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What is CanOSSEM?
The Canadian Optimized Statistical Smoke Exposure Model (CanOSSEM) is random forest machine learning model that estimates 24-hour mean all-source fine particulate matter (PM2.5) concentrations at a 5 km x 5 km spatial resolution across all populated regions of Canada (Figure 1). CanOSSEM also provides an indicator for wildfire smoke-impacted days. CanOSSEM is optimized for estimating PM2.5 related to biomass smoke by integrating regulatory air quality monitoring network observations with remotely sensed data on wildfire activity, atmospheric aerosols, smoke plumes, and meterological conditions (Figure 2). Estimates of PM2.5 concentrations and wildfire smoke days from CanOSSEM (version 3) are currently available for 2010-2023. Details on the implementation of CanOSSEM can be found in .
CanOSSEM was initially developed under the first round of Health Canada’s Addressing Air Pollution Horizontal Initiative (AAPHI) funding. CanOSSEM (version 1) estimated daily PM2.5 across all populated regions of Canada for 2010-2019. Several improvements have been made to CanOSSEM since the initial funding investment, including the expansion of the spatial grid to cover more populated raster cells and extension of the time series to include additional years. With ongoing funding from Health Canada, work is underway to further improve CanOSSEM and generate estimates of PM2.5 and wildfire smoke days for 2003-2025.
Figure 2. The workflow used to generate the PM2.5 estimates in CanOSSEM version 1, which provided estimates for 2010-2019. CanOSSEM has since been updated to include additional predictive variables, expand the spatial grid to include sparsely populated regions of Canada, and extend the time series to include additional years.