# Comments on Preliminary Bioaccumulation Assessment of Medium Chain Chlorinated Paraffins (MCCPs)

Prepared for The MCCP REACH Consortium

Final Report

Prepared by: Jon Arnot, Ph.D.

April 30 , 2013

### 2 

## Executive Summary

This r eport evaluates bioaccumulation information presented in th e draft medi um chain chlorinated paraffin ( MCCP ) REACH substance evaluation (SEV) document developed by the United Kingdom and associated 4 April 2013 draft decision letter from the European Chemicals Agency (ECHA) and provides

an assessment of the MCCP bi oaccumulation conclusions and testing recommendations

. Key technical information regarding the interpretation of bioaccumulation testing data and the bioaccumulation assessment of hydrophobic organic chemicals is first presented. This includes illustration s of the critical relationships between the chemical bioavailability in the water (i.e., the dissolved chemical fraction available for uptake into aquatic organisms ), growth dilution rates and me tabolic biotransformation rates on bioaccumulation metrics; n amely the bioconcentration factor (BCF)

. These illustrations show the relative importance of the primary biotransformation rate constant (k M ) on the level of bioaccumulation of hydrophobic organic chemicals in fish.

R obust study summaries for two bioconce ntration tests and three dietary bioaccumulation ( bioma gnification) tests for fish are provided in this report . A review of a field study including bioaccumulation factor (BAF) , biomagnification factor (BMF) and trophic magnification factor (TMF) data is a lso included. Novel k M calculations for expected MCCP constituents and related chemicals less likely to be found in commercial products (i.e., medium chain polychlorinated alkanes, MC

- PCAs) are presented. The k M s are similar in magnitude for expected MCCP constituents and MC

- PCAs that have been tested

. Growth corrected BCFs based on dissolved water concentrations calculated here are generally consistent with the BCFs calculated in the SEV with respect to the ECHA bioaccumulation
assessment. Based on this an alysis it is unlikely that further testing will provide new information for the bioaccumulation assessment of MCCPs using the BCF assessment metric and current BCF criteria. T here is s ome uncertainty in some of the existing testing data related to the use of total radioactivity ; therefore, any further testing that may be considered should be done with parent chemical specific analysis to address these uncertainties . While expected MCCP constituents show calculated growth corrected BCFs that exceed regulator y criteria, the current weight of evidence indicates that expected MCCP constituents are not likely to biomagnify in fish and in aquatic food webs based on laboratory and field BMFs and field TMFs, respectively.

### 3 

## Table of Contents

Executive Summary

................................ ................................ ................................ ................................ . 2
Table of Contents

................................ ................................ ................................ ................................ .... 3
Introduction

................................ ................................ ................................ ................................ ............ 4 Scope of Review

................................ ................................ ................................ ................................ .. 4
Bioaccumulation

– Background Terminology and Key Relevant Concepts

................................ ............. 4
Chemical bioa vailability in the water

................................ ................................ ................................ 6
Growth dilution

................................ ................................ ................................ ............................... 7
Biotransformation rates

................................ ................................ ................................ ................... 9
Measured Bioaccumulation Data Review

................................ ................................ ............................... 10 Measured Laboratory Bioconcentration Data

– BCFs

................................ ................................ ......... 10 Robust Study Summary: Fish BC F Test #1 (Thompson et al. 2000)

................................ .................. 10 Robust Study Summary: Fish BCF Test #2 (Hurd and Vaughan, 2010)

................................ ............. 11 Measured Laboratory Dieta ry Bioaccumulation Data

– BMFs

................................ ............................. 13 Robust Study Summary: Fish BMF Test #1 ( Fisk et al. 1998)

................................ ........................... 13 Robust Study Summary: Fish BMF Te st #2 ( Fisk et al. 1996)

................................ ........................... 14 Robust Study Summary: Fish BMF Test #3 ( Fisk et al. 2000)

................................ ........................... 14
Measured Field Bioaccumulation Data

– B AFs, BMFs and TMFs

................................ ......................... 16 Robust Study Summary: Fish BAF and BMF field data ( Houde et al. 2008)

................................ ...... 16
Biotransformation Rate Constants (in vivo, in silico)

- k M ................................ ................................ .... 17 Incorporating k M,N for Bioaccumulation Assessment

- BCFs

................................ ................................ 20
General Comments on SEV Document

................................ ................................ ................................ ... 21 Comment on “SEV Report section 8.1.4 Summary and overall conclusions on PBT and vPvB properties”

................................ ................................ ......................... 21
References

................................ ................................ ................................ ................................ ............ 22

### 4 

## Introduction

**Scope of Review**

This document provides general technical knowledge and commentary regarding bioconcentration and bioaccumulation in biota (primarily fish), measured data, mass balance models and quantitative
structure

- act ivity relationships (QSARs). The document provides a review and assessment of some of the bioaccumulation information presented in the draft med ium chain chlorinated paraffin (MCCP) REACH substance evaluation (SEV) document developed by the United Kingdom and associated 4 April 2013 draft decision letter from the European Chemicals Agency (ECHA). Given the time frame for preparing the present document, the objective here is not to provide a thoroughly documented review for all of the available measured (lab oratory and field) and modelled bioaccumulation data for MCCPs and medium chain length polychlorinated alkanes ( MC

- PCA s) presented in the SEV document and elsewhere. If desirable, future work could expand upon the current document by examining data not pre sented in the SEV and for other chain length PCAs and CPs . The focus here is (i) to critically examine the acceptable (usable) quality measured bioaccumulation data to better understand the bioaccumulation of MCCPs in aquatic organisms and food webs and ba sed on this analysis (ii) determine if further requested dietary based bioaccumulation testing of MCCP constituents is warranted. Through the course of this analysis key technical information relating to the bioaccumulation process of neutral organic chemi cals in aquatic organisms is briefly presented with detailed published studies cited for brevity. If desirable, more analysis and documentation can be provided in future work.

MCCPs are a mixture of chemicals , considered a UVCB substance (i.e. a substance of unknown or variable composition, complex reaction pr oducts or biological materials), registered for evaluation under a single CAS No (85535

- 85

- 9). MCCPs are linear alkanes comprised of 14 to 17 carbons with a range of chlorine substitution . Theoreticall y, there could be approximately 120,000 different constituents in the MCCP mixture

. The range of properties for constituents is therefore also large with predictions for the octanol

- water partition coefficient (K OW ) spanning a few orders of magnitude , e.g. , log K OW s from ~ 7 to

> 9.0 [ 1 ]

. While it is recognized that all of the different MCCP constituents and congeners have different bioaccumulative properties, the general focus of this review is on the available bioaccumulation data and the interpretat ion of the data included in the SEV document

.
A brief overview of relevant information for assessing the bioaccumulation potential of organic chemicals is first provided , including some key terminology and concepts

. Bioaccumulation data included in the SE V document deemed most relevant and usable (“acceptable quality”) are briefly summarized. M odelled bioaccumulation data not included in the SEV document are developed here to seek insights into differences in chlorine substitution patterns and chain length on the biotransformation rate and bioaccumulation assessment of MCCPs . Finally some general comments and responses to “ initial charge questions” are provided.


