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 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/S5P 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 different observations of the same 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，北海和波罗的海水域运营的船舶面临更严格的氮氧化物（NOx）排放限制。由于当前所有船舶合规监测方法均存在高成本和时间消耗问题，优先检测存在高违规风险的船舶至关重要。当前用于大规模船舶二氧化氮（NO2）估算的最先进方法，是基于监督机器学习的船舶羽流分割技术，应用于TROPOMI影像。然而，数据标注的挑战性及验证所用船舶排放代理模型的不足，限制了该模型在船舶合规监测中的适用性。本研究提出一种结合TROPOMI/S5P卫星数据与机器学习模型的自动化方法，用于筛选潜在违规船舶。该方法基于所提出的回归模型，该模型可预测特定属性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。通过对同一船舶不同时次观测中预测NO2与实际NO2产生量之间的差异进行整合，将其作为船舶监测优先级的关键指标。为确保结果稳健性，我们将所得结果与先前开发的基于分割方法的结果进行对比。若分割方法同样识别出显著偏差的船舶，则需进一步关注。当通过核查TROPOMI数据仍无法找到其他解释时，相应船舶将被建议列为监测候选对象。