Abstract
Atmosphere pollution brought by rapid development of shipping industry becomes increasingly serious, and has brought great challenges to maritime supervision and management. With the popularization of AIS (Automatic Identification System) technology, massive navigation logs recording rich ship activities can be collected. These logs make it possible to apply a data-driven method to quantify exhaust emissions from ships. This paper is therefore motivated to research methods of approximately estimating ship exhaust emissions and exploring its spatial distribution and variation rules. Firstly, ship static and dynamic information extracted from AIS logs are utilized to distinguish different ship navigational states. A quantitative model is then created to estimate ship emissions from main engine, auxiliary engine and boiler with varied work conditions in different states. Spatio-temporal analysis methods are further applied to comprehensively discovery knowledge hidden in ship emission inventories. Typical analyses from space, time and attributes have uncovered much useful knowledge for ship emission supervision, such as high-polluted area, peak-emission time, least environmentally friendly ship type, etc. Finally, Ningbo-Zhoushan port plays a role as case study in validating the proposed method. The experiment results illustrate this research can be helpful to make efficient emission control measures and improve the capability of environmental management.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.