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基于2016年至2022年船舶自动识别系统(AIS)数据的北极航运黑碳排放时空演变与预测

Spatiotemporal evolution and prediction of Arctic shipping black carbon emissions based on AIS data 2016 to 2022.

作者信息

Qi Xinli, Li Zhenfu, Li Shiyue, Song Chunrui, Zhou Yutao, Li Jiaqi

机构信息

College of Transportation Engineering, Dalian Maritime University, Dalian 116029, China.

College of Transportation Engineering, Dalian Maritime University, Dalian 116029, China.

出版信息

Mar Pollut Bull. 2025 Oct;219:118318. doi: 10.1016/j.marpolbul.2025.118318. Epub 2025 Jun 18.

Abstract

The rapid expansion of Arctic shipping has heightened concerns regarding its environmental impact, particularly the climate and ecological effects of black carbon (BC) emissions. Accurate quantification and a comprehensive understanding of Arctic shipping BC emission trends are essential for formulating effective mitigation policies. This study utilizes Automatic Identification System (AIS) trajectory data from 2016 to 2022 and applies a bottom-up approach to refine BC emission estimates from Arctic shipping. The analysis covers emissions across various operational states, 14 ship types, different fuel categories, and spatial distribution patterns, and predicts the 2030 and 2050 emission trends of 40 types under the global shipping shift and global route not shifted assumptions. The results show that from 2016 to 2022, the total BC emissions from Arctic shipping increased from 278.97 tons to 499.42 tons, an increase of 79.02 %. At the same time, the emissions showed obvious seasonal characteristics, with the highest emissions in summer (June to August) and the annual peak in August. In addition, Gas tankers、Fishing vessels、Cruise ships、Crude Oil tankers、Passenger ships and Bulk carriers are the six types of ships with the largest emissions (accounting for 81.29 % in 2022). From the perspective of spatial distribution, BC emissions are mainly concentrated in key shipping areas such as the Barents Sea, Kara Sea, Bering Sea, Chukchi Sea, and Davis Strait, and the scope shows a trend of expansion year by year. Furthermore, the emission forecast results show that if the "HFO (Heavy fuel oil) Ban" is implemented and the fuel is switched to MGO/MDO (Marine gas oils/Marine diesel oil), Arctic shipping BC emissions are expected to be 476.82-9157.25 tons in 2050. However, if cleaner fuels (such as liquefied natural gas LNG) are used, BC emissions can be significantly reduced, which is expected to be an effective path to achieve emission reduction targets. Finally, the model test results show that if the omission of ships in AIS data is taken into account, the total BC emissions in the Arctic can reach 1.54-1.95 times the estimated value, and fishing vessels are the main source of uncertainty.

摘要

北极航运的迅速扩张加剧了人们对其环境影响的担忧,尤其是黑碳(BC)排放对气候和生态的影响。准确量化并全面了解北极航运的黑碳排放趋势对于制定有效的减排政策至关重要。本研究利用2016年至2022年的自动识别系统(AIS)轨迹数据,采用自下而上的方法来完善北极航运的黑碳排放估算。该分析涵盖了各种运营状态、14种船舶类型、不同燃料类别以及空间分布模式下的排放情况,并预测了在全球航运航线转移和未转移假设下40种船舶类型在2030年和2050年的排放趋势。结果表明,2016年至2022年,北极航运的黑碳总排放量从278.97吨增加到499.42吨,增长了79.02%。同时,排放呈现出明显的季节性特征,夏季(6月至8月)排放量最高,8月达到年度峰值。此外,液化气船、渔船、游轮、原油油轮、客船和散货船是排放量最大的六种船舶类型(2022年占比81.29%)。从空间分布来看,黑碳排放主要集中在巴伦支海、喀拉海、白令海、楚科奇海和戴维斯海峡等关键航运区域,且范围呈逐年扩大趋势。此外,排放预测结果显示,如果实施“重质燃油禁令”并将燃料转换为MGO/MDO(船用汽油/船用柴油),2050年北极航运的黑碳排放量预计为476.82 - 9157.25吨。然而,如果使用更清洁的燃料(如液化天然气LNG),黑碳排放量可显著降低,这有望成为实现减排目标的有效途径。最后,模型测试结果表明,如果考虑到AIS数据中船舶的遗漏情况,北极黑碳总排放量可达估算值的1.54 - 1.95倍,且渔船是不确定性的主要来源。

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