09:30 〜 09:45
[MIS01-03] Modeling air quality degradation driven by wildfire events in boreal forests and tundra of Northern Eurasia
★Invited Papers
キーワード：wildfire, air quality, fine particulate matter, health outcomes
The recent string of extreme wildfires across the globe is highlighting the long-anticipated change in fire regimes towards larger, more frequent, and more severe fires under climate change. Although an important and necessary process, fire is a disturbance agent that leads to rapid and dramatic ecosystem modification and large changes in global biogeochemical cycles. Although boreal and arctic fires frequently occur in remote and sparsely populated areas, their impact on air quality is felt acutely across the entire region and, considering the long-distance transport of smoke, frequently within very densely populated areas far outside of the immediate vicinity of fires. The concern over fire-driven air quality degradation continues to grow as new evidence emerges that links wildfire smoke pollution, and specifically fine particularly matter (PM2.5), to health outcomes. A rapidly growing number of epidemiological studies have linked PM2.5 to adverse outcomes on the cardiovascular system, respiratory distress, neuropsychiatric disorders, pregnancy outcomes, and child and adolescent development. Numerous approaches have been developed to monitor, model, and forecast smoke distribution during extreme fire events. These approaches rely on a combination of numerical modeling and satellite observations of fire activity linked and constrained by surface-level observations of PM2.5 concentration available at air quality observation stations. In this study, we present a satellite data-based model of wildfire-driven PM2.5 concentrations developed using an expanded network of Low-Cost Air Quality monitors in Alaska. This model produces reliable estimates of surface PM2.5 concentrations without access to numerical models or observations of the Aerosol Optical Depth (AOD). Although MODIS-based estimates of AOD are routinely produced, our results show that only high-quality of AOD measurements deliver good estimates of PM2.5 concentrations. Thus, approaches relying on AOD measurements suffer considerably from data missingness and cannot deliver wall-to-wall assessments of fire-driven PM2.5 concentrations. We apply this model to quantify wildfire-driven PM2.5 concentrations in Northern Eurasia during the last several years and evaluate the frequency, length, and level of population exposure to fire-driven air pollution.