HBCD are considered to be comparatively low. These methods are described in Section 2.3.2. The selected input and output values for the FAV calculatio ns are summarized in Tables 9.3 and 9.4, respectively.

Table 9.3. Selected values for physical-chemical pr operties and relative variance values used as inputs for thermodynamic consistency calculations.

Substance P L , Pa S L , mol · m -3 S OL , mol · m -3 Log K AW Log K OW a Log K OA

t-HBCD 2.41×10 -5 2.01×10 -4 698 -4.15 5.63 10.71 b

α -HBCD 2.93×10 -4 3.27×10 -3 698 -4.35 5.59 9.62 b

β -HBCD 3.86×10 -5 9.86×10 -4 698 -4.71 5.44 10.50 b

γ -HBCD 2.00×10 -5 1.41×10 -4 698 -4.15 5.53 10.79 b

Relative variance

t-HBCD 3 2 4 4 3

α -HBCD 5 1 4 5 4

β -HBCD 5 1 4 5 4

γ -HBCD 5 1 4 5 4

a log K OW values corresponding with log (Co/Cw) values of 6.02, 5.97, 5.76, and 5.89 for t-, α -, β -, and γ

HBCD, respectively

b values not used as inputs for calculation of FAVs because they are not “independent” values, i.e., they were derived from vapor pressure and octanol solubility estimates (see text). The values are included for comparisons with FAVsArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 160 Table 9.4. Final adjusted values (FAVs at 25 o C), relative variance values, percent adjustments, and assigned conf idence factors used in the present study for mass balance modelling and data analyses obtained from the thermodynamic consistency calculations (Schenker et al. 2005).

Substance P L , Pa S L , mol · m -3 S OL , mol · m -3 Log K AW Log K OW a Log K OA

t-HBCD 3.03×10 -5 2.33×10 -4 380 -4.28 5.77 10.46

α -HBCD 3.00×10 -4 2.77×10 -3 1340 -4.36 5.38 9.96

β -HBCD 4.29×10 -5 9.89×10 -4 630 -4.76 5.47 10.47

γ -HBCD 2.42×10 -5 1.68×10 -4 300 -4.23 5.80 10.40

Relative variance

t-HBCD 1.95 1.27 2.13 2.13 1.95 3.45

α -HBCD 2.70 0.82 2.20 2.70 2.20 4.50

β -HBCD 2.70 0.82 2.20 2.70 2.20 4.50

γ -HBCD 2.70 0.82 2.20 2.70 2.20 4.50

Percent adjusted

t-HBCD 26% 16% -45% -26% 39% 26%

α -HBCD 2% -15% 92% -2% -38% 2%

β -HBCD 11% 0% -9% -10% 7% 11%

γ -HBCD 21% 19% -57% -18% 87% 21%

Confidence factor ( Cf )

t-HBCD 2.5 2.3 3 3 3 3

α -HBCD 4 2 4 4 4 4

β -HBCD 4 2 4 4 4 4

γ -HBCD 4 2 4 4 4 4

a log K OW values corresponding with log (Co/Cw) values of 6.22, 5.68, 5.81, 6.25 for t-, α -, β -, and γ

HBCD, respectivelyArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 161 9.2 D EGRADATION , B IOTRANSFORMATION AND H ALF

- LIVES

Table 9.5. BIOWIN TM v4.10 model output for HBCD (n ot isomer specific) (US EPA 2009).

Model Output Interpretation

BIOWIN1 (linear) 0.1650 Does NOT biodegrade fast

BIOWIN2 (non-linear ) 0.0000 Does NOT biodegrade fast

BIOWIN3 (Ultimate deg) 1.9548 Time frame : Months

BIOWIN4 (Primary deg) 3.1342 Time frame: Weeks

BIOWIN5 (MITI linear) -0.4234 NOT Readily Degradable

BIOWIN6 (MITI non-linear) 0.0000 NOT Readily Degradable

BIOWIN7 (anaerobic linear) 2.5072 Biodegrades FAST

Table 9.6. General equations used to estimate aer obic environmental biodegradation halflives from BIOWIN TM model output and corresponding values calculated for HBCD (Arnot et al. 2005; US EPA 2009).

Model Regression Equation r 2 Half-life (d)

