  Starting from 2021, more demanding $\text{NO}_\text{x}$ emission restrictions
were introduced for ships operating in the North and Baltic Sea waters. Since
all methods currently used for ship compliance monitoring are financially and
time demanding, it is important to prioritize the inspection of ships that have
high chances of being non-compliant. The current state-of-the-art approach for
a large-scale ship $\text{NO}_\text{2}$ estimation is a supervised machine
learning-based segmentation of ship plumes on TROPOMI/S5P images. However,
challenging data annotation and insufficiently complex ship emission proxy used
for the validation limit the applicability of the model for ship compliance
monitoring. In this study, we present a method for the automated selection of
potentially non-compliant ships using a combination of machine learning models
on TROPOMI satellite data. It is based on a proposed regression model
predicting the amount of $\text{NO}_\text{2}$ that is expected to be produced
by a ship with certain properties operating in the given atmospheric
conditions. The model does not require manual labeling and is validated with
TROPOMI data directly. The differences between the predicted and actual amount
of produced $\text{NO}_\text{2}$ are integrated over observations of the ship
in time and are used as a measure of the inspection worthiness of a ship. To
assure the robustness of the results, we compare the obtained results with the
results of the previously developed segmentation-based method. Ships that are
also highly deviating in accordance with the segmentation method require
further attention. If no other explanations can be found by checking the
TROPOMI data, the respective ships are advised to be the candidates for
inspection.
