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Discussion papers
https://doi.org/10.5194/acp-2019-158
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-158
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Mar 2019

Research article | 05 Mar 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data

Olli-Pekka Tikkanen1, Väinö Hämäläinen1, Grazia Rovelli2, Antti Lipponen3, Manabu Shiraiwa4, Jonathan P. Reid2, Kari E. J. Lehtinen1,3, and Taina Yli-Juuti1 Olli-Pekka Tikkanen et al.
  • 1Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
  • 2School of Chemistry, University of Bristol, Bristol, BS8 1TS, UK
  • 3Atmospheric Research Centre of Eastern Finland, Finnish Meteorological Institute, Kuopio, Finland
  • 4Department of Chemistry, University of California, Irvine, CA, USA

Abstract. The composition of organic aerosol under different ambient conditions as well as their phase state have been a subject of intense study in the recent years. One way to study the particle properties is to measure the particle size shrinkage in a diluted environment at isothermal conditions. From these measurements it is possible to separate the fraction of low volatility compounds from high volatility compounds. In this work, we analyze and evaluate a method for obtaining particle composition and viscosity from measurements using process models coupled with input optimization algorithms. Two optimization methods, Monte Carlo Genetic Algorithm and Bayesian inference, are used together with process models describing the dynamics of particle evaporation. The process model optimization scheme in inferring particle composition in a volatility-basis-set sense and composition dependent particle viscosity is tested with artificially generated data sets and real experimental data. Optimizing model input so that the output matches these data yields a good match for the estimated quantities. Both optimization methods give equally good results when they are used to estimate particle composition to artificial test data. The time scale of the experiments and the initial particle size are found to be important in defining the range of values that can be identified for the properties from the optimization.

Olli-Pekka Tikkanen et al.
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Olli-Pekka Tikkanen et al.
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Short summary
We assessed how well the organic aerosol particle composition and viscosity can be captured by optimizing process models to match particle evaporation data. We performed the analysis to both artificial and real evaporation data and tested two optimization algorithms. Our findings show that the optimization method yields good estimate for the studied properties. The timescale of the evaporation data and particle size were found to be important in identifying the volatilities of organic compounds.
We assessed how well the organic aerosol particle composition and viscosity can be captured by...
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