### Bioaccumulation

**– Background Terminology**  **and Key Relevant Concepts** 

Full definitions of key termi nology for bioaccumulation and bioaccumulation hazard assessment are available elsewhere [ 2

- 5 ] . Briefly, bioaccumulation is the net result of competing rates of che mical uptake and elimination in an organism under a defined set of exposure conditions. Fish are commonly used as a model organism for assessing bioaccumulation hazard. Key routes of chemical uptake in fish include uptake at the gill surface and through ch emical exposure in the diet and key routes of chemical elimination include gill elimination, fecal egestion and metabolic biotransformation. Growth dilution is a

### 5 process in which the chemical concentration in the organism can be reduced; however, the paren t chemical is not actually eliminated from the body. Growth rate “constants” are often included in mass balance models predicting chemical conc entrations and bioaccumulation. A typical steady

- state mass balance model for calculating chemical concentrations in fish (C F ) is [ 5

- 7 ] :

C F = [ (k 1 × C WD ) + (k D ×C D )] / (k R + k E + k G + k M )

where k 1 is the gill uptake rate constant of chemical from water , C WD is the dissolved (b ioavailable) chemical concentration in the water, k D is the dietary uptake rate constant of chemical from the diet , C D is the chemical concentration in the diet and k R , k E , k G and k M are respectively the first

- order rate constants for gill elimination, fec al egestion, growth dilution and metabolic biotransformation. The numerator reflects chemical uptake processes and the denominator reflects chemical elimination processes. The sum of the elimination rate constants is often referred to as the total eliminat ion rate constant (i.e. k T ; e.g., units d - 1 ) or depuration rate constant with various nomenclature s ( e.g., k d or k 2 ) and corresponds to the whole body total elimination half

- life t 1/2 ( e.g., units d) as ln2/k T .
Typical bioaccumulation hazard assessment m etrics include K OW , the bioconcentration factor (BCF; L/kg), the bioaccumulation factor (BAF; L/kg), and the biomagnification factor (BMF; kg

- lipid/kg

- lipid) [ 3
, 8 ] . Other metrics, such as the trophic magnification factor (TMF) and the biota

- sediment accumulation factor (BSAF; kg

- OC/kg

- lipid) are also considered useful for bioaccumulation assessment [ 3
, 8 ]

. K OW is used as a surrogate for lipid

- water partitioning and has recognized limitations for bioaccumulation assessment, primarily because it ignores key biological processes such as uptake and elimination, most notably biotransformation [ 9
, 10 ] . Therefore, K OW is no longer typically used alone to assess bioaccumulation. K OW is still an important input parameter for the majority of bioaccumulatio n models. The BCF is the steady

- state ratio of the chemical concentration in a fish to the chemical concentration in the water following chemical exposure from the water only (as occurs under controlled laboratory conditions) , i.e., BCF ss = Cf/Cw

. The BCF can also be determined from the kinetic (rate constant) information as BCF k = k 1 /k T . The BAF is the steady

- state ratio of the chemical concentration in a fish to the chemical concentration in the water as a result of all routes of exposure (i.e. water, food, and potentially sediment) , i.e., BAF ss = Cf/Cw . The BAF typically represent s environmental exposures and is derived under field conditions

. The BCF and BAF can be calculated using wet weight or lipid normalized tissue concentrations and total or dissolved water concentrations. For most neutral organic chemicals the lipid normaliz ed / dissolved water concentration BCF and BAF metrics provide information on the thermodynamic status for the chemical distribution between the organism and the water. The BMF is the steady

- state ratio of the lipid normalized chemical concentration in an organism to the lipid normalized chemical concentration in its diet [ 3
, 8 ] . The BMF can be determined through laboratory testing or field measurements. It is noted that all of these metrics used to evaluate bioaccumulation hazard are normalized to an exposure source (i.e., the concentration in the surrounding environment or the concentration in the diet). While monitoring data are useful for exposure and ri sk assessment, they are not useful for bioaccumulation hazard assessment without some quantification of the actual
exposures.

Bioaccumulation hazard assessment seeks to evaluate and compare the inherent properties of chemicals in a consistent manner. Sever al parameters other than the “inherent chemical properties” can influence chemical bioaccumulation and thus the interpretation of bioaccumulation test results. Some examples of key parameters other than the “inherent chemical properties” that can influence bioaccumulation include organism properties (lipid content) and exposure condition s (diet lipid content,
organ ic carbon content in the water). Standardized testing methods and guidance have been developed

### 6 in effort s t o assess the inherent properties of t he chemicals under defined conditions [ 11 ] , in essence these guidelines seek to provide “a testing model ” for evaluating bioaccumulation in fish . The

recom mended testing methods and technical guidance continue to evolve with the science

.
For screening

- level bioaccumulation assessments, lipid normalization, growth correction and dissolved water concentration based bioaccumulation metrics provide a consistent means for chemical comparisons. Benchmarking with a positive control or standard chemical [ 12
, 13 ] is an additional approach that may address variability and uncertainty and result in improved and more consistent data interpretation for chemical evaluations. The rationale of lipid normalization for neutral organic chemicals is well established [ 3
, 14
, 15 ] , although still not consistently adapted in al l chemical evaluations. The confounding influences of bioavailability (i.e. the actual dissolved water concentration) and growth dilution have not been commonly and consistently recognized and addressed in most historical bioaccumulation assessments; there fore, the influence of these two parameters on fish bioaccumulation data interpretation are highlighted here. The rate at which a hydrophobic chemical is biotransformed is a critical determinant of the ov erall extent of bioaccumulation for hydrophobic chem icals [ 9
, 10
, 16 ] ; t herefore, highlighting the influence of k M is also a focus of this document

.

**Chemical b ioavailability in the water** 

Understanding the concept of chemical bioavailability in the water is critical for interpreting and applying aquatic

- based bioaccumulation metrics such as the BCF and the BAF be cause the denominator of these steady

- state calculations includes an estimate of the water concentration, i.e., BCF ss = Cf/Cw. Only the dissolved chemical in the water is available for uptake through the gills [ 17 ]

. Organic carbon (OC) in the water can influence the bioavailable (dissolved) solute fraction (BSF) for hydrophobic chemicals. The f raction dissolved (or BSF) can be estimated as [ 6
, 18 ] :

BSF = 1/(1 + [ POC]×0.35×K OW + [DOC]×0.08×K OW )

where [POC] is the particulate organic carbon co ncentration, [DOC] is the dissolved organic carbon concentration (operationally defined as OC passing through a 0.45 μ m filter ), and the values 0.35 [ 19 ] and 0.08 [ 20 ] are proportionality constants relating K OW to the ty pes of organic carbon. Figure 1 illustrates the relationship between BSF, K OW
, DOC and POC [ 18 ]

. Note that e ven seemingly small quantities of OC can have significant effects on the actual fraction of chemical that is available for uptake into an aquatic organism when K OW gets large (e.g. >10 7 ) due to the high level of hydrophobicity associated with these chemicals.

The total water concentration (C WT ) for hydrophobic neutral o rganic chemicals is typically measured in bioaccumulation and toxicity testing and field studies using solvent extraction and therefore includes both the dissolved fraction and the fraction sorbed to OC (not bioavailable). The dissolved water concentration (C WD ) is therefore C WD = C WT ×BSF. As shown in Figure 1, BSF = 1 ( i.e., C WD ≈ C WT ) for lower K OW chemicals even when POC and DOC concentrations are relatively high; however, for higher K OW chemicals BSF < 1 ( i.e., C WD < C WT ) even when POC and DOC concentra tions are seemingly low (i.e., less than recommended “acceptable values” in OECD 305 BCF testing [ 11 ]

.

For bioaccumulation assessments consistent comparis ons of “inherent chemical properties ” using the BCF and BAF should therefore be based on C WD since this concentration is independent of [POC] and [DOC] . In other words, BCFs and BAFs based on C WD are most appropriate for bioaccumulation hazard categorizati on assessments so that chemicals can be compared against a consistent set of conditions

### 7 (i.e. the concentration of dissolved chemical in the exposure medium). For exposure and risk assessments, chemical partitioning to OC in the water column must be quanti fied to obtain reliable estimates of exposure based on the total (bulk) water concentrations.

