Relationship between

the Bioconcentration Factor (BCF),

the Bioaccumulation Factor (BAF), and

the Trophic Magnification Factor (TMF)

by

Aimée Rita Brisebois

B.Sc. (Hons., Environmental Biology), Queens University, 2010

Project Submitted in Partial Fulfillment of the

Requirements for the Degree of

Master of Resource and Environmental Management

Report No.: 576

in the

School of Resource and Environmental Management

Faculty of the Environment

Aimée Brisebois 2013

SIMON FRASER UNIVERSITY

Summer 2013

ii

Approval

Name: Aimée Rita Brisebois

Master of

Title of Project: Relationship between the Bioconcentration Factor (BCF), the Bioaccumulation Factor (BAF), and the Trophic Magnification Factor (TMF)

Examining Committee: Chair: Tao Eastham MRM Candidate

Dr. Frank Gobas Senior Supervisor Professor

Dr. Adrian deBruyn Supervisor Adjunct Professor

Date Defended/Approved: June 13, 2013

Degree: Resource and Environmental Management

576Report No.:

iii

Partial Copyright Licence

iv

Abstract

A degree of consensus exists among bioaccumulation scientists on the use of Trophic

Magnification Factors (TMFs), which are calculated from patterns of observed tissue

contaminant concentrations across a food web, as “conclusive” evidence of the

bioaccumulative nature of chemicals in the environment. However, most regulatory

criteria to determine whether a substance bioaccumulates rely on Bioconcentration

Factors (BCFs), which are measured in single-species laboratory tests. BCFs do not

account for chemical biomagnification via trophic transfer, nor do they reflect biodilution.

I present the results from laboratory and field studies aimed at testing the hypothesis

whether the BCF, or its field-based counterpart, the Bioaccumulation Factor (BAF) are

adequate predictors of the TMF. I conclude that the BCF can be a useful predictor of the

TMF for chemicals with certain characteristics (i.e., fat soluble substances), but that

there are two major types of errors where the BCF does not provide accurate information

about the bioaccumulative nature of chemicals in the environment.

Keywords: Bioaccumulation; biomagnification; bioconcentration; empirical; model; policy

v

Dedication

For my family,

who offered me unconditional love

throughout the process of this project, and

to Chris McDonald,

who was with me every step of the way.

vi

Acknowledgements

From the formative stages of this thesis, to the final draft, I owe an immense debt

of gratitude to my supervisor, Dr. Frank Gobas. His sound advice, careful guidance, and

mentorship was truly valued. Thank you to Victoria Otten for her support throughout my

years in the lab and also to my fellow lab mates who have inspired and challenged me.

Thank you to Adrian deBruyn for being on my committee and for your mentorship the

past 2 and half years. Thank you to my fellow REM cohort for the camaraderie,

friendship, and support the past two and half years.

Thank you to an NSERC Discovery grant for funding two summers of thesis

work. The project would not have been possible without the field data from Heather

Leslie and Pim Leonards at the Free University in Amsterdam.

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Table of Contents

Approval .......................................................................................................................... ii Partial Copyright Licence ............................................................................................... iii Abstract .......................................................................................................................... iv Dedication ....................................................................................................................... v Acknowledgements ........................................................................................................ vi Table of Contents .......................................................................................................... vii List of Tables .................................................................................................................. ix List of Figures.................................................................................................................. x List of Acronyms ............................................................................................................ xii Glossary ........................................................................................................................ xiii

1. Introduction .......................................................................................................... 1

2. Methods................................................................................................................. 5 2.1. Overview ................................................................................................................ 5 2.2. Study Area ............................................................................................................. 6 2.3. δ15N and δ13C Analysis ........................................................................................... 8 2.4. Chemical Properties ............................................................................................... 9 2.5. Calculation of the BAF, BCF and TMF .................................................................. 10

2.5.1. BAFs ......................................................................................................... 10 2.5.2. Bioconcentration Factors (BCF) ................................................................ 11 2.5.3. Trophic Magnification Factors (TMFs) ....................................................... 11

2.6. Comparison between bioaccumulation indicators ................................................. 12

3. Results and Discussion ..................................................................................... 14 3.1. Bioaccumulation Factors ...................................................................................... 14 3.2. Bioconcentration Factors ...................................................................................... 17 3.3. Trophic Magnification Factors ............................................................................... 17

3.3.1. Comparison between Trophic Magnification Factor Methods: Traditional vs. Balanced Method ............................................................... 17

3.4. Comparison of Bioaccumulation Measures (BAF, BCF, and TMF) ....................... 27 3.4.1. True Positive and True Negative Results .................................................. 28

Polychlorinated Biphenyls ......................................................................... 28 Polybrominated diphenyl ethers

(congeners 47, 99, 100, 153, 209, γ –HBCD and -HBCD) ................. 29 Benzo-a-pyrene and pyrene ...................................................................... 29 PFOS AND PFOSA .................................................................................. 30

viii

3.4.2. False Positive and False Negative Results ............................................... 30 BAF-TMF Comparison .............................................................................. 31 BCF-TMF Comparison .............................................................................. 31 Polybrominated diphenyl ethers

(congener 28, 99, 153, 153 +BB54, 209, γ-HBCD) .............................. 32 PFDA and PFOA....................................................................................... 32

4. Conclusions and Recommendations ................................................................ 36

References ................................................................................................................... 38 Appendix A. Concentrations of substances measured in organisms ............................. 42 Appendix B. Bioconcentration factors (L/Kg) from an

Arnot and Gobas (2007) database ............................................................ 45

ix

List of Tables

Table 1.1. An overview of regulatory bioaccumulation assessment endpoints (Arnot & Gobas, 2006) ............................................................................................... 2

Table 2.1. Scientific name, common name, and general diet of the species sampled in benthic-pelagic food web study in the Western Scheldt food-web (n/a indicates not applicable). .................................................................................... 7

Table 2.2. Log KOW, log KOA, and select BCFs (Arnot and Gobas, 2006) for twenty-four substances measured in benthic and pelagic organisms in the Western Scheldt Estuary. .......................................................................................... 9

Table 3.1. Bioaccumulation factors (L/kg) of various analytes in each organism of the study ...................................................................................... 16

Table 3.2. The trophic magnification factors calculated using two different methods, a) TMFs and the p-values from the linear regression of the concentration of substances in every organism and the trophic position of the organism and b) TMFs and the p-values from the linear regression of the geometric mean of substances in each species and the mean trophic position of the species. ...................................................................... 18

x

List of Figures

Figure 2.1. The Western Scheldt estuary (depicted by the arrow), the Netherlands and surrounding areas. ................................................................... 6

Figure 2.2. Illustrative representation of the benthic-pelagic water- and air-breathing food web in the Western Scheldt estuary .................................................................. 8

Figure 2.3. The relationship between normalized contaminant concentrations in biota and trophic level .............................................................................................. 12

Figure 2.4. Illustration of four possible outcomes in the comparison of a BCF or BAF (relative to a screening criterion of 5,000) with a TMF (relative to a criterion value of 1). ............................................................................ 13

Figure 3.1. The log protein normalized concentration of PFOA, PFOS, and PFDA in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF; PFOSA neither increases or decreases in log protein normalized concentration with respect to trophic level. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), (g), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f). .................... 21

Figure 3.2. The log lipid normalized concentration of PCB 28, PCB 52, PCB 101, and PCB 118 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f). ...................................................................... 22

Figure 3.3. The log lipid normalized concentration of PCB 138, PCB 152, and PCB 180 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f). ......................................................................................................... 23

Figure 3.4. The log lipid normalized concentration of pyrene and benzo-a-pyrene, in organisms on the y-axis decreases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF; HCB increases with Method 1 and neither increases nor decreases in normalized concentration with respect to trophic level in Method 2. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f) ....................................................................... 24

xi

Figure 3.5. The log lipid normalized concentration of BDE 28, BDE 47, BDE 49, and BDE 99 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), (g) on the left and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), (f), (h) on the right. .................................................... 25

Figure 3.6. The log lipid normalized concentration of BDE 100, BDE 153, BDE 154+BB53 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF; BDE 209 decreases in normalized concentration with respect to trophic level for both methods. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), (g) and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), (f), (h). ....................................................................... 26

Figure 3.7. The log lipid normalized concentration alpha-HBCD in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Gamma-HBCD decreases in normalized concentration with respect to trophic position for both TMF methods. Method 1, using individual concentrations of substances in organisms, is depicted by (a) and (c), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b) and (d). ............................................................................. 27

Figure 3.8. The Trophic Magnification Factor (TMF) as a function of the Bioaccumulation Factor (BAF), in a benthic-pelagic estuarine ecosystem including an air breathing organism. Most substances that are defined as bioaccumulative also biomagnify (i.e. BAF≥5000 and TMF>1). False Negative and False Positive results do occur with substances such as PFOS. Error bars are 95% confidence intervals ........................................ 34

Figure 3.9. The Trophic Magnification Factor (TMF) as a function of the Bioaccumulation Factor (BAF), in a benthic-pelagic estuarine ecosystem including an air breathing organism. Most substances that are defined as bioaccumulative also biomagnify (i.e. BAF≥5000 and TMF>1). False Negative and False Positive results do occur with substances such as PFOS. Error bars are 95% confidence intervals. ....................................... 35

xii

List of Acronyms

BAF Bioaccumulation Factor

BCF Bioconcentration Factor

BDE Brominated diphenyl ether

BMF Biomagnification Factor

CEPA Canadian Environmental Protection Act

HBCD Hexabromocyclodecane

KOW Octanol-water partition coefficient

KOA Octanol-air partition coefficient

OECD Organization for Economic Cooperation and Development

PCB Polychlorinated biphenyl

TMF Trophic Magnification Factor

xiii

Glossary

Bioaccumulation The combined increase in chemical concentrations in organisms compared to the surrounding environment as a result of bioconcentration and biomagnification (F. A. P. C. Gobas & Morrison, 2000)

