Articles | Volume 14, issue 2
https://doi.org/10.5194/gmd-14-675-2021
https://doi.org/10.5194/gmd-14-675-2021
Model description paper
 | 
02 Feb 2021
Model description paper |  | 02 Feb 2021

PyCHAM (v2.1.1): a Python box model for simulating aerosol chambers

Simon Patrick O'Meara, Shuxuan Xu, David Topping, M. Rami Alfarra, Gerard Capes, Douglas Lowe, Yunqi Shao, and Gordon McFiggans

Related authors

Determination of the atmospheric volatility of pesticides using Filter Inlet for Gases and AEROsols–chemical ionisation mass spectrometry
Olivia M. Jackson, Aristeidis Voliotis, Thomas J. Bannan, Simon P. O'Meara, Gordon McFiggans, Dave Johnson, and Hugh Coe
Atmos. Chem. Phys., 25, 6257–6272, https://doi.org/10.5194/acp-25-6257-2025,https://doi.org/10.5194/acp-25-6257-2025, 2025
Short summary
Theory informed, experiment based, constraint on the rate of autoxidation chemistry – An analytical approach
Lukas Pichelstorfer, Simon P. O'Meara, and Gordon B. McFiggans
Aerosol Research Discuss., https://doi.org/10.5194/ar-2024-40,https://doi.org/10.5194/ar-2024-40, 2024
Revised manuscript accepted for AR
Short summary
Impact of HO2∕RO2 ratio on highly oxygenated α-pinene photooxidation products and secondary organic aerosol formation potential
Yarê Baker, Sungah Kang, Hui Wang, Rongrong Wu, Jian Xu, Annika Zanders, Quanfu He, Thorsten Hohaus, Till Ziehm, Veronica Geretti, Thomas J. Bannan, Simon P. O'Meara, Aristeidis Voliotis, Mattias Hallquist, Gordon McFiggans, Sören R. Zorn, Andreas Wahner, and Thomas F. Mentel
Atmos. Chem. Phys., 24, 4789–4807, https://doi.org/10.5194/acp-24-4789-2024,https://doi.org/10.5194/acp-24-4789-2024, 2024
Short summary
Characterisation of the Manchester Aerosol Chamber facility
Yunqi Shao, Yu Wang, Mao Du, Aristeidis Voliotis, M. Rami Alfarra, Simon P. O'Meara, S. Fiona Turner, and Gordon McFiggans
Atmos. Meas. Tech., 15, 539–559, https://doi.org/10.5194/amt-15-539-2022,https://doi.org/10.5194/amt-15-539-2022, 2022
Short summary
Maxwell–Stefan diffusion: a framework for predicting condensed phase diffusion and phase separation in atmospheric aerosol
Kathryn Fowler, Paul J. Connolly, David O. Topping, and Simon O'Meara
Atmos. Chem. Phys., 18, 1629–1642, https://doi.org/10.5194/acp-18-1629-2018,https://doi.org/10.5194/acp-18-1629-2018, 2018
Short summary

Related subject area

Atmospheric sciences
The sensitivity of aerosol data assimilation to vertical profiles: case study of dust storm assimilation with LOTOS-EUROS v2.2
Mijie Pang, Jianbing Jin, Ting Yang, Xi Chen, Arjo Segers, Batjargal Buyantogtokh, Yixuan Gu, Jiandong Li, Hai Xiang Lin, Hong Liao, and Wei Han
Geosci. Model Dev., 18, 3781–3798, https://doi.org/10.5194/gmd-18-3781-2025,https://doi.org/10.5194/gmd-18-3781-2025, 2025
Short summary
Knowledge-inspired fusion strategies for the inference of PM2.5 values with a neural network
Matthieu Dabrowski, José Mennesson, Jérôme Riedi, Chaabane Djeraba, and Pierre Nabat
Geosci. Model Dev., 18, 3707–3733, https://doi.org/10.5194/gmd-18-3707-2025,https://doi.org/10.5194/gmd-18-3707-2025, 2025
Short summary
Tuning the ICON-A 2.6.4 climate model with machine-learning-based emulators and history matching
Pauline Bonnet, Lorenzo Pastori, Mierk Schwabe, Marco Giorgetta, Fernando Iglesias-Suarez, and Veronika Eyring
Geosci. Model Dev., 18, 3681–3706, https://doi.org/10.5194/gmd-18-3681-2025,https://doi.org/10.5194/gmd-18-3681-2025, 2025
Short summary
A novel method for quantifying the contribution of regional transport to PM2.5 in Beijing (2013–2020): combining machine learning with concentration-weighted trajectory analysis
Kang Hu, Hong Liao, Dantong Liu, Jianbing Jin, Lei Chen, Siyuan Li, Yangzhou Wu, Changhao Wu, Shitong Zhao, Xiaotong Jiang, Ping Tian, Kai Bi, Ye Wang, and Delong Zhao
Geosci. Model Dev., 18, 3623–3634, https://doi.org/10.5194/gmd-18-3623-2025,https://doi.org/10.5194/gmd-18-3623-2025, 2025
Short summary
Quantification of CO2 hotspot emissions from OCO-3 SAM CO2 satellite images using deep learning methods
Joffrey Dumont Le Brazidec, Pierre Vanderbecken, Alban Farchi, Grégoire Broquet, Gerrit Kuhlmann, and Marc Bocquet
Geosci. Model Dev., 18, 3607–3622, https://doi.org/10.5194/gmd-18-3607-2025,https://doi.org/10.5194/gmd-18-3607-2025, 2025
Short summary

Cited articles

Barley, M., Topping, D., and McFiggans, G.: Critical Assessment of Liquid Density Estimation Methods for Multifunctional Organic Compounds and Their Use in Atmospheric Science, J. Phys. Chem. A, 117, 3428–3441, https://doi.org/10.1021/jp304547r, 2013. a
Bertrand, A., Stefenelli, G., Pieber, S. M., Bruns, E. A., Temime-Roussel, B., Slowik, J. G., Wortham, H., Prévôt, A. S. H., El Haddad, I., and Marchand, N.: Influence of the vapor wall loss on the degradation rate constants in chamber experiments of levoglucosan and other biomass burning markers, Atmos. Chem. Phys., 18, 10915–10930, https://doi.org/10.5194/acp-18-10915-2018, 2018. a
Carslaw, N., Mota, T., Jenkin, M. E., Barley, M. H., and McFiggans, G.: A Significant Role for Nitrate and Peroxide Groups on Indoor Secondary Organic Aerosol, Environ. Sci. Technol., 46, 9290–9298, https://doi.org/10.1021/es301350x, 2012. a
Charan, S. M., Huang, Y., and Seinfeld, J. H.: Computational Simulation of Secondary Organic Aerosol Formation in Laboratory Chambers, Chem. Rev., 119, 11912–11944, https://doi.org/10.1021/acs.chemrev.9b00358, 2019. a, b, c, d, e
Chen, B. T., Yeh, H. C., and Cheng, Y. S.: Evaluation of an Environmental Reaction Chamber, Aerosol Sci. Tech., 17, 9–24, https://doi.org/10.1080/02786829208959556, 1992. a
Download
Short summary
User-friendly and open-source software for simulating aerosol chambers is a valuable tool for research scientists in designing and analysing their experiments. This paper describes a new version of such software and will therefore provide a useful reference for those applying it. Central to the paper is an assessment of the software's accuracy through comparison against previously published simulations.
Share