An evaluation of hexabromocyclododecane (HBCD) for Persistent Organic 

Pollutant (POP) properties and the potential 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, 2009 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation ii

EXECUTIVE SUMMARY 

This document is an environmental risk assessment of hexabromocyclododecane 

(HBCD). HBCD is also assessed with regards to persistent organic pollutant (POP) and 

persistent, bioaccumulation and toxicity (PBT) categorization criteria. Mass balance 

model simulations are used to compare HBCD 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 (NOAELs) are combined with exposure data for 

screening level assessments of risk in “source” and “remote” environments. 

The findings in the present study show that HBCD fulfils some, but not all, of the UNECE and UNEP Stockholm Convention POP criteria. For example, based on UN-ECE 

POP criteria, HBCD shows the potential for long range transboundary atmospheric 

transport (1a), and bioaccumulation (1d); however, the available environmental half-life 

data suggest an uncertain categorization for persistence (1c), and current monitoring and 

toxicity data indicate that HBCD levels in remote regions are below those associated with 

the likelihood of adversely affecting human health and/or the environment (1b). 

Furthermore, available monitoring and modelling data suggest long range transboundary 

atmospheric transportation potential (2a); however, based on current data, HBCD is not 

likely to have adverse human health and/or environmental 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 based on the available data. Due to the limited 

availability of data for the diastereomers, particularly with regards to transformation and 

isomerization, it was ultimately necessary to evaluate HBCD based on representative 

properties selected for t-HBCD. In some cases, 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 for 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. Efforts were made to reduce uncertainty in the 

estimate of these key properties, while recognizing 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. Hexabromocyclododecane (HBCD) Evaluation iii

Available monitoring data were separated into general categories of “local/near-point 

source”, “source” and “remote” regional scales based on distances, corresponding with 

production and industrial facilities, urban/rural areas, and Arctic regions, respectively. 

HBCD is detected in each of these regions indicating the potential for long range 

transport (i.e. 1a and 2a of the UN-ECE POP criteria). However, spatial trends show 

decreasing concentrations with increased distance from known point sources. 

Furthermore, remote regions are shown to have environmental concentrations that are 

orders of magnitude lower than those near-point sources. Temporal trends in the 

monitoring data were explored but no consistent trends could be identified when 

considering all of the available studies. There 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 substances that are presently 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 overall persistence (POV); however, 

other non-POP chemicals also show these properties. These properties for HBCD are 

generally found to be lower than POPs and candidate POPs, particularly when median 

and lower bound degradation half-lives for HBCD are considered The benchmark 

comparisons do not provide clear evidence for assigning HBCD as a “POP” or a “nonPOP” largely because of the uncertainties in the half-life data and the wide range of LRT 

and POV values for POPs and non-POPs. Uncertainty 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 model steady state concentrations in a range of 

representative species of varying trophic position including fish, 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 concentrations 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 birds, to have the highest exposure potential. 

The model predictions are corroborated by the available monitoring data. These findings 

support selected estimates for slow biotransformation rates in fish and the 

bioaccumulation potential categorization for HBCD (i.e. 1d of the UN-ECE criteria). A 

sensitivity analysis provides recommendations 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. Hexabromocyclododecane (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 calculations 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 indicate 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 agreement with the monitoring data. 

A toxicological evaluation of HBCD was carried out with respect 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) specifically recommends that such practices be 

avoided because of the uncertainties associated with interpreting such exposure-based test 

results. Therefore, these aquatic exposure-based results that were used in previous 

assessments are considered herein to be uncertain and of questionable reliability, 

particularly for comparison to the aquatic 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-based 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 interpretation and evaluation indicates that available key 

aquatic toxicity data for HBCD show low toxicity. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation v

The present risk assessment was used to assess HBCD with respect to POP evaluation 

criteria (e.g. UN-ECE 1b and 2b, the potential for significant adverse effects as a result of 

long range atmospheric transport). To conduct this assessment reliable toxicity endpoints 

for possible effects and no effects are needed. Aquatic exposure-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 that are not affected by the confounding issues 

related to the aqueous-exposure toxicity endpoints are the body/tissue-residue and total 

daily intake (TDI) approaches. Illustrative examples 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 establish thresholds that can be directly 

compared to organism monitoring data. As discussed below, the present study adopts two 

residue-based PNECs; one for baseline narcosis 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 NOAELs are compared with estimated HBCD 

intake rates in the environment. These two general approaches are considered viable 

methods to estimate the likelihood of significant 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 baseline 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 uncertain 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 assessment exercise, another residue-based PNEC was 

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

uncertainties associated with the mammalian toxicity data. This “worst case” toxicity 

PNEC is considered to be a more stringent and conservative threshold for the present 

screening assessment; however, it is unclear whether this “worst case” PNEC is truly 

indicative of a significant adverse effect. 

Available monitoring data were separated into general categories of “local/near-point 

source”, “source” and “remote” regional scales and subdivided into organism groupings. 

For the baseline narcosis mode of toxic action residue-based PNEC there 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” regions, the upper end of the marine mammal 

monitoring concentration distribution exceeds the “worst case” PNEC threshold while the 

upper end of the bird monitoring data enters the “worst-case” PNEC threshold. For 

“local/near point source” regions (i.e. sites near known point source emissions of 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation vi

HBCD), the majority of the marine mammal 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 warranting a more comprehensive risk assessment as 

is typically conducted for non-POP substances. 

The potential for significant adverse effects was also evaluated by comparing TDI 

exposure estimates of HBCD for upper trophic level species 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 obtain a steady state concentration of HBCD 

corresponding with the highest measured concentration 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 concentration at the 95th percentile of measured 

concentrations in marine mammals in “source” 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 piscivorous marine mammals in all three geographic 

regions were well below the NOAEL suggesting no potential for significant adverse 

effects (i.e. 1b and 2b of the UN-ECE POP criteria). 

In summary, simply because a substance meets screening level hazard categorization 

criteria and can be detected in remote environments, does not constitute sufficient 

justification for concluding that there is a likelihood of significant adverse effects in 

remote environments. The final judgement on whether a candidate substance such as 

HBCD should be categorized as a POP requires a more detailed and thorough risk-based 

assessment. A risk-based approach using organism-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 support the classification of HBCD as a POP 

since there are no indications that significant adverse effects are likely to occur in 

organisms living in remote areas distant from known point-source emissions of HBCD. 

The present assessment does not include a thorough uncertainty analysis; however, 

conservative values have been selected. 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. Hexabromocyclododecane (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 Calculations 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 Degradation 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.......................................................................................... 52 
Arnot et al. 2009. Hexabromocyclododecane (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 (eACP10) ............................. 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 Classification 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............................................................................................................... 129 

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................................................................................................. 133 
Arnot et al. 2009. Hexabromocyclododecane (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 HBCD Toxicity Data and Previous PBT Assessments... 193 

9.5.1 Water Solubility, Bioavailability and Cosolvents.......................................... 193 

9.5.2 Aquatic Toxicity Testing with Difficult Substances and Mixtures ............... 195 

9.5.3 Critical Evaluation of PBT Assessments ....................................................... 196 

9.5.4 Further Commentary on POP Assessments ................................................... 199 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation x

LIST OF TABLES 

Table 2.1. Selected values for physical-chemical properties (at 25 oC) and relative 

variance values used as inputs for thermodynamic consistency calculations..................... 9 

Table 2.2. Final adjusted values (FAVs at 25 o

C), relative variance values, percent 

adjustments, and assigned confidence factors 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 properties for the model benchmarking 

simulations. Data are from (Cowan-Ellsberry 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 total 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 diastereoisomer bioisomerization and actual total 

elimination (fecal egestion, gill respiration and biotransformation)................................. 21 

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). ......................................................................................................................... 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 transformation half-lives (HL; days) for the model 

benchmarking simulations. Data are from (Cowan-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 from (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 (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 as HLi

= HLN x (Mi/1)-0.25............................................................................................................. 27 

Table 3.1. Total estimated European Union (EU-Releases) of HBCD by emission 

compartment and emission source category (point sources versus diffuse sources). 

Adapted from TemaNord (2008). ..................................................................................... 29 

Table 3.2. Average water, suspended sediment 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 “source” regional environments 

including the range of reported values and selected median values. ................................ 44 

Table 3.5. Summary of monitoring data collected in “remote” regional environments 

including the range of reported values and selected median values. ................................ 45 

Table 3.6. Calculated doubling times (years) from sediment cores in Europe and 

Japan. ................................................................................................................................ 47 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation xi

Table 3.7. RAIDAR V2.0 Level II maximum exposure assessment factors (EAF), 

relative rankings and the organism of concern 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 listed are base 10 logarithms. .................................... 62 

Table 3.9. RAIDAR V2.0 Level III (100% 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. .................................... 63 

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. ........................................................................................................................ 64 

Table 3.11. RAIDAR V2.0 overall persistence (POV; 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 aquatic/marine mammal based on a unit emission rate 

(TBBU; ng/g) assuming equal emissions of HBCD to air and water................................ 68 

Table 3.13. Selected compounds, OH radical reactivity and eACP10 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 km2

 for different assumption concerning the mode of emission and the 

degradation half-lives in water, soil and sediment............................................................ 92 

Table 3.15. Maximum concentration of HBCD (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......................................... 95 

Table 4.1. Summary of key data on the terrestrial environmental effects of HBCD: 

Studies considered valid in previous EU assessments1

..................................................... 97 

Table 4.2. Summary of key data on the aquatic environmental effects of HBCD: 

Studies considered valid in previous EU assessments1................................................... 101 

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

effects of HBCD from European 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 according 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 octanol-water partition coefficient (KOW)............... 158 

Table 9.3. Selected values for physical-chemical properties and relative variance 

values used as inputs for thermodynamic consistency calculations. .............................. 159 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation xii

Table 9.4. Final adjusted values (FAVs at 25oC), relative variance values, percent 

adjustments, and assigned confidence factors 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. BIOWINTM 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 BIOWINTM model output and corresponding values calculated for 

HBCD (Arnot et al. 2005; US EPA 2009)...................................................................... 161 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation xiii

LIST OF FIGURES 

Figure 2.1. The three main diastereoisomers (pairs of enantiomers) 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 distribution in particulate air samples from Central USA (Hoh 

and Hites, 2005)................................................................................................................ 32 

Figure 3.4. Downstream trend for mean sediment 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. 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 Sellström et al. 1998 

(Sweden) and Schlabach et al. 2004 (Norway), Eljarrat et al., 2004 (Spain). .................. 34 

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

concentration of ΣHBCD 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 concentrations 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 total HBCD concentrations (95% CIs indicated by error 

bars) in harbour porpoise blubber (μg·kg-1 lw) from various locations 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 geometric mean total HBCD concentrations (μg·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).................................................... 47 

Figure 3.12. Geometric mean total HBCD concentrations (μg·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 (μg·kg-1 lw) in guillemot eggs (Sellström 

et al. 2003). Data from (Naturhistoriska riksmuseet 2008) not included here.................. 49 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation xiv

Figure 3.14. Mean total HBCD concentration (μg·kg-1 lw) in human breast milk 

samples (Fangstrom et al. 2008). ...................................................................................... 50 

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

Atlantic Puffin and Kittiwake from two remote island in the north of Norway, Røst 

(left panel) and Hornøya (right panel) (Knudsen et al. 2005)........................................... 51 

Figure 3.16. Reported concentrations of total HBCD in peregrine falcon eggs (μg·kg-1

lw) inhabiting Greenland (Vorkamp et al. 2005).............................................................. 51 

Figure 3.17. Estimated gas-particle partitioning behaviour of HBCD as a function of 

atmospheric temperature................................................................................................... 53 

Figure 3.18. Long range transport (LRT) estimates (characteristic travel distance, 

CTD; transfer efficiency, TE) and overall persistence (POV) 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 “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..... 70 

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

bands. ................................................................................................................................ 72 

Figure 3.22. Arctic Contamination Potential after 10 years of constant emissions 

(eACP10) of POP, non-POP, Candi-POPs and t-HBCD (under different assumptions) 

as a function of reactivity with OH radicals (kOH, cm3·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 eACP10 of CCl4 is shown on the yaxis because it was assumed to have a negligible kOH. ..................................................... 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 Bioaccumulation Potential (AC-BAP) 

(Figures from Czub et al., 2008). The circle in the lower right of each panel represents 

the approximate partitioning properties of HBCD (mixture). In the panel to the right, 

the blue line encircles areas of elevated AC-BAP10 (10 years of emissions) whereas 

the red line represents an elevated AC-BAP70 (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 (×) and 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 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. ......................................................................................................... 81

Figure 3.25. RAIDAR model predictions (×) and 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 the 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation xv

range reflects data for eggs. The gray bars are BETR-World model estimates for the 

European region................................................................................................................ 83 

Figure 3.26. RAIDAR model predictions (×) and 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 

95th 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 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 points compare to results using the 95th

percentile half-lives........................................................................................................... 87 

Figure 3.28. Atmospheric concentrations predicted by BETR-World model using the 

upper boundary half-lives. ................................................................................................ 88 

Figure 3.29. Freshwater sediment concentrations 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 emissions to air (30%) and water (70%), 

followed by 20 years of no emissions............................................................................... 90 

Figure 3.31. Comparison of simulated time trends of HBCD (long half-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 toxicity and residue-based PNECs. ...................... 110 

Figure 6.1. Residue-based risk assessment with summary monitoring data from the EU 

RAR (EC 2008)............................................................................................................... 123

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

data.................................................................................................................................. 125 

Figure 6.3. Residue-based risk assessment with available “source” region monitoring 

data.................................................................................................................................. 126 

Figure 6.4. Residue-based risk assessment with available “local/near point source” 

monitoring data. .............................................................................................................. 127 

Figure 9.1. Comparison of eACP10 values for t-, α-, β-, γ-HBCD.................................. 191 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 1

1.0 INTRODUCTION 

1.1 BACKGROUND

Chemical assessment methods and criteria have been developed over the last 40 years 

and are now being applied broadly in regulatory programs such as the United Nations 

Stockholm Convention on Persistent Organic Pollutants (POPs) (UNEP 2001). Programs 

such as these often screen intrinsic chemical properties against selected criteria or 

“trigger values” in specified and separate categories such as persistence (P), 

bioaccumulation (B), and toxicity (T). Often few experimental 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 polystyrene 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 reference has been unclear in 

previous reports or distinctions are not considered relevant 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 environmental 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 Assessment Report (RAR) for t-HBCD was 

carried out in accordance with Council Regulation (EEC) 793/93 on the evaluation and 

control of the risks of existing substances using a method supported by a Technical 

Guidance Document (TGD) (EC 2003; EC 2008). 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 necessarily provide a sufficient basis for decisionmaking regarding the hazards, exposures or the 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 sediments 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 sediments 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 2

(12 oC) and negligible degradation in soil from one particular study (Davis et al. 2004). 

Half-life data documented in the RAR and TemaNord 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 environmental effects as a result of its long range 

transboundary atmospheric transport. HBCD is very hydrophobic and complications 

related to conducting water-based tests and uncertainties interpreting the data for 

hydrophobic substances are well recognized. The PBT hazard categories are intended to 

identify chemicals that may pose potential risks to humans and the environment. These 

screening level methods and criteria may not effectively identify and prioritize chemicals 

of concern and complementary holistic methods 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 exposure models with organismbased toxicity levels has actually been advocated for a number of years (McCarty and 

Mackay 1993). Combining monitoring data and mass balance models maximizes 

theoretical and empirical knowledge for comprehensive chemical assessments (McKone 

et al. 2007; Cowan-Ellsberry et al. 2009). 

1.2 REPORT OBJECTIVES

The primary objective of the present study is to provide a scientifically defensible 

assessment of t-HBCD, and the three diastereomers where possible, using physicalchemical property data, emission estimates, multimedia fate, bioaccumulation and 

exposure models, toxicity and effects information, 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 collection and critical review of available 

monitoring and testing data. The study uses state of the science mass balance models and 

QSARs to compare HBCD with selected POPs, candidate POPs currently under risk 

review, and non-POPs. Mass balance models seek to corroborate emissions estimates, 

half-life degradation data and monitoring data 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-ECE 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 candidate POPs are also considered when 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 3

comparing model predictions with monitoring data. There are uncertainties associated 

with indoor (nearfield) exposures; therefore, a comprehensive human health risk 

assessment is not considered in the present study. The RAR found no need for further 

information or for risk reduction measures beyond 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 recognize there are differences between t-HBCD 

(mixture) and the diastereomers and to review and select physical-chemical properties 

and half-life data for applications in the present assessment. 

2.1 IDENTITY AND STEREOCHEMISTRY

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 (Becher 2005). It is noted that the 

differences in the SMILES notation for the 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 considered 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 including 16 stereoisomers (Law et al. 2005) 

and HBCD is subject to isomerization 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 majority 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 therefore 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. These three diastereoisomers are chiral and exist as 

pairs of enantiomers in t-HBCD for a total of six stereoisomers (+/-)α-, (+/-)β- and (+/-)γHBCD. 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. Hexabromocyclododecane (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 (pairs of enantiomers) in t-HBCD (recreated 

from (EC 2008); data from (Heeb et al. 2005)). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 5

2.2 ANALYTICAL METHODS AND ISOMERIZATION

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 interconversion 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 advocated over GC-MS methods whenever 

diastereomeric composition has to be preserved during analysis. Sensitivities of the 

methods are different; however, as illustrated in Figure 2.2, quantification of total HBCD 

by the two methods is comparable (Petersen et al. 2004). 

Figure 2.2. Illustration of analytical 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 the possibility of isomerization occurring at 

lower temperatures. This study also noted that the highly symmetrical conformation of αHBCD 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 isomerization 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 the environment as individual stereoisomers. 

Two recent studies suggest further uncertainties 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 absence of light Harrad and colleagues (2009) have 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 6

shown that HBCD isomers are subject to photoisomerization. This study suffers 

somewhat from an incomplete account of the HBCD mass balance; however, if verified, 

photoisomerization may also occur in various environmental compartments (atmosphere, 

soils, surfaces). The isomerization of HBCD at lower temperatures than previously 

considered (e.g. solvent extraction) and in the presence of light suggests that 

isomerization could be occurring during sampling 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 here, these issues can 

neither be fully rectified in the present study. 

2.3 PHYSICAL-CHEMICAL PROPERTIES

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 selected. The physical-chemical properties in the 

present study include molar mass (M; g·mol-1), melting point (TM; o

C or K), water 

solubility (S; mol·L-1), vapor pressure (P; Pa), Henry’s law constant (H; Pa·m3

 ·mol-1), 

and the dimensionless equilibrium partition coefficients between air and water (KAW), 

octanol and water (KOW), and octanol and air (KOA). For some parameters, such as the 

KOW, there are many measurements and estimates available, whereas for other 

parameters, such as the KOA, the number of studies reporting measurements/estimates is 

limited. Methods to seek thermodynamically consistent and thus more accurate solubility 

and partitioning properties are applied to obtain 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 monitoring data and model input 

parameters for diastereomer specific analyses. Through the course of the research it was 

recognized that diastereomer data analyses and modelling would be limited largely as a 

result of unknown isomerization rates and processes that may occur in most, if not all, 

environmental compartments. Many of the reported measured properties and tests are for 

t-HBCD, which is a mixture of isomers, each with unique properties. The poorly 

characterized, and seemingly competing, isomerization processes and rates occurring in 

the natural environment and during analytical procedures complicates the interpretation 

of monitoring and testing data and the application 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 exposure 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 activities. Previous studies have used mass 

balance models to evaluate t-HBCD as a mixture; therefore, due to a general lack of 

information to assess the isomers specifically and to compare model estimates with 

limited monitoring and testing data reported as ΣHBCD we include representative 

properties selected for t-HBCD recognizing that this is not ideal. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 7

The uncertainties associated with isomerization, in particular unknown isomerization 

rates in the multiple compartments of the environment, not only limit our ability to 

interpret the time trend and monitoring data, but also our capacity to model the behaviour 

of specific isomers. Furthermore, existing fate and bioaccumulation models are not 

capable of accounting for isomerization. If the 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 Estimates for Solubility and Partitioning 

Reliable estimates of solubility and partitioning for commercial HBCD and the three 

diastereoisomers are required to improve the interpretation of test data and to input into 

mass balance models. One objective of the present study is to obtain thermodynamically 

consistent physical-chemical properties as suggested by various authors (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 the application of the least squares adjustment 

procedure for property harmonization suggested by Schenker et al. (2005). 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 values (subscript L) using 

PL = PS/F (2.1) 

SL = SS/F (2.2) 

where F is estimated to be 0.023 based on a TM of 190o

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 diastereoisomers due to different 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 estimates selected for the calculation of FAVs 

are different from the values selected in the RAR. These discrepancies are detailed in 

Section 9.1 and summarized below. 

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

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

content of HBCD in the water is 610 μg.L-1 including 544.4 μg.L-1 of non-dissolved γand β-HBCD diastereoisomers (EC 2008). The predominant 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 interpretation of toxicity information derived from in 

experiments with water-based exposures; therefore, for consistency in the report we 

selected a value of 3.0 μg.L-1 for SS to represent t-HBCD. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 8

The RAR discusses the selection of a solid state vapor pressure PS 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 substances with vapor pressures as low as 

the values measured for HBCD (OECD 2002). Wania (2003a) determined a subcooled 

liquid state vapor pressure PL of 2.41×10-5 Pa at 25 o

C for t-HBCD relying on the gas 

chromatographic retention time method by Bidleman (1984) and using p,p’-DDT as the 

reference compound and a suite of organochlorine 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·m3

·mol-1 for t-HBCD as the ratio 

of their selected values for PS and SS (EC 2008). The different values for PS and SS 

selected in the present study result in an estimate of 0.120 Pa·m3·mol-1. The EPI Suite

bond method estimate (U.S. EPA 2009) of 0.174 Pa·m3

·mol-1 provides an independent 

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

2.3.3 Thermodynamic Consistency Calculations for Chemical Properties 

The methods of Schenker et al. (2005) were used to obtain thermodynamically consistent 

estimates for the solubility and partitioning properties of t-HBCD and the three 

diastereoisomers. The data selected as input 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, KAW

estimates derived from ratios of PS and SS were not included in the calculations because 

the selected estimates of PS and SS were already used as input. Reliability was considered 

high if multiple data sources provided similar values. Model estimates were considered as 

well as measured data. The harmonization method also requires and provides relative 

variance values for chemical properties. These values provide guidance as to the relative 

uncertainty of the different properties. 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 KOW 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 phase and of some water in the octanol phase. 

Measurements of the octanol-air partition coefficient (KOA) and of the solubility in 

octanol, on the other hand, are obtained using “dry” 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 been proposed for converting measured KOW

values to ratios of concentrations in pure octanol and pure water CO/CW. For the 

thermodynamic consistency analysis, the equation recommended by Beyer et al. (2002) 

was used to convert KOW to CO/CW prior to adjustment and to convert the adjusted CO/CW

back to KOW after adjustment. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 9

Table 2.2 lists the thermodynamically consistent FAVs for the relevant solubility and 

partitioning properties. The percentages of adjustments were modest for changes from the 

selected input values to the calculated FAVs for most properties suggesting that the data 

are generally consistent and reasonable. Notably, all of the KOW values were slightly 

increased and the solubility in octanol decreased in all cases except for α-HCBD for 

which it increased (largest percentage adjustment). 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 KOW are consistent with the rankings 

of the measured water solubilities and the independent KOW estimates by Hayward et al. 

(2006) that were not used in this procedure. The FAVs for KOA 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 generally not be used for mixtures, but rather for discrete 

substances with distinct properties. The method was applied for consistency with 

diastereomer methods and for comparing model 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 uncertainties associated with actual emissions, 

mode-of-entry, isomerization and degradation 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 PL, Pa SL, mol·m-3 SOL, mol·m-3 Log KAW Log KOW a Log KOA 

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 KOW values corresponding with log (Co/Cw) values of 6.02, 5.97, 5.76, and 5.89 for t-, α-, β-, and γHBCD, 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 Appendix). The values are included 

for comparisons with FAVs 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 10

Table 2.2. Final adjusted values (FAVs at 25 o

C), relative variance values, percent 

adjustments, and assigned confidence factors used in the present study for mass balance 

modelling and data analyses obtained from the thermodynamic consistency calculations 

(Schenker et al. 2005). 

Substance PL, Pa SL, mol·m-3 SOL, mol·m-3 Log KAW Log KOW a Log KOA 

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 KOW values corresponding with log (Co/Cw) values of 6.22, 5.68, 5.81, 6.25 for t-, α-, β-, and γHBCD, 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 parameters such as chemical properties and half-lives 

propagate uncertainty in environmental fate 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 (MacLeod et al. 2002). The 

analytical method uses a confidence factor Cf (or more appropriately an uncertainty 

factor) to express the degree to which parameter 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. Hexabromocyclododecane (HBCD) Evaluation 11

probability < X <Cf M = 0.95

Cf

M











 ⋅ (2.3) 

Thus, the variance in the lognormal distribution 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 partition coefficients, but the three solubility 

parameters are independent. It is recognized that certain properties are often used as 

surrogates in the models (e.g., KOW for lipid-water partitioning and organic carbon-water 

partitioning, KOA for lipid-air partitioning) and that variable conditions occur in the 

environment. Based on these considerations, the data review, the relative variance values 

calculated by the harmonization procedure, and professional judgement, Cfs were 

selected for the FAVs as listed in Table 2.2. 

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

diastereoisomers. 

2.3.5 Physical-Chemical Properties for Benchmarking 

The chemicals selected for this preliminary benchmarking exercise representing POPs 

were as follows: HCB, PCB28, PCB101, PCB180, Aldrin and Heptachlor. Non-POPs 

include biphenyl, p-cresol, atrazine and tetrachloromethane and candidate POPs (CandiPOPs) include α-, β-, γ-HCH, and PBDE-99. These substances represent a range of 

physical-chemical properties. Most of these substances have been used in previous POPs 

benchmarking assessments (Cowan-Ellsberry et al. 2009). Aldrin and heptachlor were 

selected because these POPs have shorter half-lives in air than most POPs and these halflife estimates are comparable to those for HBCD. 
12

Table 2.3. Selected physical-chemical properties for the model benchmarking simulations. Data are from (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 KOW log KAW log KOA POP Hexachlorobenzene (HCB) HCB 284.8 5.61 -1.51 7.12 POP 2,4,4'-trichlorobiphenyl (PCB 28) PCB28 257.6 5.66 -1.93 7.86 POP 2,2',4,5,5'-pentachlorobiphenyl (PCB 101) PCB101 326.4 6.38 -2.08 8.83 POP 2,2',3,4,4',5,5'-hexachlorobiphenyl (PCB 180) PCB180 395.3 7.15 -2.51 10.17 POP Aldrin Ald 364.9 6.25 -2.01 8.26 POP Heptachlor Hept 373.4 5.96 -1.78 7.74 NOP Biphenyl Biphenyl 154.2 4.06 -1.96 6.02 NOP p-cresol p-cresol 108.1 1.97 -4.26 6.23 NOP Atrazine Atrazine 215.7 2.73 -6.84 9.57 NOP Tetrachloromethane CCl4 153.8 2.83 0.19 2.64 Candi-POP alpha-hexachlorocyclohexane α-HCH 290.8 3.88 -3.59 7.48 Candi-POP beta-hexachlorocyclohexane β-HCH 290.8 3.91 -4.83 8.74 

Candi-POP gamma-hexachlorocyclohexane γ-HCH 290.8 3.76 -3.96 7.72 

Candi-POP 2,2',4,4',5-pentabromodiphenyl ether PBDE99 564.7 6.76 -3.67 11.26 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 13

2.4 DEGRADATION, BIOTRANSFORMATION AND HALF-LIFE DATA

Reliable estimates for environmental and biological compartment half-lives are required 

for mass balance model calculations and environmental half-lives are used for screening 

level hazard categorizations of chemical Persistence (P). The primary objective of this 

section is to describe the compilation, review and selection of primary transformation 

half-life (HL) estimates for the bulk physical compartments of the environment 

(atmosphere, water, soil and sediment) and for biological species (fish, mammals). This 

HL estimate is defined as the time it takes for half of the chemical amount present to 

undergo a change in structure. Distinctions between transformation from HBCD to a 

degradation product and isomerization will be discussed (e.g. biotransformation resulting 

in elimination from an organism and bioisomerization 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 compartments of the environment need to be 

distinguished from experimentally-derived dissipation or disappearance times since the 

latter two include losses attributable to processes other than transformation of the parent 

compound e.g. adsorption to surface of glassware, volatilization out of test system. In 

practice, distinguishing between degradation and dissipation is often difficult. 

Data characterizing the degradation potential of HBCD has been compiled and reviewed 

in the RAR (EC 2008) and the TemaNord report (2008). Experimental data are available 

for an aqueous test system, sediments and soils (aerobic, anaerobic) 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 uncertainties. 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, reactions in the gas phase with OH radicals 

are considered to be the most important degradation pathways in the atmosphere. Hence, 

a typical approach to estimate atmospheric degradation half-life (HLair, s or d) is to use a 

hydroxyl radical reaction rate constant (kOH, cm3

·molecule-1·s-1) and an assumed OH 

radical concentration ([OH], molecules·cm-3) according to the following pseudo-first 

order equation: 

 

k [OH]

HL =

OH

air

ln2 (2.4) 

kOH may be derived from experiments or field observations but due to lack of data is more 

frequently calculated using estimation software. For example, the kOH of HBCD estimated 

using AOPWIN v1.92 (U.S. EPA 2009) is 6.12×10-12cm3

·molecule-1·s-1. Based on the 

reported model performance, this estimate is expected to be accurate within a factor of 3 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 14

in either direction (U.S. EPA 2009). Since 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 1.5×106

 molecules·cm-3 whereas a value of 5×105 molecules·cm-3

was assumed in the TemaNord report (2008). The value of 1.5×106

 molecules·cm-3 is 

meant to represent a 12-hour (daylight) average whereas the value of 5×105

molecules·cm-3 represents a 24-hour average. When converting HLair 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 9.2×105

molecules·cm-3, with a value of 9.8×105

 molecules·cm-3 for the northern hemisphere and 

8.5×105

 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, reporting 24 hour global annual average values of 

9.7(±0.6)×105

 and 1.1(±0.2)×106 molecules·cm-3 respectively. Using the 24-hour average 

[OH] value calculated by Bahm and Khalil (2004) for the northern hemisphere and the 

default kOH, 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 kOH, 

the range is 0.4 to 4 days and 0.6 to 5.4 days respectively, meaning that 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 assumptions for all chemicals considered, 

particularly if HLair values rather than kOH 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 biodegradability under aerobic conditions. The results of these data 

were used to conclude that no biodegradation of HBCD occurred over a 28-day period 

(EC 2008; TemaNord 2008). However, according to Shaefer and Haberlein (1996), the 

concentration of HBCD in the test solution was 7.7 mg·L-1, which is at least three orders 

of magnitude greater than the estimated water solubility of this compound (~3 μg·L-1). 

This discrepancy introduces a bias in the interpretation of the experimental results since 

the concentration of the test substance is used to estimate biochemical oxygen demand 

(BOD, mg O2

·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 substance be present at concentrations 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 solubility. Therefore, in contrast to the RAR 

(EC 2008) and TemaNord report (2008), we do not consider the Schaefer and Haberlein 

(1996) results to be reliable and instead characterize them as inconclusive. Note that 

concerns about the validity of this test have been previously raised (Davis et al. 2005). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 15

Model output characterizing ready biodegradability generated using BIOWINTM (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 BIOWIN4 (Expert Survey, Primary Biodegradation 

Model), the expected time frame for primary degradation to an initial metabolite is on the 

order of weeks. However, as noted in the TemaNord 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 presented in the RAR (EC 2008). 