It is recognized that estimating the truly dissolved chemical concentrations of very hydrophobic chemicals in water is technically challenging and thus measureme nts and model calculations are uncertain. Furthermore, the vast majority (more than 95%) of existing measured bioaccumulation data for fish (i.e. BCFs and BAFs) are based on total water concentrations. Thus, most screening

- level bioaccumulation models ( i.e
., regression

- based BCF models using laboratory BCF data from total water concentrations ) make predictions based on the total water concentration under “typical” or a ssumed laboratory exposure conditions (i.e., relatively lower POC and DOC than the environ ment, but still some OC)

. In the MCCP SEV document, the issue of bioavailability does not seem to be well understood

.

Figure 1 . I llustration of the effect of chemical hydrophobicity (K OW ) and Particulate Organic Carbon (POC) and Dissolved Organic Carbon (DOC) concentrations on the dissolved (bioavailable) fraction of the total chemical concentration in the exposure water . This f igure is part of Figure 2 in [ 18 ]

. The fractio n dissolved is estimated here as 1/(1 + [POC]×0.35×K OW + [DOC]×0.08×K OW ) [ 1 ] where the values 0.35 [ 19 ] and 0.08 [ 20 ] are proportionality constants relating K OW to types of organic carbon.

**Growth dilution**

As illus trated below, t he organism growth rate can also be a confounding factor for certain hydrophobic chemicals and thus this parameter requires consideration for the consistent interpretation of bioaccumulation metrics. When key chemical elimination processes a re slow (i.e., gill elimination, biotransformation, fecal egestion), the growth rate can confound the true depuration or elimination rate of the chemical from the test organism , i.e., growth rate constant becomes a principal factor resulting in decreased c hemical concentrations in the organism over time , not actual chemical elimination from the body . In this regard, growth rates are expected to be most relevant for relatively hydrophobic, slowly biotransformed chemicals (i.e., key elimination rates are rela tively slow, growth dilution becomes more dominant “rate process” in the overall total elimination ). Thus there is a rationale for considering growth correction for consistent chemical hazard assessments that seek to

### 8 evaluate and compare the “ inherent prop erties ” of the chemicals independent of the exposure or organism co nditions (i.e., differences in growth rates)

. Growth correction is essentially k T - k G and is sometimes referred to as k 2g .
Figure 2 uses the mechanistic Arnot and Gobas screening

- level mas s balance model included in EPI Suite to illustrate the influence of growth rates and bioavailability on the BCF [ 1
, 21 ]

. A spreadsheet version of the model is available that allows for these parameter manipulatio ns and illustrations ( www.arnotresearch.com ). The model is parameterized here for a small fish (~10 g) with a 5% whole body lipid content (i.e., consistent with the suggested lipid content for bioaccumulation assessment against “standardized criteria” ). The water temperature is set to 15 ° C simila r to BCF and BMF testing conditions for cold

- water species (i.e., rainbow trout). For Figure 2 the biotransformation rate constant (k M ) is assumed to be zero to highlight the relative roles of growth and bioavailability (dissolved chemical fraction) on the BCF . An assumed “laboratory” growth rate constant (k G ) of 0.03 /d is initially used to parameterize the model and a second set of simulations assumes k G = 0 /d, consistent with an approach for growth correction, i.e., “growth normalization”. The se first t wo simulations include the default model parameters for POC and DOC concentrations in the water , i.e., 0.5 mg/L for both. The model shows that growth rate and bioavailability have negligible influence on the BCF for lower K OW chemicals, i.e., log K OW < ~4. 5. Growth normalization begins to influence the BCF as K OW increases , particularly when log K OW > ~6. The differences between “measured total water concentration BCFs” (including growth, blue diamonds) and “ growth corrected total water concentration BCFs ” (blue squares) can be very large for hydrophobic chemicals, when biotransformation is assumed negligible. It is emphasized that when log K OW > 5, k M ~ 0 and BCFs are corrected for growth (k G = 0) , all of the wet weight BCFs are greater than 5,000 L/kg . Und er these conditions, growth corrected BCFs calculated from total water concentrations

“ plateau ” for higher K OW chemicals because all of the other rates of chemical elimination are low (or 0) and the steady

- state BCF is essentially controlled by the fecal e gestion rate constant. When the BCF is calculated based on the dissolved water concentrations (i.e., BSF = 1) , the BCF continues to increase with increasing K OW . This is explained by inspection of the BCF and BSF calculations and Figure 1. Notably, when lo g K OW > ~ 5, k G = 0 and k M ~ 0, the BCFs are > 5,000 whether or not the BCF is calculated based on the total or dissolved water concentrations.

### 9 Figure 2. Illustration of the effect of growth rate (KG / d ) and bioavailable solute fraction (BSF, see Fig ure 1) on the bioconcentration factor (BCF) as a function of hydrophobicity (K OW ).

Growth rates can vary considerably based on energy density of the diet, digestibility of the diet, organism activity, life

- stage, etc. In general terms, organisms grow when the uptake of energy exceeds the expenditure of energy. Organisms are not expected to grow (or live very long) if they are not feeding. Fish in the environment feed and grow at various rates throughout the life

- cycle and often for some species at different rates depending on the season . While growth correction of bioaccumulation metrics is relatively straightforward if measured properly during a laboratory test , i.e., for lab BCFs and BMFs , the issue is more complicated in the environment.

**Biotransformation rates** 

Building from the illustrations in Figure 2 , the role of the biotransformation rate constant ( k M ) is illustrated next in Figure 3

. In a series of BCF calculations k M is increased from 0 to 0.03 to 0.15 to 0.3 /d. Figure 3 shows that when growth cor rected BCFs for an assumed 5% lipid content fish are calculated based on the dissolved water concentration (i.e., normalized exposure conditions), the k M needs to be about 0.15 /d for BCF < 5,000 and about 0.3 /d for BCF < 2,000 regardless of the chemical K OW . Given the uncertainty in the actual K OW of MCCP constituents (or other organic chemicals), these illustrations help put into context the importance of the biotransformation rate constant in determining the overall bioaccumulation of hydrophobic organi c chemicals. Thus the “inherent bioaccumulation properties” of hydrophobic chemicals (i.e., log K OW > 6) in aquatic organisms is largely dependent on reliable estimates of the biotransformation rate constant (k M ). For these reasons an effort was made here to estimate k M

### 10 from the “acceptable quality ” bioaccumulation test data for MCCPs. Further examples of the role of biotransformation rates on bioaccumulation and exposure assessment are available elsewhere [ 9
, 10
, 16
, 22
, 23 ]

.

Figure 3. The role of the primary biotransformation rate constant (KM /d) on the growth corrected (i.e., KG = 0 /d) bioconcentration factor (BCF) calculated from the dissolved (bioavailable) water concentration, i.e., BSF = 1.



## Meas **ured Bioaccumulation Data Review** 

**Meas u**  **red L**  **aboratory B**  **ioconcentration D**  **ata**

**– BCFs** 

**Robust Study Summary: Fish**  **BCF Test #1 (Thompson et al. 2000)**

Test substance: radiolabelled n - pentadecane

- 8

- 14 C ( C 15 H 26 Cl 6 , ~ 51% chlorinated w/w )

Test organism: rainbow trou t ( Oncorhynchus mykiss ) ~2 g initial wet weight; lipid content not measured

.

