Simulating the oxygen content of ambient organic aerosol with the 2D volatility basis set
Abstract. A module predicting the oxidation state of organic aerosol (OA) has been developed using the two-dimensional volatility basis set (2D-VBS) framework. This model is an extension of the 1D-VBS framework and tracks saturation concentration and oxygen content of organic species during their atmospheric lifetime. The host model, a one-dimensional Lagrangian transport model, is used to simulate air parcels arriving at Finokalia, Greece during the Finokalia Aerosol Measurement Experiment in May 2008 (FAME-08). Extensive observations were collected during this campaign using an aerosol mass spectrometer (AMS) and a thermodenuder to determine the chemical composition and volatility, respectively, of the ambient OA. Although there are several uncertain model parameters, the consistently high oxygen content of OA measured during FAME-08 (O:C = 0.8) can help constrain these parameters and elucidate OA formation and aging processes that are necessary for achieving the high degree of oxygenation observed. The base-case model reproduces observed OA mass concentrations (measured mean = 3.1 μg m−3, predicted mean = 3.3 μg m−3) and O:C (predicted O:C = 0.78) accurately. A suite of sensitivity studies explore uncertainties due to (1) the anthropogenic secondary OA (SOA) aging rate constant, (2) assumed enthalpies of vaporization, (3) the volatility change and number of oxygen atoms added for each generation of aging, (4) heterogeneous chemistry, (5) the oxidation state of the first generation of compounds formed from SOA precursor oxidation, and (6) biogenic SOA aging. Perturbations in most of these parameters do impact the ability of the model to predict O:C well throughout the simulation period. By comparing measurements of the O:C from FAME-08, several sensitivity cases including a high oxygenation case, a low oxygenation case, and biogenic SOA aging case are found to unreasonably depict OA aging, keeping in mind that this study does not consider possibly important processes like fragmentation that may offset mass gains and affect the prediction bias. On the other hand, many of the cases chosen for this study predict average O:C estimates that are consistent with the observations, illustrating the need for more thorough experimental characterizations of OA parameters including the enthalpy of vaporization and oxidation state of the first generation of SOA products. The ability of the model to predict OA concentrations is less sensitive to perturbations in the model parameters than its ability to predict O:C. In this sense, quantifying O:C with a predictive model and constraining it with AMS measurements can reduce uncertainty in our understanding of OA formation and aging.