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

Related authors

Advanced modeling of gas chemistry and aerosol dynamics with SSH-aerosol v2.0
Karine Sartelet, Zhizhao Wang, Youngseob Kim, Victor Lannuque, and Florian Couvidat
EGUsphere, https://doi.org/10.5194/egusphere-2025-2191,https://doi.org/10.5194/egusphere-2025-2191, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
The SAPRC Atmospheric Chemical Mechanism Generation System (MechGen)
William P. L. Carter, Jia Jiang, Zhizhao Wang, and Kelley C. Barsanti
EGUsphere, https://doi.org/10.5194/egusphere-2025-1183,https://doi.org/10.5194/egusphere-2025-1183, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Isomer Molecular Structures and Formation Pathways of Oxygenated Organic Molecules in Newly Formed Biogenic Particles
Vignesh Vasudevan-Geetha, Lee Tiszenkel, Zhizhao Wang, Robin Russo, Daniel Bryant, Julia Lee-Taylor, Kelley Barsanti, and Shan-Hu Lee
EGUsphere, https://doi.org/10.5194/egusphere-2024-2454,https://doi.org/10.5194/egusphere-2024-2454, 2024
Short summary
Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD
Chao Lin, Yunyi Wang, Ryozo Ooka, Cédric Flageul, Youngseob Kim, Hideki Kikumoto, Zhizhao Wang, and Karine Sartelet
Atmos. Chem. Phys., 23, 1421–1436, https://doi.org/10.5194/acp-23-1421-2023,https://doi.org/10.5194/acp-23-1421-2023, 2023
Short summary

Related subject area

Atmospheric sciences
Integrated Methane Inversion (IMI) 2.0: an improved research and stakeholder tool for monitoring total methane emissions with high resolution worldwide using TROPOMI satellite observations
Lucas A. Estrada, Daniel J. Varon, Melissa Sulprizio, Hannah Nesser, Zichong Chen, Nicholas Balasus, Sarah E. Hancock, Megan He, James D. East, Todd A. Mooring, Alexander Oort Alonso, Joannes D. Maasakkers, Ilse Aben, Sabour Baray, Kevin W. Bowman, John R. Worden, Felipe J. Cardoso-Saldaña, Emily Reidy, and Daniel J. Jacob
Geosci. Model Dev., 18, 3311–3330, https://doi.org/10.5194/gmd-18-3311-2025,https://doi.org/10.5194/gmd-18-3311-2025, 2025
Short summary
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Cynthia H. Whaley, Tim Butler, Jose A. Adame, Rupal Ambulkar, Steve R. Arnold, Rebecca R. Buchholz, Benjamin Gaubert, Douglas S. Hamilton, Min Huang, Hayley Hung, Johannes W. Kaiser, Jacek W. Kaminski, Christoph Knote, Gerbrand Koren, Jean-Luc Kouassi, Meiyun Lin, Tianjia Liu, Jianmin Ma, Kasemsan Manomaiphiboon, Elisa Bergas Masso, Jessica L. McCarty, Mariano Mertens, Mark Parrington, Helene Peiro, Pallavi Saxena, Saurabh Sonwani, Vanisa Surapipith, Damaris Y. T. Tan, Wenfu Tang, Veerachai Tanpipat, Kostas Tsigaridis, Christine Wiedinmyer, Oliver Wild, Yuanyu Xie, and Paquita Zuidema
Geosci. Model Dev., 18, 3265–3309, https://doi.org/10.5194/gmd-18-3265-2025,https://doi.org/10.5194/gmd-18-3265-2025, 2025
Short summary
Using a data-driven statistical model to better evaluate surface turbulent heat fluxes in weather and climate numerical models: a demonstration study
Maurin Zouzoua, Sophie Bastin, Fabienne Lohou, Marie Lothon, Marjolaine Chiriaco, Mathilde Jome, Cécile Mallet, Laurent Barthes, and Guylaine Canut
Geosci. Model Dev., 18, 3211–3239, https://doi.org/10.5194/gmd-18-3211-2025,https://doi.org/10.5194/gmd-18-3211-2025, 2025
Short summary
Pochva: a new hydro-thermal process model in soil, snow, and vegetation for application in atmosphere numerical models
Oxana Drofa
Geosci. Model Dev., 18, 3175–3209, https://doi.org/10.5194/gmd-18-3175-2025,https://doi.org/10.5194/gmd-18-3175-2025, 2025
Short summary
ClimKern v1.2: a new Python package and kernel repository for calculating radiative feedbacks
Tyler P. Janoski, Ivan Mitevski, Ryan J. Kramer, Michael Previdi, and Lorenzo M. Polvani
Geosci. Model Dev., 18, 3065–3079, https://doi.org/10.5194/gmd-18-3065-2025,https://doi.org/10.5194/gmd-18-3065-2025, 2025
Short summary

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
Download
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 %.
Share