Record Display for the EPA National Library Catalog

RECORD NUMBER: 28 OF 255

OLS Field Name OLS Field Data
Main Title Binary Recursive Partitioning Method for Modeling Hot-Stabilized Emissions from Motor Vehicles.
Author Washington, S. ; Wolf, J. ; Guensler, R. ;
CORP Author Georgia Inst. of Tech., Atlanta. School of Civil and Environmental Engineering.;Environmental Protection Agency, Research Triangle Park, NC. Air Pollution Prevention and Control Div.
Publisher 1997
Year Published 1997
Report Number EPA-R-823020; EPA/600/A-97/028;
Stock Number PB97-192884
Additional Subjects Mobile pollutant sources ; Emission factors ; Driving style effect on exhaust emissions ; Travel patterns ; Motor vehicles ; Vehicle air pollution ; Recursive functions ; Separation ; Binary processing ; Hydrocarbons ; Regression analysis ; Variables ; Least squares method ; Mutiple emission tests ; Statistical mdoels ; Mathematical models ; Binary recursive partitioning
Holdings
Library Call Number Additional Info Location Last
Modified
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Status
NTIS  PB97-192884 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 12/22/1997
Collation 24p
Abstract
The paper gives an alternative statistical modeling approach for developing modal correction factors for hydrocarbon (HC) emissions from motor vehicles. 'Modal' refers to operating modes of vehicle activity; e.g., cruise, idle, deceleration, acceleration, power, and positive kinetic energy. The modeling method used to estimate HC modal emission factors is called binary recursive partitioning, or hierarchical tree-based regression (HTBR). The paper explains the statistical theory and gives specific modeling results for modal emission factors for HCs. A data set containing 4800 vehicle tests representing 29 different laboratory testing cycles was developed and used in the analysis. HTBR methods provide statistical features that are problematic for classical ordinary least squares (OLS) regression methods, a commonly applied statistical technique for analyzing emissions data (both the California Air Resources Board (CARB) and the U.S. EPA have applied OLS regression techniques to derive emission factors in the EMFAC and MOBILE models, respectively).