Journal Article Ppublish 17 citations
PubMed: 39872628 DOI: 10.5194/acp-22-15981-2022
Inferring and evaluating satellite-based constraints on NO x emissions estimates in air quality simulations.
Atmos Chem Phys 22(24):15981-16001
Authors: James D East, Barron H Henderson, Sergey L Napelenok, Shannon N Koplitz, Golam Sarwar, Robert Gilliam, Allen Lenzen, Daniel Q Tong, R Bradley Pierce, Fernando Garcia-Menendez
Affiliations
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27606, USA. Oak Ridge Institute for Science and Education, Office of Research and Development, U.S. Environmental, Protection Agency, Research Triangle Park, NC 27711, USA.
- U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA.
- Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA.
- Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USA.
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27606, USA.
Abstract
Satellite observations of tropospheric NO 2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NO x ). Mass-balance-based methods are often applied for this purpose but do not isolate near-surface emissions from those aloft, such as lightning emissions. Here, we introduce an inverse modeling framework that couples satellite chemical data assimilation to a chemical transport model. In the framework, satellite-constrained emissions totals are inferred using model simulations with and without data assimilation in the iterative finite-difference mass-balance method. The approach improves the finite-difference mass-balance inversion by isolating the near-surface emissions increment. We apply the framework to separately estimate lightning and anthropogenic NO x emissions over the Northern Hemisphere for 2019. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare separate NO x emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO 2 and O 3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO 2 retrievals. Updated lightning NO x emissions from either satellite improve the chemical transport model's low tropospheric O 3 bias. The combined lighting and anthropogenic emissions updates improve the model's ability to reproduce measured ozone by adjusting natural, long-range, and local pollution contributions. Thus, the framework informs and supports the design of domestic and international control strategies.
MeSH
Bias, Air Pollution, Air, Lightning