title: Explaining differences between bioaccumulation measurements in laboratory and field data through use of a probabilistic modeling approach.
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.
authors.affiliation: Roskilde University, Department of Environmental, Social and Spatial Change, PO Box 260, 4000 Roskilde, Denmark. selck@ruc.dk
authors.forename: Henriette
authors.initials: H
authors.lastname: Selck
authors.forename: Ken
authors.initials: K
authors.lastname: Drouillard
authors.forename: Karen
authors.initials: K
authors.lastname: Eisenreich
authors.forename: Albert A
authors.initials: AA
authors.lastname: Koelmans
authors.forename: Annemette
authors.initials: A
authors.lastname: Palmqvist
authors.forename: Anders
authors.initials: A
authors.lastname: Ruus
authors.forename: Daniel
authors.initials: D
authors.lastname: Salvito
authors.forename: Irv
authors.initials: I
authors.lastname: Schultz
authors.forename: Robin
authors.initials: R
authors.lastname: Stewart
authors.forename: Annie
authors.initials: A
authors.lastname: Weisbrod
authors.forename: Nico W
authors.initials: NW
authors.lastname: van den Brink
authors.forename: Martine
authors.initials: M
authors.lastname: van den Heuvel-Greve
mesh_terms: D000818:Animals; D003198:Computer Simulation; D004784:Environmental Monitoring / Q000379:methods*; D004785:Environmental Pollutants / Q000032:analysis* / Q000378:metabolism / Q000633:toxicity*; D007313:Insecta / Q000187:drug effects / Q000378:metabolism; D007814:Larva / Q000187:drug effects / Q000378:metabolism; D008954:Models, Biological; D010471:Perches / Q000378:metabolism; D011077:Polychaeta / Q000187:drug effects / Q000378:metabolism; D011078:Polychlorinated Biphenyls / Q000032:analysis / Q000378:metabolism / Q000633:toxicity; D011721:Pyrenes / Q000032:analysis / Q000378:metabolism / Q000633:toxicity; D018570:Risk Assessment / Q000379:methods*; D012680:Sensitivity and Specificity; D013045:Species Specificity; D020305:Strigiformes / Q000378:metabolism; D035501:Uncertainty
chemical_list: D004785:Environmental Pollutants; D011721:Pyrenes; C030984:pyrene; D011078:Polychlorinated Biphenyls; C014024:2,4,5,2',4',5'-hexachlorobiphenyl