Assessing the Challenges of Surface‐Level Aerosol Mass Estimates From Remote Sensing During the SEAC⁴RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States

Abstract

Abstract The Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC 4 RS) campaign conducted in the southeast United States (SEUS) during the summer of 2013 provided a singular opportunity to study local aerosol chemistry and investigate aerosol radiative properties and PM 2.5 relationships, focusing on the complexities involved in simplifying the relationship into a linear regression. We utilize three Southeastern Aerosol Research and Characterization network sites and one Environmental Protection Agency Chemical Speciation Network station that afforded simultaneous Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and aerosol mass, chemistry, and light scattering monitoring. Prediction of AERONET AOD using linear regression of daily‐mean PM 2.5 during the SEAC 4 RS campaign yielded r 2 of 0.36–0.53 and highly variable slopes across four sites. There were further reductions in PM 2.5 predictive skill using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging SpetroRadiometer (MISR) AOD data, which have shorter correlation lengths and times relative to surface PM 2.5 . Long‐term trends in aerosol chemistry and optical properties in the SEUS are also investigated and compared to SEAC 4 RS period data, establishing that the SEUS experienced significant reduction in aerosol mass, corresponding with changes in both aerosol chemistry and optical properties. These changes have substantial impact on the PM 2.5 ‐AOD linear regression relationship and reinforce the need for long‐term aerosol observation stations in addition to concentrated field campaigns.