Bioaccumulation Factor (BAF)

The ratio of a chemical concentration in an organism to the surrounding environment (F. A. P. C. Gobas & Morrison, 2000)

Bioconcentration The process by which organisms’ uptake chemicals through

respiration and diffusion of hydrophobic chemicals from aqueous to organic media (F. A. P. C. Gobas & Morrison, 2000)

Biomagnification The process by which chemical concentrations increase in

predators relative to concentrations in diet items as a result of uptake from diet and transfer through the food web (F. A. P. C. Gobas & Morrison, 2000)

Biomagnification factor (BMF)

The ratio of a chemical concentration in an organism to their diet items (F. A. P. C. Gobas & Morrison, 2000)

Octanol-water partition co-efficient (KOW):

The ratio of a chemical solubility in octanol to a chemical solubility in water at equilibrium. Used as a metric to describe chemical partitioning between lipid and water phases in aquatic biota. Generally expressed in logarithmic format (log KOW) (MacKay, 1991)

Octanol-air partition co-efficient (KOA)

The ratio of a chemicals solubility in octanol to a chemical solubility in air. Used as a metric to describe chemical partitioning between lipids and air in terrestrial biota. Generally expressed in logarithmic format (log KOA) (Mackay, 1991)

Persistent organic pollutant (POP)

Class of chemicals defined by their persistence in the environment, tendency to bioaccumulate and toxicity

(Stockholm Convention on POPs, 2004)

Trophic Magnification Factor

Calculated as the slope of the logarithm of the lipid normalized chemical concentration versus the δN15/δN 14 stable isotope ratio and represents the average increase or decrease in lipid normalized chemical concentrations for a unit increase in trophic position (e.g. Fisk, Hobson, & Norstrom, 2001)

Trophic position A measure of an organism’s trophic status and thus level in a food web which, by providing non-integer quantities, considers the effects of omnivory, cannibalism, feeding loops, and scavenging on food web structure (Vander Zanden & Rassmussen, 1996)

1

1. Introduction

The Stockholm Convention on Persistent Organic Pollutants was ratified by 131

countries in 2004, with the objective of eliminating the most persistent (P),

bioaccumulative (B), and toxic (T) substances. Since this time, substances such as

polychlorinated biphenyls (PCBs), dichlordiphenyltrichloroethanes (DDTs), brominated

flame retardants, select perfluorinated carboxylic acids, and many others have emerged

as new chemicals of concern (Giesy & Kannan, 2001; Ikonomou, Rayne, & Addison,

2002). To evaluate and categorize the approximately 100,000 existing substances and

the 1,000 to 2,000 new substances developed each year, regulatory agencies in a

number of countries have developed methods to assess chemicals for their

environmental behaviour (Arnot & Gobas, 2006; Kelly, Ikonomou, Blair, Morin, & Gobas,

2007), building on the P/B/T framework of the Stockholm Convention. For example, the

Canadian Environmental Protection Act of 1999 (CEPA 1999) required that all

substances on the Domestic Substance List (DSL) be evaluated (Government of

Canada, 1999, 2000). The first phase of the evaluation was a screening assessment of

the persistence, bioaccumulation, and toxicity of 23,000 substances. Substances that

were determined to be persistent or bioaccumulative and toxic were then subject to a

comprehensive evaluation including risk assessment.

For many substances, the level of concern and regulatory attention was

determined by whether the substance was determined to be bioaccumulative.

Bioaccumulative potential is typically expressed in terms of the octanol-water partition

coefficient (KOW), the bioconcentration factor (BCF), or the bioaccumulation factor (BAF).

Most often, the degree of bioaccumulation is based on the KOW, which is a standardized,

laboratory-based, physical-chemical measurement of the partitioning of a substance

between an octanol phase (acting as a lipid surrogate) and a water phase. The KOW is

used to determine chemical bioaccumulation for the majority of substances under

evaluation because empirical bioconcentration factors (BCFs) are available for only 4%

of the chemicals on the Canadian DSL, and empirical bioaccumulation factors (BAFs) for

2

only 0.2%. Studies have shown that the KOW is useful as an indicator of a chemical’s

potential to bioaccumulate in water-breathing organisms such as fish. However, the KOW

has important limitations: most notably, it does not consider the metabolic transformation

rate of the chemical in organisms (Arnot & Gobas, 2006) and it is not particularly useful

to estimate bioaccumulation in air breathing organisms (Kelly & Gobas, 2000).

If adequate information is available, the BCF and the BAF may also be used to

assess the bioaccumulative behaviour of chemicals (Table 1, (Arnot & Gobas, 2006)).

The BCF is a standardized, laboratory-based measure of the bioaccumulation of a

substance in fish from water, calculated as the ratio of a chemical concentration in the

organism to the chemical concentration of the water, ideally measured at steady state.

The BCF does not account for dietary uptake, but it does reflect metabolic

transformation and elimination processes that take place in the study organisms. The

BAF is a field-based measure of the uptake and bioaccumulation in fish as a result of

uptake from the water via gill, dermal, and dietary exposure via all pathways. However,

the BAF is not determined under standardized conditions, resulting in variability among

calculated BAFs due to the myriad sources of natural variability among wild organisms.

Table 1.1. An overview of regulatory bioaccumulation assessment endpoints (Arnot & Gobas, 2006)

Regulatory Agency Bioaccumulation

endpoint Criteria (log values) Program

Environment Canada KOW ≥ 100 000 (5) CEPA (1999)*

Environment Canada BCF ≥5 000 (3.7) CEPA (1999)

Environment Canada BAF ≥5 000 (3.7) CEPA (1999)

European Union ‘bioaccumulative’ BCF ≥2 000 (3.3) REACh+

European Union ‘very bioaccumulative’ BCF ≥5 000 (3.7) REACh

United States ‘bioaccumulative’ BCF 1 000 (3) – 5 000 (3.7) TSCA, TRI+-

United States ‘very bioaccumulative’ BCF ≥5 000 (3.7) TSCA, TRI

United Nations Environment Programme KOW ≥ 100 000 (5) Stockholm Convention #

United Nations Environment Programme BCF ≥5 000 (3.7) Stockholm Convention

* CEPA, Canadian Environmental Protection Act, 1999 (Government of Canada 2000). + REACh, Registration Evaluation, and Authorization of Chemicals. Anne XII (European Commission 2001). +-TSCA, Toxic Substances Control Act; TRI, Toxic Release Inventory programs (USEPA, 1976) # Stockholm Convention on Persistent Organic Pollutants.

According to the United Nations Stockholm Convention on Persistent Organic

Pollutants, chemicals are considered bioaccumulative if:

3

The BCF or the BAF is greater than 5,000 L/kg wet weight or, in the absence of such data that log KOW ≥ 5;

Evidence that a chemical presents other reasons for concern such as 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 within the scope of this convention. (Kitano, 2007)

The first criterion has served as the basis for bioaccumulation regulations in the

Canadian Environmental Protection Act, the Toxic Substances Control Act in the USA,

and the Registration, Evaluation, and Assessment of Chemicals program in the EU. The

other two criteria were not included Canadian, European Union, and American

bioaccumulation regulations because they were deemed more difficult to implement and

monitor, because of a lack of relevant criteria and methods for measurement (Kitano,

2007). In a review of the application of the bioaccumulation criteria in the Stockholm

Convention, Kitano (2007) found five substances that fulfilled the requirement for

bioaccumulation despite having BCF values below 5,000. Other studies have shown that

KOW, BCF and BAF may not fully characterize bioaccumulative potential – substances

that do not meet these criteria have been shown to biomagnify, increasing in

concentration through successive steps in wild food webs (F. A. Gobas, De Wolf,

Burkhard, Verbruggen, & Plotzke, 2010; Kitano, 2007; van Wijk, Chénier, Henry,

Hernando, & Schulte, 2010; Weisbrod et al., 2010).

Another review of bioaccumulation criteria for persistent organic pollutants and

persistent, bioaccumulative, and toxic substances by a work group from a SETAC

Pellston workshop suggested that a substance should be considered bioaccumulative if

it biomagnifies in food chains (F. A. Gobas et al., 2010). The Trophic Magnification

Factor (TMF) was proposed as an empirical indicator to support this definition. The TMF

is calculated from measured contaminant concentrations in biota within a food web and

the trophic position of the organisms, usually estimated using stable nitrogen isotope

ratios (δ15N). A TMF greater than 1 indicates a substance that increases in normalized

concentrations, against the thermodynamic gradient, with increasing trophic level

(i.e., meets the definition of a biomagnifying substance). The TMF is similar to the

4

biomagnification factor (BMF), which is calculated from ratios of concentrations between

a single predator-prey pair instead of over multiple trophic levels within a food web.

In this study, I used an empirical approach to explore the relationship between

the current definition of a biaoccumulative substance (i.e., BAF or BCF ≥ 5,000) and the

definition of biomagnifying substance identified with the TMF criteria (TMF > 1). I tested

whether current criteria identifying bioaccumulative substances would also identify

substances that biomagnify, as defined by a TMF > 1. I identified which substances may

obtain false positive or false negative results in the current bioaccumulation screening

process (Figure 5), defined as follows:

A false positive result in this study occurred when a substance had a BCF or BAF above 5,000 (i.e., was screened as bioaccumulative) but a TMF below 1 (i.e., did not biomagnify in the studied food web). A false positive result could prompt regulatory attention and management that may not be warranted given the findings of the field study.

