Evaluating the spatial patterns of U.S. urban NOx emissions using TROPOMI NO2 Remote Sensing of Environment

Satellite nitrogen dioxide (NO2) datasets are increasingly used to evaluate nitrogen oxides (NOx) emissions inventories. Such studies often use a chemical transport model or a complex statistical framework involving an assumed NO2 lifetime, which can complicate the comparison. Here, we apply a novel method to compare inventory-based NOx emissions directly to Tropospheric Monitoring Instrument (TROPOMI) NO2 data without a chemical transport model by only using measurements during stagnant wind days. We oversample the satellite data over multiple years filtering to include data when near-surface wind speeds are <3.2 m/s, and then use this filtered dataset to evaluate the spatial representativeness of the 1 × 1 km2 inventory-based Neighborhood Emission Mapping Operation (NEMO). In nine out of ten US cities evaluated, spatial r2-values between NEMO NOx emissions and TROPOMI NO2 exceeded 0.73. This suggests that the 108 spatial surrogates used by NEMO to spatially disaggregate NOx emissions from the U.S. county-level (5–200 km length scale) to the neighborhood level (∼1 km length scale) are generally appropriate. However, areas with dense intermodal facilities, such as railyards and warehouses, appear to underestimate NOx emissions. Additionally, we find some evidence that NOx emissions in wealthy communities appear to be overestimated by the standard surrogates used to disaggregate the inventory. This work provides a basis for the direct use of satellite data for evaluating the spatial patterns of urban NOx emissions inventories. Grant no. NA22OAR4590515 Grant no. NA22OAR4590512