Comparing an energy-based ship emissions model with AIS and on-board emissions testing. (December 2022)

• Record Type: Journal Article
• Authors: Smit, Robin; Chu-Van, Thuy; Suara, Kabir; Brown, Richard J.
• Is Part Of: Atmospheric environment. Volume 16(2022)
• Publication Date: 2022-12
• Subjects: Shipping -- OGV -- Emissions -- On-board emission testing
• DOI: 10.1016/j.aeaoa.2022.100192
• Languages: English
• ISSNs: 2590-1621

Abstract

Abstract: On-board emission testing data for two ocean-going vessels is used to assess the performance of a new Australian ship emissions model, and to also assess the impact of local currents on emission predictions. Prediction performance is only marginally affected by AIS post-processing method and inclusion of local current information. Model performance was assessed for three different aspects, fuel-based emission factors (g/g CO2 ), engine work-based emission factors (g/kWh) and distance-based emission factors (g/km). Analysis of fuel-based and engine-work based emission factors suggest good performance and small to reasonable mean prediction errors for CO2 (±10%), PM10 (±15%) and SO2 (±20%). For NOx and CO, on-board emissions testing suggest that model emission factors are biased high and low with mean prediction errors +60–70% and −60%, respectively. The results for distance-based emission factors were not considered to be meaningful due to spatial and temporal inaccuracies in linking on-board testing with the AIS data that could not be resolved. Given the importance of AIS data as input to fuel and emissions modelling, it is recommended that the spatial and temporal accuracy of AIS data is investigated and confirmed in future studies. Moreover, the differences found in this study between model predictions and on-board measurements highlight a few limitations in application of generic fleet-based models. Graphical abstract: Image 1 Highlights: Use on-board emissions testing to validate new ship emission model. Assess the impact of local currents. On-board testing suggests accurate predictions for CO2, SO2 and PM10 . On-board testing suggests biased predictions for CO and NOx . Potential spatial issue with using AIS data in emission modelling.