AAAR 33rd Annual Conference
October 20 - October 24, 2014
Rosen Shingle Creek
Orlando, Florida, USA
Abstract View
Seasonal and Multi-Year Trends in Vehicle Emissions Measured in a Traffic Tunnel
ALBERT A. PRESTO, Xiang Li, Timothy Dallmann, Carnegie Mellon University
Abstract Number: 112 Working Group: Urban Aerosols
Abstract Even after multiple decades of regulation, emissions from motor vehicles remain a major source of urban air pollution. Thus a nearly constant need exists to characterize and better understand vehicle emissions and their changes over time. In recent years, several key regulatory changes and scientific advances have shifted traditional views of motor vehicle emissions. Notably, strict regulations for emissions of particulate matter (PM) and nitrogen oxides (NOx) from diesel engines are changing the characteristic emissions profile of diesel vehicles. Emissions of PM, NOx, and CO from gasoline-powered automobiles are also declining due to increasingly strict regulations.
In order to explore emissions from a current on-road vehicle fleet we measured the emissions of particulate (e.g., PM mass, OC, and EC) and gaseous pollutants (e.g., NOx, CO) from motor vehicles in a traffic tunnel in Pittsburgh, PA. Pollutant emissions data are reported as a function of the percentage of diesel fuel use in the tunnel, which allows for the estimation of separate emission factors for gasoline and diesel vehicles. Fuel- and fleet-averaged diesel emission factors are supplemented by measurements of emissions from plumes created by individual heavy-duty diesel vehicles. Measurements have been ongoing since January 2013 and are designed to capture seasonal and multi-year temporal trends in vehicle emissions. Seasonal trends are largely driven by changes in ambient temperature, while multi-year trends reflect the impact of turnover in the vehicle fleet.
Emissions of OC, EC, and NOx are 40-50% lower than similar measurements conducted at a nearby tunnel in 2001-2002. Initial results indicate that particulate OC emissions display slightly negative temperature dependence, with higher emissions at lower temperatures, while EC emissions are temperature independent. OC emissions data will be analyzed to determine if the temperature dependence is consistent with OC partitioning as predicted by measured volatility distributions of vehicle emissions.