Source identification of atlanta aerosol by positive matrix factorization
Eugene Kim1, Philip K Hopke, Eric S Edgerton
1Department of Chemical Engineering, Clarkson University, Potsdam, New York 13699, USA.
Journal of the Air & Waste Management Association (1995)
|June 28, 2003
Summary
Particulate matter (PM) sources in Atlanta were identified using positive matrix factorization (PMF). Fine PM was dominated by secondary aerosols and motor vehicles, while coarse PM was mainly airborne soil.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Air Quality Monitoring
Background:
- Particulate matter (PM) air pollution poses significant health risks.
- Understanding PM sources is crucial for effective air quality management.
- Atlanta's Jefferson Street site provides a key location for urban air quality studies.
Purpose of the Study:
- To identify and quantify sources of fine (PM2.5) and coarse (PM10-2.5) particulate matter in Atlanta, GA.
- To apply a bilinear positive matrix factorization (PMF) model for source apportionment.
- To correlate identified sources with local geographical and industrial data.
Main Methods:
- Analysis of daily integrated PM samples using a bilinear positive matrix factorization (PMF) model.
- Utilized 662 fine particle (PM2.5) and 685 coarse particle (PM10-2.5) samples.
- Incorporated measured PM mass concentrations and compositional data, normalized for quantitative source contributions.
Main Results:
- Identified eight sources for fine PM, with SO4(2-)-rich secondary aerosol (56%) and motor vehicle (22%) as major contributors.
- Identified five sources for coarse PM, dominated by airborne soil (60%) and NO(3-)-rich secondary aerosol (16%).
- Conditional probability functions using wind data confirmed agreement with known local point source locations.
Conclusions:
- The PMF model successfully apportioned PM mass to specific sources in Atlanta.
- Secondary aerosols and motor vehicles are key contributors to fine PM, while soil is dominant for coarse PM.
- Source identification aligns with the spatial distribution of known industrial and traffic-related activities.
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