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Modeling and Inventories

EPAct/V2/E-89 Tier 2 Gasoline Fuel Effects Study

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Understanding the effects of fuel property changes on vehicle emissions is important for assessing in-use emission inventories as well as future policy decisions. This program was cosponsored by the Department of Energy’s National Renewable Energy Laboratory and the Coordinating Research Council, and represents a major update to the large fuel effects datasets generated in the early 1990s and used in the EPA Complex and Predictive Models. Fuel parameters studied include ethanol, aromatics, vapor pressure, T50, and T90 as well as key interactions between them necessary to understand the impacts of widespread blending of ethanol. A statistically optimized matrix of 27 test fuels was tested in a fleet of 15 high-sales cars and light trucks from the 2008 model year (Tier 2 compliant) over the LA92 cycle at 75°F. Four Flexible Fuel Vehicles in the test fleet were also tested on E85 fuel.

Program design and data collection activities are described in the first report. Detailed vehicle handling and testing protocols were implemented throughout to produce the highest quality data possible. A final dataset of 956 emission tests contains phase (bag) measurements of several regulated and unregulated gaseous pollutants and particulate matter (PM), as well as second-by-second emissions and engine parameters. Exhaust speciation was performed for a large subset of tests to allow calculation of derivative measurements like NMOG, as well as estimation of fuel effects for specific air toxics such as aldehydes, acrolein, benzene and 1,3-butadiene.

A second report describes statistical analysis and modeling work done by OTAQ's Assessment and Standards Division. Models were fit to the individual phases of the LA92 cycle to allow assessment of fuel effects on cold start and hot running emissions. The database files linked below contain all regulated and speciated emission measurements by phase (bag), as well as vehicle and fuel data, which were used to perform data quality and modeling analyses.

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