Improved Surface-Level Nitrogen Dioxide Estimates at Fine Spatial Resolution using the TROPOMI Satellite Instrument

Abstract:

Satellite-based estimates of ground-level nitrogen dioxide (NO₂) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO₂ concentrations are biased low with respect to ground-based measurements. In this work we demonstrate that these biases are due to both the coarse resolution of previous satellite NO₂ products and inaccuracies in vertical mixing assumptions used to convert satellite-observed tropospheric columns to surface concentrations. We develop an algorithm that now allows for different mixing assumptions to be used based on observed NO₂ conditions. We then apply this algorithm to observations from the TROPOMI satellite instrument, which has been providing NO₂ column observations at an unprecedented spatial resolution since 2018. This new product achieves estimates of ground-level NO₂ with greater accuracy and higher resolution compared to previous satellite-based estimates from OMI. These comparisons also show that TROPOMI-inferred surface NO₂ concentrations from our updated algorithm have higher correlation and lower bias than those found using TROPOMI and the prior algorithm. TROPOMI-inferred estimates of the population exposed to NO₂ conditions exceeding health standards are at least three times higher than for OMI-inferred estimates. This algorithmic advancement provides an exciting opportunity for air quality monitoring, with the potential for even greater improvements in NO₂ monitoring from future geostationary satellite instruments.