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

Earth system science data, Journal Year: 2025, Volume and Issue: 17(1), P. 277 - 292

Published: Jan. 28, 2025

Abstract. The high-resolution ship emission inventory serves as a crucial dataset for various disciplines including atmospheric science, marine and environmental management. Here, we present global high-spatiotemporal-resolution at resolution of 0.1° × the years 2013 2016–2021, generated by state-of-the-art Shipping Emission Inventory Model (SEIMv2.2). Initially, annual 30 billion Automatic Identification System (AIS) data underwent extensive cleaning to ensure validity accuracy in temporal spatial distribution. Subsequently, integrating real-time vessel positions speeds from AIS with static technical parameters, factors, other computational SEIM simulated emissions on ship-by-ship, signal-by-signal basis. Finally, results were aggregated analyzed. In 2021, activity established based covered 109 300 vessels globally (101 400 reported United Nations Conference Trade Development). Concerning major air pollutants greenhouse gases, ships emitted 847.2×106 t CO2, 2.3×106 SO2, 16.1×106 NOx, 791.2 kt CO, 737.3 HC (hydrocarbon), 415.5 primary PM2.5, 61.6 BC (black carbon), 210.3 CH4, 45.1 N2O accounting 3.2 % 14.2 2.3 CO2 all anthropogenic sources, Community Emissions Data (CEDS). Due implementation fuel-switching policies, SO2 PM2.5 saw significant reduction 81.3 76.5 2021 compared 2019, respectively. According results, composition types contributing remained relatively stable through years, container consistently ∼ emissions. Regarding age distribution, contribution built before 2000 (without Tier standards) has been declining, dropping 10.2 suggesting that even complete phase-out these would have limited potential reducing NOx short term. On hand, after 2016 (meeting III standard) kept increasing, reaching 13.3 2021. Temporally, exhibited minimal daily fluctuations. Spatially, characteristics different delineated. Patterns contributions vary among maritime regions, predominant North South Pacific, bulk carriers Atlantic, oil tankers prevalent Arabian Sea. distribution intensity also significantly across regions. Our dataset, which is accessible https://doi.org/10.5281/zenodo.10869014 (Wen et al., 2024), provides breakdown type age; it available broad research purposes, will provide solid foundation fine-scale scientific shipping mitigation.

Language: Английский

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Insights into transportation CO2 emissions with big data and artificial intelligence

Patterns, Journal Year: 2025, Volume and Issue: 6(4), P. 101186 - 101186

Published: March 3, 2025

The ever-increasing stream of big data offers potential for deep decarbonization in the transportation sector but also presents challenges extracting interpretable insights due to its complexity and volume. This overview discusses application help understand carbon dioxide emissions introduces how artificial intelligence models, including machine learning (ML) (DL), are used assimilate these data. We suggest using ML interpret low-dimensional DL enhance predictability with spatial connections across multiple timescales. Overcoming related algorithms, data, computation requires interdisciplinary collaboration on both technology

Language: Английский

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How global container shipping emissions and marine primary production are related to: A geographically and temporally weighted analysis

Marine Environmental Research, Journal Year: 2025, Volume and Issue: 209, P. 107195 - 107195

Published: April 28, 2025

Language: Английский

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Synergistic effects of CO2 and air pollutants from ship emissions in Shanghai, China: Spatial-temporal characteristics, prediction assessment, policy implications

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 376, P. 124417 - 124417

Published: Feb. 15, 2025

Language: Английский

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