Tracking Air Pollution: Global Near Real-Time Fire PM2.5 Retrievals from Multisource Data Fusion

Wildfire smoke has raised concerns on air quality and public health with the increasing intensity, frequency, and duration of wildfires as a result of climate change. This study generates a global near real-time wildfire-related PM_2.5 (i.e., fire PM_2.5) concentration product by combining multisource data with machine learning algorithm. This is the first daily updated full-coverage high-resolution fire PM_2.5 data products that allows timely tracking of the fast-growing fire PM_2.5 globally. The gridded fire PM_2.5 data at a spatial resolution of 0.1°×0.1° are estimated by fusing surface PM_2.5 monitoring, satellite observations, meteorological fields, atmospheric composition reanalysis data, and population distribution through a three-layer random forest model. We found that during 2023-2024, wildfire smoke contributed 1.32 μg/m³ (4.7%) and 1.25 μg/m³ (4.7%) to population-weighted annual average PM_2.5 worldwide, and caused 50,700 (95% confidence interval: 33,600-68,300) and 51,500 (34,100-69,400) all-cause deaths through acute fire PM_2.5 exposure, respectively. Regionally, the record-breaking wildfires resulted to 1.42 μg/m³ (21%) and 2.53 μg/m³ (16%) increase in population-weighted annual average PM_2.5 in Canada (2023) and South America (2024), respectively. We noticed that a relatively small number of extreme wildfire episodes could disproportionately impact regional public health, emphasizing the importance of timely monitoring of wildfire-induced PM_2.5 pollution. The global fire PM_2.5 data will be publicly available on the Tracking Air Pollution platform (TAP, http://tapdata.org.cn), to support promptly health impact assessment and policymaking for wildfire risk mitigation.