Record Display for the EPA National Library Catalog

RECORD NUMBER: 7 OF 11

OLS Field Name OLS Field Data
Main Title Turbulent Diffusion Behind Vehicles: Evaluation of Roadway Models.
Author Rao, S. T. ; Sistla, G. ; Eskridge, R. E. ; Petersen, W. B. ;
CORP Author New York State Dept. of Environmental Conservation, Albany.;Environmental Protection Agency, Research Triangle Park, NC. Atmospheric Sciences Research Lab.
Year Published 1986
Report Number EPA-R-810475; EPA/600/J-86/384;
Stock Number PB87-208682
Additional Subjects Air pollution ; Exhaust emissions ; Ground vehicles ; Mathematical models ; Statistical analysis ; Highway transportation ; Atmospheric composition ; Concentration(Composition) ; Reprints ; Automobile exhaust ; Air pollution sampling ; Bootstrap model ; Tracer studies
Holdings
Library Call Number Additional Info Location Last
Modified
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Status
NTIS  PB87-208682 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. 06/21/1988
Collation 11p
Abstract
The paper presents a statistical evaluation of three highway air pollution models (CALINE 3, HIWAY-2, and ROADWAY) using the tracer data from the General Motors Sulfate Dispersion Experiment. The bootstrap resampling procedure is used to quantify the variability in the observed concentrations due to the stochastic nature of the atmosphere. The results suggest that the variability in the observations due to the random nature of the atmosphere is about 30%. Therefore, if the predicted values are within 30% of the measured concentrations, the differences between model predictions and observations should not be considered to be significant. Comparisons of the model predictions paired and unpaired in time with measurements suggest that HIWAY-2 and ROADWAY perform best, but the performance of CALINE 3 is acceptable. Application of the extreme value theory and the bootstrap resampling procedure to the modeled and measured data (unpaired) shows that all three models are capable of predicting the extreme concentrations within the model performance criteria set forth above.