Inverse Air-Pollutant Emission and Prediction Using Extended Fractional Kalman Filtering

Metia, S; Oduro, SD; Duc, HN; Ha, Q

Inverse Air-Pollutant Emission and Prediction Using Extended Fractional Kalman Filtering

Metia, S

Oduro, SD Duc, HN Ha, Q

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Publication Type:: Journal Article
Citation:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9 (5), pp. 2051 - 2063
Issue Date:: 2016-05-01

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Field	Value	Language
dc.contributor.author	Metia, S https://orcid.org/0000-0002-5676-1146	en_US
dc.contributor.author	Oduro, SD	en_US
dc.contributor.author	Duc, HN	en_US
dc.contributor.author	Ha, Q https://orcid.org/0000-0003-0978-1758	en_US
dc.date.issued	2016-05-01	en_US
dc.identifier.citation	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2016, 9 (5), pp. 2051 - 2063	en_US
dc.identifier.issn	1939-1404	en_US
dc.identifier.uri	http://hdl.handle.net/10453/43538
dc.description.abstract	© 2016 IEEE. It is essential to maintain air-quality standards and to take necessary measures when air-pollutant concentrations exceed permissible limits. Pollutants such as ground-level ozone (O3), nitrogen oxides (NOX), and volatile organic compounds (VOCs) emitted from various sources can be estimated at a particular location through integration of observation data obtained from measurement sites and effective air-quality models, using emission inventory data as input. However, there are always uncertainties associated with the emission inventory data as well as uncertainties generated by a meteorological model. This paper addresses the problem of improving the inverse air pollution emission and prediction over the urban and suburban areas using the air-pollution model with chemical transport model (TAPM-CTM) coupled with the extended fractional Kalman filter (EFKF) based on a Matérn covariance function. Here, nitrogen oxide (NO), nitrogen dioxide (NO2), and O3 concentrations are predicted by TAPM-CTM in the airshed of Sydney and surrounding areas. For improvement of the emission inventory, and hence the air-quality prediction, the fractional order of the EFKF is tuned using a genetic algorithm (GA). The proposed methodology is verified with measurements at monitoring stations and is then applied to obtain a better spatial distribution of O3 over the region.	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.2016.2541958	en_US
dc.title	Inverse Air-Pollutant Emission and Prediction Using Extended Fractional Kalman Filtering	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	9	en_US
utslib.for	090602 Control Systems, Robotics and Automation	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0909 Geomatic Engineering	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
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	9	en_US

Abstract:

© 2016 IEEE. It is essential to maintain air-quality standards and to take necessary measures when air-pollutant concentrations exceed permissible limits. Pollutants such as ground-level ozone (O3), nitrogen oxides (NOX), and volatile organic compounds (VOCs) emitted from various sources can be estimated at a particular location through integration of observation data obtained from measurement sites and effective air-quality models, using emission inventory data as input. However, there are always uncertainties associated with the emission inventory data as well as uncertainties generated by a meteorological model. This paper addresses the problem of improving the inverse air pollution emission and prediction over the urban and suburban areas using the air-pollution model with chemical transport model (TAPM-CTM) coupled with the extended fractional Kalman filter (EFKF) based on a Matérn covariance function. Here, nitrogen oxide (NO), nitrogen dioxide (NO2), and O3 concentrations are predicted by TAPM-CTM in the airshed of Sydney and surrounding areas. For improvement of the emission inventory, and hence the air-quality prediction, the fractional order of the EFKF is tuned using a genetic algorithm (GA). The proposed methodology is verified with measurements at monitoring stations and is then applied to obtain a better spatial distribution of O3 over the region.

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http://hdl.handle.net/10453/43538