Atmospheric and Oceanic Sciences Departmental Seminar Series
Multiphase Aerosol Thermodynamics – From Phase Separation to Cloud Droplet Activation
a talk by
Department of Atmospheric & Oceanic Sciences, McGill University
Aerosol particles and cloud droplets represent distinct manifestations of complex multicomponent systems in Earth’s atmosphere. The water uptake characteristics of atmospheric aerosols are among the properties controlled by chemical composition, which is relevant for particle size and interactions with radiation. Modeling the equilibrium water content of a particle involves predicting the gas–particle partitioning of water at a certain relative humidity and temperature. Furthermore, the equilibration is affected by non-ideal mixing in liquid phases and the simultaneous partitioning of semivolatile organic and inorganic species. Thermodynamic and dynamic models of these processes attempt to provide accurate predictions for use in process-level models as well as large-scale atmospheric models. However, a trade-off exists between the level of affordable chemical complexity, available information and computational costs. Features like liquid–liquid phase separation have gained interest due to their impact on multiphase chemistry, hygroscopicity, the viscosity of particle phases, predicted aerosol mass concentrations, and aerosol–cloud effects. Case studies suggest that the interplay of aerosol hygroscopicity at elevated relative humidity and the partitioning of other semivolatile species affect particle surface composition and the cloud formation potential of ultrafine particles. Modeling frameworks to capture the multiphase, multicomponent nature of atmospheric aerosols have been introduced primarily for detailed process studies. Treatments of such features within large-scale atmospheric models will benefit from a reduced-complexity approach.
In this seminar, I will introduce both a detailed framework based on the AIOMFAC model as well as a reduced-complexity organic aerosol model that was developed recently. The reduced-complexity model offers the ability to process input information typically available in large-scale air quality models and/or data from field studies. In addition, the new framework employs artificial neural networks to short-cut relatively costly numerical iteration. We will discuss this approach and its applications for aerosol hygroscopicity predictions and cloud droplet activation.