Journal Article Epublish 4 citations
PubMed: 39539465 DOI: 10.1021/acsestair.4c00097
Quantifying NO Emission Sources in Houston, Texas Using Remote Sensing Aircraft Measurements and Source Apportionment Regression Models.
ACS EST Air 1(11):1391-1401
Authors: Daniel L Goldberg, Benjamin de Foy, M Omar Nawaz, Jeremiah Johnson, Greg Yarwood, Laura Judd
Affiliations
- Department of Environmental and Occupational Health, George Washington University, Washington, D.C. 20052, United States.
- Department of Earth and Atmospheric Sciences, Saint Louis University, St. Louis, Missouri 63103, United States.
- Ramboll Americas Engineering Solutions, Novato, California 94945, United States.
- NASA Langley Research Center, Hampton, Virginia 23681, United States.
Abstract
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.
MeSH
Research Design, Bias, Air Pollution, Aircraft, Air