T he t est followed OECD 305 guidelines (1996) [ 24 ] and good laboratory practise [ 25 ]

. Uptake and elim ination kinetics were calculated based on 35 d exposure and 42 d depuration phases

. Kinetic BCF estimates of 1,087 L/kg

- ww and 349 L/kg

- ww for exposure concentration treatments of 1 and 5 μg/L (nominal) , respectively , at about 15 ° C

. Uptake rate constants (k 1 ) were 48.7 and 14.2 /d for the low and

### 11 high exposure treatments, respectively. Total elimination rate constants ( “ k 2 ”) were 0.0448 and 0.0407 /d for the low and high exposure treatments, respectively. A growth rate constant (“k G ”) of 0.021 /d can be ca lculated from the test data confirming the estimate used in the SEV document

. There are key technical issues related to data quality, uncertainty and interpretation:

• There was no correction of mass balance for parent test chemical [ 2
, 18 ] , i.e., total radioactivity for both parent and possible metabolites was quantified. G iven the flow through conditions of the test
, the tissue concentrations and ensuing BCFs represent likely upper bound estimates of the BCF based on total water concentrations. The position of the radiolabel on the test substance is not reported. This issue is a potential source of uncertainty with the reported BCF and kinetic information

.

• D epuration half

- lives were 15 and 17 days for nominal expos ure concentration treatments of 1 and 5 μg/L, respectively . Steady

- state was not approached d uring the test, i.e. time to reach ~95% of steady

- state = 4 xt 1/2 ~60 d
, suggest ing the BCF calculated as Cf/Cw is an underestimate of the steady

- state value suppor ting the rationale for using the kinetic BCF estimates

.

• The exposure concentrations are near the estimated water solubility limits for the test chemical. Based on w/w% Cl, an assumed representative SMILES notation was generated [ CC(Cl)CC(Cl)CC(Cl)CC(Cl)CC C(Cl)CC(Cl)CC] and predicted K OW and water solubility (Sw) estimates are log Kow ~8.8, Sw ~0.1 μ g/L [ 1 ] and log Kow ~6.8, Sw ~43 μ g/L (ACD Labs). It is possible that the measured exposure concentration of 5 μg/L exceeds the water solubility limit fo r the test chemical. It is recognized that there are uncertainties in measured and predicted K OW and water solubility; however, the relatively lower BCF for the higher exposure concentration is consistent with a pattern of observations of underestimated BC Fs for the same chemical when water solubility is exceeded [ 2 ]

. When chemi cal exposure concentrations are above the water solubility limit, the measured total water concentration is an overestimate of the actual bioavailable water concentration resulting in underestimates of the BCF. This issue is a key source of uncertainty with the reported BCFs , particularly for the higher exposure level.

• Total water concentrations were measured throughout the exposure; however, non

- purgeable

or ganic carbon (NPOC) concentrations during the test ranged from 0.69 to 2.4 mg/L (some higher values also reported). Although the values and nature of the OC are uncertain, the BCFs based on the dissolved (bioavailable) water concentration are higher than t he BCFs based on measurements of the total water concentration. For example, based on common model estimates for the dissolved fraction and log K OW ~8 and [DOC] ~ 1 mg/L, the dissolved fraction may be about 10 % of the total measured concentration (see Figur e 1)

. This issue is a key source of uncertainty with the reported BCFs

.

• Solvent was used, may be a minor factor; however, there are conflicting studies on possible co

solvent effects and it is probably a relatively minor source of uncertainty in this case

. **Robust Study Summary: Fish BCF Test #2 (**  **Hurd and Vaughan, 2010 )** 

Test substance: 1

- 14 C

- labelled n - tetradecane ( C 1 4 H 2 5.5 Cl 4.5 , ~42

- 48 % chlorinated w/w )

Test organism: rainbow trout ( Oncorhynchus mykiss ) ~ 2.9 g initial wet weight; mean lipid content 10.3%

T h e t est followed OECD 305 guidelines [ 24 ] and good laboratory practise [ 25 ]

. Uptake and elimination kinetics were calculated based on 35 d exposure and 42 d depuration phases . Ki netic BCF estimates of 9
, 140 L/kg

- ww (4,440 L/kg

- ww when normalized to 5% lipid content) for nominal exposure

### 12 concentration of 0. 5 μg/L

- mean measured total water concentration of 0.34 μg/L , at about 15 ° C

. Uptake rate constant (k 1 ) of 395 /d and total el imination rate constant ( “ k 2 ”) of 0.0432/d. A growth rate constant of 0.030 /d is reported. Generally a good quality study; however , there are key technical issues related to data uncertainty and interpretation:

• There was no correction of mass balance for parent test chemical [ 2
, 18 ] , i.e., total radioactivity for both parent and possible metabolites was quantified. G iven the flow through conditions of the test
, the tissue concentrations and ensuing BCFs represent likely upper bound estimates of the BCF based on total water concentrations. The position of the radiolabel on the test substance is reported to be on a terminal carbon atom. Samples from this study were subjected to further analysis to determine the prop ortions of radioactivity present as parent compound or as polar or bound metabolites (Leonards and van Beuzekom, 2010). Samples of fish at the end of the depuration phase showed that no significant extractable metabolites of chlorinated tetradecane were p resent, but non

- extractable (presumably polar) tissue metabolites were found, which accounted on average for 21% of the total 14 C activity in the fish. This issue is a potential source of uncertainty with the reported BCF and kinetic information

.

• Depuratio n half

- life was 16 days , i.e. time to ~95% of steady state is 6 4 days, indicating the BCF calculated as Cf/Cw is an underestimate of the steady

- state value supporting the rationale for using the kinetic BCF estimate.

• It is likely that the measured exposur e concentration of 0.34 μg/L is below the water solubility limit for the test chemical. Based on w/w% Cl, an assumed representative SMILES notation was generated [ CC(Cl)CCCC(Cl)CC(Cl)CCCC(Cl)CC ] and predicted K OW and water solubili ty (Sw) estimates are log Kow ~7.9, Sw ~ 1 μ g/L [ 1 ] and log Kow ~6.8, Sw ~86 μ g/L (ACD Labs). Note these predictions are assumed for 4 Cls, not 5. It is recognized that there are uncertainties in measured and predicted K OW and water solubility

.

• Total water concentrations wer e measured throughout the exposure and total organic carbon concentrations are reported as <3.0 mg/L. DOC of 0.45 mg/L was measured at one point during the test

. BCFs based on the dissolved (bioavailable) water concentration are expected to be higher than the BCFs based on measurements of the total water concentration , e.g. , based on log K OW ~7.5 and [DOC] ~0.5 mg/L, the dissolved fraction may be about 40 % of the total measured concentration. This issue is a potential source of uncertainty with the reported BCF

.

• Solvent was used, may be a minor factor; however, there are conflicting studies on possible co

solvent effects and it is probably a relatively minor source of uncertainty in this case.

The potential error in k 2 estimation is a result of uncertainties associated with chemical specific analysis (i.e., both parent and metabolites quantified )

. There may be ~ 20% error in k 2 estimation in this case; however, it is difficult to say with certainty from the available data; however, as outlined below in the est imation of biotransformation rate constants assuming a 20% faster value for k 2 , this level of uncertainty may be relatively inconsequential in terms of the overall bioaccumulation assessment

.
Summary comments of relevant BCF data

BCF Test #2 is considered to be of better quality than BCF Test #1 because the exposure concentration in Test #2 is “further from the estimated water solubility limits” than Test #1 , i.e., greater confidence that exposure concentrations are below the water solubility of the test ch emical and potential errors related to the dissolved (bioavailable) fraction of chemical in the water are expected to be lower

. It is noted that the elimination rate constants and half

- lives are comparable

### 13 (i.e., independent of potential exposure errors) , but the estimated uptake rate constants (k 1 ) were lower in Test #1, possibly an artefact of the true bioavailability of the chemical (total, not dissolved , water concentrations used in the regression equations) and water solubility uncertainties (errors).



