Daily Local-Level Estimates of Ambient Wildfire Smoke PM_2.5 for the Contiguous US

Area of Science:

• Environmental Science
• Public Health
• Atmospheric Chemistry

Background:

• Wildfire smoke is an increasing threat to public health in the US.
• Assessing health impacts requires distinguishing smoke from other pollutants and analyzing long-term data.
• Previous research lacked detailed spatial and temporal data on smoke exposure.

Purpose of the Study:

• To develop and apply a machine learning model for estimating daily wildfire smoke-driven PM2.5 concentrations.
• To characterize the spatial and temporal trends of wildfire smoke exposure across the contiguous US.
• To provide data for understanding the health and societal impacts of wildfire smoke.

Main Methods:

• Developed a machine learning model using ground, satellite, and reanalysis data.
• Generated daily estimates of smoke PM2.5 on a 10 km grid for the contiguous US (2006-2020).
• Analyzed trends in smoke PM2.5 concentrations and population exposure.

Main Results:

• Smoke PM2.5 contributions increased by up to 5 μg/m³ in the Western US over the last decade.
• This increase reversed air quality improvements and disproportionately affected higher-income and Hispanic populations.
• Population exposure to extreme smoke days (PM2.5 > 100 μg/m³) increased 27-fold in a decade, affecting 25 million people in 2020.

Conclusions:

• The developed model provides accurate, updatable daily estimates of wildfire smoke PM2.5.
• Wildfire smoke exposure is increasing, particularly in the Western US, with significant population health implications.
• The data set is crucial for future research on wildfire smoke impacts and mitigation strategies.