BIOWIN1 (linear) log 10 (t 1/2 ) = -1.32 x + 2.24 0.72 105.2

BIOWIN3 (Ultimate deg) log 10 (t 1/2 ) = -1.07 x + 4.20 0.77 128.3

BIOWIN4 (Primary deg) log 10 (t 1/2 ) = -1.46 x + 6.51 0.78 85.9

where x is the numerical output gene rated for each respective BIOWIN TM model (presented above in Table 9.5)1629.3MONITORINGDATAThis section includes summaries ofmonitoring data that were discussed in the report that may nothave been recognized in previousassessments. The abiotic data summarize allavailable data, whereas the biotic data are for studies publishedin the last 3 to4 years.Table 9.7. Summary of abiotic monitoring data.Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n ReferenceAir (pg/m3)gaseous2004 China-Guangzhou-Industrial-1 0.08 0.04 0.02 0.02 Point (Yu et al. 2008)2004 China-Guangzhou-Industrial-2 0.09 0.05 0.02 0.02 Point (Yu et al. 2008)2004 China-Guangzhou-Urban-1 0.11 0.06 0.03 0.02 Urban (Yu et al. 2008)2004 China-GuangzhouBackground-10.12 0.07 0.03 0.02 Urban (Yu et al. 2008)particulate2003 USA-Indiana 1 <0.07 Urban 0.75 0.2 3.6 37 (Hoh et al. 2005)2003 USA-Arkansas 1.6 <0.13 Semi-remote 0.4 0.2 11 30 (Hoh et al. 2005)2003 USA-Lousiana 0.6 <0.13 Remote <dl 0.16 6.2 25 (Hoh et al. 2005)2003 USA- Indiana 3.6 2.808 0.432 0.3492 <0.07 Urban 0.75 0.2 3.6 1 (Hoh et al. 2005)2003 USA- Indiana 2.9 1.334 0.2117 1.363 <0.07 Urban 0.75 0.2 3.6 1 (Hoh et al. 2005)2003 USA- Indiana 2.1 0.672 0.1281 1.281 <0.07 Urban 0.75 0.2 3.6 1 (Hoh et al. 2005)2003 USA-Arkansas 11 6.82 1.1 3.08 <0.13 Semi-remote 0.4 0.2 11 1 (Hoh et al. 2005)2003 USA-Louisiana 2.4 0.408 0.408 1.584 <0.13 Remote <dl 0.16 6.2 1 (Hoh et al. 2005)2003 Midwest USA 5.55 < 0.1 Semi-remote 0.1 11 156 (Covaci et al. 2006)2003 USA-Northern Michigan 1.2 <0.07 Remote 0.5 0.2 8 35 (Hoh et al. 2005)2003 USA-Chicago, IL 4.5 <0.13 Urban 4.2 0.9 9.6 37 (Hoh et al. 2005)2003 USA-Northern Michigan 8 6.48 0.696 0.8 <0.07 Remote 0.5 0.2 8 1 (Hoh et al. 2005)2003 USA-Chicago, IL 9.6 1.056 0.6144 7.968 <0.13 Urban 4.2 0.9 9.6 1 (Hoh et al. 2005)2004 China-Guangzhou-Industrial-1 0.6 0.36 0.08 0.16 Point (Yu et al. 2008)163Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference2004 China-Guangzhou-Industrial-2 0.79 0.54 0.1 0.15 Point (Yu et al. 2008)2004 China-Guangzhou-Urban-1 2.98 2.01 0.33 0.64 Urban (Yu et al. 2008)2004 China-GuangzhouBackground-11.52 0.92 0.18 0.42 Urban (Yu et al. 2008)total1990.5 Hoburgen and Ammarnas,Sweden5.5 Remote 5 6 2 (Covaci et al. 2006)1990.5 Sweden-Ammarnas 6.1 Remote 2 (deWit 2002)2000 Sweden-80kmSW ofAspvreten1070000 Point (Remberger et al.2004)2000 Aspvreten, Sweden 280 Remote 1 (Remberger et al.2004)2000 Rörvik, Sweden 5 1 Remote 1 (Remberger et al.2004)2000 Pallas, Finland 3 Remote 1 (Remberger et al.2004)2000.5 Remote locations in Swedenand Finland141 Remote 2 280 6 (Covaci et al. 2006)2001 Aspvreten, Sweden 25 Remote 1 (Remberger et al.2004)2001 Rörvik, Sweden 1 1 Remote 1 (Remberger et al.2004)2001 Pallas, Finland 2 Remote 1 (Remberger et al.2004)2004 City Background-1Guangzhou, China1.7 Urban 1.1 2.3 (Yu et al. 2008)2004 Industrial-1 Guangzhou, China 0.7 Point 0.3 1.2 (Yu et al. 2008)2004 Industrial-2 Guangzhou, China 0.9 Point 0.4 1.8 (Yu et al. 2008)2004 Urban-1 Guangzhou China 3.1 Urban 2.2 3.9 (Yu et al. 2008)2005 Japan-outside suburbanhomes14 Urban 13 15 (Takigami et al. 2008)2000 Sweden-30km N of Stockholm 13 Point-Landfill (Remberger et al.2004)2000 Sweden-Boras Textile Industry 19 Point (Remberger et al.2004)2000 Sweden-Stockholmschoolyard76 Urban (Remberger et al.2004)2000 Sweden-Stockholm 610 Urban (Remberger et al.2004)164Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference2001 Sweden-30km N of Stockholm 180 Point-Landfill (Remberger et al.2004)2001 Sweden-Boras Textile Industry 740 Point (Remberger et al.2004)Precipitation (ng/L)2005 Great Lakes Basin 17.5 Semi-remote 0 35 (Backus et al. 2005)Sediment(ng/g dw)core1940 Norway, Bear Island 0.14 0.06 0.03 0.05 all < Remote 1 (Christensen et al.2004)1950 Sweden-Lake Altasjon 0.5 Urban (Remberger et al.2004)1952.5 Norway, Bear Island 0.22 0.09 0.05 0.08 all < Remote 1 (Christensen et al.2004)1960 Sweden-Arstaviken Bay 0.2 Urban (Remberger et al.2004)1966 Norway, Bear Island 0.19 0.08 0.04 0.07 all < Remote 1 (Christensen et al.2004)1974 Swiss Lake-near Zurich 0.51 Urban 1 (Kohler et al. 2008)1975 Japan-Tokyo Bay TP4 0.1 0.01 Urban (Minh et al. 2007)1975 Japan-Tokyo Bay TP5 0.05 0.01 Urban (Minh et al. 2007)1979 Japan-Tokyo Bay TP1 0.05 0.01 Urban (Minh et al. 2007)1980 Norway, Bear Island 4.37 0.43 0.06 3.88 < 0.06 Remote 1 (Christensen et al.2004)1982 Japan-Tokyo Bay TP4 0.7 0.01 Urban (Minh et al. 2007)1982 Swiss Lake-near Zurich 0.4 Urban 1 (Kohler et al. 2008)1984 Japan-Tokyo Bay TP5 0.2 0.01 Urban (Minh et al. 2007)1988 Japan-Tokyo Bay TP4 0.2 0.01 Urban (Minh et al. 2007)1989 Japan-Tokyo Bay TP1 0.5 0.01 Urban (Minh et al. 2007)1989 Swiss Lake-near Zurich 1.3 Urban 1 (Kohler et al. 2008)1992 Japan-Tokyo Bay TP5 0.2 0.01 Urban (Minh et al. 2007)1994 Norway, Bear Island 0.31 0.14 0.06 0.11 all < Remote 1 (Christensen et al.2004)1995 Japan-Tokyo Bay TP4 0.7 0.01 Urban (Minh et al. 2007)1995 Swiss Lake-near Zurich 1.8 Urban 1 (Kohler et al. 2008)165Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference1996 Sweden-Arstaviken Bay 0.8 Urban (Remberger et al.2004)1996 Sweden-Fjaderholmmarnaislands0.9 Urban (Remberger et al.2004)1997 Sweden-Lake Altasjon 1.5 Urban (Remberger et al.2004)2001 Swiss Lake-near Zurich 2.5 Urban 1 (Kohler et al. 2008)estuarine1998 Japan-Tokyo Bay TP1 2.1 0.336 0.01 1.764 0.01 Remote (Minh et al. 2007)1999 Japan-Tokyo Bay TP5 0.73 0.01 0.01 0.72 0.01 Remote (Minh et al. 2007)2000 2000/58-Middelgat(Hoedekenskerke)7.7 <0.2 <0.5 7.7 1.2 Unknown 10 (Morris et al. 2004)2000 2000/64-Zandvlietsluis 99 2.6 <1.8 96 1.2 Unknown 10 (Morris et al. 2004)2000 2000/67-Terneuzen 59 2.1 0.3 57 1.2 Unknown 10 (Morris et al. 2004)2000 2000/0073-Hoofdplaat (reg.station)0.7 <0.2 <0.2 0.7 1.2 Unknown 10 (Morris et al. 2004)2000 2000/2758-Humber (EEngland)6 <2.4 <2.4 6 1.2 Unknown 1 (Morris et al. 2004)2000 Japan-Tokyo Bay TP4 2 0.94 0.01 1.06 0.01 Remote (Minh et al. 2007)2001 2001/1466-Leie, St.-MartensLeerne38 7.4 <0.1 31 1.2 Unknown 1 (Morris et al. 2004)2001 2001/1458-Scheldt, Dutchborder83 <0.2 <0.2 83 1.2 Unknown 10 (Morris et al. 2004)2001 2001/1465-Scheldt,Oudenaarde950 180 60 710 1.2 Point-downstream 10 (Morris et al. 2004)2001 Western Scheldt-Netherlands 38 Unknown 14 71 3 (Verslycke et al. 2005)2002 Japan-Tokyo Bay T2 0.056 0.01 Semi-remote 1 (Minh et al. 2007)2002 Japan-Tokyo Bay T3 0.18 0.036 0.01 0.144 0.01 Semi-remote 1 (Minh et al. 2007)2002 Japan-Tokyo Bay T4 0.11 0.0187 0.01 0.0913 0.01 Semi-remote 1 (Minh et al. 2007)2002 Japan-Tokyo Bay T5 0.13 0.0403 0.01 0.0897 0.01 Remote 1 (Minh et al. 2007)2002 Japan-Tokyo Bay TP7 1.2 0.252 0.084 0.864 0.01 Semi-remote 1 (Minh et al. 2007)2002 Japan-Tokyo Bay TP8 0.33 0.0495 0.01 0.2805 0.01 Semi-remote 1 (Minh et al. 2007)2003 DRE-1 Jernbanebrua 1.1 0.78 0.2 0.32 .3, .2, .4 Unknown (Schlabach et al.2004a)2003 DRF-1 Hovedbasseng 0.4 0.3 0.2 0.4 .3, .2, .4 Unknown (Schlabach et al.2004a)166Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference2003 DRF-2 Lierterminalen 14.14 10.15 0.65 3.34 .3,.2, .4 Point (Schlabach et al.2004a)2003 DRF-3 Teigenkaianlegg 0.4 0.3 0.2 0.4 .3, .2, .4 Unknown (Schlabach et al.2004a)2003 DRF-4 Tangenflytedokk 0.4 0.3 0.2 0.4 .3, .2, .4 Unknown (Schlabach et al.2004a)river/lake1980 Lake Ellasjøen, Bjørnøya,Norwegian Arctic3.87 0.43 0.06 3.38 Remote 4 (Evenset et al. 2007)1995 Sweden-Lake Marsjon 1 0.279 0.279 Remote 0.279 0.279 2 (Sellström et al. 1998)1995 Sweden-Lake Oresjo 2 1.54 0.28 Point-downstream 0.28 1.54 2 (Sellström et al. 1998)1995 Sweden - River Viskandownstream fromindustry/urban 373.6 Point-downstream 73.6 76.8 2 (Sellström et al. 1998)1995 Sweden-River Viskan at Moga41591 Point-downstream 1591 1591 2 (Sellström et al. 1998)1995 Sweden-River Viskanupstream from Skene 554 Point-downstream 54 54 2 (Sellström et al. 1998)1995 Sweden-River Viskan,downstream from Skene 6210 Point-downstream 192 228 2 (Sellström et al. 1998)1995 Sweden-River Hagganupstream from Fritsla 72.728 Remote 0.186 2.728 2 (Sellström et al. 1998)1995 Sweden-River Haggandownstream from Fritsla 80.468 Point-downstream 0.096 0.468 2 (Sellström et al. 1998)1995 Sweden, Lake Skaresjon 9 0.37 Remote 0.37 0.37 2 (Sellström et al. 1998)2000 2000/2345-Tees (NE England) 363 59 37 267 1.2 Point-downstream 1 (Morris et al. 2004)2000 2000/2352-Tees (NE England) 295 60 23 212 1.2 Point-downstream 1 (Morris et al. 2004)2000 2000/2359-Tees (NE England) 511 106 111 294 1.2 Point-downstream 1 (Morris et al. 2004)2000 2000/2589-Tyne (NE England) 322 32 16 274 1.2 Unknown 1 (Morris et al. 2004)2000 2000/2586-Skerne (NEEngland)1678 745 396 537 1.2 Unknown 1 (Morris et al. 2004)2000 2000/2584-Skerne (NEEngland)174 40 16 118 1.2 Point 1 (Morris et al. 2004)2000 2000/3989-Mersey (NWEngland)22 <2.4 22 <2.4 1.2 Point-downstream 1 (Morris et al. 2004)2000 2002/989-Clyde (Scotland) 187 64 31 92 1.2 Unknown 1 (Morris et al. 2004)2000 2000/1979-Rhine, Lobith 8.1 <0.6 3.9 4.2 1.2 Point-downstream 1 (Morris et al. 2004)167Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference2000 2000/1981-Hollands Diep 9.9 <1.4 <1.4 9.9 1.2 Unknown 1 (Morris et al. 2004)2000 2000/35617-Meuse, Eijsden <0.2 <0.2 <0.4 <0.2 1.2 Remote 1 (Morris et al. 2004)2000 2000/35618-Kiezersveer 2.8 2.8 <0.4 <0.2 1.2 Unknown 1 (Morris et al. 2004)2000 Sweden-River Viskandownstream9.6 Point-downstream 1 25 4 (Remberger et al.2004)2000 Sweden-River Viskanupstream0.2 < 0.2 Remote 2 (Remberger et al.2004)2000 Sweden-30km N of Stockholm 0.1 0.1 Point-Landfill (Remberger et al.2004)2000 Sweden-30km N of Stockholm 0.1 0.1 Point-Landfill (Remberger et al.2004)2000 Sweden-Boras Textile IndustryRiver Viskan 10.1 0.1 Remote (Remberger et al.2004)2000 Sweden-Boras Textile Industryriver Viskan 20.2 Remote (Remberger et al.2004)2000 Sweden-Boras Textile IndustryRiver Viskan 325 Point-downstream (Remberger et al.2004)2000 Sweden-Boras Textile IndustryRiver Viskan 41 Point-downstream (Remberger et al.2004)2000 Sweden-Boras Textile IndustryRiver Viskan 54.6 Point-downstream (Remberger et al.2004)2000 Sweden-Boras Textile IndustryRiver Viskan 67.8 Point-downstream (Remberger et al.2004)2002 Norway-landfil leachate 22 2 0.8 19 < 0.1 Unknown 0.1 84 11 (Schlabach 2002)2002 Spain-River Cinca-upstream ofsource0.1 < 0.1 Remote 2 (Eljarrat et al. 2004)2002 Spain-River Cincadownstream of source302 Point-downstream 90 514 2 (Eljarrat et al. 2004)2003 Norway-Lake Mjosa area 2.7 0.2 2.2 0.3 <0.9,<0.2,<0.3Unknown 0.9 7.9 4 (Schlabach et al.2004b)2003 Norway-DrammensRiver/Drammensfjord offshoresites2.5 1.4 0.2 0.9 < 0.2 Unknown 0.9 14 11 (Schlabach et al.2004b)2003 DRE-7 Vikersund 0.12 0.1 0.08 0.12 .1, .08,.12Remote (Schlabach et al.2004a)2003 DRE-6 Loselva 0.6 0.33 0.08 0.27 .1, .08,.12Unknown (Schlabach et al.2004a)2003 DRE-5 Hokksund 0.45 0.09 0.08 0.36 .1, .08,.12Unknown (Schlabach et al.2004a)2003 DRE-4 Mjøndalen 4.14 0.99 0.07 3.08 .1, .08,.12Unknown (Schlabach et al.2004a)168Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference2003 DRE-3 Langesøya 0.4 0.15 0.08 0.25 .1, .08,.12Unknown (Schlabach et al.2004a)2003 DRE-2 Politihuset 2.4 1.55 0.2 0.85 .3, .2, .4 Unknown (Schlabach et al.2004a)sea2000 Netherlands-North Sea 1 4.4 < 0.2 Unknown 1 (Klamer et al. 2005)2000 Netherlands-North Sea 10 0.2 < 0.2 Remote 1 (Klamer et al. 2005)2000 Netherlands-North Sea 2 3.4 < 0.2 Unknown 1 (Klamer et al. 2005)2000 Netherlands-North Sea 3 6.9 < 0.2 Unknown 1 (Klamer et al. 2005)2000 Netherlands-North Sea 4 5.4 < 0.2 Unknown 1 (Klamer et al. 2005)2000 Netherlands-North Sea 5 5 < 0.2 Unknown 1 (Klamer et al. 2005)2000 Netherlands-North Sea 6 6.2 < 0.2 Unknown 1 (Klamer et al. 2005)2000 Netherlands-North Sea 7 0.76 < 0.2 Remote 1 (Klamer et al. 2005)2000 Netherlands-North Sea 8 0.83 < 0.2 Remote 1 (Klamer et al. 2005)2000 Netherlands-North Sea 9 1 < 0.2 Remote 1 (Klamer et al. 2005)2004 German Bight 3.4 Remote 0.3 6.5 12 (Lepom et al. 2007)2005 Scheldt Basin-Netherlands,Belgium42.5 < 0.2 Unknown 14 71 3 (Verslycke et al. 2005)Soil(ng/g dw)solidsS1-Europe-1 597 Point 10 (Petersen et al. 2004)S2-Europe-1 171 Point 10 (Petersen et al. 2004)S3-Europe-1 344 Point 10 (Petersen et al. 2004)S4-Europe-1 23200 Point 10 (Petersen et al. 2004)S5-Europe-1 111 Point 10 (Petersen et al. 2004)S1-Europe-2 569 80 56 433 Point 10 (Petersen et al. 2004)S2-Europe-2 135 33 18 84 Point 10 (Petersen et al. 2004)S3-Europe-2 137 28 0 109 Point 10 (Petersen et al. 2004)S4-Europe-2 20600 2930 1520 16100 Point 10 (Petersen et al. 2004)S5-Europe-2 59 18 0 41 Point 10 (Petersen et al. 2004)169Year Location TotalHBCDαHBCDβ-HBCDγ-HBCD LOD Other Median Min Max n Reference2000 Sweden-80kmSW ofAspvreten140 Point (Remberger et al.2004)2000 Sweden-80kmSW ofAspvreten1300 Point (Remberger et al.2004)2000 Sweden-80kmSW ofAspvreten1000 Point (Remberger et al.2004)2000 Sweden-near XPS producingplant567 Point 140 1300 3 (Remberger et al.2004)Waterdissolved (μg/L)2000 Sweden-30km N of Stockholm 0.003 Point-Landfill (Remberger et al.2004)2000 Sweden-30km N of Stockholm 0.009 Point-Landfill (Remberger et al.2004)suspended solids (ng/g dw)1999 Sweden-Lake Malaren,Riddarfjarden1.6 Urban (Remberger et al.2004)1999 Sweden-Klubben 1 1 Urban (Remberger et al.2004)1999 Sweden-Slussen 2.1 Urban (Remberger et al.2004)2003 Canada/USA-Detroit River 1.3 <0.01 Urban 0.1 3.7 8 (Marvin et al. 2006)total (μg/L)W1-Europe-1 20300 Point 1 (Petersen et al. 2004)W2-Europe-1 30.4 Point 1 (Petersen et al. 2004)W3-Europe-1 318 Point 1 (Petersen et al. 2004)W4-Europe-1 9.1 Point 1 (Petersen et al. 2004)W5-Europe-1 206 Point 1 (Petersen et al. 2004)W1-Europe-2 19000 7580 3230 8220 Point 1 (Petersen et al. 2004)W2-Europe-2 36 3.8 3.5 28.7 Point 1 (Petersen et al. 2004)W3-Europe-2 361 76 17 268 Point 1 (Petersen et al. 2004)W4-Europe-2 11 1.6 0.6 8.8 Point 1 (Petersen et al. 2004)W5-Europe-2 205 51 39 115 Point 1 (Petersen et al. 2004)170Table 9.8. Summary of biotic monitoring data.Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1994 East Harbour porpoise blubber 0.88 ug kg-1 ww 64 64 < 5 < 5 Law RJ et al. (2006)1995 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 10 10 < 5 < 5 Law RJ et al. (2006)1995 Scotland Harbour porpoise blubber 0.86 ug kg-1 ww 42 34 8 < 5 Law RJ et al. (2006)1996 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 29 29 < 5 < 5 Law RJ et al. (2006)1996 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 24 16 8 < 5 Law RJ et al. (2006)1996 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 19 12 7 < 5 Law RJ et al. (2006)1996 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 106 87 9 10 Law RJ et al. (2006)1996 East Harbour porpoise blubber 0.86 ug kg-1 ww 125 125 < 5 < 5 Law RJ et al. (2006)1996 East Harbour porpoise blubber 0.92 ug kg-1 ww 103 103 < 5 < 5 Law RJ et al. (2006)1996 West Harbour porpoise blubber 0.86 ug kg-1 ww 34 34 < 5 < 5 Law RJ et al. (2006)1997 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 75 64 11 < 5 Law RJ et al. (2006)1997 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 434 434 < 12 < 5 Law RJ et al. (2006)1998 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 221 204 7 10 Law RJ et al. (2006)1998 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 22 22 < 5 < 5 Law RJ et al. (2006)1998 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 13 13 < 5 < 5 Law RJ et al. (2006)1998 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 62 62 < 5 < 5 Law RJ et al. (2006)1998 East Harbour porpoise blubber 0.85 ug kg-1 ww 82 59 23 < 5 Law RJ et al. (2006)1998 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 48 41 7 < 5 Law RJ et al. (2006)1999 East Harbour porpoise blubber 0.76 ug kg-1 ww 69 69 < 4 < 4 Law RJ et al. (2006)1999 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 216 209 7 < 5 Law RJ et al. (2006)1999 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 468 458 < 5 10 Law RJ et al. (2006)1999 West Harbour porpoise blubber 0.88 ug kg-1 ww 44 44 < 5 < 5 Law RJ et al. (2006)1999 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 287 267 11 9 Law RJ et al. (2006)2000 West Harbour porpoise blubber 0.91 ug kg-1 ww 56 56 < 5 < 5 Law RJ et al. (2006)2000 West Harbour porpoise blubber 0.85 ug kg-1 ww 240 218 12 < 5 Law RJ et al. (2006)2000 West Harbour porpoise blubber 0.86 ug kg-1 ww 42 42 < 5 < 5 Law RJ et al. (2006)2000 East Harbour porpoise blubber 0.83 ug kg-1 ww 39 39 < 4 < 4 Law RJ et al. (2006)2000 East Harbour porpoise blubber 0.84 ug kg-1 ww 41 41 < 5 < 5 Law RJ et al. (2006)2000 East Harbour porpoise blubber 0.84 ug kg-1 ww 103 103 < 5 < 5 Law RJ et al. (2006)2000 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 201 192 < 5 9 Law RJ et al. (2006)2000 West Harbour porpoise blubber 0.87 ug kg-1 ww 238 227 11 < 5 Law RJ et al. (2006)2000 West Harbour porpoise blubber 0.83 ug kg-1 ww 216 203 13 < 4 Law RJ et al. (2006)2000 East Harbour porpoise blubber 0.86 ug kg-1 ww 250 221 16 13 Law RJ et al. (2006)2000 East Harbour porpoise blubber 0.86 ug kg-1 ww 262 233 18 11 Law RJ et al. (2006)2000 East Harbour porpoise blubber 0.56 ug kg-1 ww 131 125 < 3 6 Law RJ et al. (2006)171Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2000 West Harbour porpoise blubber 0.88 ug kg-1 ww 104 93 < 5 11 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 297 279 9 9 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 368 346 13 9 Law RJ et al. (2006)2001 West Harbour porpoise blubber 0.88 ug kg-1 ww 912 875 24 13 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 125 103 14 8 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 622 603 8 11 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 185 176 < 5 9 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 149 141 8 < 5 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.85 ug kg-1 ww 10958 10900 37 21 Law RJ et al. (2006)2001 West Harbour porpoise blubber 0.76 ug kg-1 ww 707 707 < 4 < 4 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 576 543 22 11 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 2689 2660 13 17 Law RJ et al. (2006)2001 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 201 201 < 5 < 5 Law RJ et al. (2006)2001 West Harbour porpoise blubber 0.90 ug kg-1 ww 1610 1610 < 5 < 5 Law RJ et al. (2006)2001 West Harbour porpoise blubber 0.89 ug kg-1 ww 1505 1490 15 < 5 Law RJ et al. (2006)2001 West Harbour porpoise blubber 0.89 ug kg-1 ww 222 222 < 5 < 5 Law RJ et al. (2006)2002 West Harbour porpoise blubber 0.88 ug kg-1 ww 1089 1060 12 17 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 496 496 < 5 < 5 Law RJ et al. (2006)2002 West Harbour porpoise blubber 0.90 ug kg-1 ww 2702 2690 12 < 5 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 841 830 < 5 11 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 326 311 6 9 Law RJ et al. (2006)2002 West Harbour porpoise blubber 0.88 ug kg-1 ww 17609 17600 9 < 5 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 191 172 11 9 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 93 84 < 5 9 Law RJ et al. (2006)2002 West Harbour porpoise blubber 0.89 ug kg-1 ww 15316 15300 8 8 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 18437 18400 18 9 Law RJ et al. (2006)2002 East Harbour porpoise blubber 0.91 ug kg-1 ww 855 855 < 5 < 5 Law RJ et al. (2006)2002 East Harbour porpoise blubber 0.91 ug kg-1 ww 3433 3420 < 5 13 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 10442 10400 29 13 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 356 330 26 < 20 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.85 ug kg-1 ww 463 448 15 < 10 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.82 ug kg-1 ww 7223 7180 37 6 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 1354 1340 < 5 14 Law RJ et al. (2006)2002 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 1054 1030 15 9 Law RJ et al. (2006)2003 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 17411 17400 11 < 5 Law RJ et al. (2006)2003 West Harbour porpoise blubber 0.87 ug kg-1 ww 155 143 < 5 12 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.91 ug kg-1 ww 11440 11440 < 5 < 5 Law RJ et al. (2006)172Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2003 East Harbour porpoise blubber 0.85 ug kg-1 ww 7740 7740 < 5 < 5 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.83 ug kg-1 ww 8223 8210 < 4 13 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.90 ug kg-1 ww 19208 19200 < 5 8 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.88 ug kg-1 ww 15939 15900 20 18 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.91 ug kg-1 ww 9460 9460 < 5 < 5 Law RJ et al. (2006)2003 West Harbour porpoise blubber 0.89 ug kg-1 ww 132 132 < 5 < 5 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.87 ug kg-1 ww 1078 1070 < 5 8 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.85 ug kg-1 ww 6645 6570 54 21 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.85 ug kg-1 ww 2200 2200 < 5 < 5 Law RJ et al. (2006)2003 West Harbour porpoise blubber 0.88 ug kg-1 ww 1030 1030 < 5 < 5 Law RJ et al. (2006)2003 Scotland Harbour porpoise blubber 0.85 ug kg-1 ww 453 453 < 5 < 5 Law RJ et al. (2006)2003 West Harbour porpoise blubber 0.88 ug kg-1 ww 9186 9170 16 < 5 Law RJ et al. (2006)2003 East Harbour porpoise blubber 0.88 ug kg-1 ww 8460 8410 50 < 5 Law RJ et al (2006)2003 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 310 310 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 2790 2790 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 8490 8490 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.92 ug kg-1 ww 1190 1190 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 200 200 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.86 ug kg-1 ww 710 710 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.91 ug kg-1 ww 280 280 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.91 ug kg-1 ww 530 530 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.86 ug kg-1 ww 6230 6230 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.90 ug kg-1 ww 11500 11500 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 220 220 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.93 ug kg-1 ww 600 600 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.92 ug kg-1 ww 93 93 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.94 ug kg-1 ww 72 72 < 10 < 5 Law RJ et al. (2008)2003 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 560 560 < 10 < 5 Law RJ et al. (2008)2003 West England Harbour porpoise blubber 0.88 ug kg-1 ww 2140 2140 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.76 ug kg-1 ww 370 370 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 440 440 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 3870 3870 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.90 ug kg-1 ww 600 600 < 10 < 5 Law RJ et al. (2008)2004 East England Harbour porpoise blubber 0.91 ug kg-1 ww 620 620 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 3410 3410 < 10 < 5 Law RJ et al. (2008)2004 East England Harbour porpoise blubber 0.91 ug kg-1 ww 390 390 < 10 < 5 Law RJ et al. (2008)2004 East England Harbour porpoise blubber 0.93 ug kg-1 ww 200 200 < 10 < 5 Law RJ et al. (2008)173Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2004 West England Harbour porpoise blubber 0.94 ug kg-1 ww 1650 1650 < 10 < 5 Law RJ et al. (2008)2004 South England Harbour porpoise blubber 0.95 ug kg-1 ww 120 120 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.88 ug kg-1 ww 280 280 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.95 ug kg-1 ww 920 920 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 230 230 < 10 < 5 Law RJ et al. (2008)2004 South England Harbour porpoise blubber 0.94 ug kg-1 ww 170 170 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.87 ug kg-1 ww 3450 3450 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.82 ug kg-1 ww 4150 4150 < 10 < 5 Law RJ et al. (2008)2004 East England Harbour porpoise blubber 0.89 ug kg-1 ww 34 34 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.86 ug kg-1 ww 4060 4060 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.89 ug kg-1 ww 87 87 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.85 ug kg-1 ww 240 240 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.85 ug kg-1 ww 3110 3110 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.84 ug kg-1 ww 260 260 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.89 ug kg-1 ww 1120 1120 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 170 170 < 10 < 10 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.92 ug kg-1 ww 780 780 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.92 ug kg-1 ww 190 190 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.77 ug kg-1 ww 710 710 < 9 < 9 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.91 ug kg-1 ww 1170 1170 < 10 < 5 Law RJ et al. (2008)2004 South England Harbour porpoise blubber 0.93 ug kg-1 ww 94 94 < 10 < 5 Law RJ et al. (2008)2004 West England Harbour porpoise blubber 0.86 ug kg-1 ww 3720 3720 < 10 < 5 Law RJ et al. (2008)2004 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 100 100 < 10 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 167 167 < 5 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.9 ug kg-1 ww 1160 1160 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 8470 8470 < 10 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.89 ug kg-1 ww 616 616 < 5 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.92 ug kg-1 ww 55 55 < 5 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.95 ug kg-1 ww 1620 1620 < 6 < 6 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 514 508 < 5 6 Law RJ et al. (2008)2005 South England Harbour porpoise blubber 0.79 ug kg-1 ww 440 440 < 4 < 4 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.85 ug kg-1 ww 550 550 < 10 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 2105 2100 < 5 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.84 ug kg-1 ww 922 922 < 5 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.83 ug kg-1 ww 2398 2398 < 5 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.91 ug kg-1 ww 51 51 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 239 239 < 5 < 5 Law RJ et al. (2008)174Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2005 East England Harbour porpoise blubber 0.91 ug kg-1 ww 158 158 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 114 114 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.94 ug kg-1 ww 42 42 < 5 < 5 Law RJ et al. (2008)2005 South England Harbour porpoise blubber 0.91 ug kg-1 ww 1590 1590 < 5 < 5 Law RJ et al. (2008)2005 South England Harbour porpoise blubber 0.91 ug kg-1 ww 1012 1012 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 4128 4122 < 5 6 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 2720 2720 < 10 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.91 ug kg-1 ww 233 233 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 1153 1148 < 5 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 861 861 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 740 740 < 10 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.9 ug kg-1 ww 63 63 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 245 245 < 5 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.81 ug kg-1 ww 189 183 6 < 4 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 95 95 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 224 224 < 5 < 5 Law RJ et al. (2008)2005 East England Harbour porpoise blubber 0.9 ug kg-1 ww 106 99 7 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 1580 1580 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.95 ug kg-1 ww 433 433 < 6 < 6 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 69 69 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 190 190 < 10 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 193 193 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 420 420 < 10 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.89 ug kg-1 ww 150 150 < 5 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.92 ug kg-1 ww 348 348 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 84 84 < 10 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.92 ug kg-1 ww < 5 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 888 888 < 5 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.93 ug kg-1 ww 166 166 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 69 69 < 10 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.81 ug kg-1 ww 226 226 < 5 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.94 ug kg-1 ww 490 490 < 6 < 6 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.79 ug kg-1 ww 49 49 < 4 < 4 