Abiotic degradation of HBCD in surface waters is also possible via the indirect photolysis 

mechanisms, which result from the generation of photooxidants including OH radicals in 

the water column. However, in addition to knowledge of HBCD’s 2nd-order rate constant 

for reaction with OH radicals in aqueous solution, 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 reactions in the water. 

In summary, there are no reliable empirical data characterizing the degradation kinetics of 

HBCD in water. Methods to estimate degradation 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) based on experimental data from sterilized 

test systems, abiotic reaction pathways support 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 half-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 oC (EC 2008). The adopted approach for scaling 

degradation half-lives for POP and PBT screening level assessments is fundamentally 

wrong however because it is only applicable to abiotic 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 populations in different environments. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 16

Table 2.4. Degradation half-lives for total 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 “Simulation Study 1” were taken from Davis et al. 

(2003a). These data are now published in the peer-reviewed literature as Davis et al. 

(2005)). Data used to derive estimates for “Simulation Study 2” were taken from Davis et 

al. (2004), now published as Davis et al. (2006). Note that the degradation half-lives 

calculated directly from the rate constants 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 was reported in the RAR (European Commission 

2008) These discrepancies are presumably related 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 statistically significant (p > 0.05), in part due to the high level of variance in 

replicate observations. In digester sludge, the degradation 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 digester sludge but no difference 

between β- and γ-HBCD. Hence while there is some evidence to support the claim that αHBCD 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 degradation 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 constants 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. Hexabromocyclododecane (HBCD) Evaluation 17

discussed the fact that industrial chemicals are typically present in the environment at 

such low concentrations that they can be expected to be degraded as ‘secondary 

substrates’ (i.e. marginal contribution to energy flow in system) as opposed to primary 

substrates. This mode of degradation is characterized by first order kinetics with a 

constant rate parameter and it is therefore important that chemical concentrations used in 

test systems be low enough to ensure that the ‘secondary substrate’ degradation regime 

predominates (Berg and Nyholm 1996). Note that this does not necessarily mean that test 

concentrations must be similar to environmentally-relevant concentrations to yield 

representative results, only that 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 low due to a shift from firstto zero-order degradation kinetics at the high concentrations used. At elevated 

concentrations, degradation kinetics may become limited by processes such as mass 

transfer into the microbes rather than transformation of the 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 described reasonably well 

assuming a first-order decay model (Davis et al. 2006). 

With respect to mass balance modelling and exposures to air-respiring organisms, the 

most relevant degradation half-lives are those derived from tests in viable aerobic 

sediments, since these sediments are most representative 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 depending 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 layers 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. The 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 generally reliable There are three reported aerobic 

sediment degradation half-lives for commercial HBCD, 11, 32, and 94 d (or 11, 32 and 

101 d, if the values in Table 2.4 are preferred). The median value in both cases is 32 and 

the two other data points are within approximately a factor of 3. This distribution is 

consistent with a log normal distribution for degradation 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 competence for degrading HBCD (variable microbial 

communities) and it is plausible that some reduced oxygen conditions may occur. Thus 

data from the laboratory studies were extrapolated to environmental conditions using a 

confidence (uncertainty) factor of 6 in the present study. Based on a median value of ~ 32 

d, 95% of the expected values fall between 5.3 and 194 d. This upper bound level is also 

consistent with abiotic half-lives (sterile aerobic sediment environments). The lower 

bound can be considered more representative of aquatic systems where anaerobic 

conditions tend to dominate. 
Arnot et al. 2009. Hexabromocyclododecane (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-reviewed literature as Davis et 

al. (2005). Note that the degradation studies presented in this study was conducted at 

more environmentally-relevant concentrations and hence, in principal, are expected to be 

more representative, all other factors being 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 higher concentrations (mg·kg-1 dw) (Davis et al. 2004). 

Hence the issues regarding elevated concentrations and apparent degradation kinetics 

discussed above and in Davis et al. (2006) also 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 aerobic (viable) soil degradation half-life of 

63 days (20 o

C, based on Davis et al. (2003b)) while the other assumed negligible 

degradation (i.e. infinite half-life, based 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 handbooks and screening level QSAR model 

predictions for both persistent and non-persistent chemicals generally follow the pattern 

HLwater ≤ HLsoil ≤ HLsed (Howard et al. 1991; Mackay et al. 2006; U.S. EPA 2009). 

Sinkkonen and Paasivirta (2000) also proposed that, as a first approximation, the same 

empirically derived first-order biodegradation rate constant (anaerobic, aerobic) could be 

assumed for soils and sediments when estimating 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 there is no conclusive evidence upon which to 

exclude the data from Davis et al. (2004), half-lives in excess 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 BIOWINTM model output provides a qualitative assessment of 

biodegradability (bins based on numerical output) rather than quantitative estimates of 

degradation half-lives (U.S. EPA 2009). For example, bins include “ready” or “not 

ready” for general degradability and “days-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 

BIOWINTM model output. The approach calibrated BIOWINTM numerical model output 

to empirical biodegradation half-lives through linear regression. These linear regression 

equations along with the necessary BIOWINTM input are presented in the Appendix 

(Table 9.6). Based on this approach, an aerobic biodegradation half-life in the range of 85 

to 130 days is estimated for HBCD (not isomer specific). Aronson et al. (2006) also 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 19

utilized similar correlation approaches to estimate biodegradation half-lives from 

BIOWINTM model output. For example, based on BIOWIN3 model output for HBCD 

(ultimate biodegradation survey model), the recommended default 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 derived degradation halflives correlated to this category of BIOWIN3 model output is 1 to 1420 days, indicative 

of the high degree of variability and uncertainty in the experimental data. 

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

estimation in soil and sediment. Studies on the atmospheric fate of HBCD, 

particularly with respect to reactions with OH radicals, would also be valuable since 

the efficiency of this degradation pathway 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 HLair as model input of 1.3 d is based on 

AOPWIN v1.92 model output for the hydroxyl radical rate constant (kOH) and the 24 hour 

average [OH] suggested by (Bahm and Khalil 2004) for the northern hemisphere. The Cf

reflects the expected uncertainty in the hydroxyl radical rate constant only. For models 

requiring kOH only, the default value of 6.12×10-12cm3

·molecule-1·s-1 is used instead (same 

Cf). 

Water

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

derived from BIOWINTM 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. Hexabromocyclododecane (HBCD) Evaluation 20

Sediment

The default selected value of 35 d for HLsed is justifiable as either the median degradation 

half-life based on available empirical data for 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 HLsoil was based on consideration of data presented 

in Davis (2003b) and BIOWINTM model-derived estimates of aerobic biodegradation 

half-lives. The selected Cf allows the longer degradation half-lives implied by data in 

Davis et al. (2004) to be addressed in the model simulations. 

In contrast to typical default extrapolation values 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 HLwater = HLsoil ≤ HLsed. 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 mammals. 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/kM. Biotransformation half-lives and rate constants are 

dependent on body mass and to a lesser degree temperature (Hu and Hayton 2001; 

Nichols et al. 2007). Biotransformation half-lives for organisms of specific body sizes 

and temperatures (HLX) need to be calculated from the selected normalized half-life 

(HLN) as (Arnot et al. 2008b; Cowan-Ellsberry et al. 2009) 

HLX = HLN (WX/WN)

-0.25 (2.5) 

where WX is the specific mass of the organism (kg) in the model and WN 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 vitro and in vivo studies, 

were reviewed to select estimates 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 half-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 study shows the potential 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 21

for bioisomerization of the different isomers 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 another diastereomer; 

therefore these “elimination” rates do not necessarily account for the loss of HBCD from 

the organism via transformation to a metabolite. 

Table 2.6 summarizes the molar elimination and formation of the diastereoisomers during 

the “elimination” phase based on the data reported 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 the total elimination half-life is 

actually attributable to “elimination of HBCD” from the organism 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 suggests 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 αHBCD is predominantly eliminated with 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 γHBCD is converted to α-HBCD faster than α-HBCD is converted to γ-HBCD, which 

would support the overall observation 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 balance for diastereoisomer bioisomerization and actual total 

elimination (fecal egestion, gill respiration 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 γHBCD is predominantly biotransformed to α-HBCD. β-HBCD does not appear to be 

biotransformed to a metabolite. Our interpretation 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 elimination kinetics were reported for α-HBCD. Indeed 

non-first order kinetics may be the result of the relatively faster rates of conversion of γHBCD 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 loss 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 22

it appears that most of α-HBCD is actually biotransformed to a metabolite and eliminated 

from the fish, corresponding to an elimination half-life of about 75 d for α-HBCD. 

Table 2.7 summarizes the parameters 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 (Arnot et al. 2008a). There are three possible 

biotransformation rate constant (kM) estimates that could be considered. The first is “loss 

of parent” diastereomer. This includes bioisomerization from one diastereomer to a 

different diastereomer (and subsequent bioisomerization reactions) and the formation of 

metabolites (elimination). This would be calculated based on the “total depuration” rate 

constant (kT) reported by Law et al. (2006b). The second 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 diastereomer (Table 2.6), i.e., 30% of the reported total depuration 

rate constant for γ-HBCD. The third transformation rate 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 total depuration rate constant for γ-HBCD. It must be 

recognized that the measured data are uncertain and that possible 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 biotransformation (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 the 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 estimation method provides screening level 

information regarding confidence in the estimated kM 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% corresponding to a low confidence category assignment for the 

estimated kM value (Arnot et al. 2008b). Although an estimate for α-HBCD is calculated, 

the output confidence factor is greater than 10.3 corresponding to a “low” confidence 

category assignment for the estimated kM value (Arnot et al. 2008b). Using a 

rearrangement of Equation 2.5, the corresponding primary biotransformation half-lives 

normalized HLN 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 primary biotransformation half-lives for α-, and γHBCD. 
Arnot et al. 2009. Hexabromocyclododecane (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 Cƒ Value Cƒ Value Cƒ

W (kg) 0.25 1.5 0.25 1.5 0.25 1.5 

LF (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 

GD (kg/d) 0.005W 2 0.005W 2 0.005W 2 

COX (mg/L) 9.9 1.25 9.9 1.25 9.9 1.25 

ED N/R a N/A 0.41 2 0.41 2 

KOW 2.40x105 3 2.95x105 3 6.31x105 3 

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

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

Cf: confidence factor; kT: total elimination rate constant; W: whole body weight of the fish; LF: whole body 

lipid fraction of the fish; T: water temperature; GD: feeding rate; COX: dissolved oxygen concentration; ED: 

chemical transfer efficiency between the gastrointestinal tract and the organism; KOW: octanol-water 

partition coefficient; kM: primary biotransformation half-life (loss of NA: not applicable; NR: not reported. a

 Model default calculation 

b

 Reported or calculated elimination rate constants 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 parent 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 tissue and not whole body elimination rate 

constant estimates. The fish were being dosed via the food and HBCD would be subject 

to first-pass at the liver, which could be the predominant site for bioisomerization as well 

as biotransformation of the HBCD isomers to metabolites. Whole body distribution 

(toxicokinetics) is expected 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 factor in the analysis. Furthermore, 

the study recognized that steady state was not reached. 

Drottar and Krueger (2000) conducted a flow through bioconcentration 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 factors (BCFs) were 13,000 (not at steady 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 24

state) and 9,000 L·kg-1, respectively. This study reports total 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 the 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 implications 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 kM. The mass balance calculations for primary 

biotransformation of t-HBCD to metabolites provide kM estimates of 0.0027 d-1 (Cf = 7.4) 

and 0.012 d-1 (Cf = 6.7) for the low and high levels, respectively. According to the 

screening level information regarding confidence in the estimated kM 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 HLN to a 1 kg fish are 1080 and 235 d for t-HBCD for the low and 

high levels, respectively. These biotransformation rates are considered “very slow” and 

“slow”, respectively (Arnot et al. 2009). 

Nyholm et al. (2009) evaluated the dietary exposure and biotransformation of various 

brominated flame retardants, including HBCD, in Zebrafish. There was low recovery for 

HBCD in this study and only relative concentrations 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 screening level primary biotransformation rates in fish estimates 

a kM of 0.0071 d-1 for a 1 kg fish (Arnot et al. 2009; U.S. EPA 2009). This rate constant 

corresponds to a HLN of about 100 d. 

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

HLN of 250 d is selected for mass balance modelling with a Cf of 7. Thus, 95% range of 

the generated estimates for HLN will be between 35 and 1750 d. 

Mammalian Biotransformation

Geyer et al. (2004) reported total elimination half-life estimates 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 determined. 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 processes such as respiration, fecal egestion, 

urinary excretion and biotransformation. The 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 HLN (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 HLN value of 20 d (Cf
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 25

= 5) was selected for the mass balance exposure modelling for t-HBCD. The 95% range 

of the generated estimates for HLN will be between 4 and 100 d. This HLN value is about 

1 order of magnitude faster than the HLN value selected for fish. 

In vitro biotransformation studies have been conducted for laboratory rats and a harbour 

seal (sampled shortly after death) using microsomal liver preparations (Zegers et al. 

2005). Significant reductions in LC-MS chromatogram 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 γHBCD diastereromers were biotransformed; 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 parent 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 brominated 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 peaks 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 accumulation factors (wet weight-soil/wet weightworm) ranged between 0.03 and 0.08 based on the total concentration of HBCD in worm 

tissue and estimates for the wet weight concentration 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 γHBCD (EC 2008). The study suggests a degree of bioisomerization of γ-HBCD to αHBCD 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 critical 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 counterintuitive, especially with regards to the 

degree in which the values differ (e.g. aerobic sediment – 35 d; 1 kg fish – 250 d). The 

data for fish are not definitive, but are supported by bioaccumulation measurements in the 

environment showing biomagnification in food webs (low biotransformation rates). This 

suggests that the environmental half-lives may be underestimates. Therefore, some of the 

mass balance model simulations will explore the possibility of half-lives in water, soil 

and sediment that are greater 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 different conditions and natural variability is 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 26

expected among and between individuals and species. It is recognized that α-HBCD is 

usually the predominant diastereoisomer 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 result of preferential biotransformation to 

metabolites of the β- and γ-HBCD isomers and a limited capacity 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 than biotransformation rates for α-HBCD resulting in the 

preferential accumulation of α-HBCD. Different relative solubilities 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 properties 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 and the order of electronegativity is F > Cl > Br > I. These 

general trends support the hypothesis that organobrominated compounds are less 

persistent than chlorinated and fluorinated analogues (Neilson 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 CX4 (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. 
27

Table 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 based on (Arnot et al. 2005; Mackay et al. 2006; U.S. 

EPA 2009) and fish HLs are from (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 (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 as HLi = HLN x (Mi/1)-0.25. 

Status Abbrev HL_Air HL_Water HL_Soil HL_Sediment HL_Fish (1 kg) HL_Av/Mam (1 kg) POP HCB 650 1000 1500 9000 860 290 POP PCB28 6 300 450 2700 190 8.3 POP PCB101 20 1200 1800 10800 370 44 POP PCB180 80 2200 3300 19800 1100 29000 POP Ald * 0.2 1000 1000 2000 75 25 POP Hept * 1.5 25 50 150 90 30 NOP Biphenyl 3 15 22.5 135 5.2 0.2 NOP p-cresol 0.3 5 7.5 45 0.35 0.2 

NOP Atrazine 0.4 50 75 450 0.3 0.2 NOP CCl4 900 150 225 1350 0.2 0.2 Candi-POP α-HCH 50 440 660 3960 55 20 Candi-POP β-HCH 50 900 1350 8100 90 30 

Candi-POP γ-HCH 50 400 600 3600 120 40 

Candi-POP PBDE99 20 850 1275 7650 285 95 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 28

3.0 EXPOSURE CHARACTERIZATION 

3.1 PRODUCTION AND USE

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 (TemaNord 2008)). Estimated HBCD production in 

2003 was 21 900 t, suggesting that global production of this substance is still 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 insulation and packing materials (EPS, XPS), electrical and 

electronic parts (HIPS) and textile 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 the dominant release category to waste- and 

surface waters (EC 2008). 

3.2 EMISSIONS AND MODE-OF-ENTRY

Several considerations are required for the evaluation of chemical concentration trends in 

environmental and biological compartments. Predicted concentration trends and values of 

P and LRT are dependent on mode of entry of substances into the environment and the 

parameters of the modelled environment. Further, the emission history and the location of 

emissions (source area) can determine if pseudo-steady state conditions are expected. 

This is particularly important for substances with high POV, since they will take a long 

time to reach steady state, especially in soil and sediment compartments where 

degradation is usually slower. Since POV, B, T and LRTP are estimated as intrinsic 

properties independent on the amount of substance released, an ‘indirect’ model 

evaluation can be conducted using relative concentrations. 

Table 3.1 summarizes initial estimates of emissions and mode of entry to the environment 

for HBCD (EC 2008). Alternative plausible emission regimes were explored by 

corroborating mass balance model estimates with available monitoring data. Mode of 

entry information was used to determine the ratio of emissions occurring to air, water and 

soil (EC 2008). These assumptions were considered a starting point but variations were 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 29

also explored using model simulations and comparisons 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 Union (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 production sites and do not include emission estimates from a major 

HBCD production facility in the UK which 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 air 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 air 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 air 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 mixed into polymeric products) and is not 

covalently bonded to the products as some other BFRs. HBCD is therefore expected to 

have higher leaching and emissions rates during the product life cycle compared to other 

BFRs that are chemically integrated (Law et al. 2005; Nyholm et al. 2009). 

3.3 MONITORING DATA

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 LRT potential in itself. Modern analytical 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 30

methods can measure certain substances at concentrations of femtograms per cubic meter 

(10-15 g·m-3). An important consideration is potential concentration gradients from 

sources in which the transport potential relative to other chemicals can be determined. 

There are limited monitoring data available, particularly for the abiotic environment. The 

uncertainties associated with isomerization processes 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 has taken place in both remote and source regions of Sweden, 

the United States and China since the early 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 Scandinavian 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 termed “remote” in the literature. 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 the 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 atmospheric concentrations of HBCD as the highest 

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

urban areas as well as the treated consumer products utilized by the population are 

probable sources of HBCD to the urban atmosphere. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 31

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. 

Hoh and Hites (2005) show the influence of urban areas as well as the influence of 

production facilities on atmospheric concentrations of BFRs in the atmosphere in the 

United States. Levels at the Arkansas site, 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 northern 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 background sites to “near source” regions suggests 

that HBCD is emitted, presumably from non-point sources (diffuse), in sufficient 

quantities to have become detectable in many populated areas of the global environment. 

It is interesting to note is that at each of the sites in Guangzhou, the isomeric distribution 

is at least 50% α-HBCD (Figure 3.2). The similar profiles are in contrast to what was 

observed by Hoh and Hites (2005) in the US where the isomeric 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 similarities. Atmospheric degradation is an 

important loss process and photoisomerization may also occur, therefore, differences in 

latitude and cloud-cover could play a role in the variation in the American data. 
Arnot et al. 2009. Hexabromocyclododecane (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 distribution in particulate air samples from Central USA (Hoh and 

Hites 2005). 

3.3.2 Sediment 

Sediment is the most frequently monitored 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 requires information on the aerobic status. Marine 
Arnot et al. 2009. Hexabromocyclododecane (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 compared with cm or dm scales in oceanic 

sediments underlying a deep oligotrophic water column (Kristensen 2000). Estuarine and 

coastal sediments may also be anaerobic 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 sources such as textile plants and waste water 

treatment plants show the influence of these point sources to water systems. Studies 

involving point sources in fluvial and estuarine systems in Scandinavia, Switzerland, the 

Netherlands, Belgium and the UK, as well Tokyo Bay in Japan and the highly 

industrialized Detroit River in the USA/Canada show spatial sediment contamination 

trends. Figure 3.4 shows the downstream trend in average reported sediment 

concentrations. 

Figure 3.4. Downstream trend for mean sediment 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). 

Contaminant levels in isolated sediments in remote locations can implicate long range 

transport potential. Christensen et al. (Christensen et al. 2004) examined sediment cores 

from Lake Ellasjøen on Bear Island (Bjørnøya), 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 inputs of seabird guano and it receives relatively 

high rates of precipitation for the island. Relatively high levels of contaminants are 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 34

observed due to bio-transport and precipitation (Christensen et al. 2004). Sediment cores 

reveal that HBCD was also detected (4.34 μg·kg-1 dw) in a subsurface layer dating from 

1973 to 1987 with isomer ratios at ~10% α-HBCD, ~1% β-HBCD and ~89% γ-HBCD. 

This sediment core suggests a historical use pattern for HBCD and an isomeric pattern 

similar to t-HBCD. 

Surface sediment monitoring data show that releases to river systems migrate 

downstream. Eljarrat et al. (2004), Sellström et al. (1998) and Schlabach et al. (2004b) 

studied European rivers in which there is a mix of possible industrial and urban sources. 

All studies used monitoring stations upstream of the impacted areas and then at sites 

downstream. Figure 3.5 shows that although there is variation in the concentration of 

HBCD in sediments along the watercourses, there 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 Drammen River and Fjord data and shows the 

concentrations and the isomeric breakdown 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 River 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 Sellström et al. 1998 (Sweden) and 

Schlabach et al. 2004 (Norway), Eljarrat et al., 2004 (Spain). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 35

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

concentration of ΣHBCD 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). 

Estuarine environments are susceptible to contamination 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) and 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 level 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 through riverine and direct 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. Hexabromocyclododecane (HBCD) Evaluation 36

Figure 3.7. Estuarine concentrations and isomeric breakdown. 

Figure 3.8. Spatial Trends: HBCD in Tokyo Bay surface sediments from Minh et al. 

(2007). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 37

In the North Sea off the Netherlands and Germany, both nearshore and offshore 

measurements of surface sediment concentrations 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 HBCD sediment concentrations are detectable; 

however, at lower concentrations than those nearer to point sources. 

3.3.3 Water and Soil 

Remote measurements in soil and the water 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 (Petersen et al. 2004; 

Remberger et al. 2004) for water 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 sediment 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. Since air and water are the receiving 

compartments, these environments should be considered a priority. 

3.3.4 Plants 

Only one study documenting measured concentrations 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 μg·kg-1 ww) to 11 mg·kg-1 ww for total HBCD. The most 

interesting aspect of this study was the spatial trend in reported concentrations; higher 

concentrations were generally reported for the more southern locations in Norway, falling 

below detection limits at the more northern sites. No additional studies reporting HBCD 

concentrations in plants have been located in the available literature. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 38

3.3.5 Invertebrates 

Monitoring data are available for both freshwater and marine invertebrates and include 

samples “near point sources”, “regional source” and “remote” regions. Reported 

concentrations in marine invertebrates range over four orders of magnitude (EC 2008), 

indicative of strong spatial gradients in exposure 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 μg·kg-1 lw. Concentrations in blue mussels sampled along the 

Norwegian coast were substantially lower, ranging from ~50 to 400 μg·kg-1 lw. These 

latter concentrations are similar to those reported for the Kattegatt (Swedish Baltic coast) 

which ranged from 20 to 300 μg·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 μg·kg-1 lw. Reported concentrations in tissues from other 

invertebrate species (e.g. hermit crab, lobster, oysters, prawns, scallops, starfish) 

harvested in EU and Norwegian waters that are not near known point sources for 

emissions tend to fall into a similar range range, i.e., 10 to 100 μg·kg-1 lw (EC 2008). 

However, it is difficult to compare concentrations across species since habitat use 

(benthic vs. pelagic), trophic position, and possibly biotransformation capacity all 

influence exposure and corresponding body burdens of hydrophobic substances such as 

HBCD. 

Studies reporting concentrations of HBCD in invertebrates inhabiting remote regions are 

scarce. Tomy et al. (2008) reported concentrations in zooplankton (mixed, n = 5), shrimp 

(Pandalus borealis and Hymenodora glacialis, n = 5) and clams (Mya truncate and 

Serripes groenlandica, n = 5) from the Eastern Arctic sampled in 2000/2001; geometric 

mean values were 1.1, 1.9 and 1.4 μg·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 concentrations did not exceed detection limits. 

3.3.6 Fish 

Monitoring data are available for both freshwater 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 HBCD 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 (downstream HBCD production 

plant) and values typically ranged from ~ 1 to 50 mg·kg-1 lw (EC 2008). Substantially 

lower muscle tissue concentrations (≤ 35 μg·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 μg·kg-1 lw) (Sternbeck et al. 2004) and pike (Esox lucius) from 

freshwater bodies in Finland (< 5 μg·kg-1 lw, (Peltola unpublished)). Concentrations in 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 39

fish sampled upstream of known sources are also substantially lower (on average) than 

downstream (EC 2008). These observations are broadly consistent with the emission 

mode of entry scenario presented in the (EC 2008); emissions to wastewater and surface 

water (confined to downstream areas of particular drainage basins, given the low log KAW

estimated for HBCD) dominate over atmospheric emissions (which tend to disperse more 

rapidly and lead to reduced spatial gradients in exposure). Comparisons between 

freshwater fish inhabiting industrialized areas and fish inhabiting remote regions (i.e. 

Arctic) are not possible due to lack of data. 

Reported concentrations in marine fish included in (EC 2008) are lower on average and 

also span a narrower range of values in comparison to freshwater fish. As expected, fish 

species inhabiting marine environments impacted by point source discharges such as the 

Western Scheldt estuary tend to have higher reported concentrations. For example, 

measured conconcentrations of HBCD in sole (Solea vulgaris) and whiting (Merlangius 

merlangus) muscle tissue ranged from approximately 100 to 1110 μg·kg-1 lw and 20 to 

275 μg·kg-1 lw respectively (de Boer et al. 2002). In comparison, the reported 

concentration of total HBCD in whiting analyzed as part of a UK market survey (pooled 

sample, n = 60) was 28 μg·kg-1 lw (EC 2008). Reported concentrations of eel (muscle) 

sampled in the Western Scheldt estuary ranged from 10 to 310 μg·kg-1 lw (de Boer et al. 

2002; Janák et al. 2005), far below levels reported in more impacted freshwater regions 

(see above). An extensive database is available for Baltic herring (Clupea harengus) 

muscle tissue, covering the years 1999–2007 (Naturhistoriska riksmuseet 2008). These 

data are noteworthy because the Baltic Sea environment is often more seriously impacted 

by anthropogenic pollution due to the slow turnover of these waters and extensive 

drainage basin (Swedish Environmental Protection Agency 2009). Geometric mean 

concentrations in these fish over this time period range from approximately 5 to 25 μg·kg1

 lw. These concentrations are similar to levels reported for herring (muscle) available on 

the UK market (9.5 μg·kg-1 lw), assumed to be caught in UK waters (EC 2008). 

Limited data are available for marine fish inhabiting the waters of remote regions. Mean 

reported concentrations in polar cod (Boreogadus saida) and redfish (Sebastes mentella) 

muscle sampled in the Eastern Arctic (Davis Strait) in 2000/2001 were 0.4 and 2 μg·kg-1

lw respectively (Tomy et al. 2008). These measured concentrations in polar cod are 

substantially lower than values reported by (Jenssen et al. 2004) for the Svalbard area 

(range 5 to 25 μg·kg-1 lw, liver tissue) and the North-East Atlantic (range 7 to 23 μg·kg-1

lw, liver tissue) (Bytingsvik 2004). This apparent 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 also 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 industrialized and remote regions. Concentrations in 

polar bears from various regions of the Arctic are also available. 
Arnot et al. 2009. Hexabromocyclododecane (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 HBCD in eggs sampled from wild populations ranged from 

approximately 50 to 2400 μg·kg-1 lw in the southern population and 35 to 500 μg·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 unusually high in this study; when above the 

limits of detection, the reported concentrations were up to 780 μg·kg-1 lw, i.e. similar to 

concentrations reported for Sweden. Reported concentrations in eggs sampled in 

Greenland over a similar time period (1995–2000) were generally much lower, ranging 

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

Sellström et al. (2003) presented a series of measurements in guillemot (Uria aalge) eggs 

collected over the period 1969–2001. More recent measurements extending this dataset 

are presented in (Naturhistoriska riksmuseet 2008). The increasing temporal trend in 

these data is discussed in more detail in Section 3.4. Reported concentrations in eggs over 

the period 1995–2000 (pooled samples, n = 10) range from 110 to 170 μg·kg-1 lw, 

broadly similar to peregrine falcons. Concentrations of total HBCD in guillemot muscle 

issue sampled in the same area in 2000 are generally lower, ranging from 25 to 148 

μg·kg-1 lw (geometric mean = 65 μg·kg-1 lw) (Lundstedt-Enkel 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, with total HBCD concentrations reported at 

levels of 417±208 μg·kg-1 lw (Murvoll et al. 2006). Measured concentrations of HBCD in 

birds inhabiting more remote regions (e.g. Svalbard, Bjørnøya) have been reported for 

several species including 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 glaucous gulls. Here concentrations in brain 

and liver tissue were reported as 98.9±136 and 3026±4322 μg·kg-1 lw α-HBCD 

respectively. However, of the 21 birds sampled, nearly 50% were classified as completely 

or severely emaciated (total or near-complete 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 extremely inefficient and it is therefore not 

surprising that elevated lipid-normalized concentrations 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 gulls sampled for other studies are far lower. For example, 

Verrault et al. (2004) reported mean total HBCD concentrations of approximately 20 

μg·kg-1 lw in plasma samples from glaucous gulls from Svalbard for the year 2004. These 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 41

concentrations are similar to the mean total HBCD values reported in Verrault et al. 

(2004) for plasma samples from glaucous gulls inhabiting Bjørnøya (35 to 50 μg·kg-1 lw). 

Higher concentrations were reported in glaucous gull egg samples from the same island 

(140 μg·kg-1 lw). These concentrations are comparable 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 μg·kg-1 lw total HBCD. 

The most extensive dataset available 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 μg·kg-1 lw with peak values observed 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 reported measurements for 

harbour porpoises stranded over the period 2000–2003 which confirm the high levels 

reported by Law et al. (2006c; 2008) for UK waters. Harbour porpoises stranded or 

bycaught in Galicia, Spain were also included in this monitoring survery. As shown in 

Figure 3.9, the geometric mean total HBCD concentrations 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 sources such as an HBCD production 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-1 lw

Figure 3.9. Geometric mean total HBCD concentrations (95% CIs indicated by error 

bars) in harbour porpoise blubber (μg·kg-1 lw) from various locations over the period 

2000–2003 (Zegers et al. 2005). 

 

Data from grey seals (Halicoerus grypus) inhabiting the Baltic Sea 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 μg·kg-1 lw (range 31 to 554 

μg·kg-1 lw), similar to harbour porpoises from Galicia, Spain (i.e. not indicative of highly 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 42

impacted environment). Total HBCD concentrations in muscle tissue ranged from 30 to 

87 μg·kg-1 lw, somewhat lower than lipid-normalized blubber concentrations. Reported 

concentrations in marine mammals in industrialized regions of North America also 

appear to be lower than from harbour porpoises inhabiting the waters 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 μg·kg-1 lw (Peck et al. 2008) while reported concentrations of total HBCD in blubber 

from California sea lions sampled over the period 2000–2003 range from approximately 

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

2006). 

Concentrations of HBCD in mammals inhabiting 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 (Gabrielsen et al. 2004; Jenssen et al. 2004; Muir et al. 2006; 

Sormo et al. 2006). Reported total HBCD concentrations in walrus, beluga and narwhal 

blubber inhabiting the eastern Arctic (e.g. Davis Strait) sampled in 1998 were 0.6, 1.4 

and 3.4 μg·kg-1 lw respectively (Tomy et al. 2008). These values are lower than those 

reported in mammals from Svalbard. For example, the total mean HBCD concentrations 

in blubber of ringed seal sample in 2003 was 20 μg·kg-1 lw whereas mean total HBCD 

concentrations in polar bears ranged from approximately 12 to 45 μg·kg-1 lw sampled in 

2002–2003. Interestingly, mean total HBCD concentrations 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 substantially lower with a mean 

reported value of 0.4 μg·kg-1 lw (range <0.1 to 35.1 μg·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 Western 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 HBCD production and use. 