### Measured **Laboratory Dietary B ioaccumulation**  **D ata** 

**– BMFs** 

There are three published studies by Fisk and colleague s (1996, 1998, and 2000), differing mostly by the formulations of the test chemicals and the concentrations in the diet and whether or not the te st material was radiolabelled or not

. The formulation of the mixtures (i.e., actual structures being tested) could be a critical consideration when interpreting the bioaccumulation data (i.e., chain length and chlorine substitution patterns)

. Note that the bioaccumulation data by Fisk and colleagues are reported in the publications as lipid normalized and growth corrected.

**Robust Study Summary: Fish BMF Test # 1**  **( Fisk et al. 199**  **8 )** 

Test substance: C 1 4 H 26 Cl 4 ( 42 % Cl w/w)
, C 1 4 H 2 5 Cl 5 ( 48 % Cl w/w), C 14 H 24 Cl 6 (53 % Cl w/w): SCCPs ( C 10 and C 11 ) also tested

. No radiolabel led chemical s used in this test ; however, terminal Cl

- substitution not considered representative of MCCP constituents

.
Test organism: rainbow trout ( Oncorhynchus mykiss ) ~4.5 g initial wet weight; me an lipid content 2.8%

Fish were exposed to chemical in their diet for 40 days, followed by a n 80 day depuration period. A growth corrected depuration rate constant (“k 2 g ”) and associated half

- life were calculated. From these kinetic data the assimilation e fficiency (α) and BMF are calculated assuming constant feeding rates and chemical concentrations in the food. T he test chemicals were derived from alkenes with double bonds on both of the terminal carbons resulting in terminal and adjacent chlorination, i. e. 4 chlorine atoms

- two at both ends

. After those double bonds are saturated, the location of the remaining chlorine substitutions is believed to be rando m for C 1 4 H 2 5 Cl 5 and C 1 4 H 24 Cl 6 ; therefore, the se test chemicals are not specifically defined with res pect to structure. Fisk et al. [ 26 ] point out that “the PCAs used in these experiments are no t likely to be found at high proportions in commercial CP mixtures because they contain Cl on terminal and adjacent carbons, which is chemically and energetically unfavourable under free radical reaction conditions used in the production of commercial CP p roducts”. With respect to primary biotransformation, precisely where the non

- terminal Cls are on the alkane chain may be less significan t relative to the known substitution of the two Cls at both ends of the alkane chain. This is examined in more detail be low.

The bioaccumulation data from Fisk et al. 1998 [ 26 ] using the terminal Cl

- substituted PC As include several data points in which the calculated chemical assimilation efficiencies (α) are > 100%. Bioaccumulation kinetic data with chemical assimilation efficiencies >100% should generally be considered to have some source of critical error since the only plausible explanation for a “ true assimilation efficiency ” > 100% is the formatio n of the chemical in the organism (or perhaps the GIT) during the testing and analysis. Note this has been hypothesized for certain polybrominated diphenyl ethers, but it is not considered a likely event for the chlorinated alkane data here

. The assimilati on efficiency (α) and BMF are calculated from the depuration rate constants and assumed “totally consistent” feeding rates and chemical concentrations in the food. When assimilation efficiency is > 10 0% this suggests some source of error, perhaps in one of these paramet ers used in the calculation. In particular, the bioaccumulation data for C 14 H 24 Cl 6a reported in Table 26 of the SEV should not be used for

### 14 the bioaccumulation assessment since α > 100%

. The other test chemicals with assimilation efficiency estimates > 100% reported in Fisk et al. [ 26 ] , and elsewhere, should also be considered highly uncertain and not used for bioaccumulation assessment. Also note in Table 26 of the SEV document, the BMF for C 14 H 2 5 Cl 5b is not correct

- it is not 29 ; the reported value by Fisk et al. [ 26 ] is 2. 9

.

A general finding in Fisk et al. 1998 [ 26 ] is that half

- lives ( t 1/2 s ) of PCAs increase with increasing chlorination, chain length, and K OW

. This is consistent with other studies cited in their paper ; however, the precise location of the Cl substitution coul d have some influence on biotransformation rates and hence overall half

- life. Excluding the uncertain data point for C 14 H 24 Cl 6a the whole body half

- lives for MC

PCAs range from 39 to 53 days, with a median value of 44.5 days . These growth corrected half

- li ves are from juvenile rainbow trout that weighed about 4.5 g at the start of the 120 day test. Based on the reported growth rate of about 0.016 /d [ 26 ] , the estimated average fish mass at 80 days (i.e., mid

- point of depuration) is about 30 g. Note the kinetic, growth corrected lipid normalized BMFs are > 1 for these test chemicals

. **Robust Study Su mmary: Fish BMF Test #**  **2 (**  **Fisk et al. 1996)**

Test substance: 14 C 16 H 31 Cl 3 (3 2 % Cl w/w) and 14 C 16 H 21 Cl 13 ( 68 % Cl w/w), other CPs also evaluated

. Radiolabelled test chemicals used in this test

.
Test organism: rainbow trout ( Oncorhynchus mykiss ) ~4.5 g initial w et weight; mean lipid content ~ 5.7 %

Fisk et al. 1996 [ 27 ] tested CPs that were more similar to the commercial industrial mixture than the Fisk et al. 1998 tests that were synthesized from diene s , i.e., terminal Cls

. Fish were exposed to chemical in their diet for 40 days, followed by a 160

- 173 day depuration period. A growth corrected depuration rate constant (“k 2 g ”) and associated half

- life were calculated. From these kinetic data the assimilat ion efficiency (α) and BMF are calculated from the assuming constant feeding rates and chemical concentrations in the food. Whole

- body t 1/2 s based on total radioactive residue (TRR), i.e

. , parent and metabolites, ranged from 37 to 77 d for MCCPs (median) a nd from 39 to 87 d for SCCPs. These t 1/2 s are from juvenile rainbow trout that weighed about 4.5 g at the start of the ~200 day test. Based on the reported growth rate of about 0.012 /d [ 27 ] , t he estimated average fish mass at 120 days (i.e., mid

- point of depuration) is about 20 g. Kinetic, growth corrected BMFs for MCCPs based on total radioactive residue (TRR), i.e., parent and metabolites, ranged from 0.4 to 1.1 with a median of 0.72. A ssimil ation efficiencies range from 9.4% to 35.1% for the MCCPs, note assimilation efficiency estimates <50%

. D epuration rate constants are based on TRR; and all other kinetic parameters use these data for calculation and therefore because this does not constitu te chemical specific analysis there are uncertain ties in these data [ 2
, 18 ]

. **Robust Study Summary: Fish BMF Test # 3**  **( Fisk et al.**  **2000 )** 

Test substance: 14 C 1 4 H 21.3 Cl 6.7 ( ~5 5% Cl w/w), SCCP and LCCPs also evaluated

. Radiolabelled test chemicals used in this test

.
Test organism: rainbow trout ( Oncorhynchus mykiss ) ~3 g initial wet weight; mean lipid content 5

- 11 %

In 2000, Fisk and colleagues [ 28 ] published another fish bioaccumulation study for CPs using two different dietary exposure concentratio ns for each group of CP . In this case they used CPs that are generally more consistent with the structures expected in the industrial mixture, i.e., no apparent

### 15 terminal Cls. Fish were exposed to chemical in their diet for 40 days, followed by a 160 day de puration period. A growth corrected depuration rate constant (“k 2 g ”) and associated half

- life were calculated. From these kinetic data the assimilation efficiency (α) and BMF are calculated assuming constant feeding rates and chemical concentrations in the food. Based on the reported growth rate of about 0.014 /d , the estimated average fish mass at 120 days (i.e., mid

- point of depuration) is about 16 g . The reported half

- lives for the two MCCP treatments were 58 and 41 d for the low and high dose, respectiv ely. The corresponding assimilation efficiencies and k inetic, growth corrected BMFs for MCCPs based on total radioactive residue (TRR), i.e

. , parent and metabolites, were 10% and 11% and 0.43 and 0.27, respectively. Note the kinetic, growth corrected lipid normalized BMFs are < 1 for all MCCPs for these test chemicals

.
Summary comments of relevant B MF data

Reduced chemical dietary uptake (assimilation) efficiency ( α , or sometimes referred to as E D ) has been observed for very hydrophobic organics in a range of species (see, e.g., [ 6 , 7 , 21 , 29 , 30 ]

. Empirical models (i.e., regression s based on measured data) for α for organic chemica ls in fish show median values for α of about 50% for chemicals with log K OW from ~5 to 6 .5 and decreasing values for α when log K OW > ~ 6.5 [ 6 , 30 ]

. There fore, the rationale in Fisk et al. 2000 [ 28 ] and elsewhere to assume assimilation efficiencies of 50% and 90% to calculate some of the BMFs for CPs and PCAs instead of α values derived from the test data is questionable and seemingly unjustified. Th e BMFs calculated with the measured α are considered more reliable and applicable for bioaccumulation hazard assessment

.

