Abstract View
Improved Prediction of Near-Road Vehicle Emissions for Gasoline and Diesel Vehicles Between Emission Simulators and Measured Data from PEMS and Laboratory Measurements
AYLA MORETTI, David R. Cocker III, Matthew Barth, University of California, Riverside
Abstract Number: 222
Working Group: Urban Aerosols
Abstract
Vehicle emissions are measured using dynamometers (coupled with test-cell emission instruments) and/or portable emissions measurement systems (PEMS); however, these systems operate at temperatures and dilution ratios that are not representative of the ambient atmosphere. Estimates of near-road particulate matter (PM) concentrations using these emission factors (EF) within emission models, such as the EPA’s Motor Vehicle Emission Simulator (MOVES), are not in agreement with measured near-road PM concentrations. A majority of differences between the near-road studies and current emission simulators could be due to MOVES, and other emission simulators, treating OA as non-volatile and not adjusting the PM based on the gas-particle partitioning that occurs immediately after the emissions rapidly dilute and cool in the ambient atmosphere. Gas-particle partitioning suggests that we need a better way to predict roadside emissions by extrapolating from PEMS and dynamometer-based measurements.
This research is a continuation of data that was presented last year and uses published volatility basis set (VBS) data coupled with a newly developed model to improve the prediction of near-road PM2.5 from gasoline and diesel vehicles. Using the VBS approach, the gas-particle partitioning of OA from gasoline and diesel vehicles were modeled using Python to create a correction factor and empirical formula that can work with the outputted MOVES EF to correct for primary PM2.5 from vehicles. This correction factor helps to bridge the gap between regulatory model estimations and what is measured near-road. Results indicate that, as suspected, the gas-particle partitioning plays a major role in final PM levels present in the atmosphere due to vehicle exhaust. This research explores sensitivity of sampling dilution & temperature (from the PEMS and dynamometers), ambient temperature & background PM, distance from the vehicle, and the vehicles EC/TC ratio, and shows that there is a bias in predicted roadside PM using the current transportation models.