Estimation of Surface NO2 Using Remote Sensing Data and CMAQ Model Output from DISCOVER-AQ Campaigns

Satellite-based observations of NO₂ vertical column densities have enabled development of long-term global NO₂ datasets at reasonably high spatial resolution. However, these column data are underutilized by air quality specialists, who need surface concentrations to augment sparse ground measurement networks. We describe a method for inferring estimates of surface-level NO₂ from retrievals of NO₂ tropospheric column density. This methodology is first applied to airborne remotely sensed NO₂ column data from the four NASA DISCOVER-AQ field campaigns. We use high-resolution CMAQ simulations to provide a priori information for the retrievals. The spatially and temporally varying relationship between surface and column from the CMAQ model is used in estimating surface NO₂ from the column observations. These estimates are evaluated against in-situ surface concentrations during the NASA DISCOVER-AQ field campaigns. Uncertainties in the estimates are addressed through evaluation of the CMAQ NO₂ profile shapes using in-situ profile data from the NASA P-3B aircraft. A similar method is then applied to OMI NO₂ column data to yield estimates of surface NO₂ mixing ratios over larger-scale regions. The method utilized here can be considered a prototype for potential use in estimating surface NO₂ from column data observed by the future TEMPO geostationary air quality satellite instrument.