Smoke from wildfires contains many air pollutants of concern and epidemiological studies have identified associations between exposure to wildfire smoke PM2.5 and mortality and respiratory morbidity, and a possible association with cardiovascular morbidity. For this study, a retrospective analysis of air quality modelling was performed to quantify the exposure to wildfire-PM2.5 across the Canadian population. The model included wildfire emissions from across North America for a 5-month period from May to September (i.e. wildfire season), between 2013 and 2015 and 2017-2018. Large variations in wildfire-PM2.5 were noted year-to-year, geospatially, and within fire season. The model results were then used to estimate the national population health impacts attributable to wildfire-PM2.5 and the associated economic valuation. The analysis estimated annual premature mortalities ranging from 54-240 premature mortalities attributable to short-term exposure and 570-2500 premature mortalities attributable to long-term exposure, as well as many non-fatal cardiorespiratory health outcomes. The economic valuation of the population health impacts was estimated per year at $410M-$1.8B for acute health impacts and $4.3B-$19B for chronic health impacts for the study period. The health impacts were greatest in the provinces with populations in close proximity to wildfire activity, though health impacts were also noted across many provinces indicating the long-range transport of wildfire-PM2.5. Understanding the population health impacts of wildfire smoke is important as climate change is anticipated to increase wildfire activity in Canada and abroad.

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.

A lockdown was implemented in Canada mid-March 2020 to limit the spread of COVID-19. In the wake of this lockdown, declines in nitrogen dioxide (NO${}_2$) were observed from the TROPOspheric Monitoring Instrument (TROPOMI). A method is presented to quantify how much of this decrease is due to the lockdown itself as opposed to variability in meteorology and satellite sampling. The operational air quality forecast model, GEM-MACH (Global Environmental Multi-scale - Modelling Air quality and CHemistry), was used together with TROPOMI to determine expected NO${}_2$ columns that represents what TROPOMI would have observed for a non-COVID scenario. Applying this methodology to southern Ontario, decreases in NO${}_2$ emissions due to the lockdown were seen, with an average $40\%$ (roughly 10 kt[NO${}_2$]/yr) in Toronto and Mississauga and even larger declines in the city center. Natural and satellite sampling variability accounted for as much as 20–30%, which demonstrates the importance of taking meteorology into account. A model run with reduced emissions (from 65 kt[NO${}_2$]/yr to 40 kt[NO${}_2$]/yr in the Greater Toronto Area) based on emission activity data during the lockdown period was found to be consistent with TROPOMI NO${}_2$ columns.