A method is proposed for remotely detecting sulfur content in fuel oil used by ships in emission control areas (ECAs) based on the direct collection of SO2 emission data from the plume of the ship and numerical simulation models. Assuming a ship in the ECA uses fuel oil with compliance fuel sulfur content (FSC), activity-based ship emission assessment model is firstly utilized to calculate the SO2 concentration emitted from the ship. Numerical simulation models are then used to estimate the theoretical SO2 diffusion concentration at the location of the monitoring equipment. The observed SO2 concentration at the same time can then be integrated to estimate the FSC of the ship. A sample of eleven ships was selected to verify the proposed method in Yantian port, Shenzhen, China in June 2018. The result illustrates that the method can remotely detect ships using fuel oil with the FSC greater than 0.146%m/m in the ECAs, the relative error is 46%.@en

The purpose of this paper is to evaluate the effect of the seasonal flow field on inland ship emissions and to improve calculation accuracy. Firstly, the flow field model is built through numerical simulation to correct the sailing speed of the ship from the Automatic Identification System (AIS) information in real-time. Then, an optimal emission estimation model for inland ships considering flow field factors is proposed. Finally, the effectiveness of the optimization model is demonstrated by a case study, and the influence of the seasonal flow field on emission calculation is analyzed. It indicates that the calculation results of the model considering the influence of the flow field are more accurate. Without considering the flow field, the results of emission calculations are often underestimated, especially in summer, which shows the importance of incorporating the flow field factors into the calculation of inland ship emissions.@en

In this study, a method for dynamically establishing ship domain in inland waters is proposed to help make decisions about ship collision avoidance. The surrounding waters of the target ship are divided to grids and then calculating the grid densities of ships in each moment to deter-mine the shape and size of ship domain of different types of ships. At last, based on the spatiotem-poral statistical method, the characteristics of ship domains of different types of ship in different navigational environments were analyzed. The proposed method is applied to establish ship domains of different types of ship in Wuhan section of the Yangtze River in January, February, July, and August in 2014. The results show that the size of ship domain increases as the ship size increases in each month. The domain size is significantly influenced by the water level, and the ship domain size in dry seasons is larger than in the wet seasons of inland waters.@en

Recognizing ship behavior is important for maritime situation awareness and intelligent transportation management. Some scholars extracted ship behaviors from massive trajectory data by statistical analysis. However, the meaning of the behaviors, i.e., semantic meanings of behaviors and their relationships, are not explicit. Ship behaviors are affected by navigational area and traffic rules, so their meanings can be obtained only in specific maritime situations. The work establishes the semantic model of ship behavior (SMSB) to represent and reason the meaning of the behaviors. Firstly, a semantic network is built based on maritime traffic rules and good seamanship. The corresponding detection methods are then proposed to identify basic ship behaviors in various maritime scenes, including dock, anchorage, traffic lane, and general scenes. After that, dynamic Bayesian network (DBN) is used to reason potential ship behaviors. Finally, trajectory annotation and semantic query of the model are validated in the different scenes of harbor. The basic behaviors and potential behaviors in all typical scenes of any harbor can be obtained accurately and expressed conveniently using the proposed model. The model facilitates the ships behavior research, contributing to the semantic trajectory analysis.@en

The remote monitoring method based on air-quality monitoring sensors is a common way to monitor the FSC (fuel sulfur content) of oils for ships. Considering the influences of geographical environments, atmospheric conditions, regional ship traffic flow, emission characteristics of ships, and height of monitoring sensors on the monitor station chosen, a new method was proposed to optimize the site selection for monitoring the FSC of fuel oils used by ships in waters of the port. SO2 numeric simulation and observation from sensors were integrated to estimate the FSCs. The proposed method was utilized to recommend the sites of the fixed sniffing monitoring stations in Yantian port, Shenzhen, China from June and July 2018. The results showed that the experimental stations could monitor FSCs, and the relative difference between the estimated and actual FSCs of ships was 16.34%. The proposed method for recommending sites of FSC monitoring sensors contributed to intelligently supervising air pollutants emitted from ships and fuel oils of ships in the emission control areas of China.@en

To improve the ability of ship behavior detection in maritime safety surveillance, a new ship behavior of frequent turning within a certain spatial range, called ship loitering, was found under the ship behavior framework of "anchored-off, straight-sailing, turning". The concept of trajectory redundancy is defined as the quantitative representation of trajectory characteristics of ship loitering. A multi-scale sliding window-based ship loitering detection method is designed to process ship trajectories with different spatial ranges and time duration. Further, a shape recognition model based on a convolutional neural network is constructed and trained to identify four typical shapes of loitering trajectories. To verify the effectiveness of the proposed method, experiments were conducted using the AIS trajectory data collected from the sea off the Strait of Juan de Fuca in the eastern North Pacific Ocean in 2017 and validated against the manual labeling results. The results illustrate that six general categories of ships produce loitering behaviors with different intentions. The characteristics of the spatial-temporal distribution of ship loitering behaviors can be represented as different shapes. The average precision of ship loitering detection reaches 96.5% and the average accuracy of four typical loitering trajectory shapes reaches 86.2%.@en

The special SOX Emission Control Areas (ECAs) had been established to reduce pollution in coastal region. Real-time ship emissions monitoring should be strengthened in SECA. The mainstream remote measurements of gas concentrations are the optical or sniffer monitoring methods. Exhaust emissions location and intensity could not be acquired by methods of remote measurements. Based on monitoring multiple sensors, this paper presents a new inversion method for characteristics of single ship emissions. The Gaussian plume diffusion model (GPDM) and Genetic Algorithm (GA) to calculate the position and concentration of single ship emissions. Simultaneous measurements of CO2, SO2 and NOX concentrations can be achieved by several monitoring stations on the wharf. Multiple solutions of the ship emissions location and intensity have been calculated by the selection, crossover and mutation of GA. As parameters of Gaussian plume diffusion model, those solutions are employed to calculate the gas concentrations on the location of the monitoring stations. The optimal one can be acquired by comparing the measurement and the calculation. The presented algorithm is verified by the real practice for Yantian harbor in Shenzhen.@en

With the booming of marine industry, occurrence of maritime oil spills will not only loss of life and property, but also has brought about a long-term and bad influence to ocean environment and ecosystem system. This paper introduces a platform architecture for marine oil spill response capacity visualization, and elaborates the visualization of oil spill emergency elements, emergency resources dynamic interconnection and regional oil spill response capability with marine oil spill accidents. The platform is based on the B/S (browser/server) framework along with the tools of Baidu map API and MapV visualization system to realize the oil spill emergency resources visualization, oil spill emergency capability visualization and oil spill emergency response. The successful running of the platform provides scientific support for the decision-making in handling the oil spill emergency.@en

With the rapid development of information technology in shipping industry, massive network meteorological datasets come available and valuable. Intuitive visualization of surrounding marine meteorological data is helpful to improve the safety and reliability of ship navigation. In this article, build a system framework of network marine meteorological information processing and visualization that can automatically obtain, store and process these network meteorological information and provide well-designed and content-rich meteorological thematic map for ship navigation. Tested, the system can effectively provide the sharing service of marine meteorological information.@en