Abstract
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. 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.
Highlights
It has been estimated that organic aerosols (OAs) comprise a large fraction of global aerosol particle mass (Kanakidou et al, 2005; Jimenez et al, 2009)
A significant fraction of OA is of secondary origin, i.e. OA formed from oxidation of volatile organic compounds and their subsequent condensation onto pre-existing particles (Hallquist et al, 2009)
If the literature values were input to Eq (1), they would produce higher viscosity than what is estimated with the process model optimization method
Summary
It has been estimated that organic aerosols (OAs) comprise a large fraction of global aerosol particle mass (Kanakidou et al, 2005; Jimenez et al, 2009). In SOA systems, there are gaps of knowledge in the composition and phase state of the particles and their response to atmospheric conditions such as relative humidity or temperature (Hallquist et al, 2009; Virtanen et al, 2010; Pajunoja et al, 2015) These properties are important since they control the evolution of atmospheric organic particles and their subsequent effect on climate (Tsigaridis et al, 2014; Shiraiwa et al, 2017). The current challenges of accurately estimating OA component volatility and particle viscosity raises a need for studies that assess how accurately they can be inferred by fitting process model output to time-dependent evaporation measurements, which is the aim of this study. In the last section the findings are summarized and conclusions are drawn
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
