Abstract
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.
Highlights
Organic aerosol (OA), a significant component of submicron particles throughout the world, is poorly simulated by predictive models when compared to particulate inorganic constituents (Kanakidou et al, 2005; Fuzzi et al, 2006)
The following five OA source classes are resolved: (1) aSOA is OA formed from the oxidation products of anthropogenic VOCs, (2) biogenic SOA (bSOA) is OA formed from oxidation products of biogenic VOCs, (3) primary organic aerosol (POA) is fresh emitted OA with C∗ ≤ 1000 μg m−3
This mass may evaporate and recondense during its atmospheric lifetime, but once it reacts with OH it is removed from this classification becoming (4) semi-volatile secondary OA (SOA). (5) intermediate volatility SOA mass is emitted largely in the gas phase with C∗ > 1000 μg m−3
Summary
Organic aerosol (OA), a significant component of submicron particles throughout the world, is poorly simulated by predictive models when compared to particulate inorganic constituents (Kanakidou et al, 2005; Fuzzi et al, 2006). Thousands of individual organic compounds make up this OA mixture, making its bulk properties (e.g. reactivity, volatility, hygroscopicity, etc.) difficult to assess and represent in mathematical models of the atmosphere (Goldstein and Galbally, 2007) These chemical transport models (CTMs) typically use surrogate species with averaged properties to represent dominant OA formation, evolution, and removal pathways in order to predict ambient OA mass concentrations at the Earth’s surface or aloft. The two-dimensional volatility basis set (2D-VBS) tracks the oxygen content as well as the saturation concentration of model species (Donahue et al, 2011), and this additional information could be used to compare predictions to the results from AMS observations Simulating this added dimension may help constrain some of the uncertainties in OA aging as well as provide more precise predictions of the concentration of organic carbon, which is reported by ambient monitoring networks (Chow et al, 2001; Watson et al, 2005). Knowing the magnitude of these impacts, we recommend areas where detailed experimental characterization would lead to significant improvement in accuracy for ambient OA models
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