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.
Highlights
Shipping has proven to be the most energy efficient mode for mass transport, emissions from ship engines are harmful to the environment at both regional and global scales [1,2].The International Maritime Organization (IMO) has introduced a number of measures designed to reduce exhaust emissions from global shipping [3]
The activity-based emission prediction method aims to calculate the emissions from the fleet or various groups of marine vessels operating around ports and coast-lines for a particular area, which involve sailing statistics and Symmetry 2018, 10, 452; doi:10.3390/sym10100452
The estimation methodology of ship emissions in the present study is developed using based on the total amount of bunker fuels during maritime transport, i.e., the shipping bigdata analysis, which is based on the analysis on a huge amounts of traffic statistics in Qingdao port
Summary
Shipping has proven to be the most energy efficient mode for mass transport, emissions from ship engines are harmful to the environment at both regional and global scales [1,2]. Relevant studies that aim to predict the vessel emission inventory in Qingdao port have not been found yet. To address the challenge in emission control and environment protection, a new prediction model for vessel emission inventory of Qingdao port was developed using bigdata analysis in this paper. The link between shipping activities and air pollution emission in Qingdao port were examined, and an emission inventory that takes into account the different types of ships under various operation modes was developed. The contribution of this study is that, for the first time, a bigdata analysis-based methodology is proposed for vessel emission prediction in Qingdao port. The paper is organized as follows: Section 2 introduces the Qingdao port and Section 3 presents the new methodology for vessel emission prediction.
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