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| Content Provider | World Health Organization (WHO)-Global Index Medicus |
|---|---|
| Author | Li, Cheng Ye, Siqi Yuan, Zibing Zheng, Junyu Shi, Yuqi Ng, Simon K. W. Xiao, Teng Huang, Zhijiong Fan, Xiaoli Ou, Jiamin Zhong, Zhuangmin |
| Description | Country affiliation: China Author Affiliation: Li C ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Yuan Z ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Ou J ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Fan X ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Ye S ( Guangdong Environmental Monitoring Center, Modiesha Street, Haizhu District, Guangzhou, 510308, China.); Xiao T ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Shi Y ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Huang Z ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Ng SK ( Civic Exchange, 23/F, Chun Wo Commercial Centre, 23-29 Wing Wo Street, Central, Hong Kong.); Zhong Z ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China.); Zheng J ( School of Environment and Energy, South China University of Technology, Guangzhou 510006, China. Electronic address: zheng.junyu@gmail.com.) |
| Abstract | Ship emissions contribute significantly to air pollution and impose health risks to residents along the coastal area. By using the refined data from the Automatic Identification System (AIS), this study developed a highly resolved ship emission inventory for the Pearl River Delta (PRD) region, China, home to three of ten busiest ports in the world. The region-wide SO , NO , CO, PM , PM , and VOC emissions in 2013 were estimated to be 61,484, 103,717, 10,599, 7155, 6605, and 4195t, respectively. Ocean going vessels were the largest contributors of the total emissions, followed by coastal vessels and river vessels. In terms of ship type, container ship was the leading contributor, followed by conventional cargo ship, dry bulk carrier, fishing ship, and oil tanker. These five ship types accounted for >90% of total emissions. The spatial distributions of emissions revealed that the key emission hot spots all concentrated within the newly proposed emission control area (ECA) and ship emissions within ECA covered >80% of total ship emissions in the PRD, highlighting the importance of ECA in emissions reduction in the PRD. The uncertainties of emission estimates of pollutants were quantified, with lower bounds of -24.5% to -21.2% and upper bounds of 28.6% to 33.3% at 95% confidence intervals. The lower uncertainties in this study highlighted the powerfulness of AIS data in improving ship emission estimates. The AIS-based bottom-up methodology can be used for developing and upgrading ship emission inventory and formulating effective control measures on ship emissions in other port regions wherever possible. |
| ISSN | 00489697 |
| Journal | Science of The Total Environment |
| Volume Number | 573 |
| e-ISSN | 18791026 |
| Language | English |
| Publisher | Elsevier |
| Publisher Date | 2016-12-15 |
| Publisher Place | Netherlands |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Discipline Environmental Science |
| Content Type | Text |
| Resource Type | Article |
| Subject | Environmental Chemistry Waste Management and Disposal Pollution Environmental Engineering |
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