# Supervised Segmentation of NO2 Plumes from Individual Ships Using Troppomi Satellite Data
## 1 Introduction  
The international shipping sector is one of the strongest sources of anthropogenic emission of NOx--a substance that has a negative impact both on ecology and human health. The contribution of the shipping industry is estimated to vary from 15% to 35% worldwide [1, 2], which leads to approximately 60,000 premature deaths annually [3]. While over the last 20 years, the pollution produced by power plants, the industry sector, and cars has been constantly decreasing, the impact of maritime transport continues to grow [4]. This causes a big societal pressure, resulting in regulations [5] that put restrictions on emission levels that can be produced by individual ships. All methods currently used for ship emission monitoring such as in situ [6, 7], onboard [8], and airborne-platform-based [9] have several disadvantages: they all require close proximity to a ship; they are costly; they do not allow for monitoring on a global scale. A potential solution to the problem is the application of remote sensing instruments [10]. Studies [11, 12] show that NO2 traces of some individual ship plumes can be visually distinguished on images from the TROPOspheric Monitoring Instrument on board the Copernicus Sentinel 5 Precursor (TROPOMI/SSP) satellite [13], which was launched in 2018. An important next step required for the development of an automated global-scale monitoring system is an automated interpretable method for the evaluation of NO\({}_{\rm x}\) emission produced by individual ships. Among the main challenges for this development are a low temporal sampling rate and spatial resolution, resulting in an extremely low signal-to-noise ratio. In addition, there is a high risk of interference of the ship plume with other NO\({}_{\rm x}\) sources and a high frequency of occurrence of plume-like objects that cannot be associated with any ship. Finally, the ground truth for this task is not available. In this paper, we present a methodology that allows addressing the above-mentioned challenges. Using machine learning and taking advantage of the spatial characteristics of a ship plume, the developed method allows automatic segmentation of NO\({}_{2}\) plumes from the background, simultaneously assigning the detected signal to a ship-emitter, circumventing the listed limitations. We used NO\({}_{2}\) retrievals from the TROPOMI/S5P as this is the only available remote sensing instrument that performs NO\({}_{2}\) measurements with a resolution high enough (The ground pixel resolution of the TROPOMI/S5P instrument equals \(3.5\times 5.5\) km\({}^{2}\) at nadir) to distinguish plumes from individual ships. To increase the number of potentially distinguishable plumes, we enhanced the contrast between the ship plumes and the background. The used enhancement technique allows for a differentiation between the ship plumes and random co-occurring concentration peaks in the ships' neighborhood. The application of the image enhancement technique also allows for an improvement of the low signal-to-noise ratio. Then, for each analyzed ship, we performed an automatic generation of a Region Of Interest (ROI) that we call a _ship sector_. The purpose of the _ship sector_ is to focus the area of analysis on the region where the ship plume is expected to be located. Subsequently, we normalized the _ship sector_ and divided it into sub-regions. This way, we distinguish the plume of interest from all the other NO\({}_{2}\) plumes or land-origin outflows that potentially might be located within the _ship sector_. Based on the _ship sector_ division, we created a set of spatial features that characterize the location of the NO\({}_{2}\) plume within the _ship sector_. Due to the absence of other sources of ground truth, each pixel of the _ship sectors_ we manually labeled as a \"plume\" or \"not a plume\". Trained on the manually labeled data, a machine learning model enabled us to automatically segment plumes in unseen images.