The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Kinetic model framework for aerosol and cloud surface chemistry and gas-particle interactions – Part 1: General equations, parameters, and terminology
Open accessAbstract
Abstract. Aerosols and clouds play central roles in atmospheric chemistry and physics, climate, air pollution, and public health. The mechanistic understanding and predictability of aerosol and cloud properties, interactions, transformations, and effects are, however, still very limited. This is due not only to the limited availability of measurement data, but also to the limited applicability and compatibility of model formalisms used for the analysis, interpretation, and description of heterogeneous and multiphase processes. To support the investigation and elucidation of atmospheric aerosol and cloud surface chemistry and gas-particle interactions, we present a comprehensive kinetic model framework with consistent and unambiguous terminology and universally applicable rate equations and parameters. It enables a detailed description of mass transport and chemical reactions at the gas-particle interface, and it allows linking aerosol and cloud surface processes with gas phase and particle bulk processes in systems with multiple chemical components and competing physicochemical processes. The key elements and essential aspects of the presented framework are: a simple and descriptive double-layer surface model (sorption layer and quasi-static layer); straightforward flux-based mass balance and rate equations; clear separation of mass transport and chemical reactions; well-defined and consistent rate parameters (uptake and accommodation coefficients, reaction and transport rate coefficients); clear distinction between gas phase, gas-surface, and surface-bulk transport (gas phase diffusion, surface and bulk accommodation); clear distinction between gas-surface, surface layer, and surface-bulk reactions (Langmuir-Hinshelwood and Eley-Rideal mechanisms); mechanistic description of concentration and time dependences (transient and steady-state conditions); flexible addition of unlimited numbers of chemical species and physicochemical processes; optional aggregation or resolution of intermediate species, sequential processes, and surface layers; and full compatibility with traditional resistor model formulations. The outlined double-layer surface concept and formalisms represent a minimum of model complexity required for a consistent description of the non-linear concentration and time dependences observed in experimental studies of atmospheric multiphase processes (competitive co-adsorption and surface saturation effects, etc.). Exemplary practical applications and model calculations illustrating the relevance of the above aspects are presented in a companion paper (Ammann and Pöschl, 2007). We expect that the presented model framework will serve as a useful tool and basis for experimental and theoretical studies investigating and describing atmospheric aerosol and cloud surface chemistry and gas-particle interactions. It shall help to end the "Babylonian confusion" that seems to inhibit scientific progress in the understanding of heterogeneous chemical reactions and other multiphase processes in aerosols and clouds. In particular, it shall support the planning and design of laboratory experiments for the elucidation and determination of fundamental kinetic parameters; the establishment, evaluation, and quality assurance of comprehensive and self-consistent collections of rate parameters; and the development of detailed master mechanisms for process models and derivation of simplified but yet realistic parameterizations for atmospheric and climate models.
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Cites
Aerosols, Climate, and the Hydrological Cycle
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., & Rosenfeld, D. (2001) · Science · doi:10.1126/science.1064034 · Cited by 3,831
The effect of air pollution on lung development from 10 to 18 years of age
Gauderman, W. J., Avol, E., Gilliland, F., Vora, H., Thomas, D., Berhane, K., McConnell, R., Kuenzli, N., Lurmann, F., Rappaport, E., et al. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040610 · Cited by 1,440
Al-Hosney, H. A., & Grassian, V. H. (2005) · Physical Chemistry Chemical Physics · doi:10.1039/b417872f · Cited by 236
Preexponential factors in surface reactions
BAETZOLD, R. (1976) · Journal of Catalysis · doi:10.1016/0021-9517(76)90059-2 · Cited by 140
Uptake of HNO3 on water‐ice and coadsorption of HNO3 and HCl in the temperature range 210–235 K
Hynes, R. G., Fernandez, M. A., & Cox, R. A. (2002) · Journal of Geophysical Research: Atmospheres · doi:10.1029/2001jd001557 · Cited by 61
Cited by
Sadezky, A., Muckenhuber, H., Grothe, H., Niessner, R., & Pöschl, U. (2005) · Carbon · doi:10.1016/j.carbon.2005.02.018 · Cited by 4,850
Self-citationAtmospheric composition change – global and regional air quality
Monks, P. S., Granier, C., Fuzzi, S., Stohl, A., Williams, M. L., Akimoto, H., Amann, M., Baklanov, A., Baltensperger, U., Bey, I., et al. (2009) · Atmospheric Environment · doi:10.1016/j.atmosenv.2009.08.021 · Cited by 872
Self-citationPöschl, U., & Shiraiwa, M. (2015) · Chemical Reviews · doi:10.1021/cr500487s · Cited by 657
Self-citationNighttime radical observations and chemistry
Brown, S. S., & Stutz, J. (2012) · Chemical Society Reviews · doi:10.1039/c2cs35181a · Cited by 637
An overview of current issues in the uptake of atmospheric trace gases by aerosols and clouds
Kolb, C. E., Cox, R. A., Abbatt, J. P. D., Ammann, M., Davis, E. J., Donaldson, D. J., Garrett, B. C., George, C., Griffiths, P. T., Hanson, D. R., et al. (2010) · Atmospheric Chemistry and Physics · doi:10.5194/acp-10-10561-2010 · Cited by 405
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Metadata
- Authors
- Pöschl, U., Rudich, Y., & Ammann, M.
- Venue
- Atmospheric Chemistry and Physics
- Publisher
- Copernicus GmbH
- Published
- 2007-12-10
- Volume
- 7
- Issue
- 23
- Pages
- 5989-6023
- ISSN
- 1680-7316 (ISSN-L)
- 1680-7324
- OpenAlex
- W2075199607
- Cited by
- 321
- References
- 5
- FWCI
- 11.11
- Type
- journal article
- Language
- en
- Open access
- gold
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- Pöschl, U., Rudich, Y., & Ammann, M. (2007). Kinetic model framework for aerosol and cloud surface chemistry and gas-particle interactions – Part 1: General equations, parameters, and terminology. Atmospheric Chemistry and Physics. https://doi.org/10.5194/acp-7-5989-2007
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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