American Association for Aerosol Research - Abstract Submission

AAAR 32nd Annual Conference
September 30 - October 4, 2013
Oregon Convention Center
Portland, Oregon, USA

Abstract View


Cloud Condensation Nuclei Closure Study for Transient Drive Cycles

DIEP VU, Daniel Short, Mark Villela, Georgios Karavalakis, Thomas D. Durbin, Akua Asa-Awuku, University of California, Riverside

     Abstract Number: 374
     Working Group: Aerosols, Clouds, and Climate

Abstract
Particles emitted in the exhaust may vary in composition under different driving conditions. This poses challenges when characterizing ephemeral changes in aerosol composition for vehicle-testing procedures conducted over transient cycles. Hence, cloud condensation nuclei (CCN) properties of the exhaust from vehicle studies with gasoline fuel blends are currently not well understood. In this study, the potential to activate as CCN and form cloud droplets in the atmosphere was investigated for aerosols derived from various biofuel/gasoline blends. Measurements were collected for gas direct injection passenger (GDI) vehicles using different blends of ethanol and iso-butanol in gasoline over the Federal Test Procedure (FTP) and California Unified Cycle (UC) on a chassis dynamometer.
A Continuous Flow Streamwise Thermal Gradient CCN Counter was operated in parallel with two different sizing instruments; a Scanning Mobility Particle Sizer (SMPS) and an Engine Exhaust Particle Sizer (EEPS). The EEPS provides a full particle size distribution within each second. The SMPS has longer time resolution and provides size distributions every 2.25 min. All instruments are operated during transient driving cycles. This is the first study to combine fast resolution CCN activation information from both a continuous CCN counter and an EEPS (10Hz). Results show the critical diameter shifts throughout the cycle, thereby reflecting the sensitivity of aerosol composition to the transient nature of the cycle. Measured versus predicted values of CCN are presented with EEPS and SMPS data sets.