3.3.8 Humans 

HBCD levels have been reported in human breast 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 samples in Japanese women (age 25–29) over the 

period 1973–2006; total mean HBCD concentrations over the period 2000 – 2006 ranged 

from 1 to 4 μg·kg-1 lw. These levels are higher than values reported for women in the 

Tromsø area (northern Norway); HBCD was detected in only 1/10 samples, at a 

concentration of 0.13 μg·kg-1 lw (Polder et al. 2008a). In contrast, substantially higher 

values were reported in a survey of women living in Northwestern Spain conducted in 

2006/2007 (Eljarrat et al. 2009). In this location, the geometric mean total HBCD 

concentration was 27 μg·kg-1 lw (range 3 to 190 μg·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 αHBCD 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 concentrations in human breast milk in 

populations inhabiting industrialized areas appears to be <1 to 5 μg·kg-1 lw. No data on 
Arnot et al. 2009. Hexabromocyclododecane (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 adipose tissue obtained by liposuction in the 

Czech Republic (Pulkrabová et al. 2009). Reported concentrations ranged from <0.5 to 

7.5 μg kg-1 lw. No other human adipose tissue data are available for comparison. 

Table 3.3. Summary of monitoring data collected near known point sources including the 

range of reported values and 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-lw 
Arnot et al. 2009. Hexabromocyclododecane (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 data 
Arnot et al. 2009. Hexabromocyclododecane (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-lw 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 46

3.4 TIME TRENDS OF ENVIRONMENTAL CONCENTRATIONS

3.4.1 Abiotic Compartments 

Sediment cores from depositional zones within study areas can be used to estimate 

historical levels of contamination. Typically, sediments are very slow to respond to 

changes in emission patterns and act as a repository and can even be a source for certain 

chemicals after emissions have been restricted. The ages of the subsections of the 

sediment core are determined using 210Pb 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 (Christensen et al. 2004). 

Of the sediment cores included in this analysis, the maximum concentration is 2.5 μg·kg-1

dw from 2001 in Lake Greifensee (CH) (Kohler et al., 2008). Figure 3.10 shows a clear 

increase in concentrations with time. Using simple linear regression, doubling times were 

calculated and are presented in Table 3.6. Although the absolute concentrations are near 

background levels, the Swiss and Japanese sediments show a dramatic increase, with 

doubling times of approximately 8 years for Greifensee 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 deeper, older and possibly more 

anaerobic sediments. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 47

Table 3.6. Calculated doubling times (years) from sediment 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, particularly 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 concentrations 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 geometric mean total HBCD concentrations (μg·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 significant decrease between 2003 and 2004 (p < 

0.05). Neither trend was confounded by age, sex, nutritional status, or location”. The 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 48

significance of the apparent decreases in measured concentrations (all samples) for 2005 

and 2006 could not be confirmed by the statistical 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 interest 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-1to surface 

water and 2 t·yr-1 to wastewater from the onsite sewage 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 apparent decline of HBCD over the period 

2003–2004 with respect to understanding response times of the environment and food 

webs to changes in emissions of HBCD. Considering the outcomes of all trend analyses 

presented in Law et al. (2008), the limited number of data points for the post-emissions 

phase for these organisms in addition to the paucity of monitoring data for any other 

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

decline in HBCD concentrations in harbour porpoises is representative of the post-2003 

period. Consequently, no comparisons of the response times of biota to emission 

reductions between HBCD and other chemicals 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, mean concentrations of PBDEs 

declined by a factor of nearly 35, possibly reflecting the restriction of penta-PBDE 

technical mixture in 2004. While it is possible 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 conclusions regarding the overall persistence 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. Hexabromocyclododecane (HBCD) Evaluation 49

Figure 3.12. Geometric mean total HBCD concentrations (μg·kg-1 lw) in herring muscle 

sampled at various locations in the Baltic Sea (Naturhistoriska riksmuseet 2008).

Figure 3.13. Mean total HBCD concentration (μg·kg-1 lw) in guillemot eggs (Sellström et 

al. 2003). Data from (Naturhistoriska riksmuseet 2008) not included here. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 50

Figure 3.14. Mean total HBCD concentration (μg·kg-1 lw) in human breast milk samples 

(Fangstrom et al. 2008). 

Reported concentrations in guillemot eggs (Sellström et al. 2003) and human breast milk 

(Fangstrom et al. 2008) show slight increases over the period 1980–1995. The overall 

increasing trends in reported 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 proportional to body size. This fact implies that the time to reach 

near-steady state body burdens (assuming constant 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 relevant 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 were highest in 1979 is somewhat counterintuitive 

given the expectation that production/use (and hence emissions) has increased even in 

North America over the past two decades. Further information on production/use and 

emissions from this region could help clarify the interpretation of these data. 
Arnot et al. 2009. Hexabromocyclododecane (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 (μg·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 panel) (Knudsen et al. 2005). 

Figure 3.16. Reported concentrations of total HBCD in peregrine falcon eggs (μg·kg-1 lw) 

inhabiting Greenland (Vorkamp et al. 2005). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 52

As shown in Figures 3.15 and 3.16, the temporal 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-statistically significant decrease is observed for Peregrine 

falcon eggs. (Braune et al. 2007) also documented declining concentrations of total 

HBCD in the eggs of ivory gulls (Pagophila eburnea) inhabiting Seymour Island 

(Canadian Arctic) based on levels in samples 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 trends reflect different 

exposure sources (e.g. North America vs Europe) and their relative importance in various 

regions of the Arctic but there is no additional information to corroborate this assertion at 

this time. 

3.4.4 Key Considerations 

Temporal trends in reported HBCD concentration in organisms from different locations 

do not show a uniform pattern. In some species, concentrations of HBCD may have 

stabilized over the past decade or even begun to decrease whereas there are indications 

from other studies that concentrations are still increasing in other species, including 

humans. 

Analysis of temporal trend data in biota would be greatly facilitated by i) information on 

historical and current production/use of HBCD as well as the corresponding environment 

releases and ii) data characterizing the temporal trends in relevant exposure media (e.g. 

air, water, sediment cores). These data gaps limit the ability to interpret the temporal 

evolution of HBCD concentrations in the environment and compare them to the temporal 

evolution of other hydrophobic compounds which have been subject to restrictions or 

bans on use. 

3.5 FATE AND EXPOSURE MODELLING

Basic mass balance modelling concepts are described in the Appendix (Section 9.4.1). 

The models were used to (i) characterize environmental distributions and long range 

transport potential of HBCD using available 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 studies, it is useful to first explore the 

partitioning behaviour of HBCD in the atmosphere. This important partitioning 

equilibrium determines the rate of atmospheric deposition and degradation of semivolatile organic chemicals (Lei et al. 2004). The gas-particle partitioning of HBCD as a 

function of temperature can be estimated using a simple KOA based equation and applying 

the log KOA 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 to 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 53

-35 °C) (Figure 3.17). Depending on the concentration and composition of the particles, 

that transition can occur within a slightly different temperature range. In a highly polluted 

atmosphere, a larger fraction of HBCD is expected to be particle bound than in the 

remote atmosphere at the same temperature. 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 1011

volume fraction of organic carbon in particles: 0.1 

log KOA at 25 °C = 10.46

∆HOA = -80 kJ/mol

Figure 3.17. Estimated gas-particle partitioning behaviour of HBCD as a function of 

atmospheric temperature. 

This suggests that HBCD will be subject to different atmospheric processes at different 

temperatures. At higher temperatures, a significant 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 association with particles may prevent the 

oxidation of HBCD, but also increase the rate of dry deposition. 

Interestingly, the air-water equilibrium partition coefficient (KAW) of HBCD also places it 

in a transition area with respect to the partitioning 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 tend to be washed out 

efficiently by precipitation. 

The long range transport of HBCD would presumably be most pronounced during cold, 

dry periods. Low temperature would cause HBCD to sorb to particles, where it is likely 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 54

shielded from OH radical attack. In the absence of precipitation, small particles can 

remain airborne for extended periods of time and can therefore be transported long 

distances. Most simple long range transport assessment models (e.g. the Tool) would fail 

to recognize the potential for such transport, because they either only consider relatively 

high temperature conditions and/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 pathway for HBCD, the situation may be quite a 

bit different in a cold environment. By extension, 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 properties 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 characteristic travel distance (CTD, km), transfer 

efficiency (TE; %), and overall persistence (POV, d) for t-HBCD and the benchmark 

chemicals. CTD is an estimate of the distance at which the concentration of a chemical in 

a moving parcel of air that interacts 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 adjacent to the region receiving the emissions 

(Wegmann et al. 2009). POV 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 partitioning. 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 substances under all environmental conditions (see above). 

Three calculations were made for t-HBCD. The first assumes selected median HLs for 

degradation in air, water and 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 scenarios”, 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 (most 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 55

conservative, or ‘worst case’) value for each parameter (CTD, TE, and POV). Unless 

otherwise noted below, only the results for the ‘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 POV

for the t-HBCD scenarios and the benchmark chemicals. The CTD predictions are 

approximately 200, 600 and 1,500 km for the 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 for 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 HBCD (100% emissions to air, upper bound 

degradation HLs) are near the middle of the values for the benchmark chemicals. The 

lowest CTD value for the benchmark chemicals is for a non-POP: atrazine (125 km); 

however, the CTD for aldrin (a POP) is only about twice as high (230 km). The highest 

CTD values are for a non-POP: tetrachloromethane (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 ‘worst 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 that these CTD values are lower than CTD 

predictions for candidate POPs. HBCD CTD predictions are generally closer to values 

observed for “traditional non-POPs”, regardless of degradation HL assumption. Clearly 

these CTD predictions for HBCD are much lower 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 are 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, respectively. The TE predictions for the lower 

bound and median HL assumptions are in the range 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 for 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 POV Benchmarking

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

HBCD lower bound HL scenario to 4,700 days for PCB 180. The POV predictions are 
Arnot et al. 2009. Hexabromocyclododecane (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 POV 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 POV

predictions for HBCD are approximately 12, 120 and 970 days for the lower bound, 

median, and upper bound HL estimates, respectively. The ‘worst case’ POV predictions 

for HBCD (median and lower bound HLs) are similar to most of the ‘worst case’ POV

predictions for non-POPs (≤120 d) and separate from most POPs and candidate POPs 

(≥600 d). The upper bound degradation HL scenario results in POV predictions that are 

comparable to many POP and candidate POP benchmarks. The results highlight the 

uncertainty in environmental degradation half-life estimates on predictions of POV for 

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

with a ‘worst case’ POV comparable to most non-POPs and that there is a non-POP 

(tetrachloromethane; 1,270 d) with a ‘worst case’ POV comparable to many POPs and 

candidate POPs. 

The mode-of-entry to the environment and the rates of degradation are shown to have 

significant impacts on LRT potential and POV predictions for HBCD. In particular, the 

mode-of-entry assumption influences LRT potential to a greater extent than the 

degradation HLs. The degradation HL assumptions have a greater influence on the 

predictions for POV. Due to the uncertainties in these two key parameters, there are 

uncertainties in comparisons with the benchmark chemicals. In general, however, HBCD 

shows a trend of having LRT and POV predictions that are more consistent with values for 

most of the benchmark non-POPs than values for most of the benchmark POPs and 

candidate POPs. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 57

Figure 3.18. Long range transport (LRT) estimates (characteristic travel distance, CTD; 

transfer efficiency, TE) and overall persistence (POV) estimates for the selected 

substances using The Tool V2.0 model. 

10

100

1000

10000

100000

1000000

1 10 100 1000 10000

CTD, 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. Hexabromocyclododecane (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). Briefly, RAIDAR is a screening level steady state evaluative 

model that combines information on chemical partitioning, degradation, environmental 

fate and transport, food web bioaccumulation, exposure, effect endpoint and emission 

rate in a coherent mass balance framework. RAIDAR includes a regional scale 

environment with an area of 100,000 km2

 (~90% land, ~10% water with underlying 

sediment) and a variety of representative plant, invertebrate and vertebrate species 

including fish, wildlife, agricultural crops, livestock, and humans. Primary producers and 

invertebrates bioconcentrate chemical from their ambient environment of air, water, soil, 

or sediment while all other species bioaccumulate chemical from their ambient 

environment and from their diet. The mass balance bioaccumulation models include 

major routes of chemical uptake and elimination. Only farfield exposures are currently 

considered for humans. The model does not include 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 there are no losses or additions of chemical to 

food as a result of processing (e.g. animal husbandry) and preparation (e.g. cooking, 

washing). 

RAIDAR "active" soil depth is 20 cm and sediment 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 assumed constant temperature approximating 

temperatures associated with the physical-chemical property estimates. Since substances 

such as HBCD can be transported between regions greater than the RAIDAR evaluative 

environmental scale (i.e. neighbouring source regions), advective losses in air and water 

were considered negligible for all model calculations and benchmarking exercises in the 

present assessment. 
Arnot et al. 2009. Hexabromocyclododecane (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 determine the potential for chemical exposure while 

QPB determine actual exposures. RAIDAR combines P and B information into an 

exposure assessment factor (EAF) thus providing single values for transparent chemical 

comparisons and rankings based on exposure 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 unit emission rate for comparing HBCD with 

benchmark chemicals. Estimates of actual emissions can be used to scale unit emissions 

for comparisons of RAIDAR predictions with monitoring data. 

RAIDAR EAF Benchmarking

RAIDAR V2.0 was used to calculate maximum exposure assessment factors (EAF) in the 

regional environment for HBCD and the benchmark 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 species in the RAIDAR environment. EAF is 

similar to the human intake fraction; however, it also includes absorption efficiency. It is 

calculated using a unit emission rate (EU) of 1 kg·h-1 for each chemical; therefore, the 

Air

Sediment

Soil

Water
Arnot et al. 2009. Hexabromocyclododecane (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 considered as t-HBCD (low) and t-HBCD 

(high), respectively, to assess the uncertainty of these parameters on the EAF and 

RAIDAR POV 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 conditions between the physical environmental 

compartments. For the Level III fate calculations, three scenarios were considered for 

mode-of-entry. The first assumes 100% of the unit emissions are to air, the second 

assumes that 100% of the emissions are to water and the third assumes that 50% of the 

emissions are to air and 50% are to water. 

Table 3.7 lists the organisms identified as having the highest potential for exposure, the 

associated EAF estimates, i.e., maximum exposures, and the relative rankings of the 

chemicals based on the Level II fate calculations. The relative exposure potential for tHBCD and the three diastereomers (median HLs) 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 are the representative organisms that show the second 

highest exposure potential (data not shown). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 61

Table 3.7. RAIDAR V2.0 Level II maximum exposure assessment factors (EAF), relative 

rankings and the organism of concern for HBCD 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 mammal 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 62

Table 3.8 lists the organisms identified as having the highest potential for exposure, the 

associated EAF estimates (i.e. maximum EAFs), and the relative rankings of the 

chemicals based on the Level III (100% emissions 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 ranking 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% emissions to air) 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 -1.15 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 carnivore 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 63

Table 3.9 lists the organisms identified as having the highest potential for exposure, the 

associated EAF estimates (i.e. maximum EAFs), and the relative rankings of the 

chemicals based on the Level III (100% emissions to water) fate calculations. These 

relative results are similar to the Level II results. 

Table 3.9. RAIDAR V2.0 Level III (100% 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 -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 mammal 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 64

Table 3.10 lists the organisms identified as having the highest potential for exposure, the 

associated EAF estimates (i.e. maximum EAFs), 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 highlights the low air-water partition coefficient 

(KAW) of HBCD and the moderately high KOW (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 mammal 
Arnot et al. 2009. Hexabromocyclododecane (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 the current study. The 

general similarities in the results for t-HBCD and the diastereomers suggests that the 

representative properties for t-HBCD are likely sufficient to characterize HBCD for the 

modelling exercises in the context of the present study. Notably all diastereomers and tHBCD currently share the same environmental degradation and biotransformation halflives as a result of substance specific data limitations and the available estimates for the 

partitioning properties are comparable. In general, 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 slow HLs for degradation), 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 POV Bencmarking

Table 3.11 compares the predictions for POV based on the different assumptions used in 

the RAIDAR model simulations. For HBCD, Level III simulations show lower POV

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 the same for all forms of HBCD in these 

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

for the non-POP benchmark chemicals than to POV 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 POV predictions of HBCD typically below, or well below, POV 

predictions for benchmark POP and candidate POP chemicals. The POV predictions for tHBCD are higher using The Tool than using RAIDAR, particularly for emissions to 

water, because The Tool does not consider degradation in sediment. 

The Level III simulations show that mode-of-entry is important for POV predictions for 

most, if not all, substances. For HBCD, POV predictions are lower when emissions are to 

air than when emissions are to water assuming either the median or lower bound HL 

estimates. However, when the upper bound HL estimates are selected there is little 

difference between the POV predictions based on the mode-of-entry assumption. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 66

Table 3.11. RAIDAR V2.0 overall persistence (POV; 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 using 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 based on the assumed unit emission rate (TBBU; 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 analysis indicates the influence of model input 

parameters (physical chemical properties and 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 contribution of variance (CV) 

for each parameter on t-HBCD exposure to the representative marine mammal can be 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 67

determined. For example, the sensitivity (Sens) of an input parameter (Pi) on TBB 

calculations (model output) can be approximately quantified as (MacLeod et al. 2002) 

Sens = (∆TBB/TBB)/(∆P/Pi) (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 (CVi) on the TBB calculations are evaluated as a function of the 

variance (uncertainty) σ

2

i and sensitivity (Sensi) 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 TBBU calculations are the 

primary biotransformation HLs in the marine mammal and in fish (its primary dietary 

item). The degradation HLs in water and sediment are also sensitive input parameters, but 

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

influence the level in the marine mammal because it largely determines the residence 

time in the body. Biotransformation is particularly important for chemicals with high 

bioaccumulation potential in air breathing organisms (high KOW and high KOA). Due to 

these partitioning properties and the high lipid content of the organism, other routes of 

chemical elimination (respiration, urination) are very slow. Thus 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 sensitivity of the model to this parameter it has the highest 

contribution to variance in the prediction. Approximately 94% of the uncertainty 

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

the marine mammal. 

It is emphasized that this analysis is dependent 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 invertebrates, or concentrations in 

different compartments) will result in different 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 water 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. Hexabromocyclododecane (HBCD) Evaluation 68

Table 3.12. Summary of RAIDAR model sensitivity for input parameters on estimated 

total body burdens in the aquatic/marine mammal based on a unit emission rate (TBBU; 

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 

KOW 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 elsewhere (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 (TBBU; 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 assessed 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 transport to the remote region is considered 

and not transport in oceans or river water. The estimated body burden in the “remote 

region” is calculated by scaling the body burden in the “source region” by the fraction of 

the chemical that is transported to the “remote region”. For this screening assessment 

identical environmental conditions (e.g. temperature, surface coverage of water, soil and 

vegetation) and exposure pathways (e.g. dietary selection) describe both the source and 

remote regions. This screening level approach 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. Hexabromocyclododecane (HBCD) Evaluation 69

Figure 3.20 compares the TBBU predictions for the various substances in source and 

remote evaluative environments. For the source region simulation, the TBBU predictions 

span 9 orders of magnitude from a POP (PCB 180) to a non-POP (p-cresol). HBCD 

“source region” TBBU prediction is lower than most POPs and candidate POPs; however, 

it is comparable to some existing POPs (heptachlor, aldrin). For the “remote” region 

benchmarking scenario, the TBBU predictions span >20 orders of magnitude from a POP 

(PCB 180) to a non-POP (atrazine); however, certain non-POPs have higher TBBU

predictions in remote regions than listed POPs. The HBCD TBBU prediction is 

acomparable to certain POPs (heptahclor), but several orders of magnitude lower than 

other POPs (HCB, PCB 180). The HBCD TBBU prediction is also about 3 orders of 

magnitude lower than the TBBU predictions for the candidate POPs. It is noteworthy that 

The Tool long range transport predictions assume 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. Hexabromocyclododecane (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

4

HCB

PCB28

PCB101PCB180

Aldrin

Heptachlor

Biphenyl

p-cresol

Atrazine

CCl4

a-HCH

b-HCH

g-HCH

PBDE99

t-HBCD

log TBB / mmol

-8

-6

-4

-2

0

2

4

HCB

PCB28

PCB101PCB180

Aldrin

Heptachlor

Biphenyl

p-cresol

Atrazine

CCl4

a-HCH

b-HCH

g-HCH

PBDE99

t-HBCD

log TBB / mmol

10-1310-1410-19
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 71

3.5.1.4 GloboPOP: Arctic Contamination Potential (eACP10) 

Wania (2003b; 2006) introduced the Arctic Contamination Potential (ACP) as a metric of 

long range transport efficiency, a model-calculated value which is independent of 

emission rate. There are two different formulations 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

T A

M

M M mACP − = (3.3) 

where MT1 is the total mass of contaminant in the N-polar zone (all compartments), MA1 is 

the total mass of contaminant in the atmosphere of the N-polar zone and MTG is the total 

mass of contaminant in the global environment (all compartments). eACP represents the 

potential for absolute contamination of the northernmost climatic zone and is calculated 

as 

(100%) 1 1

TG

T A

E

M M eACP − = (3.4) 

where ETG is the total mass of contaminant emitted into global environment over the 

model simulation period (default is 10 years). mACP and eACP 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 (LRT) potential that has been compared to 

other model-derived indicators of LRT potential in the past (Fenner et al. 2005). Both 

ACPs are sensitive to the assumed emission 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 (Wania 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 version of the model, the default 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 eACP10 (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. Hexabromocyclododecane (HBCD) Evaluation 72

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

bands. 

Note that GloboPOP differs from some of the other model tools applied in this 

assessment with regards to the treatment of atmospheric degradation. Here, kOH is used as 

model input and the degradation half-life is calculated as a function of latitudinallyspecific OH radical concentration and temperature. 

We first conducted eACP10 simulations using physical-chemical property values for α-, 

β-, and γ-HBCD as well as values characterizing the technical mixture (t-HBCD). The 

results of these preliminary simulations are presented in the Appendix (Figure 9.1). Since 

no substantial differences in eACP10 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 range of degradation half-lives presented in 

Table 2.5 (lower bound, median and upper bound estimates). 

The calculated eACP10 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 (kOH, cm3

·molecule-1·s-1). Note that this figure should not be interpreted to 

mean that OH radical reactivity is the sole determinant of long range transport (LRT) 

potential. Atmospheric deposition and air-surface exchange are also important processes 

influencing environmental fate, hence degradation half-lives in other media also affect 

model output. As a general trend however, substances which are more recalcitrant to OH 

radical reactions have longer degradation half-lives in other media. Hence kOH can 

broadly serve as a proxy for overall persistence. The kOH and eACP10 values are also 

presented in Table 3.13. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 73

Figure 3.22. Arctic Contamination Potential after 10 years of constant emissions 

(eACP10) of POP, non-POP, Candi-POPs and t-HBCD (under different assumptions) as a 

function of reactivity with OH radicals (kOH, cm3

·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 eACP10 of CCl4 is shown on the y-axis because it was 

assumed to have a negligible kOH.

The eACP10 values of the POP compounds range from 0.004–1.6%. Aldrin and 

heptachlor had the lowest eACP10 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 Aldrin are roughly 1 to 3 orders of 

magnitude more susceptible to OH radical attack compared to the other POPs. HCB had 

the highest eACP10 (1.61%) while the PCBs ranged from 0.17% (PCB28) to 0.65% 

(PCB180). 

The eACP10 values for the non-POPs included in the benchmarking exercise ranged from 

0.004–0.18%. The lowest eACP10 values were calculated for biphenyl and p-cresol 

(0.004%) followed by atrazine (0.016%). These three compounds are characterized by 

low degradation half-lives in all bulk environmental compartments. In contrast, CCl4 had 

a relatively high eACP10 due to its persistence in the atmosphere and longer half-lives in 

water, soil and sediments compared to the other non-POPs. 

The eACP10 values for the candidate POPs included in the benchmarking exercise ranged 

from 0.37–0.55%. eACP10 values of the HCH isomers were similar (0.50–0.55%) while a 

lower value was calculated for PBDE99 (0.37%). These calculated eACP10 values fall 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 74

within the same range as the PCBs indicating that these candidate POPs are broadly 

similar to other POPs with respect to long range transport potential. 

eACP10 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 compartments) 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 eACP10 of t-HBCD ranges 

from 0.005% (lower bound HLs) to 0.4% (upper bound HLs); the eACP10 assuming the 

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

eACP10 is reduced to 0.02% demonstrating the influence of mode of entry on this metric 

of long range transport potential. The fact that the range of estimated eACP10 for t-HBCD 

spans two-orders of magnitude indicates the sensitivity of the model output to 

assumptions regarding degradation half-lives. Interestingly, eACP10 is least sensitive to 

the assumed half-life in freshwater 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 water) have a greater influence since these 

compartments have comparatively large volumes. Therefore, if experimental studies 

demonstrate the degradation half-lives in these compartments are substantially longer 

than the maximum values assumed here, it is no longer reasonable to assume that the 

maximum eACP10 calculated here is representative. 

The overall interpretation of these results is complicated by the range of eACP10 values 

exhibited by the POP compounds. Assuming median parameter values, the eACP10 of tHBCD is lower than PCBs, HCB and the other candidate POPs but higher than Aldrin 

and Heptachlor. In comparison to t-HBCD (median kOH value), Heptachlor and Aldrin 

are roughly 1 order of magnitude more susceptible to OH radical attack. The eACP10 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 eACP10 primarily because it is 

degraded in other compartments with similar HL as t-HBCD (median values). 

Partitioning properties (i.e. distribution) also influence overall persistence and eACP10. 

For example, Aldrin and Heptachlor have log KAW values 2 orders of magnitude higher. 

Strictly speaking, this benchmarking exercise could be interpreted to mean that t-HBCD 

has a long range transport potential similar to other POPs. However, the long range 

transport potential of Aldrin and Heptachlor is clearly different from other POPs (and 

Candidate POPs) included in the benchmarking 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 POPs in order for a similar mass of chemical 

to reach this environment. Under default assumptions regarding degradation half-life, the 

same argument could be made for t-HBCD based on its eACP10. It is also worth noting 

that the eACP10 of all non-POPs falls within the overall 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 and mode of entry of these 

emissions must also be considered. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 75

Two additional eACP10 simulations were conducted to further elucidate the fate and 

transport of t-HBCD in the global environment, one assuming perfect persistence in all 

compartments (i.e. negligible degradation) and one assuming no atmospheric degradation 

(default degradation half-lives in all other compartments). The model output is also 

included in Table 3.13. The eACP10 assuming perfect persistence is 0.93%, 

approximately 20 times higher than the default value. In this simulation, the main 

processes limiting long range transport are atmospheric deposition and other sink 

processes (e.g. sediment burial, transport to deep ocean via particle settling). The eACP10

value assuming no degradation in the atmosphere (and default half-lives in all other 

compartments) is 0.11%. This model output confirms the fact the i) degradation in other 

compartments has an influence on the long range transport potential of t-HBCD and ii) 

air-surface exchange processes (e.g. wet/dry deposition) transfer significant quantities of 

t-HBCD from the atmosphere to other compartments in source regions. 

Table 3.13. Selected compounds, OH radical reactivity and eACP10 calculated using 

GloboPOP model. 

Status Chemical Scenario kOH eACP10 

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 CCl4 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. Hexabromocyclododecane (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 application of the combined 

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

The Arctic contamination and bioaccumulation 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 model 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 mH

(g·person-1) and the cumulative emissions of a chemical to the global environment eTG

(g): 

AC− BAP=

mH

eTG (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 years1

. The resulting AC-BAP-values were plotted as a function of the 

octanol-air and air-water partition coefficient, yielding a chemical space diagram which 

identifies partitioning property combinations that favour accumulation of a globally 

emitted, persistent chemical in Arctic 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 complete persistence of HBCD in the 

environment and in food webs (“persistent HBCD”) 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 approximately 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. Hexabromocyclododecane (HBCD) Evaluation 77

estimate assuming 10 years of emissions for all chemicals. “Persistent HBCD” is 

expected to have approximately 15-25% of the maximum AC-BAP estimate assuming 70 

years of emissions for all chemicals. 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” into the part of the chemical space with 

elevated AC-BAP (>10 % of the maximum). This suggests that HBCD does have 

partitioning properties that favour transport to the Arctic environment and 

bioaccumulation in the Arctic human food chain. 

HBCD HBCD

HBCD

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 Bioaccumulation Potential (AC-BAP) (Figures from 

Czub et al., 2008). The circle in the lower right of each panel represents the approximate 

partitioning properties of HBCD (mixture). In the panel to the right, the blue line 

encircles areas of elevated AC-BAP10 (10 years of emissions) whereas the red line 

represents an elevated AC-BAP70 (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. 

The AC-BAP of “persistent HBCD” is somewhat higher than that of persistent PBDE 47 

and persistent 2,3,7,8-tetrachlorodibenzo-p-dioxin. This suggests that if degradation and 

biotranformation of HBCD are similar to these 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 study 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 resemblance with a selection of known POPs2

, rather than 

partitioning properties that would yield an elevated 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. Hexabromocyclododecane (HBCD) Evaluation 78

persistent. Brown and Wania (2008) used partitioning properties 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 emissions estimates allow for comparisons 

between model predictions and available monitoring data. These comparisons can be 

used to corroborate emission estimates and selected properties for HBCD. 

3.5.2.1 RAIDAR and Real World Regional Scale Monitoring Data 

The RAIDAR model was used to predict concentrations in general source regions of the 

European environment (e.g. regional areas in Sweden) and these estimates were 

compared to available monitoring data representative of regional non-point sources 

(Table 3.4). This comparison seeks to corroborate actual emission rate estimates and 

uncertainties in persistence estimates (e.g. degradation half-lives in water). This is done 

by considering various assumptions for emissions in a regional area (100,000 km2

) and 

different mode-of-entry scenarios and comparing 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 (EU) of 1 kg·h-1. For example, if the actual 

estimated emission rate (EA) is 0.5 kg·h-1 the unit output from the model is scaled lower 

by a factor of 2. Therefore if the unit concentration in a marine mammal, CU (ng·g-1 lw) is 

100, based on the actual emission rate estimate the corresponding actual concentration 

predicted by the model CA (ng·g-1 lw) is 50. This approach exploits the linear calculations 

of the model. 

It is emphasized that the model predicts steady state chemical concentrations in the 

environment. The application of a steady state model for comparisons with monitoring 

data is not appropriate if chemical concentrations in the environment have not 

approached steady state. Whether or not a chemical 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 time; 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; temporally-resolved emission 

data are not available and time-trend data, while showing a slight 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. Levels of HBCD in the environment appear to 

be near steady state as a result of long term production 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 applicability 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 the regional environments in which the monitoring data were 
Arnot et al. 2009. Hexabromocyclododecane (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 simple approach can reasonably reproduce 

environmental observations (Cowan-Ellsberry et al. 2009). For simplicity in this analysis, 

and due to data limitations, only t-HBCD predictions will be considered for comparisons 

with ΣHBCD monitoring data. 

