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Receptor Autoradiography Protocol for the Localized Visualization of Angiotensin II Receptors
Published on: June 7, 2016
Evaluation of regional-scale receptor modeling
Douglas H Lowenthal1, John G Watson, Darko Koracin
1Desert Research Institute, Reno, NV 89512, USA. Doug.Lowenthal@dri.edu
Journal of the Air & Waste Management Association (1995)
|January 28, 2010
Summary
Receptor models like PMF and Unmix showed limitations in accurately estimating regional fine particulate matter (PM2.5) contributions. The trajectory mass balance regression (TMBR) model offered a better approach for sulfate apportionment, though results varied with meteorological data.
Area of Science:
- Environmental Science
- Atmospheric Chemistry
- Air Quality Modeling
Background:
- Receptor models are crucial for source apportionment of fine particulate matter (PM2.5).
- Accurate regional source attribution is vital for effective air quality management.
Purpose of the Study:
- To assess the performance of receptor models (PMF, Unmix) and trajectory models (TMBR) in estimating regional PM2.5 contributions.
- To compare model-derived source contributions with "true" contributions from synthetic data.
Main Methods:
- Generated synthetic PM2.5 chemical concentrations using the Community Multiscale Air Quality (CMAQ) model.
- Applied positive matrix factorization (PMF) and Unmix receptor models.
- Utilized trajectory mass balance regression (TMBR) with HYSPLIT trajectories and MM5/EDAS meteorological data.
Main Results:
- PMF explained 99% of data variability but struggled to separate specific sources like vehicles.
- Unmix also identified a secondary sulfate factor but combined or missed some PMF factors.
- TMBR qualitatively agreed with true regional sulfate contributions, with local regions being the largest contributors.
Conclusions:
- Receptor models have limitations in resolving specific regional PM2.5 sources.
- TMBR shows promise for regional sulfate apportionment, but results are sensitive to trajectory elevation and meteorological input.
- Further refinement of models is needed for accurate source attribution.
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