Detection of anomalous NO2 emitting ships using AutoML on TROPOMI satellite data

Starting from 2021 International Maritime Organization (IMO) introduced more demanding NO_x emission restrictions for ships operating in waters of the North and Baltic Seas. All methods currently used for ship compliance monitoring are financially and time-demanding. Thus, it is important to prioritize the inspection of ships that have a high chance of being non-compliant. 

 

TROPOMI/S5P instrument for the first time allows a distinction of NO₂ plumes from individual ships. Here, we present a method for the selection of potentially non-compliant ships using automated machine learning (AutoML) on TROPOMI/S5P satellite data. The study is based on the analysis of 20 months of data in the Mediterranean Sea region. To each ship, we assign a Region of Interest (RoI), where we expect the ship plume to be located. We then train a regression model to predict the amount of NO₂ that is expected to be produced by a ship with specific properties operating in the given atmospheric conditions. We use a genetic algorithm-based AutoML for the automatic selection and configuration of a machine-learning pipeline that maximizes prediction accuracy. The difference between the predicted and actual amount of produced NO₂ is a measure of inspection worthiness. We rank the analyzed ships accordingly. 

 

We cross-check the obtained ranks using a previously developed method for supervised ship plume segmentation.  We quantify the amount of NO₂ produced by a given ship by summing up concentrations within the pixels identified as a “plume”. We rank the ships based on the difference between the obtained concentrations and the ship emission proxy.

 

Ships that are also ranked as highly deviating by the segmentation method need further attention. For example, by checking their data for other explanations. If no other explanations are found, these ships are advised to be the candidates for fuel inspection.