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Modeling gas/particle partitioning of polybrominated diphenyl ethers (PBDEs) in the atmosphere: A review

期刊

SCIENCE OF THE TOTAL ENVIRONMENT
卷 729, 期 -, 页码 -

出版社

ELSEVIER
DOI: 10.1016/j.scitotenv.2020.138962

关键词

Polybrominated diphenyl ethers; Gas/particle partition; Adsorption; Absorption; Steady state; Atmosphere; Model

资金

  1. National Natural Science Foundation of China [21577030, 41671470]
  2. Polar Academy, Harbin Institute of Technology [PA-HIT-201901]
  3. State Key Laboratory of Urban Water Resource and Environment (Harbin Institute of Technology) [2019DX04]

向作者/读者索取更多资源

Gas/particle (G/P) partitioning of semi-volatile organic compounds (SVOCs) such as polybrominated diphenyl ethers (PBDEs), is an important atmospheric process due to its significance in governing atmospheric fate, wet/dry deposition, and long-range atmospheric transport. In this article, eight models published to predict the G/P partitioning of PBDEs are reviewed. These eight models are used to calculate the G/P partitioning quotient and particulate phase fraction of selected PBDE congeners. A comparison of the predicted results from the eight models with monitoring data published by several research groups worldwide leads to the following conclusions: 1) when the values of the logarithm of the octanol-air partition coefficient (logK(OA)) fall below 11.4 (the first threshold value, logK(OA1)), all 8 models perform well in predicting the G/P partitioning of PBDEs in the atmosphere, and 2) when logKOA is >11.4, and especially above 12.5 (the second threshold value, logK(OA2)), the Li-Ma-Yang model, a steady-state model developed based on wet and dry deposition of the particles (Li et al., Atmos. Chem. Phys. 2015; 15:1669-1681), shows the best performance with highest conformity to the measurements for selected PBDEs (94.4 +/- 1.6% data points within +/- 1 log unit). Overall, the Li-Ma-Yang model appears to capture the most important factors that affect the partitioning of PBDEs between gaseous and particular phases in the atmosphere. (C) 2020 Elsevier B.V. All rights reserved.

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