Detecting ship-produced NO2 plumes and shipping routes in TROPOMI data
with a deep learning model
- Tianle Yuan,
- Fei Liu,
- Lok N. Lamsal,
- 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.23 Jun 2023Submitted to ESS Open Archive 25 Jun 2023Published in ESS Open Archive