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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
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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.:
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Partial Copyright Licence
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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
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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.
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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
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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
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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
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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
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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
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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
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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)
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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
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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:
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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
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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.
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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.
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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.
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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
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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):
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(‰) = [(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
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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
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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.
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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.
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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).
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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
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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.
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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
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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
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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
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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
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trophic level and γ-HBCD decreases in lipid normalized concentration with increasing
trophic level (Figure 3-7)
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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).
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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).
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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).
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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)
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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.
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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).
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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)
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(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
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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.
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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,
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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.
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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
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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.
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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
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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.
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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
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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.
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