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An Improved Volatility Basis Set for Modeling Organic Aerosol in Both CAMx and CMAQ

  • Bonyoung KooEmail author
  • Eladio Knipping
  • Greg Yarwood
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Atmospheric organic aerosol (OA) is highly complex and detailed mechanistic descriptions include hundreds or thousands of compounds and are impractical for use in photochemical grid models (PGMs). Therefore, PGMs adopt simplified OA modules where organic compounds with similar properties and/or origin are lumped together. The first generation volatility basis set (VBS) module grouped OA compounds by volatility and provided a unified framework for gas-aerosol partitioning of both primary and secondary OA and their chemical aging. However, a VBS approach with one dimension of variation (volatility) is unable to describe observed variations in OA oxidation state (i.e., O:C ratio) at a fixed volatility level. A two-dimensional VBS approach was introduced that tracks degree of oxidation in addition to volatility but further study is needed to fully parameterize 2-D VBS modules.

We developed a new OA module based on the VBS approach and implemented it in two widely-used PGMs. Our scheme uses four basis sets to describe oxidation state: two basis sets for oxygenated OA (anthropogenic and biogenic) and two for freshly emitted OA (from anthropogenic sources and biomass burning). Each basis set has five volatility bins including a zero-volatility bin for essentially non-volatile compounds. The scheme adjusts both carbon number and oxidation state in response to chemical aging by simplifying the 2-D VBS scheme. The new OA module is implemented in both the CAMx and CMAQ PGMs and evaluated for summer and winter 2005 episodes over the eastern US.

Keywords

Emission Inventory Secondary Organic Aerosol Fractional Error Fractional Bias Smog Chamber 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was funded by the Electric Power Research Institute. We appreciate valuable discussion with Drs. Spyros Pandis, Neil Donahue and Allen Robinson at Carnegie Mellon University and Dr. Heather Simon at US Environmental Protection Agency.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.ENVIRON International CorporationNovatoUSA
  2. 2.Electric Power Research Institute (EPRI)Washington, DCUSA

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