Characterizing the Spatial and Temporal Variability of Pollution in an Urban Oil Field Using Low-cost Sensors

CAROLINE FRISCHMON, Venezia Ramirez, Brittney Lu-Jones, Nancy Lam, Richard Parks, Bhavna Shamasunder, Jill Johnston, Michael Hannigan, University of Colorado Boulder

     Abstract Number: 71
     Working Group: Urban Aerosols

Abstract
The Las Cienegas oilfield in South Los Angeles sits among the most environmentally burdened communities in California. In addition to pollution exposure from oil extraction, residents are also exposed to traffic-related and other typical urban sources of pollution. We set up a dense network of air sensor packages, called HAQ-Pods, in the community to better understand how pollution varies spatially and temporally within this complex environment.

The HAQ-Pods measure multiple pollutants, including PM_2.5, NO₂, total volatile organic compounds (TVOCs), CO, and CH₄. Measuring and exploring the relationships between these pollutants provides insight into potential emission sources. For example, CO and CH₄ can serve as tracer species for traffic and oil extraction, respectively. We show how correlations with these tracers can reveal likely sources of more broadly emitted pollutants, such as PM_2.5.

We also use wavelet decomposition to separate each concentration time series into short-lived (<2 h), longer-lived (2-8 h), and regional (>8 h) components. Through this analysis, we found that variance in PM_2.5 predominantly came from the regional component, indicating PM2.5 levels in the community are not driven by the local sources of concern. Other pollutants showed more significant contribution from all three components. We use bivariate polar plotting to visualize how the direction of origin varies across the regional, long-lived, and short-lived contributions of each pollutant. Combining these analytical tools provides a more comprehensive understanding of air quality in a complex urban environment.