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Source apportionment: findings from the U.S. Supersites Program.

John G Watson1, L W Antony Chen, Judith C Chow

  • 1Division of Atmospheric Sciences, Desert Research Institute, Reno, NV 89512, USA. john.watson@dri.edu

Journal of the Air & Waste Management Association (1995)
|March 6, 2008
PubMed
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Receptor models help identify pollution sources like particulate matter and volatile organic compounds. Advanced datasets were used to test chemical mass balance solutions, but better source characterization is needed.

Area of Science:

  • Environmental chemistry
  • Atmospheric science
  • Air quality modeling

Background:

  • Receptor models identify and quantify source contributions to air pollutants like particulate matter and volatile organic compounds.
  • Chemical mass balance (CMB) models rely on source profiles and measurements at receptor sites.
  • UNMIX, positive matrix factorization (PMF), and effective variance are common CMB solutions implemented in software.

Purpose of the Study:

  • To apply and evaluate different chemical mass balance (CMB) receptor model solutions using advanced datasets.
  • To assess the capabilities of UNMIX and PMF in source apportionment.
  • To identify areas for improvement in receptor modeling techniques.

Main Methods:

  • Utilized advanced datasets from the U.S. Supersites Program in various urban settings.

Related Experiment Videos

  • Applied and compared different solutions to chemical mass balance (CMB) receptor model equations, including UNMIX and PMF.
  • Examined the role of source profiles and their justification in derived source factors.
  • Main Results:

    • Demonstrated the application of CMB solutions (UNMIX, PMF) using real-world urban air quality data.
    • Highlighted that while UNMIX and PMF do not explicitly use source profiles, they require measured profiles for validation.
    • Showcased the potential of CMB models to integrate spatial, temporal, and particle size information for enhanced source resolution.

    Conclusions:

    • Advanced datasets facilitate the application of CMB receptor models for air pollution source identification.
    • Further research is needed for improved source emission characterization and methods to account for profile changes.
    • Systematic sensitivity testing is crucial to understand deviations from receptor model assumptions for robust source apportionment.