Research Team: Aydogan Ozcan (lead), Yifang Zhu, and Arnold Suwarnasarn
UC Campus(es): UCLA
Problem Statement: Traffic-related emissions are divided into two general categories: exhaust- and non-exhaust-related. Due to decades of efforts to reduce exhaust-related emissions, the relative contribution from non-exhaust sources has increased. The major contributors to non-exhaust particulate matter are brake and tire wear, while minor contributors include clutch and engine particle emissions. Both brake and tire wear particles are rich in metallic content which has been found to cause various health effects ranging from pulmonary inflammation to cardiac responses. Therefore, one of the conventional methods for estimating brake and tire wear is to measure the trace metals emitted from brakes and tires in a lab, but real-time measurement in the field is not available with current measurement technologies.
Project Description: In this project, researchers developed a portable computational imaging and deep-learning enhanced aerosol analysis device (c-Air) to identify and measure particulate emissions directly from traffic sources. Time-lapsed holograms of continuously collected particles emitted by a moving vehicle were taken using the c-Air device. 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. These results were in agreement with measures obtained from traditional aerosol sampling instruments.
Project Partner(s): California Air Resources Board (CARB)