AAAR 36th Annual Conference October 16 - October 20, 2017 Raleigh Convention Center Raleigh, North Carolina, USA
Abstract View
Towards Understanding the Physical Conditions Governing the Relationship between Aerosol Optical Depth and Surface PM2.5 Mass in the Western U.S.
SANDRA-MARCELA LORÍA-SALAZAR, Anna Panorska, W. Patrick Arnott, James Barnard, Cesunica Ivey, Jayne Boehmler, Heather Holmes, University of Nevada, Reno
Abstract Number: 219 Working Group: Remote and Regional Atmospheric Aerosols
Abstract Monitoring surface PM2.5 using satellite retrievals is desired because of the improvement in spatial resolution of the sampling with respect to ground-based monitoring stations. Data fusion models based on statistical techniques aim to create relationships that rely on columnar aerosol optical depth (AOD) from satellite retrievals as a spatial surrogate of surface PM2.5. Those models show optimistic results in the eastern U.S. because of the strong correlation between AOD and PM2.5. However, data fusion models based on purely statistical approaches are not able to represent surface concentrations of aerosol pollution in the western U.S. because they are challenged by complex atmospheric physics (e.g planetary boundary layer mixing, transport of aerosol pollution), air pollution sources, and uncertainty of satellite retrievals due to instrument calibration and non-ideal model parametrizations and assumptions. Therefore, data fusion or exposure models based on purely statistical relationships may not be able to capture the physical conditions governing the relationship between AOD and PM2.5 in the western U.S. Thereby requiring an intensive examination of the atmospheric conditions to improve surface estimates of PM2.5. The main goals of this investigation are: 1) Study the atmospheric processes that impact the complex relationship between AOD and PM2.5 using ground-based sunphotometry as “ground-truth” and 2) investigate the atmospheric and aerosol pollution scenarios under which AOD from satellite retrievals can be used as spatial predictors of surface PM2.5. Future aims include using the results of this investigation to help identify atmospheric variables that will improve results from data fusion models that estimate near surface PM2.5.