A false negative result occurred when a substance had a BCF or BAF below 5,000 (i.e., was screened as not bioaccumulative) but a TMF above 1 (i.e., biomagnified in the study food web). A false negative result could identify a substance as being of low concern, when the findings of the field study indicate that biomagnification if occurring.

5

2. Methods

2.1. Overview

Pim Leonards and Heather Leslie at the Institute of Environmental Studies at the

Free University, the Netherlands, completed the field sampling in the Western Scheldt

Estuary in the Netherlands and analytical aspects of the project, including the stable

nitrogen isotope calculations. The purpose of the field study was to test predictions of

bioaccumulation based on laboratory BCF studies. The sampling was organized in

cooperation with colleagues from the Dutch Institute Deltares and with local fisherman

for the fieldwork itself.

All permits required by Dutch law for the sampling of biota were pre arranged in

cooperation with Deltares, which also had the accredited personnel on board during

sampling. The field work took place on Sept 17-18 2008. Both pelagic and benthic food

webs were sampled ensuring that the food chains are as long as possible. The link to

the terrestrial food chain is provided by the tern eggs from a colony feeding within the

study area that were sampled in advance of the main sampling round. Water samples

were taken in 1L glass and plastic bottles. Composite samples of sediment were taken

at various locations using a core sampler. The freely dissolved chemical concentrations

in the water phase and the total water were used to estimate the bioavailability of

chemicals in the water phase and for calculating BAFs.

In the analytical design, target substances were identified in advance to ensure a

span of log KOW, KOA , BCF, and biotransformation capacity. The selected substances

also include high production volume chemicals, which have reported BCFs above and

below the 2000 limit.

6

2.2. Study Area

The Western Scheldt, the estuary of the Scheldt River in southwestern

Netherlands, contains many different substances above detection limits due to land-

based industrial activity on shore and in nearby Antwerp (Baeyens, Van Eck, Lambert,

Wollast, & Goeyens, 1997) (Figure 2.1). The sampling area was located near the

Terneuzen harbor and the sandbar known as the Middenplaat. The Western Scheldt is

well-studied in terms of national monitoring programs and surveys and extensive

knowledge exists of the resident food web and the trophic positions of organisms in this

region. The benthic-pelagic food web includes suspended particulate matter,

phytoplankton, zooplankton, lugworms, rag worms, cockles, green crabs, goby, sole,

flounder, plaice, sand eels, pouting, herring, and the common tern (Figure 2.2).

Figure 2.1. The Western Scheldt estuary (depicted by the arrow), the Netherlands and surrounding areas.

7

Table 2.1 lists species names and general dietary information for each species

sampled in the food web.

Table 2.1. Scientific name, common name, and general diet of the species sampled in benthic-pelagic food web study in the Western Scheldt food-web (n/a indicates not applicable).

Species Common name General Diet

Suspended matter (n/a) (n/a)

Phytoplankton Phytoplankton The phytoplankton samples collected are a mixture of phytoplankton and suspended matter.

Mysis sp. Mysids, (zooplankton) Phytoplankton

Arenicola marina Lugworm Algae, small worms, detritus

Nepthys Rag worm Lugworms, nematodes, small shrimps, clams,

Cerastoderma edule

Cockle Plankton, including suspended matter

Pleurobrachia sp. Jellyfish Zooplankton.

Clupea harengus Herring Phyto- and zooplankton

Ammodytes sp. Sand eel Zooplankton and some large diatoms

Carcinus maenas Green crab Young of bivalves and fish

Goby sp. Goby Molluscs, crustacean, insect larvae

Solea solea Sole Worms, molluscs and small crustaceans

Platichthys flesus Flounder Juveniles of less than a year old feed on plankton and larvae of insects, juveniles of more than a year and adults feed on benthic fauna, including small fishes and invertebrates (cockles)

Pleuronectes platessa

Plaice Thin-shelled mollusks and polychaetes (lugworm, rag worm)

Trisopterus lucus Pouting Benthic crustaceans but also on small fish, mollusks and polychaetes

Myoxocephalus scorpius

Sculpin Fish, and large crustaceans

Sterna hirundo Common tern Herring and flatfish

8

Figure 2.1. Illustrative representation of the benthic-pelagic water- and air-breathing

food web in the Western Scheldt estuary

1.1. δ15N and δ13C Analysis

The nitrogen (δ15N) and carbon (δ13C) isotopic composition of tissues are integrated

measures of diet that can be used to distinguish between food web positions and help interpret

trophic transfer of chemicals that bioaccumulate (e.g., Fisk et al. 2001). Stable isotopes ratios

(15N/14N, 13C/12C) were determined in biotic samples using an elemental analyzer (NC2500,

ThermoQuest Italia, Rodana, Italy) coupled with an Isotope Ratio Mass Spectrometer (Delta

Plus, Thermo-Quest Finnigan, Bremen, Germany). Stable isotope values were expressed as a

ratio (R) of the heavy to the lighter isotope (15N/14N or 13C/12C or) standardized with respect to

internationally recognized reference materials (e.g. atmospheric air for nitrogen and Vienna Pee

Dee Belemnite for carbon) as follows (Eqn 1):

9

(‰) = [(Rsample/Rreference) – 1] x 1000 (1)

The standards used for δ13C determination were USGS-24 graphite (δ13C = -

16.05 ‰), IAEA-601 benzoic acid (δ13C = -28.81 ‰) and IAEA-CH7 PEF (δ13C = -32.15

‰). The standards used for δ15N measurements were IAEA-N1 (δ15N = 0.43 ‰) and

IAEA-N2 (δ15N = 20.41 ‰). Glutamic acid, with a known value of δ13C = -24.08 ‰ and

δ15N = 3.20 ‰, was used as a quality control sample. Instrument precision was better

than 0.15 ‰ for carbon and nitrogen based on replicate analysis of standard reference

materials.

2.4. Chemical Properties

The 24 substances in this study exhibit a wide range of KOW and octanol-air (KOA)

partition coefficients (Table 2.2) and chemical classes, including polychlorinated

biphenyls, perfluorinated compounds, brominated compounds, hexachlorobenzene,

pyrenes, and hexabromocyclodecanes. Appendix B contains more information about

BCFs from the Arnot and Gobas database.

Table 2.2. Log KOW, log KOA, and select BCFs (Arnot and Gobas, 2006) for twenty-four substances measured in benthic and pelagic organisms in the Western Scheldt Estuary.

Chemical Abbreviation Log KOW Log KOA Geometric mean BCF

(L/kg wet weight)

Benzo-a-pyrene n/a 7.6 10.86 6,945

Brominated diphenyl ether 28 BDE 28 5.88 9.5 -

Brominated diphenyl ether 47 BDE 47 6 10.69 -

Brominated diphenyl ether 49 BDE 49 - - -

Brominated diphenyl ether 99 BDE 99 6.8 11.16 -

Brominated diphenyl ether 100 BDE 100 - - -

Brominated diphenyl ether 153 BDE 153 8.3 11.82 -

Brominated diphenyl ether 154 +BB153 BDE 154 +BB153 - 9.3 -

Brominated diphenyl ether 209 BDE 209 9.9 18.42 7

Diisopropylnaphtalene n/a 5.68 7.37 9,208*

α-Hexabromocyclodecane α-HBCD 899

γ- Hexabromocyclodecane γ-HBCD 173

Hexachlorobenzene HCB 5.86 7.38 4,780

Polychlorinated biphenyl-28 PCB 28 5.97 8.77 -

Polychlorinated biphenyl-52 PCB 52 6.04 8.4 12,133

10

Chemical Abbreviation Log KOW Log KOA Geometric mean BCF

(L/kg wet weight)

Polychlorinated biphenyl-101 PCB 101 6.8 9.1 10,165

Polychlorinated biphenyl- 118 PCB 118 7.12 9.8 61,353

Polychlorinated biphenyl- 138 PCB 138 7.44 9.5 96,713

Polychlorinated biphenyl- 153 PCB 153 6.91 10.44 48,416

Polychlorinated biphenyl- 180 PCB 180 7.26 10.99 18,804

Perfluorooctanoic acid PFOA 6.30** 5.73 1,258

Perfluorooctane sulfonic acid PFOS 6.28** 6.63 3,981

Perfluorooctanesulfonamide PFOSA 7.58** - -

Perfluoro-n-decanoic acid PFDA - 6.22 2,511

Pyrene Pyrene 6.11 8.19 2,827

-Terphenyl n/a 5.52 9.3 3,558*

- indicates data not available * indicates only one BCF value was available and is not a geometric mean ** value predicted from KOWWIN v1.67

2.5. Calculation of the BAF, BCF and TMF

Field-based biota concentrations were calculated from whole-body samples.

Concentrations of the substances found in biota and in water data are summarized in

Appendix A. Field data were excluded from the BAF and TMF calculations if water or

biota concentrations were unavailable or were below the detection limit.

2.5.1. BAFs

BAFs (L/kg ww) were calculated from observed concentrations of substances

measured in organisms and in water for each organism in the food web (Eqn.2):

where Cbiota is the geometric mean, wet-weight concentration of the observed

chemical in a given organism (mg/kg ww) and Cwater is the geometric mean total

concentration of each substance observed in water (mg/L) in the Western Scheldt

estuary. Chemical concentrations were measured in 11 components of the food web and

9 water samples.For each component of the food web (i.e. water, sediment, organisms

etc…), multiple individual BAFs (calculated for individual samples) were summarized as

11

a geometric mean. The 5th and 95th percentiles of the distribution of BAFs for each

component were calculated from the standard deviation of log BAF values.

