Exposure of the Irish population to PBDEs in food: consideration of parameter uncertainty and variability for risk assessment

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  • David Trudel
  • Christina Tlustos
  • Natalie von Goetz
  • Martin Scheringer
  • Peter Reichert
  • Konrad Hungerbühler

Polybrominated diphenyl ethers (PBDEs) are brominated flame retardants used to retard the ignition and/or spread of fire. PBDEs are used in various consumer products, such as textiles, mattresses and TV screens. This study presents a chemical risk assessment for the Irish population based on exposure to PBDEs from food. Special regard is given to the influence of parameter uncertainty and variability on the margins of safety. To quantitatively model uncertainty and variability in concentration data and variability in consumer behavior, a hierarchical probabilistic model was constructed. This model was evaluated using a two-dimensional Monte Carlo simulation (2D-MCS) approach. By considering uncertainty and variability in concentration data, margins of safety (MOS) were derived that are lower by a factor of ∼2 compared to MOS based on dose estimates that only consider variability. The lowest MOS is 7.5 × 10(4) for BDE-99, with impaired spermatogenesis as toxic endpoint. Assuming an MOS of 10(4) as acceptable, we conclude that there is no significant risk for human health through intake of contaminated food. To investigate whether additional measurements could improve the quality of dose estimates, the statistic "uncertainty-to-variability (UVR)" was developed. By applying the UVR to our dose estimates, we show that, in our case, the datasets contain little uncertainty and additional measurements would not significantly improve the quality of dose estimates.

Original languageEnglish
JournalFood additives & contaminants. Part A, Chemistry, analysis, control, exposure & risk assessment
Issue number7
Pages (from-to)943-955
Number of pages13
Publication statusPublished - 07.2011

    Research areas

  • Chemistry - polybromitated diphenyl ether, food exposure, Two-dimensional Monte Carlo simulation, uncertainty-to-variability, risk assessment