Demonstrating New Tools for Measuring Particulate Emissions from Vehicles

Research Team: Aydogan Ozcan (lead), Yifang Zhu, Arnold Suwarnasarn, and Farzan Oroumiyeh

UC Campus(es): UCLA

Problem Statement: Traffic-related emissions are divided into two general categories: exhaust- and non-exhaust-related. Previous studies have shown that the contribution from non-exhaust sources to PM10 (particulate matter with an aerodynamic diameter equal to or less than 10 micrometers) which can cause severe respiratory problems, is approximately equal to exhaust-related sources. The major contributors to non-exhaust particulate matter are brake and tire wear, while minor contributors include clutch and engine particle emissions. In contrast to exhaust-related emissions, non-exhaust sources are not well studied and their characteristics such as emission factors and associated health effects need more investigation.

Project Description: This project developed a portable computational imaging and deep-learning enhanced aerosol analysis device (c-Air) to identify and measure particulate emissions directly from traffic sources. Researchers found that significantly higher numbers of particles were collected per second when the car was in motion compared to the background particle levels measured when the vehicle was stationary. In addition, even more particles were generated during acceleration and braking. This mobile and cost-effective device is able to distinguish non-volatile as well as volatile and evaporating particles caused by brake and tire wear generated by a moving vehicle from background road dust, with a high degree of accuracy in the field. In addition to counting and sizing particles, this system can also classify particles based upon physical features, shape, color and volatility using computational imaging and deep learning.

Status: Completed

Budget: $114,932