Date: August 1, 2021
Author(s): Adyogan Ozcan, Yifang Zhu
Abstract
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. 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. 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. Both brake and tire wear particles are rich in metallic content. 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. This study developed a portable computational imaging and deep-learning enhanced aerosol analysis device (c-Air) and 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.
About the Project
Evaluation of particulate matter (PM) due to transportation systems is of interest to public health professionals and policymakers in California and Southern California, specifically. Poor air quality can lead to short-term eye, throat, and nose irritations, as well as long-term cancers. While PM can be reduced through new regulations including bus-only lane projects, carpooling, and the adoption of clean air vehicles, there is a need for highly accurate, yet cost-effective sensors which can assess the efficacy of these improvements. UCLA will develop a field-portable computational imaging and deep-learning enhanced aerosol analysis device, termed c-Air, to characterize PM due to transportation systems. In addition to particle counting and sizing, UCLA will further enhance its system above the current gold standard by classifying particles based upon physical features and volatility using computational imaging and deep learning.