Application of the Statistical Oxidation Model (SOM) to Secondary Organic Aerosol formation from photooxidation of C<sub>12</sub> alkanes

Abstract

Abstract. Laboratory chamber experiments are the main source of data on the mechanism of oxidation and the secondary organic aerosol (SOA) forming potential of volatile organic compounds. Traditional methods of representing the SOA formation potential of an organic do not fully capture the dynamic, multi-generational nature of the SOA formation process. We apply the Statistical Oxidation Model (SOM) of Cappa and Wilson (2012) to model the formation of SOA from the formation of the four C12 alkanes, dodecane, 2-methyl undecane, cyclododecane and hexylcyclohexane, under both high- and low-NOx conditions, based upon data from the Caltech chambers. In the SOM, the evolution of reaction products is defined by the number of carbon (NC) and oxygen (NO) atoms, and the model parameters are (1) the number of oxygen atoms added per reaction, (2) the decrease in volatility upon addition of an oxygen atom and (3) the probability that a given reaction leads to fragmentation of the molecules. Optimal fitting of the model to chamber data is carried out using the measured SOA mass concentration and the aerosol O:C atomic ratio. The use of the kinetic, multi-generational SOM is shown to provide insights into the SOA formation process and to offer promise for application to the extensive library of existing SOA chamber experiments that is available.

Technical note: Monte Carlo genetic algorithm (MCGA) for model analysis of multiphase chemical kinetics to determine transport and reaction rate coefficients using multiple experimental data sets

“…chemical reactions, physical processes), data from various measurement campaigns could be combined, reconciled, and in a further step used to reduce the number of model input parameters to the key processes necessary to describe all measurement data. MCGA may be a powerful and useful tool to constrain kinetic parameters and reaction rate coefficients in models that study the formation of secondary organic aerosol in reaction chambers (Chan et al, 2007;Shiraiwa et al, 2013;Cappa et al, 2013;Riedel et al, 2016). It could be suitable for fine-tuning of reaction rates in large reaction mechanisms of atmospheric chemistry, such as the Master Chemical Mechanism (MCM; Jenkin et al, 1997;Saunders et al, 2003), the Gas-Aerosol Model for Mechanism Analysis (GAMMA; McNeill et al, 2012), or the Chemical Aqueous Phase Radical Mechanism (CAPRAM; Herrmann et al, 1999).…”

Hygroscopicity, CCN and volatility properties of submicron atmospheric aerosol in a boreal forest environment during the summer of 2010

“…Among these six variables, the hygroscopic growth factor correlated best with gas phase O 3 and aerosol sulfate concentration. The correlation between GF H and O 3 might be linked to the fact that high ozone concentrations indicate more effective atmospheric oxidation, which in turn, increases aerosol hygroscopicity (Cappa et al, 2013). The positive correlation between GF H and sulphate is due to the fact that aerosol particles containing more sulphate are more hygroscopic.…”

Multi-generational oxidation model to simulate secondary organic aerosol in a 3-D air quality model

“…This last term, LVP i , reflects the average change in vapor pressure due to the functional group added upon oxidation (e.g., alcohol, ketone) and is determined by fitting the SOM to chamber data. Differences in values of LVP i between different SOA precursors reflect differences in chemical reaction pathways between these precursors (Cappa and Wilson, 2012;Cappa et al, 2013). The SOM model species are allowed to dynamically partition to the particle phase as per Eq.…”

“…The NO xdependence of SOA formation is consequently treated in a binary manner because the SOM in its current configuration does not allow for continuous variation in the dependence of SOA on NO x . More details about the fitting process and the experimental chamber data can be found in Cappa et al (2013) and Zhang et al (2014). Briefly, measurements of VOC decay during the chamber experiment were used to estimate OH concentrations that were then used to represent the oxidation of the SOM model species.…”

“…Recently, there has been the development of two frameworks of intermediate complexity that allow for the treatment of multi-generational oxidation (and other aerosol processes) during SOA formation: the two-dimensional volatility basis set (2D-VBS) that uses vapor pressure and the O : C (oxygen to carbon) ratio as the independent variables Donahue et al, 2012a) and the statistical oxidation model (SOM) that uses the number of carbon atoms and oxygen atoms per molecule as independent variables (Cappa and Wilson, 2012). Both have provisions to treat fragmentation of the reactants as a function of their oxygen content and can be parameterized from chamber measurements (Cappa et al, 2013;Zhao et al, 2015). Both frameworks require tracking on the order of hundreds of model species, which is more computationally expensive than models with less detail, but still sufficiently modest to be realistically implemented in 3-D models today.…”