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.7 ug kg-1 ww 492 492 < 4 < 4 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 8310 8310 < 10 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.78 ug kg-1 ww 1201 1201 < 4 < 4 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.8 ug kg-1 ww 280 280 < 10 < 5 Law RJ et al. (2008)175Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2005 Scotland Harbour porpoise blubber 0.84 ug kg-1 ww 201 201 < 5 < 5 Law RJ et al. (2008)2005 South England Harbour porpoise blubber 0.85 ug kg-1 ww 1031 1031 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 193 193 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 869 869 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 110 110 < 4 < 2 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 390 390 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.9 ug kg-1 ww 4225 4225 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 2689 2689 < 5 < 5 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.92 ug kg-1 ww 4113 4113 < 5 < 5 Law RJ et al. (2008)2005 West England Harbour porpoise blubber 0.93 ug kg-1 ww 2363 2357 < 5 6 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.97 ug kg-1 ww 1038 1038 < 6 < 6 Law RJ et al. (2008)2005 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 635 631 4 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 278 278 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 548 548 < 5 < 5 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.72 ug kg-1 ww 344 200 99 45 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.87 ug kg-1 ww 311 311 < 5 < 5 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.9 ug kg-1 ww 6358 6358 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 2415 2415 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 218 218 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.93 ug kg-1 ww 81 81 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.89 ug kg-1 ww 132 132 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.91 ug kg-1 ww 652 652 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.94 ug kg-1 ww 88 88 < 6 < 6 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.93 ug kg-1 ww 517 508 < 5 9 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.89 ug kg-1 ww 115 115 < 5 < 5 Law RJ et al. (2008)2006 Scotland Harbour porpoise blubber 0.88 ug kg-1 ww 64 64 < 5 < 5 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.86 ug kg-1 ww 115 85 12 18 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.9 ug kg-1 ww 267 267 < 5 < 5 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.91 ug kg-1 ww 524 524 < 5 < 5 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.83 ug kg-1 ww 697 677 12 8 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.89 ug kg-1 ww 1139 1139 < 5 < 5 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.9 ug kg-1 ww 120 120 < 5 < 5 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.9 ug kg-1 ww 1554 1554 < 5 < 5 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.92 ug kg-1 ww 130 130 < 5 < 5 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.82 ug kg-1 ww 415 415 < 4 < 4 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.6 ug kg-1 ww 1535 1535 < 3 < 3 Law RJ et al. (2008)2006 East England Harbour porpoise blubber 0.86 ug kg-1 ww 489 365 80 44 Law RJ et al. (2008)176Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2006 South England Harbour porpoise blubber 0.69 ug kg-1 ww 277 277 < 4 < 4 Law RJ et al. (2008)2006 South England Harbour porpoise blubber 0.86 ug kg-1 ww 112 105 7 < 5 Law RJ et al. (2008)2006 West England Harbour porpoise blubber 0.9 ug kg-1 ww 122 122 < 5 < 5 Law RJ et al. (2008)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 448.8 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 781.3 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 2593.2 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 770.2 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 497.0 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 1054.5 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 1564.3 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 1068.2 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 744.3 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 393.3 Zegers et al. (2005)2001 - 2003 North, North Sea Harbour porpoise blubber ug kg-1 lw 637.5 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 678.9 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1218.5 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1232.8 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 537.9 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 666.0 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1676.0 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1160.2 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 815.9 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1596.5 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1064.7 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 1143.8 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 2311.8 Zegers et al. (2005)2000 - 2003 South, North Sea Harbour porpoise blubber ug kg-1 lw 754.0 Zegers et al. (2005)2001 - 2003 NW Scotland Harbour porpoise blubber ugkg-1 lw 1502.6 Zegers et al. (2005)2001 - 2003 NW Scotland Harbour porpoise blubber ugkg-1 lw 9590.5 Zegers et al. (2005)2001 - 2003 NW Scotland Harbour porpoise blubber ugkg-1 lw 6324.8 Zegers et al. (2005)2001 - 2003 NW Scotland Harbour porpoise blubber ugkg-1 lw 1009.0 Zegers et al. (2005)2001 - 2003 NW Scotland Harbour porpoise blubber ugkg-1 lw 5056.3 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 930.8 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 6301.3 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 5343.2 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 466.3 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 3440.8 Zegers et al. (2005)177Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 1981.2 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 8786.4 Zegers et al. (2005)2002 - 2003 Irish Sea Harbour porpoise blubber ug kg-1 lw 2383.0 Zegers et al. (2005)2001 - 2003 South Ireland Harbour porpoise blubber ug kg-1 lw 2269.0 Zegers et al. (2005)2001 - 2003 South Ireland Harbour porpoise blubber ug kg-1 lw 1158.4 Zegers et al. (2005)2001 - 2003 South Ireland Harbour porpoise blubber ug kg-1 lw 710.0 Zegers et al. (2005)2001 - 2003 Galicia, Spain Harbour porpoise blubberug kg-1 lw 78.8 Zegers et al. (2005)2001 - 2003 Galicia, Spain Harbour porpoise blubberug kg-1 lw 142.6 Zegers et al. (2005)2001 - 2003 Galicia, Spain Harbour porpoise blubberug kg-1 lw 142.3 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 174.9 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 630.5 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 279.1 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 97.6 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 180.2 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 418.4 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 344.0 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 199.1 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 388.8 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 898.6 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 460.6 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 717.1 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 188.2 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 197.3 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 215.0 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 253.4 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 235.7 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 442.7 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 619.3 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 472.2 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 438.5 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 597.3 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 417.9 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 365.7 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 568.9 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 468.5 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 760.6 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 702.7 Zegers et al. (2005)178Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2002 NW France Common dolphin blubber ug kg-1lw 820.6 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 503.6 Zegers et al. (2005)2002 NW France Common dolphin blubber ug kg-1lw 377.7 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 64.0 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 280.6 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 251.4 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 183.6 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 259.3 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 290.8 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 90.5 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 368.4 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 205.0 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 454.5 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 109.4 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 159.8 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 90.7 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 170.6 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 51.5 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 123.9 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 216.2 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 162.3 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 163.4 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 115.8 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 246.6 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 93.6 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 89.9 Zegers et al. (2005)2001 - 2003 Galicia, Spain Common dolphin blubber ugkg-1 lw 182.2 Zegers et al. (2005)2001 - 2002 West Ireland Common dolphin blubber ug kg-1 lw 727.3 Zegers et al. (2005)2001 - 2002 West Ireland Common dolphin blubber ug kg-1 lw 411.1 Zegers et al. (2005)2001 - 2002 West Ireland Common dolphin blubber ug kg-1 lw 193.2 Zegers et al. (2005)2001 - 2002 West Ireland Common dolphin blubber ug kg-1 lw 3416.3 Zegers et al. (2005)2001 - 2002 West Ireland Common dolphin blubber ug kg-1 lw 1591.0 Zegers et al. (2005)2001 - 2002 West Ireland Common dolphin blubber ug kg-1 lw 998.0 Zegers et al. (2005)1997 US East Coast white-sided dolphin liver 0.031 ug kg-1 lw 68 Peck et al. (2008)1993 US East Coast white-sided dolphin liver 0.0186 ug kg-1 lw 36 Peck et al. (2008)1997 US East Coast white-sided dolphin liver 0.0275 ug kg-1 lw 29 Peck et al. (2008)1993 US East Coast white-sided dolphin liver 0.0216 ug kg-1 lw 23 Peck et al. (2008)179Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1993 US East Coast white-sided dolphin liver 0.0254 ug kg-1 lw 5.8 Peck et al. (2008)1999 US East Coast white-sided dolphin liver 0.0265 ug kg-1 lw 140 Peck et al. (2008)1998 US East Coast white-sided dolphin liver 0.0224 ug kg-1 lw 4.5 Peck et al. (2008)1998 US East Coast white-sided dolphin liver 0.0193 ug kg-1 lw 4.4 Peck et al. (2008)1999 US East Coast white-sided dolphin liver 0.0268 ug kg-1 lw 130 Peck et al. (2008)1998 US East Coast white-sided dolphin liver 0.0338 ug kg-1 lw 7.7 Peck et al. (2008)1998 US East Coast white-sided dolphin liver 0.0208 ug kg-1 lw 11 Peck et al. (2008)1999 US East Coast white-sided dolphin liver 0.0275 ug kg-1 lw 25 Peck et al. (2008)1999 US East Coast white-sided dolphin liver 0.032 ug kg-1 lw 81 Peck et al. (2008)1998 US East Coast white-sided dolphin liver 0.0334 ug kg-1 lw 13 Peck et al. (2008)1998 US East Coast white-sided dolphin liver 0.0174 ug kg-1 lw 3 Peck et al. (2008)2000 US East Coast white-sided dolphin liver 0.0217 ug kg-1 lw 26 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.744 ug kg-1 lw 118 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.814 ug kg-1 lw 89.4 Peck et al. (2008)1993 US East Coast white-sided dolphin blubber 0.848 ug kg-1 lw 214 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.695 ug kg-1 lw 79.4 Peck et al. (2008)1997 US East Coast white-sided dolphin blubber 0.725 ug kg-1 lw 245 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.803 ug kg-1 lw 136 Peck et al. (2008)1993 US East Coast white-sided dolphin blubber 0.86 ug kg-1 lw 164 Peck et al. (2008)1993 US East Coast white-sided dolphin blubber 0.748 ug kg-1 lw 360 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.789 ug kg-1 lw 100 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.759 ug kg-1 lw 41 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.699 ug kg-1 lw 55 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.702 ug kg-1 lw 73 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.725 ug kg-1 lw 79 Peck et al. (2008)2000 US East Coast white-sided dolphin blubber 0.744 ug kg-1 lw 80 Peck et al. (2008)2001 US East Coast white-sided dolphin blubber 0.797 ug kg-1 lw 73 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.668 ug kg-1 lw 190 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.792 ug kg-1 lw 22 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.779 ug kg-1 lw 32 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.789 ug kg-1 lw 62 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.669 ug kg-1 lw 52 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.745 ug kg-1 lw 84 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.776 ug kg-1 lw 19 Peck et al. (2008)1997 US East Coast white-sided dolphin blubber 0.435 ug kg-1 lw 260 Peck et al. (2008)2000 US East Coast white-sided dolphin blubber 0.59 ug kg-1 lw 78 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.719 ug kg-1 lw 200 Peck et al. (2008)180Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1997 US East Coast white-sided dolphin blubber 0.771 ug kg-1 lw 140 Peck et al. (2008)1997 US East Coast white-sided dolphin blubber 0.545 ug kg-1 lw 190 Peck et al. (2008)1994 US East Coast white-sided dolphin blubber 0.758 ug kg-1 lw 110 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.695 ug kg-1 lw 110 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.793 ug kg-1 lw 86 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.725 