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. The composition of organic aerosol under different ambient conditions aswell as their phase state have been a subject of intense study in recentyears. One way to study particle properties is to measure the particlesize shrinkage in a diluted environment at isothermal conditions. From thesemeasurements it is possible to separate the fraction of low-volatilitycompounds from high-volatility compounds. In this work, we analyse andevaluate a method for obtaining particle composition and viscosity frommeasurements using process models coupled with input optimizationalgorithms. Two optimization methods, the Monte Carlo genetic algorithm andBayesian inference, are used together with process models describing thedynamics of particle evaporation. The process model optimization scheme ininferring particle composition in a volatility-basis-set sense andcomposition-dependent particle viscosity is tested with artificiallygenerated data sets and real experimental data. Optimizing model input sothat the output matches these data yields a good match for the estimatedquantities. Both optimization methods give equally good results when theyare used to estimate particle composition to artificially test data. The timescale of the experiments and the initial particle size are found to beimportant in defining the range of values that can be identified for theproperties from the optimization.

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