2.5.2. Bioconcentration Factors (BCF)

Bioconcentration Factors (BCFs) are generally standardized, laboratory-based

bioaccumulation indicators (OECD 305, Eqn. 3).

BCFs were compiled from the database in Arnot and Gobas (2006) (Appendix B).

Only substances included in the Arnot and Gobas (2006) database were included in the

BCF-TMF comparison. For substances with more than one BCF (i.e., PCB 52, PCB 101,

PCB 118, PCB 153, PCB 180, HCB, pyrene, benzo(a)pyrene, and BDE 209), a

geometric mean BCF and associated percentile values were calculated as described for

BAFs.

2.5.3. Trophic Magnification Factors (TMFs)

Trophic magnification factors (TMFs), also sometimes termed Food Web

Magnification Factors (Fisk et al., 2001; Hop, Borgå, Gabrielsen, Kleivane, & Skaare,

2002) were calculated for each substance using two different methods. The first method

of calculating the TMF, referred to herein as the ‘traditional method’, is from the antilog

of the slope of a regression between the log chemical concentration (normalized to lipid,

or to nitrogen for perfluorinated substances) and trophic level for each organism of the

tested food web (Eqn. 4) (Figure 2.3). Concentrations of perfluorinated substances were

normalized to percent nitrogen as an index of the protein content, because these

substances are known to associate with protein, rather than lipid. Phytoplankton were

removed from the linear regression for perfluorinated substances because protein

normalization of phytoplankton is not feasible due to the very low nitrogen content of

algae.

12

The second method for calculating the TMF, referred to herein as the ‘balanced

method’, was based on a regression of geometric mean concentrations and trophic

levels, rather than concentrations and trophic levels of each individual organism.

Calculating the geometric mean for each species reduces the influence of unbalanced

sampling, i.e., a greater number of samples at certain trophic positions, in this study

between trophic position 2.5 and 3.5, compared to other trophic positions. The balanced

method was tested in response to concerns that the traditional method was unduly

influenced by larger samples of organisms with higher trophic positions.

Figure 2.3. The relationship between normalized contaminant concentrations in biota and trophic level

2.6. Comparison between bioaccumulation indicators

The relationships between the BAF, BCF, and TMF were explored to test if the

current regulatory criteria (i.e., BCF and BAF ≥ 5,000) adequately indicate a substance’s

biomagnification behaviour in the studied estuarine food web, as indicated by a TMF > 1.

I assumed a finding of TMF > 1 to be a true indicator of a biomagnifying substance. The

BAF and the BCF were compared to the TMF to identify one of four possible outcomes:

I. The contaminant was screened as bioaccumulative (BCF or BAF ≥ 5,000) but showed no biomagnification in the food web (TMF < 1). This outcome was referred to as a false positive.

II. The contaminant was screened to be not bioaccumulative (BCF and BAF < 5,000) but the chemical biomagnified (TMF > 1). This outcome was referred to as a false negative.

III. The contaminant was screened as bioaccumulative (BCF and BAF ≥ 5,000) and biomagnified (TMF > 1). This outcome was referred to as a true positive.

13

IV. The contaminant was screened to be not bioaccumulative (BCF and BAF < 5,000) and did not biomagnify (TMF < 1). This outcome was referred to as a true negative.

Figure 2.4 illustrates these four outcomes.

Figure 2.4. Illustration of four possible outcomes in the comparison of a BCF or BAF (relative to a screening criterion of 5,000) with a TMF (relative to a criterion value of 1).

14

3. Results and Discussion

3.1. Bioaccumulation Factors

Table 3.1 lists the empirical BAFs for each tested substance and for each

sampled aquatic organism. The BAFs range from 2 L/kg ww for BDE 209 in mid to upper

trophic level organisms such as pouting and flounder to 463 629 L/kg ww for PCB 118

in slimy sculpin. All tested substances with the exception of PFOA obtained at least one

BAF above the current regulatory threshold of 5000 L/Kg. Among samples, i.e. the

multiple BAFs calculated for each species, the BAFs generally remained consistent

within a species. However, the zooplankton/jellyfish samples obtained one sample

consistently higher than the other sample.

The variation of BAFs between species may be explained due to the diverse

bioaccumulative nature of the studied substances, i.e. a substance with a high log KOW

or log KOA will be more likely to bioaccumulate than a substances with a low log KOW or

log KOA. the ability of the organism to metabolize the substance, and the trophic level of

the organism. Specifically, among the perfluorinated substances, BAFs tend to be

highest between pouting and herring with the exception of PFOA where lugworm

obtained the highest BAF. The BAFs for the PCBs also have high BAFs among the

pouting and herring. BAFs for the PCBs are also relatively higher than other species in

sole, flounder, and goby. Pouting and herring also obtain the highest BAFs for HCB.

Suspended particulate matter, lugworm, and cockle obtained the highest BAFs for

pyrene and benzo-a-pyrene; the lowest BAFs for the same substances were found in

upper trophic level benthic and pelagic organisms such as herring, pouting, sole, and

flounder. The less brominated congeners of the brominated flame-retardants obtained

low BAFs in suspended particulate matter, phytoplankton, lugworms, and cockle

compared to upper trophic level organisms with higher BAFs. Herring and sole obtained

the highest BAFs of all the species sampled for BDE 99. Phytoplankton and cockle

obtained BAFs below the rest of the species sampled for BDE 100. The BAFs for BDE

15

154+BB153 and BDE 153 were similar in that highest BAFs were found in sole whereas

the lowest were obtained for phytoplankton and zooplankton. Alpha-HBCD obtained low

BAFs in phytoplankton, zooplankton and the common shore crab compared to Gamma-

HBCD where low BAFs were obtained for the majority of species sampled with the

exception of phytoplankton and suspended particulate matter. The variability may also

be attributed to different biotransformation capabilities of the organisms for each

substance.

The geometric means of the BAFs obtained for each substance studied generally

agree with the prediction that a substance defined as bioaccumulative under current

regulatory criteria, in this case BAF≥5000, also biomagnifies as defined by a TMF>1

(Figure 3.8).

The BAF for each substance studied varied depending on which organism was

used for the calculation. For example, the BAFs were different between species i.e. one

BAF for PFDA in herring was 1757 L/kg and one BAF for PFDA in pouting was 6003

L/kg. The 95% confidence intervals for BAFs range an order of magnitude and within

that range many substances obtained BAFs both above and below the current regulatory

criteria of 5000. For example, the substance PFDA may obtain a False Negative result

or a True Positive result depending on which BAF was selected for evaluation because

the 95% confidence intervals span across the line indicating the current regulatory

criteria. In this study, we selected the geometric mean to represent the BAF for the

substance to include as much field information as possible in the measurement. The

inconsistency between BAF values may have implications when used as a

bioaccumulation indicator in Canadian legislation (i.e. BAF ≥ 5000). The implications of

using the BAF as a bioaccumulation indicator include the decision of which BAF is

selected for evaluation may be a decision of the regulator or industry professional who

may not be aware of the bioaccumulative nature of the substance or of the implications

of selecting a BAF for a benthic fish compared to a pelagic fish.

16

Table 3.1. Bioaccumulation factors (L/kg) of various analytes in each organism of the study PFOA PFDA PFOS PFOSA PCB 28 PCB 52 PCB 101 PCB 118 PCB 138 PCB 153 PCB 180 HCB Pyrene Benzo-a-pyrene BDE 28 BDE 49 BDE 47 BDE 100 BDE 99

