Quantifying NOx Emission Sources in Houston, Texas Using Remote Sensing Aircraft Measurements and Source Apportionment Regression Models

Abstract

Air quality managers in areas exceeding air pollution standards are motivated to understand where there are further opportunities to reduce NO_x emissions to improve ozone and PM_2.5 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 NO_x emissions from individual source-sectors in Houston, TX. In prior work, GCAS column NO₂ was shown to be close to the “truth” for 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 NO_x 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 (MLR) model, we isolated on-road, railyard, and “other” NO_x emissions as the likeliest cause of this low bias, and simultaneously identified a potential overestimate of shipping NO_x emissions. Based on the MLR, we modified on-road and shipping NO_x 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 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_x emissions.

Sector NO_x emissions in Houston, Texas are constrained by combining GCAS remote sensing NO₂ measurements and source apportioned chemical transport modeling using regression analysis; on-road NO_x underestimated, shipping NO_x overestimated.