Zengqi Xiao, Jasmine Siu Lee Lam

    Exhaust emissions from ships negatively affect air quality, climate and human health. Emissions from tankers are often neglected while no existing study focuses on emissions from bunkering vessels within a port limit. This paper firstly utilises automatic identification system (AIS) data and establishes an emission accounting model to estimate the amount of exhaust emissions from oil tankers in the Port of Singapore, and secondly focuses on emissions from bunkering vessels as a major ship emission segment to draw policy implications and recommendations for maritime and port cities. Big data analytics and the bottom-up method are used to develop the model. To have a comprehensive study, all major types of pollutants and greenhouse gases are analysed, which include carbon monoxide, carbon dioxide, sulphur dioxide, nitrogen oxides, nitrous oxide, methane, non-methane volatile organic compounds, and particulate matters. Findings show that boilers and tankers at berth generate the most emission. In terms of vessel type, despite being the smallest in fleet size, bunkering tankers generate the most emission. Policies to motivate the adoption of cleaner fuels by bunkering vessels are recommended.

Key words:exhaust emission, ship emission, greenhouse gases, big data, automatic identification system, port, oil tanker, bunkering tanker

DOI:https://doi.org/10.1504/IJSTL.2023.129864

Date:2023