Simulating spatio-temporal dynamics of surface PM_2.5 emitted from Alaskan wildfires

Area of Science:

• Environmental Science
• Atmospheric Science
• Public Health

Background:

• Wildfires are increasing in Arctic regions due to climate change, posing health risks.
• Alaska's remote communities have limited air quality monitoring, hindering health impact studies.
• Existing monitoring is insufficient to assess wildfire pollution's full impact on Alaskan populations.

Purpose of the Study:

• To develop a method for estimating daily PM2.5 concentrations in Alaska from wildfires.
• To map the spatial and temporal distribution of wildfire-driven air pollution in Alaska.
• To provide data for understanding wildfire health effects and informing mitigation strategies.

Main Methods:

• Integrated the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model, Wildland Fire Emissions Inventory System (WFEIS), and Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset.
• Developed a framework to estimate daily gridded surface-level PM2.5 concentrations.
• Analyzed data for Alaska from 2001 to 2015.

Main Results:

• Estimated daily PM2.5 concentrations across Alaska attributable to wildfires from 2001-2015.
• Revealed spatio-temporal patterns of wildfire impacts on regional air quality.
• Identified wildfire as the dominant source of PM2.5 during Alaska's fire season.

Conclusions:

• Wildfire emissions are the primary driver of PM2.5 concentrations in Alaska during fire seasons.
• The developed framework provides essential data for public health research in under-monitored regions.
• This work supports the development of effective mitigation efforts for wildfire-related health impacts.