Articles | Volume 17, issue 12
https://doi.org/10.5194/acp-17-8021-2017
https://doi.org/10.5194/acp-17-8021-2017
Technical note
 | 
30 Jun 2017
Technical note |  | 30 Jun 2017

Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

Thomas Berkemeier, Markus Ammann, Ulrich K. Krieger, Thomas Peter, Peter Spichtinger, Ulrich Pöschl, Manabu Shiraiwa, and Andrew J. Huisman

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Cited articles

Abbatt, J. P. D., Lee, A. K. Y., and Thornton, J. A.: Quantifying trace gas uptake to tropospheric aerosol: recent advances and remaining challenges, Chem. Soc. Rev., 41, 6555–6581, 2012.
Ammann, M. and Pöschl, U.: Kinetic model framework for aerosol and cloud surface chemistry and gas-particle interactions – Part 2: Exemplary practical applications and numerical simulations, Atmos. Chem. Phys., 7, 6025–6045, https://doi.org/10.5194/acp-7-6025-2007, 2007.
Arangio, A. M., Slade, J. H., Berkemeier, T., Pöschl, U., Knopf, D. A., and Shiraiwa, M.: Multiphase Chemical Kinetics of OH Radical Uptake by Molecular Organic Markers of Biomass Burning Aerosols: Humidity and Temperature Dependence, Surface Reaction, and Bulk Diffusion, J. Phys. Chem. A, 119, 4533–4544, https://doi.org/10.1021/jp510489z, 2015.
Arora, J. S., Elwakeil, O. A., Chahande, A. I., and Hsieh, C. C.: Global optimization methods for engineering applications: A review, Struct. Optimization, 9, 137–159, https://doi.org/10.1007/bf01743964, 1995.
Berkemeier, T., Huisman, A. J., Ammann, M., Shiraiwa, M., Koop, T., and Pöschl, U.: Kinetic regimes and limiting cases of gas uptake and heterogeneous reactions in atmospheric aerosols and clouds: a general classification scheme, Atmos. Chem. Phys., 13, 6663–6686, https://doi.org/10.5194/acp-13-6663-2013, 2013.
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Short summary
Kinetic process models are efficient tools used to unravel the mechanisms governing chemical and physical transformation in multiphase atmospheric chemistry. However, determination of kinetic parameters such as reaction rate or diffusion coefficients from multiple data sets is often difficult or ambiguous. This study presents a novel optimization algorithm and framework to determine these parameters in an automated fashion and to gain information about parameter uncertainty and uniqueness.
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