ug kg-1 lw 150 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.778 ug kg-1 lw 68 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.746 ug kg-1 lw 118 Peck et al. (2008)2002 US East Coast white-sided dolphin blubber 0.822 ug kg-1 lw 55 Peck et al. (2008)2002 US East Coast white-sided dolphin blubber 0.825 ug kg-1 lw 41 Peck et al. (2008)2002 US East Coast white-sided dolphin blubber 0.789 ug kg-1 lw 47 Peck et al. (2008)2003 US East Coast white-sided dolphin blubber 0.923 ug kg-1 lw 65 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.645 ug kg-1 lw 210 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.557 ug kg-1 lw 150 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.671 ug kg-1 lw 170 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.601 ug kg-1 lw 140 Peck et al. (2008)2000 US East Coast white-sided dolphin blubber 0.685 ug kg-1 lw 76 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.702 ug kg-1 lw 68 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.577 ug kg-1 lw 350 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.627 ug kg-1 lw 100 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.66 ug kg-1 lw 86 Peck et al. (2008)1998 US East Coast white-sided dolphin blubber 0.744 ug kg-1 lw 120 Peck et al. (2008)2000 US East Coast white-sided dolphin blubber 0.523 ug kg-1 lw 250 Peck et al. (2008)2000 US East Coast white-sided dolphin blubber 0.352 ug kg-1 lw 330 Peck et al. (2008)2000 US East Coast white-sided dolphin blubber 0.451 ug kg-1 lw 170 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.685 ug kg-1 lw 93 Peck et al. (2008)1999 US East Coast white-sided dolphin blubber 0.582 ug kg-1 lw 190 Peck et al. (2008)2001 US East Coast white-sided dolphin blubber 0.705 ug kg-1 lw 34 Peck et al. (2008)2001 US East Coast white-sided dolphin blubber 0.669 ug kg-1 lw 82 Peck et al. (2008)2003 US East Coast white-sided dolphin blubber 0.671 ug kg-1 lw 160 Peck et al. (2008)2003 US East Coast white-sided dolphin blubber 0.598 ug kg-1 lw 170 Peck et al. (2008)2004 US East Coast white-sided dolphin blubber 0.399 ug kg-1 lw 220 Peck et al. (2008)1993 California coast California sea lion blubber 0.1325 ug kg-1 ww 0.71 0.71 < 0.3 < 0.3 Stapleton et al. (2006)1996 California coast California sea lion blubber 0.7917 ug kg-1 ww < 0.3 < 0.3 < 0.3 Stapleton et al. (2006)1996 California coast California sea lion blubber 0.6054 ug kg-1 ww 1.33 1.33 < 0.3 < 0.3 Stapleton et al. (2006)1996 California coast California sea lion blubber 0.0816 ug kg-1 ww 1.12 1.12 < 0.3 < 0.3 Stapleton et al. (2006)1996 California coast California sea lion blubber 0.5538 ug kg-1 ww < 0.3 < 0.3 < 0.3 Stapleton et al. (2006)181Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1996 California coast California sea lion blubber 0.1893 ug kg-1 ww < 0.3 < 0.3 < 0.3 Stapleton et al. (2006)1996 California coast California sea lion blubber 0.3598 ug kg-1 ww 0.31 0.31 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.3027 ug kg-1 ww 1.26 1.26 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.5492 ug kg-1 ww 2.38 2.38 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.6396 ug kg-1 ww 1.17 1.17 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.0632 ug kg-1 ww < 0.3 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.2017 ug kg-1 ww 1.51 0.94 0.58 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.5359 ug kg-1 ww 2.67 1.79 0.87 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.4429 ug kg-1 ww 1.54 1.54 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.5356 ug kg-1 ww < 0.3 < 0.3 < 0.3 Stapleton et al. (2006)1997 California coast California sea lion blubber 0.4236 ug kg-1 ww 6.56 6.26 0.3 < 0.3 Stapleton et al. (2006)1998 California coast California sea lion blubber 0.5831 ug kg-1 ww 0.63 0.63 < 0.3 < 0.3 Stapleton et al. (2006)1999 California coast California sea lion blubber 0.4436 ug kg-1 ww 2.15 2.15 < 0.3 < 0.3 Stapleton et al. (2006)1999 California coast California sea lion blubber 0.6635 ug kg-1 ww 3.18 3.18 < 0.3 < 0.3 Stapleton et al. (2006)2000 California coast California sea lion blubber 0.0703 ug kg-1 ww 6.75 6.75 < 0.3 < 0.3 Stapleton et al. (2006)2000 California coast California sea lion blubber 0.495 ug kg-1 ww 4.71 4.71 < 0.3 < 0.3 Stapleton et al. (2006)2000 California coast California sea lion blubber 0.5099 ug kg-1 ww 5.91 5.91 < 0.3 < 0.3 Stapleton et al. (2006)2000 California coast California sea lion blubber 0.6097 ug kg-1 ww 2.31 2.31 < 0.3 < 0.3 Stapleton et al. (2006)2002 California coast California sea lion blubber 0.6268 ug kg-1 ww 5.6 5.6 < 0.3 < 0.3 Stapleton et al. (2006)2002 California coast California sea lion blubber 0.6783 ug kg-1 ww 8.63 8.63 < 0.3 < 0.3 Stapleton et al. (2006)2003 California coast California sea lion blubber 0.6904 ug kg-1 ww 11.85 11.85 < 0.3 < 0.3 Stapleton et al. (2006)1979 Lake Ontario (east) lake trout whole body 0.129 ug kg-1 lw 33 25 0.94 6.5 (Ismail et al. 2009)1983 Lake Ontario (east) lake trout whole body 0.141 ug kg-1 lw 28 25 0.4 2.9 Ismail et al. (in press)1988 Lake Ontario (east) lake trout whole body 0.114 ug kg-1 lw 18 15 0.26 2.5 Ismail et al. (in press)1993 Lake Ontario (east) lake trout whole body 0.104 ug kg-1 lw 32 27 0.38 4.6 Ismail et al. (in press)1998 Lake Ontario (east) lake trout whole body 0.121 ug kg-1 lw 25 22 0.28 2.3 Ismail et al. (in press)2004 Lake Ontario (east) lake trout whole body 0.163 ug kg-1 lw 16 15 0.16 1.4 Ismail et al. (in press)1972 Offshore Japan Northern fur seal blubber 0.64 -0.69ug kg-1 lw < 0.1 (Kajiwara et al. 2006)1976 Offshore Japan Northern fur seal blubber 0.027- 0.60ug kg-1 lw 2.4 Kajiwara et al. (2006)1980 Offshore Japan Northern fur seal blubber 0.71 -0.79ug kg-1 lw 0.77 Kajiwara et al. (2006)1982 Offshore Japan Northern fur seal blubber 0.23 -0.38ug kg-1 lw 6.5 Kajiwara et al. (2006)1985 Offshore Japan Northern fur seal blubber 0.48 -0.60ug kg-1 lw 1.6 Kajiwara et al. (2006)1988 Offshore Japan Northern fur seal blubber 0.70 -0.82ug kg-1 lw 3.6 Kajiwara et al. (2006)182Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1991 Offshore Japan Northern fur seal blubber 0.64 -0.80ug kg-1 lw 15 Kajiwara et al. (2006)1994 Offshore Japan Northern fur seal blubber 0.72 -0.80ug kg-1 lw 33 Kajiwara et al. (2006)1997 Offshore Japan Northern fur seal blubber 0.77 -1ug kg-1 lw 33 Kajiwara et al. (2006)1998 Offshore Japan Northern fur seal blubber 0.72 -0.88ug kg-1 lw 24 Kajiwara et al. (2006)1982 Pacific Coast,JapanMelon-headed whale blubber 0.46 -0.80ug kg-1 lw 7 Kajiwara et al. (2006)2001 Pacific Coast,JapanMelon-headed whale blubber 0.63 -0.74ug kg-1 lw 390 Kajiwara et al. (2006)1990 S.China Sea Finless porpoise blubber 0.87 ug kg-1 lw 5.6 4.8 0.34 0.51 (Isobe et al. 2007)1990 S.China Sea Finless porpoise blubber 0.73ug kg-1 lw 37 12 4 21 Isobe et al. (2007)1990 S.China Sea Finless porpoise blubber 0.92 ugkg-1 lw 23 10 1.5 11 Isobe et al. (2007)1990 S.China Sea Finless porpoise blubber 0.78 ugkg-1 lw 9.4 9.4 Isobe et al. (2007)1990 S.China Sea Finless porpoise blubber 0.83 ugkg-1 lw 30 15 1.3 14 Isobe et al. (2007)1990 S.China Sea Finless porpoise blubber 0.91 ug kg-1 lw 4.7 4.4 0.33 Isobe et al. (2007)1990 S.China Sea Finless porpoise blubber 0.71 ugkg-1 lw 14 11 0.71 2.4 Isobe et al. (2007)2000 Hong Kong Finless porpoise blubber 0.4 ug kg-1 lw 55 55 Isobe et al. (2007)2000 Hong Kong Finless porpoise blubber 0.34 ug kg-1 lw 30 29 0.21 Isobe et al. (2007)2001 Hong Kong Finless porpoise blubber 0.63 ug kg-1 lw 21 21 Isobe et al. (2007)2001 Hong Kong Finless porpoise blubber 0.65 ug kg-1 lw 28 28 Isobe et al. (2007)2001 Hong Kong Finless porpoise blubber 0.32 ug kg-1 lw 43 43 Isobe et al. (2007)1997 Hong Kong Humpback dolphin blubber 0.53 ug kg-1 lw 83 83 Isobe et al. (2007)1998 Hong Kong Humpback dolphin blubber 0.13 ug kg-1 lw 31 31 0.16 Isobe et al. (2007)2000 Hong Kong Humpback dolphin blubber 0.34 ug kg-1 lw 120 120 Isobe et al. (2007)2001 Hong Kong Humpback dolphin blubber 0.29 ug kg-1 lw 51 50 0.64 Isobe et al. (2007)2001 Hong Kong Humpback dolphin blubber 0.32 ug kg-1 lw 64 64 Isobe et al. (2007)2001 Hong Kong Humpback dolphin blubber 0.17 ug kg-1 lw 380 370 4.6 Isobe et al. (2007)2001 Hong Kong Humpback dolphin blubber 0.18 ug kg-1 lw 47 46 0.59 0.55 Isobe et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.0078 ug kg-1 lw 12 (Ramu et al. 2007)2005 South Korea, coast blue mussel soft-flesh 0.017 ug kg-1 lw 6 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.0093 ug kg-1 lw Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.016 ug kg-1 lw 66 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.018 ug kg-1 lw 45 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.01 ug kg-1 lw Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.021 ug kg-1 lw 39 Ramu et al. (2007)183Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2005 South Korea, coast blue mussel soft-flesh 0.0081 ug kg-1 lw Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.018 ug kg-1 lw 500 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.021 ug kg-1 lw 21 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.023 ug kg-1 lw 73 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.019 ug kg-1 lw 53 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.014 ug kg-1 lw Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.012 ug kg-1 lw Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.0075 ug kg-1 lw 140 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.012 ug kg-1 lw 30 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.0086 ug kg-1 lw 49 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.013 ug kg-1 lw 350 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.0072 ug kg-1 lw 42 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.013 ug kg-1 lw 52 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.012 ug kg-1 lw 18 Ramu et al. (2007)2005 South Korea, coast blue mussel soft-flesh 0.0095 ug kg-1 lw 38 Ramu et al. (2007)1998 N-Pacific-1 skipjack tuna muscle 0.037 ug kg-1 lw 25 22 0.22 1.6 (Ueno et al. 2006)1997 N-Pacific-2 skipjack tuna muscle 0.018 ug kg-1 lw 29 24 0.63 4.2 Ueno et al. (2006)1998 N-Pacific-3 skipjack tuna muscle 0.008 ug kg-1 lw 1.1 0.86 0.25 < 0.5 Ueno et al. (2006)1997 off-Japan-1 skipjack tuna muscle 0.049 ug kg-1 lw 32 30 0.27 1.9 Ueno et al. (2006)1997 off-Japan-2 skipjack tuna muscle 0.048 ug kg-1 lw 45 40 0.3 4.2 Ueno et al. (2006)1997 Japan Sea skipjack tuna muscle 0.074 ug kg-1 lw 6.5 5 0.66 1.4 Ueno et al. (2006)1997 E-China Sea-1 skipjack tuna muscle 0.026 ug kg-1 lw 44 29 0.75 14 Ueno et al. (2006)1997 E-China Sea-2 skipjack tuna muscle 0.008 ug kg-1 lw 28 21 0.41 6.5 Ueno et al. (2006)1998 off-Taiwan skipjack tuna muscle 0.004 ug kg-1 lw 27 24 0.38 2.3 Ueno et al. (2006)2001 S-China Sea skipjack tuna muscle 0.012 ug kg-1 lw 3.2 2.5 0.1 0.65 Ueno et al. (2006)1997 off-Philippines skipjack tuna muscle 0.006 ug kg-1 lw 0.86 0.86 < 0.1 < 0.4 Ueno et al. (2006)1998 Bay of Bengal skipjack tuna muscle 0.008 ug kg-1 lw 0.27 0.27 < 0.1 < 0.4 Ueno et al. (2006)1999 off-Indonesia skipjack tuna muscle 0.008 ug kg-1 lw 0.41 0.41 < 0.1 < 0.4 Ueno et al. (2006)1999 off-Seychelles skipjack tuna muscle 0.007 ug kg-1 lw < 0.1 < 0.1 < 0.4 Ueno et al. (2006)2000 off-Brazil skipjack tuna muscle 0.031 ug kg-1 lw 0.28 0.28 < 0.03 < 0.1 Ueno et al. (2006)Unknown Unknown Human Milk N/R ug kg-1 lw 0.7 Geyer et al. 20041969 Baltic Sea Guillemot Egg 0.111 ug kg-1 lw 78 Sellström et al., 20031971 Baltic Sea Guillemot Egg 0.105 ug kg-1 lw 66 Sellström et al., 20031972 Baltic Sea Guillemot Egg 0.121 ug kg-1 lw 58 Sellström et al., 20031973 Baltic Sea Guillemot Egg 0.117 ug kg-1 lw 52 Sellström et al., 20031975 Baltic Sea Guillemot Egg 0.114 ug kg-1 lw 100 Sellström et al., 20031976 Baltic Sea Guillemot Egg 0.153 ug kg-1 lw 140 Sellström et al., 2003184Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1976 Baltic Sea Guillemot Egg 0.153 ug kg-1 lw 150 Sellström et al., 20031977 Baltic Sea Guillemot Egg 0.122 ug kg-1 lw 95 Sellström et al., 20031978 Baltic Sea Guillemot Egg 0.114 ug kg-1 lw 45 Sellström et al., 20031980 Baltic Sea Guillemot Egg 0.159 ug kg-1 lw 47 Sellström et al., 20031981 Baltic Sea Guillemot Egg 0.178 ug kg-1 lw 34 Sellström et al., 20031982 Baltic Sea Guillemot Egg 0.119 ug kg-1 lw 45 Sellström et al., 20031983 Baltic Sea Guillemot Egg 0.124 ug kg-1 lw 48 Sellström et al., 20031985 Baltic Sea Guillemot Egg 0.13 ug kg-1 lw 76 Sellström et al., 20031986 Baltic Sea Guillemot Egg 0.125 ug kg-1 lw 120 Sellström et al., 20031987 Baltic Sea Guillemot Egg 0.125 ug kg-1 lw 83 Sellström et al., 20031988 Baltic Sea Guillemot Egg 0.128 ug kg-1 lw 98 Sellström et al., 20031989 Baltic Sea Guillemot Egg 0.123 ug kg-1 lw 130 Sellström et al., 20031990 Baltic Sea Guillemot Egg 0.128 ug kg-1 lw 110 Sellström et al., 20031992 Baltic Sea Guillemot Egg 0.122 ug kg-1 lw 81 Sellström et al., 20031992 Baltic Sea Guillemot Egg 0.109 ug kg-1 lw 97 Sellström et al., 20031993 Baltic Sea Guillemot Egg 0.111 ug kg-1 lw 150 Sellström et al., 20031994 Baltic Sea Guillemot Egg 0.106 ug kg-1 lw 130 Sellström et al., 20031995 Baltic Sea Guillemot Egg 0.113 ug kg-1 lw 170 Sellström et al., 20031996 Baltic Sea Guillemot Egg 0.112 ug kg-1 lw 130 Sellström et al., 20031997 Baltic Sea Guillemot Egg 0.122 ug kg-1 lw 110 Sellström et al., 20031998 Baltic Sea Guillemot Egg 0.11 ug kg-1 lw 110 Sellström et al., 20031999 Baltic Sea Guillemot Egg 0.124 ug kg-1 lw 130 Sellström et al., 20032000 Baltic Sea Guillemot Egg 0.117 ug kg-1 lw 140 Sellström et al., 20032001 Baltic Sea Guillemot Egg 0.121 ug kg-1 lw 140 Sellström et al., 20032000-2002 Baltic Sea Guillemot egg ug kg-1 lw 138 Lundstedt-Enkel et al.,2006b2000-2002 Baltic Sea Guillemot muscle 0.029 ug kg-1 lw 64.7 Lundstedt-Enkel et al.,2006b2000 Baltic Sea Guillemot muscle ug kg-1 lw 66.7 Lundstedt-Enkel et al., 20052002 Svalbard Guillemot 0.28 ug kg-1 lw 35.4 Murvoll et al., 20072002 Svalbard Eider 0.21 ug kg-1 lw 6.23 Murvoll et al., 20071980 Sweden Human Milk ug kg-1 lw 0.11 Fangstrom et al. 20081984 Sweden Human Milk ug kg-1 lw 0.09 Fangstrom et al. 20081988 Sweden Human Milk ug kg-1 lw 0.22 Fangstrom et al. 20081990 Sweden Human Milk ug kg-1 lw 0.21 Fangstrom et al. 20081992 Sweden Human Milk ug kg-1 lw 0.31 Fangstrom et al. 20081994 Sweden Human Milk ug kg-1 lw 0.41 Fangstrom et al. 2008185Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1995 Sweden Human Milk ug