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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  04 May 2020

04 May 2020

Review status
A revised version of this preprint is currently under review for the journal ACP.

Identification of molecular cluster evaporation rates, cluster formation enthalpies and entropies by Monte Carlo method

Anna Shcherbacheva1, Tracey Balehowsky2, Jakub Kubečka1, Tinja Olenius3, Tapio Helin4, Heikki Haario4,5, Marko Laine5, Theo Kurtén6,1, and Hanna Vehkamäki1 Anna Shcherbacheva et al.
  • 1Institute for Atmospheric and Earth System Research, P.O. Box 64, 00014 University of Helsinki, Finland
  • 2Department of Mathematics and Statistics Subunit, P.O. Box 64, 00014 University of Helsinki, Finland
  • 3Department of Environmental Science and Analytical Chemistry & Bolin Centre for Climate Research, Stockholm University, Svante Arrhenius väg 8, 11418 Stockholm, Sweden
  • 4LUT School of Engineering Science, Lappeenranta-Lahti University of Technology, P.O.Box 20, 53851 Lappeenranta, Finland
  • 5Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland Finland
  • 6Department of Chemistry, P.O. Box 55, 00014 University of Helsinki, Finland

Abstract. We address the problem of identifying the evaporation rates for neutral molecular clusters from synthetic (computer-simulated) cluster concentrations. We applied Bayesian parameter estimation using a Markov chain Monte Carlo (MCMC)algorithm to determine cluster evaporation/fragmentation rates from known cluster distributions, assuming that the clustercollision rates are known. We used the Atmospheric Cluster Dynamic Code (ACDC) with evaporation rates based on quantumchemical calculations to generate cluster distributions for a set of electrically neutral sulphuric acid and ammonia clusters. We then treated these concentrations as synthetic experimental data, and tested two approaches for estimating the evaporation rates. First we have studied a scenario where at one single temperature time-dependent cluster distributions are measured before thesystem reaches a time-independent steady-state. In the second scenario only steady-state cluster distributions are measured, butat several temperatures. This allowed us to use multiple sets of concentrations at different temperatures. Additionally, in thelatter case the evaporation rates were represented in terms of cluster formation enthalpies and entropies which were considered to be free parameters. This reparametrization reduced the number of unknown parameters, since several evaporation ratesdepend on the same cluster formation enthalpy and entropy values.

We show that in the second setting, even if only two temperatures were used, the temperature-dependent steady-state dataoutperforms the first setting for parameter identification. We can thus conclude that for experimentally determining evaporationrates, cluster distribution measurements at several temperatures are recommended over time-dependent measurements at one temperature.

Anna Shcherbacheva et al.

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Status: final response (author comments only)
Status: final response (author comments only)
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Anna Shcherbacheva et al.

Model code and software

AnnaShcher/Shcherbacheva_ACDP: Release 1 (Version v1.0). Zenodo A. Shcherbacheva

Anna Shcherbacheva et al.


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Latest update: 26 Sep 2020
Publications Copernicus
Short summary
Atmospheric new particle formation and cluster growth to aerosol particles is an important field of research, in particular due to climate change phenomenon. Evaporation rates are very difficult to account for but they are important to explain the formation and growth of particles. Different Quantum Chemistry (QC) methods produce substantially different values for the evaporation rates. We propose a novel approach for inferring evaporation rates of clusters from available measurements.
Atmospheric new particle formation and cluster growth to aerosol particles is an important field...