Preprints
https://doi.org/10.5194/egusphere-2025-107
https://doi.org/10.5194/egusphere-2025-107
05 Mar 2025
 | 05 Mar 2025

Improved detection of global NOx emissions from shipping in Sentinel-5P TROPOMI data

Miriam Latsch, Andreas Richter, John P. Burrows, and Hartmut Bösch

Abstract. Shipping is an important source of nitrogen oxide (NOx) emissions worldwide, contributing to air pollution and negatively affecting marine environments, ecosystems, and biodiversity. TROPOMI (TROPOspheric Monitoring Instrument) aboard the Sentinel-5 Precursor (S5P) has significantly enhanced the ability to detect ship emissions from space due to its low measurement noise levels and high spatial resolution of 5.5 x 3.5 km2. This study uses the TROPOMI tropospheric NO2 slant column density (tSCD) to identify global shipping routes. Preprocessing techniques, including iterative high-pass and Fourier filtering, markedly improve the detection of shipping lanes, revealing previously undetectable routes. Our analysis examines the impact of high-pass filter box sizes, demonstrating that smaller sizes enhance the visibility of narrow shipping features, while larger box sizes increase overall NO2 signals. Additionally, we investigate various flagging criteria that affect NO2 signal distribution, highlighting the critical importance of careful selection for accurate emission monitoring. Filtered TROPOMI NO2 tSCDs over oceans show a strong correlation with shipping activities, as confirmed by comparison with the CAMS-GLOB-SHIP (Copernicus Atmospheric Monitoring Service for Global Shipping) inventory, and also reveal unknown shipping routes in regions such as the Bering Sea. Furthermore, TROPOMI effectively captures NO2 emissions from offshore oil and gas platforms, with NO2 hotspots in the TROPOMI data aligning well with locations of offshore installations listed in the OSPAR (Oslo and Paris Commission) and BOEM (Bureau of Ocean Energy Management) inventories. Lastly, the filtered TROPOMI NO2 tropospheric vertical column densities (tVCDs) are compared with the tVCDs from the CAMS (Copernicus Atmospheric Monitoring Service) model, which has a coarse spatial resolution of 0.4°. While both data sets effectively identify global shipping lanes, the CAMS NO2 tVCDs are significantly higher compared to the filtered TROPOMI tVCDs, with differences of up to a factor of 100 in the South Atlantic Ocean.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Atmospheric Measurement Techniques.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share

Journal article(s) based on this preprint

10 Sep 2025
Improved detection of global NO2 signals from shipping in Sentinel-5P TROPOMI data
Miriam Latsch, Andreas Richter, John P. Burrows, and Hartmut Bösch
Atmos. Meas. Tech., 18, 4373–4395, https://doi.org/10.5194/amt-18-4373-2025,https://doi.org/10.5194/amt-18-4373-2025, 2025
Short summary
Miriam Latsch, Andreas Richter, John P. Burrows, and Hartmut Bösch

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-107', Anonymous Referee #4, 27 Mar 2025
    • AC1: 'Reply on RC1', Miriam Latsch, 02 Jun 2025
  • RC2: 'Comment on egusphere-2025-107', Anonymous Referee #1, 27 Apr 2025
    • AC2: 'Reply on RC2', Miriam Latsch, 02 Jun 2025

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2025-107', Anonymous Referee #4, 27 Mar 2025
    • AC1: 'Reply on RC1', Miriam Latsch, 02 Jun 2025
  • RC2: 'Comment on egusphere-2025-107', Anonymous Referee #1, 27 Apr 2025
    • AC2: 'Reply on RC2', Miriam Latsch, 02 Jun 2025

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Miriam Latsch on behalf of the Authors (02 Jun 2025)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to technical corrections (24 Jun 2025) by Lok Lamsal
AR by Miriam Latsch on behalf of the Authors (25 Jun 2025)  Author's response   Manuscript 

Journal article(s) based on this preprint

10 Sep 2025
Improved detection of global NO2 signals from shipping in Sentinel-5P TROPOMI data
Miriam Latsch, Andreas Richter, John P. Burrows, and Hartmut Bösch
Atmos. Meas. Tech., 18, 4373–4395, https://doi.org/10.5194/amt-18-4373-2025,https://doi.org/10.5194/amt-18-4373-2025, 2025
Short summary
Miriam Latsch, Andreas Richter, John P. Burrows, and Hartmut Bösch
Miriam Latsch, Andreas Richter, John P. Burrows, and Hartmut Bösch

Viewed

Total article views: 717 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
579 115 23 717 15 30
  • HTML: 579
  • PDF: 115
  • XML: 23
  • Total: 717
  • BibTeX: 15
  • EndNote: 30
Views and downloads (calculated since 05 Mar 2025)
Cumulative views and downloads (calculated since 05 Mar 2025)

Viewed (geographical distribution)

Total article views: 709 (including HTML, PDF, and XML) Thereof 709 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 10 Sep 2025
Download

The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
This study presents our advanced methods to better detect ship-related nitrogen dioxide (NO2) emissions in TROPOMI satellite data. By applying filtering techniques, we identify numerous global shipping routes, including previously undetectable ones, and emissions from offshore platforms. Additionally, we compare the filtered satellite data with CAMS global model data to estimate the differences between observed and modelled NO2 emissions.
Share