Record Display for the EPA National Library Catalog


OLS Field Name OLS Field Data
Main Title Improvements to Single-Source Model. Volume 2. Testing and Evaluation of Model Improvements.
Author Mills, Michael T. ; Stern, Roger W. ; Vincent., Linda M. ;
CORP Author GCA Corp., Bedford, Mass. GCA Technology Div.;Environmental Protection Agency, Research Triangle Park, N.C.
Year Published 1977
Report Number GCA-TR-76-6-G(2); EPA-68-02-1376; EPA/450/3-77/003b;
Stock Number PB-271 922
Additional Subjects Mathematical models ; Sulfur dioxide ; Electric power plants ; Atmospheric diffusion ; Substitutes ; Dispersion ; Concentration(Composition) ; Industrial wastes ; Combustion products ; Numerical analysis ; Sites ; Monitoring ; Wind velocity ; Wind direction ; Computer programs ; Graphic methods ; Air pollution ; Plumes ; CRSTER models ; Air pollution sampling ; Single source models
Library Call Number Additional Info Location Last
NTIS  PB-271 922 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 06/23/1988
Collation 174p
The main purpose of this study was to determine whether alternate methods for stability index assignment and dispersion calculation would yield better agreement between measured and calculated cumulation frequency distributions of 1-hour SO2 concentrations when used in the EPA Single Source Model. The following dispersion curves were tested: Pasquill-Turner, Gifford-Briggs, Smith-Singer and F. B. Smith. A fractional stability assignment technique based upon the work of F. B. Smith was also investigated. Based upon model validation results for the Canal Power Plant in Massachusetts and the Muskingum Power Plant in Ohio, the Pasquill-Turner dispersion curves and stability index assignment algorithm currently used in the model were found to give the best agreement with measured concentration distributions. During the course of the study the incorporation of a variable stack gas exit velocity was evaluated and found not to appreciably affect the model predictions.