23 Jan 2024
 | 23 Jan 2024
Status: this preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).

A light-weight NO2 to NOx conversion model for quantifying NOx emissions of point sources from NO2 satellite observations

Sandro Meier, Erik Koene, Maarten Krol, Dominik Brunner, Alexander Damm, and Gerrit Kuhlmann

Abstract. Nitrogen oxides (NOx = NO + NO2) are air pollutants which are co-emitted with CO2 during high-temperature combustion processes. Monitoring NOx emissions is crucial for assessing air quality and for providing proxy estimates of CO2 emissions. Satellite observations, such as those from the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite, provide global coverage at high temporal resolution. However, satellites measure only NO2, necessitating a conversion to NOx. Previous studies applied a constant NO2-to-NOx conversion factor. In this paper, we develop a more realistic model for NO2 to NOx conversion and apply it to TROPOMI data of 2020 and 2021. To achieve this, we analysed plume-resolving simulations from the MicroHH Large Eddy Simulation model with chemistry for the power plants Bełchatów (PL), Jänschwalde (DE), Matimba and Medupi (ZA), as well as a metallurgical plant in Lipetsk (RU). We used the cross-sectional flux method to calculate NO, NO2, and NOx line densities from simulated NO and NO2 columns and derived NO2-to-NOx conversion factors as a function of the time since emission. Since the method of converting NO2 to NOx presented in this paper assumes steady-state conditions as well as that the conversion factors can be modeled by a negative exponential function, we validated the conversion factors using the same MicroHH data. Finally, we applied the derived conversion factors to TROPOMI NO2 observations of the same sources. The validation of the NO2-to-NOx conversion factors shows that they can account for the NOx chemistry in plumes, in particular for the conversion between NO and NO2 near the source and for the chemical loss of NOx further downstream. When applying these time-since-emission-dependent conversion factors, biases in NOx emissions estimated from TROPOMI NO2 images are greatly reduced from between -50 and -42 % to only -9.5 to -0.5 % in comparison with reported emissions. Single-overpass estimates can be quantified with an uncertainty of 20–27 %, while annual NOx emission estimates have uncertainties in the range of 4–21 % but are highly dependent on the number of successful retrievals. Although more simulations covering a wider range of meteorological and trace gas background conditions will be needed to generalize the approach, this study marks an important step towards a global, uniform, high-resolution, and near real-time estimation of NOx emissions – especially with regard to upcoming NO2 monitoring satellites such as Sentinel-4 and -5 and CO2M.

Sandro Meier, Erik Koene, Maarten Krol, Dominik Brunner, Alexander Damm, and Gerrit Kuhlmann

Status: open (until 19 Mar 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-159', Anonymous Referee #1, 09 Feb 2024 reply
Sandro Meier, Erik Koene, Maarten Krol, Dominik Brunner, Alexander Damm, and Gerrit Kuhlmann

Data sets

CoCO2 WP4.1 Library of Plumes Erik Koene and Dominik Brunner

Copernicus Sentinel-5P (processed by ESA), TROPOMI Level 2 Nitrogen Dioxide total column products. Version 2.4.0 European Space Agency

ERA5 hourly data H. Hersbach et al.

Model code and software

Data-driven emission quantification (ddeq) Gerrit Kuhlmann, Erik F. M. Koene, Sandro Meier, Diego Santaren, Grégoire Broquet, Frédéric Chevallier, Janne Hakkarainen, Janne Nurmela, Laia Amorós, Johanna Tamminen, and Dominik Brunner

Sandro Meier, Erik Koene, Maarten Krol, Dominik Brunner, Alexander Damm, and Gerrit Kuhlmann


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Short summary
Nitrogen oxides (NOx = NO + NO2) are important air pollutants. This study addresses the challenge of accurately estimating NOx emissions from NO2 satellite observations. We develop a realistic model to convert NO2 to NOx by using simulated plumes from various power plants. We apply the model to satellite NO2 observations, significantly reducing biases in estimated NOx emissions. The study highlights the potential for a global, high-resolution estimation of NOx emissions using satellite data.