Gao Xinjia, Yu Qifeng, Dai Wei, Chen Qinyang
College of Transport and Communications, Shanghai Maritime University, Shanghai, 201306, China.
Heliyon. 2024 Dec 12;11(1):e41208. doi: 10.1016/j.heliyon.2024.e41208. eCollection 2025 Jan 15.
There is a direct and close relationship between ship emissions in port waters and the operational status of the ships. Precisely identifying the operational status of ships in port waters and thoroughly exploring the specific relationship between these activities and ship emissions is crucial for achieving accurate control and scientific reduction of emissions from ships in port areas. With advancements in technology, AIS data can accurately capture the operational status of ships, facilitating a macro-level analysis of ship behavior and emission characteristics. This study proposes a method for extracting ship activity states using AIS data and conducts a detailed examination of ship activities at various container terminals and berths within Shanghai Port. Additionally, it analyzes the correlation between ship activities and their emissions. The research findings indicate that container ships are the most prevalent ships at Shanghai Port, followed by bulk carriers, general cargo ships, and tankers. Notably, bulk carriers and tankers exhibit the longest berthing times, with tankers showing the most significant fluctuations. Mingdong Terminal is identified as the most active, featuring the longest anchorage and berthing time, whereas Jungonglu and Zhanghuabang Terminals have the shortest. The calculation of pollutant emissions reveals that container ships and oil tankers are the primary contributors, particularly for CO and NO emissions. Mingdong Terminal recorded the highest total CO emissions, followed by Guandong and Shengdong Terminals. These findings emphasize the importance of berth service efficiency, suggesting that increased emissions are associated with heightened ship activity. CO emissions from container ships increase linearly with the number of berthed ships and show a cubic nonlinear relationship with berthing time. The analysis highlights terminals such as Guandong, Zhendong, Shengdong, and Yangshan Phase IV as critical areas for emission reduction strategies. This study not only enhances our understanding of the dynamics of ship emissions in port waters but also provides a theoretical foundation for implementing effective emission control and reduction strategies. Through this detailed research approach, it is possible to more accurately identify emission sources and optimize port operations, thereby offering strong data support and scientific guidance for environmental management and policy-making in port waters.
港口水域船舶排放与船舶运行状态之间存在直接且紧密的关系。准确识别港口水域船舶的运行状态，并深入探究这些活动与船舶排放之间的具体关系，对于实现港口区域船舶排放的精确控制和科学减排至关重要。随着技术的进步，船舶自动识别系统（AIS）数据能够准确捕捉船舶的运行状态，有助于从宏观层面分析船舶行为和排放特征。本研究提出了一种利用AIS数据提取船舶活动状态的方法，并对上海港内各集装箱码头和泊位的船舶活动进行了详细考察。此外，还分析了船舶活动与其排放之间的相关性。研究结果表明，集装箱船是上海港最常见的船舶类型，其次是散货船、杂货船和油轮。值得注意的是，散货船和油轮的停泊时间最长，其中油轮的波动最为显著。明东码头被确定为最活跃的码头，锚泊和停泊时间最长，而军工路和张华浜码头的时间最短。污染物排放计算结果显示，集装箱船和油轮是主要排放源，尤其是一氧化碳（CO）和氮氧化物（NO）排放。明东码头的总CO排放量最高，其次是冠东和盛东码头。这些发现强调了泊位服务效率的重要性，表明排放量增加与船舶活动加剧有关。集装箱船的CO排放量随靠泊船舶数量呈线性增加，与停泊时间呈三次非线性关系。分析突出了冠东、振东、盛东和洋山四期等码头作为减排策略的关键区域。本研究不仅增进了我们对港口水域船舶排放动态的理解，还为实施有效的排放控制和减排策略提供了理论基础。通过这种详细的研究方法，可以更准确地识别排放源并优化港口运营，从而为港口水域的环境管理和政策制定提供有力的数据支持和科学指导。
