Abstract
Abstract. Regional-scale chemical transport model predictions of urban organic aerosol to date tend to be biased low relative to observations, a limitation with important implications for applying such models to human exposure health studies. We used a nested version of Environment Canada's AURAMS model (42- to- 15- to- 2.5-km nested grid spacing) to predict organic aerosol concentrations for a temporal and spatial domain corresponding to the Border Air Quality and Meteorology Study (BAQS-Met), an air-quality field study that took place in the southern Great Lakes region in the summer of 2007. The use of three different horizontal grid spacings allowed the influence of this parameter to be examined. A domain-wide average for the 2.5-km domain and a matching 15-km subdomain yielded very similar organic aerosol averages (4.8 vs. 4.3 μg m−3, respectively). On regional scales, secondary organic aerosol dominated the organic aerosol composition and was adequately resolved by the 15-km model simulation. However, the shape of the organic aerosol concentration histogram for the Windsor urban station improved for the 2.5-km simulation relative to those from the 42- and 15-km simulations. The model histograms for the Bear Creek and Harrow rural stations were also improved in the high concentration "tail" region. As well the highest-resolution model results captured the midday 4 July organic-aerosol plume at Bear Creek with very good temporal correlation. These results suggest that accurate simulation of urban and large industrial plumes in the Great Lakes region requires the use of a high-resolution model in order to represent urban primary organic aerosol emissions, urban VOC emissions, and the secondary organic aerosol production rates properly. The positive feedback between the secondary organic aerosol production rate and existing organic mass concentration is also represented more accurately with the highest-resolution model. Not being able to capture these finer-scale features may partly explain the consistent negative bias reported in the literature when urban-scale organic aerosol evaluations are made using coarser-scale chemical transport models.
Highlights
Atmospheric aerosols have important human health impacts and are estimated to cause more than a million premature deaths globally per year (Davidson et al, 2005)
Of the STN organic aerosol (OA) measurements for all eastern sites for the BAQS-Met period was 6.4 ± 2.7 μg m−3, which resulted in a mean model bias of −1.7 μg m−3 and a root mean square error (RMSE) of 3.2 μg m−3 with the 15-km grid spacing
This is a significant improvement in mean bias compared to prior order-of-magnitude OA under-predictions (e.g., McKeen et al, 2007; Smyth et al, 2009; Gong et al, 2010b), which were based on earlier OA yield data from traditional SOA precursors (Odum et al, 1996), lower monoterpene SOA yields, and no ISOP SOA, IVOC SOA or sesquiterpene species (SESQ) SOA production
Summary
Atmospheric aerosols have important human health impacts and are estimated to cause more than a million premature deaths globally per year (Davidson et al, 2005). Organics comprise approximately half of atmospheric PM2.5 (particulate matter with aerodynamic diameter less than 2.5 μm) and may be a major player in the aerosol health effects. Modelling studies to date have generally under-predicted OA concentrations, especially in urban air masses (Griffin et al, 2005; Heald et al, 2005; Chen et al, 2006; McKeen et al, 2007; Ying et al, 2007; Yu et al, 2007; Carlton et al, 2008; Murphy and Pandis, 2009; Smyth et al, 2009). There have been a number of hypotheses proposed to explain these model under-predictions, namely (1) unaccounted-for intermediate volatile organic compounds
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