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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/gmd-2020-274
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-274
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: development and technical paper 04 Sep 2020

Submitted as: development and technical paper | 04 Sep 2020

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This preprint is currently under review for the journal GMD.

Influence of biomass burning vapor wall loss correction on modeling organic aerosols in Europe by CAMx v6.50

Jianhui Jiang1, Imad El-Haddad1, Sebnem Aksoyoglu1, Giulia Stefenelli1, Amelie Bertrand1,2, Nicolas Marchand2, Francesco Canonaco1, Jean-Eudes Petit3, Olivier Favez3, Stefania Gilardoni4, Urs Baltensperger1, and André S. H. Prévôt1 Jianhui Jiang et al.
  • 1Laboratory of Atmospheric Chemistry, Paul Scherrer Institute, 5232 Villigen PSI, Switzerland
  • 2Aix Marseille Univ, CNRS, LCE, Marseille, France
  • 3Institut National de l'Environnement Industriel et des Risques (INERIS), Verneuil-en-Halatte, France
  • 4Italian National Research Council – Institute of Polar Sciences, Bologna, Italy

Abstract. Increasing evidence from experimental studies suggests that the losses of semi-volatile vapors to the chamber walls could be responsible for the underestimation of organic aerosol (OA) in air quality models which use parameters obtained from the chamber experiments. In this study, a box model with volatility basis set (VBS) scheme was developed and the secondary organic aerosol (SOA) yields with vapor wall loss corrected were optimized by a genetic algorithm based on advanced chamber experimental data for biomass burning. The vapor wall loss correction increases the SOA yields by a factor of 1.9–4.9, and leads to a better agreement with the measured OA for 14 chamber experiments under different temperatures and emission loads. To investigate the influence of vapor wall loss correction on regional OA simulations, the optimized parameterizations (SOA yields, emissions of intermediate-volatility organic compounds from biomass burning, and enthalpy of vaporization) were implemented in the regional air quality model CAMx (Comprehensive Air Quality Model with extensions). The modeled results from the standard and vapor wall loss corrected VBS schemes, as well as the traditional two-product approach were compared and evaluated by OA measurements from five Aerodyne aerosol chemical speciation monitor (ACSM)/aerosol mass spectrometer (AMS) stations in the winter of 2011. The vapor wall loss corrected VBS (VBS_WLS) generally shows the best performance for predicting OA among all OA schemes, and reduces the mean fractional bias from −72.9 % (with the standard VBS (VBS_BASE)) to −1.6 % for the winter OA. In Europe, the VBS_WLS produces the highest domain average OA in winter (2.3 µg m−3), which is 106.6 % and 26.2 % higher than the standard VBS and the reference scenario (VBS_noWLS, same parameterization as VBS_WLS, except with the default yields without vapor wall loss correction), respectively. Compared to VBS_noWLS, VBS_WLS leads to an increase in SOA by up to ~ 80 % in Romania. VBS_WLS also leads to a better agreement between the modeled SOA fraction in OA (fSOA) and the estimated measured values in the literature. The substantial influence of vapor wall loss correction on modeled OA in Europe highlights the importance of further improvements in the parameterizations based on laboratory studies with a wider range of chamber conditions and field observations with higher spatial and temporal coverage.

Jianhui Jiang et al.

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Short summary
This study developed a box model with volatility basis set to simulate organic aerosol (OA) from biomass burning, and optimized the vapor wall loss corrected OA yields by genetic algorithm. The optimized parameterizations were then implemented in the air quality model CAMx v6.5. The comparisons with ambient measurements indicate the vapor wall loss corrected parameterization effectively improve the model performance on predicting OA, which reduced the mean fractional bias from -72.9% to -1.6%.
This study developed a box model with volatility basis set to simulate organic aerosol (OA) from...
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