Comparing an energy-based ship emissions model with AIS and on-board emissions testing

Smit, R; Chu-Van, T; Suara, K; Brown, RJ

Comparing an energy-based ship emissions model with AIS and on-board emissions testing

Smit, R Chu-Van, T Suara, K Brown, RJ

Permalink

Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Atmospheric Environment: X, 2022, 16, pp. 100192
Issue Date:: 2022-12-01

Recently Added

	Filename	Description	Size
	1-s2.0-S2590162122000466-main.pdf	Published version	5.44 MB	Adobe PDF	View/Open

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is new to OPUS and is not currently available.

Full metadata record

Field	Value	Language
dc.contributor.author	Smit, R
dc.contributor.author	Chu-Van, T
dc.contributor.author	Suara, K
dc.contributor.author	Brown, RJ
dc.date.accessioned	2023-06-29T04:59:31Z
dc.date.available	2023-06-29T04:59:31Z
dc.date.issued	2022-12-01
dc.identifier.citation	Atmospheric Environment: X, 2022, 16, pp. 100192
dc.identifier.issn	2590-1621
dc.identifier.issn	2590-1621
dc.identifier.uri	http://hdl.handle.net/10453/170995
dc.description.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.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Atmospheric Environment: X
dc.relation.isbasedon	10.1016/j.aeaoa.2022.100192
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.title	Comparing an energy-based ship emissions model with AIS and on-board emissions testing
dc.type	Journal Article
utslib.citation.volume	16
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	recently_added	*
dc.date.updated	2023-06-29T04:59:29Z
pubs.publication-status	Published
pubs.volume	16

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/170995