Preprints
https://doi.org/10.5194/gmd-2022-292
https://doi.org/10.5194/gmd-2022-292
Submitted as: methods for assessment of models
16 Dec 2022
Submitted as: methods for assessment of models | 16 Dec 2022
Status: this preprint is currently under review for the journal GMD.

Can TROPOMI-NO2 satellite data be used to track the drop and resurgence of NOx emissions between 2019–2021 using the multi-source plume method (MSPM)?

Enrico Dammers1, Janot Tokaya1, Christian Mielke2, Kevin Hausmann2, Debora Griffin3, Chris McLinden3, Henk Eskes4, and Renske Timmermans1 Enrico Dammers et al.
  • 1Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, Princetonlaan 6, 3584 CB, The Netherlands
  • 2Umweltbundesamt (UBA), Dessau-Roßlau, Wörlitzer Pl. 1, 06844, Germany
  • 3Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin St, M3H 5T4, Toronto, Canada
  • 4KNMI, De Bilt, Utrechtseweg 297, 3731 GA, The Netherlands

Abstract. Nitrogen dioxide (NOx) is an important primary air pollutant, dominantly produced by anthropogenic, mostly combustion based, activities from sectors such as industry, traffic and transport. NOx is directly linked to negative health and environmental impacts. Currently, the construction of emission inventories to keep track of NOx emissions is based on official national reported emissions and proxies such as activity data as well as direct measurements. The effort to properly construct an accurate inventory is significant and time consuming which causes a reporting offset between one and five years with respect to the current date. Next to this temporal lag difficulties in composed inventories can arise from legislative and protocol differences between countries and over time in reporting of emissions. Satellite based atmospheric composition measurements provide a unique opportunity to fill this gap and independently estimate emissions on a large scale in a consistent, transparent and comprehensible way. They give the possibility to check for compliance with emission reduction targets in a timely manner as well as to observe rapid emission reductions such as experienced during the COVID-19 lock-downs. In this study we apply a consistent methodology to derive NOx emissions over Germany for the years of 2019–2021. For the years where reporting is available differences between satellite estimates and inventory totals were within 100 kt. The large reduction of NOx emissions related to the COVID-19 lock-downs were observed in both the inventory and satellite derived emissions. The recent projections for the inventory emissions pointed to a recovery of the emissions towards pre-COVID19 levels this increase was not observed. While emissions from the larger power-plants did rebound to earlier levels, others sectors such as road transport and shipping did not and could be linked to a reduction in the number of heavier transport trucks. This again illustrates the value of having a consistent satellite based methodology for faster projections to guide and check the conventional emission inventory reporting. The method described in this manuscript also meet the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues. Transparency and comparability, two key values for emission reporting, are thus bolstered by this technique.

Enrico Dammers et al.

Status: open (until 10 Feb 2023)

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

Enrico Dammers et al.

Enrico Dammers et al.

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Short summary
Nitrogen dioxide (NOx) is produced by sources such as industry, and traffic, and directly linked to negative impacts on health and the environment. The current construction of emission inventories to keep track of NOx emissions is slow and time consuming. Satellite measurements provide a way to quickly and independently estimate emissions. In this study we apply a consistent methodology to derive NOx emissions over Germany, and illustrate the value of having such a method for fast projections.