Nitrogen dioxide (NO2) is emitted during high temperature combustion from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The EPA operates ground monitors across the U.S. which take hourly measurements of NO2 concentrations, providing precise measurements for assessing human pollution exposure but with sparse spatial distribution. Satellite-based instruments capture NO2 amounts through the atmospheric column with global coverage at regular spatial resolution, but do not directly measure surface NO2. This study compares regression methods using satellite NO2 data from the TROPospheric Ozone Monitoring Instrument (TROPOMI) to estimate annual surface NO2 concentrations in varying geographic and land use settings across the continental U.S. We then apply the best-performing regression models to estimate surface NO2 at 0.01o by 0.01o resolution, and we term this estimate as quasi-NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross-validation (CV) R2 = 0.72) and away from major roads (CV R2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R2 = 0.89) and agrees least in the Southwest (CV R2 = 0.65). To account for the non-Gaussian distribution of TROPOMI NO2, we apply data transforms, with the Anscombe transform yielding highest agreement across the continental U.S. (CV R2 = 0.78). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.

Heterogeneous reactions occurring at the surface of atmospheric aerosol particles regulate the production and lifetime of a wide array of atmospheric gases. Aerosol surface area plays a critical role in setting the rate of heterogeneous reactions in atmospheric chemistry. Despite the central role for aerosol surface area, there are few assessments of the accuracy of aerosol surface area concentrations in regional and global models. In this study, we compare aerosol surface area concentrations in the EPA’s CMAQ (Community Multiscale Air Quality) model with commensurate observations from the 2011 NASA flight-based DISCOVER-AQ (Deriving Information on Surface Conditions from COlumn and VERtically Resolved Observations Relevant to Air Quality) campaign. The study region for the 2011 DISCOVER-AQ campaign focused primarily on the Baltimore and Washington, DC region. Dry aerosol surface area was measured aboard the NASA P-3B aircraft using a combination of a Scanning Mobility Particle Sizer (SMPS), Aerodynamic Particle Sizer (APS), and Ultra-High Sensitivity Aerosol Spectrometer (UHSAS). In this study, we focus on the continuous 1s UHSAS measurements in size range of 60-1000nm, as it captures the majority of the dry surface area distribution. Over the course of 13 flight campaigns, we show strong agreement between measured and modeled aerosol number concentration (CMAQ Number/UHSAS Number= 0.88). In contrast, the total surface area showed a larger discrepancy (CMAQ surface area/UHSAS surface area = 0.44). We hypothesize that the emissions and chemistry in CMAQ relating to the production and loss of each moment play a large part in the model/measurement discrepancy. Despite the disagreement, this analysis suggests that modeled aerosol surface area is accurate to within a factor of two, highlighting that uncertainty in the rate of heterogeneous reactions is largely driven by uncertainty in the reactive uptake coefficients. We would like to thank the DISCOVER-AQ NASA Langley Aerosol Research Group Experiment (LARGE) research team, including Richard Moore, Bruce Anderson, Andreas Beyersdorf, Luke Ziemba, Lee Thornhill, and Edward Winstead for the use of their data in this study.