Explaining Differences Between Bioaccumulation Measurements in Laboratory and Field Data Through Use of a Probabilistic Modeling Approach

Henriette Selck, Ken Drouillard, Karen Eisenreich, Albert A Koelmans, Annemette Palmqvist, Anders Ruus, Daniel Salvito, Irv Schultz, Robin Stewart, Annie Weisbrod, Nico W van den Brink, Martine van den Heuvel-Greve

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Resumé

In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps
OriginalsprogEngelsk
TidsskriftIntegrated Environmental Assessment and Management
Vol/bind8
Udgave nummer1
Sider (fra-til)42-63
ISSN1551-3777
DOI
StatusUdgivet - 2012

Emneord

  • Bioaccumulation
  • Modeling

Citer dette

Selck, Henriette ; Drouillard, Ken ; Eisenreich, Karen ; Koelmans, Albert A ; Palmqvist, Annemette ; Ruus, Anders ; Salvito, Daniel ; Schultz, Irv ; Stewart, Robin ; Weisbrod, Annie ; van den Brink, Nico W ; van den Heuvel-Greve, Martine. / Explaining Differences Between Bioaccumulation Measurements in Laboratory and Field Data Through Use of a Probabilistic Modeling Approach. I: Integrated Environmental Assessment and Management. 2012 ; Bind 8, Nr. 1. s. 42-63.
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abstract = "In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps",
keywords = "Bioaccumulation, Modeling, Bioaccumulation, Modeling",
author = "Henriette Selck and Ken Drouillard and Karen Eisenreich and Koelmans, {Albert A} and Annemette Palmqvist and Anders Ruus and Daniel Salvito and Irv Schultz and Robin Stewart and Annie Weisbrod and {van den Brink}, {Nico W} and {van den Heuvel-Greve}, Martine",
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Selck, H, Drouillard, K, Eisenreich, K, Koelmans, AA, Palmqvist, A, Ruus, A, Salvito, D, Schultz, I, Stewart, R, Weisbrod, A, van den Brink, NW & van den Heuvel-Greve, M 2012, 'Explaining Differences Between Bioaccumulation Measurements in Laboratory and Field Data Through Use of a Probabilistic Modeling Approach', Integrated Environmental Assessment and Management, bind 8, nr. 1, s. 42-63. https://doi.org/10.1002/ieam.217

Explaining Differences Between Bioaccumulation Measurements in Laboratory and Field Data Through Use of a Probabilistic Modeling Approach. / Selck, Henriette; Drouillard, Ken; Eisenreich, Karen ; Koelmans, Albert A ; Palmqvist, Annemette; Ruus, Anders; Salvito, Daniel ; Schultz, Irv ; Stewart, Robin; Weisbrod, Annie ; van den Brink, Nico W ; van den Heuvel-Greve, Martine.

I: Integrated Environmental Assessment and Management, Bind 8, Nr. 1, 2012, s. 42-63.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

TY - JOUR

T1 - Explaining Differences Between Bioaccumulation Measurements in Laboratory and Field Data Through Use of a Probabilistic Modeling Approach

AU - Selck, Henriette

AU - Drouillard, Ken

AU - Eisenreich, Karen

AU - Koelmans, Albert A

AU - Palmqvist, Annemette

AU - Ruus, Anders

AU - Salvito, Daniel

AU - Schultz, Irv

AU - Stewart, Robin

AU - Weisbrod, Annie

AU - van den Brink, Nico W

AU - van den Heuvel-Greve, Martine

PY - 2012

Y1 - 2012

N2 - In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps

AB - In the regulatory context, bioaccumulation assessment is often hampered by substantial data uncertainty as well as by the poorly understood differences often observed between results from laboratory and field bioaccumulation studies. Bioaccumulation is a complex, multifaceted process, which calls for accurate error analysis. Yet, attempts to quantify and compare propagation of error in bioaccumulation metrics across species and chemicals are rare. Here, we quantitatively assessed the combined influence of physicochemical, physiological, ecological, and environmental parameters known to affect bioaccumulation for 4 species and 2 chemicals, to assess whether uncertainty in these factors can explain the observed differences among laboratory and field studies. The organisms evaluated in simulations including mayfly larvae, deposit-feeding polychaetes, yellow perch, and little owl represented a range of ecological conditions and biotransformation capacity. The chemicals, pyrene and the polychlorinated biphenyl congener PCB-153, represented medium and highly hydrophobic chemicals with different susceptibilities to biotransformation. An existing state of the art probabilistic bioaccumulation model was improved by accounting for bioavailability and absorption efficiency limitations, due to the presence of black carbon in sediment, and was used for probabilistic modeling of variability and propagation of error. Results showed that at lower trophic levels (mayfly and polychaete), variability in bioaccumulation was mainly driven by sediment exposure, sediment composition and chemical partitioning to sediment components, which was in turn dominated by the influence of black carbon. At higher trophic levels (yellow perch and the little owl), food web structure (i.e., diet composition and abundance) and chemical concentration in the diet became more important particularly for the most persistent compound, PCB-153. These results suggest that variation in bioaccumulation assessment is reduced most by improved identification of food sources as well as by accounting for the chemical bioavailability in food components. Improvements in the accuracy of aqueous exposure appear to be less relevant when applied to moderate to highly hydrophobic compounds, because this route contributes only marginally to total uptake. The determination of chemical bioavailability and the increase in understanding and qualifying the role of sediment components (black carbon, labile organic matter, and the like) on chemical absorption efficiencies has been identified as a key next steps

KW - Bioaccumulation

KW - Modeling

KW - Bioaccumulation

KW - Modeling

U2 - 10.1002/ieam.217

DO - 10.1002/ieam.217

M3 - Journal article

VL - 8

SP - 42

EP - 63

JO - Integrated Environmental Assessment and Management

JF - Integrated Environmental Assessment and Management

SN - 1551-3777

IS - 1

ER -