BDE153 +BB153 BDE 153 BDE 209 α-HBCD γ-HBCD

SPM-1 222.2 4505.7 1092.8 1778.2 19587.8 3751.2 11855.2 20157.8 28048.5 20202.5 43589.6 3125.2 13221.5 164481.9 3872.4 2140.4 8134.9 2773.5 896905.8 2919.4 6172.1 18418.8 17788.5 6371.9 SPM-2 130.3 4224.1 850.0 1258.4 15235.0 3506.5 10943.2 17335.7 25498.7 17677.2 35234.9 2430.7 14405.5 167838.6 3872.4 2140.4 6507.9 3466.9 413956.5 3892.5 3857.6 5709.8 12197.8 5602.9 SPM-3 131.5 1903.7 736.6 1636.0 22852.5 6197.6 17326.8 28220.9 48447.5 29462.0 65384.4 3472.5 25259.0 285325.7 3872.4 3567.4 13015.8 4160.3 945200.7 3892.5 7715.2 16576.9 37609.9 27465.2 Phytoplankton 1 51.8 1610.8 399.9 647.3 3083.3 570.8 2029.1 4082.0 5737.2 3998.4 8173.1 1215.4 1628.0 20979.8 2904.3 1427.0 3254.0 2080.1 1034891.3 1946.2 1543.0 483.5 1016.5 98.9 Phytoplankton 2 47.8 1449.7 336.7 654.4 5223.4 822.0 3064.1 6386.0 9179.5 6464.8 12379.4 1000.1 5967.4 72506.3 968.1 713.5 2342.8 693.4 620934.8 973.1 771.5 3890.0 8538.5 3849.5 Zooplankton jelly1 73.1 2181.9 2050.4 23643.4 374.2 439.3 1567.8 1779.0 3945.5 2990.9 3747.1 138.9 90.5 769.5 2904.3 3543.6 13675.1 2861.0 203528.6 1946.2 1543.0 79.2 44272.4 151.1 Zooplankton jelly2 19.1 602.4 604.0 4763.5 86.8 81.9 239.9 260.3 463.5 346.3 382.3 8.3 5.7 32.1 740.9 1092.1 2594.2 663.3 68992.8 291.9 308.6 33.0 1166.9 3.7 Mysis-1 66.9 1933.0 2399.3 16217.5 870.6 9394.2 1750.9 1886.8 3365.8 2626.3 2746.1 347.2 71.0 167.8 7744.8 3805.2 9761.9 5547.0 2759710.1 5838.7 4629.1 49.7 3476.4 19.8 Jelly fish 1 836.7 9225.5 8629.1 71129.3 145.1 2120.2 182.4 201.6 382.5 336.7 3269.2 69.4 39.5 1342.7 5808.6 3329.6 8134.9 4853.6 2414746.4 4865.6 3857.6 32.2 1524.7 43.9 Pouting-1 19.5 5857.4 7576.8 78242.2 18136.9 19571.3 58363.9 102804.8 140242.7 134683.6 196153.2 3125.2 39.5 335.7 5808.6 8323.9 39589.8 10400.6 1000394.9 1946.2 1543.0 3.7 21854.4 33.0 Pouting-2 23.5 4393.1 6629.7 78242.2 16323.2 15493.9 46508.8 96757.4 117293.9 126265.9 177990.9 2604.4 19.7 335.7 4840.5 6421.3 33624.2 9013.9 758920.3 1946.2 1543.0 2.8 18805.0 22.0 Pouting-3 51.8 6003.9 10523.3 113806.9 21764.2 25279.5 51980.4 120946.8 178490.7 151519.1 399571.4 4687.8 78.9 335.7 6776.7 9037.4 46097.7 12480.8 1276365.9 3892.5 3857.6 3.7 31511.0 87.9 Pouting-4 107.6 4539.5 7892.5 92468.1 20313.3 17940.3 51068.4 104820.6 127493.3 143101.3 152563.6 2951.6 59.2 335.7 4840.5 6659.1 40674.5 11094.0 965898.5 1946.2 1543.0 6.4 21346.2 54.9 Pouting-5 33.1 4539.5 8102.9 92468.1 27205.3 27725.9 85722.0 147151.9 229488.0 218860.9 294229.9 4167.0 118.4 335.7 3872.4 4756.5 27116.3 5547.0 1414351.4 2919.4 2314.6 1.8 15247.3 22.0 Herring-1 99.6 2050.1 3577.9 64727.7 43528.5 52190.0 145909.8 201578.0 318733.3 294620.4 261537.6 5729.6 98.7 671.4 10649.0 33295.6 86772.2 21494.6 1345358.7 2919.4 3086.1 1.8 76236.3 131.8 Herring-2 47.8 1757.2 3472.7 54058.3 23577.9 33434.2 109432.4 177388.6 305984.0 277785.0 308759.7 3472.5 59.2 1007.0 4840.5 23782.6 75925.7 25654.9 827913.0 3892.5 3086.1 12.9 33035.7 65.9 Herring-3 75.7 2343.0 4840.7 52635.7 9793.9 14678.4 74778.8 126994.1 242237.3 218860.9 250640.2 3472.5 39.5 1342.7 7744.8 12129.1 40132.1 14560.9 2759710.1 5838.7 5400.6 4.6 14230.8 43.9 Herring-4 51.8 1610.8 3367.5 49079.2 9068.4 15493.9 75690.7 116915.2 216738.7 202025.4 214315.6 2951.6 39.5 1007.0 6776.7 14031.7 42301.4 16641.0 2414746.4 4865.6 4629.1 2.8 12706.0 22.0 Herring-5 95.6 2050.1 4525.0 55480.9 9068.4 10601.1 64747.5 102804.8 216738.7 176772.3 203418.2 4340.6 59.2 1678.4 9680.9 12129.1 42301.4 15254.3 3449637.7 6811.9 6172.1 5.5 9148.4 54.9 Sandeel 51.8 7468.2 11575.6 61171.2 15235.0 17124.9 91193.6 114899.5 254986.7 218860.9 276067.5 7292.2 118.4 2685.4 17425.7 10464.3 81348.9 13867.5 6554311.6 12650.6 10801.2 8.3 20329.7 54.9 Lugworm,depurated 2709.3 937.2 1368.0 66861.6 5078.3 7094.6 24622.3 38299.8 54822.1 48822.8 33418.7 347.2 2368.0 10070.3 1700.4 2863.0 7332.7 3054.9 88938.9 2267.6 1996.4 8.6 10673.1 9.9 Lugworm,not depur. 1992.1 820.0 1262.8 60459.9 5803.8 5626.7 19150.7 22173.6 39522.9 37879.8 27243.5 1041.7 4538.7 33567.7 1633.7 2661.5 7177.5 3219.9 79882.0 2458.1 2036.2 22.4 14230.8 33.0 Nepthys 336.7 3426.6 4514.5 1756.9 5187.1 2014.2 11025.3 10482.1 19889.0 16414.6 80277.5 1736.2 333.5 3021.1 578.7 687.3 3592.9 2651.4 44021.5 2390.0 154.3 6.4 4228.6 22.0 Cockle-1 8.4 336.8 157.8 1493.7 1450.9 1304.8 4559.7 6853.7 14024.3 10943.0 14166.6 347.2 1184.0 10070.3 565.5 734.4 2597.9 1720.4 133677.0 1574.5 1110.9 180.8 3049.5 87.9 Cockle-2 6.8 336.8 168.4 1849.4 2176.4 1630.9 5654.0 8466.3 14024.3 10943.0 14166.6 520.9 1381.4 10070.3 882.9 580.1 2418.8 1524.1 91316.7 1405.9 771.5 198.5 3049.5 98.9 Cockle-3 6.8 307.5 157.8 1849.4 1450.9 1304.8 4833.3 7861.5 14024.3 10101.3 14166.6 347.2 1578.7 10070.3 1286.9 710.4 5180.2 3216.9 217793.9 2954.8 2197.6 181.8 3557.7 285.6 Shore crab1 71.7 5271.7 2209.9 46234.1 20313.3 16309.4 77514.6 131025.7 280485.3 252531.8 181623.4 2778.0 118.4 1107.7 8411.1 1288.5 53057.7 6638.3 67408.8 3528.5 540.1 43.4 1524.7 76.9 Shore crab2 91.6 5857.4 1894.2 44100.2 25391.6 17124.9 76602.7 151183.5 254986.7 286202.7 167093.5 2778.0 138.1 1107.7 7227.1 1388.4 49733.5 5741.8 54840.0 3390.5 617.2 39.1 1524.7 11.0 Goby 10.4 1903.7 2315.1 12803.3 11970.3 11416.6 57452.0 78615.4 140242.7 134683.6 108974.0 2083.5 789.3 6713.5 8386.5 10647.7 45601.7 16074.1 88807.1 4474.8 3568.4 12.9 26936.8 33.0 Goby 7.6 1332.6 1683.7 17071.0 18136.9 13047.5 64747.5 90710.1 203989.3 159936.8 141666.2 4861.5 78.9 1040.6 8187.5 11546.8 62240.6 22988.4 64276.7 8570.0 3652.9 13.7 37609.9 54.9 Sole-1 37.1 4100.2 6314.0 14225.9 9793.9 16309.4 62923.6 84662.8 242237.3 185190.0 348716.9 520.9 118.4 1208.4 3572.6 3026.6 42993.4 40082.0 851362.3 27204.2 11809.0 18.9 22870.9 33.0 Sole-2 298.8 4832.4 6840.1 45522.8 19950.6 25279.5 72954.9 112883.7 267736.0 235696.3 308759.7 520.9 118.4 1107.7 5826.4 6171.4 114566.4 87051.5 4949742.8 58532.4 48218.4 42.8 71153.9 76.9 Sole- 231.1 3221.6 3893.6 32008.2 17411.4 20386.7 66571.4 143120.4 242237.3 235696.3 214315.6 347.2 197.3 1107.7 5076.3 4765.5 61238.6 44016.7 2283513.4 32768.7 24580.4 20.6 31002.8 33.0 Sole-4 518.0 6003.9 9471.0 64727.7 19950.6 21202.2 60187.8 131025.7 203989.3 185190.0 170726.0 347.2 118.4 1074.2 5698.3 5810.6 23649.1 17430.8 99717.6 9687.9 3074.2 10.9 60989.0 22.0 Sole-5 597.6 5418.1 8313.4 41255.0 15235.0 18755.8 58363.9 102804.8 331482.7 227278.6 254272.7 347.2 78.9 1107.7 7614.9 6241.9 36258.6 30328.6 50317.1 12860.4 2326.3 5.5 50824.2 11.0 Flounde 25.1 3953.8 9997.1 5903.7 24303.4 28541.4 91193.6 201578.0 318733.3 286202.7 286964.9 2604.4 78.9 1141.3 9016.8 10303.8 102526.5 31684.4 42825.6 9703.0 2689.9 1.8 12197.8 11.0 Plaice 35.1 4246.6 7892.5 16359.7 14146.8 17124.9 47420.7 74583.9 203989.3 151519.1 159828.6 2257.1 78.9 1040.6 5887.4 4942.7 59450.8 25970.2 97818.6 10063.0 2908.7 2.8 2541.2 11.0 Sculpin-1 23.1 1098.3 1894.2 6828.4 11970.3 1060.1 1641.5 98773.2 140242.7 134683.6 94444.2 3819.7 19.7 1074.2 12587.0 383.2 99526.6 13474.4 6420.9 9245.5 980.1 37.4 46250.0 43.9 Sculpin-2 135.5 4539.5 8629.1 21338.8 36273.7 16309.4 23710.3 463629.4 255.0 168.4 726.5 4167.0 59.2 1107.7 27476.4 621.1 174231.4 25849.8 13063.6 14544.9 1149.8 34.0 86401.1 54.9

17

3.2. Bioconcentration Factors

Appendix B lists the BCFs for the substances tested obtained from the Gobas

and Arnot database (2007) for the available substances. The BCFs were highly variable

within a species because the database contains BCFs from hundreds of different lab-

based studies. The 95% confidence intervals for certain BCFs (i.e. pyrene, benzo-a-

pyrene, and hexachlorobenzene) range above and below the current regulatory criteria

in this case, BCF ≥ 5000 (i.e.Figure 3.9).

