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

Smit Department of Environment and Science, GPO Box 2454, Brisbane, 4001, QLD, Australia|
Richard J. (56962718900) Transport Energy/Emission Research, Brisbane, 4068, QLD, Australia| Kabir (53980606000); Brown Vietnam Maritime University (VMU), Haiphong, 180000, Viet Nam| Thuy (57196456361); Suara Biofuel Engine Research Facility (BERF), Queensland University of Technology (QUT), 4000, QLD, Australia| Robin (23490602500); Chu-Van University of Technology Sydney, Faculty of Engineering and Information Technology, Sydney, Australia|

Atmospheric Environment: X Số , năm 2022 (Tập 16, trang -)

ISSN: 25901621

ISSN: 25901621

DOI:

Tài liệu thuộc danh mục:

Article

English

Từ khóa: Engines; Forecasting; Fuels; Ocean currents; Ships; 1,3 butadiene; arsenic; benzene; benzo[a]pyrene; cadmium; carbon dioxide; carbon monoxide; ethylbenzene; formaldehyde; fuel; lead; manganese; methane; nickel; nitric oxide; nitrogen oxide; oxygen; sulfur dioxide; toluene; vanadium; volatile organic compound; xylene; 'current; Distance-based emission factors; Emission; Emission factors; Emission model; Emission testing; OGV; On-board emission testing; Performance; Ship emissions; diesel engine; air pollution control; Article; carbon emission; exhaust gas; particulate matter 10; physical chemistry; prediction; prediction error; salinity; shipping; temperature; Carbon dioxide
Tóm tắt tiếng anh
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. © 2022 The Authors

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