EGU26-16312, updated on 14 Mar 2026
https://doi.org/10.5194/egusphere-egu26-16312
EGU General Assembly 2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Poster | Friday, 08 May, 14:00–15:45 (CEST), Display time Friday, 08 May, 14:00–18:00
 
Hall X5, X5.26
Big Data–Driven Emission Inventory of Ship Exhaust Gases Considering Operational Scenarios
Min An1, JeongSeok Lee2, and TaeHoon Kim2
Min An et al.
  • 1Korea Institute of Ocean Science and Technology, Marine Bigdata & A.I. Center (UST Student)
  • 2Korea Institute of Ocean Science and Technology, Marine Bigdata & A.I. Center

Ship exhaust emissions are recognized as a major source of air pollution in coastal regions and an important target of international maritime regulations. With the strengthening of fuel sulfur content regulations, there is an increasing demand for quantitative emission estimates that explicitly account for operational conditions and regulatory application criteria. The U.S. Environmental Protection Agency (EPA) provides guidelines for ship emission estimation, including engine power and load factor calculations based on AIS-derived operational data, low-load operation adjustments, and the application of fuel sulfur regulations. In this study, these EPA-recommended procedures are implemented as a computational framework and algorithm applicable to large-scale AIS data, and are applied to ships operating in Korean waters. The analysis targets oceangoing vessels operating in Korean waters during the period 2021–2024, using AIS-based operational data combined with detailed ship specification data from IHS. Operating time is derived from the time differences between consecutive AIS records, while engine load factors are calculated using relationships between vessel speed and Maximum speed, as well as between draft and Maximum draft. Operating conditions with load factors below 20% are defined as low-load operation, and corresponding low-load adjustment factors recommended by the EPA are applied. Fuel sulfur content regulations are incorporated by reflecting time-dependent and spatially differentiated regulatory criteria to classify fuel types, and emission factors are applied at the level of individual AIS records according to these conditions. Through this procedure, ship emissions are estimated while simultaneously accounting for operational characteristics and regulatory applicability. The estimated pollutants include NOx, CO, HC, PM10, PM2.5, SO₂, and CO₂. The results indicate that low-load operation accounts for 42.56% of all valid AIS records, including 13.69% under stationary conditions (SOG = 0) and 28.87% under low-speed operation. The average SO₂ emission intensity is estimated at 2446.95 g/h in non-regulated areas and 20.30 g/h in regulated areas. These results suggest that ship emission characteristics in Korean waters vary substantially depending on operational conditions and the application of time and space dependent fuel sulfur regulations. The resulting emission inventory enables comparisons of emission characteristics across regions and periods, and can serve as a basis for discussions related to coastal air quality analysis, evaluation of emission control effectiveness, and assessments of emission changes under different regulatory scenarios.

How to cite: An, M., Lee, J., and Kim, T.: Big Data–Driven Emission Inventory of Ship Exhaust Gases Considering Operational Scenarios, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16312, https://doi.org/10.5194/egusphere-egu26-16312, 2026.