Port pollution prediction and management via multi-view intelligent computing: A case study of Tianjin Port
文献类型：期刊论文
| 作者 | Xue, Tong1; Li, Yong1; Mei, Qiang2,4; Bai, Yundi1; Yang, Yang5; Cui, Lei2; Wang, Peng6; Zhang, Beibei7; Wang, Shaohua3 |
| 刊名 | REGIONAL STUDIES IN MARINE SCIENCE
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| 出版日期 | 2026-03-01 |
| 卷号 | 95页码:27 |
| 关键词 | Pollutants Greenhouse gases Emission inventory Tianjin port AIS data Time series forecasting |
| ISSN号 | 2352-4855 |
| DOI | 10.1016/j.rsma.2026.104805 |
| 英文摘要 | Ship emissions exacerbate air pollution in ports, and their assessment and control have attracted widespread attention from both government and academia. To enable scientific tracing and management of port-related ship pollutants, this study proposes an multi-view intelligent spatiotemporal computing framework that integrates emission inventory construction and emission forecasting. The emission inventory is constructed based on AIS data, combined with vessel activity parameters and emission factor models, covering channels, anchorages, and berths to achieve high spatiotemporal resolution. In the prediction module, Transformer-, MLP-, TCN-, and RNN-based models are employed, and CEEMDAN is introduced for multi-scale decomposition to address the non-stationary nature of emission sequences. Experimental results for Tianjin Port from 2015 to 2018 show that CO2, SOX, and NOX emissions increased from 8.66 x 105 t, 1.28 x 104 t, and 6.6 x 103 t in 2015 to 1.35 x 106 t, 1.74 x 104 t, and 9.6 x 103 t in 2018, respectively. Spatially, the Xingang main channel and Beijiang Port area are emission hotspots. Among vessel types, oil tankers, bulk carriers, and container ships collectively contribute over 90% of total emissions. Source analysis indicates that main engine emissions in channels exceeded 70%, while berths and anchorages together contributed over 90%. Comparative prediction results demonstrate that CEEMDAN decomposition enhances the fine-grained representation of emission forecasts across all model types. The hybrid model SCINet_D, which integrates the strengths of SCINet and DLinear, exhibits relatively favorable predictive performance. The proposed spatiotemporal computing framework enables a coordinated analysis of emission inventory construction and trend prediction, providing scientific support for refined port air quality management, emission reduction strategies, and evaluation of domestic Emission Control Area (DECA) policies. |
| 资助项目 | National Natural Science Foundation of China[52372316] ; National Natural Science Foundation of China[42471495] ; Natural Science Foundation of Fujian Province[2024J01719] ; Natural Science Foundation of Fujian Province[2021J01821] ; Natural Science Foundation of Fujian Province[2023J01804] ; National Key R&D Program of China[2023YFF0805904] ; Talent introduction Program Youth Project of the Chinese Academy of Sciences[E43302020D] ; Talent introduction Program Youth Project of the Chinese Academy of Sciences[E2Z105010F] |
| WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology |
| 语种 | 英语 |
| WOS记录号 | WOS:001686436700001 |
| 出版者 | ELSEVIER |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/42823]  |
| 专题 | 中国科学院计算技术研究所 |
| 通讯作者 | Mei, Qiang; Wang, Shaohua |
| 作者单位 | 1.Beijing Univ Technol, Coll Comp Sci, Beijing 100124, Peoples R China 2.Jimei Univ, Nav Coll, Xiamen 361021, Peoples R China 3.Aerosp Informat Res Inst, Chinese Acad Sci, State Key Lab Remote Sensing & Digital Earth, Beijing 100094, Peoples R China 4.Shanghai Maritime Univ, Merchant Marine Coll, Shanghai 201306, Peoples R China 5.East China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China 6.Chinese Acad Sci, Inst Comp Technol, Beijing 100086, Peoples R China 7.Zhengzhou Univ, Sch Cyber Sci & Engn, Zhengzhou 450001, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xue, Tong,Li, Yong,Mei, Qiang,et al. Port pollution prediction and management via multi-view intelligent computing: A case study of Tianjin Port[J]. REGIONAL STUDIES IN MARINE SCIENCE,2026,95:27. |
| APA | Xue, Tong.,Li, Yong.,Mei, Qiang.,Bai, Yundi.,Yang, Yang.,...&Wang, Shaohua.(2026).Port pollution prediction and management via multi-view intelligent computing: A case study of Tianjin Port.REGIONAL STUDIES IN MARINE SCIENCE,95,27. |
| MLA | Xue, Tong,et al."Port pollution prediction and management via multi-view intelligent computing: A case study of Tianjin Port".REGIONAL STUDIES IN MARINE SCIENCE 95(2026):27. |
入库方式： OAI收割
来源：计算技术研究所
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