Identifying Sources of NOx emissions from Aircraft through Source Apportionment and Regression Models

Abstract

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NOx emissions to improve ozone and PM2.5 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 NOx emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO2 was shown to be close to the “truth” in validating column NO2 in model simulations. Column NO2 from CAMx was substantially low biased compared to Pandora (–20%) and GCAS measurements (–31%), suggesting an underestimate of local NOx emissions. We applied a flux divergence method to the GCAS and CAMx data to distinguish the linear shape of major highways and identify NO2 underestimates at highway locations. Using a multiple linear regression model, we isolated on-road, railyard, and “other” NOx emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NOx emissions. We modified on-road and shipping NOX emissions in a new CAMx simulation and increased the background NO2, and better agreement was found with GCAS measurements: bias improved from –31% to –10% and r2 improved from 0.78 to 0.80. This study outlines how remote sensing data, including fine spatial information from newer instruments such as TEMPO, can be used in concert with chemical transport models to provide actionable information for air quality managers to identify further opportunities to reduce NOx emissions.