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

Abstract

The composition of organic aerosol under different ambient conditions as well as their phase state have been a subject of intense study in recent years. One way to study 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 analyse and evaluate a method for obtaining particle composition and viscosity from measurements using process models coupled with input optimization algorithms. Two optimization methods, the 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 artificially test data. Finally, the timescale of the experiments and the initial particle size are found to be important in defining the range of values that can be identifiedmore » for the properties from the optimization.« less

Authors:
ORCiD logo [1];  [1]; ORCiD logo [2]; ORCiD logo [3];  [4]; ORCiD logo [2];  [5];  [1]
  1. Univ. of Eastern Finland, Kuopio (Finland)
  2. Univ. of Bristol (United Kingdom)
  3. Finnish Meteorological Inst., Kuopio (Finland)
  4. Univ. of California, Irvine, CA (United States)
  5. Univ. of Eastern Finland, Kuopio (Finland); Finnish Meteorological Inst., Kuopio (Finland)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Science (SC); Academy of Finland; National Science Foundation (NSF)
OSTI Identifier:
1580894
Grant/Contract Number:  
AC02-05CH11231; 307331; 299544; NE/M004600/1; AGS-1654104
Resource Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 19; Journal Issue: 14; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY

Citation Formats

Tikkanen, Olli-Pekka, Hämäläinen, Väinö, Rovelli, Grazia, Lipponen, Antti, Shiraiwa, Manabu, Reid, Jonathan P., Lehtinen, Kari E. J., and Yli-Juuti, Taina. Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data. United States: N. p., 2019. Web. doi:10.5194/acp-19-9333-2019.
Tikkanen, Olli-Pekka, Hämäläinen, Väinö, Rovelli, Grazia, Lipponen, Antti, Shiraiwa, Manabu, Reid, Jonathan P., Lehtinen, Kari E. J., & Yli-Juuti, Taina. Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data. United States. doi:10.5194/acp-19-9333-2019.
Tikkanen, Olli-Pekka, Hämäläinen, Väinö, Rovelli, Grazia, Lipponen, Antti, Shiraiwa, Manabu, Reid, Jonathan P., Lehtinen, Kari E. J., and Yli-Juuti, Taina. Mon . "Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data". United States. doi:10.5194/acp-19-9333-2019. https://www.osti.gov/servlets/purl/1580894.
@article{osti_1580894,
title = {Optimization of process models for determining volatility distribution and viscosity of organic aerosols from isothermal particle evaporation data},
author = {Tikkanen, Olli-Pekka and Hämäläinen, Väinö and Rovelli, Grazia and Lipponen, Antti and Shiraiwa, Manabu and Reid, Jonathan P. and Lehtinen, Kari E. J. and Yli-Juuti, Taina},
abstractNote = {The composition of organic aerosol under different ambient conditions as well as their phase state have been a subject of intense study in recent years. One way to study 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 analyse and evaluate a method for obtaining particle composition and viscosity from measurements using process models coupled with input optimization algorithms. Two optimization methods, the 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 artificially test data. Finally, the timescale 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.},
doi = {10.5194/acp-19-9333-2019},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 14,
volume = 19,
place = {United States},
year = {2019},
month = {7}
}

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