Articles | Volume 15, issue 18
https://doi.org/10.5194/gmd-15-7257-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-7257-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
29 Sep 2022
Model description paper |
| 29 Sep 2022
| 29 Sep 2022
Atmospherically Relevant Chemistry and Aerosol box model – ARCA box (version 1.2)
Institute for Atmospheric and Earth Systems Research/Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Carlton Xavier
Institute for Atmospheric and Earth Systems Research/Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Lukas Pichelstorfer
Department of Chemistry and Physics of Materials, University of Salzburg, 5020, Salzburg, Austria
Putian Zhou
Institute for Atmospheric and Earth Systems Research/Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
Tinja Olenius
Research Department, Unit of Meteorology/Environment and Climate,
Swedish Meteorological and Hydrological Institute (SMHI), 601 76 Norrköping, Sweden
Pontus Roldin
Division of Nuclear Physics, Department of Physics, Lund University, P.O. Box 118, 221 00 Lund, Sweden
Michael Boy
Institute for Atmospheric and Earth Systems Research/Physics, University of Helsinki, P.O. Box 64, 00014 Helsinki, Finland
LUT School of Engineering Science, Lappeenranta-Lahti University of Technology, P.O. Box 20, 53851 Lappeenranta, Finland
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Total article views: 3,816 (including HTML, PDF, and XML)
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Total article views: 1,517 (including HTML, PDF, and XML)
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Cited
15 citations as recorded by crossref.
- Molecular Composition of Anthropogenic Oxygenated Organic Molecules and Their Contribution to Organic Aerosol in a Coastal City C. Yang et al. https://doi.org/10.1021/acs.est.3c03244
- Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019 Y. Tang et al. https://doi.org/10.1016/j.scitotenv.2024.172591
- Oxygenated Organic Molecules over the Boundary Layer Aloft in Beijing N. Yao et al. https://doi.org/10.1021/acs.est.5c01020
- A theory-informed, experiment-based constraint on the rate of autoxidation chemistry – an analytical approach L. Pichelstorfer et al. https://doi.org/10.5194/ar-3-417-2025
- Modelling the impact of anthropogenic aerosols on CCN concentrations over a rural boreal forest environment P. Clusius et al. https://doi.org/10.5194/acp-26-1967-2026
- An overlooked oxidation mechanism of toluene: computational predictions and experimental validations Z. Fu et al. https://doi.org/10.1039/D3SC03638C
- Box Model Applications for Secondary Inorganic Aerosol: A Review Focused on Nitrate Formation S. Park https://doi.org/10.5572/KOSAE.2024.40.1.1
- Advanced modeling of gas chemistry and aerosol dynamics with SSH-aerosol v2.0 K. Sartelet et al. https://doi.org/10.5194/gmd-19-389-2026
- A cross-correlation-based method for determining size-resolved particle growth rates J. Lampilahti et al. https://doi.org/10.5194/ar-3-637-2025
- The role of local shipping emissions in aerosol-cloud interactions in the central Arctic B. Heutte et al. https://doi.org/10.1088/1748-9326/ae6673
- A Lagrangian view on severe haze in Beijing: local and long-range sources of trace gases and primary and secondary aerosols B. Foreback et al. https://doi.org/10.1016/j.atmosenv.2025.121602
- Characterization of volatile organic compounds and submicron organic aerosol in a traffic environment S. Saarikoski et al. https://doi.org/10.5194/acp-23-2963-2023
- Traffic-Emitted Amines Promote New Particle Formation at Roadsides J. Brean et al. https://doi.org/10.1021/acsestair.5c00119
- Current and future machine learning approaches for modeling atmospheric cluster formation J. Kubečka et al. https://doi.org/10.1038/s43588-023-00435-0
- Neural network emulator for atmospheric chemical ODE Z. Liu et al. https://doi.org/10.1016/j.neunet.2024.107106
15 citations as recorded by crossref.
- Molecular Composition of Anthropogenic Oxygenated Organic Molecules and Their Contribution to Organic Aerosol in a Coastal City C. Yang et al. https://doi.org/10.1021/acs.est.3c03244
- Diurnal emission variation of ozone precursors: Impacts on ozone formation during Sep. 2019 Y. Tang et al. https://doi.org/10.1016/j.scitotenv.2024.172591
- Oxygenated Organic Molecules over the Boundary Layer Aloft in Beijing N. Yao et al. https://doi.org/10.1021/acs.est.5c01020
- A theory-informed, experiment-based constraint on the rate of autoxidation chemistry – an analytical approach L. Pichelstorfer et al. https://doi.org/10.5194/ar-3-417-2025
- Modelling the impact of anthropogenic aerosols on CCN concentrations over a rural boreal forest environment P. Clusius et al. https://doi.org/10.5194/acp-26-1967-2026
- An overlooked oxidation mechanism of toluene: computational predictions and experimental validations Z. Fu et al. https://doi.org/10.1039/D3SC03638C
- Box Model Applications for Secondary Inorganic Aerosol: A Review Focused on Nitrate Formation S. Park https://doi.org/10.5572/KOSAE.2024.40.1.1
- Advanced modeling of gas chemistry and aerosol dynamics with SSH-aerosol v2.0 K. Sartelet et al. https://doi.org/10.5194/gmd-19-389-2026
- A cross-correlation-based method for determining size-resolved particle growth rates J. Lampilahti et al. https://doi.org/10.5194/ar-3-637-2025
- The role of local shipping emissions in aerosol-cloud interactions in the central Arctic B. Heutte et al. https://doi.org/10.1088/1748-9326/ae6673
- A Lagrangian view on severe haze in Beijing: local and long-range sources of trace gases and primary and secondary aerosols B. Foreback et al. https://doi.org/10.1016/j.atmosenv.2025.121602
- Characterization of volatile organic compounds and submicron organic aerosol in a traffic environment S. Saarikoski et al. https://doi.org/10.5194/acp-23-2963-2023
- Traffic-Emitted Amines Promote New Particle Formation at Roadsides J. Brean et al. https://doi.org/10.1021/acsestair.5c00119
- Current and future machine learning approaches for modeling atmospheric cluster formation J. Kubečka et al. https://doi.org/10.1038/s43588-023-00435-0
- Neural network emulator for atmospheric chemical ODE Z. Liu et al. https://doi.org/10.1016/j.neunet.2024.107106
Saved (final revised paper)
Latest update: 30 May 2026
Short summary
Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and solving problems regarding air quality or climate requires detailed modelling and coupling of the processes. The models involved are often very complex to use. We have addressed this problem with the new ARCA box model. It puts much of the current knowledge of the nano- and microscale aerosol dynamics and chemistry into usable software and has the potential to become a valuable tool in the community.
Atmospheric chemistry and aerosol processes form a dynamic and sensitively balanced system, and...
