Record Display for the EPA National Library Catalog

RECORD NUMBER: 39 OF 81

OLS Field Name OLS Field Data
Main Title Evaluation of Four Urban-Scale Photochemical Air Quality Simulation Models.
Author Shreffler, Jack H. ; Schere, Kenneth L. ;
CORP Author Environmental Sciences Research Lab., Research Triangle Park, NC.
Year Published 1982
Report Number EPA-600/3-82-043;
Stock Number PB82-239278
Additional Subjects Air pollution ; Urban areas ; Mathematical models ; Ozone ; Concentration(Composition) ; Air quality ; Photochemical box model ; Lagrangian photochemical model ; Urban Airshed Model ; Livermore regional air quality model
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB82-239278 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 06/23/1988
Collation 179p
Abstract
The purpose of this research was to determine the accuracy of four photochemical air quality simulation models using data from the Regional Air Pollution Study in St. Louis. The models evaluated in this report are: The Photochemical Box Model (PBM) built in-house by EPA, The Lagrangian Photochemical Model (LPM) built by Environmental Research and Technology, Inc., The Urban Airshed Model (UAM) built by Systems Applications, Inc., and The Livermore Regional Air Quality Model (LIRAQ) built by Lawrence Livermore Laboratory. Emphasis in this report is directed at the ability of the models to reproduce the maximum 1-hour ozone concentrations observed on 10 days selected from nearly two years of data. The PBM, LPM, and UAM have been successfully tested and show potential as air quality management tools. LIRAQ does not show potential as a model for general use, irrespective of its accuracy (which was impossible to judge at this time). The standard deviation of the differences between observed ozone maxima and predicted concentrations at the same place and time tend to be large, ranging 0.04-0.1 ppm for maxima of 0.19-0.26 ppm. Although some problems exist whose resolution could improve model performance, this high variability should be recognized by decision-makers.