Assessing source characteristics of PM2.5 in the eastern United States using positive matrix factorization

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Air Quality Monitoring

Background:

• Fine aerosol (PM2.5) poses significant health risks.
• Understanding PM2.5 sources is crucial for effective air quality management.
• Nationwide monitoring networks provide valuable data for source apportionment.

Purpose of the Study:

• To identify and characterize sources of fine aerosol (PM2.5) in the eastern United States.
• To analyze seasonal variations in PM2.5 concentrations and their contributing factors.
• To apply the positive matrix factorization (PMF) technique to a large dataset.

Main Methods:

• Utilized PM2.5 mass concentration data from the EPA's AIRS database.
• Applied the positive matrix factorization (PMF) technique to data from over 350 monitoring sites.
• Analyzed 24-hour averaged PM2.5 concentrations measured every third day from April to December 1999.

Main Results:

• The PMF model identified six distinct factors representing PM2.5 source influences.
• A specific factor (Factor 5), prevalent in the Appalachian states, demonstrated significant seasonal behavior.
• Spatial and temporal patterns of PM2.5 sources were investigated across the eastern US.

Conclusions:

• Source apportionment of PM2.5 is feasible using PMF on national monitoring data.
• Seasonal variations in PM2.5 sources, particularly in the Appalachian region, warrant further investigation.
• The study provides a foundational understanding of PM2.5 sources for air quality policy development.