The degree of fit between model predictions and observations can be quantified providing 

an indication of Model Bias (MB = CA / CO). If the predicted concentration CA is greater 

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

concentration CA is lower than the observed concentration CO 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 data. Assuming the structure of the model is 

representative of the environment and the input parameters are reasonable and that the 

system is near steady state, the estimates for the actual emission rate, mode-of-entry, and 

half-lives can be explored 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 

km2

. 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 km2 regional scale European environment. A confidence factor of 

5 was assigned to the actual emissions estimate. 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 Norway (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-entry 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 source (upstream estimates). There are no 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 80

representative regional monitoring data for soil. All soil measurements have been near 

production facilities. Estimates for RAIDAR regional scale water 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 environment are too low. The underestimation of 

measured levels in air and terrestrial organisms (eating mostly crop materials) suggests 

that emissions to air may be higher than presently estimated. This is further supported by 

monitoring data for dust in indoor environments and regional air concentrations from 

other global regions with lower use patterns than Europe. Assuming the model is 

representative of the conditions, this slight underestimation suggests that either the halflife estimates are too short, the emissions estimates are too low, the mode-of-entry 

proportions are different, or some combination of these three factors. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 81

Figure 3.24. RAIDAR model predictions (×) and 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 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/m3

)

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 TL
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 82

RAIDAR does not consider wastewater treatment. It is estimated that approximately 20% 

of emissions to treated wastewater are prevented from entering the natural environment 

(EC 2008). This would suggest that the proportional mode-of-entry for total emissions is 

therefore higher to air than 5% and total emissions may be lower 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 adjustments 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-lives. Another alternative scenario considered 

was to use the original emissions scaling factor, the 30:70 ratio for air : water mode-ofentry and the upper bound estimates for environmental degradation half-lives (Table 2.5). 

Figure 3.26 illustrates the results of these assumptions in comparison to the source 

regional scale monitoring data. There is still reasonable agreement in many 

compartments; however, there is now a greater MB in predictions for biota with an 

overall average MB for biological compartments of 2.23. 

Figure 3.26 also includes model predictions for humans, marine mammals and birds 

assuming the biotransformation half-lives for these organisms are equivalent to the 

estimates for fish on a body weight basis. When 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 biotransformed at faster rates 

in birds and mammals than in fish. 

In summary, the steady state model predictions are in good agreement 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. Hexabromocyclododecane (HBCD) Evaluation 83

Figure 3.25. RAIDAR model predictions (×) and 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 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/m3

)

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 TL
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 84

Figure 3.26. RAIDAR model predictions (×) and 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 95th

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 triangles are estimates assuming metabolic 

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

-4 -3 -2 -1 0 1 2 3

log concentration

Air (pg/m3

)

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 TL
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 85

The predicted concentrations in humans are somewhat higher than the median of the 

measured value; however, many uncertainties affect the comparisons of human 

predictions with monitoring data for humans. RAIDAR calculates concentrations for 

adult male humans; whereas the available monitoring data are predominantly for nursing 

mothers. In general, reproductive and lactational losses are recognized as lowering lipid 

normalized concentrations of hydrophobic chemicals in reproductive females compared 

to males. Thus, predictions for males in RAIDAR may over predict monitoring data for 

females. Humans are included in this analysis because the general human diet is 

composed of a range of items including meat and dairy products, fish, vegetables and 

fruits which represent a range of possible sources 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 differences in diet (omnivorous, vegetarian), and the 

import of foods from various regions, dietary 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 near-field exposures to humans such as the 

indoor environment. 

The model-measurement comparison can be affected by detection limits. Typically, when 

summary statistics are calculated from monitoring data with 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, piscivorous birds and their eggs, are 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, degradation half-lives, physical-cemical properties, 

mode-of-entry and the model, it is difficult to ascertain all parameters at the same time. In 

general, the model – monitoring data comparison suggests that current parameterization 

estimates are reasonable. If current emission and 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 that there is an inverse relationship between 

emissions estimates and degradation 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 transport potential of chemicals from source regions and create a 

mass-balance of these chemicals through the definition of historical or estimated regional 

loadings. The model consists of 25 predominantly terrestrial or oceanic regions that are 

linked through quantified atmospheric, fluvial and oceanic advective pathways. Each 

region consists of a maximum of seven environmental media, upper atmosphere, lower 

atmosphere, freshwater, freshwater sediments, soil, vegetation and coastal and/or oceanic 

water. The terrestrial regions are delineated to incorporate latitudinal climatic changes as 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 86

well as political boundaries. This marriage of environmental and political consideration 

effectively captures both the conditions affecting long range atmospheric transport of 

chemicals as well as providing a framework to support regulatory decisions (Toose et al. 

2004). The model accommodates both dynamic and steady state conditions and the 

efficiency of transport of chemical emitted from each region to a target region, such as 

the Arctic, can be determined (Reid and Mackay 2008). 

For the BETR-World model calculations emission estimates (ca. 2001) were derived 

from various sources; but mainly those described 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 proportion releases per unit demand in Europe. 

Australia has regulated the use of HBCD and the annual use for 2001 was about 40 

metric tonnes (NICNAS Australian Government 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 around the world including 

South Africa (Polder et al. 2008b). The assumed 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” environmental half-lives and a second time for 

“upper 95th 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 precipitation 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, hotspots 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 highly industrial and urbanized area that is not 

well predicted by this large global-scale low-resolution model. The BETR-World model 

predicts background concentrations in the global environment and is not of sufficiently 

high spatial resolution to predict concentration “hot-spots”. Predictions using the upperboundary half-lives underpredict the average concentrations by 3x in air and 10x in 

freshwater sediments. The predictions approach lower values of measured sediment 

concentrations in Tokyo Bay (0.056 to1.3 ng·g-1in the middle to outer bay, respectively 

(Minh et al. 2007)). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 87

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 points compare to results using the 95th percentile 

half-lives. 

Figures 3.28 and 3.29 show the air and freshwater 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 Atlantic Ocean. Other locations, notably the 

Arctic and the Orient, are predicted to be above detectable 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 predicted 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. Hexabromocyclododecane (HBCD) Evaluation 88

Figure 3.28. Atmospheric concentrations predicted by BETR-World model using the 

upper boundary half-lives. 

Figure 3.29. Freshwater sediment concentrations 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-point source sediment concentrations agrees well with 

measured background (upstream) concentrations (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 Stockholm, SE range between 0.8 to 1.5 μg·kg-
Arnot et al. 2009. Hexabromocyclododecane (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 sediments (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 (primarily 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 the underlying processes - the entire 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 concentration changes in response to changes in 

emissions. Specifically, it can estimate the time 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 are 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 km2

 and year (1 t·yr-1 per 1Mio km2), followed 

by 20 years without any emissions. In the default 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 default parameterization for the environment and 

human food chain of the Western 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 these simulations. Additionally, a second set of 

simulations with the half-lives in water, sediment and soil compartments at the upper 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 90

bound (HLsoil = HLwater = 850 days, HLsediment = 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 concentrations in air, soil, water, sediment, herring 

and humans. The water concentrations are for 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 between 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 emissions 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 40

concentraion in pg/m3

Soil

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40

concentration in pg/g OC

Water

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30 35 40

concentration in pg/L

Sediment

0

5

10

15

20

0 5 10 15 20 25 30 35 40

concentration in ng/g OC

Herring

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30 35 40

concentration in ng/g lipid

Human

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30 35 40

concentration in ng/g lipid

HBCD assuming HLsoil/water = 85 days, HLsediment = 35 days

HBCD assuming HLsoil/water = 850 days, HLsediment = 210 days

constant emissions 

at 1 ton/year/1Mio km2

(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 40

concentraion in pg/m3

Soil

0

50

100

150

200

250

0 5 10 15 20 25 30 35 40

concentration in pg/g OC

Water

0

2

4

6

8

10

12

14

16

0 5 10 15 20 25 30 35 40

concentration in pg/L

Sediment

0

5

10

15

20

0 5 10 15 20 25 30 35 40

concentration in ng/g OC

Herring

0

10

20

30

40

50

60

70

80

0 5 10 15 20 25 30 35 40

concentration in ng/g lipid

Human

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30 35 40

concentration in ng/g lipid

HBCD assuming HLsoil/water = 85 days, HLsediment = 35 days

HBCD assuming HLsoil/water = 850 days, HLsediment = 210 days

constant emissions 

at 1 ton/year/1Mio km2

(30 % air, 70 % water)

no emissions
Arnot et al. 2009. Hexabromocyclododecane (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 years of continuous emissions. With the longer 

degradation half lives (upper bound estimates for soil, water and sediment), the time to 

steady state is longer, yet still 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 acceptable for HBCD, as long as 

emission rates remain fairly constant on the time scale of a decade and 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 are 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 more 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 reveals that the extent of air concentration variability during a 

year depends on the degradation half-lives and 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 limited seasonal monitoring data for air. 

Absolute Concentration Values

Figure 3.30 also reveals that the absolute concentration levels in most media would be 

considerably higher if HBCD is more persistent in the surface media than the default 

degradation half-lives suggest. The difference is media dependent, ranging from less than 

a factor of two for air concentrations to an order of magnitude 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. Table 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. Hexabromocyclododecane (HBCD) Evaluation 92

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 

km2

 for different assumption concerning the mode 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/m3

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 model is linear with 

respect to the emissions. This implies that the results can be scaled for emissions rates 

other than 1 t·yr-1·per 1Mio km2

 by simple multiplication. For example, if the real 

emission rate is 5 t·yr-1·per 1Mio km2

 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 km2

), the unit emissions would be multiplied by a factor of about 2.2. 

Whereas it is not surprising that air concentrations 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 higher 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 water compartment receiving the emissions. 

Apparently, the atmosphere is more efficient in delivering contaminant to the estuarine 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 93

water compartment than water advection – because most of the emitted HBCD is retained 

in the sediments of the receiving water compartment. 

Overall, it is clear that reproducing 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 constrained. 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 concentration variability may provide some 

clues as to the likely mode of emission. Table 3.14 suggests that much higher 

summer-winter differences in air concentrations 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 choosing values for the half-lives that 

reconcile modelled and measured concentrations and time trends in soils and 

sediments. 

To place into context the time to steady state 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 simulations, 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 normalized to the average over the 40 year simulation period to 

focus on the differences in trends rather than the absolute concentration levels being 

predicted. The time trend for HBCD in Figure 3.31 refers to the simulations using the 

upper bound half-lives in surface media (water and soil). In all media, HBCD displays a 

shorter time to steady state than the three 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 emissions than categorized 

POPs. 
Arnot et al. 2009. Hexabromocyclododecane (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 40

concentraion 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 40

concentrati 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 40

concentrati 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 40

concentrati 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 40

concentration 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 40

concentrati on i n ng/g lipid

P CB-180

HCB

PCB-101

HBCD

Figure 3.31. Comparison of simulated time trends 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 concentration 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 used, CoZMoMAN predicts higher maximum 

concentrations for HBCD in water and herring 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 highlights that a chemical that is generally less persistent than 

categorized POPs, could potentially bioaccumulate to higher concentrations in organisms 

than those POPs, if they were emitted in similar fashion and in comparable amounts. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 95

Table 3.15. Maximum concentration of HBCD (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/m3 0.9 1.1 194 14 2.5 

Soil in pg/g OC 21 208 1700 500 261 

Water in ng/m3

 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 used to screen “chemicals 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 adverse 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 address. The objective of this section is to 

compile and critically review the available ecological and mammalian 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 the effects characterization 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 including a review of the current scientific 

literature. 

4.1 DATA COMPILATION AND REVIEW

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 the 
Arnot et al. 2009. Hexabromocyclododecane (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 regulatory 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 terrestrial toxicity data selected for review in the present 

study from the European Commission RAR (EC 2008). Other available laboratory studies 

with mammals indicate that HBCD is not carcinogenic, mutagenic, or toxic to 

reproduction. 

The soil micro-organism and vascular plant data are from good quality tests; however, the 

results are of limited use for establishing HBCD toxicity as they are 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 organisms 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 Dose Lower-confidence Limit (BMD-L) is 

considered equivalent to a chronic no observed adverse effect level (NOAEL). The 

corresponding critical effect dose (CED) for this effect endpoint of liver weight increase 

in females is 22.9 mg·(kg-bw·d)-1. The 2-generation rat reproduction study of Ema et al. 

(2008) used relatively widely spaced exposure levels with the lower 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 were 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 proposal that it be used to assess risk in bird eggs is an 

assumption not supported by a detailed justification in the RAR (EC 2008). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 97

Table 4.1. Summary of key data on the terrestrial environmental effects of HBCD: 

Studies considered valid in previous EU assessments1

. 

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. Hexabromocyclododecane (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 the 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 for risk assessment and POP categorization 

because either (i) test organisms were exposed 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) bioavailability was not quantified (the truly dissolved, 

bioavailable chemical concentration was not measured), (iii) cosolvents were used 

without quantifying the influence of the cosolvent on bioavailability and partitioning 

between the bulk water compartment and the test 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 available 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 solubility 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 mixture are considered (i.e. 58 

μg·L-1), there are undissolved precipitates of the β- and γ-HBCD isomers in the water 

column as they are substantially above their water solubility limits. The issue of β- and γHBCD precipitates forming at the water solubility 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 seemingly ignored in the evaluation 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 keeping 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. Thus 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 solubility (i.e. hydrophobic), such as HBCD, some 

fraction of the total water column concentration will not be bioavailable to the test 

organism because it partitions into or onto organic phases in the bulk water compartment. 

Typical measurements of the bulk exposure water concentration use solvent extraction 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 99

methods that measure both the truly dissolved and the bound, or sorbed, non-bioavailable 

fraction of chemical. This confounds the quantification of the concentration in the water 

that is actually being absorbed by the organism during the toxicity test. 

There can be complications interpreting toxicity data for hydrophobic substances when 

cosolvents are used. The use of cosolvents affects the bioavailability of a substance in 

aqueous exposure media. The degree to which the altered bioavailability occurs is 

speculative without a greater quantitative understanding of the influence of the cosolvent 

on toxicity endpoints in comparison to “pure” 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 practice of including a solvent control in the 

design does not address this issue. The solvent control establishes whether the solvent 

itself is causing any adverse effects. It does not provide any information about how the 

cosolvent may be affecting the test substance or test organisms (e.g. bulk phase water 

solubility, bioavailablity) and thereby altering toxicity. 

Evaluating the toxicity of mixtures is a challenge in terms of a theoretical foundation and 

in practical application (McCarty and Borgert 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, synergistic or antagonistic 

interactions. In addition to the broad general 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 specifically contraindicated for toxicity testing of 

difficult substances and mixtures such as HBCD. Generator systems are not considered 

appropriate for substances which differ in their water solubility since these differences 

will result in selective depletion of the more water soluble components from the column 

or disk matrix and thus influence their relative concentrations in the water phase (OECD 

2000). 

4.1.2.2 Aquatic Toxicity Data Review 

Table 4.2 summarizes the key aquatic toxicity data selected for review in the present 

study from the European Commission RAR (EC 2008). The available sediment 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 that could be used in the present risk 

assessment. 

Plants, Algae and Microorganisms

There are no toxicity data for freshwater or marine vascular plants but there are some 

results for freshwater and marine algae. In addition to the usual challenges 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 water, and measuring effects at in a specific 

exponential growth phase) almost all of the reported results were obtained with 

cosolvents. As different cosolvents were used at different concentrations, effects on 

bioavailability and toxicity are unquantified. However, since many of these tests were 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 100

near or in excess of water solubility levels, the results when cosolvents were used must be 

considered uncertain and likely overestimates of the actual aquatic toxicity. In other 

words, if the test had been conducted without cosolvent any toxicity that may have been 

reported would have occurred at a higher 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 for the aquatic invertebrate Daphnia. The acute 

test is of little utility as no effects were attributed 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 indicated 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 toxicity 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 NOECs was greater than the highest exposure 

level of 3.7 μg·L-1. Neither of these studies found any 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 these conditions. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 101

Table 4.2. Summary of key data on the aquatic environmental effects of HBCD: Studies 

considered valid in previous EU assessments1

. 

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 μg/l. 

(Graves and Swigert 

1997b) 

dimethylformamide 

Chronic toxicity 

Rainbow trout 

(Oncorhynchus 

mykiss) 

Flow-through 

OECD 210 and 

OPPTS 850.1400 

NOEC μg/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 EC50 >3.2 μg/l (Graves and Swigert 

1997a) 

dimethylformamide 

Chronic toxicity 

Daphnia magna TSCA , OECD 

Flow through 21 

day test. 

NOEC 3.1 μg/l 

LOEC length 5.6 μg/l (Drottar and Krueger 

1998) 

dimethylformamide 

ALGAE 

Selenastrum 

capricornutum 

OECD 201 and 

TSCA40/797/1050 

72 h EC50 and LOEC 

>2.5 μg/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 EC50 = 

9 μg/l (lowest value) 

72 h EC50 = 

40 μg/l (lowest value) 

96h EC50 >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 μg/l 

EC50 >40.6 μg/l 

Study 4 

(Desjardins et al. 

2004) 

No cosolvent, generator 

column used for single 

exposure concentration 

Skeletonema 

costatum 

OECD 201 NOEC >10 μg/l 

EC50 52 μg/l 

Study 5 

(Desjardins et al. 

2005) 

NOEC: 

dimethylformamide 

EC50: no cosolvent, 

generator column used 

for single exposure 

concentration 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 102

Compartment & 

Species 

Method Results Remark and 

Reference 

Cosolvent Used 

SEWAGE TREATMENT PLANT 

MICROORGANISMS  

Activated sludge Respiration 

inhibition 

OECD 209 

EC50 15 mg/l Limit test with one 

test concentration, 

EC50 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 EFFECT VALUES FOR PBT OR POPS CLASSIFICATION AND RISK ASSESSMENT

The European Commission RAR (2008) employs toxicity 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 toxicity data in their 

screening level POPs classification process. In the present assessment we first critically 

evaluate the values selected in the aforementioned assessments for screening HBCD as a 

potential PBT chemical (hazard) and for assessing the potential of significant adverse 

effects from exposure to HBCD in the environment (risk). Then, based on our analysis of 

the available data, alternative toxicity endpoints are proposed for assessing the potential 

of significant adverse effects from exposure to HBCD in the environment (risk). 

4.2.1 PBT and POPs "T" Classification 

The key toxicity data considered valid and diagnostic in the European Commission RAR 

(2008) and TemaNord POP proposal (2008) are the aquatic toxicity data. These data 

included the reproduction NOEC 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 solubility limit. The testing data appear to 

demonstrate some adverse effects (e.g. LOEC) above the water solubility of γ-HBCD; 

however, these “effect data” are highly uncertain due to the unquantified influence of 

undissolved HBCD (precipitate) and 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 Reichenberg (2006) also discuss a melting point 

cut-off for baseline toxicity. The melting point 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 concentration 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 toxic 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 103

effects at the water solubility limit, this and other observations suggests that it may 

possess only a baseline narcotic mode of toxic 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 there are no effects observed at the highest 

exposure concentration. Although reporting a NOEC value in this manner is correct it can 

be misleading, particularly if there are no reported concentrations of an effect 

concentration (i.e. LOEC). A NOEC clearly indicates that no effects were found at the 

specified exposure; however, for the majority of the reported NOEC values there were no 

indications of an exposure concentration where some effects might be expected (i.e. 

LOEC). Thus, an actual toxic exposure concentration 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 information: no adverse toxic effects 

were observed. In such circumstances the use of “no effects” data to indicate possible 

“toxic effects” for chemical assessments is deemed to be of poor scientific defensibility, 

particularly when there are other viable alternatives to assess the likelihood for significant 

adverse effects in the environment. Remaining 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 NOEC for rainbow trout (Drottar et al. 2001) is 

actually reported as > 3.7 μg·L-1 as no effects for several endpoints were observed at any 

exposure concentration tested. In both of these fish studies the measured exposure levels 

were only about 1/3 to 1/2 of the nominal 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 observed 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. Hexabromocyclododecane (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 indicate 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, the reported values are most likely confounded 

and overestimate toxicity such that studies carried out without cosolvent would likely 

generate toxicity estimate with a higher numerical 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 solubility issues, a detailed examination of the 

results from the 21-day studies (Drottar and Krueger 1998) indicates some problems in 

the latter part of this test, as can be seen in Table 3-157 in European Commission (2008). 

Mortality began to be observed in the negative 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 deaths 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 deaths 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 involved in causing the mortality. This also 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 105

raises uncertainty about the validity of the growth and reproductive effects reported, since 

a general increase in some other stressor sufficient to cause general increases in mortality 

will also affect non-lethal endpoints. 

The available Daphnia data do not clearly and unequivocally establish 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 well, 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 combination of no-effect and 

effects data and most obtained with the use of cosolvents. In the first study for a 

freshwater alga (Roberts and Swigert 1997) little information is reported on effects. An 

NOEC could not be estimated and the EC50 and LOEC were both reported as greater 

than 2.5 μg·L-1. A cosolvent was employed in this test. In the second study with three 

marine algae (Walsh et al. 1987b) several growth EC50s were obtained that varied both 

within and between species and no NOEC values were calculated. A cosolvent was also 

employed in this study. Since this work deviated from standard testing methodology the 

European Commission (2008) judged that this information should only be used in support 

of other work obtained with standard methods. 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 inhibition 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 growth 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 produce the exposure media. Problems with this 

methodology are discussed elsewhere (Section 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 alga (Desjardins et al. 2005) contains 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 obtained using a generator column to produce the 

exposure media (see Section 9.5 and (OECD 2000)) The study with cosolvent reported no 

adverse effects after 72 -hours of exposure in any of the test concentrations employed. 

The NOEC was reported as being greater than 10 μg·L-1, which was the highest exposure 
Arnot et al. 2009. Hexabromocyclododecane (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 establish that the chronic toxicity of HBCD to algae is below 

the European Commission PBT “T” criterion of 10 μg·L-1. In fact, adverse effects to 

algae are only reported at exposure levels in excess of the water solubility for the main γisomer. Perhaps the best algae data set (Desjardins et al. 2004; Desjardins et al. 2005), 

indicates that, despite the confounding issues, the chronic toxicity of HBCD to algae is 

above the “T” criterion of 10 μg·L-1 and therefore it should not be 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) identified and selected key 

environmental toxicity data for HBCD. The present 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 POP classification. Much of the HBCD data 

considered valid and applicable 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 implications of uncertainty 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 classification. Clearly, due to water solubility limitations, it is 

impossible to have a valid aquatic toxicity test result for HBCD that can be adequately 

assessed using the existing PBT NOEC criterion. The simplest and most informative 

statement on the aquatic toxicity testing data for HBCD is that no effects have been 

reported for exposure concentrations at or below 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 reliable or appropriate methodology to establish 

whether HBCD should be classified as a PBT or a POP on the basis of toxicity. A 

detailed interpretive review of available data does not support a conclusion that HBCD 

meets the “T” criterion for PBT or POPs classification, as presented in European 

Commission (2008) and TemaNord (2008), respectively. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 107

4.2.2 Previous Approaches for Effect Characterization 

Table 4.3 contains a summary of the critical toxicity data and the recommended 

assessment factors considered for PNEC estimation by the European Commission RAR 

(2008). The TemaNord (2008) report did not present a general 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 recommended for comprehensive risk assessment. 

The Technical Guidance Document (EC 2003) used for the European Commission RAR 

(2008) also suggests that the lowest NOEC should be used in the PNEC estimation. Both 

a NOEC and the lowest effect exposure level (LOEC) are considered more appropriate 

for developing a PNEC since the threshold for the occurrence of adverse effects is 

somewhere between these two estimated endpoints. 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. Rather 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 effects. The body/tissueresidue and the Total Daily Intake (TDI) approaches are viable alternatives examined in 

the next section. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 108

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

effects of HBCD from European 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 cause significant adverse effects in different 

regions of the global environment. First the body/tissue-residue approach is discussed 

followed by the Total Daily Intake (TDI) approach. 

Body/tissue-residue Approach

McCarty and Mackay (1993) examined some disparate trends in environmental fate and 

environmental toxicity and suggested a consolidated approach. It was suggested that 

routinely providing more detailed information for toxicity data, particularly some 

quantitative indication of the amount of substance 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 provide both an improved understanding of the 

toxicity of the substance and facilitate regulatory use of that enhanced toxicity 

information. The facilitated regulatory use would occur in two areas. The first is by more 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 109

direct comparison of laboratory-derived body/tissue residue dose-effect data with 

measured body/tissue concentrations of substances obtained from monitoring. The second 

is by enhancing both retrospective and prospective use of increasingly sophisticated 

environmental distribution, fate, and exposure models to explain and predict toxicity. For 

hydrophobic substances, such as HBCD, a further advantage is that an organism-based 

dose metric is largely free of the aforementioned issues that plague aquatic exposurebased metrics (bioavailability, cosolvents). Toxicity information developed based on 

body/tissue-residues is more directly comparable with the monitoring data and model 

predictions for various organisms at various trophic levels in the environment. A recent 

study provides a good example of using this approach to evaluate 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 Environmental 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 (Solomon 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 Protection of Aquatic and 

Aquatic-Dependent Wildlife document (U.S. EPA 2005). The U.S. EPA's Office of 

Pesticide Programs has adopted a residue-based approach for pesticide registration and 

has published a white paper on the registration process for candidate pesticides with 

PBT-like properties (Anderson et al. 2008a). Residue-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 and 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 extremes of the range of toxic potency appear 

to be defined by baseline narcosis and TCDD-like toxicity. 

For HBCD a comprehensive review of all available 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 original publications. Some residue-based data can be gleaned 

from the reviews provided in the European Commission risk assessment (2008). This, 

along with some other pertinent information, is presented in Figure 4.2. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 110

Figure 4.2. Residue-based organism toxicity and residue-based PNECs. 

On the left side of Figure 4.2 the approximate whole-body residue-effect ranges for the 

least toxic (baseline narcosis) and most toxic modes of action (TCDD-like toxicity) for 

small aquatic organisms of the order of 5% lipid (McCarty and Mackay 1993). This 

provides perspective on the range of possible 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 developed below and depicted in Figure 4.2. A marine fish 

no-observed effect residue (NOER) is based 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 content 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 levels 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. Hexabromocyclododecane (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 survival and reproduction study with earthworm (Aufderheide 

et al. 2003) at 28-days the highest 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 after 56 days at intermediate exposure 

levels but no BCF or tissue residue information was available. Earthworms have been 

shown to exhibit residue-effect relationships similar to fish and aquatic invertebrates for a 

number of organic chemicals (Lanno and McCarty 1997). 

There are three mammalian toxicity studies with 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 and is unlikely to be 

a site where toxic action occurs. This information 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 generation reproduction study (Lilienthal et al. 2009) 

where some liver residue data was collected. In the 28-d study the lowest effect levels 

were reported for female rats which exhibited 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 content of 5%, corresponds to residues of 0.0034 and 0.015 

mmol·kg-1 ww, respectively. The European Commission (2008) considered the BMD-L 

based on liver weight change to be the most robust. van der Ven et al. (2009) found a 

variety of effects due to HBCD exposure over one generation. The most sensitive 

response they noted was modest behavior changes in rats (BMD-L based on changes in 

auditory thresholds and effects of catalepsy) at HBCD liver levels of 7 and 16-20 μg·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 significance of some of the detected changes is 

uncertain. However, in the interests of presenting an alternative to the baseline narcosis 

scenario, the lowest data for the modest behaviorial changes (more than an order of 

magnitude below the robust BMD-L for liver weight change) will be used as a “worst 

case” exercise to develop a tentative residue-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 illustrative 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 data range up into the lower part of the 

chronic baseline narcosis residue range established for fish and some invertebrates. 
Arnot et al. 2009. Hexabromocyclododecane (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/bioaccumulation potential. However, the very low water 

solubility of HBCD limits the amount of exposure that any organism will encounter. The 

exposure-based toxicity cutoff that is encountered in aquatic exposures with highly 

hydrophobic, very low water solubility substances is operational here and this suggests 

that other exposure routes - primarily dietary exposure - 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-toxicity PNEC, it is 

assumed that the lowest residue-based dose-response data represents the lower end of a 

range of toxic effects caused 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 narcosis 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 pathway/receptor, exhibit 

effects due to the specific mode of action at a lower residue level than baseline narcosis, 

thus preempting effects due to baseline narcosis. 

Two residue-based PNEC development scenarios 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 starting just below the lower end of the observed 

chronic toxicity residue range. In this case the rat behavior data can be viewed as the 

extreme lower tail of chronic narcotic effects that are not routinely evaluated and are of 

uncertain and questionable relevance to the 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 specific but unknown mode of toxic action that is 

further assumed to be operational for all organisms. 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 this 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. Hexabromocyclododecane (HBCD) Evaluation 113

Total Daily Intake (TDI) Approach

The other general alternative approach to characterize the potential for adverse effects in 

the environment is to utilize available dietary exposure data as estimates for allowable 

Total Daily Intake (TDI). Dietary exposure tests of HBCD to laboratory models (rats) 

have been conducted to determine NOAEL and LOAEL based on exposure to t-HBCD 

dosed in food for a range of toxic endpoints. The European Commission RAR includes a 

detailed summary of the test data available ca. 2007 – 2008 (EC 2008). These data are 

primarily from 28 day exposure periods. 

From studies by Chengelis (Chengelis 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 endpoints tested in the van der Ven et al. 

study (2006), there were no significant effects reported at the highest exposure level (200 

mg·(kg-bw·d)-1), particularly in males. 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 significantly induced enzymatic 

activities were detected for CYP 2B in males 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 based on a two-generation 

reproductive toxicity study in rats (EC 2008). Thus, for the purpose of risk 

characterization in the present study a NOAEL of 10 mg·(kg-bw·d)-1 was selected. 

5.0 HAZARD CHARACTERIZATION (PBT/POP) 

5.1 SCREENING CRITERIA 

Table 5.1 presents the PBT and vPvB screening criteria proposed in the European 

Commission Technical Guidance Document (TGD) (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 similar with a few exceptions. In the UN-ECE 

Protocol a vapour pressure less than 1000 Pa is also included as a screening 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 the substance is likely to have 

significant adverse human health and/or environmental effects as a result 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 predicted levels of a chemical resulting or anticipated from its long 

range environmental transport (UNEP 2001). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 114

Table 5.1. PBT and vPvB screening criteria 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 CMRb 

Or Endocrine disrupting effects 

- 

a

 Bioconcentration Factor 

b Carcinogenic, Mutagenic and Toxic to Reproduction 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 115

Table 5.2. POP screening criteria according to the Stockholm Convention (UNEP 2001). 

Category Criteria 

1b. Persistence (P) Half-life in water > 2 months 

Half-life in sediments > 6 months 

Half-life in soils > 6 monthsa

1c. Bioaccumulation (B) Bioconcentration factor (BCF) > 5000 

Bioaccumulaiton factor (BAF) > 5000b 

log KOW > 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 for 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 Convention 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 116

Table 5.3. POP screening criteria according to the UN-ECE Protocol (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 monthsa

1d. Bioaccumulation (B) Bioconcentration factor (BCF) > 5000 

Bioaccumulaiton factor (BAF) > 5000b 

log KOW > 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 daysc 

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 lower 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 ASSESSMENT OF PERSISTENCE AND LONG RANGE TRANSPORT 

This section discusses the categorization of P and LRT. A caveat of the P categorization 

methodology is that a substance can be categorized 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 recommendations in the EU RAR and compare 

model predictions of overall persistence (POV) for HBCD with benchmark chemicals. 

