高分辨率船舶排放清单是大气科学、海洋科学、环境管理等各个学科的重要数据集。在这里，我们展示了 2013 年、2016 年至 2021 年分辨率为 0.1° × 0.1° 的全球高时空分辨率船舶排放清单，由最先进的航运排放清单模型 （SEIMv2.2） 生成。在主要空气污染物和温室气体方面，2021 年全球船舶排放了 8.472 亿吨二氧化碳、230 万吨二氧化硫、1610 万吨氮氧化物、791.2 万吨二氧化碳、737.3 千吨氢化合物、415.5 吨初级 PM2.5、61.6 千吨 BC、210.3 千吨 CH4、45.1 千吨 N2O，占二氧化硫的 3.2%; 根据社区排放数据系统 （CEDS），全球所有人为来源的 NOx 和 CO2 排放量的 14.2% 和 2.3%。根据盘点结果，导致全球船舶排放的船舶类型构成保持相对稳定。从时间上看，全球船舶排放的每日波动最小。在空间上，高分辨率排放数据集揭示了不同海洋区域之间不同类型船舶的船舶排放贡献模式。", "ds_source": "
利用 300 亿个自动识别系统 （AIS） 信号，SEIMv2.2 集成了实时船舶位置、速度和技术参数，以对 CO2、NOx、SO2、PM2.5、CO、HC、N2O 和 CH4 等关键物种的船舶排放进行建模。", "ds_process_way": "
最初，每年 300 亿个自动识别系统 （AIS） 数据进行了广泛的清理，以确保数据在时间和空间分布方面的有效性和准确性。随后，将 AIS 数据的实时船舶位置和速度与静态技术参数、排放因子和其他计算参数相结合，SEIM 逐船、逐个信号地模拟船舶排放。最后，对结果进行汇总和分析。", "ds_quality": "
提供按船舶类型和船龄划分的每日细分数据，可用于广泛的研究目的，将为精细的科学研究和航运减排提供坚实的数据基础。", "ds_acq_start_time": "2013-01-01 00:00:00", "ds_acq_end_time": "2021-12-31 00:00:00", "ds_acq_place": "全球", "ds_acq_lon_east": 180.0, "ds_acq_lat_south": -90.0, "ds_acq_lon_west": -180.0, "ds_acq_lat_north": 90.0, "ds_acq_alt_low": null, "ds_acq_alt_high": null, "ds_share_type": "login-access", "ds_total_size": 3493130699, "ds_files_count": 5, "ds_format": "CSV", "ds_space_res": "0.1度", "ds_time_res": "年", "ds_coordinate": "无", "ds_projection": "", "ds_thumbnail": "d3d8c3b2-d684-44a4-b15a-3f6af4073d96.png", "ds_thumb_from": 2, "ds_ref_way": "", "paper_ref_way": "", "ds_ref_instruction": "", "ds_from_station": null, "organization_id": "0a4269e1-65f4-45f1-aeba-88ea3068eebf", "ds_serv_man": "敏玉芳", "ds_serv_phone": "0931-4967596", "ds_serv_mail": "ncdc@lzb.ac.cn", "doi_value": "", "subject_codes": [ "170.60" ], "quality_level": 3, "publish_time": "2024-10-29 09:39:28", "last_updated": "2026-01-14 10:34:30", "protected": false, "protected_to": null, "lang": "zh", "cstr": "11738.11.NCDC.ZENODO.DB6617.2024", "i18n": { "en": { "title": "Global shipping emissions for the years 2013 and 2016-2021", "ds_format": "CSV", "ds_source": "
Using 30 billion Automatic Identification System (AIS) signals, SEIMP2.2 integrates real-time ship position, speed, and technical parameters to model ship emissions of key species such as CO2, NOx, SO2, PM2.5, CO, HC, N2O, and CH4.", "ds_quality": "
Providing daily segmented data by ship type and age, which can be used for a wide range of research purposes, will provide a solid data foundation for fine scientific research and shipping emissions reduction.", "ds_ref_way": "", "ds_abstract": "
The high-resolution ship emission inventory is an important dataset for various disciplines such as atmospheric science, marine science, and environmental management. Here, we present the global high-resolution ship emission inventory with a resolution of 0.1 °× 0.1 ° from 2013, 2016 to 2021, generated by the state-of-the-art shipping emission inventory model (SEIMP2.2). In terms of major air pollutants and greenhouse gases, in 2021, global ships emitted 847.2 million tons of carbon dioxide, 2.3 million tons of sulfur dioxide, 16.1 million tons of nitrogen oxides, 7.912 million tons of carbon dioxide, 737.3 million tons of hydrogen compounds, 415.5 tons of primary PM2.5, 61.6 million tons of BC, 210.3 million tons of CH4, and 45.1 million tons of N2O, accounting for 3.2% of sulfur dioxide emissions; According to the Community Emissions Data System (CEDS), 14.2% and 2.3% of all anthropogenic NOx and CO2 emissions worldwide are from human sources. According to the inventory results, the composition of ship types that contribute to global ship emissions remains relatively stable. In terms of time, the daily fluctuations in global ship emissions are the smallest. In terms of space, high-resolution emission datasets reveal the contribution patterns of ship emissions from different types of vessels in different marine regions.
", "ds_time_res": "年", "ds_acq_place": "Global", "ds_space_res": "0.1度", "ds_projection": "", "ds_process_way": "Initially, 30 billion Automatic Identification Systems (AIS) data were extensively cleaned each year to ensure their effectiveness and accuracy in terms of temporal and spatial distribution. Subsequently, by combining the real-time ship position and speed of AIS data with static technical parameters, emission factors, and other calculation parameters, SEIM simulates ship emissions on a ship by ship and signal by signal basis. Finally, summarize and analyze the results.", "ds_ref_instruction": "" } }, "submit_center_id": "ncdc", "data_level": 0, "license_type": "CC BY 4.0", "doi_reg_from": "reg_outside", "cstr_reg_from": "reg_outside", "doi_not_reg_reason": null, "cstr_not_reg_reason": null, "is_paper_in_submitting": false, "ds_topic_tags": [ "船舶排放", "大气污染", "海洋污染", "时空分析" ], "ds_subject_tags": [ "海洋科学" ], "ds_class_tags": [], "ds_locus_tags": [ "全球" ], "ds_time_tags": [ 2013, 2016, 2017, 2018, 2019, 2020, 2021 ], "ds_contributors": [ { "true_name": "刘欢", "email": "liu_env@tsinghua.edu.cn", "work_for": "清华大学", "country": "中国" } ], "ds_meta_authors": [ { "true_name": "刘欢", "email": "liu_env@tsinghua.edu.cn", "work_for": "清华大学", "country": "中国" } ], "ds_managers": [ { "true_name": "刘欢", "email": "liu_env@tsinghua.edu.cn", "work_for": "清华大学", "country": "中国" } ], "category": "其他" }