Daniel L Goldberg, Benjamin de Foy, M Omar Nawaz, Jeremiah Johnson, Greg Yarwood, Laura Judd
Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO emissions to improve ozone and PM air quality. In this project, we use a combination of aircraft remote sensing (i.e., GCAS), source apportionment models (i.e., CAMx), and regression models to investigate NO emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO was shown to be close to the "truth" for validating column NO in model simulations. Column NO from CAMx was substantially low biased compared to Pandora (-20%) and GCAS measurements (-31%), suggesting an underestimate of local NO emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO underestimates at highway locations. Using a multiple linear regression (MLR) model, we isolated on-road, railyard, and "other" NO emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO emissions. Based on the MLR, we modified on-road and shipping NO emissions in a new CAMx simulation and increased the background NO, and better agreement was found with GCAS measurements: bias improved from -31% to -10% and r improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer geostationary instruments, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NO emissions.
