The shipping industry is one of the strongest anthropogenic emitters of $\text{NO}_\text{x}$ -- substance harmful both to human health and the environment. The rapid growth of the industry causes societal pressure on controlling the emission levels produced by ships. All the methods currently used for ship emission monitoring are costly and require proximity to a ship, which makes global and continuous emission monitoring impossible. A promising approach is the application of remote sensing. Studies showed that some of the $\text{NO}_\text{2}$ plumes from individual ships can visually be distinguished using the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/S5P). To deploy a remote sensing-based global emission monitoring system, an automated procedure for the estimation of $\text{NO}_\text{2}$ emissions from individual ships is needed. The extremely low signal-to-noise ratio of the available data as well as the absence of ground truth makes the task very challenging. Here, we present a methodology for the automated segmentation of $\text{NO}_\text{2}$ plumes produced by seagoing ships using supervised machine learning on TROPOMI/S5P data. We show that the proposed approach leads to a more than a 20\% increase in the average precision score in comparison to the methods used in previous studies and results in a high correlation of 0.834 with the theoretically derived ship emission proxy. This work is a crucial step toward the development of an automated procedure for global ship emission monitoring using remote sensing data.

翻译：航运业是人为排放NOx（一种对健康和环境有害的物质）的主要来源之一。该行业的快速增长导致社会对控制船舶排放水平产生了压力。目前用于船舶排放监测的所有方法均成本高昂且需靠近船舶，这使得全球连续排放监测难以实现。遥感技术的应用是一种有前景的途径。研究表明，利用哥白尼哨兵5号先导卫星上的对流层监测仪（TROPOMI/S5P），可以视觉上分辨部分船舶个体排放的NO2烟羽。为部署基于遥感的全球排放监测系统，需开发自动化流程以估算船舶个体的NO2排放量。但可用数据信噪比极低且缺乏地面真值，使此任务极具挑战性。本文提出了一种基于TROPOMI/S5P数据的有监督机器学习方法，用于自动分割海船产生的NO2烟羽。结果表明，与先前研究方法相比，所提方法使得平均精确率提升超过20%，且与理论推导的船舶排放代理指标呈现0.834的高相关性。本研究是利用遥感数据开发全球船舶排放自动化监测流程的关键一步。