Abstract
On 14–17 March 1982, the U.S. Environmental Protection Agency sponsored the Mathematical and Empirical Receptor Models Workshop (Quail Roost II) at the Quail Roost Conference Center, Rougemont, NC. Thirty-five scientists were invited to participate. The objective of the workshop was to document and compare results of source apportionment analyses of simulated and real aerosol data sets. The simulated data set was developed by scientists from the National Bureau of Standards. It consisted of elemental mass data generated using a dispersion model that simulated transport of aerosols from a variety of sources to a receptor site. The real data set contained the mass, elemental, and ionic species concentrations of samples obtained in 18 consecutive 12-h sampling periods in Houston, TX. Some participants performed additional analyses of the Houston filters by X-ray powder diffraction, scanning electron microscopy, or light microscopy. Ten groups analyzed these data sets using a variety of modeling procedures. The results of the modeling exercises were evaluated and structured in a manner that permitted model intercomparisons. The major conclusions and recommendations derived from the intercomparisons were: (1) using aerosol elemental composition data, receptor models can resolve major emission sources, but additional analyses (including light microscopy and X-ray diffraction) significantly increase the number of sources that can be resolved; (2) simulated data sets that contain up to 6 dissimilar emission sources need to be generated, so that different receptor models can be adequately compared; (3) source apportionment methods need to be modified to incorporate a means of apportioning such aerosol species as sulfate and nitrate formed from SO 2 and NO, respectively, because current models tend to resolve particles into chemical species rather than to deduce their sources and (4) a source signature library may be required to be compiled for each airshed in order to improve the resolving capabilities of receptor models.
Published Version
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