The differences in the BCFs can be explained by a potential inconsistency in the

laboratory tests used to calculate the bioconcentration factor. The laboratory tests for

many of the BCFs used in this study occurred prior to the OECD standardization

protocol. However, with the OECD 305 standard protocol, the variability in BCFs for

individual chemicals may decrease over time. The variability in BCFs for a given

substance has consequences such as subjectivity in the selection of which BCF value to

use for regulatory purposes. The database contained only one BCF for certain

substances such as PFOS and PFDA and as a result no confidence intervals could be

calculated for these substances.

3.3. Trophic Magnification Factors

3.3.1. Comparison between Trophic Magnification Factor Methods: Traditional vs. Balanced Method

Table 1-4 lists the trophic magnification factors obtained from two different

methods used to calculate trophic magnification factors. Column (a) lists TMFs

calculated from the use of individual concentrations of substances in organisms with

increasing trophic level and column (b) lists TMS calculated from the geometric mean

concentration of substances in a species with increasing mean trophic level. The 95 %

confidence intervals calculated for the trophic magnification factor did not span the same

order of magnitude as the confidence intervals calculated for the BAFs or BCFs (Figures

18

3.8 and 3.9). 95% confidence intervals for TMFs ranged less than 1 order of magnitude

and few 95% confidence intervals overlapped the TMF threshold of 1.

The TMFs calculated for substances in this study using the Balanced Method

were consistent with TMFs calculated traditionally with the exception of three substance,

PFOSA, PCB 28, and BDE 49 (Table 3.2). Assuming the traditional method to be the

true indicator of a substance’s capacity to biomagnify, the Balanced Method over-

estimates a substances’ biomagnifying ability for PFOSA and BDE 49, and under-

estimates a substances biomagnifying ability. A comparison between the two methods

used to calculate a trophic magnification factor has not yet been documented to the best

of this author’s knowledge. The consistency of TMFs calculated from both methods to be

above or below 1 for the majority of substances suggests there may not be a need to

ensure a balance of trophic levels within the food web prior to determining the slope in

the linear regression. The traditional method to calculating a TMF incorporates the

available information of concentrations of substances in organisms.

Table 3.2. The trophic magnification factors calculated using two different methods, a) TMFs and the p-values from the linear regression of the concentration of substances in every organism and the trophic position of the organism and b) TMFs and the p-values from the linear regression of the geometric mean of substances in each species and the mean trophic position of the species.

Substance Trophic

Magnification Factor (a)

p-value (0.05)

Trophic Magnification Factor using the

Balanced Method (b)

p-value (0.05)

PFOA 3.85 2.68 x 10 -3 3.30 1.8 x 10-1

PFDA 6.69 1.87 x 10 -8 4.96 1.0 x10 -2

PFOS 8.33 1.61 x 10 -11 7.15 7.2 x 10 -4

PFOSA 0.84 6.37 x 10 -1 1.04 9.5 x 10 -1

PCB 28 1.13 2.86 x 10 -1 0.95 8.6 x 10 -1

PCB 52 1.06 7.94 x 10 -1 1.38 2.2 x 10 -1

PCB 101 1.49 9.68 x 10 -3 1.48 1.9 x 10 -1

PCB 118 1.89 8.00 x 10 -5 1.70 1.1 x 10 -1

PCB 138 1.77 8.68 x 10 -3 1.81 7.0 x 10-2

PCB 153 2.15 2.00 x 10 -5 1.95 5.0 x 10-2

PCB 180 1.94 1.70 x 10 -4 1.67 1.3 x 10-1

Hexacholorbenzene 1.24 2.08 x 10 -1 1.00 1.0 x 10 0

Pyrene 0.19 1.21 x 10 -7 0.17 1.8 x 10 -4

Benzo-a-pyrene 0.32 2.30 x 10 -3 0.12 9.2 x 10 -4

BDE 28 1.34 3.74 x 10 -2 1.27 1.3 x 10 -1

19

Substance Trophic

Magnification Factor (a)

p-value (0.05)

Trophic Magnification Factor using the

Balanced Method (b)

p-value (0.05)

BDE 49 0.97 9.14 x 10 -1 1.38 3.0 x 10 -1

BDE 47 1.93 1.88 x 10 -7 1.87 5.0 x 10 -2

BDE 99 1.27 2.99 x 10 -1 1.11 7.5 x 10 -1

BDE 100 1.91 1.50 x 10 -4 1.80 2.0 x 10 -2

BDE 153 1.32 2.24 x 10 -1 1.06 7.6 x 10 -1

BDE 138 0.91 3.69 x 10 -1 0.89 3.3 x 10 -1

BDE 154+BB153 1.53 5.39 x 10 -3 1.22 2.9 x 10 -1

BDE 209 0.14 2.85 x 10 -10 0.13 3.3 x 10 -4

α-HBCD 1.51 4.57 x 10 -3 1.47 1.5 x 10 -1

γ-HBCD 0.27 1.13 x 10 -3 0.36 1.0 x 10 -2

The change of concentration for substances in organisms at different trophic

levels varies depending on the analyte and on the method of TMF calculation, however

the majority of substances studied increase in normalized, either protein-normalized or

lipid normalized with increasing trophic position; select substances either no not increase

in normalized concentration with respect to increasing trophic level or show no

discernible pattern.(Figure 3.1 to Figure 3.7).

The fluorinated substances generally increase in protein-normalized

concentrations in organisms with increasing trophic level, with the exception of PFOSA

for which no discernible pattern of biomagnification is observed with increasing trophic

level (3.1, (g) and (h)). The PCBs in this study also increase in lipid-normalized

concentrations in organisms with increasing trophic level and is particularly evident in the

higher-chlorinated congeners such as PCB 180 (Figures 3.2, 3.3 (e) (f)).

Hexachlorobenzene also increases in lipid-normalized concentration in organisms with

increasing trophic level (Figure 3.4, (e) and (f)). Pyrene and benzo-a-pyrene decrease in

lipid-normalized concentration in organisms with increasing trophic level (Figure 3.4, (a),

(b), (c), (d)) The BDEs in this study generally increase in lipid normalized concentrations

with increasing trophic level with the exception of BDE 209 (Figure 3.5). BDE 209

decreases in lipid-normalized concentration in organisms with increasing trophic level.

The HBCD isomers α and γ have opposite biomagnification patterns in the studied food

web; α-HBCD increases in lipid normalized concentrations in organisms with increasing

20

trophic level and γ-HBCD decreases in lipid normalized concentration with increasing

trophic level (Figure 3-7)

21

Figure 3.1. The log protein normalized concentration of PFOA, PFOS, and PFDA in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF; PFOSA neither increases or decreases in log protein normalized concentration with respect to trophic level. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), (g), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f).

22

Figure 3.2. The log lipid normalized concentration of PCB 28, PCB 52, PCB 101, and PCB 118 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f).

23

Figure 3.3. The log lipid normalized concentration of PCB 138, PCB 152, and PCB 180 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f).

24

Figure 3.4. The log lipid normalized concentration of pyrene and benzo-a-pyrene, in organisms on the y-axis decreases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF; HCB increases with Method 1 and neither increases nor decreases in normalized concentration with respect to trophic level in Method 2. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), and (f)

25

Figure 3.5. The log lipid normalized concentration of BDE 28, BDE 47, BDE 49, and BDE 99 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), (g) on the left and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), (f), (h) on the right.

26

Figure 3.6. The log lipid normalized concentration of BDE 100, BDE 153, BDE 154+BB53 in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF; BDE 209 decreases in normalized concentration with respect to trophic level for both methods. Method 1, using individual concentrations of substances in organisms, is depicted by (a), (c), (e), (g) and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b), (d), (f), (h).

27

Figure 3.7. The log lipid normalized concentration alpha-HBCD in organisms on the y-axis increases with respect to increasing tropic position on the x-axis for both methods of calculating the TMF. Gamma-HBCD decreases in normalized concentration with respect to trophic position for both TMF methods. Method 1, using individual concentrations of substances in organisms, is depicted by (a) and (c), and Method 2, using geometric mean concentrations of substances in a species and mean trophic level is depicted in graphs (b) and (d).

3.4. Comparison of Bioaccumulation Measures (BAF, BCF, and TMF)

In general, for both BCFs and BAFs, the current regulatory criteria for a

bioaccumulative substance (the BCF and BAF ≥5000) adequately describes a

biomagnifying substance as defined by a TMF>1(True Positive or True Negative results)

28

(Figure 3.8, Figure 3.9). Exceptions to the agreement between BCF-BAF and TMF do

exist however, and certain groups of substances either biomagnify without being

classified as bioaccumulative, or do not biomagnify despite being classified as

bioaccumulative according to current regulatory criteria (False Positive or False

Negative). The following sections describe the True Positive, True Negative, False

Positive, and False Negative results and the corresponding substances.

3.4.1. True Positive and True Negative Results

For the substances included in this study the BAF and BCF regulatory criteria to

identify a bioaccumulative substance (BAF or BCF ≥ 5000) adequately described the

biomagnifying nature of most substances as defined by a TMF >1; the majority of

substances studied obtained a True Positive or True Negative result.

