A near-field Gaussian plume inversion flux quantification method, applied to unmanned aerial vehicle sampling
A near-field Gaussian plume inversion flux quantification method, applied to unmanned aerial vehicle sampling
The accurate quantification of methane emissions from point sources is required to better quantify emissions for sector-specific reporting and inventory validation. An unmanned aerial vehicle (UAV) serves as a platform to sample plumes near to source. This paper describes a near-field Gaussian plume inversion (NGI) flux technique, adapted for downwind sampling of turbulent plumes, by fitting a plume model to measured flux density in three spatial dimensions. The method was refined and tested using sample data acquired from eight UAV flights, which measured a controlled release of methane gas. Sampling was conducted to a maximum height of 31 m (i.e. above the maximum height of the emission plumes). The method applies a flux inversion to plumes sampled near point sources. To test the method, a series of random walk sampling simulations were used to derive an NGI upper uncertainty bound by quantifying systematic flux bias due to a limited spatial sampling extent typical for short-duration small UAV flights (less than 30 min). The development of the NGI method enables its future use to quantify methane emissions for point sources, facilitating future assessments of emissions from specific source-types and source areas. This allows for atmospheric measurement-based fluxes to be derived using downwind UAV sampling for relatively rapid flux analysis, without the need for access to difficult-to-reach areas.
Shah, Adil
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Allen, Grant
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Pitt, Joseph R.
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Ricketts, Hugo
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Williams, Paul. I
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Hellmore, Jonathan
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Finlayson, Andrew
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Robinson, Rod
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Kabbabe, Khristopher
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Hollingsworth, Peter
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Rees-White, Tristan
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Beaven, Richard
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Scheutz, Charlotte
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Bourn, Mark
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Shah, Adil
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Allen, Grant
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Pitt, Joseph R.
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Ricketts, Hugo
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Williams, Paul. I
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Hellmore, Jonathan
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Finlayson, Andrew
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Robinson, Rod
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Kabbabe, Khristopher
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Hollingsworth, Peter
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Rees-White, Tristan
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Beaven, Richard
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Scheutz, Charlotte
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Bourn, Mark
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Shah, Adil, Allen, Grant, Pitt, Joseph R., Ricketts, Hugo, Williams, Paul. I, Hellmore, Jonathan, Finlayson, Andrew, Robinson, Rod, Kabbabe, Khristopher, Hollingsworth, Peter, Rees-White, Tristan, Beaven, Richard, Scheutz, Charlotte and Bourn, Mark
(2019)
A near-field Gaussian plume inversion flux quantification method, applied to unmanned aerial vehicle sampling.
Atmosphere, 10 (7), [396].
(doi:10.3390/atmos10070396).
Abstract
The accurate quantification of methane emissions from point sources is required to better quantify emissions for sector-specific reporting and inventory validation. An unmanned aerial vehicle (UAV) serves as a platform to sample plumes near to source. This paper describes a near-field Gaussian plume inversion (NGI) flux technique, adapted for downwind sampling of turbulent plumes, by fitting a plume model to measured flux density in three spatial dimensions. The method was refined and tested using sample data acquired from eight UAV flights, which measured a controlled release of methane gas. Sampling was conducted to a maximum height of 31 m (i.e. above the maximum height of the emission plumes). The method applies a flux inversion to plumes sampled near point sources. To test the method, a series of random walk sampling simulations were used to derive an NGI upper uncertainty bound by quantifying systematic flux bias due to a limited spatial sampling extent typical for short-duration small UAV flights (less than 30 min). The development of the NGI method enables its future use to quantify methane emissions for point sources, facilitating future assessments of emissions from specific source-types and source areas. This allows for atmospheric measurement-based fluxes to be derived using downwind UAV sampling for relatively rapid flux analysis, without the need for access to difficult-to-reach areas.
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Accepted/In Press date: 9 July 2019
e-pub ahead of print date: 15 July 2019
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Local EPrints ID: 432985
URI: http://eprints.soton.ac.uk/id/eprint/432985
PURE UUID: 4bea9235-7fc5-493d-b8fa-c906ca7b2461
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Date deposited: 05 Aug 2019 16:30
Last modified: 17 Mar 2024 02:44
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Contributors
Author:
Adil Shah
Author:
Grant Allen
Author:
Joseph R. Pitt
Author:
Hugo Ricketts
Author:
Paul. I Williams
Author:
Jonathan Hellmore
Author:
Andrew Finlayson
Author:
Rod Robinson
Author:
Khristopher Kabbabe
Author:
Peter Hollingsworth
Author:
Charlotte Scheutz
Author:
Mark Bourn
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