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Exploring the Use of Multimedia Fate and Bioaccumulation Models to Calculate Trophic Magnification Factors (TMFs)
Citation:
ARNOT, J. A. AND L. BURKHARD. Exploring the Use of Multimedia Fate and Bioaccumulation Models to Calculate Trophic Magnification Factors (TMFs). Presented at SETAC North America 31st Annual Meeting, Portland, OR, November 07 - 11, 2010.
Impact/Purpose:
The trophic magnification factor (TMF) is considered to be a key metric for assessing the bioaccumulation potential of organic chemicals in food webs. Fugacity is an equilibrium criterion and thus reflects the relative thermodynamic status of a chemical in the environment and in aquatic, terrestrial, and human food webs. For most neutral organic chemicals the TMF is calculated using lipid normalized concentrations measured in organisms of a food web and their relative trophic position. This lipid normalized TMF calculation is analogous to calculating the change in chemical fugacities in the organisms of a food web with respect to their relative trophic position. For example, a TMF > 1 indicates increasing fugacity (or chemical activity) with increasing trophic level and thus food web biomagnification. Fugacity based fate and food web bioaccumulation models have been used to estimate the fate and bioaccumulation of organic chemicals in the environment. RAIDAR is a fugacity based mass balance model that combines environmental fate and food web calculations to assess exposures and potential risks of organic chemicals to humans and ecological receptors. The model includes representative species comprising aquatic, terrestrial, and human food webs. In this study, the RAIDAR model is used to calculate fugacity ratios in the representative food webs (i.e. TMFs) and to illustrate the range of TMFs as a function of chemical space. TMFs are then calculated for a subset of well studied chemicals (e.g. PCBs, HCB, PBDEs, HCHs, pyrene, DEHP, D5) and compared with measured TMFs. The holistic mass balance modelling framework is exploited to show how chemical mode-of-entry influences assumed steady state TMFs for certain chemicals and to identify key parameters in TMF estimates through sensitivity and uncertainty analyses. The merits and limitations of the screening level modelling approach for TMF calculations are discussed.
Description:
The trophic magnification factor (TMF) is considered to be a key metric for assessing the bioaccumulation potential of organic chemicals in food webs. Fugacity is an equilibrium criterion and thus reflects the relative thermodynamic status of a chemical in the environment and in aquatic, terrestrial, and human food webs. For most neutral organic chemicals the TMF is calculated using lipid normalized concentrations measured in organisms of a food web and their relative trophic position. This lipid normalized TMF calculation is analogous to calculating the change in chemical fugacities in the organisms of a food web with respect to their relative trophic position. For example, a TMF > 1 indicates increasing fugacity (or chemical activity) with increasing trophic level and thus food web biomagnification. Fugacity based fate and food web bioaccumulation models have been used to estimate the fate and bioaccumulation of organic chemicals in the environment. RAIDAR is a fugacity based mass balance model that combines environmental fate and food web calculations to assess exposures and potential risks of organic chemicals to humans and ecological receptors. The model includes representative species comprising aquatic, terrestrial, and human food webs. In this study, the RAIDAR model is used to calculate fugacity ratios in the representative food webs (i.e. TMFs) and to illustrate the range of TMFs as a function of chemical space. TMFs are then calculated for a subset of well studied chemicals (e.g. PCBs, HCB, PBDEs, HCHs, pyrene, DEHP, D5) and compared with measured TMFs. The holistic mass balance modelling framework is exploited to show how chemical mode-of-entry influences assumed steady state TMFs for certain chemicals and to identify key parameters in TMF estimates through sensitivity and uncertainty analyses. The merits and limitations of the screening level modelling approach for TMF calculations are discussed.