Quantifying NO<sub><i>x</i></sub> Emission\nSources in Houston, Texas Using Remote Sensing Aircraft Measurements\nand Source Apportionment Regression Models

Air quality managers in areas exceeding air pollution\nstandards\nare motivated to understand where there are further opportunities\nto reduce NO<sub><i>x</i></sub> emissions to improve ozone\nand PM<sub>2.5</sub> air quality. In this project, we use a combination\nof aircraft remote sensing (i.e., GCAS), source apportionment models\n(i.e., CAMx), and regression models to investigate NO<sub><i>x</i></sub> emissions from individual source-sectors in Houston,\nTX. In prior work, GCAS column NO<sub>2</sub> was shown to be close\nto the “truth” for validating column NO<sub>2</sub> in\nmodel simulations. Column NO<sub>2</sub> from CAMx was substantially\nlow biased compared to Pandora (−20%) and GCAS measurements\n(−31%), suggesting an underestimate of local NO<sub><i>x</i></sub> emissions. We applied a flux divergence method to\nthe GCAS and CAMx data to distinguish the linear shape of major highways\nand identify NO<sub>2</sub> underestimates at highway locations. Using\na multiple linear regression (MLR) model, we isolated on-road, railyard,\nand “other” NO<sub><i>x</i></sub> emissions\nas the likeliest cause of this low bias, and simultaneously identified\na potential overestimate of shipping NO<sub><i>x</i></sub> emissions. Based on the MLR, we modified on-road and shipping NO<sub><i>x</i></sub> emissions in a new CAMx simulation and increased\nthe background NO<sub>2</sub>, and better agreement was found with\nGCAS measurements: bias improved from −31% to −10% and\nr<sup>2</sup> improved from 0.78 to 0.80. This study outlines how\nremote sensing data, including fine spatial information from newer\ngeostationary instruments, can be used in concert with chemical transport\nmodels to provide actionable information for air quality managers\nto identify further opportunities to reduce NO<sub><i>x</i></sub> emissions.