Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires
Document Type: Journal Article
Author(s): Dong Chen; Michael Billmire; Christopher P. Loughner; Allison Bredder; Nancy H. F. French; Hyun Cheol Kim; Tatiana V. Loboda
Publication Year: 2023

Cataloging Information

  • air pollution
  • air quality
  • biomass burning
  • boreal forests
  • human health
  • HYSPLIT - Hybrid Single-Particle Lagrangian Integrated Trajectory
  • PM2.5
  • remote sensing
  • wildfires
Record Maintained By:
Record Last Modified: July 18, 2023
FRAMES Record Number: 68401


Wildfire is a major disturbance agent in Arctic boreal and tundra ecosystems that emits large quantities of atmospheric pollutants, including PM2.5. Under the substantial Arctic warming which is two to three times of global average, wildfire regimes in the high northern latitude regions are expected to intensify. This imposes a considerable threat to the health of the people residing in the Arctic regions. Alaska, as the northernmost state of the US, has a sizable rural population whose access to healthcare is greatly limited by a lack of transportation and telecommunication infrastructure and low accessibility. Unfortunately, there are only a few air quality monitoring stations across the state of Alaska, and the air quality of most remote Alaskan communities is not being systematically monitored, which hinders our understanding of the relationship between wildfire emissions and human health within these communities. Models simulating the dispersion of pollutants emitted by wildfires can be extremely valuable for providing spatially comprehensive air quality estimates in areas such as Alaska where the monitoring station network is sparse. In this study, we established a methodological framework that is based on an integration of the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, the Wildland Fire Emissions Inventory System (WFEIS), and the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, an Arctic-oriented fire product. Through our framework, daily gridded surface-level PM2.5 concentrations for the entire state of Alaska between 2001 and 2015 for which wildfires are responsible can be estimated. This product reveals the spatio-temporal patterns of the impacts of wildfires on the regional air quality in Alaska, which, in turn, offers a direct line of evidence indicating that wildfire is the dominant driver of PM2.5 concentrations over Alaska during the fire season. Additionally, it provides critical data inputs for research on understanding how wildfires affect human health which creates the basis for the development of effective and efficient mitigation efforts.

Online Link(s):
Chen, Dong; Billmire, Michael; Loughner, Christopher P.; Bredder, Allison; French, Nancy H.F.; Kim, Hyun Cheol; Loboda, Tatiana V. 2023. Simulating spatio-temporal dynamics of surface PM2.5 emitted from Alaskan wildfires. Science of The Total Environment online early.