Investigating Hyper-Local NOx Emissions in Houston and New York City Using GCAS, TROPOMI, and Sub-Kilometer Model Simulations

Nitrogen dioxide (NO₂) is an air pollutant with concentrations that are often largest in densely populated urban areas and environmental justice communities, and therefore can affect large and vulnerable populations. Rapid conversion of nitric oxide (NO) emissions to NO₂ (collectively NOx) coupled with strong spatial variations in NO emission intensity challenge NOx emission inventory validation with source specificity. Methods to map concentrations between monitoring locations are crucial to better understand the relative influence of different sources of NO₂ and to ameliorate its public health burden.

In this study we leverage spatially continuous column NO₂ measurements from the GeoCAPE Airborne Simulator (GCAS), and the Tropospheric Monitoring Instrument (TROPOMI) to validate a 444 x 444 m² CAMx model simulation and characterize potential biases in the NO_x emissions inventory in the Houston metropolitan area during September 2021. First we compare GCAS and TROPOMI column NO₂ measurements to the “gold-standard” Pandora measurements. We find that column NO­₂ from GCAS has excellent agreement with Pandora measurements (R²=0.81 and NMB of +2.4%), column NO₂ from TROPOMI had good correlation with Pandora (R²=0.62), but there was a low bias that remained even when a CAMx-based air mass factor was used (NMB of –11%). The model output was then thoroughly compared to observations from the GCAS instrument, Pandora instruments, and TROPOMI to identify gaps in our understanding of NO_X emissions and NO₂ dispersion within CAMx. Column NO₂ from CAMx showed a substantial low bias when compared with Pandora (NMB of –20%) and GCAS measurements (NMB of –37%). Through this comparison and a multiple linear regression model, we were able to isolate on-road and railyard emissions as probable sources of a NO_X emissions inventory underestimate. Preliminary work from the New York City and Chicago areas will also be shared. Lessons learned and techniques developed for this project could be applied to other cities and potentially using data from the Tropospheric Emissions Monitoring of Pollution satellite (TEMPO) when available.