Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 18, 6075-6093, 2018
https://doi.org/10.5194/acp-18-6075-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Research article
02 May 2018
Decadal evolution of ship emissions in China from 2004 to 2013 by using an integrated AIS-based approach and projection to 2040
Cheng Li1, Jens Borken-Kleefeld2, Junyu Zheng1,3, Zibing Yuan3, Jiamin Ou4, Yue Li5, Yanlong Wang3, and Yuanqian Xu3 1Institute for Environmental and Climate Research, Jinan University, Guangzhou 511442, China
2The International Institute for Applied Systems Analysis, Air Quality and Greenhouse Gases Program, 2361 Laxenburg, Austria
3School of Environment and Energy, South China University of Technology, Guangzhou 510006, China
4School of International Development, University of East Anglia, Norwich, NR4 7TJ, UK
5Transport Planning and Research Institute, Ministry of Transport No. 2 Building, 6A Shuguangxili, Chaoyang District, Beijing 100028, China
Abstract. Ship emissions contribute significantly to air pollution and pose health risks to residents of coastal areas in China, but the current research remains incomplete and coarse due to data availability and inaccuracy in estimation methods. In this study, an integrated approach based on the Automatic Identification System (AIS) was developed to address this problem. This approach utilized detailed information from AIS and cargo turnover and the vessel calling number information and is thereby capable of quantifying sectoral contributions by fuel types and emissions from ports, rivers, coastal traffic and over-the-horizon ship traffic. Based upon the established methodology, ship emissions in China from 2004 to 2013 were estimated, and those to 2040 at 5-year intervals under different control scenarios were projected. Results showed that for the area within 200 nautical miles (Nm) of the Chinese coast, SO2, NOx, CO, PM10, PM2.5, hydrocarbon (HC), black carbon (BC) and organic carbon (OC) emissions in 2013 were 1010, 1443, 118, 107, 87, 67, 29 and 21 kt yr−1, respectively, which doubled over these 10 years. Ship sources contributed  ∼ 10 % to the total SO2 and NOx emissions in the coastal provinces of China. Emissions from the proposed Domestic Emission Control Areas (DECAs) within 12 Nm constituted approximately 40 % of the all ship emissions along the Chinese coast, and this percentage would double when the DECA boundary is extended to 100 Nm. Ship emissions in ports accounted for about one-quarter of the total emissions within 200 Nm, within which nearly 80 % of the emissions were concentrated in the top 10 busiest ports of China. SO2 emissions could be reduced by 80 % in 2020 under a 0.5 % global sulfur cap policy. In comparison, a similar reduction of NOx emissions would require significant technological change and would likely take several decades. This study provides solid scientific support for ship emissions control policy making in China. It is suggested to investigate and monitor the emissions from the shipping sector in more detail in the future.
Citation: Li, C., Borken-Kleefeld, J., Zheng, J., Yuan, Z., Ou, J., Li, Y., Wang, Y., and Xu, Y.: Decadal evolution of ship emissions in China from 2004 to 2013 by using an integrated AIS-based approach and projection to 2040, Atmos. Chem. Phys., 18, 6075-6093, https://doi.org/10.5194/acp-18-6075-2018, 2018.
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Short summary
We developed an integrated approach based on the Automatic Identification System (AIS) to promote the estimation of sectoral ship emissions in China. Based upon this approach, the sector-based contributions, decadal evolution from 2004 to 2013, emission projection to 2040, and impact of different sizes of Emission Control Areas (ECAs) on emission reductions were investigated, aiming to provide solid scientific support for ship emissions control policy making in China.
We developed an integrated approach based on the Automatic Identification System (AIS) to...
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