Preprints
https://doi.org/10.22541/essoar.168771101.14987378/v1
https://doi.org/10.22541/essoar.168771101.14987378/v1
07 Dec 2023
 | 07 Dec 2023

Detecting ship-produced NO2 plumes and shipping routes in TROPOMI data with a deep learning model

Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song

Abstract. Ship emissions are important contributor to air pollution and the climate through interacting with clouds. They are the dominant anthropogenic source over the oceans. However, their magnitudes still have large uncertainty. Here we develop a deep learning model to detect ship-emitted NO2 plumes at the pixel level in TROPOMI tropospheric NO2 data. The trained model performs well and, when applied to a year of data, it finds major shipping routes, but misses several other routes. We show that high cloudiness in these shipping routes is the culprit because clouds block signals from reach the sensor. Indeed, detected shipping routes in this study complements shipping routes detected using ship-tracks that is only available in cloudy regions. Our method can find application in several areas such as improving ship emission estimates and compliance verifications. Our method will benefit from improved tropospheric NO2 retrievals since their quality is critical for plume detection.

Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on Egusphere-2023-2469', Anonymous Referee #1, 16 Dec 2023
  • RC2: 'Comment on egusphere-2023-2469', Anonymous Referee #2, 02 Jan 2024
  • RC3: 'Comment on egusphere-2023-2469', Anonymous Referee #3, 10 Jan 2024
Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song
Tianle Yuan, Fei Liu, Lok N. Lamsal, and Hua Song

Viewed

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 158 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
157 0 1 158 0 0
  • HTML: 157
  • PDF: 0
  • XML: 1
  • Total: 158
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 07 Dec 2023)
Cumulative views and downloads (calculated since 07 Dec 2023)

Viewed (geographical distribution)

Since the preprint corresponding to this journal article was posted outside of Copernicus Publications, the preprint-related metrics are limited to HTML views.

Total article views: 156 (including HTML, PDF, and XML) Thereof 156 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Feb 2024
Download
Short summary
We train and apply a state-of-the-art deep learning model to detect NO2 plumes emitted by ships using NO2 retrievals from TROPOMI. By applying the model, we can detect individual plumes with excellent fidelity. The aggregated data show major shipping routes, but miss other routes. The missing routes are due to high cloudiness. Our method can be potentially useful for monitoring ship emissions of NOx and verifying compliance of emission standards.