There are merits and limitations to all of the existi ng laboratory BMF test data. BMF Test #1 included chemical specific analysis (albeit, perhaps unconfirmed congener analysis) , whereas Test s #2 and #3 are based on total radioactivity generating some uncertainty in the derived kinetics and bioaccumulation d ata

. BMF T est #1 uses chemical formulation s that are less likely commercial MCCP products (i.e., terminal Cl

- substitution). Chain lengths for the MCCPs and MC

- PCAs studied in dietary bioaccumulation testing range from 14 to 16 carbons and chlorine substitu tion ranges from 32 to 68 % w/w

. Estimated log K OW s range from about 7 to greater than 9. T he kinetic, growth corrected lipid normalized BMFs for the expected MCCP constituents from these three studies are < 1 , except for 1 of the 11 reliable quality measur ement s (i.e., 1.07 for low dose C 16 H 31 Cl 3 )

. The BMFs for the MC

- PCAs (i.e. terminal Cl

- substitution) show BMFs >1. A potential difference between the BMF results for terminal Cl

- substituted MC

- PCAs and the expected MCCP constituents may be the slightly hig her α generally observed for the MC

- PCAs. Assuming that all of the data are reasonably accura te and that TRR use in the MCCP tests (i.e., #2 and #3) is an insignificant variable, t his apparent difference could be due to some slow, but relevant , degradation rate of MCCPs in the gastro

- intestinal tract (GIT) by microorganisms and/or some first

- pass effects in the GIT tissues and liver that do not occur , or do not occur as quickly, for the terminal Cl

- substituted MC

- PCAs.

The reliable quality whole body growth corrected half

- lives for these MCCPs and MC

- PCAs from the dietary studies range from 37 to 77 days. The median t 1/2 of 50 days from the dietary testing is greater than the median t 1/2 from the BCF testing (16 days ); however, it is recognized that t 1/2 s ar e a function of organism properties (e.g., body size , lipid content ).

### 16 **Measured Field B**  **ioaccumulation D**  **ata**

**– BAFs, BMFs and TMFs** 

**Robust Study Summary: Fish B A**  **F and BMF field data**  **( Houde**  **et al. 2008**  **)**

BAFs

Houde et al

. [ 31 ] report MCCP concentrati ons in biota and water samples from Lake Ontario and Lake Michigan. Lipid weight BAFs are calculated between water and four species of fish and plankton from Lake Ontario. W hole body wet weight BAFs for “total MCCP” range from about 24,000 (Lake trout)
to about 163,000 (Alewife). As noted in Houde et al. [ 31 ] and by Thompson (2010) these BAFs are based on water samples including some different locations and years (mostly 2002

- 2004) to the biota samples (mostly 2001)

. T he measured water concentra tion of Σ MCCP varied from 47 pg/ l in 2002 to <0.5 pg/l in 2004 at the same sample location

. The former value is considered an “outlier” by the authors [ 31 ]

. T he biota were sampled from the western, industrialised end of Lake Ontario, whereas th e water samples were taken in the western, central and eastern areas of Lake Ontario . Furthermore, as also noted by Thompson (2010), sample sizes were small and several water samples were below the detection limit (for BAF calculations 0.5DLs assumed)

. It is unclear if the BAFs are based on ratios of the same MCCP constituents. For these reasons general confidence in the reported BAFs is low . Due to the water measurement uncertainties and the spatial and temporal discrepancies between fish and water, the BA Fs are not considered to be usable for definitive quantification; however, as outlined in the present study it is likely that many of the MCCP BAFs based on dissolved water concentrations are > 5,000 L/kg

ww

.

BMFs

As noted by Gobas et al. [ 3 ] and Burkhard et al. [ 8 ] assessing whether or not a chemical biomagnifies in the environment is a critic al consideration for bioaccumulation hazard assessment. The b est use of the Houde et al. [ 31 ] data is for the BMF calculation s (water and spatial/temporal issues less problematic in this regard; however, sample sizes are still rather small). Tr out BMF data are considered more reliable than the Sculpin

- Diporeia BMF data since n =1 for the latter. BMF data should be lipid normalized for a thermodynamic analysis, i.e., is the chemical fugacity increasing from diet to consumer [ 8 ]

.

From Lake Ontario all l ipid normalized fish BMFs f or t rout/ alewife, t rout/ sculpin, trout/smelt ( n =36 ) for MCCP isomers analyzed were < 1 (ranging from 0.01 for C 16 Cl 7 to 0. 86 for C 14 Cl 7 )

. From Lake Michigan all l ipid normalized fish BMFs for t rout/ alewife ( n =8) were < 1 (ranging from 0.14 to 0.40 for C14C l5 up to C15Cl6). In Lake Michigan some l ipid normalized fish BMFs for t rout/ sculpin ( n =3) MCCPs isomers were > 1 and some ( n =5) were < 1 (ranging from 0. 61 to 1.2 for C 14 Cl 5 up to C 15 Cl 6 ). For MCCPs with molar mass greater than C 15 Cl 6 , BMFs could not be c alculated in Lake Michigan and Lake Ontario because the isomer concentration s were below detection limits or not analyzed. A weight of evidence from the field BMF data (i.e. 94% of the data) indicat e that these MCCPs do no t biomagnify in fish

.

TMFs

TMFs we re calculated for the C14, C15 and C16 isomer groups and for total MCCP f rom the Lake Ontario samples

. T MF values for Lake Ontario ranged from 0.14 (C16) to 0.29 (C14), with a value of 0.22 for total MCCP. These TMF data also indicate that these MCCP const ituents do not biomagnify in aquatic food webs in the environment.

### 17 Other measured data

– Other BCFs, BSAFs

Given the time frame of the current project, the other bioaccumulation studies listed in the various documents were not examined and reviewed here. Such data could be evaluated and could possibly to add to the analysis and weight of evidence presented herein

.



### B **iotransformation Rate C**  **onstants (in vivo, in silico)**

**- k**  **M**

As highlighted in Figure 3 and noted in the literature, the biotransformation rate co nstant (k M ) is a critical parameter determining the overall bioconcentration and bioaccumulation for hydrophobic organic chemicals in aquatic and terrestrial organisms

. Based on in vitro and in vivo biotransformation studies for fish, birds and mammals, th e primary biotransformation pathway for n

- linear alkanes in fish is through hydroxylation at the end of the chain into corresponding alcohols, which are then transformed into the fatty acid, which are then subject to β

- oxidation [ 32
, 33 ] . Chain length can influence which enzymes are involved in the biotransformation reaction. Variation in the chlorine substitution pattern, primarily at the ends of the alkane chain, may therefore also be key determinants of the kinetics for the first transformation reaction. There is clear evidence that MCCP congeners and MC

- PCAs are biotransformed from the in vivo dietary fish bioaccumulation studies. The objective here i s to estimate the rates a t which these chemicals are biotransform ed from the in vivo laboratory test data and compare these values in terms of chain length, chlorine substitution and exposure route (Water

- BCF vs. Diet

- BMF ).

