AQmap in Action: Tracking wildfire smoke in real time for cleaner air decisions

Abstract

Wildfire activity is rising across Canada, leading to increasingly severe smoke episodes that pose significant health risks to communities. Unlike traditional air pollution, wildfire smoke disperses over vast areas, often far from the emission source. Timely, localized air quality information is critical for public health response, yet access to this information remains inequitable due to Canada’s expansive geography and the high cost of conventional monitoring networks. The emergence of low-cost monitors like PurpleAir is helping to bridge some of these gaps, but real-time smoke data is fragmented across multiple platforms, making it challenging to access and interpret.

AQmap addresses these critical barriers by integrating data from diverse sensor networks, satellite observations, and modeling outputs into a single, bilingual, user-friendly platform that delivers real-time situational awareness during wildfire smoke events. This presentation will explore current inequities in air quality monitoring, and will demonstrate practical applications of AQmap for empowering environmental health professionals, air quality forecasters, and the public across Canada to monitor and respond effectively to wildfire smoke exposure.

Additional Resources: https://aqmap.ca

Speaker

Brayden Nilson is an Applied Air Quality Scientist with the Western Air Quality Science Unit at Environment and Climate Change Canada (ECCC). He holds an MSc and BSc in Environmental Science from the University of Northern British Columbia and has nearly a decade of experience working with air quality data. Brayden is the developer of AQmap—an online platform that integrates real-time PM_2.5 data from across Canada to support wildfire smoke response and air quality awareness. He has played a key role in evaluating the feasibility of low-cost monitors, such as PurpleAir, to expand access to air quality observations nationwide. His current work involves improvements to AQmap, assessing existing inequities in Canada’s PM_2.5 monitoring network, as well as developing a machine-learning based quality control and bias correction system for Canadian PurpleAir observations.