Preprints
https://doi.org/10.5194/acp-2022-435
https://doi.org/10.5194/acp-2022-435
 
25 Jul 2022
25 Jul 2022
Status: this preprint is currently under review for the journal ACP.

Inferring and evaluating satellite-based constraints on NOx emissions estimates in air quality simulations

James D. East1,2, Barron H. Henderson3, Sergey L. Napelenok3, Shannon N. Koplitz3, Golam Sarwar3, Robert Gilliam3, Allen Lenzen4, Daniel Q. Tong5, R. Bradley Pierce4, and Fernando Garcia-Menendez1 James D. East et al.
  • 1Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27695, USA
  • 2Oak Ridge Institute for Science and Education, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
  • 3U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA
  • 4Space Science and Engineering Center, University of Wisconsin-Madison, Madison, WI 53706, USA
  • 5Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030 USA

Abstract. Satellite observations of tropospheric NO2 columns can provide top-down observational constraints on emissions estimates of nitrogen oxides (NOx). 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 and infers satellite-constrained emissions totals using 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 estimate lightning and anthropogenic NOx emissions over the Northern Hemisphere. Using overlapping observations from the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI), we compare NOx emissions inferences from these satellite instruments, as well as the impacts of emissions changes on modeled NO2 and O3. OMI inferences of anthropogenic emissions consistently lead to larger emissions than TROPOMI inferences, attributed to a low bias in TROPOMI NO2 retrievals. Updated lightning NOx emissions from either satellite improve the chemical transport model’s low tropospheric O3 bias. Combined lightning and anthropogenic updates inferred from satellite observations can improve the model’s ability to represent background and ground-level O3 concentrations, an ongoing policy consideration in the U.S. as domestic and international emissions control strategies evolve.

James D. East et al.

Status: open (until 05 Sep 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

James D. East et al.

James D. East et al.

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Short summary
We present a framework that uses a computer model of air quality, along with air pollution data from satellite instruments, to estimate emissions of nitrogen oxides (NOx) across the Northern Hemisphere. The framework, which advances current methods to infer emissions from satellite observations, provides observationally-constrained NOx estimates, including in regions of the world where emissions are highly uncertain, and can improve simulations of air pollutants relevant for health and policy.
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