A Comparison of Regression Methods for Inferring Near-Surface NO2 With Satellite Data

Nitrogen dioxide (NO2) is an atmospheric pollutant emitted from anthropogenic and natural sources. Human exposure to high NO2 concentrations causes cardiovascular and respiratory illnesses. The Environmental Protection Agency 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 0.01 degrees by 0.01 degrees resolution, and we term this estimate as quasi-NO2 (qNO2). qNO2 agrees best with measurements at suburban sites (cross-validation (CV) R-2 = 0.72) and away from major roads (CV R-2 = 0.75). Among U.S. regions, qNO2 agrees best with measurements in the Midwest (CV R-2 = 0.89) and agrees least in the Southwest (CV R-2 = 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 R-2 = 0.77). The interpretability, minimal computational cost, and health relevance of qNO2 facilitates use of satellite data in a wide range of air quality applications.<br /> Plain Language Summary Nitrogen dioxide (NO2) is an air pollutant which causes cardiovascular and respiratory illnesses and reacts in the atmosphere to form other harmful pollutants. This necessitates accurate and reliable quantification of NO2 concentrations in the air. Ground monitors directly observe NO2 concentrations near the Earth's surface. However, monitors do not have sufficient spatial coverage to quantify NO2 at large scales. Satellite-based instruments capture NO2 amounts across the Earth at increasingly high spatial resolution. However, satellite instruments cannot directly observe surface NO2 concentrations. In this study, we compare regression methods for estimating surface NO2 over the continental U.S. using satellite data and auxiliary land-use variables. We find that NO2 estimated using multivariate regression models with transforms applied to inputs result in the highest agreement with surface NO2 among the regression methods we investigated. We then use the regression models to quantify surface NO2 concentration across the U.S. at 0.01 degrees by 0.01 degrees spatial resolution. Our work leverages the precision of ground observations and the high resolution of satellite data to accurately quantify surface NO2. The interpretable, generalizable, and easily applicable methods used in our study will facilitate the use of satellite data for air quality and human health assessments.