Fine particulate matter (PM2.5) air pollution is a significant health concern for the global population. The increasing frequency, intensity, and duration of wildfire smoke events continues to worsen people’s exposure, regardless of their proximity to other major sources such as industry and roadways. To mitigate this exposure, it is crucial to understand the variability of PM2.5 in both space and time. However, the current PM2.5 monitoring network in Canada is limited to major population centres, due to the prohibitive costs of maintenance and deployment. In this study, we propose that low-cost air quality monitors are a viable solution to supplementing this monitoring network. Chapter 2 presents a comprehensive evaluation of these low-cost monitors by comparison with monitors from regulatory networks, including the development of a general-use bias correction model to improve their data quality. The focus of this analysis was to ensure optimal performance in the moderate to high concentration range, where variations in the concentrations have the greatest impact on human health. Chapter 3 demonstrates the application of this bias correction model to a network of low-cost monitors in a northern Canadian city. The data generated by this network was then combined with a novel interpolation method to assess the spatial and temporal variation in PM2.5 concentrations. This analysis represents a valuable resource for any population centre with a sufficient number of monitors installed, and has the potential to inform the siting of new regulatory monitors in locations without existing coverage.
