A State of the Science Review of Wildfire-Specific Fine Particulate Matter Data Sources, Methods, and Models.

Area of Science:

• Environmental Health Sciences
• Atmospheric Science
• Public Health

Background:

• Wildfire frequency and intensity are increasing due to climate change, posing significant health risks.
• Accurate estimation of wildfire-generated fine particulate matter (PM2.5) is crucial for health research and policy.
• Existing industrial pollution reduction efforts are overshadowed by growing wildfire impacts.

Purpose of the Study:

• To review and evaluate methodologies and data sources for estimating wildfire-generated PM2.5.
• To improve the accuracy and accessibility of wildfire PM2.5 data for health research and policy.
• To compare publicly available datasets for wildfire PM2.5 estimation.

Main Methods:

• Systematic literature search (Jan 2018-Mar 2024) across multiple databases.
• Inclusion criteria: publicly available, North America focus, wildfire-attributable PM2.5 data.
• Data analysis using R at ZIP code level, examining correlations and causality.

Main Results:

• Three main estimation approaches identified: chemical extraction, thresholding, and data integration.
• Wildfire PM2.5 contribution varied: 11.2%-36.9% (2010) and 13.7%-21.2% (2018) in California.
• Correlations between datasets were stronger in areas with fewer monitors and during high fire activity.

Conclusions:

• Wildfire smoke's contribution to PM2.5 increases with fire activity.
• Limited publicly available datasets restrict comprehensive comparisons and health research.
• Further development of accessible data sources is needed for effective wildfire impact assessment.