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Abstract. 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. 

Abstract. Information on the rate of diffusion of organic moleculeswithin secondary organic aerosol (SOA) is needed to accurately predict theeffects of SOA on climate and air quality. Diffusion can be important forpredicting the growth, evaporation, and reaction rates of SOA under certainatmospheric conditions. Often, researchers have predicted diffusion rates oforganic molecules within SOA using measurements of viscosity and theStokes–Einstein relation (D∝1/η, where D is the diffusioncoefficient and η is viscosity). However, the accuracy of thisrelation for predicting diffusion in SOA remains uncertain. Usingrectangular area fluorescence recovery after photobleaching (rFRAP), wedetermined diffusion coefficients of fluorescent organic molecules over8 orders in magnitude in proxies of SOA including citric acid, sorbitol,and a sucrose–citric acid mixture. These results were combined withliterature data to evaluate the Stokes–Einstein relation for predictingthe diffusion of organic molecules in SOA. Although almost all the data agreewith the Stokes–Einstein relation within a factor of 10, a fractionalStokes–Einstein relation (D∝1/ηξ) with ξ=0.93is a better model for predicting the diffusion of organic molecules in the SOAproxies studied. In addition, based on the output from a chemical transportmodel, the Stokes–Einstein relation can overpredict mixing times of organicmolecules within SOA by as much as 1 order of magnitude at an altitudeof ∼3 km compared to the fractional Stokes–Einstein relation with ξ=0.93. These results also have implications for other areas such as infood sciences and the preservation of biomolecules. 

The ozonolysis kinetics of viscous aerosol particles containing maleic acid are studied. Kinetic fits are constrained by measured particle viscosities.