Estimation of Power Plant Emissions with Unscented Kalman Filter

Metia, S; Ha, QP; Duc, HN; Azzi, M

Estimation of Power Plant Emissions with Unscented Kalman Filter

Metia, S

Ha, QP

Duc, HN Azzi, M

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Publication Type:: Journal Article
Citation:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11 (8), pp. 2763 - 2772
Issue Date:: 2018-08-01

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Field	Value	Language
dc.contributor.author	Metia, S https://orcid.org/0000-0002-5676-1146	en_US
dc.contributor.author	Ha, QP https://orcid.org/0000-0003-0978-1758	en_US
dc.contributor.author	Duc, HN	en_US
dc.contributor.author	Azzi, M	en_US
dc.date.available	2020-06-08T19:07:31Z
dc.date.issued	2018-08-01	en_US
dc.identifier.citation	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11 (8), pp. 2763 - 2772	en_US
dc.identifier.issn	1939-1404	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131786
dc.description.abstract	© 2008-2012 IEEE. Emissions from power plants constitute a major part of air pollution and should be adequately estimated. In this paper, we consider the problem of estimating nitrogen dioxide (NO-X ) emission of power plants by developing an inverse method to integrate satellite observations of atmospheric pollutant column concentrations with species concentrations and direct sensitivities predicted by a regional air quality model, in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields are analyzed using a 'bottom-up' approach, with an inversion performed by an unscented Kalman filter (UKF) to improve estimation profiles from emissions inventories data for the Sydney metropolitan area. The idea is to integrate information from the original inventories with tropospheric nitrogen dioxide (NO-2) emissions estimated during one month from the air pollution model-chemical transport model, and then, for validation, to compare the resulting model with satellite retrievals from the ozone monitoring instrument (OMI) above the region. The UKF-based estimation of NO-2 emissions shows better agreement with OMI observations, implying a significant improvement in accuracy as compared with the original inventories. Therefore, the proposed method is a promising tool for estimation of air emissions in urban areas.	en_US
dc.relation.ispartof	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing	en_US
dc.relation.isbasedon	10.1109/JSTARS.2018.2832131	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Estimation of Power Plant Emissions with Unscented Kalman Filter	en_US
dc.type	Journal Article
utslib.citation.volume	8	en_US
utslib.citation.volume	11	en_US
utslib.for	0909 Geomatic Engineering	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
pubs.embargo.period	Not known	en_US
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 Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CBI - Centre for Built Infrastructure
utslib.copyright.status	open_access	*
pubs.issue	8	en_US
pubs.publication-status	Published	en_US
pubs.volume	11	en_US

Abstract:

© 2008-2012 IEEE. Emissions from power plants constitute a major part of air pollution and should be adequately estimated. In this paper, we consider the problem of estimating nitrogen dioxide (NO-X ) emission of power plants by developing an inverse method to integrate satellite observations of atmospheric pollutant column concentrations with species concentrations and direct sensitivities predicted by a regional air quality model, in order to discern biases in the emissions of the pollutant precursors. Using this method, the emission fields are analyzed using a 'bottom-up' approach, with an inversion performed by an unscented Kalman filter (UKF) to improve estimation profiles from emissions inventories data for the Sydney metropolitan area. The idea is to integrate information from the original inventories with tropospheric nitrogen dioxide (NO-2) emissions estimated during one month from the air pollution model-chemical transport model, and then, for validation, to compare the resulting model with satellite retrievals from the ozone monitoring instrument (OMI) above the region. The UKF-based estimation of NO-2 emissions shows better agreement with OMI observations, implying a significant improvement in accuracy as compared with the original inventories. Therefore, the proposed method is a promising tool for estimation of air emissions in urban areas.

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http://hdl.handle.net/10453/131786