We evaluated the uncertainty associated with secondary pollutants formation due to different chemical mechanisms in photochemical modelling. The CMAQ modelling system was utilized in conjunction with CB6R3, SAPRC07, and RACM2 chemical mechanisms and compared the concentrations of various chemical species, including ozone (O₃) and particulate matter (PM_2.5). Using datasets from ambient monitoring stations, we assessed the performance of each of the mechanism in summer and winter. The concentrations of various chemical species predicted by the three mechanisms varied significantly. The differences are more evident in summer than in winter for most of the species, except for hydrogen peroxide (H₂O₂), methyl hydroperoxide (MEPX), and Secondary Organic Aerosol—Anthropogenic. We observed that the summer daytime O₃ predictions showed reasonable peaks at the three air quality monitoring sites, but the nighttime values under-predicted for all three mechanisms. In the winter, all three mechanisms tend to under-predict O₃. Differences in the mean O₃ values (bias) at the different sites, for the different seasons, are consistent with corrections made to previous modelling studies that modified KZMIN. PM_2.5 predictions with RACM2 were slightly better. The dominant PM_2.5 species in summer were sulfate and SOA-Bio, which may be attributed to non-mobile sources in the region, while NO₃ became dominant in winter due to more favorable conditions for forming this species, including lower temperatures and an elevated NH₄ to SO₄ ratio. We concluded that the differences in O₃ and PM_2.5 predictions between the three mechanisms are significant, implying that when developing strategic and management actions are based on modelling, the most appropriate mechanism should be considered.

Understanding the combustion dynamics of fuels, and the generation and propagation of smoke in a wildland fire, can inform short-range and long-range pollutant transport models, and help address and mitigate air quality concerns in communities. Smoldering smoke can cause health issues in nearby valley bottoms, and can create hazardous road conditions due to low-visibility. We studied near-field smoke dynamics in a prescribed fire of 3.4 hectares of land in a boreal black spruce forest in central Alberta. Smoke generated from the fire was monitored through a network of five field-deployable micro sensor systems. Sensors were placed within 500–1000 m of the fire area at various angles in downwind. Smoke generated from flaming and smoldering combustions showed distinct characteristics. The propagation rates of flaming and smoldering smoke, based on the fine particulate (PM_2.5) component, were 0.8 and 0.2 m/s, respectively. The flaming smoke was characterized by sharp rise of PM_2.5 in air with concentrations of up to 940 µg/m³, followed by an exponential decay with a half-life of ~10 min. Smoldering combustion related smoke contributed to PM_2.5 concentrations above 1000 µg/m³ with slower decay half-life of ~18 min. PM_2.5 emissions from the burn area during flaming and smoldering phases, integrated over the combustion duration of 2.5 h, were ~15 and ~16 kilograms, respectively, as estimated by our mass balance model.