Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models

Aromatic hydrocarbons, including benzene, toluene, and xylenes, play an important role in atmospheric chemistry, but the associated chemical mechanisms are complex and uncertain. Sparing representation of this chemistry in models is needed for computational tractability. Here, we develop a new compact mechanism for aromatic chemistry (GC13) that captures current knowledge from laboratory and computational studies with only 17 unique species and 44 reactions. We compare GC13 to six other currently used mechanisms of varying complexity in box model simulations of environmental chamber data and diurnal boundary layer chemistry, and show that GC13 provides results consistent with or better than more complex mechanisms for oxygenated products (alcohols, carbonyls, dicarbonyls), ozone, and hydrogen oxide (HOx≡OH+HO2) radicals. Specifically, GC13 features increased radical recycling and increased ozone destruction from phenoxy–phenylperoxy radical cycling relative to other mechanisms. We implement GC13 into the GEOS-Chem global chemical transport model and find higher glyoxal yields and net ozone loss from aromatic chemistry compared with other mechanisms. Aromatic oxidation in the model contributes 23 %, 5 %, and 8 % of global glyoxal, methylglyoxal, and formic acid production, respectively, and has mixed effects on formaldehyde. It drives small decreases in global tropospheric OH (−2.2 %), NOx (≡NO+NO2; −3.7 %), and ozone (−0.8 %), but a large increase in NO3 (+22 %) from phenoxy–phenylperoxy radical cycling. Regional effects in polluted environments can be substantially larger, especially from the photolysis of carbonyls produced by aromatic oxidation, which drives large wintertime increases in OH and ozone concentrations.

Development and evaluation of a new compact mechanism for aromatic oxidation in atmospheric models

Aromatic hydrocarbons (mainly benzene, toluene, and xylenes) play an important role in atmospheric chemistry but the associated chemical mechanisms are complex and uncertain. Spare representation of this chemistry in models is needed for computational tractability. Here we develop a new compact mechanism for aromatic chemistry (GC13) that captures current knowledge from laboratory and computational studies with only 17 unique species and 44 reactions. We compare GC13 to six other currently used mechanisms of varying complexity in box model simulations of environmental chamber data and diurnal boundary layer chemistry, and show that GC13 provides results consistent with or better than more complex mechanisms for oxygenated products (alcohols, carbonyls, dicarbonyls), ozone, and hydrogen oxide (HOx ≡ OH + HO2) radicals. GC13 features in particular increased radical recycling and increased ozone destruction from phenoxy-phenylperoxy radical cycling relative to other mechanisms. We implement GC13 into the GEOS-Chem global chemical transport model and find higher glyoxal yields and net ozone loss from aromatic chemistry compared to other mechanisms. Aromatic oxidation in the model contributes 23 %, 5 %, and 8 % of global glyoxal, methylglyoxal, and formic acid production respectively, and has mixed effects on formaldehyde. It drives small decreases in global tropospheric OH (−2.2 %), NOx (≡ NO + NO2; −3.7 %) and ozone (−0.8 %), but a large increase in NO3 (+22 %) from phenoxy-phenylperoxy radical cycling. Regional effects in polluted environments can be substantially larger, especially from photolysis of carbonyls produced by aromatic oxidation, which drives large wintertime increases in OH and ozone concentrations.

Simulating secondary organic aerosol from anthropogenic and biogenic precursors: comparison to outdoor chamber experiments, effect of oligomerization on SOA formation and reactive uptake of aldehydes

New parameterizations for the formation of organic aerosols have been developed. These parameterizations cover SOA formation from biogenic and anthropogenic precursors, NOx dependency, oligomerization and the reactive uptake of pinonaldehyde. Those parameterizations are based on available experimental results. The effects of those parameterizations are tested against various experiments carried out in previous studies inside the outdoor chamber Euphore. Two datasets of experiments were used: the anthropogenic experiments (were SOA is formed mainly from a mixture of toluene, 1,3,5-trimethylbenzene and o-xylene) and the biogenic experiments (were SOA is formed mainly from α-pinene and limonene). SOA formation inside the chamber is simulated by using the Secondary Organic Aerosol Processor (SOAP) model to take into account the dynamic evolution of concentrations. When assuming no wall deposition of organic vapors, satisfactory results were obtained for the biogenic experiments and most of the anthropogenic experiments. However, the anthropogenic experiments seem to indicate a complex NOx dependency that could not be reproduced by the model. Oligomerization was found to have a strong effect on SOA composition and could have a strong effect on the formation of SOA. The uptake of pinonaldehyde (which is a high volatility SVOC) onto acidic aerosol was found to be too slow to occur under atmospheric conditions but less volatile or more reactive aldehydes could react in acidic aerosols indicating that the parameterization of Pun and Seigneur (2007) used in some air quality models may lead to an overestimation of SOA concentrations. However, taking into account wall deposition of organic vapors (with the parameters of Zhang et al. (2014)) leads to a strong underestimation of SOA concentrations. This feature is consistent with the fact that the SOA mechanisms are based on environmental chamber data. This underestimation could however be corrected by decreasing the volatility of SVOC by a factor 3. The effect of particle viscosity was also estimated. Low changes in concentrations were found when taking into account SOA viscosity. Taking it into account leads to a decrease of SVOC evaporation and prevents changes in concentration due to changes of temperature during the experiments.

Evaluation of detailed aromatic mechanisms (MCMv3 and MCMv3.1) against environmental chamber data

A high quality dataset on the photo-oxidation of benzene, toluene, p-xylene and 1,3,5-trimethylbenzene has been obtained from experiments in the European Photoreactor (EUPHORE), a large outdoor environmental reaction chamber. The experiments were designed to test sensitive features of detailed aromatic mechanisms, and the dataset has been used to evaluate the performance of the Master Chemical Mechanism Version 3 (MCMv3). An updated version (MCMv3.1) was constructed based on recent experimental data, and details of its development are described in a companion paper. The MCMv3.1 aromatic mechanisms have also been evaluated using the EUPHORE dataset. Significant deficiencies have been identified in the mechanisms, in particular: 1) an over-estimation of the ozone concentration, 2) an under-estimation of the NO oxidation rate, 3) an under-estimation of OH. The use of MCMv3.1 improves the model-measurement agreement in some areas but significant discrepancies remain.

Evaluation of detailed aromatic mechanisms (MCMv3 and MCMv3.1) against environmental chamber data

A high quality dataset on the photo-oxidation of benzene, toluene, p-xylene and 1,3,5-trimethylbenzene has been obtained from experiments in the European Photoreactor (EUPHORE), a large outdoor environmental reaction chamber. The experiments were designed to test sensitive features of detailed aromatic mechanisms, and the dataset has been used to evaluate the performance of the Master Chemical Mechanism Version 3 (MCMv3). An updated version (MCMv3.1) was constructed based on recent experimental data, and details of its development are described in a companion paper. The MCMv3.1 aromatic mechanisms have also been evaluated using the EUPHORE dataset. Significant deficiencies have been identified in the mechanisms, in particular: 1) an over-estimation of the ozone concentration, 2) an under-estimation of the NO oxidation rate, 3) an under-estimation of OH. The use of MCMv3.1 improves the model-measurement agreement in some areas but significant discrepancies remain.