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.

翻译：自2021年起，对在北海和波罗的海水域航行的船舶实施了更严格的NOx排放限制。由于目前用于船舶合规监测的所有方法均耗时耗力，因此优先检查具有高违规概率的船舶至关重要。当前大尺度船舶NO2估算的最先进方法是基于监督式机器学习的分割技术，用于识别TROPOMI/S5P图像上的船舶羽流。然而，具有挑战性的数据标注以及用于验证的船舶排放代理模型复杂度不足，限制了该模型在船舶合规监测中的适用性。本研究提出了一种结合TROPOMI卫星数据与机器学习模型的自动化方法，用于筛选潜在不合规船舶。该方法基于一个回归模型，该模型可预测具有特定属性的船舶在给定大气条件下应产生的NO2量。该模型无需人工标注，并直接使用TROPOMI数据进行验证。通过将船舶随时间观测到的NO2实际排放量与预测排放量之间的差异进行积分，以此作为船舶检查优先级的衡量标准。为确保结果稳健性，我们将所得结果与先前基于分割的方法进行对比。若船舶在两种方法中均表现出高度偏差，则需进一步关注。通过核查TROPOMI数据无法找到其他解释时，相应船舶将被建议列为检查候选对象。