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.
Highlights
Polycyclic aromatic hydrocarbons (PAHs) are semi-volatile atmospheric pollutants that have numerous negative health effects (Kim et al, 2013)
We focus our model evaluation on spatial variations at high resolution, estimating the level of model sensitivity to uncertainties in the PAH emission factors, gas– particle partitioning, and wet deposition, which are all related to novel aspects of the GEM-MACH-PAH model
By month (Fig. 11b), the wet deposition from the model is best represented in June and July, whereas, wet deposition is greatly overestimated in the winter, implying that the current snow adsorption parametrization may be too effective at removing PAHs in the model
Summary
Polycyclic aromatic hydrocarbons (PAHs) are semi-volatile atmospheric pollutants that have numerous negative health effects (some are carcinogenic, mutagenic, and teratogenic) (Kim et al, 2013). The AURAMSPAH gas–particle partitioning over-predicted the gas phase for the lighter PAH species, and was employed at relatively poor time and spatial resolutions It was missing two known PAH loss processes: the surface reaction of O3 on particulate BaP (Kwamena et al, 2004, 2007; Ringuet et al, 2012; Keyte et al, 2013; Liu et al, 2014), and snow scavenging of gas-phase PAHs (Franz and Eisenreich, 1998; Daly and Wania, 2004; Lei and Wania, 2004; Skrdlíková et al, 2011). The following sections will further describe the GEMMACH-PAH model (Sect. 2), the measurements used for evaluation (Sect. 3), the results of the model evaluation (Sect. 4), and the conclusions (Sect. 5)
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