GEM-MACH-PAH (rev2488): a new high-resolution chemical transport model for North American polycyclic aromatic hydrocarbons and benzene

Abstract

Abstract. Environment and Climate Change Canada's online air quality forecasting model, GEM-MACH, 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. 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. Added process parametrizations include the particle phase benzo(a)pyrene reaction with ozone via the Kwamena scheme and gas-phase scavenging of PAHs by snow via vapour 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, centred on the PAH data-rich region of southern Ontario, Canada and the northeastern US. Model output for two seasons was compared to measurements from three monitoring networks spanning Canada and the US Average spring–summertime model results were found to be statistically unbiased from measurements of benzene and all seven PAHs. The same was true for the fall–winter seasonal mean, except for benzo(a)pyrene, which had a statistically significant positive bias. We present evidence that the benzo(a)pyrene results may be ameliorated via further improvements to particulate matter 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 ∕ PM2.5 at a 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-MACH-PAH can provide seasonal estimates for benzene and PAHs and is suitable for emissions scenario simulations.

How much does traffic contribute to benzene and PAH air pollution? Results from a high-resolution North American air quality model centered on Toronto, Canada

“…We carried out four sensitivity simulations with GEM-MACH-PAH with the BENZ and PAH emissions from on-road 290 vehicles scaled by factors of 0.5 and 2 in both seasons. This range corresponds approximately to the 25th and 75th percentiles of the range of EFs reported in the recent peer-reviewed literature (Whaley et al, 2018b). The model responded consistently to on-road vehicle emission scaling with average changes to the vehicle contribution amount of -5 to -10% and +20 to +30%, depending on species, for halved and doubled vehicle emissions, respectively (Table 1 vs 2).…”

“…Ratios of modelled to measured concentrations were close to unity for the lowest molecular weight compounds in springsummer, and increased modestly in the fall-winter and with increasing molecular weight. Modelled concentrations were found to be statistically unbiased relative to measurements (paired t-test with t < 1, p > 0.01) for all compounds and seasons except for BaP in fall-winter (Whaley et al, 2018b), which was biased high. Model output for the latter compound and season combination was therefore excluded from this study.…”

“…Modelled concentrations for BENZ and PAHs are higher in fall-winter than in spring-summer (Whaley et al, 2018b) due to lower fall-winter temperatures and solar radiation. These factors lead to to reduced photochemical degradation and increased 155 vertical stability, which in turn induce less vertical mixing and dilution, and lower boundary layer heights.…”

“…The good overall performance (Whaley et al, 2018b) of GEM-MACH-PAH demonstrates its validity for calculating ambient concentrations and for assessing source contributions at its evaluated resolution (2.5 km grid size and seasonal time scale).…”

“…Model output for the latter compound and season combination was therefore excluded from this study. GEM-MACH-PAH's particle/gas partitioning parametrization was evaluated at six IADN stations and the results showed a 140 substantial improvement over the previous AURAMS-PAH partitioning (Galarneau et al, 2014) due to an empirically-based update in partitioning parameters (Whaley et al, 2018b).…”