Articles | Volume 15, issue 24
https://doi.org/10.5194/gmd-15-8957-2022
https://doi.org/10.5194/gmd-15-8957-2022
Model description paper
 | 
14 Dec 2022
Model description paper |  | 14 Dec 2022

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

Zhizhao Wang, Florian Couvidat, and Karine Sartelet

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Cited articles

Aumont, B., Szopa, S., and Madronich, S.: Modelling the evolution of organic carbon during its gas-phase tropospheric oxidation: development of an explicit model based on a self generating approach, Atmos. Chem. Phys., 5, 2497–2517, https://doi.org/10.5194/acp-5-2497-2005, 2005. a
Breysse, P. N., Delfino, R. J., Dominici, F., Elder, A. C. P., Frampton, M. W., Froines, J. R., Geyh, A. S., Godleski, J. J., Gold, D. R., Hopke, P. K., Koutrakis, P., Li, N., Oberdörster, G., Pinkerton, K. E., Samet, J. M., Utell, M. J., and Wexler, A. S.: US EPA particulate matter research centers: summary of research results for 2005–2011, Air Qual. Atmos. Health, 6, 333–355, https://doi.org/10.1007/s11869-012-0181-8, 2013. a
Carter, W. P.: Development of the SAPRC-07 chemical mechanism, Atmos. Environ., 44, 5324–5335, https://doi.org/10.1016/j.atmosenv.2010.01.026, 2010. a
Chen, Q., Li, Y. L., McKinney, K. A., Kuwata, M., and Martin, S. T.: Particle mass yield from β-caryophyllene ozonolysis, Atmos. Chem. Phys., 12, 3165–3179, https://doi.org/10.5194/acp-12-3165-2012, 2012. a, b, c, d
Compernolle, S., Ceulemans, K., and Müller, J.-F.: EVAPORATION: a new vapour pressure estimation methodfor organic molecules including non-additivity and intramolecular interactions, Atmos. Chem. Phys., 11, 9431–9450, https://doi.org/10.5194/acp-11-9431-2011, 2011. a
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Short summary
Air quality models need to reliably predict secondary organic aerosols (SOAs) at a reasonable computational cost. Thus, we developed GENOA v1.0, a mechanism reduction algorithm that preserves the accuracy of detailed gas-phase chemical mechanisms for SOA formation, thereby improving the practical use of actual chemistry in SOA models. With GENOA, a near-explicit chemical scheme was reduced to 2 % of its original size and computational time, with an average error of less than 3 %.