A mass balance method for estimating in vivo biotransfor mation rate constants (k M /d) from laboratory data (i.e., BCF and BMFs tests) was used here [ 9 ] . Because the method accounts for test

specific i nformation (i.e., fish body size, lipid content, temperature, feeding rate, growth rate, etc) and these factors are known to influence bioaccumulation kinetics (e.g. [ 34 ] ) , t he m ass balance estimation method is superior to the k M estimates provided by Fisk et al. [ 35 ] (see also [ 9
, 36 ] )

. The mass balance method used here also includes a screening

- level uncertainty analysis to characterize uncertainty in calculated k M s based on the uncertainty in the measured data (i.e., rate constants, body size, lipid
content ), key model parameters and chemical properties used to parameterized the model (i.e., K OW ). The method was parameterized here using median values from the laboratory tests. For example, from the data reported in BCF Test #2 for C 14 H 25.5 Cl 4.5 and a log K OW of 8 a median k M estima te is 0.013 /d. A QSAR model for estimating k M from chemical structure was also used to compare with the in vivo estimates [ 1
, 37 ]

.
Table 1 summarizes in vivo and QSAR estimates for k M ,N (note “normalized for body mass” , i.e., 10 g ) for the chemicals that have been tested and revie wed to be of usable quality in the present document. The uncertainty factors for these in vivo k M ,N estimates are about 2, i.e., 95% probability that the k M ,N value lies between a factor of 2 of the listed median. A general rule of thumb for the QSAR predi ctions is about a factor of 5 [ 37 ] . There is notable overlap between the in vivo and QSAR k M ,N estimates for a selected constituent thus providing some confidence in the plausible range of values

. It is recognized that the meas ured k 2 g values derived without chemical

- specific analyses are more uncertain than k 2 g derived using parent chemical specific analysis. The uncertainties related to this issue are not captured in the uncertainty assigned to the measured k 2 g data for these k M estimates . However, as a case study using the BCF Test #2 data ( Hurd and Vaughan, 2010 ) and noted in Table 1 as C 14 H 25.5 Cl 4.5b , the measured k 2 value (before growth correction) was assumed to be 20% faster to approximate a potential difference in the ra tes based on in the percentage of metabolites estimated at the end of the test. Note that this

### 18 “20% faster assumption” is a first approximation based only on the percentage of metabolites determined at the end of the test; the approximation is assumed and unconfirmed.

The k M ,N estimates are all within the same “ order of magnitude ”. The in vivo values range from 0.010 to 0.022 /d and the median value is 0.016 /d (i.e., < factor of 3 for the range of values) regardless of exposure route (i.e., BCF

- water or BM F

- diet) and fish lipid content and body size. There are no easily recognizable differences in k M ,N estimates for these chemicals based on differences in carbon chain length and chlorine substitution ; however, given the relatively small differences for the chemicals studied here compared to the range of structural differences for all PCAs and CPs, there may be relationships between biotransformation rates and structure when greater chemical diversity is considered . There were no clear difference s in the Cl

- t erminal substituted chemical k M ,N estimates compared to the non Cl

- terminal substituted chemicals (i.e., expected MCCP constituents )

. In general,
t he degree of chlorination based on %w/w may be misleading with respect to the potential differences in biotra nsformation rates. The in silico values range from 0.003 to 0.022 with a median value of 0.016 /d. Given the general uncertainties in biological data such as biotransformation rate constant s and predicting such data from structure, the strong similarities between the in vivo and QSAR rates shown here are notable

. The QSAR predictions reflect a relationship of slower biotransformation rates for larger, more chlorinated CPs; however, the pattern is not as recogniz able in the in vivo estimates for these MCCPs

. The potential reason for this trend in the QSAR predictions is because the QSAR includes K OW as a predictor and K OW predictions by KOWWIN are automatically included in the screening

- level EPI Suite k M,N QSAR predictions.

Decreasing biotransformation rate s of PCAs has been associated with increasing Cl content and carbon chain length in birds and fish [ 27 ] . This observation and inference may be more apparent by considering a broader range of CPs (i.e., including SCCP s and LCCPs); however, based on the current analysis for MC

- PCAs and MCCPs in fish the differences in the biotransformation rate constant appear to be relatively small. Further analysis of the hypothesis could be examined using the laboratory data for SCCP s, etc.

I t is recognized that it is not feasible to measure and test all MCCP constituents. There is no notable trend in differences in k M for the range of carbon length and chlorine substitution included in the available MC

- PCA and MCCP test data. This su ggests that additional requested testing may not provide any “new” information, although a test with non Cl

- terminal substitution MCCP constituents conducted w ith chemical

- specific analysis c ould perhaps provide more definitive estimates (less uncertainty) of key bioaccumulation parameters such as BCF, BMF, BAF, half

- life and k M .
The upper bound k M ,N estimate is less than the value suggested by the model calculations in Figure 3 to lower the dissolved water concentration BCF below 5,000.Table 1. Summary of in vivo and in silico (QSAR) k M estimates

.

in vivo in vivo in silico in silico
Test Reference MolFormula k 2g k M,N k M,N log Kow Representative structure BCF Test #2 Hurd and Vaughan, 2010 C14H25.5Cl4.5a 0.013 0.014 0.022 7.37 CC(Cl)CCCC(Cl)CC(Cl) CCCC(Cl)CC BCF Test #2 a Hurd and Vaughan, 2010 C14H25.5Cl4.5b 0.022 0.024 0.022 7.37 CC(Cl)CCCC(Cl)CC(Cl)CCCC(Cl)CC BMF Test #1 Fisk et al. 1998 C14H26Cl4 0.018 0.019 0.018 7.74 C(Cl)C(Cl)CCCCCCCCCCC(Cl)C(Cl) BMF Test #1 Fisk et al. 1998 C14H25Cl5a 0.01 3 0.014 0.017 7.69 C(Cl)C(Cl)CCCCC(Cl)CCCCCC(Cl)C(Cl) BMF Test #1 Fisk et al. 1998 C14H25Cl5b 0.015 0.016 0.017 7.69 C(Cl)C(Cl)CCCCCCCCC(Cl)CC(Cl)C(Cl)
BMF Test #1 Fisk et al. 1998 C14H24Cl6b 0.016 0.017 0.015 7.64 C(Cl)C(Cl)CCCC(Cl)CCCCC(Cl)CC(Cl)C(Cl) BMF Test #3 Fisk et al. 2000 low dose C14H23.3Cl6.7 0.012 0.013 0.015 7.64 C C(Cl)CC (Cl) CC(Cl)CCCCC(Cl)CC(Cl)C(Cl) BMF Test #3 Fisk et al. 2000 high dose C14H23.3Cl6.7 0.017 0.020 0.015 7.64 C C(Cl)CC (Cl) CC(Cl)CCCCC(Cl)CC(Cl)C(Cl) BMF Test #2 Fisk et al. 1996 low dose C16H31Cl3 0.014 0.016 0.013 8.41 CC(Cl)CCCC(Cl)CCCCCCCC(Cl)CC BMF Test #2 Fisk et al. 1996 high dose C16H31Cl3 0.019 0.022 0.013 8.41 CC(Cl)CCCC(Cl)CCCCCCCC(Cl)CC BMF Test #2 Fisk et al. 1996 low dose C16H21Cl13 0.012 0.014 0.003 8.70 C C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C (Cl) C(Cl)C(Cl)C(Cl)C(Cl)CC BMF Test #2 Fisk et al. 1996 med dose C16H21Cl13 0.011 0.013 0.003 8.70 C C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C (Cl) C(Cl)C(Cl)C(Cl)C(Cl)CC BMF Test #2 Fisk et al. 1996 high dose C16H21Cl13 0.009 0.010 0.003 8.70 C C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C(Cl)C (Cl) C(Cl)C(Cl)C(Cl)C(Cl)CC

a Note these data are for an assumed and unconfirmed faster rate of elimination based on measured 20% metabolites at the end of the test, thus they are considered “exploratory”

