Integration of Ground Measurement Networks, Numerical Models, and Satellite Data in the Characterization of PM2.5 Levels in the US

YANG LIU, Emory University, Atlanta, GA

     Abstract Number: 419
     Working Group: Aerosols Spanning Spatial Scales: Measurement Networks to Models and Satellites

Abstract
Over the past two decades, satellite aerosol remote sensing data products have seen applications beyond their original design parameters and made great advancement in generating a wide range of societal benefits including ambient PM2.5 monitoring. However, satellite data often suffer from substantial missingness due to cloud cover or unfavorable retrieval conditions. Meanwhile, the paradigm of ground air quality monitoring is shifting with the citizen science movement where many individuals voluntarily collect air quality data through low-cost air quality sensors. However, using this emerging technology to improve PM2.5 pollution mapping still deserves careful planning given the greater measurement errors of low cost sensors. Chemical transport models (CTMs) such as the Community Multiscale Air Quality (CMAQ) Model can simulate PM2.5 with full coverage in space and time, greatly expanding the study population of air pollution health effect studies to cover both urban and rural populations. However, uncalibrated CTM simulations frequently suffer from substantial prediction errors caused by imperfect characterization of atmospheric chemistry, inaccurate emission inventory and rapidly changing local meteorology. This presentation provides a framework to take advantage of the strengths of the individual techniques towards high-quality and full-coverage PM2.5 mapping.