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Modeling Air Quality in Los Angeles During the COVID-19 Pandemic Using CMAQ: Organic Aerosol Chemistry, Speciation, and Source Apportionment
ELYSE PENNINGTON, Yuan Wang, Karl Seltzer, Jiani Yang, Benjamin Schulze, Meemong Lee, Havala Pye, Benjamin Murphy, Christopher Kenseth, Benjamin Moul, Lelia Hawkins, Harrison Parker, John Crounse, Paul Wennberg, John Seinfeld, California Institute of Technology
Abstract Number: 105
Working Group: Urban Aerosols
Abstract
The COVID-19 pandemic had large impacts on human behavior and air quality in urban areas around the world. Modeling these changes on a regional scale has been difficult due to the complex nature of pandemic-induced variations in emissions, meteorology, and multi-phase atmospheric chemistry. Here we utilize the Community Multiscale Air Quality (CMAQ) model version 5.3.2 with new chemical mechanism updates to understand air quality in the Los Angeles Basin during March-June, 2020. We use a random forest regression model to incorporate real-time traffic data into an existing emissions model, yielding accurate mobile source emissions that are specific to our simulation period. Volatile chemical product (VCP) emissions are provided by the VCPy framework, and sensitivity simulations are performed to understand the effect of local shutdowns on VCP emissions and the resultant air quality. The chemical mechanism of CMAQ was updated to include secondary organic aerosol (SOA) formation from VCPs and mobile source intermediate volatility organic compounds (IVOCs). Model predictions of PM2.5, ozone, and speciated SOA are compared to concurrent measurements made in Pasadena, Claremont, and routine monitoring stations throughout the Los Angeles Basin. The impacts of meteorology (e.g. temperature and precipitation) on elevated ozone and reduced particulate matter (PM) concentrations are investigated using sensitivity experiments in the Weather Research and Forecasting (WRF) model. We aim to understand the primary emissions sources and atmospheric oxidation pathways of organic aerosol (OA) by investigating the individual components of SOA and use 1x1 km resolution to explore fine-scale spatial variations. Understanding the impact of COVID-19 on human behavior and the subsequent changes in air quality provides an unparalleled opportunity to predict the impact of future policy changes on air quality and human health.