Abstract

Abstract. In this paper the Secondary Organic Aerosol Processor (SOAP v1.0) model is presented. This model determines the partitioning of organic compounds between the gas and particle phases. It is designed to be modular with different user options depending on the computation time and the complexity required by the user. This model is based on the molecular surrogate approach, in which each surrogate compound is associated with a molecular structure to estimate some properties and parameters (hygroscopicity, absorption into the aqueous phase of particles, activity coefficients and phase separation). Each surrogate can be hydrophilic (condenses only into the aqueous phase of particles), hydrophobic (condenses only into the organic phases of particles) or both (condenses into both the aqueous and the organic phases of particles). Activity coefficients are computed with the UNIFAC (UNIversal Functional group Activity Coefficient; Fredenslund et al., 1975) thermodynamic model for short-range interactions and with the Aerosol Inorganic–Organic Mixtures Functional groups Activity Coefficients (AIOMFAC) parameterization for medium- and long-range interactions between electrolytes and organic compounds. Phase separation is determined by Gibbs energy minimization. The user can choose between an equilibrium representation and a dynamic representation of organic aerosols (OAs). In the equilibrium representation, compounds in the particle phase are assumed to be at equilibrium with the gas phase. However, recent studies show that the organic aerosol is not at equilibrium with the gas phase because the organic phases could be semi-solid (very viscous liquid phase). The condensation–evaporation of organic compounds could then be limited by the diffusion in the organic phases due to the high viscosity. An implicit dynamic representation of secondary organic aerosols (SOAs) is available in SOAP with OAs divided into layers, the first layer being at the center of the particle (slowly reaches equilibrium) and the final layer being near the interface with the gas phase (quickly reaches equilibrium). Although this dynamic implicit representation is a simplified approach to model condensation–evaporation with a low number of layers and short CPU (central processing unit) time, it shows good agreements with an explicit representation of condensation–evaporation (no significant differences after a few hours of condensation).

Highlights

  • Fine particles are regulated because of their impact on human health (WHO, 2003)

  • As the amount of inorganics may not be recomputed after Secondary Organic Aerosol Processor (SOAP), we considered that the amount of water is at least equal to the amount of water given by ISORROPIA; that is, the amount of water being recomputed only to provide a better estimate of the amount of water when water absorption are mainly due to organics

  • The dynamic implicit representation of SOAP is first compared to an explicit representation to check that the dynamic evolution of surrogates between the gas and particle phases is well represented by the implicit representation

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Summary

Introduction

Fine particles are regulated because of their impact on human health (WHO, 2003). they degrade atmospheric visibility (Larson et al, 1989) and influence climate change (Kanakidou et al, 2005). In 3-D air quality models, several assumptions are made on the thermodynamics of organic aerosols (OAs) such as equilibrium between the gas phase and the particle phase, ideality or no phase separation These assumptions could strongly impact simulated OA formation. To represent organic aerosol formation and take into account non-ideality, phase separation and the viscous state of OAs, the Secondary Organic Aerosol Processor (SOAP), destined to be implemented in 3-D air quality models, is developed and presented here. This model is designed to be modular with different user options depending on the computation time and the complexity required by the user. As the dynamic representation of SOAP is implicit in order to work with low numbers of particle layers and short CPU times, comparisons to an explicit representation of condensation–evaporation are presented

Overview
Equilibrium between the gas phase and one organic phase
Equilibrium between the gas phase and an aqueous phase
Equilibrium between the gas phase and several particulate phases
Saturation and separation of organic phases
Dynamic gas uptake by organic particles
Diffusion of organic compounds in spherical organic particles
Diffusion of organic compounds in more complex particles
Condensation–evaporation of organic compounds into a viscous particle
Characteristic time to reach equilibrium with the gas phase
Generalization to several organic phases
Absorption into a particle with an aqueous phase and organic phases
Redistribution of compounds between layers
Thermodynamic equilibrium
2.3.10 Methodology used to compute the evolution of concentrations
Results
Validation of the dynamic implicit representation
Setup of the test cases
Equilibrium representation
Hydrophobic versus hydrophilic
Dynamic representation
Time analysis
Conclusions
Full Text
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