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

Tikkanen, Olli-Pekka; Hämäläinen, Väinö; Rovelli, Grazia; Lipponen, Antti; Shiraiwa, Manabu; Reid, Jonathan P.; Lehtinen, Kari E.  J.; Yli-Juuti, Taina

doi:10.5194/acp-19-9333-2019

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

Journal Article · Mon Jul 22 00:00:00 EDT 2019 · Atmospheric Chemistry and Physics (Online)

DOI:https://doi.org/10.5194/acp-19-9333-2019· OSTI ID:1580894

^[1]; Hämäläinen, Väinö ^[1];

^[2];

^[3]; Shiraiwa, Manabu ^[4];

^[2]; Lehtinen, Kari E. J. ^[5]; Yli-Juuti, Taina ^[1]

Univ. of Eastern Finland, Kuopio (Finland)
Univ. of Bristol (United Kingdom)
Finnish Meteorological Inst., Kuopio (Finland)
Univ. of California, Irvine, CA (United States)
Univ. of Eastern Finland, Kuopio (Finland); Finnish Meteorological Inst., Kuopio (Finland)

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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC); Academy of Finland; National Science Foundation (NSF)

Grant/Contract Number:: AC02-05CH11231; 307331; 299544; NE/M004600/1; AGS-1654104

OSTI ID:: 1580894

Journal Information:: Atmospheric Chemistry and Physics (Online), Vol. 19, Issue 14; ISSN 1680-7324

Publisher:: European Geosciences UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 9 works

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