### Incorporating k **M,N** 

### for Bioaccumulation A **ssessment**

**- BCFs** 

Although the BMF and TMF are purported to provide the most appropriate answers (insights) to the question as to whether or not a chemical biomagnifies in organisms and food webs, th ere is seemingly endless interest in the BCF for bioaccumulation hazard assessment. Thus some BCF estimates for the chemicals in Table 1 are provided in Table 2 using a BCF model and the in vivo k M estimates derived above. For the BCFs predicted based on total water concentrations, a value of 1 mg/L DOC was assumed based on values inferred from BCF Test #2. The BCFs are for a fish of 10 g, 5% lipid content at 15 ° C generated using the spreadsheet version of the lower trophic level fish in the BCFBAF model [ 1
, 21 ] ( www.arnotresearch.com )

. Note that the column on the right shows growth corrected BCFs based on the dissolved water concentrations , i.e., BCF data most appropriate for comparing the “inherent chemical properties”

. Also note that the most relevant BCF model calculation for comparison with the measured BCF data from Test #2 (best quality BCF available : 4,440 ) is the value of 4,500 L

- total water / kg

- wet weight.

The BCF findings here are generally consistent with the recommendations in the SEV report with respect to growth corrected BCF calculations and REACH BCF bioaccumulation criteria; howe ver, these bioaccumulation hazard categorizations seemingly contradict the measured observations for potential chemical biomagnification in the field (i.e., BMFs and TMFs < 1). Th er e is uncertainty in all measured and modelled data, including those data co mpiled, evaluated and applied in the SEV document and discussed here. Burkhard and colleagues [ 8 ] developed a weight of evidence approach for assessing whether or not a chemical is expected to biomagnify in the environment and such an approach could be considered for MCCPs for further da ta analysis

.

Table 2. Summary of some BCF calculations using in vivo k M estimates from various studies. BCF (L

- total/kg

- ww) BCF (L

- total/kg

- ww) BCF (L

- diss/kg

- ww)
Test MolFormula Including growth 1 G rowth

- corrected Growth

- corrected BCF Test #2 C14H25 .5Cl4.5a 4500 17400 33700 BCF Test #2 C14H25.5Cl4.5b 3800 11600 22500 BMF Test #1 C14H26Cl4 2200 8900 28300 BMF Test #1 C14H25Cl5a 2700 12500 37100 BMF Test #1 C14H25Cl5b 2600 11000 32600 BMF Test #1 C14H24Cl6b 2800 11200 30400 BMF Test #3 C14H23.3Cl 6.7 3100 14100 38300 BMF Test #3 C14H23.3Cl6.7 2700 9900 27000 BMF Test #2 C16H31Cl3 600 3200 36600 BMF Test #2 C16H31Cl3 500 2400 27000 BMF Test #2 C16H21Cl13 300 2100 43300 BMF Test #2 C16H21Cl13 400 2300 47400 BMF Test #2 C16H21Cl13 400 2700 57700

1 assuming k G = 0.03 /d, simulating BCF Test #2

### 21 

## General **C omments on SEV**  **D ocument** 

The REACH SEV document presents a review and analysis on bioaccumulation assessment for MCCPs. The analysis includes REACH methods and criteria and shows creative effor ts to characterize the bioaccumulation of different untested expected MCCP congeners based on different measured endpoints. There are some apparent misunderstandings related to BCF calculations (i.e. total vs. dissolved water concentrations) for BCF data a nd current model interpretation. The SEV document includes an “ overabundance ” of k 1 models for calculating BCFs from dietary test data ( i.e., Tables 27 and 28 ); a single model or an average of k 1 models would have been sufficient in the proper context. Ext rapolation methods applied to “scale BCFs” are questionable (page s 67

- 71 and pages 178

- 186 ), since the bioaccumulation conditions and chemicals are not consistent; thus the approximated scaling approach is limited and confusing in the current context. Mech anistic interpretations of the measured data would be more appropriate (e.g., as illustrated here) . The half

- life comparison is good, but could be improved as outlined here to address differences body composition, mass and temperature

.

There is uncertaint y in the actual MCCP constituent / congener structure (i.e., chlorine substitution); however, this is an issue for all interpretations of the data and is not only an issue in the SEV document. The approach to examine different K OW estimates in the SEV docu ment is a reasonable first approach to address uncertainty in constituents and K OW ; however, a s shown here the uncertainty in the actual structure of MCCP constituents and K OW may be relatively inconsequential for the bioaccumulation
assessment when using BCF and BAF endpoints and criteria

. 

### Comment on “SEV Report section 8.1.4 **Summary and overall conclusions on PBT and vPvB properties”** 

“The PBT assessment is complicated because both the biodegradability and bioconcentration factor of MCCP constituents depen d on chlorine content and chain length, and the trends in these are opposite (i.e. persistence appears to increase with increasing chlorine content whilst the bioconcentration factor appears to decrease).”

The analysis provided in the present document expl ains some of the “apparent contradictions” mentioned in the SEV report when interpreting measured and modeled BCF data. In particular
, the present analysis illustrates why some measured BCFs “ go up ” while some predicted Arnot and Gobas BCFs “ go down ” , i.e. , the Arnot and Gobas BCF and BAF models in BCFBAF use default POC and DOC values typical for North American surface water, i.e., 0.5 mg/L and 0.5 mg/L, respectively. POC and DOC values in filtered tap water will be lower than surface water , thus increasin g the bioavailable solute fraction (and hence measured BCFs) for hydrophobic organics under most laboratory conditions. This apparent contradiction does not exist when the fish bioconcentration data are considered in terms of the estimated bioavailable (di ssolved) concentrations in the water. The reduced dietary bioaccumulation for higher chlorinated, higher K OW MCCP constituents is also consistent and explained by the measured reduced dietary uptake efficiency for very hydrophobic organics (see, e.g., [ 6
, 7 , 21
, 29
, 30 ]

.

### 22 

## References

1. U.S. EPA Estimation Programs Interface (EPI) Suite  for Microsoft® Windows, Ver. 4.1. , Released October, 2011; U. S. Environmental Protection Agency: Washington, D.C., 201 1. 2. Arnot, J. A.; Gobas, F. A. P. C., A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in fish. Environmental Reviews 2006, 14 , (4), 257

- 297. 3. Gobas, F. A. P. C.; de Wolf, W.; Burkhard, L. P.; Verbruggen, E.; Plotzke, K., Revisiting bioaccumulation criteria for POPs and PBT assessments. Integrated Environmental Assessment and Management 2009, 5 , (4), 624

- 637. 4. Gobas, F. A. P. C.; Morrison, H. A., Bioconcentration and Biomagnification in the Aquatic Environment. In Handbook of Property Estimation Methods for Chemicals: Environmental and Health Sciences. , Boethling, R. S.; Mackay, D., Eds. CRC Press: Boca Raton, FL, 2000. 5. Mackay, D.; Fraser, A., Bioaccumulation of Persistent Organic Chemica ls: Mechanisms and Models. Environmental Pollution 2000, 110 , 375

- 391. 6. Arnot, J. A.; Gobas, F. A. P. C., A Food Web Bioaccumulation Model for Organic Chemicals in Aquatic Ecosystems. Environmental Toxicology and Chemistry 2004, 23 , (10), 2343

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