kg-1 lw 0.51 Fangstrom et al. 20081996 Sweden Human Milk ug kg-1 lw 0.32 Fangstrom et al. 20081997 Sweden Human Milk ug kg-1 lw 0.29 Fangstrom et al. 20081999 Sweden Human Milk ug kg-1 lw 0.39 Fangstrom et al. 20082001 Sweden Human Milk ug kg-1 lw 0.53 Fangstrom et al. 20082002 Sweden Human Milk ug kg-1 lw 0.64 Fangstrom et al. 20082003 Sweden Human Milk ug kg-1 lw 0.05 Fangstrom et al. 20082004 Sweden Human Milk ug kg-1 lw 0.37 Fangstrom et al. 20081980 Sweden Human Milk ug kg-1 lw 0.06 Fangstrom et al. 20081984 Sweden Human Milk ug kg-1 lw 0.10 Fangstrom et al. 20081988 Sweden Human Milk ug kg-1 lw 0.26 Fangstrom et al. 20081990 Sweden Human Milk ug kg-1 lw 0.19 Fangstrom et al. 20081992 Sweden Human Milk ug kg-1 lw 0.26 Fangstrom et al. 20081994 Sweden Human Milk ug kg-1 lw 0.35 Fangstrom et al. 20081995 Sweden Human Milk ug kg-1 lw 0.52 Fangstrom et al. 20081996 Sweden Human Milk ug kg-1 lw 0.35 Fangstrom et al. 20081997 Sweden Human Milk ug kg-1 lw 0.30 Fangstrom et al. 20081999 Sweden Human Milk ug kg-1 lw 0.35 Fangstrom et al. 20082001 Sweden Human Milk ug kg-1 lw 0.55 Fangstrom et al. 20082002 Sweden Human Milk ug kg-1 lw 0.53 Fangstrom et al. 20082003 Sweden Human Milk ug kg-1 lw 0.49 Fangstrom et al. 20082004 Sweden Human Milk ug kg-1 lw 0.40 Fangstrom et al. 2008~2007 Czech Republic Human fat tissue 0.83 ug kg-1 lw 1.2 Pulkrabová et al., 2009~2007 Spain Human Milk ug kg-1 lw 27 Eljarrat et al. 20092001 Sweden Human Milk ug kg-1 lw 0.5 Cited in Eljarrat et al. 20092002-2003 Spain Human Milk ug kg-1 lw 0.5 Cited in Eljarrat et al. 20092002-2003 Canada Human Milk ug kg-1 lw 3.8 Cited in Eljarrat et al. 20092005 France Human Milk ug kg-1 lw 3 Antignac et al. 2008~2007 Norway Human Blood ug kg-1 lw 4.1 Thomsen et al., 2008~2007 Norway Human Blood ug kg-1 lw 2.6 Thomsen et al., 20081999 Sweden Bovine meat Unknown 0.87 ug kg-1 lw 1.4 Remberger et al. 20041999 Sweden Pork meat Unknown 0.82 ug kg-1 lw 1.0 Remberger et al. 20041999 Sweden Lamb Unknown 0.81 ug kg-1 lw 4.1 Remberger et al. 20041999 Sweden Chicken Unknown 0.63 ug kg-1 lw 6.5 Remberger et al. 20041999 Sweden Egg Yolk Unknown 0.25 ug kg-1 lw 9.4 Remberger et al. 20041999 Sweden Milk Unknown 0.038 ug kg-1 lw 1.8 Remberger et al. 20041999 Sweden Fish (mixed) Unknown 0.06 ug kg-1 lw 48.0 Remberger et al. 2004186Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2004-2007 Norway Farmed trout Unknown 0.195 ug kg-1 lw 3.6 Knutsen et al., 20082004-2007 Norway Herring Unknown 0.114 ug kg-1 lw 3.9 Knutsen et al., 20082004-2007 Norway Sardines Unknown 0.291 ug kg-1 lw 2.7 Knutsen et al., 20082004-2007 Norway Farmed salmon Unknown 0.148 ug kg-1 lw 1.8 Knutsen et al., 20082004-2007 Norway Mackerel Unknown 0.257 ug kg-1 lw 3.1 Knutsen et al., 20082004-2007 Norway Smoked salmon Unknown 0.111 ug kg-1 lw 2.7 Knutsen et al., 20082004-2007 Norway Shrimp Unknown 0.012 ug kg-1 lw 1.1 Knutsen et al., 20082004-2007 Norway Crab Unknown 0.092 ug kg-1 lw 3.0 Knutsen et al., 20082004-2007 Norway Bovine meat Unknown 0.178 ug kg-1 lw 0.3 Knutsen et al., 20082004-2007 Norway Pork meat Unknown 0.248 ug kg-1 lw 0.1 Knutsen et al., 20082004-2007 Norway Dairy products Unknown 0.521 ug kg-1 lw 0.3 Knutsen et al., 20082004-2007 Norway Hen egg Unknown 0.098 ug kg-1 lw 1.6 Knutsen et al., 20082004-2007 Norway Seagull egg Unknown 0.094 ug kg-1 lw 70.4 Knutsen et al., 20082004-2007 Norway Vegetable oil Unknown 1 ug kg-1 lw 0.4 Knutsen et al., 20082004-2007 Norway Banana Unknown 0.001 ug kg-1 lw 0.04 Knutsen et al., 20082001-2003 Czech Republic barbel (fish) muscle 0.034ug kg-1 lw 68 Pulkrabová et al., 20072001-2003 Czech Republic barbel (fish) muscle 0.048ug kg-1 lw 174 Pulkrabová et al., 20072001-2003 Czech Republic barbel (fish) muscle 0.04ug kg-1 lw 389 Pulkrabová et al., 20072001-2003 Czech Republic barbel (fish) muscle 0.036ug kg-1 lw 101 Pulkrabová et al., 20072001-2003 Czech Republic barbel (fish) muscle 0.024ug kg-1 lw 75 Pulkrabová et al., 20072002 Spain barbel (fish) muscle ug kg-1 ww 309 Eljarrat et al., 20042002 Spain barbel (fish) muscle ug kg-1 ww 750.4 Eljarrat et al., 20042002 Spain barbel (fish) liver ug kg-1 ww 432.3 Eljarrat et al., 20042002 Spain barbel (fish) liver ug kg-1 ww 554.4 Eljarrat et al., 20042002 Spain barbel (fish) liver ug kg-1 ww 624.8 Eljarrat et al., 20042002 Spain barbel (fish) liver ug kg-1 ww 483.9 Eljarrat et al., 20042002 Spain barbel (fish) muscle ug kg-1 ww 89.5 Eljarrat et al., 20042002 Spain barbel (fish) muscle ug kg-1 ww 529.7 Eljarrat et al., 20042002 Spain barbel (fish) muscle ug kg-1 ww 0.02 Eljarrat et al., 20042002 Spain barbel (fish) liver ug kg-1 ww 0.02 Eljarrat et al., 20042002 Spain barbel (fish) liver ug kg-1 ww 0.02 Eljarrat et al., 20042002 Spain barbel (fish) muscle ug kg-1 ww 0.02 Eljarrat et al., 20042002 Spain bleak (fish) whole ug kg-1ww 0.02 Eljarrat et al., 20052002 Spain bleak (fish) whole ug kg-1ww 0.02 Eljarrat et al., 20052002 Spain bleak (fish) whole ug kg-1ww 1501 Eljarrat et al., 20052002 Spain bleak (fish) whole ug kg-1ww 172 Eljarrat et al., 20052002 Spain bleak (fish) whole ug kg-1ww 760 Eljarrat et al., 2005187Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference2002 Spain bleak (fish) whole ug kg-1ww 381 Eljarrat et al., 20052001-2003 Czech Republic bream (fish) muscle 0.041 ug kg-1 lw 2 Pulkrabová et al., 20072001-2003 Czech Republic bream (fish) muscle 0.023 ug kg-1 lw 34 Pulkrabová et al., 20072001-2003 Czech Republic bream (fish) muscle 0.035 ug kg-1 lw 39 Pulkrabová et al., 20072001-2003 Czech Republic bream (fish) muscle 0.046 ug kg-1 lw 161 Pulkrabová et al., 20072001-2003 Czech Republic bream (fish) muscle 0.021 ug kg-1 lw 108 Pulkrabová et al., 20072001-2003 Czech Republic bream (fish) muscle 0.021 ug kg-1 lw 328 Pulkrabová et al., 20072005 Czech Republic bream (fish) muscle 0.021ug kg-1 lw 576.2 Hajslova et al., 20072001-2003 Czech Republic chub (fish) muscle 0.027ug kg-1 lw 58 Pulkrabová et al., 20072001-2003 Czech Republic chub (fish) muscle 0.021ug kg-1 lw 64 Pulkrabová et al., 20072001-2003 Czech Republic chub (fish) muscle 0.032ug kg-1 lw 115 Pulkrabová et al., 20072001-2003 Czech Republic chub (fish) muscle 0.019ug kg-1 lw 215 Pulkrabová et al., 20072001-2003 Czech Republic chub (fish) muscle 0.021ug kg-1 lw 183 Pulkrabová et al., 20072001-2003 Czech Republic chub (fish) muscle 0.03ug kg-1 lw 46 Pulkrabová et al., 20072005 Czech Republic chub (fish) muscle 0.018 ugkg-1 lw 116.7 Hajslova et al., 20072001-2003 Czech Republic perch (fish) muscle 0.007ug kg-1 lw 14 Pulkrabová et al., 20072001-2003 Czech Republic perch (fish) muscle 0.01ug kg-1 lw 42 Pulkrabová et al., 20072001-2003 Czech Republic perch (fish) muscle 0.006ug kg-1 lw 82 Pulkrabová et al., 20072001-2003 Czech Republic perch (fish) muscle 0.008ug kg-1 lw 114 Pulkrabová et al., 20072001-2003 Czech Republic perch (fish) muscle 0.007ug kg-1 lw 123 Pulkrabová et al., 20072001-2003 Czech Republic perch (fish) muscle 0.009ug kg-1 lw 177 Pulkrabová et al., 20072005 Czech Republic perch (fish) muscle 0.017 ugkg-1 lw 158.8 Hajslova et al., 20072001-2003 Czech Republic trout (fish) muscleug kg-1 lw 6 Pulkrabová et al., 20072001-2003 Czech Republic trout (fish) muscleug kg-1 lw 5 Pulkrabová et al., 20072001-2003 Czech Republic trout (fish) muscleug kg-1 lw 4 Pulkrabová et al., 20072004 Svalbard glaucous gulls plasma 0.0154 ug kg-1 ww 0.34 (Verrault et al. 2005)2004 Svalbard glaucous gulls plasma 0.0148 ug kg-1 ww 0.32 Verrault et al. (2005)2002 Svalbard polar bears plasma 0.0101 ug kg-1ww Verrault et al. (2005)2002, 2004 Bear Island glaucous gullsplasma 0.0143 ug kg-1 ww 0.51 Verrault et al. (2005)2002, 2004 Bear Island glaucous gullsplasma 0.0138 ug kg-1 ww 0.7 Verrault et al. (2005)2002, 2004 Bear Island gull eggs egg fat 0.0961 ug kg-1 ww 13.3 Verrault et al. (2005)1983 Hornoya Herring gull egg 0.11 ug kg-1 ww 1.8 1.8 < DL < DL Knudsen et al. (2005)1993 Hornoya Herring gull egg 0.102 ug kg-1 ww 1.9 1.9 < DL < DL Knudsen et al. (2005)2003 Hornoya Herring gull egg 0.097 ug kg-1 ww 9.3 9.3 < DL < DL Knudsen et al. (2005)1983 Hornoya A. puffin egg 0.126 ug kg-1 ww 1.9 1.9 < DL < DL Knudsen et al. (2005)1993 Hornoya A. puffin egg 0.117 ug kg-1 ww 4.9 4.9 < DL < DL Knudsen et al. (2005)2003 Hornoya A. puffin egg 0.137 ug kg-1 ww 10.1 10.1 < DL < DL Knudsen et al. (2005)188Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1983 Hornoya Kittiwake egg 0.098 ug kg-1 ww 1.7 1.7 < DL < DL Knudsen et al. (2005)1993 Hornoya Kittiwake egg 0.096 ug kg-1 ww 3 3 < DL < DL Knudsen et al. (2005)2003 Hornoya Kittiwake egg 0.095 ug kg-1 ww 10.8 10.8 < DL < DL Knudsen et al. (2005)1983 Rost/Hekkingen Herring gull egg 0.095 ug kg-1 ww 1.4 1.4 < DL < DL Knudsen et al. (2005)1993 Rost/Hekkingen Herring gull egg 0.083 ug kg-1 ww 3.7 3.7 < DL < DL Knudsen et al. (2005)2003 Rost/Hekkingen Herring gull egg 0.104 ug kg-1 ww 11.6 11.6 < DL < DL Knudsen et al. (2005)1983 Rost/Hekkingen A. puffin egg 0.122 ug kg-1 ww 1.1 1.1 < DL < DL Knudsen et al. (2005)1993 Rost/Hekkingen A. puffin egg 0.122 ug kg-1 ww 2.2 2.2 < DL < DL Knudsen et al. (2005)2003 Rost/Hekkingen A. puffin egg 0.137 ug kg-1 ww 6.1 6.1 < DL < DL Knudsen et al. (2005)1983 Rost/Hekkingen Kittiwake egg 0.071 ug kg-1 ww 2.9 2.9 < DL < DL Knudsen et al. (2005)1993 Rost/Hekkingen Kittiwake egg 0.094 ug kg-1 ww 7.1 7.1 < DL < DL Knudsen et al. (2005)2003 Rost/Hekkingen Kittiwake egg 0.099 ug kg-1 ww 17.3 17.3 < DL < DL Knudsen et al. (2005)1997 Bear Island glaucous gull egg 0.09 ug kg-1 ww2.3 2.3 < DL < DL Knudsen et al. (2005)2002 Bear Island glaucous gull egg 0.085 ug kg-1ww 12 12 < DL < DL Knudsen et al. (2005)1986 South Greenland peregrine falcon egg 0.07 ug kg-1 lw < 0.8 Vorkamp et al. (2005)1987 South Greenland peregrine falcon egg 0.094 ug kg-1 lw 34 Vorkamp et al. (2005)1988 South Greenland peregrine falcon egg 0.189 ug kg-1 lw Vorkamp et al. (2005)1988 South Greenland peregrine falcon egg 0.224 ug kg-1 lw Vorkamp et al. (2005)1988 South Greenland peregrine falcon egg 0.203 ug kg-1 lw Vorkamp et al. (2005)1989 South Greenland peregrine falcon egg 0.107 ug kg-1 lw 7.8 Vorkamp et al. (2005)1990 South Greenland peregrine falcon egg 0.063 ug kg-1 lw 4.1 Vorkamp et al. (2005)1990 South Greenland peregrine falcon egg 0.073 ug kg-1 lw 230 Vorkamp et al. (2005)1990 South Greenland peregrine falcon egg 0.063 ug kg-1 lw 9 Vorkamp et al. (2005)1991 South Greenland peregrine falcon egg 0.075 ug kg-1 lw < 1.1 Vorkamp et al. (2005)1991 South Greenland peregrine falcon egg 0.068 ug kg-1 lw 10 Vorkamp et al. (2005)1991 South Greenland peregrine falcon egg 0.052 ug kg-1 lw 22 Vorkamp et al. (2005)1992 South Greenland peregrine falcon egg 0.093 ug kg-1 lw 1.2 Vorkamp et al. (2005)1992 South Greenland peregrine falcon egg 0.048 ug kg-1 lw < 1.1 Vorkamp et al. (2005)1992 South Greenland peregrine falcon egg 0.068 ug kg-1 lw 26 Vorkamp et al. (2005)1992 South Greenland peregrine falcon egg 0.056 ug kg-1 lw 32 Vorkamp et al. (2005)1994 South Greenland peregrine falcon egg 0.075 ug kg-1 lw 2.4 Vorkamp et al. (2005)1994 South Greenland peregrine falcon egg 0.036 ug kg-1 lw 2.1 Vorkamp et al. (2005)1994 South Greenland peregrine falcon egg 0.067 ug kg-1 lw 77 Vorkamp et al. (2005)1994 South Greenland peregrine falcon egg 0.079 ug kg-1 lw 67 Vorkamp et al. (2005)1994 South Greenland peregrine falcon egg 0.057 ug kg-1 lw < 0.1 Vorkamp et al. (2005)1995 South Greenland peregrine falcon egg 0.065 ug kg-1 lw < 0.8 Vorkamp et al. (2005)1995 South Greenland peregrine falcon egg 0.063 ug kg-1 lw 3.2 Vorkamp et al. (2005)189Year Location Organism Tissue fLIPIDUnits Total HBCDα-HBCDβ-HBCDγ-HBCD Reference1998 South Greenland peregrine falcon egg 0.058 ug kg-1 lw 2.6 Vorkamp et al. (2005)1998 South Greenland peregrine falcon egg 0.076 ug kg-1 lw < 0.8 Vorkamp et al. (2005)1999 South Greenland peregrine falcon egg 0.039 ug kg-1 lw < 0.9 Vorkamp et al. (2005)1999 South Greenland peregrine falcon egg 0.06 ug kg-1 lw < 1.2 Vorkamp et al. (2005)1999 South Greenland peregrine falcon egg 0.04 ug kg-1 lw < 0.1 Vorkamp et al. (2005)2000 South Greenland peregrine falcon egg 0.036 ug kg-1 lw 14 Vorkamp et al. (2005)2000 South Greenland peregrine falcon egg 0.028 ug kg-1 lw Vorkamp et al. (2005)2000 South Greenland peregrine falcon egg 0.039 ug kg-1 lw 2.7 Vorkamp et al. (2005)2000 South Greenland peregrine falcon egg 0.046 ug kg-1 lw < 0.1 Vorkamp et al. (2005)2001 South Greenland peregrine falcon egg 0.065 ug kg-1 lw < 0.8 Vorkamp et al. (2005)2002 South Greenland peregrine falcon egg 0.069 ug kg-1 lw 1.6 Vorkamp et al. (2005)2002 South Greenland peregrine falcon egg 0.053 ug kg-1 lw < 1.0 Vorkamp et al. (2005)2003 South Greenland peregrine falcon egg 0.07 ug kg-1 lw 27 Vorkamp et al. (2005)2003 South Greenland peregrine falcon egg 0.06 ug kg-1 lw < 0.1 Vorkamp et al. (2005)2003 Svalbard calanoid copepods whole body 0.0996 ug kg-1 lw NA Sormo et al. (2006)2003 Svalbard Thysanoessainermiswhole body 0.1242 ug kg-1 lw NA Sormo et al. (2006)2003 Svalbard Themisto libellula whole body 0.0901ug kg-1 lw NA Sormo et al. (2006)2003 Svalbard Gammarus wilkitzkii whole body 0.0385 ug kg-1 lw ND Sormo et al. (2006)2003 Svalbard polar cod whole body 0.1201 ug kg-1lw 1.89 Sormo et al. (2006)2003 Svalbard ringed seal blubber 0.8923 ug kg-1lw 19.56 Sormo et al. (2006)2003 Svalbard polar bears adiposetissue0.8385 ug kg-1 lw 11.53 Sormo et al. (2006)2002 Svalbard polar bears adiposetissue0.91 ug kg-1 lw 44.4 Muir et al (2006)1999 - 2001 East Greenland polar bears adiposetissue0.9 ug kg-1 lw 44.5 Muir et al (2006)1994 - 2002 Bering-ChukchiSeapolar bears adiposetissue0.6 ug kg-1 lw 0.4 Muir et al (2006)2002 eastern Arctic zooplankton whole body 0.0369 ug kg-1 lw 1.1 0.6 0.5 Tomy et al. (2008)2000 - 2001 eastern Arctic shrimp whole body 0.075 ug kg-1 lw 1.9 1.4 0.5 Tomy et al. (2008)2002 eastern Arctic clam whole body 0.0101 ug kg-1 lw 1.4 0.2 1.2 Tomy et al. (2008)2000 - 2001 eastern Arctic red fish whole body 0.0181 ug kg-1 lw 2 1.4 0.6 Tomy et al. (2008)2000 - 2001 eastern Arctic polar cod whole body 0.1419 ugkg-1 lw 0.42 0.4 0.02 Tomy et al. (2008)1998 eastern Arctic walrus blubber 0.8879 ug kg-1 lw 0.6 0.2 0.4 Tomy et al. (2008)1996 eastern Arctic beluga blubber 0.9232 ug kg-1 lw 1.4 1.2 0.2 Tomy et al. (2008)2000 eastern Arctic narwhal blubber 0.8296 ug kg-1 lw 3.4 2.9 0.5 Tomy et al. (2008)Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 190 9.4 M ASS B ALANCE M ODELS

