AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Low-Cost Sensing to Assess Personal Exposure in a Heavily Burdened Air Basin
KHANH DO, Haofei Yu, Cesunica E. Ivey, University of California, Riverside
Abstract Number: 155 Working Group: Air Quality Sensors: Low-cost != Low Complexity
Abstract The South Coast Air Basin of California (USA) is well-known for its historically poor air quality. In recent years, the Basin has seen tremendous progress due to the implementation of effective emissions mitigation strategies. The Basin is still subject to poor air quality due to the expansive network of rail and interstate corridors, high volume of shipping activity in the ports of Los Angeles and Long Beach, and operations of several industrial point sources. Further, meteorological conditions in the Basin are conducive to photochemical smog formation due to landward sea breeze, the opposing mountain range, temperature inversions, and infrequent rainfall. The approximately 18 million residents of the South Coast Air Basin are subjected to high levels of primary and secondary particulate and ozone pollution as a result. In this work, we seek to determine the spatiotemporal variability in personal exposure to PM2.5 in the inland portion of the Basin, where emissions sources are commonly adjacent to residential areas and secondary pollutant formation is extensive. Further, few personal exposure studies have been conducted for the inland Basin compared to the neighboring, coastal counties of Orange and Los Angeles. In a pilot study, we measure daily PM2.5 exposure for 18 community participants from diverse backgrounds each for one week using real-time, wearable monitoring technology that samples every 15 seconds. Participants are also outfitted with fast-response GPS data loggers for precise microenvironment characterization. Results elucidate the microenvironments in the Inland Basin that pose the highest risks for PM2.5 exposure. We stratify results using 2010 Census data to investigate the relationship between socioeconomic status and exposure in this unique, mixed land-use area.