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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 08 Apr 2020

Submitted as: research article | 08 Apr 2020

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This preprint is currently under review for the journal ACP.

Improving NO2 and ozone simulations through global NOx emission inversions

Zhen Qu1,2, Daven K. Henze1, Owen R. Cooper3,4, and Jessica L. Neu5 Zhen Qu et al.
  • 1Department of Mechanical Engineering, University of Colorado Boulder, Boulder,CO, 80309, USA
  • 2School of Engineering and Applied Science, Harvard University, Cambridge, MA, 02138, USA
  • 3Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO, 80309, USA
  • 4NOAA Chemical Sciences Laboratory, Boulder, CO, 80305, USA
  • 5Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, 91109, USA

Abstract. Tropospheric ozone simulations have large uncertainties, but their biases, seasonality and trends can be improved with more accurate estimates of precursor gas emissions. We perform global top-down estimates of monthly NOx emissions using two OMI NO2 retrievals (NASAv3 and DOMINOv2) from 2005 to 2016 through a hybrid 4D-Var/mass balance inversion. The 12-year averages of regional NOx budgets from the NASA posterior emissions are 37 % to 53 % smaller than the DOMINO posterior. Compared to surface NO2 measurements, GEOS-Chem adjoint NO2 simulations using the DOMINO posterior NOx emissions have smaller biases in China (by 15 %) and the US (by 22 %), but NO2 trends have more consistent decreases (by 26 %) with the measurements (by 32 %) in the US from 2006 to 2016 when using the NASA posterior. The two posterior NOx emissions datasets have different strengths with respect to simulation of ozone concentrations. Simulations using NASA-based emissions provide better agreement for polluted conditions. Ozone from these shows the highest correlation (R2 = 0.88) with annual MDA8 ozone trends and alleviates the double peak in the prior simulation of global ozone seasonality. The DOMINO-based emissions, on the other hand, are better for simulating ozone at remote sites, making them well-suited to generation of boundary conditions for regional models, and in capturing the interannual variability of daytime-average (R2 = 0.72–0.81) and 24-hour average (R2 = 0.88–0.96) surface ozone. We recommend using NOx emission datasets that have the best performance in the corresponding spatial domain and temporal focus to improve ozone simulations.

Zhen Qu et al.

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Latest update: 26 May 2020
Publications Copernicus
Short summary
We use satellite observations and chemical transport modeling to quantify sources of NOx, a major air pollutant, over the past decade. We find improved simulations of the magnitude, seasonality and trends of NO2 and ozone concentrations using these derived emissions. Changes in ozone pollution driven by human and natural sources are identified in different regions. This work shows the benefits of remote sensing data and inverse modeling for more accurate ozone simulations.
We use satellite observations and chemical transport modeling to quantify sources of NOx, a...