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Talk Abstract
Modeling Gas-to-Particle Conversion in Atmospheric Chemical Transport Models

Spyros N. Pandis
Department of Chemical Engineering
Carnegie Mellon University
Pittsburgh, PA 15213
spyros+@andrew.cmu.edu



Significant progress has been made in recent years in improving our understanding of gas-to-particle conversion processes (gas-phase reactions and condensation/evaporation of inorganic and organic aerosol components, aerosol-cloud interactions, homogeneous and heterogeneous nucleation, etc.). A brief overview of this progress and the remaining challenges is presented. The Multicomponent Aerosol Dynamics Model (MADM) is one of the models recently developed to simulate the condensation/evaporation of inorganic and organic aerosol components. For the inorganic constituents a number of thermodynamic modules (ISORROPIA, SCAPE2, etc.) can be used by MADM to predict the physical state of the particle, i.e. whether the aerosol is liquid or solid. MADM is able to simulate aerosol deliquescence, crystallization, solid to solid phase transitions, and acidity transitions. Aerosols of different sizes can be in different physical states (solid, liquid, or partially solid and partially liquid). Novel constraints on the electroneutrality of the species flux between the gas and aerosol phases are presented for both liquid and solid aerosols. These constraints aid in the stability of the algorithm, yet still allow changes in aerosol acidity. The formation of secondary organic aerosol is described using a lumped-species approach and assuming a pseudo-ideal solution. The organic components are partially dissolved in the aqueous-phase and alter the partitioning of the inorganics between the gas and aerosol phases.

Dynamic mass transfer method, like those used by MADM, are computationally expensive. Hybrid methods combining the speed of equilibrium methods with the accuracy of the dynamic methods represent an alternative approach for use in 3D atmospheric chemical transport models. Two such hybrid methods are discussed and are evaluated against the fully dynamic approach used by MADM.


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