5.2.1 Persistence 

The selected degradation half-lives (HLs) for 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 was derived from model estimates due to the 

lack of reliable data. Although there are uncertainties associated with the degradation 

studies showing elevated persistence in sediments and soils (e.g. (Davis et al. 2004)), 

there is no definitive evidence demonstrating that these data should be completely 

disregarded. Overall, based on available data, it can be concluded that HBCD likely 

fulfills some but not all of the bright-line cut-off “P” criteria presented above 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 117

However, it has been argued that overall persistence (POV) 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 models are necessary to assess “P” because it 

is not possible to trace a chemical in the actual environment. There is no screening “P” 

criterion for the POV metric; therefore, POV predictions for HBCD were compared to POV

predictions for POPs, non-POPs and candidate POPs (Figure 3.18 and Table 3.11). When 

default (median) HLs for HBCD were selected the POV is usually at least 1 order of 

magnitude, or more, lower than five of the six benchmark POPs. Notable exceptions are 

for benchmark comparisons with aldrin and heptachlor when emissions are assumed to 

air. HBCD POV predictions (median HLs) are also about 1 order of magnitude lower than 

the four candidate POPs. Certain non-POP benchmarks have POV predictions greater than 

HBCD. When upper bound HL estimates were selected for HBCD and 100% emissions 

are to air, the POV 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 POV predictions are generally much lower than many 

benchmark POPs and the candidate POPs (Table 3.11). When lower bound HL estimates 

for HBCD were selected the POV predictions are much lower than benchmark POPs 

(except for heptachlor) and the candidate POPs and more similar with most non-POPs. 

Without reduced uncertainty for the HL estimates it is difficult to arrive at an 

unambiguous conclusion regarding POV benchmarking for HBCD. 

Recommendations/Conclusions

It is not possible to definitively conclude whether or not HBCD fulfills the “P” 

degradation half-life criteria based on the available experimental data. Model 

assessments of POV against benchmark chemicals do not provide any definitive 

categorization for persistence, largely due to a wide range of POV values for listed 

POPs. Additional studies on aerobic degradation in water, sediments and soils may 

help to reduce the uncertainty in the estimated half-lives for HBCD and the “P” 

criteria comparisons. Further data on atmospheric chemistry of this compound 

would also be highly valuable for assessments of POV. Regardless, in the context of 

POP assessments, HBCD detected in remote regions 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 vapour pressures of HBCD (t-, α-, β-, γ-) were presented 

in Table 2.2. All values are far below the vapour pressure criterion 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 concentrations. While the median value (1.3 d) 

is below the criterion value of 2 days, the uncertainty associated with the estimate 

includes values that exceed the criterion. 

The CTD, TE and eACP10 of HBCD compared to POPs, non-POPs and candidate POPs 

are shown in Figures 3.18 and 3.22, respectively. Similar to POV, the CTD, TE and 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 118

eACP10 predictions for HBCD are lower than some POPs (e.g. HCB, PCBs) included as 

benchmarks but higher than others (e.g. aldrin, heptachlor). The non-POPs included in 

the study also exhibit a range of CTD, TE and eACP10 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 transport potential (compared with existing POPs). Long range 

transport potential benchmark estimates also show that the potential is lower when 

emissions are greater to the water 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 evidence 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 “source” emissions in “remote” regions may be 

exacerbated by the common practice of burning garbage (products containing HBCD) in 

Northern communities. The potential emissions related to use and disposal of HBCDcontaining products in remote areas is unclear. Thus, without improved knowledge of 

relative contributions of quantities released in remote regions 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 explicitly the result of long range transport. 

HBCD seems to have a “low” to “moderately-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 these 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 transport 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 characterizing the degradation half-life of HBCD in air, water, 

soil and sediment were available. In particular, empirical data on atmospheric 

chemistry of this compound (e.g. OH radical reactivity) would be highly valuable. 

However, available monitoring data indicate that HBCD is present in remote 

regions. This is considered sufficient evidence that HBCD has long range transport 

potential as identified by the screening criteria. 

5.3 ASSESSMENT OF BIOACCUMULATION

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, which introduces some uncertainty into the 

interpretation of the BCFs since guidelines recommend against the use of solvents for 
Arnot et al. 2009. Hexabromocyclododecane (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 uncertainty generally associated with estimating 

BCFs for hydrophobic substances (Arnot and Gobas 2006). However, Yakata et al. 

(2006) tested six single compounds 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 derived 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. Furthermore, the estimated log KOW values (Final Adjusted 

Value, see Table 2.2) for the three major isomers 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 support the bioaccumulation categorization using 

BCF data such that HBCD (or at least certain 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 perhaps other compartments of the environment. 

Hence, it remains uncertain if all diastereomers 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 eastern Arctic food web. There are other 

examples where different conclusions could be reached regarding bioaccumulation 

potential depending on what data are considered. Trophic level and lipid-normalized 

biomagnification factors (BMFTLs) 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 BMFTL < 1). Data from a food web study in the Western Canadian Arctic 

(Ismail et al. 2009) seem to contradict the patterns seen in Tomy et al. (2008). For 

example, the calculated BMFTL of α-HBCD for beluga : polar cod is 0.4 (although 

BMFTLs greater than one were observed in other cases, e.g., beluga : herring and beluga :

Arctic cisco). There also appear to be discrepancies in field-derived BMFs for ringed seal 

: polar cod feeding relationships among different 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 and polar cod sampled from 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 bioaccumulation potential, the weight of evidence 

suggests that HBCD is “B” according to the screening criteria presented in Tables 

5.1, 5.2 and 5.3. It remains uncertain if all diastereomers are bioaccumulative in all 

species based on the uncertainty of the bioisomerization and biotransformation 

rates for each isomer. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 120

5.4 ASSESSMENT OF TOXICITY (HAZARD) 

The toxicity hazard evaluation of HBCD was carried 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 and 5.3). There is no available evidence to 

suggest that HBCD is carcinogenic, mutagenic, a reproductive toxicant or an endocrine 

disrupting chemical. The exposure-based aquatic ecotoxicity data available for HBCD are 

of limited value for screening categorizations as all exposure data are confounded by 

water solubility/bioavailability issues including the use of a cosolvent or use of a 

generator column. In OECD testing guidance for difficult substances and mixtures 

(HBCD is both) there is a specific caution 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 exposure-based toxicity data 

cannot be obtained at concentrations that exceed 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 exposure 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 baseline narcotic mode of toxic action, the least toxic mode of 

action for organic chemicals. Based on the 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. 2002). Following this approach, available 

mammalian and ecotoxicity data for HBCD suggest that HBCD is a Category 1 chemical 

(the least “toxic” category). The majority of the qualitative statements for screening 

potential toxicity and adverse effects in the UN Stockholm Convention (Table 5.2) and 

the UN-ECE POP Protocol (Table 5.3), respectively, seek to identify substances with the 

potential for toxic effects. All substances have the “potential for adverse effects”, based 

on a function of the dose and the duration of exposure. Thus, strictly speaking, these 

qualifying statements are ineffective for screening potential POPs for “T”. More 

appropriately, both the UN-ECE Protocol and the UN Stockholm Convention recognize 

that substances should be evaluated for their potential adverse effects as a result of 

environmental exposures due to long range environmental (or atmospheric) tranport, i.e., 

the risk of toxicity occurring in remote regions. These more comprehensive assessments 

are beyond the hazard based screening criteria, but are arguably more relevant for 

chemical management objectives. The following section provides comprehensive risk 

characterizations based on the likelihood of toxic effects from HBCD in “local/near point 

source”, “source” region and “remote” regional scales. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 121

Recommendations/Conclusions

A detailed evaluation of key exposure-based aquatic toxicity studies for HBCD 

indicates that there are a number of uncertainties. The data are largely unusable for 

a valid screening assessment of the toxicity of HBCD for PBT/POPs categorization. 

The PBT and POP screening criteria are not effective for classifying very 

hydrophobic substances, especially mixtures such as HBCD, for their toxicity 

potential. It is recommended that future toxicity 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 ECOLOGICAL RISK ASSESSMENT

It is critical to assess whether HBCD concentrations measured or modelled in different 

environmental compartments of remote areas and elsewhere are likely to cause adverse 

effects. Adverse effects of HBCD are necessarily a function of actual dose estimates, i.e., 

exposure, and estimates for effects or no effects levels. In this section the critically 

evaluated and selected metrics for effect and no effects levels based on the two 

alternative approaches - body/tissue-residue and TDI - are compared with estimates of 

actual exposure in the environment. The primary focus is the likelihood of significant 

adverse effects in remote regions (i.e. the Arctic); however, evaluations for the likelihood 

of adverse effects in source regions and near 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-based PNECs for the two proposed scenarios 

that were developed in Section 4.2.3 and presented in Figure 4.2. The ranges of European 

monitoring data for invertebrates, freshwater 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 invertebrates are combined with the more 

abundant and broader range of marine invertebrate data in Figure 4.2. The lower section 

of each monitoring data range represents “remote” regions, including Arctic sites, while 

the upper section represents mostly sampling data near known industrial sites (“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 below this residue-based PNEC indicating that 

no adverse effects are expected due to exposures of organisms 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 assessment 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 invertebrate distribution at this PNEC is for mussels 
Arnot et al. 2009. Hexabromocyclododecane (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 upper limit of the the bird data also enters this range. 

These results suggest that freshwater fish and marine mammals in contaminated regions 

near local point source emissions may exhibit some adverse effects 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 occur 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 persued 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 monitoring data reviewed there were examined 

and specific information judged to be representative of “local” (near known point sources 

of HBCD release), “source” (in the general region where point sources are found), and 

“remote” (largely Arctic) observations were selected. This is appropriate as the initial risk 

assessment analysis, presented above, indicated 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 results 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 included. Discussions on the suitability of using 

some of the bird monitoring data were presented in Section 3.3.7.
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 123

Figure 6.1. Residue-based risk assessment with summary monitoring data from the EU 

RAR (EC 2008). 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 124

Inspection of Figure 6.2 (monitoring data from “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 mode of toxic action assumptions, 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 the 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 observations for marine mammals are likely due to the 

wide ranging habits of some of these organisms and their very high lipid content (related 

to the very substantial blubber content in their bodies). Lipid concentrations represent a 

challenge for interpretation due to a lack of knowledge of target-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 presented in Figures 6.1 to 6.4 suggest that 

there is potential for adverse effects occurring in organisms 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 unlikely to occur in any organisms in remote 

regions including those occupying higher trophic levels (i.e. marine 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. Hexabromocyclododecane (HBCD) Evaluation 125

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

data. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 126

Figure 6.3. Residue-based risk assessment with available “source” region monitoring 

data. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 127

Figure 6.4. Residue-based risk assessment with available “local/near point source” 

monitoring data. 
Arnot et al. 2009. Hexabromocyclododecane (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 intakes 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 ringed seals (8 to 25 yrs 

old) (Sormo et al 2006). The average, median, 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 concentration of 34.5 ng·g-lipid-1 in the representative piscivorous 

marine mammal. The corresponding total daily 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 dietary intake of tHBCD in piscivorous marine mammals in remote 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 the laboratory studies; however, a similar 

general approach for characterizing potential 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 95th percentile of concentrations in piscivorous marine mammals from 

source region and local (near point source) scale monitoring data were also selected for 

comparisons with the NOAEL TDI estimate of 10 mg·(kg-bw·d)-1 using the approach 

described above. The upper bound concentration 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 RAIDAR total daily dietary 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, respectively. 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 organisms in which the data are reported (i.e. 

ringed seal) rather than the general parameters used to calculate bioaccumulation for the 

representative marine mammal in RAIDAR. A more comprehensive assessment should 

also include TDI estimates for pups and calves 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 considered for other monitoring data in remote 

regions, most notably for polar bears and for certain bird species. There are presently no 

effects data available for risk characterization from bird species; therefore, the 

mammalian data would likely have to be considered. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 129

7.0 CONCLUSIONS 

7.1 ECOLOGICAL RISK ASSESSMENT

Two versions of a body/tissue residue-based risk assessment were conducted. The first 

was based on baseline narcosis and the other was based on an unidentified and assumed 

mode of toxic action associated with PNEC body/tissue residues 100 times lower than 

baseline narcosis. Neither supported classification of HBCD as a POP on the basis of 

toxicity as neither assessment indicated that adverse effects are expected in upper trophic 

level organisms living in remote areas distant from known point-source emissions of 

HBCD. 

The likelihood of adverse effects 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 environment with estimates of total daily intakes 

associated with NOAELs from laboratory studies. The corresponding 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 corresponding total daily dietary intake of tHBCD to obtain a steady state concentration corresponding with the upper bound of 

measured concentrations in piscivorous marine mammals in “source” and local scale 

(near point source) regional environments ranges from about 2 to 3 orders of magnitude 

below the NOAEL. 

Simply because a substance meets screening level hazard categorization 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 actual 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 remote 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 source” regions. It is recommended that these 

instances be considered for more comprehensive site-specific risk assessments conducted 

within regulatory frameworks typically employed for non-POP substances. These 

recommendations are further supported by the modelled and measured spatial trends in 

the environment. 

7.2 POP ASSESSMENT

7.2.1 Persistence and Long Range Transport Potential 

Based on the assessment of degradation studies and model output considered in this 

report, it cannot be definitively concluded that 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 considered reliable. Thus, according to the POP 
Arnot et al. 2009. Hexabromocyclododecane (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 HBCD is persistent and exhibits sufficient LRT 

potential to warrant consideration as a potential 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 criteria, 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 uncertainties associated with the laboratory BCF data for fish and 

assessments of bioaccumulation potential based on field biomonitoring data, the weight 

of evidence suggests that HBCD is bioaccumulative according to existing POPs 

screening criteria (BCF > 5000). All isomers have estimated log KOW > 5 (See Table 2.2) 

which is also sufficient to fulfill this particular 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 classification of HBCD as “B”.

7.2.3 Toxicity and Potential for Adverse Effects 

There are no quantitative screening criteria for 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 determine if the substance is likely to have 

significant adverse human health and/or environmental effects as a result of its long range 

transboundary atmospheric transport (UN-ECE 1998) including, where possible, a 

comparison of toxicity or ecotoxicity 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 presented residue-based and TDI-based risk 

assessments indicate that no adverse effects are expected in upper trophic level organisms 

in regions remote from locations associated with the release of HBCD to the 

environment. Thus, HBCD does not meet the general and vigorous component of the 

POPs “T” criterion relating to the likelihood of adverse effects in remote regions. 

Neither a detailed interpretation of available 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. Hexabromocyclododecane (HBCD) Evaluation 131

7.3 COMMENTARY ON PREVIOUS ASSESSMENTS

Previous Assessments

HBCD has been subject to a PBT assessment according to criteria presented in the 

European Commission Technical Guidance Document (TGD) (EC 2003; EC 2008). 

According to the screening criteria of the UN Stockholm Convention (UNEP 2001) and 

the UN-ECE Protocol, the TemaNord assessment 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 unequivocally fulfill the specific P-criterion, 

with some reliable studies indicating biodegradation 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 scientific 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 presence of HBCD in deep sediment layers. 

The final decision, that HBCD fulfills the PBT-criteria overall, was also justified based 

on the fact that the TGD specifically advises that, “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 exceeded considerably” (EC 2003). 

Similar data and arguments were presented in the POP proposal (TemaNord 2008) to 

arrive at a decision that HBCD fulfills the screening 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 aerobic sediments and ii) a study showing “no 

degradation observed” for soil. The presence 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 consideration of available monitoring 

data in remote regions. (TemaNord 2008) concluded 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 model output). The strongest evidence of LRT 

potential presented in (TemaNord 2008) is the summary of monitoring data from remote 

regions which demonstrate the HBCD is present 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 KOW (5.62). 

The POP proposal (TemaNord 2008) concluded that HBCD has “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 ecotoxicity” proposal. Further, the confounding 

factors associated with these tests (e.g. use of cosolvent) were not thoroughly discussed. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 132

While available laboratory studies with mammals do not indicate that HBCD is 

carcinogenic, mutagenic or toxic to reproduction, 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 adversely affect human health and/or the 

environment. Appendix 4 of the TemaNord POP proposal includes a summary table of 

listed sources concluded to cause potential risks as conducted in the EU RAR 

(“conclusion iii”) (TemaNord 2008); however, it is noteworthy that all of the listed 

sources are specific or generic industrial sites and not remote locations. 

Critical Appraisal of Previous Assessments

Substances identified in category-based assessments may not always 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 analysis suggests that the proportion of 

emissions to air may be greater than previously 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 environment. The findings in the 

present study also support conclusions from the previous assessments that there are 

concerns for HBCD released to the environment at local scales near industrial sites. 

HBCD was classified as “P” in (TemaNord 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 particular study. There is no consensus on the 

validity of applying a temperature-correction to adjust biodegradation rate constants. In 

fact, Boethling et al. (2009) specifically recommend against such adjustments for 

screening level persistence categorization using half-life criteria. However, according to 

both Annex D of the Stockholm Convention (UNEP 2001) and the UN-ECE POPs 

protocol (UN-ECE 1998), a substance can be classified as “P” if there is evidence that the 

substance is, “otherwise sufficiently persistent to be of concern within the scope of the 

convention/protocol”. Since the presence of HBCD in remote regions can be interpreted 

as evidence that a compound is sufficiently persistent and the quality and 

representativeness of the monitoring data are not in dispute, the overall screening 

conclusions in TemaNord (2008) regarding “P” are deemed reasonable. The monitoring 

data from remote regions also provide evidence of sufficient long range transport (LRT) 

potential to be considered under the POP protocol. Similarly, 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 classification (TemaNord 2008) is not reasonable. 

As detailed in the present study, there are significant reliability issues with regards to 

most of the previously selected aquatic-exposure based “T” endpoints. For the PBT 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 133

assessment, HBCD is essentially categorized as “T” because the substance is 

hydrophobic. There are no indications 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 TemaNord (2008) justification for toxicity and 

potential adverse effects is also considered to be unsatisfactory. Key concerns regarding 

the applicability of available toxicity data are not thoroughly 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 toxicity or ecotoxicity data with detected or 

predicted levels of HBCD in the POP proposal (TemaNord 2008) for either industrialized 

or remote regions despite the 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 previous assessments are deemed reasonable 

despite the discussed uncertainties; however, as discussed in the present study there 

are concerns regarding the previous assessments of toxicity and potential for 

adverse effects. The ecological risk assessment summarized in 7.1 provides an 

example of the type of analysis that should be considered. 

7.4 RECOMMENDATIONS

The risk characterization in the present study is considered to be a more scientifically 

defensible approach for assessing the potential adverse effets for HBCD and its isomers 

on human health and the environment than previous assessments. Evaluations for remote 

regions show low (residue-based approaches) to very low (total daily-intake-based 

approach) risk based on available 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 environmental 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 may require additional site-specific risk 

assessment work to establish appropriate regulatory activites. 

The proposed methods and current results can be considered a starting point for any 

further assessments for HBCD, and more appropriately, the individual diastereomers. The 

present data limitations prevent a more comprehensive quantification of associated 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 134

uncertainties with the risk assessment results. There are a number of recommendations 

throughout the document that should be considered to reduce uncertainties in future 

assessments of HBCD. In particular, the model sensitivity analysis shows that metabolic 

biotransformation rates are the chemical properties that contribute the greatest variance to 

estimated exposure levels in higher trophic level 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 storage 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 effect or no effect endpoints. 

Most monitoring data presently available are for “site-specific” areas near production 

sites. Monitoring data are needed for regional 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 environmental compartments need to be measured 

to have a more complete characterization of time trends and chemical distribution. 

Environmental compartments in remote regions also need to be monitored more 

thoroughly to test the hypotheses proposed in the present evaluation based on the models 

and available measurements. As more monitoring and test data become available, more 

modelling scenarios can also be considered. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 135

8.0 REFERENCES 

AMAP. 2005. Brominated flame retardants in the arctic. Fact sheet. 

Anderson B, Bailey T, Garber K, Hetrick J, Hoffman M, Kahn F, Odenkirchen E, Parker 

R, Ruhman M, Sappington K, Steeger T, Sunzenauer I. 2008a. White paper on 

methods for assessing ecological risk of pesticides with persistent, 

bioaccumulative and toxic characteristics. Office of Prevention, Pesticides and 

Toxic Substances, Office of Pesticide Programs, U.S. EPA, Washington, DC. p. 

221. 

Anderson B, Corbin M, Hetrick J, Odenkirchen E. 2008b. Revised supplement to the new 

chemical risk assessment (d289708, d293146; September, 2004) using alternative 

framework based on newly submitted accumulation data. Office of Prevention, 

Pesticides and Toxic Substances, U.S. Environmental Protection Agency. 

Washington DC. 

Antignac JP, Cariou R, Maume D, Marchand P, Monteau R, Zalko D, Berrebi A, Cravedi 

JP, Andre F, Le Bizec B. 2008. Exposure assessment of fetus and newborn to 

brominated flame retardants in France: Preliminary data. Molecular Nutrition & 

Food Research 52: 258-265. 

Armitage JM, Cousins IT, Hauck M, Harbers JV, Huijbregts MAJ. 2007. Empirical 

evaluation of spatial and non-spatial European-scale multimedia fate models: 

Results and implications for chemical risk assessment. Journal of Environmental 

Monitoring 9: 572-581. 

Arnot JA and Gobas FAPC. 2006. A review of bioconcentration factor (BCF) and 

bioaccumulation factor (BAF) assessments for organic chemicals in fish. 

Environmental Reviews 14: 257-297. 

Arnot JA, Gouin T, Mackay D. 2005. Practical methods for estimating environmental 

biodegradation rates. Report for environment Canada. Canadian Environmental 

Modelling Network, Trent University, Peterborough, ON. p. 50. 

Arnot JA and Mackay D. 2008. Policies for chemical hazard and risk priority setting: Can 

persistence, bioaccumulation, toxicity and quantity information be combined? 

Environmental Science and Technology 42: 4648-4654. 

Arnot JA, Mackay D, Bonnell M. 2008a. Estimating metabolic biotransformation rates in 

fish from laboratory data. Environmental Toxicology and Chemistry 27: 341-351. 

Arnot JA, Mackay D, Parkerton TF, Bonnell M. 2008b. A database of fish 

biotransformation rates for organic chemicals. Environmental Toxicology and 

Chemistry 27: 2263-2270. 

Arnot JA, Mackay D, Webster E, Southwood JM. 2006. Screening level risk assessment 

model for chemical fate and effects in the environment. Environmental Science 

and Technology 40: 2316-2323. 

Arnot JA, Meylan W, Tunkel J, Howard PH, Mackay D, Bonnell M, Boethling RS. 2009. 

A quantitative structure-activity relationship for predicting metabolic 

biotransformation rates for organic chemicals in fish. Environmental Toxicology 

and Chemistry 28: 1168-1177. 

Aronson D, Boethling R, Howard P, Stiteler W. 2006. Estimating biodegradation halflives for use in chemical screening. Chemosphere 63: 1953-1960. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 136

Aufderheide J, Jones A, MacGregor JA, Nixon WB. 2003. Effect of 

hexabromocyclododecane on the survival and reproduction of the earthworm, 

Eisenia fetida. ABC Laboratories, Inc. and Wildlife International Ltd, Columbia, 

Missouri and Easton, Maryland, USA. p. 94. 

Aune M, Barregård L, Claesson A, Darnerud PO. 2002. Organic environmental pollutants 

in breast milk from Gothenburg, Sweden, 2001. Report 219 0108. Swedish 

Environmental Protection Agency. 

Backus S, Archer M, Harrison B, Williams D, Muir DCG, Alaee M (2005). Spatial and 

temporal distributions of brominated flame retardants (BFRs) in wet-only 

precipitation collected in the great lakes basin. SETAC North America, 

Baltimore, MD. 

Bahm K and Khalil MAK. 2004. A new model of tropospheric hydroxyl radical 

concentrations. Chemosphere 54: 143-166. 

Becher G. 2005. The stereochemistry of 1,2,5,6,9,10-hexabromocyclododecane and its 

graphic representation. Chemosphere 58: 989-991. 

Bennett, DH, Kastenberg WE, McKone TE. 1999. General formulation of characteristic 

time for persistent chemicals in a multimedia environment. Environmental 

Science and Technology 33(3): 503-509. 

Berg UT and Nyholm N. 1996. Biodegradability simulation studies in semicontinuous 

activated sludge reactors with low (μg/l range) and standard (ppm range) 

chemical concentrations. Chemosphere 33: 711-735. 

Bergander L, Kierkegaard A, Sellstrom U, Wideqvist U, de Wit C. 1995. Are brominated 

flame retardants present in ambient air? 6th Nordic symposium on organic 

pollutants. Smygehuk, September 17-20. 

Beyer A, Wania F, Gouin T, Mackay D, Matthies M. 2002. Selecting internally 

consistent physicochemical properties of organic compounds. Environmental 

Toxicology and Chemistry 21: 941-953. 

Bidleman TF. 1984. Estimation of vapor pressures for nonpolar organic compounds by 

capillary gas chromatography. Analytical Chemistry 56: 2490-2496. 

Boethling RS, Fenner K, Howard PH, Klecka G, Madsen T, Snape JR, Whelan MJ. 2009. 

Evaluating environmental persistence of organic pollutants: Guidance for 

development and review of POP risk profiles. Integrated Environmental 

Assessment and Management in press. 

Braune BM, Mallory ML, Gilchrist HG, Letcher RJ, Drouillard KG. 2007. Levels and 

trends of organochlorines and brominated flame retardants in ivory gull eggs from 

the Canadian arctic, 1976 to 2004. Science of the Total Environment 378: 403 - 

417. 

Breivik K, Czub G, McLachlan MS, Wania F. Introducing cozmoman: A dynamic 

mechanistic model of the link between environmental emissions and human body 

burdens of organic contaminants. Submitted to Environ. Sci. Technol. (April 9, 

2009). 

Brown TN and Wania F. 2008. Screening chemicals for the potential to be persistent 

organic pollutants: a case study of arctic contaminants. Environmental Science & 

Technology 42: 5202-5209. 

Bytingsvik JG, H.; Pettersvik-Salmer, M.; Soermo, E.G.; Baek, K.; Föreid, S.; Ruus, A.; 

Skaare, J.U.; Jenssen, B.M. 2004. Spatial and temporal trends of BFRs in Atlantic 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 137

cod and polar cod in the north-east Atlantic. Organohalogen Compounds 66: 

3869-3873. 

Chengelis CP. 2001. A 90-day oral (gavage) toxicity study of HBCD in rats. Unpublished 

study report Wil-186012. Wil Research Laboratories, Inc., Ashland, Ohio, USA.

p. 1527. 

Christensen GN, Evenset A, Zaborska A, Berger U, Carroll J. 2004. Sediment dating and 

historical development of contaminants in Lake Ellasjøen, Bear Island. 

Cole JG, Mackay D. 2000. Correlation of environmental partitioning properties of 

organic compounds: The three solubilities approach. Environmental Toxicology 

and Chemistry 19: 265-270. 

Covaci A, Gerecke AC, Law RJ, Voorspoels S, Kohler M, Heeb NV, Leslie H, Allchin 

CR, de Boer J. 2006. Hexabromocyclododecanes (HBCDs) in the environment 

and humans: a review. Environmental Science & Technology 40: 3679-3688. 

Cowan-Ellsberry CE, McLachlan MS, Arnot JA, MacLeod MJ, McKone TE, Wania F. 

2009. Modeling exposure to persistent chemicals in hazard and risk assessment. 

SETAC Pellston Workshop Proceedings. Integrated Environmental Assessment 

and Management in press. 

Crane M and Newman MC. 2000. What level of effect is a no observed effect? 

Environmental Toxicology and Chemistry 19: 516-519. 

Czub G and McLachlan MS. 2004. A food chain model to predict the levels of lipophilic 

organic contaminants in humans. Environmental Toxicology and Chemistry 23: 

2356-2366. 

Czub G, Wania F, McLachlan MS. 2008. Combining long range transport and 

bioaccumulation considerations to identify potential arctic contaminants. 

Environmental Science & Technology 42: 3704-3709. 

Davis JW, Gonsior S, Marty G, Ariano J. 2005. The transformation of 

hexabromocyclododecane in aerobic and anaerobic soils and aquatic sediments. 

Water Research 39: 1075-1084. 

Davis JW, Gonsior SJ, Markham DA, Friederich U, Hunziker RW, Ariano JM. 2006. 

Biodegradation and product identification of C-14 hexabromocyclododecane in 

wastewater sludge and freshwater aquatic sediment. Environmental Science & 

Technology 40: 5395-5401. 

Davis JW, Gonsior SJ, Markham DA, Marty GT. 2004. Investigation of the 

biodegradation of 14C-hexabromocyclododecane in sludge, sediment and soil. 

Toxicology and Enviornmental Research and Consulting, The Dow Chemical 

Company., Midland, Michigan, USA. p. 113. 

Davis JW, Gonsior SJ, Marty GT. 2003a. Evaluation of aerobic and anaerobic 

transformation of hexabromocyclododecane in aquatic sediment systems. Dow 

Chemical Company, Midland, MI. p. 87. 

Davis JW, Gonsior SJ, Marty GT. 2003b. Evaluation of aerobic and anaerobic 

transformation of hexabromocyclododecane in soil. Dow Chemical Company, 

Midland, MI. p. 61. 

de Boer J, Allchin C, Zegers B, Boon JP, Brandsma SH, Morris S, Kruijt AW, van der 

Veen I, van Hesselingen JM, Haftka JJH. 2002. HBCDD and TBBP-A in sewage 

sludge, sediments and biota, including interlaboratory study. RIVO - Netherlands 

Institute for Fisheries Reserach, Wageningen, The Netherlands. p. 40. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 138

de Boer J and Wells DE. 2006. Pitfalls in the analysis of brominated flame retardants in 

environmental, human and food samples - including results of three international 

interlaboratory studies. Trends in Analytical Chemistry 25: 364-372. 

Deichmann WB, Henschler D, Holmstedt B, Keil G. 1986. What is there that is not 

poison? A study of the third defense by Paracelsus. Archives of Toxicology 58: 

207-213. 

Desjardins D, MacGregor JA, Krueger HO. 2004. Hexabromocyclododecane (HBCD): A 

72-hour toxicity test with the marine diatom (Skeletonema costatum), final report. 

Wildlife International, Ltd., Easton, Maryland, USA. p. 66. 

Desjardins D, MacGregor JA, Krueger HO. 2005. Chapter 1, hexabromocyclododecane 

(HBCD): A 72-hour toxicity test with the marine diatom (Skeletonema costatum) 

using a co-solvent. Chapter 2, hexabromocyclododecane (HBCD): A 72-hour 

toxicity test with the marine diatom (Skeletonema costatum) using generator 

column saturated media. Final report. Wildlife International, Ltd., Easton, 

Maryland, USA. p. 

deWit CA. 2002. An overview of brominated flame retardants in the environment. 

Chemosphere 46: 583-624. 

Drever JI. 1997. The geochemistry of natural waters. Upper Saddle River, NJ, PrenticeHall, Inc. 

Drottar KR and Krueger HO. 1998. Hexabromocyclododecane (HBCD): A flow-through 

life-cycle toxicity test with the cladoceran (Daphnia magna). Final report. 439a108. Wildlife International Ltd., Easton, Maryland, USA. p. 78. 

Drottar KR and Krueger HO. 2000. Hexabromocyclododecane (HBCD): A flow-through 

bioconcentration test with the rainbow trout (Oncorhynchus mykiss). Final report. 

Wildlife International Ltd,, Easton, MD. p. 137. 

Drottar KR, MacGregor JA, Krueger HO. 2001. Hexabromocyclododecane (HBCD): An 

early life-stage toxicity test with the rainbow trout (Oncorhynchus mykiss). Final 

report. Wildlife International, Ltd., Easton, Maryland, USA. p. 102. 

EC. 2003. 2nd Edition of the Technical Guidance Document in support of Commission 

Directive 93/67/EEC on Risk Assessment for New Notified Substances. 

Commission Regulation (EC) No 1488/94 on Risk Assessment for Existing 

Substances and Directive 98/8/EC of the European Parliament and of the Council 

Concerning the Placing of Biocidal Products on the Market. European 

Commission. Luxembourg. BE, Office for Official Publications of the European 

Communitie. 

