Article: Analysing exhaust emission of oil tanker vessels using big data in the port of Singapore Journal: International Journal of Shipping and Transport Logistics (IJSTL) 2023 Vol.16 No.3/4 pp.231 - 255 Abstract: 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. Inderscience Publishers - linking academia, business and industry through research

Title: Analysing exhaust emission of oil tanker vessels using big data in the port of Singapore

Authors: Zengqi Xiao; Jasmine Siu Lee Lam

Addresses: Maritime Energy and Sustainable Development Centre of Excellence, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore ' Maritime Energy and Sustainable Development Centre of Excellence, School of Civil and Environmental Engineering, Nanyang Technological University, Singapore

Abstract: 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.

Keywords: exhaust emission; ship emission; greenhouse gases; big data; automatic identification system; port; oil tanker; bunkering tanker.

DOI: 10.1504/IJSTL.2023.129864

International Journal of Shipping and Transport Logistics, 2023 Vol.16 No.3/4, pp.231 - 255

Received: 07 May 2020
Accepted: 19 Jun 2021
Published online: 03 Apr 2023 *

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Title: Analysing exhaust emission of oil tanker vessels using big data in the port of Singapore

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