Articles | Volume 17, issue 1
https://doi.org/10.5194/essd-17-277-2025
https://doi.org/10.5194/essd-17-277-2025
Data description paper
 | 
28 Jan 2025
Data description paper |  | 28 Jan 2025

The high-resolution global shipping emission inventory by the Shipping Emission Inventory Model (SEIM)

Wen Yi, Xiaotong Wang, Tingkun He, Huan Liu, Zhenyu Luo, Zhaofeng Lv, and Kebin He

Related authors

Impacts of shipping emissions on ozone pollution in China
Zhenyu Luo, Li Peng, Zhaofeng Lv, Tingkun He, Wen Yi, Yongyue Wang, Kebin He, and Huan Liu
EGUsphere, https://doi.org/10.5194/egusphere-2025-2027,https://doi.org/10.5194/egusphere-2025-2027, 2025
This preprint is open for discussion and under review for Atmospheric Chemistry and Physics (ACP).
Short summary

Related subject area

Domain: ESSD – Ocean | Subject: Chemical oceanography
An updated synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2023: the SNAPO-CO2-v2 dataset
Nicolas Metzl, Jonathan Fin, Claire Lo Monaco, Claude Mignon, Samir Alliouane, Bruno Bombled, Jacqueline Boutin, Yann Bozec, Steeve Comeau, Pascal Conan, Laurent Coppola, Pascale Cuet, Eva Ferreira, Jean-Pierre Gattuso, Frédéric Gazeau, Catherine Goyet, Emilie Grossteffan, Bruno Lansard, Dominique Lefèvre, Nathalie Lefèvre, Coraline Leseurre, Sébastien Petton, Mireille Pujo-Pay, Christophe Rabouille, Gilles Reverdin, Céline Ridame, Peggy Rimmelin-Maury, Jean-François Ternon, Franck Touratier, Aline Tribollet, Thibaut Wagener, and Cathy Wimart-Rousseau
Earth Syst. Sci. Data, 17, 1075–1100, https://doi.org/10.5194/essd-17-1075-2025,https://doi.org/10.5194/essd-17-1075-2025, 2025
Short summary
A global monthly 3D field of seawater pH over 3 decades: a machine learning approach
Guorong Zhong, Xuegang Li, Jinming Song, Baoxiao Qu, Fan Wang, Yanjun Wang, Bin Zhang, Lijing Cheng, Jun Ma, Huamao Yuan, Liqin Duan, Ning Li, Qidong Wang, Jianwei Xing, and Jiajia Dai
Earth Syst. Sci. Data, 17, 719–740, https://doi.org/10.5194/essd-17-719-2025,https://doi.org/10.5194/essd-17-719-2025, 2025
Short summary
Mapping the global distribution of lead and its isotopes in seawater with explainable machine learning
Arianna Olivelli, Rossella Arcucci, Mark Rehkämper, and Tina van de Flierdt
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-17,https://doi.org/10.5194/essd-2025-17, 2025
Revised manuscript accepted for ESSD
Short summary
A machine-learning reconstruction of sea surface pCO2 in the North American Atlantic Coastal Ocean Margin from 1993 to 2021
Zelun Wu, Wenfang Lu, Alizée Roobaert, Luping Song, Xiao-Hai Yan, and Wei-Jun Cai
Earth Syst. Sci. Data, 17, 43–63, https://doi.org/10.5194/essd-17-43-2025,https://doi.org/10.5194/essd-17-43-2025, 2025
Short summary
Tracer-based Rapid Anthropogenic Carbon Estimation (TRACE)
Brendan R. Carter, Jörg Schwinger, Rolf Sonnerup, Andrea J. Fassbender, Jonathan D. Sharp, and Larissa M. Dias
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-560,https://doi.org/10.5194/essd-2024-560, 2024
Revised manuscript accepted for ESSD
Short summary

Cited articles

Browse, J., Carslaw, K. S., Schmidt, A., and Corbett, J. J.: Impact of future Arctic shipping on high-latitude black carbon deposition, Geophys. Res. Lett., 40, 4459–4463, https://doi.org/10.1002/grl.50876, 2013. 
Chen, D., Wang, X., Li, Y., Lang, J., Zhou, Y., Guo, X., and Zhao, Y.: High-spatiotemporal-resolution ship emission inventory of China based on AIS data in 2014, Sci. Total Environ., 609, 776–787, https://doi.org/10.1016/j.scitotenv.2017.07.051, 2017. 
Chen, D., Fu, X., Guo, X., Lang, J., Zhou, Y., Li, Y., Liu, B., and Wang, W.: The impact of ship emissions on nitrogen and sulfur deposition in China, Sci. Total Environ., 708, 134636, https://doi.org/10.1016/j.scitotenv.2019.134636, 2020. 
Chen, X. and Yang, J.: Analysis of the uncertainty of the AIS-based bottom-up approach for estimating ship emissions, Mar. Pollut. Bull., 199, 115968, https://doi.org/10.1016/j.marpolbul.2023.115968, 2024. 
Corbett, J. J., Fischbeck, P. S., and Pandis, S. N.: Global nitrogen and sulfur inventories for oceangoing ships, J. Geophys. Res.-Atmos., 104, 3457–3470, https://doi.org/10.1029/1998jd100040, 1999. 
Download
Short summary
This study presents a detailed global dataset on ship emissions, covering the years 2013 and 2016–2021, using advanced modeling techniques. The dataset includes emissions data for four types of greenhouse gases and five types of air pollutants. The data, available for research, offer valuable insights into ship emission spatiotemporal patterns by vessel type and age, providing a solid data foundation for fine-scale scientific research and shipping emission mitigation.
Share
Altmetrics
Final-revised paper
Preprint