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

Abstract

Satellite-based estimates of ground-level nitrogen dioxide (NO _2 ) concentrations are useful for understanding links between air quality and health. A longstanding question has been why prior satellite-derived surface NO _2 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 NO _2 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 NO _2 conditions. We then apply this algorithm to observations from the TROPOMI satellite instrument, which has been providing NO _2 column observations at an unprecedented spatial resolution for over a year. This new product achieves estimates of ground-level NO _2 with greater accuracy and higher resolution compared to previous satellite-based estimates from OMI. These comparisons also show that TROPOMI-inferred surface NO _2 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 NO _2 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.

Keywords

• air quality
• NO2
• TROPOMI
• remote sensing