The global increase in shipping activity has contributed to the degradation of air quality, which particularly affects traffic-dense port areas. Due to the environmental and public health impacts of air quality in port cities, a number of inventories using varying methodologies have been conducted over the past two decades to manage gas emissions in specific areas. The objective of this work is to determine one relevant methodology for estimating ship emissions in ports through a systematic review of the relevant literature. In this research, PRISMA guidelines were followed through a multi-layer bottom-up analysis approach to ensure the validity of the proposed methodology. The aforementioned methodology, as the end result of this research, is intended to provide an empirically structured basis for further development of a novel indexing model of ship gas emissions in port areas.

Intensive shipping activity in port areas is considered one of the leading problems in the maritime sector, which has a negative effect on climate change and local air quality. The compilation of detailed inventories of combustion gases released by ships should therefore provide a more accurate overview of emission levels, which can serve as a basis for analysing impacts on the port community and lead to the establishment of better environmental measures. Thus, the aim of this study was to develop an adaptable and relevant analytical model capable of integrating a comprehensive methodology with large databases of ship movements and technical details to provide clear ship-related emission estimates in large port areas. Considering the lack of research in Croatia that includes the mentioned approach and the insufficient monitoring of air pollutants in ports, the model was used to produce an initial overall emissions inventory for the Port of Split, the busiest passenger port in Croatia. In the model, bottom-up logic with an energy-based method was applied to detailed technical and near-real-time shipping data from AIS, creating the first high-density spatial and temporal overview of shipping emissions in the City port basin. The results showed strong seasonal fluctuations and large discrepancies in the quantities emitted between different ship types and operating modes. The analysis therefore raised the question of the need for the future development and implementation of a scalable system that would provide a more transparent and efficient overview of the important characteristics of air pollution from ships and port areas.

Seaports, as major transportation hubs, generate significant air pollution due to intensive ship traffic, directly affecting local air quality. While emission inventories are commonly used to manage ship-based air pollution, they reflect only the emission-related aspect of a specified period and area, limiting the broader interpretability and comparability of the results. To overcome the mentioned challenges, this research presents the PrE-PARE model, which enables the prediction, analysis, and risk evaluation of ship-sourced air pollution in port areas. The model comprises three interconnected modules. The first is applied for quantifying emissions using detailed technical and movement datasets, which are combined into individual voyage trajectories to enable a high-resolution analysis of ship-based air pollutants. In the second module, the Multivariate Adaptive Regression Splines (MARS) machine learning method is adapted to predict emissions in varying operational scenarios. In the third module, novel metric methods are introduced, enabling a standardised efficiency comparison between ships. These methods are supported by a unique classification system to determine the emission risk in different periods, evaluate the intensity of various ship types, and rank individual ships based on their operational efficiency and emission optimisation potential. By combining new methods with technical and operational shipping data, the model provides a transparent, comparable, and adaptable system for emissions monitoring. The results demonstrate that the PrE-PARE model has the potential to improve strategic planning and air quality management in ports while remaining flexible enough to be applied in different contexts and future scenarios.

The problem of maritime traffic in Singapore Strait is traffic density, also the probability of collision, which is increased beside the existing Vessel Traffic System (VTS). The paper discusses the synthesis early warning system, or automatic crossing supervisor for Singapore Strait by using the Timed Petri Nets (TPN). Authors proposes dividing the strait in zones, so called crossings, where routes are crossing and in which the number of ships must be limited. The maximum number of vessels in period of time of the highest traffic density through the crossings are determined. Derived constraints are used for synthesis of crossing supervisor. The authors uses Petri nets to make model Singapore Strait, and use P-invariant method to syntheses crossing supervisor, which limits the number of ships in all critical crossings. Finally, the author verified derived supervisor by using Visual Object Net ++ programme for computer simulation. With the aid of Transas nautical simulator, the traffic in the strait is analysed.

The purpose of this study is to show how coloured Petri nets can be used to select the crossing rules guaranteeing that two ships avoid collisions at sea in accordance with the international regulations on this matter. This paper is exclusively focused on the solution to this small sub-problem within the overall “Collision Avoidance System” that the authors of the present study are currently developing. For easier understanding, the overall system is also briefly presented. How the Petri net in the CPN Tools software is fed with “Real Time Real World” data is presented. These data are generated outside the Petri net from the function block to predict a possible collision and from the current meteorological data. We also demonstrate how the rule selections made by the Petri net are transferred from the CPN Tools software into the “Real Time Real World”. This transferred information is used outside the Petri net in the function block to calculate avoidance routes. The definition of the colour sets that are used and the individual operations applied to these colour sets in the coloured Petri net are presented.

From a scientific viewpoint, as well as from the perspective of navigation practice, it is clear that the Adriatic Sea feeder service is relatively underdeveloped. Hence, the objective of this study is to suggest a model for selecting the hub port and to optimize the network of seaports engaged in the feeder service. Accordingly, an appropriate hub port has been identified through the methods of multi-criteria decision making and expert assessment, and the optimum shipping route has been calculated by applying the travelling salesman algorithm (TSA). In order to analyze whether there is a possibility of obtaining better optimization results, an integration of a sub-hub port system is suggested. Optimization has been achieved by applying a minimum spanning tree algorithm (MST) and a combination of these algorithms. The proposed methodology for selecting the hub port, sub-hub port and optimizing the feeder network can be implemented globally. The practical application of the achieved model would result in cost minimization, owing to shorter shipping routes or a combination of different transportation means (feeders).