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Development of a methodology for estimation of ballast water Imported to Australian ports


Yoong, JLX and Enshaei, H, Development of a methodology for estimation of ballast water Imported to Australian ports, Journal of Basic and Applied Scientific Research, 6, (5) pp. 14-25. ISSN 2090-4304 (2016) [Refereed Article]


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Copyright 2016 TextRoad Publication Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)

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The importance of identifying the location and magnitude of risks imposed from ship-mediated bioinvasion in Australia is significant after the assessment of previous events that have impacted the Australian ecosystem and economy. This paper provides an overview of the developed methodology adopted for the estimation of ballast water imported to Australian ports. The resultant amount of ballast water discharged for a total of 31ports in a period of five years was estimated and results were presented. A high level of risk was identified at the northwest of Australia, where 60 percent of the total ballast water imported was discharged for the year 2013. A significantly large amount of ballast water was also discovered in the regions of Newcastle and Hay Point. It was discovered that bulk carriers account for 94 percent of mediated ballast water. Proportion factors for predictions have been established based on the relation between the mass of freight exported with the amount of ballast water discharged. The study recommends a sensitivity analysis of proportion factors based on varying selected deadweight for individual ship type and size categories. To mitigate risks from ship-mediated bioinvasion, the origin of the ballast water imported should be investigated as well as the type of foreign marine life introduced. The development of a methodology in the classification of ship sizes for general cargo ships, LPG/LNG, livestock and vehicle carriers was suggested to improve the accuracy of the estimation.

Item Details

Item Type:Refereed Article
Keywords:bioinvasion, vessel traffic, automatic identification system, artificial neuron network, deadweight tonnage, gross tonnage, proportion factor
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Ocean engineering
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the environmental sciences
UTAS Author:Yoong, JLX (Mr Joel Lim)
UTAS Author:Enshaei, H (Dr Hossein Enshaei)
ID Code:109748
Year Published:2016
Deposited By:Seafaring and Maritime Operations
Deposited On:2016-06-29
Last Modified:2017-10-19
Downloads:303 View Download Statistics

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