The substances that obtained True Positive results in BAF-TMF relationship were

PCB congeners 28, 52, 99, 101, 118, 138, 153, 180, BDE congeners 28, 47,49, 99,100,

α-HBCD, PFOS, PFOSA. True Negative results in the food web containing aquatic

organisms and air-breathing organisms were associated with the following substances:

BDE 209, benzo-a-pyrene, pyrene, and -HBCD.

The substances that obtained True Positive results in the BCF-TMF comparison

were: PCB congeners 52, 101, 118, 138, 153, 180, BDE congener 47, and α-HBCD.

Substances with True Negative results were pyrene and benzo-a-pyrene. Certain

substances were excluded from the BCF-TMF comparison because of unavailable BCF

information from the database (Table 2).The following three sections contain a

description of the different types of substances in this study with True Positive or True

Negative results:

Polychlorinated Biphenyls

The biomagnification of lipophilic organochlorines (e.g. PCBs) is explained by the

high KOW and high KOA properties, the general resistance to metabolic transformation,

high gastrointestinal uptake rates, and low respiratory elimination rates in aquatic

organisms (resulting in a BCF or BAF≥5000) and air-breathing organisms (resulting in a

TMF>1)(Kelly et al., 2009). Current bioaccumulation understanding involves lipophilic

29

substances in an equilibrium environment where the higher the octanol-water partition

coefficient (KOW) the higher the tendency for a substance to accumulate in biota. This

relationship generally holds true for lipophilic chemicals in an aquatic environment and

explains why PCBs were adequately screened for bioaccumulation by the regulatory

indicators that rely on the KOW to measure a substance’s bioaccumulative nature.

Polybrominated diphenyl ethers (congeners 47, 99, 100, 153, 209, γ –HBCD

and -HBCD)

BDE 47 and BDE 99 in the BCF-TMF relationship only, and PBDE 100, obtained

true positive results and bioaccumulate and biomagnify due to chemical properties such

as high lipophilicity (log Kow: 5.9–10) (Braekevelt, Tittlemier, & Tomy, 2003; de Wit, 2002)

and resistance to metabolism (Gustafsson, Björk, Burreau, & Gilek, 1999). The less

brominated congeners (e.g. BDE 28 and 47) are known to be more bioaccumulative than

their parent compounds due to metabolic debromination of higher congeners (i.e. BDE

209) into lower congener PBDEs resulting in higher concentrations of the lower

substituted BDEs (i.e. BDE 47) in organisms relative to the aquatic environment and

consequently results in BAF or BCF measurements above the current bioaccumulation

screening standard of greater than or equal to 5000(Law et al., 2003; Shaw et al., 2008;

Stapleton, Letcher, & Baker, 2004; Wan, Hu, Zhang, & An, 2008). The highly brominated

congeners BDE 209 obtained True Negative results because of the metabolic

debromination of these compounds into less brominated congeners (i.e. BDE 49) with

increasing trophic level.

Benzo-a-pyrene and pyrene

Benzo-a-pyrene and pyrene had BAF and BCF values below 5000 and TMF

values below 1. Polycyclic aromatic hydrocarbons are well known to metabolize by the

inducible Cytochrome P450 pathway and aquatic organisms can metabolize these

substances generally reducing the measured value in an organism resulting in a low

BAF or BCF. PAHs are also known to biodilute, or decrease in concentration up a food-

web, due to low assimilation efficiencies and efficient metabolic transformation at higher

trophic levels, resulting in a TMF less than 1 (Wan, Jin, Hu, & Jin, 2007). Overall, benzo-

a-pyrene is known to biodilute with increasing trophic level and the BCF was not deemed

significantly different from the current screening criteria of 5000.

30

PFOS AND PFOSA

PFOS and PFOSA bioaccumulate and biomagnify in all BCF-BAF-TMF

comparisons. Although lipid-based partitioning chemical characteristics are difficult to

measure in a laboratory setting, predicted log KOWs (log KOW≥ 5) of the substances in this

study may indicate the ability of PFOS and PFOSA to bioaccumulate in aquatic

ecosystems. However, the difficulty in calculating the lipid-based partitioning constants in

the lab result in increased reliance on empirical data to indicate the biomagnification of

these substances. The relatively high amounts of PFOS in the seabirds in the food web

may be due to an ability to biotransform and excrete other less persistent fluorinated

substances (i.e. shorter chain fluorinated substances) (Haukås, Berger, Hop, Gulliksen,

& Gabrielsen, 2007; Kelly et al., 2009; Walker & Livingstone, 1992) .The

biotransformation of PFOSA and other PFOS precursors may also influence the

increased concentration of PFOS in estuarine organisms by increasing the relative

amount of PFOS (Tomy, Budakowski, et al., 2004; Tomy, Tittlemier, et al., 2004)

3.4.2. False Positive and False Negative Results

Exceptions to the general agreement between the current definition of a

bioaccumulative substance (BCF or BAF≥5000) and the definition of a biomagnifying

substance (TMF>1) do exist for the substances studied. Substances studied in both the

BAF-TMF and BCF-TMF comparisons obtained False Negative and/or False Positive

results.

False Positive and False Negative results have implications for the effectiveness

of the current measures used to indicate a bioaccumulative substance as defined by the

Canadian Environmental Protection Act. False Positive result may be costly from an

environmental perspective because a non-biomagnifying substance is classified as

biomagnifying and therefore removed from commerce due to its mis-classification. The

mistake may result in lost revenues for the distributor or the chemical may be important

to society yet it is banned from production. A False Negative result may also be costly to

environmental and human health. Biomagnifying substances are classified as non-

bioaccumulative and as a result are released into the environment potentially posing a

threat to upper trophic level organisms (Fisk et al., 2005; Letcher, Norstrom, & Bergman,

31

1995). The PFCAs, however, remain near the boundaries between the True Positive and

True Negative results and the False Negative and False Positive results. The proximity

of these substances to the boundaries may suggest that even in aquatic food webs the

BCF and BAF are inadequate or inappropriate bioaccumulation screening tools for the

perfluorinated substances.

BAF-TMF Comparison

No substances tested in this study obtained False Positive results in the BAF-

TMF comparison, however, some substances obtained False Negative results. Select

fluorinated substances PFDA and PFOA, polybrominated diphenyl ethers congeners 153

and 154+BB153, and hexachlorobenzene were defined as not bioaccumulative

according to current regulatory criteria (BAF 1). Two substances are classified as True Positive substances

yet fall significantly close to the False Negative results; PFOS and BDE congener 28 are

found near the BCF boundary of 5000 and have TMFs above one and despite the

geometric mean of the BCFs remaining outside the False Negative result the substances

may have potential to be inadequately characterized as bioaccumulative.

BCF-TMF Comparison

γ-HBCD and benzo-a-pyrene obtained False Positive results in the BCF-TMF

comparison. γ-HBCD obtained a False Positive result. BDE 209, PFOA, PFDA, and

hexachlorobenzene obtained False Negative results.

The following two sections describe the substances that generally obtained False

Positive or False Negative results in the BAF-TMF and BCF-TMF relationships . Overall,

substances in this study with False Positive or False Negative results may have other

mechanisms influencing bioaccumulation not captured in current bioaccumulation

screening criteria (e.g. BCF or BAF ≥5000). For example, these substances may not

partition easily into the air phase (e.g. high log KOA or protein partitioning) and/or ii) are

ionizable.

32

Polybrominated diphenyl ethers (congener 28, 99, 153, 153 +BB54, 209, γ-HBCD)

The disparity between the results of the PBDE congeners, i.e. some with True

Positive and True Negative results and others with False Negative results, may be due

to the biotransformation of higher brominated congeners into lower brominated

congeners over time and subsequently up the food chain. More specifically, when the

higher brominated substances biotransform into the lower brominated substances, the

lower brominated congeners are the available substance for uptake by organisms and

over time accumulate in biota and upper trophic levels. When looking at concentrations

of the higher congener PBDEs in organisms up a food- chain, high congener PBDEs

actually decrease in concentration with increasing trophic level.

BDE 99 may obtain a True Positive result in the BAF-TMF comparison because

air-breathing organisms are present in the tested food web; BDE 99 is known to

debrominate in the intestines of fish (Mizukawa et al., 2013; Stapleton et al., 2004)

however few studies have tested the debromination of BDE 99 in avian species such as

the common tern in this study. As a result, BDE 99 may not be biotransformed in the

common tern as it is in the upper trophic level aquatic species, resulting in an

accumulation of BDE 99 with increasing trophic level producing a trophic magnification

factor above 1.

PFDA and PFOA

Based on BCF or BAF values used in current bioaccumulation criteria, some

fluorinated substances are classified as ‘not biomagnifying’, but in the field, specifically in

air-breathing food webs, these chemicals have high potential for food web magnification

obtaining a false negative result. The False Negative results may occur because

bioaccumulation of fluorinated compounds may not be captured in aquatic-based

indicators such as BCF or BAF. Fluorinated substances may require an indicator that

captures bioaccumulation mechanisms in air-breathing food webs to adequately be

assessed for biomagnification.

PFOA is also characterized as “low KOW and high KOA” substances and are

known to biomagnify in air-breathing food webs (Kelly et al., 2007). Kelly et al. (2007)

also reported that a large number of chemicals (ca. 4000) currently in use in Canada can

33

be classified as “low KOW − high KOA” and may also be at risk of improper screening for

biomagnification.

