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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. During the COVID-19 lockdown, the dramatic reduction of anthropogenicemissions provided a unique opportunity to investigate the effects ofreduced anthropogenic activity and primary emissions on atmospheric chemicalprocesses and the consequent formation of secondary pollutants. Here, weutilize comprehensive observations to examine the response of atmosphericnew particle formation (NPF) to the changes in the atmospheric chemicalcocktail. We find that the main clustering process was unaffected by thedrastically reduced traffic emissions, and the formation rate of 1.5 nmparticles remained unaltered. However, particle survival probability wasenhanced due to an increased particle growth rate (GR) during the lockdownperiod, explaining the enhanced NPF activity in earlier studies. For GR at1.5–3 nm, sulfuric acid (SA) was the main contributor at high temperatures,whilst there were unaccounted contributing vapors at low temperatures. ForGR at 3–7 and 7–15 nm, oxygenated organic molecules (OOMs) played amajor role. Surprisingly, OOM composition and volatility were insensitive tothe large change of atmospheric NOx concentration; instead theassociated high particle growth rates and high OOM concentration during thelockdown period were mostly caused by the enhanced atmospheric oxidativecapacity. Overall, our findings suggest a limited role of traffic emissionsin NPF.