Ships operating on fossil fuel release pollutant emissions into the atmosphere. Released pollutants have a negative effect on the environment and human health, especially in port cities. For this reason, it is very important to properly evaluate these emissions so they can be managed. The current and most common methodologies for shipping pollution evaluation are used for whole port areas or larger terminals over a long period of time and are not analyzed in terms of detailed activity, which may lead to underestimations in certain areas. This study aims to evaluate emissions from ships in port by combining ships’ technical, AIS and EMEP data that allow us to evaluate emissions in port, not as a singular area source but enables individual ship emissions evaluation at any given point in time. To achieve this emission calculation, an algorithm was compiled by using EMEP/EEA Tier 3 methodology. The developed method presents a way to evaluate emissions in a detailed manner not only for groups of ships but also for individual ships if that is required. This method also lets us analyze shipping emissions’ intensity throughout all port territory and identify the most excessive pollution sources. However, the method adds additional work for researchers because of the huge data arrays required for complex calculations.

In the last decade the reduction of carbon dioxide emissions in the transport sector, including the marine sector, has become the direction of its strategic development. Increased air pollution in the air is one of the main reasons for premature deaths around the globe. It was determined that while many methods provide adequate information about pollution levels, improvements could be made to avoid major errors. The traditional methods are either expensive or require a lot of data and human resources to correctly evaluate those data arrays. To avoid these problems, artificial neural networks (ANN) and other machine learning methods are widely used nowadays. Many ANN models for ship pollution evaluation in ports either included the whole port area or went even further and included cities near port areas. These studies show that ANNs can be effectively used to evaluate air pollution in a wide area. However, there is a lack of research on ANN usage for individual ship pollution or ship plume evaluation. This study attempts to fill this gap by developing an ANN model to evaluate an individual ship’s plumes by combining several data sources such as AIS data, meteorological data, and measured the ship’s plume pollutants concentration. Results show good correlation; however, additional limitations have to be overcome regarding data filtering and the overall accuracy of the model.

Global shipping generates substantial emissions that can adversely affect air quality in port cities, yet the detectability of ship-related pollution by urban monitoring locations remains insufficiently understood. This study aims to identify the meteorological conditions under which ship exhaust plumes can be detected at a stationary air-quality monitoring station located 1.4 km from the Port of Klaipeda. Night-time particulate matter and NO measurements from an AQMesh station were synchronized with Automatic Identification System (AIS) ship-tracking data, and an artificial neural network was applied to determine the environmental parameters most strongly associated with detectable pollution peaks. Kernel Density Estimation (KDE) was used to map the spatial patterns of ship activity by vessel type. The results indicate that plume detection is most likely to be detected with moderate wind speeds (8–12.5 m/s for PM and 7.5–9.6 m/s for NO), elevated humidity (>84%), and higher-pressure ranges for particulate matter. Warmer night-time conditions further enhance pollutant transport by reducing atmospheric stability. KDE analysis shows that potential pollutant accumulation zones differ by vessel type, with the most intense hotspots forming near anchorage locations rather than along transit routes. Overall, the findings demonstrate that ship-related pollution can be detected at distances exceeding 1 km under specific meteorological conditions and highlight the parameters that most strongly govern plume penetration into the urban environment.