EC. 2008. Risk assessment hexabromocyclododecane, CAS-No.: 25637-99-4, EINECSNo.: 247-148-4, final draft May 2008. European Commission. Sundbyberg, 

Sweden, Swedish Chemicals Agency: p. 492. 

Eljarrat E, De La Cal A, Raldua D, Duran C, Barcelo D. 2004. Occurrence and 

bioavailability of polybrominated diphenyl ethers and hexabromocyclododecane 

in sediment and fish from the Cinca River, a tributary of the Ebro River (spain). 

Environmental Science & Technology 38: 2603-2608. 

Eljarrat E, Guerra P, Martinez E, Farre M, Alvarez JG, Lopez-Teijon M, Barcelo D. 

2009. Hexabromocyclododecane in human breast milk: Levels and enantiomeric 

patterns. Environmental Science & Technology 43: 1940-1946. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 139

Ema M, Fujii S, Hirata-Koizumi M, Matsumoto M. 2008. Two-generation reproductive 

toxicity study of the flame retardant hexabromocyclododecane in rats. 

Reproductive Toxicology 25: 335-351. 

Eriksson P, Fischer C, Wallin M, Jakobsson E, Fredriksson A. 2006. Impaired behaviour, 

learning and memory, in adult mice neonatally exposed to 

hexabromocyclododecane (HBCDD). Environmental Toxicology and 

Pharmacology 21: 317-322. 

Evenset A, Christensen GN, Carroll J, Zaborska A, Berger U, Herzke D, Gregor D. 2007. 

Historical trends in persistent organic pollutants and metals recorded in sediment 

from Lake Ellasjoen, bjornoya, Norwegian arctic. Environmental Pollution 146: 

196-205. 

Fangstrom B, Athanassiadis L, Odsjo T, Noren K, Bergman A. 2008. Temporal trends of 

polybrominated diphenyl ethers and hexabromocyclododecane in milk from 

Stockholm mothers, 1980-2004. Molecular Nutrition & Food Research 52: 187-

193. 

Fenner K, Scheringer M, MacLeod M, Matthies M, McKone T, Stroebe M, Beyer A, 

Bonnell M, Le Gall AC, Klasmeier J, Mackay D, Van De Meent D, Pennington 

D, Scharenberg B, Suzuki N, Wania F. 2005. Comparing estimates of persistence 

and long-range transport potential among multimedia models. Environmental 

Science & Technology 39: 1932-1942. 

Fjeld E, Schlabach M, Berge JA, Green N, Eggen T, Snilsberg P, Vogelsang C, Rognerud 

S, Kjellberg G, Enge EK, Dye C, Gundersen H. 2005. Bromerte 

flammehemmere,perfluoralkylstoffer, irgarol, diuron, bht og dicofol. Kartlegging 

av utvalgtenye organiske miljøgifter 2004. NIVA, Norway. p. 105. 

Förster B. 2007. Hexabromocyclododecane (HBCD): Effects on soil microorganisms. 

ECT study number: AU1BB., p. 1-20. 

Gabrielsen GW, Knudsen LB, Verreault J, Push K, Muir DDC, Letcher RJ. 2004. 

Halogenated organic contaminants and metabolites in blood and adipose tissues of 

polar bears (Ursus maritimus) from Svalbard. Norwegian Polar Institute, Tromsø, 

Norway. p. 32. 

Gerecke AC, Giger W, Hartmann PC, Heeb NV, Kohler HPE, Schmid P, Zennegg M, 

Kohler M. 2006. Anaerobic degradation of brominated flame retardants in sewage 

sludge. Chemosphere 64: 311-317. 

Germer S, Piersma AH, van der Ven L, Kamyschnikov A, Fery Y, Schmitz H-J, Schrenk 

D. 2006. Subacute effects of the brominated flame retardants 

hexabromocyclododecane and tetrabromobisphenol A on hepatic cytochrome 

P450 levels in rats. Toxicology 218: 229-236. 

Geyer HJ, Schramm KW, Darnerud PO, Aune M, Anton Feicht E, Fried KW, 

Henkelmann B, Lenoir D, Schmid P, McDonald TA. 2004. Terminal elimination 

half-lives of the brominated flame retardants TBBPA, HBCD, and lower 

brominated PBDEs in humans. Organohalogen Compounds 66: 3867-3872. 

Gobas FAPC, Purdy R, Granville G, Cowan C, Lewis M, Socha A, Chapman PM. 2001. 

Proposal for hazard identification of organic chemicals based on inherent toxicity. 

SETAC Globe 2: 33-34. 

Göransson P, Börjesson L, Karlsson M. 2004. Kustkontrollprogram för helsingborg. 

Årsrapport 2003. Miljönämnden i Helsingborg, Helsingborg, Sweden. p. 84. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 140

Goss KU, Arp HPH, Bronner G, Niederer C. 2008. Partition behavior of 

hexachlorocyclohexane isomers. Journal of Chemical and Engineering Data 53: 

750-754. 

Gouin T and Wania F. 2007. Time trends of arctic contamination in relation to emission 

history and chemical persistence and partitioning properties. Environmental 

Science & Technology 41: 5986-5992. 

Graves WC and Swigert JP. 1997a. Hexabromocyclododecane (HBCD): A 48-hour flowthrough acute toxicity test with the cladoceran (Daphnia magna). Final report. 

439a-102. Wildlife International Ltd., Easton, Maryland, USA. p. 

Graves WC and Swigert JP. 1997b. Hexabromocyclododecane (HBCD): A 96-hour flowthrough acute toxicity test with the rainbow trout (Oncorhynchus mykiss). Final 

report. 439a-101. Wildlife International Ltd., Easton, Maryland, USA. p. 70. 

Harrad S, Abdallah MA-E, Covaci A. 2009. Causes of variability in concentrations and 

diastereomer patterns of hexabromocyclododecanes in indoor dust. Environment 

International 35: 573-579.

Hayward S, Lei Y, Wania F. 2006. Comparative evaluation of three high-performance 

liquid chromatography-based kow estimation methods for highly hydrophobic 

organic compounds: Polybrominated diphenyl ethers and 

hexabromocyclododecane. Environmental Toxicology and Chemistry 25: 2018-

2027. 

Heeb NV, Schweizer WB, Kohler M, Gerecke AC. 2005. Structure elucidation of 

hexabromocyclododecanes - a class of compounds with a complex 

stereochemistry. Chemosphere 61: 65-73. 

Heeb NV, Schweizer WB, Mattrel P, Haag R, Gerecke AC, Schmid P, Zennegg M, 

Vonmont H. 2008. Regio- and stereoselective isomerization of 

hexabromocyclododecanes (HBCDs): Kinetics and mechanism of γ- to α-HBCD 

isomerization. Chemosphere 73: 1201-1210. 

Hendriks AJ, van der Linde A, Cornelissen G, Sijm DTHM. 2001. The power of size. 1. 

Rate constants and equilibrium ratios for accumulation of organic substances 

related to octanol-water partition ratio and species weight. Environmental 

Toxicology and Chemistry 20: 1399-1420. 

Hoh E and Hites RA. 2005. Brominated flame retardants in the atmosphere of the eastcentral United States. Environmental Science & Technology 39: 7794-7802. 

Howard PH, Boethling RS, Jarvis WF, Meylan WM, Michalenko EM. 1991. Handbook 

of Environmental Degradation Rates. Chelsea, MI, Lewis Publishers. 

Hu TM and Hayton WL. 2001. Allometric scaling of xenobiotic clearance: Uncertainty 

versus universality. American Association of Pharmaceutical Scientists Journal 3: 

1-14. 

Ismail N, Gewurtz SB, Pleskach K, Whittle DM, Helm PA, Marvin CH, Tomy GT. 2009. 

Brominated and chlorinated flame retardants in lake ontario, Canada, lake trout 

(Salvelinus namaycush) between 1979 and 2004 and possible influences of food 

web changes. Environmental Toxicology and Chemistry in press. On-line 

December 2, 2008. 

Isobe T, Ramu K, Kajiwara N, Takahashi S, Lam PKS, Jefferson TA, Zhou K, Tanabe S. 

2007. Isomer specific determination of hexabromocyclododecanes (HBCDs) in 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 141

small cetaceans from the South China Sea - levels and temporal variation. Marine 

Pollution Bulletin 54: 1139 - 1145. 

Jager T, Heugens EHW, Kooijman SA. 2006. Making sense of ecotoxicological test 

results: Towards application of process-based models. Ecotoxicology 15: 305-314. 

Janák K, Covaci A, Voorspoels S, Becher G. 2005. Hexabromocyclododecane in marine 

species from the western Scheldt estuary: Diastereoisomer- and enantiomerspecific accumulation. Environmental Science & Technology 39: 1987-1994. 

Janák K, Thomsen C, Becher G (2004). Enantiomer specific determination of HBCD 

diastereomers by LC-MS-MS. The Third International Workshop on Brominated 

Flame Retardants - BFR 2004. 

Jenssen BM, Sørmo EG, Salmer MP, Bæk K, Skaare JU (2004). Brominated flame 

retardants (BFRs) in the arctic marine food chain. In The Third International 

Workshop on Brominated Flame Retardants - BFR 2004. 

Jouyban A. 2008. Review of the cosolvency models for predicting solubility of drugs in 

water-cosolvent mixtures. Journal of Pharmaceutical Sciences 11: 32-57. 

Kajiwara N, Isobe T, Ramu K, Tanabe S. 2006. Temporal trend studies on 

hexabromocyclododecane (HBCD) in marine mammals from asia-pacific. 

Organohalogen Compounds 68: 515 - 518. 

Kajiwara N, Sueoka M, Ohiwa T, Takigami H. 2009. Determination of flame-retardant 

hexabromocyclododecane diastereomers in textiles. Chemosphere 74: 1485-1489. 

Kakimoto K, Akutsu K, Konishi Y, Tanaka Y. 2008. Time trend of 

hexabromocyclododecane in the breast milk of japanese women. Chemosphere

71: 1110-1114. 

Kelly BC, Ikonomou M, Blair JD, Surridge B, Hoover D, Grace R, Gobas FAPC. 2009. 

Perfluoroalkyl contaminants in an arctic marine food web: Trophic magnification 

and wildlife exposure. Environmental Science & Technology published on-line 

April 30, 2009. 

Klamer HJC, Leonards PEG, Lamoree MH, Villerius LA, Akerman JE, Bakker JF. 2005. 

A chemical and toxicological profile of Dutch North Sea surface sediments. 

Chemosphere 58: 1579-1587. 

Klasmeier J, Matthies M, MacLeod M, Fenner K, Scheringer M, Stroebe M, Le Gall AC, 

McKone TE, van de Meent D, Wania F. 2006. Application of multimedia models 

for screening assessment of long-range transport potential and overall persistence. 

Environmental Science and Technology 40: 53–60. 

Knudsen et al. 2007. Halogenated organic contaminants and mercury in dead or dying 

seabirds on Bjørnøya (Svalbard). SPFO, Statens Forurensningstilsyn. p. 45. 

Knudsen LB, Gabrielsen GW, Verreault J, Barrett R, Skåre JU, Polder A, Lie E. 2005. 

Temporal trends of brominated flame retardants, cyclodeca-1,5,9-triene and 

mercury in eggs of four seabird species from northern Norway and Svalbard. 

Norwegian Polar Institute, Tromsø, Norway. p. 43. 

Knutsen HK, Kvalem HE, Thomsen C, Froshaug M, Haugen M, Becher G, Alexander J, 

Meltzer HM. 2008. Dietary exposure to brominated flame retardants correlates 

with male blood levels in a selected group of Norwegians with a wide range of 

seafood consumption. Molecular Nutrition & Food Research 52: 217-227. 

Kohler M, Zennegg M, Bogdal C, Gerecke AC, Schmid P, Heeb NV, Sturm M, Vonmont 

H, Kohler HPE, Giger W. 2008. Temporal trends, congener patterns, and sources 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 142

of octa-, nona-, and decabromodiphenyl ethers (PBDE) and 

hexabromocyclododecanes (HBCD) in swiss lake sediments. Environmental 

Science & Technology 42: 6378-6384. 

Koppen R, Becker R, Jung C, Nehls I. 2008. On thermally induced isomerization of 

hexabromocyclododecane stereoisomers. Chemosphere 71: 656-662. 

Kristensen E. 2000. Organic matter diagenesis at the oxic/anoxic interface in coastal 

marine sediments, with emphasis on the role of burrowing animals. 

Hydrobiologia 426: 1-24. 

Kuiper RV, Canton RF, Leonards PEG, Jenssen BM, Dubbeldam M, Wester PW, van 

den Berg M, Vos JG, Vethaak AD. 2007. Long-term exposure of European 

flounder (Platichthys flesus) to the flame-retardants tetrabromobisphenol a 

(TBBPA) and hexabromocyclododecane (HBCD). Ecotoxicology and 

Environmental Safety 67: 349-360. 

Lanno RP and McCarty LS. 1997. Worm bioassays. What knowledge can be applied 

from aquatic toxicity testing. Soil Biology and Biochemistry 29: 693-697. 

Law K, Halldorson T, Danell R, Stern G, Gewurtz S, Alaee M, Marvin C, Whittle M, 

Tomy G. 2006a. Bioaccumulation and trophic transfer of some brominated flame 

retardants in a Lake Winnipeg (Canada) food web. Environmental Toxicology and 

Chemistry 25: 2177-2186. 

Law K, Palace VP, Halldorson T, Danell R, Wautier K, Evans B, Alaee M, Marvin C, 

Tomy GT. 2006b. Dietary accumulation of hexabromocyclododecane 

diastereoisomers in juvenile rainbow trout (Oncorhynchus mykiss) I: 

Bioaccumulation parameters and evidence of bioisomerization. Environmental 

Toxicology and Chemistry 25: 1757-1761. 

Law RJ, Allchin CR, de Boer J, Covaci A, Herzke D, Lepom P, Morris S, Tronczynski J, 

de Wit CA. 2006c. Levels and trends of brominated flame retardants in the 

European environment. Chemosphere 64: 187-208. 

Law RJ, Bersuder P, Barry J, Wilford BH, Allchin CR, Jepson PD. 2008. A significant 

downturn in levels of hexabromocyclododecane in the blubber of harbor 

porpoises (Phocoena phocoena) stranded or bycaught in the UK: An update to 

2006. Environmental Science & Technology 42: 9104-9109. 

Law RJ, Kohler M, Heeb NV, Gerecke AC, Schmid P, Voorspoels S, Covaci A, Becher 

G, Janak K, Thomson C. 2005. Hexabromocyclododecane challenges scientists 

and regulators. Environmental Science & Technology 39: 281A-287A. 

Lei YD, Wania F, Mathers D, Mabury SA. 2004. Determination of vapor pressures, 

octanol-air, and water-air partition coefficients for polyfluorinated sulfonamide, 

sulfonamidoethanols, and telomer alcohols. Journal of Chemical and Engineering 

Data 49: 1013-1022. 

Lei, Y. D. and F. Wania (2004). Is rain or snow a more efficient scavenger of organic 

chemicals? Atmospheric Environment 38(22): 3557-3571. 

Leonards PEG, van Hattum B, Leslie H. 2008. Assessing the risks of persistent organic 

pollutants to top predators: A review of approaches. Integrated Environmental 

Assessment and Management 4: 386-398. 

Lepom P, Sawal G, Theobald N, Warmbrunn-Suckrow E (2007). PBDEs and HBCD in 

marine sediments from the North Sea, Germany. 4th International Workshop on 

Brominated Flame Retardants, Amersterdam, Netherlands. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 143

Lerche D, van d Plassche E, Schwegler A, Balk F. 2002. Selecting chemical substances 

for the UN-ECE POP Protocol. Chemosphere 47: 617-630. 

Leslie HA, Leonards PEG, Kwadijk CJAF, Kruijt AW, Brandsma SH, de Boer J, 

Bersuder P, Allchin CR. 2004. Analysis of hexabromocyclododecane in peregrine 

falcon (Falco peregrinus) and sparrowhawk (Accipiter nisus). Netherlands 

Institute for Fisheries Research, RIVO. p. 18. 

Lide DR, Ed. 2000. CRC Handbook of Chemistry and Physics, 81st Edn. Boca Raton, 

FL, USA, CRC Press. 

Lignell S, Darnerud PO, Aune M, Tömkvist A. 2003. Persistent organic pollutants (pop) 

in breast milk from primiparae women in Uppsala County, Sweden 2002–2003. 

Swedish Environmental Protection Agency., p. 

Lilienthal H, van der Ven LTM, Piersma AH, Vos JG. 2009. Effects of the brominated 

flame retardant hexabromocyclododecane (HBCD) on dopamine-dependent 

behavior and brainstem auditory evoked potentials in a one-generation 

reproduction study in Wistar rats. Toxicology Letters 185: 63-72. 

Lindberg P, Sellstrom U, Haggberg L, de Wit CA. 2004. Higher brominated diphenyl 

ethers and hexabromocyclododecane found in eggs of peregrine falcons (Falco 

peregrinus) breeding in Sweden. Environmental Science & Technology 38: 93-96. 

Lundstedt-Enkel K, Asplund L, Nylund K, Bignert A, Tysklind M, Olsson M, Orberg J. 

2006. Multivariate data analysis of organochlorines and brominated flame 

retardants in Baltic Sea guillemot (Uria aalge) egg and muscle. Chemosphere 65: 

1591-1599. 

MacGregor JA and Nixon WB. 2004. Determination of water solubility of 

hexabromocyclododecane (HBCD) using a generator column method. Wildlife 

International, Ltd., Easton, Maryland, USA. p. 52. 

Mackay D. 2001. Multimedia Environmental Models: The Fugacity Approach - Second 

Edition. Boca Raton, FL, Lewis Publishers. 

Mackay D, McCarthy LS, MacLeod M. 2001. On the validity of classifying chemicals for 

persistence, bioaccumulation, toxicity and potential for long-range transport. 

Environmental Toxicology and Chemistry 20: 1491-1498. 

Mackay D, Shiu WY, Ma KC, Lee SC. 2006. Handbook of Physical-Chemical Properties 

and Environmental Fate for Organic Chemicals, Second Edition. Vol I-IV., CRC 

Press. 

MacLeod M, Fraser A, Mackay D. 2002. Evaluating and expressing the propagation of 

uncertainty in chemical fate and bioaccumulation models. Environmental 

Toxicology and Chemistry 21: 700-709. 

Maeder V, Escher BI, Scheringer M, Hungerbuhler K. 2004. Toxic ratio as an indicator 

of the intrinsic toxicity in the assessment of persistent, bioaccumulative, and toxic 

chemicals. Environmental Science & Technology 38: 3659-3666. 

Marvin CH, Tomy GT, Alaee M, MacInnis G. 2006. Distribution of 

hexabromocyclododecane in Detroit River suspended sediments. Chemosphere

64: 268-275. 

Mayer P and Reichenberg F. 2006. Can highly hydrophobic organic substances cause 

aquatic baseline toxicity and can they contribute to mixture toxicity? 

Environmental Toxicology and Chemistry 25: 2639-2644. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 144

McCarty LS and Arnot JA. 2008. Ecological risk assessment for pyridalyl. Prepared for 

Valent U.S.A. Corporation, Walnut Creek CA USA. p. 101. 

McCarty LS and Borgert CJ. 2006a. Review of the toxicity of chemical mixtures 

containing at least one organochlorine. Regulatory Toxicology and Pharmacology

45: 104-118. 

McCarty LS and Borgert CJ. 2006b. Review of the toxicity of chemical mixtures: theory, 

policy, and regulatory practice. Regulatory Toxicology and Pharmacology 45: 

119-143. 

McCarty LS and Mackay D. 1993. Enhancing ecotoxicological modeling and assessment: 

Body residues and modes of toxic action. Environmental Science and Technology

27: 1719-1728. 

McKone TE, Castorina R, Harnly ME, Kuwabara Y, Eskenazi B, Bradmanm A. 2007. 

Merging models and biomonitoring data to characterize sources and pathways of 

human exposure to organophosphorus pesticides in the Salinas Valley of 

California. Environmental Science & Technology 41: 3233-3240. 

Meador JP. 2006. Rationale and procedures for using the tissue-residue approach for 

toxicity assessment and determination of tissue, water, and sediment quality 

guidelines for aquatic organisms. Human and Ecological Risk Assessment 12: 

1018-1073. 

Meador JP, McCarty LS, Escher BI, Adams WJ. 2008. The tissue-residue approach for 

toxicity assessment: Concepts, issues, application, and recommendations. Journal 

of Environmental Monitoring 10: 1486-1498. 

Minh NH, Isobe T, Ueno D, Matsumoto K, Mine M, Kajiwara N, Takahashi S, Tanabe S. 

2007. Spatial distribution and vertical profile of polybrominated diphenyl ethers 

and hexabromocyclododecanes in sediment core from Tokyo bay, japan. 

Environmental Pollution 148: 409-417. 

Montzka SA, Spivakovsky CM, Butler JH, Elkins JW, Lock LT, Mondeel DJ. 2000. New 

observational constraints for atmospheric hydroxyl on global and hemispheric 

scales. Science 288: 500-503. 

Morris AD, Muir DCG, Teixeira C, Epp J, Sturman S, Solomon KR (2007). 

Bioaccumulation and distribution of brominated flame retardants and current-use 

pesticides in an arctic marine food-web. 28th Annual Meeting of the Society of 

Environmental Toxicology and Chemistry, Milwakee, WI. 

Morris S, Allchin CR, Zegers BN, Haftka JJH, Boon JP, Belpaire C, Leonards PEG, Van 

Leeuwen SPJ, De Boer J. 2004. Distributon and fate of HBCD and TBBPA 

brominated flame retardants in North Sea estuaries and aquatic food webs. 

Environmental Science & Technology 38: 5497-5504. 

Muir DCG, Backus S, Derocher AE, Dietz R, Evans TJ, Gabrielsen GW, Nagy J, 

Norstrom RJ, Sonne C, Stirling I, Taylor MK, Letcher RJ. 2006. Brominated 

flame retardants in polar bears (Ursus maritimus) from Alaska, the Canadian 

arctic, east Greenland, and Svalbard. Environmental Science & Technology 40: 

449-455. 

Muir DCG and Howard PH. 2006. Are there other persistent organic pollutants? A 

challenge for environmental chemists. Environmental Science & Technology 40: 

7157 -7166. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 145

Murvoll KM, Skaare JU, Anderssen E, Jenssen BM. 2006. Exposure and effects of 

persistent organic pollutants in European shag (Phalacrocorax aristotelis) 

hatchlings from the coast of Norway. Environmental Toxicology and Chemistry

25: 190-198. 

Naturhistoriska riksmuseet. 2008. Sakrapport: Metaller och organiska miljögifter i marin 

biota, trend- och områdesövervakning. Överenskommelse 212 0714, dnr 721-

4235-07mm. Swedish Museum of Natural History, Department of Contaminant 

Research, Stockholm, Sweden. 

Neilson AH, Ed. 2003. The Handbook of Environmental Chemistry 3R: Organic Bromine 

and Iodine Compounds. Berlin Heidelberg, Springer-Verlag. 

Nichols JW, Fitzsimmons PN, Burkhard LP. 2007. In vitro-in vivo extrapolation of 

quantitative hepatic biotransformation data for fish. II. Modeled effects on 

chemical bioaccumulation. Environmental Toxicology and Chemistry 26: 1304-

1319. 

NICNAS Australian Government Department of Health and Ageing. 2005. Current 

Australian use and regulatory activities on polybrominated flame retardants. 

Existing chemicals information sheet. 

Nyholm JR, Norman A, Norrgren L, Haglund P, Andersson PL. 2009. Uptake and 

biotransformation of structurally diverse brominated flame retardants in zebrafish 

(Danio rerio) after dietary exposure. Environmental Toxicology and Chemistry

28: 1035-1042. 

OECD. 1992. OECD Guidelines for Testing of Chemicals. Test No. 301: Ready 

biodegradability. 

OECD. 1996. Bioconcentration: Flow-through fish test, 305. OECD Guidelines for 

Testing of Chemicals. Organization for Economic Co-operation and 

Development, Paris. 

OECD. 1998. Report on the OECD workshop on statistical analysis of aquatic toxicity 

data. OECD series on testing and assessment, number 10. Environment 

Directorate, Organization for Economic Co-operation and Development, Paris. p. 

133. 

OECD. 2000. Guidance document on aquatic toxicity testing of difficult substances and 

mixtures. OECD Series on Testing and Assessment, No. 23. Environment 

Directorate, Organization for Economic Co-operation and Development, Paris. p. 

53. 

OECD. 2002. Proposal for updating guideline 104: Vapour pressure. Organization for 

Economic Co-operation and Development, Paris. 

Peck AM, Pugh RS, Moors A, Ellisor MB, Porter BJ, Becker PR, Kucklick JR. 2008. 

Hexabromocyclododecane in white-sided dolphins: Temporal trend and 

stereoisomer distribution in tissues. Environmental Science & Technology 42: 

2650-2655. 

Peltola J. unpublished. An environmental screeing of four brominated flame retardants in 

Finland. 

Petersen M, Hamm S, Schafer A, Esser U. 2004. Comparative GC/MS and LC/MS 

detection of hexabromocyclododecane (HBCD) in soil and water samples. 

Organohalogen Compounds 66: 226-233. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 146

Polder A, Thomsen C, Lindstrom G, Loken KB, Skaare JU. 2008a. Levels and temporal 

trends of chlorinated pesticides, polychlorinated biphenyls and brominated flame 

retardants in individual human breast milk samples from northern and southern 

Norway. Chemosphere 73: 14-23. 

Polder A, Venter B, Skaare JU, Bouwman H. 2008b. Polybrominated diphenyl ethers and 

HBCD in bird eggs of South Africa. Chemosphere 73: 148-154. 

Porch JR, Kendall TZ, Krueger HO. 2002. Hexabromocyclododecane (HBCD): A 

toxicity test to determine the effects of the test substance on seedling emergence 

of six species of plants. Final report. Wildlife International, Ltd., Easton, 

Maryland. p. 126. 

Prinn RG, Weiss RF, Miller BR, Huang J, Alyea FN, Cunnold DM, Fraser PJ, Hartley 

DE, Simmonds PG. 1995. Atmospheric trends and lifetime of CH3CCl3 and global 

OH concentrations. Science 269: 187-192. 

Pulkrabová J, Hradkova P, Hajslova J, Poustka J, Napravnikova M, Polacek V. 2009. 

Brominated flame retardants and other organochlorine pollutants in human 

adipose tissue samples from the Czech Republic. Environment International 35: 

63-68. 

Ramu K, Kajiwara N, Isobe T, Takahashi S, Kim E-Y, Min B-Y, We S-U, Tanabe S. 

2007. Spatial distribution and accumulation of brominated flame retardants, 

polychlorinated biphenyls and organochlorine pesticides in blue mussels (Mytilus 

edulis) from coastal waters of Korea. Environmental Pollution 148: 562-569. 

Reid L and Mackay D. 2008. Local and distant residence times of contaminants in multicompartment models. Part II: Application to assessing environmental mobility 

and long-range atmospheric transport. Environmental Pollution 156: 1182-1189. 

Remberger M, Sternbeck J, Palm A, Kaj L, Strömberg K, Brorström-Lundén E. 2004. 

The environmental occurrence of hexabromocyclododecane in Sweden. 

Chemosphere 54: 9-21. 

Roberts CA and Swigert JP. 1997. Hexabromocyclododecane (HBCD): A 96-hour 

toxicity test with the freshwater alga (Selenastrum capricornutum). Final report. 

439a-103. Wildlife International Ltd., Easton, Maryland, USA. p. 

Roosens L, Dirtu AC, Goemans G, Belpaire C, Gheorghe A, Neels H, Blust R, Covaci A. 

2008. Brominated flame retardants and polychlorinated biphenyls in fish from the 

river Scheldt, Belgium. Environment International 34: 976-983. 

Ryan JJ, Wainman BC, Schecter A, Moisey J, Kosarac I, Sun WF. 2006. Trends of the 

brominated flame retardants, pbdes and HBCD, in human milks from North 

America. Organohalogen Compounds 68: 778-781. 

Schaefer E and Haberlein D. 1996. Hexabromocyclododecane (HBCD): Closed bottle 

test. Wildlife International Ltd., Easton, MD. 

Schaefer EC and Siddiqui AI. 2003. Hexabromocyclododecane (HBCD): An activated 

sudge respiration inhibitory test. 439a-108a. Wildlife International Ltd., Easton, 

Maryland, USA. p. 32. 

Schenker U, MacLeod M, Scheringer M, Hungerbuehler K. 2005. Improving data quality 

for environmental fate models: A least-squares adjustment procedure for 

harmonizing physicochemical properties of organic compounds. Environmental 

Science & Technology 39: 8434-8441. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 147

Schlabach M, Fjeld E, Borgen AR (2004a). Brominated flame retardants in drammenriver 

and the drammensfjord, Norway. Third International Workshop on Brominated 

Flame Retardants, Toronto, Canada. 

Schlabach M, Fjeld E, Gundersen H, Mariussen E, Kjellberg G, Breivik E. 2004b. 

Pollution of Lake Mjøsa by brominated flame retardants. Organohalogen 

Compounds 66: 3779-3785. 

Schlabach MM, E.; Borgen, A.; Dye, C.; Enge, E-K.; Steinnes, E.; Green, N.; Mohn, H. 

2002. Kartlegging av bromerte flammehemmere og klorerte parafiner. SFT - 

Stalig program for forurensningsovervåking, Norsk Institutt for Luftforskning 

(NILU), Oslo. p. 71. 

Schmid P, Gujer E, Zennegg M, Lanfranchi M. 2004. POPs and other persistent organic 

compounds in fish from remote alpine lakes in the Grisons, Switzerland. 

Organohalogen Compounds 66: 1716-1719. 

Sellström U, Bignert A, Kierkegaard A, Haggberg L, De Wit CA, Olsson M, Jansson B. 

2003. Temporal trend studies on tetra-and pentabrominated diphenyl ethers and 

hexabromocyclododecane in guillemot egg from the Baltic Sea. Environmental 

Science & Technology 37: 5496-5501. 

Sellström U, Kierkegaard A, de Wit C, Jansson B. 1998. Polybrominated diphenyl ethers 

and hexabromocyclododecane in sediment and fish from a Swedish river. 

Environmental Toxicology and Chemistry 17: 1065-1072. 

Sellström U, Lindberg P, Häggberg L, de Wit C. 2001. Bromerade flamskyddsmedel 

(PBDEs) funna i ägg av pilgrimsfalkar (Falco peregrinus) häckande i sverige. 

Svenska Naturskyddsföreningen i samarbete med TCO-Utveckling AB, 

Stockholm, Sweden. p. 33. 

Sinkkonen S and Paasivirta J. 2000. Degradation half-life times of PCDDs, PCDFs and 

PCBs for environmental fate modeling. Chemosphere 40: 943-949. 

Slob W. 1994. Uncertainty analysis in multiplicative models. Risk Analysis 14: 571-576. 

Smith K, Liu C-H, El-Hiti GA, Kang GS, Jones E, Clement SG, Checquer AD, Howarth 

OW, Hursthouse MB, Coles SJ. 2005. An extensive study of bromination of 

cis,trans,trans-1-5-9-cyclododecatriene: Product structures and confirmations. 

Organic and Biomolecular Chemistry 3: 1880-1892. 

Solomon K, Dohmen P, Fairbrother A, Marchand M, McCarty LS. 2009. Use of 

(eco)toxicity data as screening criteria for the identification and classification of 

PBT / POP compounds in science based guidance and framework for the 

evaluation and identification of PBTs and POPs, GM Klecka and DCG Muir 

(RFs), SETAC Pellston Workshop Proceedings. Integrated Environmental 

Assessment and Management in press. 