9.4.1 General Background

All models, including the mass balance models used in the present study, are necessarily based on simplifying assumptions and cannot be expected to replicate the inherent complexity of actual systems. Model deve lopment, evaluation (sometimes termed validation), refinement, and application are th erefore key elements in the evolutionary scientific process towards improved understand ing. Models cannot provide “truths” or make decisions; however, they can be used to inform decisions, particularly if they are designed for a specific purpose. It must be recognized that measurements are also limited for regulatory decision-making because they ar e usually incomplete and often variable and uncertain. A strategy for improving environmental and human health assessments is to combine both models and measurements (McKone et al. 2007). Further detailed discussions on models, particularly for ch emical regulatory purposes, are provided elsewhere (Mackay 2001; Webster et al. 2005). Mass balance modelling concepts for organic chemical evaluations are first briefly described followed by brief overviews for

the mass balance models used in the present study.

Mass balance multi-compartment models use mathematical expressions to combine chemical transport processes (e.g., diffu sion in water and advection by wind) and chemical transformation processes (e.g., mi crobial degradation, hydrolysis) with thermodynamic principles to quantify chemical fate, transport and behavior. The models are grounded in laws for the conservation of mass and energy. The fugacity concept has proven particularly useful in the developm ent of environmental mass balance models (Mackay 2001). In particular, the fugacity a pproach facilitates development of the mass balance equations and the results are more readily interpreted. For example, since fugacity is an equi librium criterion anal ogous to chemical poten tial, the equilibrium status of the system can be readily assessed.

In a simplistic sense the biosphere is co mprised of a multitude of interconnected compartments and phases of various com position and size. Examples include the atmosphere, water bodies, bottom sediment, soil, biological organisms, tissues and cells. These compartments and phases can be viewed as a series of “boxes” in which chemicals can exist either in a fixed state, pass thr ough or be in the process of transformation or

reaction. In the enviro nment most of these compartments are heterogeneous in nature (e.g., sediments and soils); however, some are relatively well-mixed phases (e.g., ponds). Properties such as volume, density and co mposition of each comp artment in the mass balance equation must be defi ned. A single phase (e.g. air) can be modelled as a single

box or as a number of boxes t hus providing greater spatial resolution for the total air compartment. Representing heterogeneous conc entrations in mass balance models can be difficult resulting in complex and parameter-intensive models; therefore, a typical simplifying assumption for model development is that concentrations in a particular box are homogeneously distributed. This is often re ferred to as the Continuous Stirred Tank Reactor (CSTR) assumption. In certain case s, particularly for substances that areArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 191 transformed rapidly, the appl ication of this assumption re quires further consideration (Warren et al. 2009).

Spatially, mass balance equations link the transport of chemical into and out of the various compartments (e.g. from air to water or water into a fish) and quantify processes within these compartments (e.g. reaction or formation). The model equations can be

derived and solved as a function of time or at steady state. Steady state reflects a situation

in which the chemical concentrations no l onger change as a function of time, i.e., chemical input quantities equal chemical “l oss” or output quantities. The steady state assumption is often used because it simplif ies the mathematical and computational requirements to solve the equations. It is r ecognized that complex environmental systems are never truly at steady state; however, in certain cases “pseudo-steady state” conditions

can be observed. Commercial chemicals rel eased to the environment continuously or semi-continuously over years of production and use may approximate “pseudo-steady state” conditions, at least in regi ons where they are discharged.

Models can be developed, parameterized and applied to specific conditions with a view to describing an actual environment or situatio n. Alternatively, models can also be developed and applied in an evaluative sense in which the system is parameterized to be representative of general environmental conditions.

9.4.2 Supplementary Data for Globo-POP Modelling

Comparison of eACP 10 values calculate for t-, α -, β -, γ -HBCD where t-HBCD is based on the physical-chemical properties esti mated for the technical mixture

0.001% 0.010% 0.100% 1.000%Min HLDefaultMax HLReducedE to Air*Min HLDefaultMax HLMin HLDefaultMax HLMin HLDefaultMax HL

t-HBCD a-HBCD b-HBCD g-HBCD

Figure 9.1. Comparison of eACP 10 values for t-, α -, β -, γ -HBCDArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 192 9.4.3 Supplementary Data for BETR-World Modelling

Table 9.9. Total Estimated Global Market Demand for HBCD in 2001 (AMAP, 2005).

Region HBCD % total emission

Americas 2800 16.77%

Europe 9500 56.89%

Asia 3900 23.35%

Rest of World 500 2.99%

Total 16700

Table 9.10. Emissions to BETR -World Regions (kg/year).

Region emission kg/year % total

emission

1 NA - Continental Arctic 79 0.5%

2 NA - Canadian Provinces 513 3.27%

3 NA - Continental United States 2043 13%

4 NA - SA - Carribean 79 0.5%

5 SA - Centro 79 0.5%

6 SA - Arg. Chile 79 0.5%

7 EU - Europe 8942 56.89%

8 AS - Middle East 39 0.25%

9 AS - Russia 1313 8.35%

10 AS - Orient 2358 15%

11 OC - Oceania 38 0.24%

12 AF - Northern Africa 79 0.5%

13 AF - Southern Africa 79 0.5%

Total Global 15718Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 193 Table 9.11. Transport pathways of HBCD to the Arctic.

UA-UA LA-LA FW-MW MW-MW

1 NA - Continental Arctic 8.61% 0.88% 0.00% 0.03%

2 NA - Canadian

Provinces 2.50% 0.78% 0.00% 1.99%

7 EU - Europe 17.89% 3.80% 0.06% 15.66%

9 AS - Russia 30.51% 6.83% 0.13% 0.90%

17 North North Atlantic 0.34% 0.16% 0.00% 8.93%

Total 59.85% 12.45% 0.20% 27.50%

9.4.4 Supplementary Data for RAIDAR Modelling

For the application of RAIDAR in the presen t assessment there were some minor dietary preference changes to the default values. These changes were made so that the model predictions are more reflective of the region al environments from which the monitoring data were obtained. Feeding preferences fo r the avian piscivore (scavenger) were: 70% lower trophic level fish, 20% upper trophic level fish and 10% terrestr ial invertebrates. Feeding preferences for the ma rine (aquatic) mammal were: 30% lower trophic level fish, 50% upper trophic level fish and 20% benthic invertebrates. Feeding preferences for the adult male human were changed from 4% upp er trophic level fish to 2% upper trophic level fish and 2% lower trophic level fish. Th e remaining default human diet preferences were unchanged (Arnot and Mackay 2008).

9.5 D ETAILED C OMMENTS ON HBCD T OXICITY D ATA AND P REVIOUS PBT

A SSESSMENTS

9.5.1 Water Solubility, Bioavailability and Cosolvents

There is some discussion in the European Commission (2008) draft report about water solubility issues in general as well as the di fferences in water solubi lity and perhaps other characteristics of the three main HBCD isomers ( α -, β -, and γ -). As discussed in Section 2.3.2, FAVs for the water solubility of the α -, β -, and γ - isomers are 41, 15, and 2.5 μg·L 1 , respectively, giving a water solubility estimate of 58 μg·L -1 for t-HBCD (the sum of all isomers). The γ - isomer makes up about 80% of t-HBCD . Thus, when the solubility limit for all components of the mixture is considered (i.e. 58 μg·L -1 ), there are undissolved precipitates of the β - and γ - isomers in the water column as they are substantially above their individual water solubility limits. On ly by keeping the t-HBCD concentration near the water solubility limit for the γ - isomer, can it be assumed that all of the HBCD present is dissolved in the water. Thus, a water solubility of about 2 or 3 μg·L -1 represents
a concentration at which all isomers of HB CD are truly dissolved and there are no precipates expected. Thus, th ere are unresolved and unquantif ied bioavailability issues for all testing data above the solubility of the γ -HBCD isomer. This issue has not been addressed in any of the previous re views of the toxicity test data.

There are also a number of problems in terpreting toxicity data for hydrophobic

substances when cosolvents are used. Toxic ity test design is based on a simple modelArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 194 approach that allows the external exposure conc entration to be used as a surrogate for the usually unknown amount of substance that en ters the body of the organism, a portion of which ultimately reaches the site(s) of toxic action and initiates the adverse effect being examined. A key requirement for using such surrogate dose estimates in toxicity comparisons between various test results for various substances is that the toxicity modifying factors be the same in all cases (Mackay et al. 2001) . Bioavailability of the test substance in the water column is a key toxic ity modifying factor and any change in it, such as would be caused by the use of a cosolvent, changes the conditions and renders such test data not directly comparable with data obtained without coso lvent. If the nature and magnitude of any bioavailability change has been quantified, this would allow changes to be understood and an appropria te correction made. Unfortunately, such information does not appear to have been collected in any of the key HBCD studies.

Cosolvents affect the bioavail ability of a substance in a queous exposure media; however, to what degree is speculative w ithout a greater quanti tative understanding of the influence of the cosolvent in comparison to “pure” water phase exposures. The success of cosolvents in increasing effective bioavailabli ty is well established. In a variety of tests with hydrophobic chemicals no effects were repo rted unless the exposure to the substance is carried out with a cosolvent. This qualitative change is usually all that is reported, and contrary to guidance from the OECD (OECD 2000), key quantitative parameters are not reported. These oversights make it difficult to compare aqueous exposure measurements to other substances (relative toxicity) and ag ainst screening criteria that have not been developed to account for the influe nce of cosolvents on test data.

