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In the last years, two major findings concerning secondary organic aerosol (SOA) were reported. Firstly, the aerosol particles formed by the organic compounds are higher viscous than previously thought. Up to now, SOA formation modeling has mostly based on gas-particle equilibrium partitioning of semi-volatile species. This approach implicates sufficient diffusion of the organic compounds into the particle phase to keep the condensed phase in equilibrium with the gas phase, thus the phase state of the particle phase is important for SOA modeling. Secondly, highly oxidized multifunctional organic compounds (HOMs) are found to influence the early aerosol growth. In order to investigate both aerosol phase state and HOMs in detail in the SPACCIM model framework, a kinetic partitioning approach was implemented in the box model and the gas-phase chemistry mechanism was updated by HOMs. Finally, the insights of the performed box model studies have been utilized to improve SOA modeling within a 3D model and first results are shown in the present study.

This paper describes the implementation of a kinetic gas-particle partitioning approach used for the simulation of secondary organic aerosol (SOA) formation within the SPectral Aerosol Cloud Chemistry Interaction Model (SPACCIM). The kinetic partitioning considers the diffusion of organic compounds into aerosol particles and the subsequent chemical reactions in the particle phase. The basic kinetic partitioning approach is modified by the implementation of chemical backward reaction of the solute within the particle phase as well as a composition dependent particle-phase bulk diffusion coefficient. The adapted gas-phase chemistry mechanism for α-pinene oxidation has been updated due to the recent findings related to the formation of highly oxidized multifunctional organic compounds (HOMs). Experimental results from a LEAK (Leipziger Aerosolkammer) chamber study for α-pinene ozonolysis were compared with the model results describing this reaction system. The performed model studies reveal that the particle-phase bulk diffusion coefficient and the particle phase reactivity are key parameters for SOA formation. Using the same particle phase reactivity for both cases we find that liquid particles with higher particle-phase bulk diffusion coefficients have 310-times more organic material formed in the particle phase compared to higher viscous semi-solid particles with lower particle-phase bulk diffusion coefficients. The model results demonstrate that, even with a moderate particle phase reactivity, about 61 % of the modeled organic mass consists of reaction products that are formed in the liquid particles. This finding emphasizes the potential role of SOA processing. Moreover, the initial organic aerosol mass concentration and the particle radius are of minor importance for the process of SOA formation in liquid particles. A sensitivity study shows that a 22-fold increase in particle size merely leads to a SOA increase of less than 10 %. Due to two additional implementations, allowing backward reactions in the particle phase and considering a composition dependent particle-phase bulk diffusion coefficient, the potential overprediction of the SOA mass with the basic kinetic approach is reduced by about 40 %. HOMs are an important compound group in the early stage of SOA formation because they contribute up to 65 % of the total SOA mass at this stage. HOMs also induce further SOA formation by providing an absorptive medium for SVOCs (semi-volatile organic compounds). This process contributes about 27 % of the total organic mass. The model results are very similar to the LEAK chamber results. Overall, the sensitivity studies demonstrate that the particle reactivity and the particle-phase bulk diffusion require a better characterization in order to improve the current model implementations and to validate the assumptions made from the chamber simulations. The successful implementation and testing of the current kinetic gas-particle partitioning approach in a box model framework will allow further applications in a 3D model for regional scale process investigations.