GEM-MACH-PAH (rev2488): a new high-resolutionchemical transport model for North American PAHsand benzene

Abstract. Environment and Climate Change Canada’s online air quality forecasting model, GEMMACH, was extended to simulate atmospheric concentrations of benzene and seven polycyclic aromatic hydrocarbons (PAHs): phenanthrene, anthracene, fluoranthene, pyrene, benz(a)anthracene, chrysene, and benzo(a)pyrene (BaP). In the expanded model, benzene and PAHs are emitted from major point, area, and mobile sources, with emissions based on recent emission factors. Modelled PAHs undergo gas-particle partitioning (whereas benzene is only in the gas phase), atmospheric transport, oxidation, cloud processing, and dry and wet deposition. To represent PAH gas-particle partitioning, the Dachs-Eisenreich scheme was used, and we have improved gas-particle partitioning parameters based on an empirical analysis to get significantly better gas-particle partitioning results than the previous North American PAH model, AURAMS-PAH. Other added process parameterizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapor sorption to the snow surface. The resulting GEM-MACH-PAH model was used to generate the first online model simulations of PAH emissions, transport, chemical transformation and deposition for a high resolution domain (2.5-km grid cell spacing) in North America, centered on the PAH-data-rich region of southern Ontario, Canada and the north-eastern United States. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the U.S. Average summertime model results were found to be statistically indistinguishable from measurements of benzene and all seven PAHs. The same was true for the winter seasonal mean, except for BaP, which had a statistically significant positive bias.We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to PM and oxidant processes and transport. Our analysis focused on four key components to the prediction of atmospheric PAH levels: spatial variability; sensitivity to mobile emissions; gas-particle partitioning; and wet deposition. Spatial variability of PAHs/PM 2.5 at 2.5-km resolution was found to be comparable to measurements. Predicted ambient surface concentrations of benzene and the PAHs were found to be critically dependent on mobile emission factors, indicating the mobile emissions sector has a significant influence on ambient PAH levels in the study region. PAH wet deposition was overestimated due to additive precipitation biases in the model and the measurements. Our overall performance evaluation suggests that GEM-MACHPAH can provide seasonal estimates for benzene and PAHs and be suitable for emissions scenario simulations.