Inferring ground-level nitrogen dioxide concentrations at fine spatial resolution applied to the TROPOMI satellite instrument

Abstract

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 for over a year. 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. These developments provide an exciting opportunity for air quality monitoring.