Bioavailability can also be changed by coso lvent in another way concurrent with the water solubility. Water solubility is determ ined in pure water, without dissolved or suspended organic material. Both toxicity a nd bioconcentration tests are carried out in water with living organisms which continuously release a variety of organic material to

the water column. Food and feces also contri bute to organic matter in the water column and certain exposure media used in algal and some invertebrate/bacte rial testing contain other added substances that may be probl ematic. For very hydrophobic substances some portion of the total water column concentrat ion will not be in solution because it partitions into these organic phases. Thus, the actual bioavailable fraction of chemical in the water is also a function of the organic matter in the water column. Substances in the dissolved/suspended organic phase are likely to be less bioavailable to normal respiratory uptake but may, in fact, be more bioavailab le to some organisms in some cases via alternative uptake routes - e.g., diet. Depe nding on the amount and effectiveness of the cosolvent, this reduction in bioavailability may be c ounteracted or overwhelmed by increased “apparent water solubility” noted earlier. Both of these effects on bioavailability can occur at concentrations above and somewhat below the pure-water solubility limit. When water column concen trations of HBCD are above about 2 to 3 μg·L -1 , the presence of undissolved precipitates of γ -HBCD (and β -HBCD isomer above about 15 μg·L -1 ), and possible interactions with solvents further complicate the problem.

In addition to the issue of the effect on th e amount of substance taken up into the exposed organisms which affect amounts accumulated for either BCF or toxicity testing, it alsoArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 195 means that the total water column concentr ation is a confounded me asure of the true exposure concentration experienced by the organi sms. All in all, inte rpretation of aquatic testing results for very hydrophobic substances is very problematic and the simple process and general assumptions generally ac cepted for more water soluble substances should not be blindly adopted for candidate PBT substances. Since, essentially the same issues are applicable to evaluation of candi date POP substances, if the European PBT critieria or any scr eening criteria or ev aluation schemes for St ockholm Convention and UN-ECE candidate POPs are to be employed in a scientifically-s ound and fair decisionmaking process, a more thorough and detaile d technical analysis and evaluation is needed. There is a substantial published litera ture on the effects of cosolvents produced by pharmaceutical researchers that should be brought to bear on this problem (e.g. (Jouyban 2008).

In summary, a variety of c oncerns regarding water solub ility, use of co-solvent and bioavailablity render all availa ble aquatic toxicity data for HBCD problematic since these issues confound accurate evaluation of the testing data. Concerns about the uncertainty that are introduced by these issues brings into question the reliability and validity for use of such compromised data in PBT, or fo r that matter POP, classification decisions.

9.5.2 Aquatic Toxicity Testing with Difficult Substances and Mixtures

Mixture toxicity remains a substantial challenge in toxicity, both in terms of a theoretical foundation and in practical application (McCarty and Borgert 2006b; McCarty and Borgert 2006a). In addition to the broad ge neral challenge, there is another pertinent point about the use of cosolven ts in toxicity te sting with mixtures. The current OECD (2000) guidance document on test ing of difficult substances and mixtures specifically advises against the use of cosolvents in cases such as encountered with HBCD (mixture of isomers).

"Water-miscible solvents provide a vehicle in which some poorly soluble substances can be dissolved to produce a stock solution which is more amenable to adding to, and mixing with, the test media. In particular, solvents could be helpful for hydrolytically unstable and highly viscous substances. However, because of the poten tial for interaction with the test substance resulting in an alte red response in the test, their use should be

restricted to situations where no other acceptable method of me dia preparation is available. If solvents are used, their effect s on the test results, if any, need to be determined. It should be emphasized that solv ents are not appropriate for mixtures where the use of the solvent can give preferential dissolution of one or more components and thereby affect the toxicity. This could also be true where the technical grade of a substance is tested and a toxic impurity disso lves preferentially." (OECD, 2000, p. 29)

Similarly, the use of generator systems su ch as generator columns is specifically contraindicated for toxicity testing of diffi cult substances and mixtures such as HBCD.

"Generator systems are not considered appr opriate for substances containing components or impurities which differ in their water so lubility. Differences in water solubility willArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 196 result in selective depletion of the more water soluble co mponents from the column or

disk matrix and their relativ e concentration in the water phase." (OECD, 2000, p. 23)

In summary, based on internati onally recognized toxicity test ing guidance it is clear that that most, if not all, of the aquatic toxi city data currently available for HBCD are unreliable due to lack of qua ntitative information about te st-specific water solubility information, especially on the influence of co solvents used with a difficult substance that

is a mixture of isomers with diffe rent physical-chemical properties.

9.5.3 Critical Evaluation of PBT Assessments

Section 3.4.6 in the European Commission RAR (2008) entitled PBT-assessment outlines the screening process used in identifying substances with PBT characteristics and applies that policy to HBCD information. This PBT evaluation process is presented in detail in Section 4.4 Part II of the European Techni cal Guidance Manual (EC 2003). There are two general issues that must be raised with respect to the employed guidance: "inherent" properties and the use of NOEC data. First th e issue of “inherent pr operties is discussed followed by the application of NO EC data without LOEC data.

Inherent Properties There is an emphasis on screening inform ation for PBT evaluation based on some relatively simplistic decision criteria. Although screening is a necessary first step, definitive categorization of a substance as a PBT require s a more thorough evaluation. However, the European Commission evaluative process indicates that screening may be

sufficient to allow scientif ically-sound PBT categorizatio n appears to be based on a

fundamental scientific error. There is repeated reference to "inherent" properties in the categories of persistence, bioaccumulation, and toxicity that appear to be the basis of enabling and justifying a relativ ely simple screening process as scientifically sound. The

definition of the word "inherent" is instructive in this matter. There are various dictionary definitions with the following composite phrase providing a good perspective: an essential constituent or characteristic or a permanent and inseparable element in someone or something.

The property of toxicity is definitely not i nherent to a substance. Toxicity requires an interaction - i.e., an exposure - with a living organism to occur, a nd then a given adverse effect still requires a sufficient degree of exposure (related to the amount accumulated and duration) for the effect to occur. Thus, there are many exposure scenarios for "inherently toxic" chemicals where no adverse effects will occur. Therefore, it is clear that the toxicity is not "inherent" to the substance. A more detailed review of this issue as it relates to PBT, derived from fundamental principles of ch emistry, appears in Mackay et
al. (Mackay et al. 2001). At the very leas t the term "inherent" is redundant as all substances are potentially toxic and the key de terminant of exposure is clearly stated in a founding toxicological principle:

"What is there that is not poison? All things are poison a nd nothing is without poison. Solely the dose determines that a thing is not a poison" - Paracelsus, 1564 (Deichmann et al. 1986)Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 197 The European Commission PBT assessment doe s not appear to fully consider the

interaction between the "inhere nt" toxicity concept and the screening process. Where there are good toxicity data in terms of quality and scope, PBT screening may remove some substances from being categorized as PBTs as the toxicity test results indicate relatively low toxicity. However, that does not mean that substances that are screened in are necessarily PBTs.

Exposure-medium-based toxicity tests such as aquatic toxicity test s relate the adverse

effects observed to a substance concentration in aqueous exposure media. That estimate is used as a surrogate dose metric for th e usually unknown concentr ation in the body of the exposed organisms; specifically, the am ounts at the usually unknown site(s) of toxic action. For organic chemicals the media- based dose surrogate is a product of two processes. One is the mode of toxic action of the substance, which determines the molar amounts of substance at the site(s ) of toxic action in the organi sm that initiate the adverse effect in question. The other is the part itioning behavior of the substance, which determines bioaccumulation from the media in question.

To further illustrate this point, for a given toxicity e ndpoint an identical value can be

obtained for a substance that combines low toxicity (i.e. baseli ne narcosis) and high hydrophobicity and a substance with higher toxici ty (i.e. non-narcotic specific toxicity) and lower hydrophobicity. There is nothing "inherent" in anLC50 or chronic exposurebased toxicity estimate. The estimate is very explicitly a function of toxic potency and partitioning from the exposure medium. For this reason aquatic exposure toxicity metrics are not directly indicative of toxic potency, because of the inclusion of partitioning behavior, i.e., bioconcentr ation. Thus, some high K OW chemicals can appear more toxic

than lower K OW chemicals because of higher bioaccu mulation, rather than higher toxic potency. Knowledge of both th e contribution of toxic mode of action/potency and the contribution of partitioning/bioaccumulation is imperative for a scientifically sound risk assessment. A number of publications have hi ghlighted this issue and possible errors in interpreting toxicity data fo r hydrophobic chemicals in aquatic based exposure tests (e.g., (McCarty and Mackay 1993; Gobas et al. 2001; Maeder et al. 2004).

The use of screening trigger values for PBT assessments, or POPs assessments for that matter, do not consider partitioning because they use exposure criteria and thus many highly hydrophobic chemicals may be categorized as "T" when they are only baseline narcotic toxicants, the least toxic mode of ac tion for organic chemicals. There appears to be little in the way of detailed consider ation of the potency-partitioning issue in the European Commission (2008) PBT evaluation or risk assessment, even when it is clear that, due to the low water solubility of HBCD , this would provide valuable insights and a better understanding its t oxic characteristics.

Using Lowest NOECs and NOECs without Associated LOECs Although the European Commission (2008) report on HBCD provides a good overview of the available toxicity inform ation, there appears to be li ttle in the way of a thorough weight-of-evidence evaluation of the available data. In fact, the lowest toxicity valuesArnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 198 from testing carried out with protocols j udged acceptable are simply compared to the defined PBT toxicity trigger values. For aqua tic data the PBT screening trigger values

are:

"... a substance is considered to be poten tially toxic when the L(E)C50 to aquatic organisms is less than 0.1 mg·L -1 ." while for the more defi nitive chroni c endpoint the trigger is "... the long-term NO EC for marine or freshwater organisms is less than 0.01 mg·L -1 ."

The acute screening trigger of an LC50 or EC50 makes sense as these are a measure of some specific effect and the lowest effect leve l represents valuable information about real toxicity. The definitive chr onic trigger defined as a NOEC level makes little sense as presented. NOECs are poor toxi city metrics with unreliable quantification since they do not refer to a known adverse dose-response rela tionship for an exposure level. In fact, they specifically refer to an exposure wher e no adverse effects ha ve been found. NOECs may be found to be of some real use onl y when a corresponding known effect level is available. This problem is exacerbated by NOEC estimation methodology as, due to the generally low statistical power available in many toxicity tests, many NOECs can be considered to be largely statistical artifacts. The ultimate validity and utility of this metric has been widely questioned in the litera ture (OECD 1998; Crane and Newman 2000; Jager et al. 2006).

NOECs are commonly estimated as the exposur e dose level employed in a test that is below the lowest exposure dose level where some adverse effects were reported. In such cases perspective on a NOEC estimate can be obtained by comparison to the known

effect level. The difference between the NOEC and effect level can often be of the order of 10 or 100 times, but at least the NOEC can be put into perspective and an informed judgment made. A problem arises when the NO EC is the highest exposure level and there is no exposure level where adverse effects we re reported. Here the NOEC is of little value for use in comparison with a trigger valu e as an actual effect level is not known. Where the range of available NOECs span the trigger value, risk assessors are placed in the difficult position of differentiating betw een the informational value of various NOECs, and making judgments based on that differentiation, when all of the NOECs

indicate the same information: no toxic effects were observed.

The proposed use of NOECs in the Eur opean Commission RAR (2008) to identify candidate PBT substances is not considered to be scientifically defensible for a final PBT

classification. Some additional direction to address NOEC issues, as discussed above, must be developed for final PBT and PO P classification purposes. Additionally, the quality of the toxicity data must be more thoroughly evaluated than the simple observation that an acceptable testing protocol was employed . For example, results from aquatic toxicity tests for hydr ophobic substances, such as HBCD , are difficult to interpret and to compare with substances that do not have low water so lubility. Although an appropriate testing protocol may have been used, there is addi tional testing guidance (e.g., OECD, 2000) that must be followed before a specific test result should be judged as valid and acceptable.Arnot et al. 2009. Hexabromoc yclododecane (HBCD) Evaluation 199 9.5.4 Further Commentary on POP Assessments

The UN-ECE Protocal and UN Stockholm Convention do not contain clear quantitative endpoints for evaluating “T” as summari zed in the following (UN-ECE 1998; UNEP 2001).

The screening criteria in Annex D of the St ockholm Convention for ad verse effects are as

follows:

(i) Evidence of adverse effects to human health or to the environment that justifies consideration of the chemical within the scope of this Convention; or

(ii) Toxicity or ecotoxicity data that indicate the potential for damage to human health or

to the environment.

We interpret screening criterion (i) to imply some sort of risk assessment is required as evidence of adverse effects (i.e. comparis ons of measured body burdens to relevant toxicity endpoints). Criterion (ii) is more vaguely expresse d and hence it is unclear what sort of data or analysis is sufficient to “indicate the potential for damage”. However, Annex D also contains the following statement:

“The proposing Party shall provide a st atement of the reasons for concern including, where possible, a comparison of toxicity or ecoto xicity data with detected or predicted levels of a chemical resulting or anticipated from its long range environmental

transport , and a short statement indicati ng the need for global control.”

With respect to the UN-ECE POPs protocol (UN-ECE 1998), the screening criterion for toxicity states the following:

1(b) Toxicity: potential to adversely a ffect human health and /or the environment

A more detailed assessment may occur followi ng submission of the ri sk profile to the UN-ECE Executive Body, if, “further considerati on of the substance is determined to be warranted”, including th e following evaluation:

2(b) Whether sufficient information exists to suggest that the substance is likely to have significant adverse human hea lth and/or environmental effect s as a result of its long range transboundary atmospheric transport

In summary, the present assessment considers assessing the potential for adverse effects in “remote regions” based on comparisons of exposure and effect levels (i.e. risk characterization / risk assessment) to be a more definitive assessment of a substance’s POP potential in preference to comparisons of chemical properties against screening

criteria.