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Estimation of vessel emissions inventory in Qingdao port based on big data analysis

Journal Article


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Abstract


  • Exhaust emissions from vessels have increasingly attracted attention in the continuously growing marine transport world trade market. The International Maritime Organization (IMO) has introduced a number of measures designed to reduce exhaust emissions from global shipping. As one of the busiest ports in the world, Qingdao port has been studied to propose possible support to the development of efficient emission reduction. In this study, a large amount data of emissions inventory in Qingdao port was used to predict its annual exhaust emissions, and hence, to help understand maritime pollution in Qingdao port. Bigdata analysis methodology was employed to perform accurate predictions on vessel emissions. The analysis results show that the emissions were dominated by container ships, oil tankers, and bulk cargo ships. The comparison between Qingdao port and other ports in emission control areas demonstrates the necessity of control measures for exhaust emissions. The adoption of shore power and efficient cargo handling seems to be a potential solution to reduce exhaust emissions. The findings of this study are meaningful for maritime safety administration to understand the current emission situation in Qingdao port, propose corresponding control measures, and perform pollution prevention.

UOW Authors


  •   Sun, Xing (external author)
  •   Tian, Zhe (external author)
  •   Malekian, Reza (external author)
  •   Li, Zhixiong (external author)

Publication Date


  • 2018

Citation


  • Sun, X., Tian, Z., Malekian, R. & Li, Z. (2018). Estimation of vessel emissions inventory in Qingdao port based on big data analysis. Symmetry, 10 (10), 452-1-452-11.

Scopus Eid


  • 2-s2.0-85055742389

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3059&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2054

Start Page


  • 452-1

End Page


  • 452-11

Volume


  • 10

Issue


  • 10

Place Of Publication


  • Switzerland

Abstract


  • Exhaust emissions from vessels have increasingly attracted attention in the continuously growing marine transport world trade market. The International Maritime Organization (IMO) has introduced a number of measures designed to reduce exhaust emissions from global shipping. As one of the busiest ports in the world, Qingdao port has been studied to propose possible support to the development of efficient emission reduction. In this study, a large amount data of emissions inventory in Qingdao port was used to predict its annual exhaust emissions, and hence, to help understand maritime pollution in Qingdao port. Bigdata analysis methodology was employed to perform accurate predictions on vessel emissions. The analysis results show that the emissions were dominated by container ships, oil tankers, and bulk cargo ships. The comparison between Qingdao port and other ports in emission control areas demonstrates the necessity of control measures for exhaust emissions. The adoption of shore power and efficient cargo handling seems to be a potential solution to reduce exhaust emissions. The findings of this study are meaningful for maritime safety administration to understand the current emission situation in Qingdao port, propose corresponding control measures, and perform pollution prevention.

UOW Authors


  •   Sun, Xing (external author)
  •   Tian, Zhe (external author)
  •   Malekian, Reza (external author)
  •   Li, Zhixiong (external author)

Publication Date


  • 2018

Citation


  • Sun, X., Tian, Z., Malekian, R. & Li, Z. (2018). Estimation of vessel emissions inventory in Qingdao port based on big data analysis. Symmetry, 10 (10), 452-1-452-11.

Scopus Eid


  • 2-s2.0-85055742389

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3059&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2054

Start Page


  • 452-1

End Page


  • 452-11

Volume


  • 10

Issue


  • 10

Place Of Publication


  • Switzerland