FireWork is an on-line, one-way coupled meteorology–chemistry model based on near-real-time wildfire emissions. It was developed by Environment and Climate Change Canada to deliver operational real-time forecasts of biomass-burning pollutants, in particular fine particulate matter (PM_2.5), over North America. Such forecasts provide guidance for early air quality alerts that could reduce air pollution exposure and protect human health. A multi-year (2013–2016) analysis of FireWork forecasts over a five-month period (May to September) was conducted. This work used an archive of FireWork outputs to quantify wildfire contributions to total PM_2.5 surface concentrations across North America. Different concentration thresholds (0.2 to 28 µg/m³) and averaging periods (24 h to five months) were considered. Analysis suggested that, on average over the fire season, 76% of Canadians and 69% of Americans were affected by seasonal wildfire-related PM_2.5 concentrations above 0.2 µg/m³. These effects were particularly pronounced in July and August. Futhermore, the analysis showed that fire emissions contributed more than 1 µg/m³ of daily average PM_2.5 concentrations on more than 30% of days in the western USA and northwestern Canada during the fire season.

Surface-level ozone (O₃) continues to be a significant health risk in the Greater Toronto Hamilton Area (GTHA) of Canada even though precursor emissions in the area have decreased significantly over the past two decades. In July 2015, Environment and Climate Change Canada (ECCC) led an intensive field study coincident with Toronto hosting the 2015 Pan American Games. During the field study, the daily 1-h maximum O₃ standard (80 ppbv) was exceeded twice at a measurement site in North Toronto, once on July 12 and again on July 28. In this study, ECCC’s 2.5-km configuration of the Global Environmental Multi-scale (GEM) meteorological model was combined with the Modelling Air-quality and CHemistry (MACH) on-line atmospheric chemistry model and the Town Energy Balance (TEB) urban surface parameterization to create a new urban air quality modelling system. In general, the model results showed that the nested 2.5-km grid-spaced urban air quality model performed better in statistical scores compared to the piloting 10-km grid-spaced GEM-MACH model without TEB. Model analyses were performed with GEM-MACH-TEB for the two exceedance periods. The local meteorology for both cases consisted of light winds with the highest O₃ predictions situated along lake-breeze fronts. For the July 28 case, O₃ production sensitivity analysis along the trajectory of the lake-breeze circulation showed that the region of most efficient O₃ production occurred in the updraft region of the lake-breeze front, as the precursors to O₃ formation underwent vertical mixing. In this updraft region, the ozone production switches from volatile organic compound (VOC)-sensitive to NOx-sensitive, and the local net O₃ production rate reaches a maximum. This transition in the chemical regime is a previously unidentified factor for why O₃ surface-level mixing ratios maximize along the lake-breeze front. For the July 12 case, differences between the model and observed Lake Ontario water temperature and the strength of lake-breeze opposing wind flow play a role in differences in the timing of the lake-breeze, which impacts the predicted location of the O₃ maximum north of Toronto.

The sensitivities of meteorological and chemical predictions to urban effects over four major North American cities are investigated using the high-resolution (2.5-km) Environment and Climate Change Canada’s air quality model with the Town Energy Balance (TEB) scheme. Comparisons between the model simulation results with and without the TEB effect show that urbanization has great impacts on surface heat fluxes, vertical diffusivity, air temperature, humidity, atmospheric boundary layer height, land-lake circulation, air pollutants concentrations and Air Quality Health Index. The impacts have strong diurnal variabilities, and are very different in summer and winter. While the diurnal variations of the impacts share some similarities over each city, the magnitudes can be very different. The underlying mechanisms of the impacts are investigated. The TEB impacts on the predictions of meteorological and air pollutants over Toronto are evaluated against ground-based observations. The results show that the TEB scheme leads to a great improvement in biases and root-mean-square deviations in temperature and humidity predictions in downtown, uptown and suburban areas in the early morning and nighttime. The scheme also leads to a big improvement of predictions of NO${}_x$, PM${}_{2.5}$ and ground-level ozone in the downtown, uptown and industrial areas in the early morning and nighttime.