Sormo EG, Salmer MP, Jenssen BM, Hop H, Baek K, Kovacs KM, Lydersen C, FalkPetersen S, Gabrielsen GW, Lie E, Skaare JU. 2006. Biomagnification of 

polybrominated diphenyl ether and hexabromocyclododecane flame retardants in 

the polar bear food chain in Svalbard, Norway. Environmental Toxicology and 

Chemistry 25: 2502-2511. 

Stapleton HM, Dodder NG, Kucklick JR, Reddy CM, Schantz MM, Becker PR, Gulland 

F, Porter BJ, Wise SA. 2006. Determination of HBCD, PBDEs and MEO-BDEs 

in California sea lions (Zalophus californianus) stranded between 1993 and 2003. 

Marine Pollution Bulletin 52: 522-531. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 148

Stenzel JI and Markley BJ. 1997. Hexabromocyclododecane (HBCD): Determination of 

the water solubility. 439c-105. Wildlife International Ltd., Easton, Maryland, 

USA. p. 55. 

Sternbeck J, Kaj L, Remberger M, Palm A, Junedahl E, Bignert A, Haglund P, Lindkvist 

K, Adolfsson-Erici M, Nylund K, Asplund L. 2004. Organiska miljögifter i fisk 

från svenska bakgrundslokaler. IVL Svenska Miljöinstitutet AB, Stockholm, 

Sweden. p. 48. 

Swedish Environmental Protection Agency. 2009. Sources, transport, reservoirs and fate 

of dioxins, PCBs and HCB in the Baltic Sea environment. Report 5912. 

Naturvårdsverket, Stockholm, Sweden.

Takigami H, Suzuki G, Hirai Y, Sakai S. 2008. Transfer of brominated flame retardants 

from components into dust inside television cabinets. Chemosphere 73: 161-169. 

TemaNord. 2008. Hexabromocyclododecane as a possible global POP. Nordic Council of 

Ministers, Copenhagen, DK. p. 91. 

Tetko IV, Gasteiger J, Todeschini R, Mauri A, Livingstone D, Ertl P, Palyulin VA, 

Radchenko EV, Zefirov NS, Makarenko AS, Tanchuk VY, Prokopenko VV. 

2005. Virtual computational chemistry laboratory - design and description. 

Journal Of Computer-Aided Molecular Design 19: 453-463. 

Tomy GT, Budakowski W, Halldorson T, Whittle DM, Keir MJ, Marvin C, Macinnis G, 

Alaee M. 2004. Biomagnification of alpha- and gamma-hexabromocyclododecane 

isomers in a Lake Ontario food web. Environmental Science & Technology 38: 

2298-2303. 

Tomy GT, Pleskach K, Oswald T, Halldorson T, Helm PA, Macinnis G, Marvin CH. 

2008. Enantioselective bioaccumulation of hexabromocyclododecane and 

congener-specific accumulation of brominated diphenyl ethers in an eastern 

Canadian arctic marine food web. Environmental Science & Technology 42: 

3634-3639. 

Toose L, Woodfine DG, MacLeod M, Mackay D, Gouin J. 2004. BETR-World: A 

geographically explicit model of chemical fate: Application to transport of a-HCH 

to the arctic. Environmental Pollution 128: 223-240. 

U.S. EPA. 2005. Overview of proposed revisions to the guidelines for deriving ambient 

water quality criteria for the protection of aquatic and aquatic-dependent wildlife. 

Science advisory board consultation document prepared by the aquatic life criteria 

guidelines committee. U.S. Environmental Protection Agency, Washington, DC.

p. 33. 

U.S. EPA. 2009. Exposure assessment tools and models, Estimation Programs Interface 

(epi) Suite, ver. 4.0. Washington, D.C., U. S. Environmental Protection Agency, 

Exposure Assessment Branch. 

Ueno D, Alaee M, Marvin C, Muir DCG, Macinnis G, Reiner E, Crozier P, Furdui VI, 

Subramanian A, Fillmann G, Lam PKS, Zheng GJ, Muchtar M, Razak H, 

Prudente M, Chung KH, Tanabe S. 2006. Distribution and transportability of 

hexabromocyclododecane (HBCD) in the asia-pacific region using skipjack tuna 

as a bioindicator. Environmental Pollution 144: 238-247. 

UN-ECE. 1998. Protocol to the 1979 Convention on Long Range Transboundary Air 

Pollution on Persistent Organic Pollutants and Excecutive Body Decision 1998/2 

on Information to be Submitted and the Procedure for adding Substances to 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 149

Annexes I, II or III to the Protocol on Persistent Organic Pollutants. 

ECE/EB.AIR/60, United Nations, New York and Geneva. 

UNEP. 2001. Final Act of the Conference of Plenipotentiaries on the Stockholm 

Convention on Persistent Organic Pollutants. Geneva, Switzerland, United 

Nations Environment Program: 44. 

van der Ven LTM, van de Kuil T, Leonards PEG, Slob W, Lilienthal H, Litens S, Herlin 

M, Hakansson H, Canton RF, Van den Berg M, Visser TJ, Van Loveren H, Vos 

JG, Piersma AH. 2009. Endocrine effects of hexabromocyclododecane (HBCD) 

in a one-generation reproduction study in Wistar rats. Toxicology Letters 185: 51-

62. 

van der Ven LTM, Verhoef A, van de Kuil T, Slob W, Leonards PEG, Visser TJ, Hamers 

T, Herlin M, Hakansson H, Olausson H, Piersma AH, Vos JG. 2006. A 28-day 

oral dose toxicity study enhanced to detect endocrine effects of 

hexabromocyclododecane in Wistar rats. Toxicological Sciences 94: 281-292. 

Veith GD, Call DJ, Brooke LT. 1983. Structure toxicity relationships for the fathead 

minnow, Pimephales promelas: Narcotic industrial chemicals. Canadian Journal 

of Fisheries and Aquatic Sciences 40: 743-748. 

Veith GD, Defoe DL, Bergstedt BV. 1979. Measuring and estimating the 

bioconcentration factor of chemicals in fish. Journal of the Fisheries Research 

Board of Canada 36: 1040-1048. 

Verrault J, Gabrielsen GW, Chu S, Muir DCG, Andersen M, Hamaed A, Letcher RJ. 

2005. Flame retardants and methoxylated and hydroxylated polybrominated 

diphenyl ethers in two Norwegian arctic top predators: Glaucous gulls and polar 

bears. Environmental Science & Technology 39: 6021 - 6028. 

Verreault J, Gabrielsen GW, Letcher RJ, Muir DDC, Chu S. 2004. New and established 

organohalogen contaminants and their metabolites in plasma and eggs of glaucous 

gulls from Bear Island. Norwegian Polar Institute, Tromsø, Norway. p. 26. 

Verslycke TA, Vethaak AD, Arijs K, Janssen CR. 2005. Flame retardants, surfactants and 

organotins in sediment and mysid shrimp of the Scheldt estuary (The 

Netherlands). Environmental Pollution 136: 19-31. 

Vorkamp K, Thomsen M, Falk K, Leslie H, Moller S, Sorensen PB. 2005. Temporal 

development of brominated flame retardants in peregrine falcon (Falco 

peregrinus) eggs from south Greenland (1986-2003). Environmental Science & 

Technology 39: 8199-8206. 

Walsh GE, Yoder MJ, McLaughlin LL, Lores EM. 1987. Responses of marine unicellular 

algae to brominated organic compounds in six growth media. Ecotoxicolocy and 

Environmental Safety 14: 215-222. 

Wania F. 2003a. Assessing the long-range transport potential of tetrabromobisphenol A 

and hexabromocyclododecane using several multimedia transport models. A 

report to the Bromine Science and Environment Forum (BSEF). WECC Wania 

Environmental Chemists Corp., Toronto, ON. p. 13. 

Wania F. 2003b. Assessing the potential of persistent organic chemicals for long-range 

transport and accumulation in polar regions. Environmental Science & 

Technology 37: 1344-1351. 

Wania F. 2006. Potential of degradable organic chemicals for absolute and relative 

enrichment in the arctic. Environmental Science & Technology 40: 569-577. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 150

Wania F, Breivik K, Persson NJ, McLachlan MS. 2006. COZMO-POP 2 – a fugacitybased dynamic multi-compartmental mass balance model of the fate of persistent 

organic pollutants. Environmental Modelling and Software 21: 868-884. 

Wania F and Mackay D. 1995. A global distribution model for persistent 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. Evaluating 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 models of chemical fate in Canada: 

Modelling guidance document. Canadian Environmental Modelling Centre, Trent 

University, Peterborough, ON. p. 129. 

Wegmann F, Cavin L, MacLeod M, Scheringer M, Hungerbuehler K. 2009. The OECD 

software tool for screening chemicals for persistence and long-range transport 

potential. Environmental Modelling and Software 24: 228-237. 

Wu RSS, Chan AKY, Richardson BJ, Au DWT, Fang JKH, Lam PKS, Giesy JP. 2008. 

Measuring and monitoring persistent organic 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. Influence 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 hexabromocyclododecane in the atmosphere 

of an urban city in south China. Environmental Science & Technology 42: 3996-

4001. 

Zegers BN, Mets A, Van Brommel R, Minkenberg C, Hamers T, Kamstra JH, Pierce JG, 

Boon JP. 2005. Levels of hexabromocyclododecane 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. Hexabromocyclododecane (HBCD) Evaluation 151

9.0 APPENDIX 

9.1 PHYSICAL CHEMICAL PROPERTIES

The general objectives in this section are to obtain and review measured and predicted 

estimates of physical-chemical properties relevant for modelling the fate and 

bioaccumulation of HBCD in the environment. Based on this review, values were then 

selected to derive thermodynamically consistent estimates, final adjusted values (FAVs), 

which were then used for modelling and data analysis in the report. For the physicalchemical property consistency procedure, relative variance values were selected for the 

property estimates (Schenker et al. 2005). Discrepancies 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 melting 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 TM (K) is selected for technical 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 γHBCD, respectively (EC 2008). 

Fugacity ratio

The fugacity ratio F (unitless) was not included in the RAR and is necessary for 

estimating the properties of the subcooled liquid which are used as a reference state for 

fugacity and activity calculations and for the thermodynamic consistency calculations. 

The fugacity ratio can be estimated as 

F = exp(6.79(1 – TM/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 (TM = 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 there is greater uncertainty 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 entropy of fusion be measured. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 152

Water solubility

Table 9.1 lists the solid state water solubilities SS for HBCD from three studies using the 

generator column method, which is considered reliable in the EU RAR (EC 2008). The 

first two studies were reported to be in general accordance with OECD guidelines and 

GLP compliant. The test substance in the first 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 diastereoisomer-specific analysis (Stenzel and Markley 1997). 

The third study reports solubilities of the three diastereoisomers in a salt-water medium 

used for an algal growth inhibition test. The solubilities 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 considered reliable in the 

present study. The first study had fewer impurities and unknowns then the second study 

and all isomers could be quantified; 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 20o

C and the analyses in the present study (e.g. 25oC) 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 illustrated 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 μg/l (with a total HBCD 

dissolved concentration of 2.4 μg/l), β-HBCD at 14.7 μg/l (with a total HBCD 

dissolved concentration of 39 μg/l) and α-HBCD at 48.8 μg/l (with a total 

dissolved HBCD concentration of 65.6 μg/l). At this level, 65.6 μg/l dissolved 

HBCD, the total content of HBCD in the water is 610 μg/l, with 544.4 μg/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 discussed in the EU RAR; however, a value of 66 

μg.L-1 was selected as a “worst case” estimate for the technical product for input in 

EUSES. When a phase is “super-saturated” in this manner, then the mass balance 

equations must consider a “compartment” for the residual pool of precipitated chemical. 

The residual pool can then supplement the water phase as concentrations decrease in a 

manner analogous to the generator column. Most models, including EUSES, are not 

programmed to address “super-saturation” in the pure water 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 water. As discussed in the EU RAR and above, 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 153

when a value of 66 μg.L-1 is assumed for the water solubility, the concentration of total 

HBCD in the water (dissolved and precipitate) is approximately 300 times greater than 

the value for γ-HBCD. It is recommended that, if possible, diastereoisomer-specific 

model simulations be conducted using data for 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 between diastereoisomers. When a log KOW value of 5.63 is 

used as input, EPI Suite Ver. 4.0 estimates SS values of 1.3 μg/L and 3.0 μg/L from the 

WSKOWWIN Ver. 1.41 and WATERNT Ver. 1.01 submodels, respectively (U.S. EPA 

2009). These SS estimates are generally consistent with the measured values for γ-HBCD. 

The SPARC model Ver. 4.2 (http://ibmlc2.chem.uga.edu/sparc/) generates two slightly 

different sets of physical-chemical properties depending on the SMILES notation used. 

The SMILES notation obtained for CAS# 25637-99-4 shows a substitution pattern of 

1,3,5,7,9,11-HBCD, which is inconsistent with the diastereoisomer configurations. The 

SMILES notation obtained for CAS# 3194-55-6 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 pattern that is also inconsistent with the recognized 

diastereoisomer structures. Thus, the SMILES notation for CAS# 3194-55-6 was used for 

SPARC calculations. SPARC estimates an SS of 0.31 μg/L and an activity coefficient in 

water of 109.58 (TM=190o

C). The SS estimate is about 1 order of magnitude lower than the 

measured values for γ-HBCD. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 154

There is general consistency between the different estimates for water solubility. For the 

present study the measured SS from Study 1 were the selected values for the individual 

HBCD diastereoisomers. The SS (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, respectively. These estimates correspond to 

subcooled liquid water solubility values SL (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, respectively. A value of 3.0 μg/L (SS = 

4.67×10-6 mol.m-3, SL = 2.01×10-4 mol.m-3) is selected for technical (commercial) 

mixture HBCD for the thermodynamic consistency 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 uncertainty in the water solubility estimates for the thermodynamic 

consistency calculations. A relative 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 vapor pressure PS for HBCD from two 

measurements (EC 2008). Measured values for PS of HBCD include 1.6×10-5 Pa at 20o

C 

using the effusion method and 6.27×10-5 Pa at 21o

C using the spinning rotor method. The 

OECD vapor pressure guidelines include three different effusion methods; however, only 

the isothermal thermogravimetry effusion 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 were reported for the spinning rotor method: 

α-HBCD 6.0; β-HBCD 8.5; and γ-HBCD 79.1. According to OECD guidelines, both of 

these methods are not recommended for substances with vapor pressures as low as the 

values measured for HBCD; however, both methods produced similar values. A value of 

6.3×10-5 Pa at 21°C for PS was used in the EU risk assessment. 

Wania (2003a) reported a liquid state vapor pressure PL (subcooled) of 2.41×10-5 Pa at 

25o

C for t-HBCD. The value was obtained using the gas chromatographic-retention time 

method, which is suitable for substances with vapor pressures as low as 10-5 Pa. This 

value is selected for the FAV calculations. 

EPI Suite Ver. 4.0 estimates a PS of 2.25×10-6 Pa (U.S. EPA 2009) and SPARC Ver. 4.2 

estimates a PS of 2.63×10-9 Pa (http://ibmlc2.chem.uga.edu/sparc/). The EPI Suite 

estimate is about 3 times higher than the measured estimate by Wania whereas the 

SPARC estimate is 275 times lower. 

At present there are no isomer-specific estimates for vapor pressure. Goss et al. (2008) 

used the COSMOtherm model to calculate octanol-water (KOW) and air-water (KAW) 

partitioning of α-, β-, γ-HBCD diastereoisomers at 25o

C (see details below). From the 

isomer-specific quantum chemical software estimates of KAW and from isomer-specific 

estimates of water solubility SS it is possible to estimate PS for the three isomers as 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 155

PS = KAW.RT.SS (9.2) 

where R is the gas constant (8.314 Pa.m3

(mol.K)-1) and T is the temperature (298 K). 

This approach assumes that temperature differences (20 to 25o

C) are minimal. The 

resulting isomer-specific estimates for PS are 2.72×10-10 Pa, 3.58×10-11 Pa, and 1.86×10-11 

Pa for α-, β-, γ-HBCD, respectively. These model estimates are about 104 times lower 

than the measured values measured by Wania. Goss et al. (2008) note that “the absolute 

values of the calculated air-water partition coefficients may be considerably off the real 

values”. COSMOtherm also underestimates KAW for hexachlorocyclohexane (HCH) 

(Goss et al. 2008). Since the KOW estimates for HCH and HBCD are similar to measured 

estimates, this suggests that errors in COSMOtherm KAW estimates relate to errors in P. 

Thus, the COSMOtherm estimates for KAW and PS are not considered reliable; however, 

in relative terms the PS results are similar to the measured values for SS such that values 

for γ-HBCD are about 1 order of magnitude less than values for α-HBCD. 

There are limited data and conflicting results for estimates of the vapor pressure. For the 

present study the measured PS estimate 7.23×10-7 Pa (PL = 3.11×10-5 Pa using an F of 

0.023) using the gas saturation method is considered the most reliable estimate for the 

commercial HBCD mixture and is selected as an input for thermodynamic consistency 

analyses (see below). Estimates for the diastereoisomers were derived by assuming PL for 

γ-HBCD is approximately 2.0×10-5 Pa and that the relative differences in values 

estimated by COSMOtherm are applicable such that PL 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 estimates. A relative variance value of 3 was 

assigned to the PL of the technical mixture and a value of 5 was assigned to the 

diastereoisomers. 

It is suggested that measured estimates for isomer-specific vapor pressures be 

obtained using OECD recommended methods for low vapor pressure substances. 

Solubility in Octanol

There are no known reported measurements for 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-water partition coefficient)

To our knowledge there are no measured estimates for Henry’s law constant H 

(Pa.m3

.mol-1) currently available. The dimensionless air-water partition coefficient KAW

is H/RT. EPI Suite Ver. 4.0 estimates (HENRYWIN Ver. 3.2) for Henry’s law constants 

are 0.174 and 6.52×10-6 Pa.m3

.mol-1 using the bond and group methods, respectively 

(U.S. EPA 2009). The group method estimate is considered erroneous. The log KAW

estimate corresponding to the bond method is -4.15. H can also be estimated from vapor 

pressure and water solubility as PS/SS or PL/SL. 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.m3

.mol-1
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 156

(log KAW = -4.32). SPARC Ver. 4.2 estimates a value for H of 0.0055 Pa.m3

.mol-1 (log 

KAW = -5.66) using the same approach, i.e., PL/SL (http://ibmlc2.chem.uga.edu/sparc/). 

The estimates from EPI Suite bond method and the relative solubilities of the selected 

values for vapor pressure and water solubility are consistent, independent 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 KAW have been reported by Goss et al. using the 

COSMOtherm model (Goss et al. 2008). The log KAW 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.m3

.mol-1 for α-, β-, γ-HBCD, respectively. As discussed above and 

by the authors the absolute estimates are considered to be erroneous, but the relative 

values are considered reasonable. Diastereoisomer estimates of log KAW from selected 

values for PL/SL are -4.27, -4.63, and -4.07 for α-, β-, γ-HBCD, respectively. These 

estimates are dependent on the values for PL/SL, which have already been selected for 

thermodynamic consistency calculations. Independent estimates for the diastereoisomers 

were derived by assuming log KAW 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 KAW for α- and β-HBCD are -4.35 and -4.71, respectively. 

It is recommended that isomer-specific Henry’s law constants be measured. 

Octanol-water partition coefficient

The EU RAR reports a measured log KOW of HBCD of 5.625 at 25±0.05°C using the 

generator column method, which is also in accordance with GLP 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 study was generally carried out according to the guidelines, 

although the concentration of the stock solution was approximately 0.2 % instead of 1 %. 

As noted above in the discussion on water solubility and in the RAR (EC 2008), the 

generator column method for mixtures may result in excess, non-dissolved quantities of 

diastereoisomers in the water column. MacGregor and Nixon (2004) report a mean water 

concentration of 3.97 μg-HBCD.L-1 (standard deviation 1.53 μg-HBCD.L-1). The water 

concentration is near the water solubility limit for γ-HBCD. 

The RAR also summarizes the diastereoisomer-specific log KOW values estimated for α-, 

β- and γ-HBCD using reversed-phase high-performance liquid chromatography (RPHPLC) methods (Hayward et al. 2006). The estimated log KOW values for α-, β- and γHBCD were 5.07±0.09, 5.12±0.09 and 5.47±0.10, respectively. The calibration 

compounds were chlorobenzenes and polychlorinated biphenyls, which as discussed by 

the authors, are not ideal because of the structural dissimilarities between the 

organochlorines and HBCD (Hayward et al. 2006). Assuming the solubility in octanol is 

similar for the diastereoisomers, the KOW estimates are relatively consistent with 

measurements of water solubility such that KOW increases with decreasing water 

solubility. The HPLC derived KOW values for the diastereoisomers are comparable but 

lower than the measured KOW estimate for commercial HBCD using the generator 

column method. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 157

The RAR includes a few other measured and modelled estimates for log KOW that were 

considered unreliable. The RAR selected a log KOW 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 value of the model estimates for log KOW is 6.73 

with a standard deviation of 0.76. These model estimates do not differentiate between 

KOW values for the diastereoisomers. Goss et al. (2008) reported diastereoisomer log KOW

estimates of 5.59, 5.44 and 5.53, for α-, β- and γ-HBCD, respectively, using the 

COSMOtherm model. The COSMOtherm model estimates are of the same general 

magnitude as the generator column KOW measurement for commercial HBCD and the 

diastereoisomers determined from calibration 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 KOW for the commercial 

mixture and the diastereoisomers. The solubilities of the diastereoisomers in octanol are 

expected to be similar and differences in KOW are expected to be the result of differences 

in water solubility. SPARC Ver. 4.2 calculates an activity coefficient in octanol γO of 

12.88 (unitless) and a solid state solubility in octanol SOS of 16.21 mol·m-3 (liquid state 

solubility in octanol SOL of 698 mol·m-3) for HBCD. The SOL value was selected as input 

for the thermodynamic consistency calculations for the commercial HBCD mixture and 

the diastereoisomers. A relative variance value of 4 was assigned to this property 

estimate. 

The measured log KOW value of 5.63 was selected as input for the consistency 

calculations for t-HBCD and a relative variance value of 3 was assigned. For the 

diastereoisomers the estimates by Goss et al. were selected as input for the consistency 

calculations and relative variance values of 4 were assigned. Although these estimates 

were derived in silico they are close to measured estimates and considering the potential 

underestimation of HBCD using the generator column method and the lack of ideal 

calibration substances for the RP-HPLC method, the selected values are considered 

reasonable for the thermodynamic consistency calculations. 

It is recommended that isomer-specific octanol-water partition coefficients and 

solubility in octanol be measured. 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 158

Table 9.2. Model estimates for the octanol-water partition coefficient (KOW). 

log KOW 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 KOW 

0.76 Standard Deviation 

Octanol-air partition coefficient

There are no reported measurements for the octanol-air partition coefficient (KOA, 

dimensionless); however, there are a few methods for estimation. EPI Suite Ver. 4.0 

estimates a log KOA of 10.47 with the KOAWIN program and a log KOA of 11.89 (based 

on EPI estimates for KOW and KAW as log KOW - log KAW). KOA can also be estimated 

from the relative fugacity capacities of octanol and air, ZO and ZA (mol·m-3·Pa-1), 

respectively as ZO / ZA. ZA is 1/RT and ZO can be estimated as 1/(νOγOPL), where νO is the 

molar volume of “dry” octanol (1.57×10-4 m3

·mol-1). The predicted γO of 12.88 (SPARC 

Ver. 4.2) and the selected PL 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 KOA, i.e., ZO / ZA. At 25 o

C, log 

KOA values are 10.71, 9.62, 10.50, and 10.79 for the t-HBCD and α-, β- and γ-HBCD 

diastereoisomers, respectively. Log KOA can also be estimated from the COSMOtherm 

KAW and KOW; however, as discussed above the estimates for KAW are considered 

erroneous. 

No KOA values were selected as inputs for the thermodynamic consistency calculations 

because the values for the diastereoisomers are dependent on the estimates for the 

extrapolated estimates for PL; however, the estimates provided above for KOA 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. Hexabromocyclododecane (HBCD) Evaluation 159

Thermodynamic consistency for model input parameters

The methods of Schenker et al. (Schenker et al. 2005) were used to obtain 

thermodynamically consistent estimates for the solubility and partitioning properties of tHBCD and the three diastereoisomers. The method was applied to t-HBCD in the current 

study; however, the thermodynamic consistency calculation should generally not be used 

for mixtures, but rather for discrete substances with distinct properties. The method was 

applied for consistency with diastereomer methods and for comparing model 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 uncertainties 

associated with actual emissions, mode-of-entry, isomerization and degradation in the 

environment, the errors in calculating “representative” 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 calculations are summarized in Tables 9.3 and 9.4, 

respectively. 

Table 9.3. Selected values for physical-chemical properties and relative variance values 

used as inputs for thermodynamic consistency calculations. 

Substance PL, Pa SL, mol·m-3 SOL, mol·m-3 Log KAW Log KOW a Log KOA 

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 KOW values corresponding with log (Co/Cw) values of 6.02, 5.97, 5.76, and 5.89 for t-, α-, β-, and γHBCD, 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 FAVs 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 160

Table 9.4. Final adjusted values (FAVs at 25o

C), relative variance values, percent 

adjustments, and assigned confidence factors used in the present study for mass balance 

modelling and data analyses obtained from the thermodynamic consistency calculations 

(Schenker et al. 2005). 

Substance PL, Pa SL, mol·m-3 SOL, mol·m-3 Log KAW Log KOW a Log KOA 

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 KOW values corresponding with log (Co/Cw) values of 6.22, 5.68, 5.81, 6.25 for t-, α-, β-, and γHBCD, respectively 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 161

9.2 DEGRADATION, BIOTRANSFORMATION AND HALF-LIVES

Table 9.5. BIOWINTM v4.10 model output for HBCD (not 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 aerobic environmental biodegradation halflives from BIOWINTM model output and corresponding values calculated for HBCD 

(Arnot et al. 2005; US EPA 2009). 

Model Regression Equation r2 Half-life (d) 

BIOWIN1 (linear) log10(t1/2) = -1.32x + 2.24 0.72 105.2 

BIOWIN3 (Ultimate deg) log10(t1/2) = -1.07x + 4.20 0.77 128.3 

BIOWIN4 (Primary deg) log10(t1/2) = -1.46x + 6.51 0.78 85.9 

where x is the numerical output generated for each respective BIOWINTM model (presented above in Table 9.5) 
162

9.3 MONITORING DATA

This section includes summaries of monitoring data that were discussed in the report that may not have been recognized in previous 

assessments. The abiotic data summarize all available data, whereas the biotic data are for studies published in the last 3 to 4 years. Table 9.7. Summary of abiotic monitoring data. 

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

Air (pg/m3) gaseous 

2004 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-1 0.12 0.07 0.03 0.02 Urban (Yu et al. 2008) 

particulate 

2003 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) 
163

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

2004 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-1 1.52 0.92 0.18 0.42 Urban (Yu et al. 2008) 

 total 

1990.5 Hoburgen and Ammarnas, 

Sweden 

5.5 Remote 5 6 2 (Covaci et al. 2006) 

1990.5 Sweden-Ammarnas 6.1 Remote 2 (deWit 2002) 

2000 Sweden-80kmSW of 

Aspvreten 

1070000 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 Sweden 

and Finland 

141 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-1 

Guangzhou, China 

1.7 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 suburban 

homes 

14 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-Stockholmschoolyard 76 Urban (Remberger et al. 2004) 

2000 Sweden-Stockholm 610 Urban (Remberger et al. 

2004) 
164

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

2001 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) core 

1940 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) 
165

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

1996 Sweden-Arstaviken Bay 0.8 Urban (Remberger et al. 

2004) 

1996 Sweden-Fjaderholmmarna 

islands 

0.9 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) 

estuarine 

1998 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 (E 

England) 

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.-MartensLeerne 38 7.4 <0.1 31 1.2 Unknown 1 (Morris et al. 2004) 2001 2001/1458-Scheldt, Dutch 

border 

83 <0.2 <0.2 83 1.2 Unknown 10 (Morris et al. 2004) 

2001 2001/1465-Scheldt, 

Oudenaarde 

950 180 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) 
166

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

2003 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/lake 

1980 Lake Ellasjøen, Bjørnøya, 

Norwegian Arctic 

3.87 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 from 

industry/urban 3 

73.6 Point-downstream 73.6 76.8 2 (Sellström et al. 1998) 

1995 Sweden-River Viskan at Moga 

4 

1591 Point-downstream 1591 1591 2 (Sellström et al. 1998) 

1995 Sweden-River Viskan 

upstream from Skene 5 

54 Point-downstream 54 54 2 (Sellström et al. 1998) 

1995 Sweden-River Viskan, 

downstream from Skene 6 

210 Point-downstream 192 228 2 (Sellström et al. 1998) 

1995 Sweden-River Haggan 

upstream from Fritsla 7 

2.728 Remote 0.186 2.728 2 (Sellström et al. 1998) 

1995 Sweden-River Haggan 

downstream from Fritsla 8 

0.468 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 (NE 

England) 

1678 745 396 537 1.2 Unknown 1 (Morris et al. 2004) 

2000 2000/2584-Skerne (NE 

England) 

174 40 16 118 1.2 Point 1 (Morris et al. 2004) 

2000 2000/3989-Mersey (NW 

England) 

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) 
167

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

2000 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 Viskandownstream 9.6 Point-downstream 1 25 4 (Remberger et al. 2004) 2000 Sweden-River Viskanupstream 0.2 < 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 Industry 

River Viskan 1 

0.1 0.1 Remote (Remberger et al. 

2004) 

2000 Sweden-Boras Textile Industry 

river Viskan 2 

0.2 Remote (Remberger et al. 

2004) 

2000 Sweden-Boras Textile Industry 

River Viskan 3 

25 Point-downstream (Remberger et al. 

2004) 

2000 Sweden-Boras Textile Industry 

River Viskan 4 

1 Point-downstream (Remberger et al. 

2004) 

2000 Sweden-Boras Textile Industry 

River Viskan 5 

4.6 Point-downstream (Remberger et al. 

2004) 

2000 Sweden-Boras Textile Industry 

River Viskan 6 

7.8 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 of 

source 

0.1 < 0.1 Remote 2 (Eljarrat et al. 2004) 

2002 Spain-River Cincadownstream of source 302 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, 

<0.3 

Unknown 0.9 7.9 4 (Schlabach et al. 

2004b) 

2003 Norway-Drammens 

River/Drammensfjord offshore sites 

2.5 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, 

.12 

Remote (Schlabach et al. 

2004a) 

2003 DRE-6 Loselva 0.6 0.33 0.08 0.27 .1, .08, 

.12 

Unknown (Schlabach et al. 

2004a) 

2003 DRE-5 Hokksund 0.45 0.09 0.08 0.36 .1, .08, 

.12 

Unknown (Schlabach et al. 

2004a) 

2003 DRE-4 Mjøndalen 4.14 0.99 0.07 3.08 .1, .08, 

.12 

Unknown (Schlabach et al. 

2004a) 
168

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

2003 DRE-3 Langesøya 0.4 0.15 0.08 0.25 .1, .08, 

.12 

Unknown (Schlabach et al. 

2004a) 

2003 DRE-2 Politihuset 2.4 1.55 0.2 0.85 .3, .2, .4 Unknown (Schlabach et al. 

2004a) 

 sea 

2000 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, 

Belgium 

42.5 < 0.2 Unknown 14 71 3 (Verslycke et al. 2005) 

Soil (ng/g dw) 

solids 

 S1-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) 
169

Year Location Total 

HBCD 

αHBCD β- HBCD γ-HBCD LOD Other Median Min Max n Reference 

2000 Sweden-80kmSW of 

Aspvreten 

140 Point (Remberger et al. 

2004) 

2000 Sweden-80kmSW of 

Aspvreten 

1300 Point (Remberger et al. 

2004) 

2000 Sweden-80kmSW of 

Aspvreten 

1000 Point (Remberger et al. 

2004) 

2000 Sweden-near XPS producing 

plant 

567 Point 140 1300 3 (Remberger et al. 

2004) 

Water dissolved (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, 

Riddarfjarden 

1.6 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) 
170

Table 9.8. Summary of biotic monitoring data. 

