Preprints
https://doi.org/10.5194/egusphere-2022-245
https://doi.org/10.5194/egusphere-2022-245
 
10 Jun 2022
10 Jun 2022
Status: this preprint is open for discussion.

GENerator of reduced Organic Aerosol mechanism (GENOA v1.0): An automatic generation tool of semi-explicit mechanisms

Zhizhao Wang1,2, Florian Couvidat2, and Karine Sartelet1 Zhizhao Wang et al.
  • 1CEREA, École des Ponts ParisTech, EDF R&D, 77 455 Marne-la-Vallée, France
  • 2INERIS, Institut National de l’Environnement Industriel et des Risques, Verneuil-en-Halatte, France

Abstract. This paper describes the GENerator of Reduced Organic Aerosol Mechanisms (GENOA) that produces semi-explicit mechanisms for simulating the formation and evolution of secondary organic aerosol (SOA) in air-quality models. Using a series of predefined reduction strategies and evaluation criteria, GENOA trains and reduces SOA mechanisms from explicit chemical mechanisms (e.g., the master chemical mechanism (MCM)) under representative atmospheric conditions. As a consequence, these trained SOA mechanisms can preserve the accuracy of explicit VOC mechanisms on SOA formation (e.g., molecular structures of crucial compounds, the effect of non-ideality and hydrophilic/hydrophobic partitioning of aerosols), with a size (in terms of reaction and species numbers) that is manageable for three-dimensional aerosol modeling (e.g., regional chemical transport models). Applied to the degradation of a sesquiterpene (β-caryophyllene) from MCM, GENOA builds a concise SOA mechanism (2 % of the MCM size), consisting of 23 reactions and 15 species, six of them being condensable. The generated SOA mechanism has been evaluated for its ability to reproduce SOA concentrations under varying atmospheric conditions encountered over Europe, with an average error lower than 3 %.

Zhizhao Wang et al.

Status: open (until 05 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-245', William Carter, 27 Jun 2022 reply

Zhizhao Wang et al.

Data sets

Dataset used to run the BCARY MCM reduction Wang, Zhizhao; Couvidat, Florian; Sartelet, Karine https://doi.org/10.5281/zenodo.6483088

Model code and software

GENOA v1.0 source code Wang, Zhizhao; Couvidat, Florian; Sartelet, Karine https://doi.org/10.5281/zenodo.6482978

Zhizhao Wang et al.

Viewed

Total article views: 188 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
134 48 6 188 18 2 3
  • HTML: 134
  • PDF: 48
  • XML: 6
  • Total: 188
  • Supplement: 18
  • BibTeX: 2
  • EndNote: 3
Views and downloads (calculated since 10 Jun 2022)
Cumulative views and downloads (calculated since 10 Jun 2022)

Viewed (geographical distribution)

Total article views: 154 (including HTML, PDF, and XML) Thereof 154 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Discussed

Latest update: 02 Jul 2022
Download
Short summary
3D air-quality models need to represent the complexity of SOA formation with a manageable computational cost. Therefore, we developed GENOA v1.0, an algorithm to generate semi-explicit SOA mechanisms preserving the accuracy of the explicit VOC chemical mechanisms on SOA formation. When applying to the MCM degradation scheme of beta-caryophyllene, GENOA managed to reduce the mechanism from 1626 to 23 reactions and from 579 to 15 species with an average error lower than 3 %.