Additionally, hydrophobic substances with lower KOW values, e.g. from log KOW~5

to 2, that are poorly metabolized and have high octanol-air partition coefficients ( KOA )

may accumulate in upper trophic level organisms resulting in a TMF greater than 1(Kelly

et al., 2007). As a result of low KOW, high KOA biomagnification occurs in food webs with

air-breathing animals, but do not tend to magnify in water-respiring animals, although we

do see evidence of biomagnification in aquatic organisms for certain substances such as

PFOS. Air-breathing organisms have a low rate of respiratory elimination to air for these

chemicals (high KOA) resulting in a TMF greater than 1.

Further complicating the bioaccumulation of fluorinated substances is the

ionizing capability of these compounds. Arnot and Gobas (Arnot & Gobas, 2006) called

for further research on the bioaccumulation of ionizable substances due to their high

production volume and likeliness to enter the environment. For example, 33% of

ionizable substances registered under REACh ionize and dissociate at pH of 7 and 77%

of pharmaceuticals are ionizing substances(Franco, Ferranti, Davidsen, & Trapp, 2010;

Rendal, Kusk, & Trapp, 2011) . Bioaccumulation indicators of ionizing substances, such

as the BCF, are sensitive to pH, log D (sum of log KOW of neutral and ionic molecule),

dissociation, the pH-dependent ion trap, and the electrical attraction of cations (Fu,

Franco, & Trapp, 2009), none of which are incorporated in conventional bioaccumulation

screening tests.

Current regulatory bioaccumulation criteria do not incorporate protein-water

partitioning for aquatic organisms or protein-air partitioning for terrestrial organisms and

these partition coefficients may be necessary to adequately identify fluorinated

substances as bioaccumulative. Chemical-class specific bioaccumulation indicators may

reduce the False Negative and False Negative results for ionizable and non-lipid

partitioning substances such as the fluorinated substances in this study.

34

Figure 3.8. The Trophic Magnification Factor (TMF) as a function of the Bioaccumulation Factor (BAF), in a benthic-pelagic estuarine ecosystem including an air breathing organism. Most substances that are defined as bioaccumulative also biomagnify (i.e. BAF≥5000 and TMF>1). False Negative and False Positive results do occur with substances such as PFOS. Error bars are 95% confidence intervals

35

Figure 3.9. The Trophic Magnification Factor (TMF) as a function of the Bioaccumulation Factor (BAF), in a benthic-pelagic estuarine ecosystem including an air breathing organism. Most substances that are defined as bioaccumulative also biomagnify (i.e. BAF≥5000 and TMF>1). False Negative and False Positive results do occur with substances such as PFOS. Error bars are 95% confidence intervals.

36

4. Conclusions and Recommendations

Overall, most hydrophobic, biotransformed substances measured in the Western

Scheldt estuarine food web including air breathing organisms and aquatic breathing

bioaccumulate according to the current definition of a bioaccumulative substance and

also biomagnify. However some substances are listed as bioaccumulative because they

meet the bioaccumulation criteria but do not show evidence of biomagnifying in a food

web. In this study we explored the relationship between bioaccumulative and

biomagnifying substances and find general agreement between the two, however

exceptions to the agreement between a bioaccumulative and biomagnifying substance

do exist.

The substances with the False Positive and False Negative results suggest that

bioaccumulation may involve other mechanisms not included in current regulatory

criteria of bioaccumulation such as protein-water partitioning, protein-air partitioning, and

trophic transfer via diet. To reduce the False Positive and False Negative results and to

include alternate bioaccumulation mechanisms while screening for a bioaccumulative

substance, we suggest a change to the current definition of a bioaccumulative

substance. Currently a bioaccumulative substance is one which obtains a BCF or

BAF≥5000 or a log KOW≥5, however we suggest a bioaccumulative substance is a

substance that biomagnifies in a food chain. Biomagnification may be indicated by a

trophic magnification factor or biomagnification factor greater than 1. Modifying the

definition of a bioaccumulative substance would reduce False Positive and False

Negative results without having to rely on the relationship between the bioconcentration,

bioaccumulation factor, and trophic magnification factor.

Many bioaccumulation specialists already agree that a substance is

bioaccumulative if it biomagnifies in a food web. The modified definition broadens the

scope of the current definition of a bioaccumulative substance because it includes

mechanisms of bioaccumulation such as trophic transfer and biotransformation and for

certain substances reduces the reliance on the irrelevant criteria of a BCF or BAF. I also

37

suggest chemical-class specific bioaccumulation criteria where substances are classified

into groups with appropriate bioaccumulation screening and evaluation methodologies

for the respective chemical characteristic. Chemical-class appropriate screening may

streamline the bioaccumulation screening process and reduce False Positive and False

Negative results. An example of a chemical-class specific screening would be using

protein partitioning for substances such as the perfluorinated compounds because the

current lipid-partitioning indicators are irrelevant for the bioaccumulative nature of these

substances.

The current study explores new ideas for further research in the study of

bioaccumulation screening criteria. Field studies are required and are important to

validate the laboratory and model-predicted bioaccumulation indicators used in many

bioaccumulation screening decisions. Field studies can provide assurance that what is

observed in the lab is also occurring in the field, or field studies can demonstrate

differences between field results and lab results. Lab studies continue to be relevant and

contribute information to the field studies and also help bioaccumulation scientists

understand real-life issues such as the debromination of highly brominated BDEs in the

intestines of carp. The field study identified a decrease of BDE 209 in upper trophic level

organisms and lab studies identified the reason why. Increased research on the complex

relationship between current bioaccumulation screening criteria and other indicators

such as the TMF is important to support regulators in making informed decisions about

the bioaccumulative nature of a substance in a regulatory setting. I also suggest more

research further defining the difference between the two methods to determine the

trophic magnification factor.

38

References

Arnot, J. A., & Gobas, F. A. (2006). A review of bioconcentration factor (BCF) and bioaccumulation factor (BAF) assessments for organic chemicals in aquatic organisms. Environmental Reviews, 14(4), 257–297.

Baeyens, W., Van Eck, B., Lambert, C., Wollast, R., & Goeyens, L. (1997). General description of the Scheldt estuary. Hydrobiologia, 366(1), 1–14. doi:10.1023/A:1003164009031

Braekevelt, E., Tittlemier, S. A., & Tomy, G. T. (2003). Direct measurement of octanol–water partition coefficients of some environmentally relevant brominated diphenyl ether congeners. Chemosphere, 51(7), 563–567. doi:10.1016/S0045-6535(02)00841-X

De Wit, C. A. (2002). An overview of brominated flame retardants in the environment. Chemosphere, 46(5), 583–624. doi:10.1016/S0045-6535(01)00225-9

Fisk, A. T., De Wit, C. A., Wayland, M., Kuzyk, Z. Z., Burgess, N., Letcher, R., … Muir, D. C. G. (2005). An assessment of the toxicological significance of anthropogenic contaminants in Canadian arctic wildlife. Science of The Total Environment, 351–352, 57–93. doi:10.1016/j.scitotenv.2005.01.051

Fisk, A. T., Hobson, K. A., & Norstrom, R. J. (2001). Influence of Chemical and Biological Factors on Trophic Transfer of Persistent Organic Pollutants in the Northwater Polynya Marine Food Web. Environmental Science & Technology, 35(4), 732–738. doi:10.1021/es001459w

Franco, A., Ferranti, A., Davidsen, C., & Trapp, S. (2010). An unexpected challenge: ionizable compounds in the REACH chemical space. The International Journal of Life Cycle Assessment, 15(4), 321–325. doi:10.1007/s11367-010-0165-6

Fu, W., Franco, A., & Trapp, S. (2009). Methods for Estimating the Bioconcentration Factor of Ionizable Organic Chemicals. Environmental Toxicology and Chemistry, 28(7), 1372–1379.

Giesy, J. P., & Kannan, K. (2001). Global Distribution of Perfluorooctane Sulfonate in Wildlife. Environmental Science & Technology, 35(7), 1339–1342. doi:10.1021/es001834k

39

Gobas, F. A., De Wolf, W., Burkhard, L. P., Verbruggen, E., & Plotzke, K. (2010). Revisiting Bioaccumulation Criteria for POPs and PBT Assessments. Integrated Environmental Assessment and Management, 5(4), 624–637. doi:10.1897/IEAM_2008-089.1

Gobas, F. A. P. C., & Morrison, H. (2000). Bioconcentration and Biomagnification in the Aquatic Environment. In Handbook of property estimation methods for chemicals, environmental and health sciences. Boca Raton, USA: CRC Press. Retrieved from http://scholar.google.ca/scholar?q=Bioconcentration+and+Biomagnification+in+the+Aquatic+Environment&hl=en&as_sdt=0&as_vis=1&oi=scholart&sa=X&ei=DS-kUdTEFKOKiALNmoEo&ved=0CC4QgQMwAA

Gustafsson, K., Björk, M., Burreau, S., & Gilek, M. (1999). Bioaccumulation kinetics of brominated flame retardants (polybrominated diphenyl ethers) in blue mussels (Mytilus edulis). Environmental Toxicology and Chemistry, 18(6), 1218–1224. doi:10.1002/etc.5620180621

Haukås, M., Berger, U., Hop, H., Gulliksen, B., & Gabrielsen, G. W. (2007). Bioaccumulation of per- and polyfluorinated alkyl substances (PFAS) in selected species from the Barents Sea food web. Environmental Pollution, 148(1), 360–371. doi:10.1016/j.envpol.2006.09.021

Hop, H., Borgå, K., Gabrielsen, G. W., Kleivane, L., & Skaare, J. U. (2002). Food Web Magnification of Persistent Organic Pollutants in Poikilotherms and Homeotherms from the Barents Sea. Environmental Science & Technology