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1994 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) 
171

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2000 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) 
172

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2003 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) 
173

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2004 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) 
174

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2005 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) 
175

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2005 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) 
176

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2006 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 ug kg-1 lw 1502.6 Zegers et al. (2005) 2001 - 2003 NW Scotland Harbour porpoise blubber ug kg-1 lw 9590.5 Zegers et al. (2005) 2001 - 2003 NW Scotland Harbour porpoise blubber ug kg-1 lw 6324.8 Zegers et al. (2005) 2001 - 2003 NW Scotland Harbour porpoise blubber ug kg-1 lw 1009.0 Zegers et al. (2005) 2001 - 2003 NW Scotland Harbour porpoise blubber ug kg-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) 
177

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2002 - 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 blubber ug kg-1 lw 78.8 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Harbour porpoise blubber ug kg-1 lw 142.6 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Harbour porpoise blubber ug kg-1 lw 142.3 Zegers et al. (2005) 

2002 NW France Common dolphin blubber ug kg-1 lw 174.9 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 630.5 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 279.1 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 97.6 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 180.2 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 418.4 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 344.0 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 199.1 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 388.8 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 898.6 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 460.6 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 717.1 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 188.2 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 197.3 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 215.0 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 253.4 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 235.7 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 442.7 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 619.3 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 472.2 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 438.5 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 597.3 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 417.9 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 365.7 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 568.9 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 468.5 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 760.6 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 702.7 Zegers et al. (2005) 
178

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2002 NW France Common dolphin blubber ug kg-1 lw 820.6 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 503.6 Zegers et al. (2005) 2002 NW France Common dolphin blubber ug kg-1 lw 377.7 Zegers et al. (2005) 

2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 64.0 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 280.6 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 251.4 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 183.6 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 259.3 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 290.8 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 90.5 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 368.4 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 205.0 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 454.5 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 109.4 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 159.8 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 90.7 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 170.6 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 51.5 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 123.9 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 216.2 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 162.3 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 163.4 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 115.8 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 246.6 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 93.6 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-1 lw 89.9 Zegers et al. (2005) 2001 - 2003 Galicia, Spain Common dolphin blubber ug kg-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) 
179

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1993 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) 
180

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1997 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) 
181

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1996 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 - 

0.69 

ug kg-1 lw < 0.1 (Kajiwara et al. 2006) 

1976 Offshore Japan Northern fur seal blubber 0.027 

- 0.60 

ug kg-1 lw 2.4 Kajiwara et al. (2006) 

1980 Offshore Japan Northern fur seal blubber 0.71 - 

0.79 

ug kg-1 lw 0.77 Kajiwara et al. (2006) 

1982 Offshore Japan Northern fur seal blubber 0.23 - 

0.38 

ug kg-1 lw 6.5 Kajiwara et al. (2006) 

1985 Offshore Japan Northern fur seal blubber 0.48 - 

0.60 

ug kg-1 lw 1.6 Kajiwara et al. (2006) 

1988 Offshore Japan Northern fur seal blubber 0.70 - 

0.82 

ug kg-1 lw 3.6 Kajiwara et al. (2006) 
182

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1991 Offshore Japan Northern fur seal blubber 0.64 - 

0.80 

ug kg-1 lw 15 Kajiwara et al. (2006) 

1994 Offshore Japan Northern fur seal blubber 0.72 - 

0.80 

ug kg-1 lw 33 Kajiwara et al. (2006) 

1997 Offshore Japan Northern fur seal blubber 0.77 - 

1 

ug kg-1 lw 33 Kajiwara et al. (2006) 

1998 Offshore Japan Northern fur seal blubber 0.72 - 

0.88 

ug kg-1 lw 24 Kajiwara et al. (2006) 

1982 Pacific Coast, 

Japan 

Melon-headed whale blubber 0.46 - 

0.80 

ug kg-1 lw 7 Kajiwara et al. (2006) 

2001 Pacific Coast, 

Japan 

Melon-headed whale blubber 0.63 - 

0.74 

ug 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 0.73 ug kg-1 lw 37 12 4 21 Isobe et al. (2007) 1990 S.China Sea Finless porpoise blubber 0.92 ug kg-1 lw 23 10 1.5 11 Isobe et al. (2007) 1990 S.China Sea Finless porpoise blubber 0.78 ug kg-1 lw 9.4 9.4 Isobe et al. (2007) 1990 S.China Sea Finless porpoise blubber 0.83 ug kg-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 ug kg-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) 
183

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2005 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. 2004 

1969 Baltic Sea Guillemot Egg 0.111 ug kg-1 lw 78 Sellström et al., 2003 1971 Baltic Sea Guillemot Egg 0.105 ug kg-1 lw 66 Sellström et al., 2003 1972 Baltic Sea Guillemot Egg 0.121 ug kg-1 lw 58 Sellström et al., 2003 1973 Baltic Sea Guillemot Egg 0.117 ug kg-1 lw 52 Sellström et al., 2003 1975 Baltic Sea Guillemot Egg 0.114 ug kg-1 lw 100 Sellström et al., 2003 1976 Baltic Sea Guillemot Egg 0.153 ug kg-1 lw 140 Sellström et al., 2003 
184

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1976 Baltic Sea Guillemot Egg 0.153 ug kg-1 lw 150 Sellström et al., 2003 1977 Baltic Sea Guillemot Egg 0.122 ug kg-1 lw 95 Sellström et al., 2003 1978 Baltic Sea Guillemot Egg 0.114 ug kg-1 lw 45 Sellström et al., 2003 1980 Baltic Sea Guillemot Egg 0.159 ug kg-1 lw 47 Sellström et al., 2003 1981 Baltic Sea Guillemot Egg 0.178 ug kg-1 lw 34 Sellström et al., 2003 1982 Baltic Sea Guillemot Egg 0.119 ug kg-1 lw 45 Sellström et al., 2003 1983 Baltic Sea Guillemot Egg 0.124 ug kg-1 lw 48 Sellström et al., 2003 1985 Baltic Sea Guillemot Egg 0.13 ug kg-1 lw 76 Sellström et al., 2003 1986 Baltic Sea Guillemot Egg 0.125 ug kg-1 lw 120 Sellström et al., 2003 1987 Baltic Sea Guillemot Egg 0.125 ug kg-1 lw 83 Sellström et al., 2003 1988 Baltic Sea Guillemot Egg 0.128 ug kg-1 lw 98 Sellström et al., 2003 1989 Baltic Sea Guillemot Egg 0.123 ug kg-1 lw 130 Sellström et al., 2003 1990 Baltic Sea Guillemot Egg 0.128 ug kg-1 lw 110 Sellström et al., 2003 1992 Baltic Sea Guillemot Egg 0.122 ug kg-1 lw 81 Sellström et al., 2003 1992 Baltic Sea Guillemot Egg 0.109 ug kg-1 lw 97 Sellström et al., 2003 1993 Baltic Sea Guillemot Egg 0.111 ug kg-1 lw 150 Sellström et al., 2003 1994 Baltic Sea Guillemot Egg 0.106 ug kg-1 lw 130 Sellström et al., 2003 1995 Baltic Sea Guillemot Egg 0.113 ug kg-1 lw 170 Sellström et al., 2003 1996 Baltic Sea Guillemot Egg 0.112 ug kg-1 lw 130 Sellström et al., 2003 1997 Baltic Sea Guillemot Egg 0.122 ug kg-1 lw 110 Sellström et al., 2003 1998 Baltic Sea Guillemot Egg 0.11 ug kg-1 lw 110 Sellström et al., 2003 1999 Baltic Sea Guillemot Egg 0.124 ug kg-1 lw 130 Sellström et al., 2003 2000 Baltic Sea Guillemot Egg 0.117 ug kg-1 lw 140 Sellström et al., 2003 2001 Baltic Sea Guillemot Egg 0.121 ug kg-1 lw 140 Sellström et al., 2003 

2000-2002 Baltic Sea Guillemot egg ug kg-1 lw 138 Lundstedt-Enkel et al., 

2006b 

2000-2002 Baltic Sea Guillemot muscle 0.029 ug kg-1 lw 64.7 Lundstedt-Enkel et al., 

2006b 

2000 Baltic Sea Guillemot muscle ug kg-1 lw 66.7 Lundstedt-Enkel et al., 2005 2002 Svalbard Guillemot 0.28 ug kg-1 lw 35.4 Murvoll et al., 2007 2002 Svalbard Eider 0.21 ug kg-1 lw 6.23 Murvoll et al., 2007 1980 Sweden Human Milk ug kg-1 lw 0.11 Fangstrom et al. 2008 1984 Sweden Human Milk ug kg-1 lw 0.09 Fangstrom et al. 2008 1988 Sweden Human Milk ug kg-1 lw 0.22 Fangstrom et al. 2008 1990 Sweden Human Milk ug kg-1 lw 0.21 Fangstrom et al. 2008 1992 Sweden Human Milk ug kg-1 lw 0.31 Fangstrom et al. 2008 1994 Sweden Human Milk ug kg-1 lw 0.41 Fangstrom et al. 2008 
185

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1995 Sweden Human Milk ug kg-1 lw 0.51 Fangstrom et al. 2008 1996 Sweden Human Milk ug kg-1 lw 0.32 Fangstrom et al. 2008 1997 Sweden Human Milk ug kg-1 lw 0.29 Fangstrom et al. 2008 1999 Sweden Human Milk ug kg-1 lw 0.39 Fangstrom et al. 2008 2001 Sweden Human Milk ug kg-1 lw 0.53 Fangstrom et al. 2008 2002 Sweden Human Milk ug kg-1 lw 0.64 Fangstrom et al. 2008 2003 Sweden Human Milk ug kg-1 lw 0.05 Fangstrom et al. 2008 2004 Sweden Human Milk ug kg-1 lw 0.37 Fangstrom et al. 2008 1980 Sweden Human Milk ug kg-1 lw 0.06 Fangstrom et al. 2008 1984 Sweden Human Milk ug kg-1 lw 0.10 Fangstrom et al. 2008 1988 Sweden Human Milk ug kg-1 lw 0.26 Fangstrom et al. 2008 1990 Sweden Human Milk ug kg-1 lw 0.19 Fangstrom et al. 2008 1992 Sweden Human Milk ug kg-1 lw 0.26 Fangstrom et al. 2008 1994 Sweden Human Milk ug kg-1 lw 0.35 Fangstrom et al. 2008 1995 Sweden Human Milk ug kg-1 lw 0.52 Fangstrom et al. 2008 1996 Sweden Human Milk ug kg-1 lw 0.35 Fangstrom et al. 2008 1997 Sweden Human Milk ug kg-1 lw 0.30 Fangstrom et al. 2008 1999 Sweden Human Milk ug kg-1 lw 0.35 Fangstrom et al. 2008 2001 Sweden Human Milk ug kg-1 lw 0.55 Fangstrom et al. 2008 2002 Sweden Human Milk ug kg-1 lw 0.53 Fangstrom et al. 2008 2003 Sweden Human Milk ug kg-1 lw 0.49 Fangstrom et al. 2008 2004 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. 2009 

2001 Sweden Human Milk ug kg-1 lw 0.5 Cited in Eljarrat et al. 2009 

2002-2003 Spain Human Milk ug kg-1 lw 0.5 Cited in Eljarrat et al. 2009 2002-2003 Canada Human Milk ug kg-1 lw 3.8 Cited in Eljarrat et al. 2009 

2005 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., 2008 

1999 Sweden Bovine meat Unknown 0.87 ug kg-1 lw 1.4 Remberger et al. 2004 1999 Sweden Pork meat Unknown 0.82 ug kg-1 lw 1.0 Remberger et al. 2004 1999 Sweden Lamb Unknown 0.81 ug kg-1 lw 4.1 Remberger et al. 2004 1999 Sweden Chicken Unknown 0.63 ug kg-1 lw 6.5 Remberger et al. 2004 1999 Sweden Egg Yolk Unknown 0.25 ug kg-1 lw 9.4 Remberger et al. 2004 1999 Sweden Milk Unknown 0.038 ug kg-1 lw 1.8 Remberger et al. 2004 1999 Sweden Fish (mixed) Unknown 0.06 ug kg-1 lw 48.0 Remberger et al. 2004 
186

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2004-2007 Norway Farmed trout Unknown 0.195 ug kg-1 lw 3.6 Knutsen et al., 2008 2004-2007 Norway Herring Unknown 0.114 ug kg-1 lw 3.9 Knutsen et al., 2008 2004-2007 Norway Sardines Unknown 0.291 ug kg-1 lw 2.7 Knutsen et al., 2008 2004-2007 Norway Farmed salmon Unknown 0.148 ug kg-1 lw 1.8 Knutsen et al., 2008 2004-2007 Norway Mackerel Unknown 0.257 ug kg-1 lw 3.1 Knutsen et al., 2008 2004-2007 Norway Smoked salmon Unknown 0.111 ug kg-1 lw 2.7 Knutsen et al., 2008 2004-2007 Norway Shrimp Unknown 0.012 ug kg-1 lw 1.1 Knutsen et al., 2008 2004-2007 Norway Crab Unknown 0.092 ug kg-1 lw 3.0 Knutsen et al., 2008 2004-2007 Norway Bovine meat Unknown 0.178 ug kg-1 lw 0.3 Knutsen et al., 2008 2004-2007 Norway Pork meat Unknown 0.248 ug kg-1 lw 0.1 Knutsen et al., 2008 2004-2007 Norway Dairy products Unknown 0.521 ug kg-1 lw 0.3 Knutsen et al., 2008 2004-2007 Norway Hen egg Unknown 0.098 ug kg-1 lw 1.6 Knutsen et al., 2008 2004-2007 Norway Seagull egg Unknown 0.094 ug kg-1 lw 70.4 Knutsen et al., 2008 2004-2007 Norway Vegetable oil Unknown 1 ug kg-1 lw 0.4 Knutsen et al., 2008 2004-2007 Norway Banana Unknown 0.001 ug kg-1 lw 0.04 Knutsen et al., 2008 2001-2003 Czech Republic barbel (fish) muscle 0.034 ug kg-1 lw 68 Pulkrabová et al., 2007 2001-2003 Czech Republic barbel (fish) muscle 0.048 ug kg-1 lw 174 Pulkrabová et al., 2007 2001-2003 Czech Republic barbel (fish) muscle 0.04 ug kg-1 lw 389 Pulkrabová et al., 2007 2001-2003 Czech Republic barbel (fish) muscle 0.036 ug kg-1 lw 101 Pulkrabová et al., 2007 2001-2003 Czech Republic barbel (fish) muscle 0.024 ug kg-1 lw 75 Pulkrabová et al., 2007 

2002 Spain barbel (fish) muscle ug kg-1 ww 309 Eljarrat et al., 2004 2002 Spain barbel (fish) muscle ug kg-1 ww 750.4 Eljarrat et al., 2004 2002 Spain barbel (fish) liver ug kg-1 ww 432.3 Eljarrat et al., 2004 2002 Spain barbel (fish) liver ug kg-1 ww 554.4 Eljarrat et al., 2004 2002 Spain barbel (fish) liver ug kg-1 ww 624.8 Eljarrat et al., 2004 2002 Spain barbel (fish) liver ug kg-1 ww 483.9 Eljarrat et al., 2004 2002 Spain barbel (fish) muscle ug kg-1 ww 89.5 Eljarrat et al., 2004 2002 Spain barbel (fish) muscle ug kg-1 ww 529.7 Eljarrat et al., 2004 2002 Spain barbel (fish) muscle ug kg-1 ww 0.02 Eljarrat et al., 2004 2002 Spain barbel (fish) liver ug kg-1 ww 0.02 Eljarrat et al., 2004 2002 Spain barbel (fish) liver ug kg-1 ww 0.02 Eljarrat et al., 2004 2002 Spain barbel (fish) muscle ug kg-1 ww 0.02 Eljarrat et al., 2004 2002 Spain bleak (fish) whole ug kg-1 ww 0.02 Eljarrat et al., 2005 2002 Spain bleak (fish) whole ug kg-1 ww 0.02 Eljarrat et al., 2005 2002 Spain bleak (fish) whole ug kg-1 ww 1501 Eljarrat et al., 2005 2002 Spain bleak (fish) whole ug kg-1 ww 172 Eljarrat et al., 2005 2002 Spain bleak (fish) whole ug kg-1 ww 760 Eljarrat et al., 2005 
187

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

2002 Spain bleak (fish) whole ug kg-1 ww 381 Eljarrat et al., 2005 

2001-2003 Czech Republic bream (fish) muscle 0.041 ug kg-1 lw 2 Pulkrabová et al., 2007 2001-2003 Czech Republic bream (fish) muscle 0.023 ug kg-1 lw 34 Pulkrabová et al., 2007 2001-2003 Czech Republic bream (fish) muscle 0.035 ug kg-1 lw 39 Pulkrabová et al., 2007 2001-2003 Czech Republic bream (fish) muscle 0.046 ug kg-1 lw 161 Pulkrabová et al., 2007 2001-2003 Czech Republic bream (fish) muscle 0.021 ug kg-1 lw 108 Pulkrabová et al., 2007 2001-2003 Czech Republic bream (fish) muscle 0.021 ug kg-1 lw 328 Pulkrabová et al., 2007 

2005 Czech Republic bream (fish) muscle 0.021 ug kg-1 lw 576.2 Hajslova et al., 2007 

2001-2003 Czech Republic chub (fish) muscle 0.027 ug kg-1 lw 58 Pulkrabová et al., 2007 2001-2003 Czech Republic chub (fish) muscle 0.021 ug kg-1 lw 64 Pulkrabová et al., 2007 2001-2003 Czech Republic chub (fish) muscle 0.032 ug kg-1 lw 115 Pulkrabová et al., 2007 2001-2003 Czech Republic chub (fish) muscle 0.019 ug kg-1 lw 215 Pulkrabová et al., 2007 2001-2003 Czech Republic chub (fish) muscle 0.021 ug kg-1 lw 183 Pulkrabová et al., 2007 2001-2003 Czech Republic chub (fish) muscle 0.03 ug kg-1 lw 46 Pulkrabová et al., 2007 

2005 Czech Republic chub (fish) muscle 0.018 ug kg-1 lw 116.7 Hajslova et al., 2007 

2001-2003 Czech Republic perch (fish) muscle 0.007 ug kg-1 lw 14 Pulkrabová et al., 2007 2001-2003 Czech Republic perch (fish) muscle 0.01 ug kg-1 lw 42 Pulkrabová et al., 2007 2001-2003 Czech Republic perch (fish) muscle 0.006 ug kg-1 lw 82 Pulkrabová et al., 2007 2001-2003 Czech Republic perch (fish) muscle 0.008 ug kg-1 lw 114 Pulkrabová et al., 2007 2001-2003 Czech Republic perch (fish) muscle 0.007 ug kg-1 lw 123 Pulkrabová et al., 2007 2001-2003 Czech Republic perch (fish) muscle 0.009 ug kg-1 lw 177 Pulkrabová et al., 2007 

2005 Czech Republic perch (fish) muscle 0.017 ug kg-1 lw 158.8 Hajslova et al., 2007 

2001-2003 Czech Republic trout (fish) muscle ug kg-1 lw 6 Pulkrabová et al., 2007 2001-2003 Czech Republic trout (fish) muscle ug kg-1 lw 5 Pulkrabová et al., 2007 2001-2003 Czech Republic trout (fish) muscle ug kg-1 lw 4 Pulkrabová et al., 2007 

2004 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-1 ww Verrault et al. (2005) 

2002, 2004 Bear Island glaucous gulls plasma 0.0143 ug kg-1 ww 0.51 Verrault et al. (2005) 2002, 2004 Bear Island glaucous gulls plasma 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) 
188

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1983 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 ww 2.3 2.3 < DL < DL Knudsen et al. (2005) 2002 Bear Island glaucous gull egg 0.085 ug kg-1 ww 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) 
189

Year Location Organism Tissue fLIPID Units Total HBCD α-HBCD β-HBCD γ-HBCD Reference 

1998 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 Thysanoessa 

inermis 

whole body 0.1242 ug kg-1 lw NA Sormo et al. (2006) 

2003 Svalbard Themisto libellula whole body 0.0901 ug 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-1 lw 1.89 Sormo et al. (2006) 2003 Svalbard ringed seal blubber 0.8923 ug kg-1 lw 19.56 Sormo et al. (2006) 2003 Svalbard polar bears adipose 

tissue 

0.8385 ug kg-1 lw 11.53 Sormo et al. (2006) 

2002 Svalbard polar bears adipose 

tissue 

0.91 ug kg-1 lw 44.4 Muir et al (2006) 

1999 - 2001 East Greenland polar bears adipose 

tissue 

0.9 ug kg-1 lw 44.5 Muir et al (2006) 

1994 - 2002 Bering-Chukchi 

Sea 

polar bears adipose 

tissue 

0.6 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 ug kg-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. Hexabromocyclododecane (HBCD) Evaluation 190

9.4 MASS BALANCE MODELS

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 development, evaluation (sometimes termed 

validation), refinement, and application are therefore key elements in the evolutionary 

scientific process towards improved understanding. 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 are 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 chemical 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., diffusion in water and advection by wind) and 

chemical transformation processes (e.g., microbial 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 development of environmental mass balance models 

(Mackay 2001). In particular, the fugacity approach facilitates development of the mass 

balance equations and the results are more readily interpreted. For example, since 

fugacity is an equilibrium criterion analogous to chemical potential, the equilibrium 

status of the system can be readily assessed. 

In a simplistic sense the biosphere is comprised of a multitude of interconnected 

compartments and phases of various composition 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 through or be in the process of transformation or 

reaction. In the environment 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 composition of each compartment in the mass 

balance equation must be defined. A single phase (e.g. air) can be modelled as a single 

box or as a number of boxes thus providing greater spatial resolution for the total air 

compartment. Representing heterogeneous concentrations 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 referred to as the Continuous Stirred Tank 

Reactor (CSTR) assumption. In certain cases, particularly for substances that are 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 191

transformed rapidly, the application of this assumption requires 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 longer change as a function of time, i.e., 

chemical input quantities equal chemical “loss” or output quantities. The steady state 

assumption is often used because it simplifies the mathematical and computational 

requirements to solve the equations. It is recognized that complex environmental systems 

are never truly at steady state; however, in certain cases “pseudo-steady state” conditions 

can be observed. Commercial chemicals released to the environment continuously or 

semi-continuously over years of production and use may approximate “pseudo-steady 

state” conditions, at least in regions where they are discharged. 

Models can be developed, parameterized and applied to specific conditions with a view to 

describing an actual environment or situation. 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 eACP10 values calculate for t-, α-, β-, γ-HBCD where t-HBCD is based on 

the physical-chemical properties estimated for the technical mixture 

0.001%

0.010%

0.100%

1.000%

Min HL

Default

Max HL

Reduced

E to Air*

Min HL

Default

Max HL

Min HL

Default

Max HL

Min HL

Default

Max HL

t-HBCD a-HBCD b-HBCD g-HBCD

Figure 9.1. Comparison of eACP10 values for t-, α-, β-, γ-HBCD 
Arnot et al. 2009. Hexabromocyclododecane (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 15718 
Arnot et al. 2009. Hexabromocyclododecane (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 present 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 regional environments from which the monitoring 

data were obtained. Feeding preferences for the avian piscivore (scavenger) were: 70% 

lower trophic level fish, 20% upper trophic level fish and 10% terrestrial invertebrates. 

Feeding preferences for the marine (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% upper trophic level fish to 2% upper trophic 

level fish and 2% lower trophic level fish. The remaining default human diet preferences 

were unchanged (Arnot and Mackay 2008). 

9.5 DETAILED COMMENTS ON HBCD TOXICITY DATA AND PREVIOUS PBT

ASSESSMENTS

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 differences in water solubility 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·L1

, 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. Only 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 HBCD are truly dissolved and there are no 

precipates expected. Thus, there are unresolved and unquantified 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 reviews of the toxicity test data. 

There are also a number of problems interpreting toxicity data for hydrophobic 

substances when cosolvents are used. Toxicity test design is based on a simple model 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 194

approach that allows the external exposure concentration to be used as a surrogate for the 

usually unknown amount of substance that enters 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 toxicity 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 cosolvent. If the nature 

and magnitude of any bioavailability change has been quantified, this would allow 

changes to be understood and an appropriate correction made. Unfortunately, such 

information does not appear to have been collected in any of the key HBCD studies. 

Cosolvents affect the bioavailability of a substance in aqueous exposure media; however, 

to what degree is speculative without a greater quantitative understanding of the influence 

of the cosolvent in comparison to “pure” water phase exposures. The success of 

cosolvents in increasing effective bioavailablity is well established. In a variety of tests 

with hydrophobic chemicals no effects were reported 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 against screening criteria that have not been 

developed to account for the influence of cosolvents on test data. 

Bioavailability can also be changed by cosolvent in another way concurrent with the 

water solubility. Water solubility is determined in pure water, without dissolved or 

suspended organic material. Both toxicity and 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 contribute to organic matter in the water column 

and certain exposure media used in algal and some invertebrate/bacterial testing contain 

other added substances that may be problematic. For very hydrophobic substances some 

portion of the total water column concentration 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 bioavailable to some organisms in some cases via 

alternative uptake routes - e.g., diet. Depending on the amount and effectiveness of the 

cosolvent, this reduction in bioavailability may be counteracted 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 concentrations 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 the amount of substance taken up into the exposed 

organisms which affect amounts accumulated for either BCF or toxicity testing, it also 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 195

means that the total water column concentration is a confounded measure of the true 

exposure concentration experienced by the organisms. All in all, interpretation of aquatic 

testing results for very hydrophobic substances is very problematic and the simple 

process and general assumptions generally accepted for more water soluble substances 

should not be blindly adopted for candidate PBT substances. Since, essentially the same 

issues are applicable to evaluation of candidate POP substances, if the European PBT 

critieria or any screening criteria or evaluation schemes for Stockholm Convention and 

UN-ECE candidate POPs are to be employed in a scientifically-sound and fair decisionmaking process, a more thorough and detailed technical analysis and evaluation is 

needed. There is a substantial published literature 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 concerns regarding water solubility, use of co-solvent and 

bioavailablity render all available 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 for 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 general challenge, there is another pertinent 

point about the use of cosolvents in toxicity testing with mixtures. The current OECD 

(2000) guidance document on testing 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 potential for interaction 

with the test substance resulting in an altered response in the test, their use should be 

restricted to situations where no other acceptable method of media preparation is 

available. If solvents are used, their effects on the test results, if any, need to be 

determined. It should be emphasized that solvents 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 dissolves preferentially." (OECD, 2000, p. 29) 

Similarly, the use of generator systems such as generator columns is specifically 

contraindicated for toxicity testing of difficult substances and mixtures such as HBCD. 

"Generator systems are not considered appropriate for substances containing components 

or impurities which differ in their water solubility. Differences in water solubility will 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 196

result in selective depletion of the more water soluble components from the column or 

disk matrix and their relative concentration in the water phase." (OECD, 2000, p. 23) 

In summary, based on internationally recognized toxicity testing guidance it is clear that 

that most, if not all, of the aquatic toxicity data currently available for HBCD are 

unreliable due to lack of quantitative information about test-specific water solubility 

information, especially on the influence of cosolvents used with a difficult substance that 

is a mixture of isomers with different 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 Technical 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 the issue of “inherent properties is discussed 

followed by the application of NOEC data without LOEC data. 

Inherent Properties

There is an emphasis on screening information 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 requires a more thorough evaluation. 

However, the European Commission evaluative process indicates that screening may be 

sufficient to allow scientifically-sound PBT categorization 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 relatively 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 inherent to a substance. Toxicity requires an 

interaction - i.e., an exposure - with a living organism to occur, and 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 chemistry, appears in Mackay et 

al. (Mackay et al. 2001). At the very least the term "inherent" is redundant as all 

substances are potentially toxic and the key determinant of exposure is clearly stated in a 

founding toxicological principle: 

"What is there that is not poison? All things are poison and 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. Hexabromocyclododecane (HBCD) Evaluation 197

The European Commission PBT assessment does not appear to fully consider the 

interaction between the "inherent" 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 tests relate the adverse 

effects observed to a substance concentration in aqueous exposure media. That estimate 

is used as a surrogate dose metric for the usually unknown concentration in the body of 

the exposed organisms; specifically, the amounts 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 organism that initiate the adverse 

effect in question. The other is the partitioning behavior of the substance, which 

determines bioaccumulation from the media in question. 

To further illustrate this point, for a given toxicity endpoint an identical value can be 

obtained for a substance that combines low toxicity (i.e. baseline narcosis) and high 

hydrophobicity and a substance with higher toxicity (i.e. non-narcotic specific toxicity) 

and lower hydrophobicity. There is nothing "inherent" in an LC50 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., bioconcentration. Thus, some high KOW chemicals can appear more toxic 

than lower KOW chemicals because of higher bioaccumulation, rather than higher toxic 

potency. Knowledge of both the 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 highlighted this issue and possible errors in 

interpreting toxicity data for 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 action for organic chemicals. There appears to 

be little in the way of detailed consideration 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 toxic 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 information, there appears to be little in the way of a thorough 

weight-of-evidence evaluation of the available data. In fact, the lowest toxicity values 
Arnot et al. 2009. Hexabromocyclododecane (HBCD) Evaluation 198

from testing carried out with protocols judged acceptable are simply compared to the 

defined PBT toxicity trigger values. For aquatic data the PBT screening trigger values 

are: 

 

"... a substance is considered to be potentially toxic when the L(E)C50 to aquatic 

organisms is less than 0.1 mg·L-1." while for the more definitive chronic endpoint the 

trigger is "... the long-term NOEC 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 level represents valuable information about real 

toxicity. The definitive chronic trigger defined as a NOEC level makes little sense as 

presented. NOECs are poor toxicity metrics with unreliable quantification since they do 

not refer to a known adverse dose-response relationship for an exposure level. In fact, 

they specifically refer to an exposure where no adverse effects have been found. NOECs 

may be found to be of some real use only 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 literature (OECD 1998; Crane and Newman 2000; 

Jager et al. 2006). 

NOECs are commonly estimated as the exposure 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 NOEC is the highest exposure level and there 

is no exposure level where adverse effects were reported. Here the NOEC is of little 

value for use in comparison with a trigger value 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 between 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 European 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 POP 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 hydrophobic substances, such as HBCD, are difficult to interpret 

and to compare with substances that do not have low water solubility. Although an 

appropriate testing protocol may have been used, there is additional 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. Hexabromocyclododecane (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 summarized in the following (UN-ECE 1998; UNEP 

2001). 

The screening criteria in Annex D of the Stockholm Convention for adverse 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. comparisons of measured body burdens to relevant 

toxicity endpoints). Criterion (ii) is more vaguely expressed 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 statement of the reasons for concern 

including, where possible, a comparison of toxicity or ecotoxicity data with detected or 

predicted levels of a chemical resulting or anticipated from its long range environmental 

transport, and a short statement indicating 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 affect human health and/or the environment 

A more detailed assessment may occur following submission of the risk profile to the 

UN-ECE Executive Body, if, “further consideration of the substance is determined to be 

warranted”, including the following evaluation: 

2(b) Whether sufficient information exists to suggest that the substance is likely to have 

significant adverse human health and/or environmental effects 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.