A near-field Gaussian plume inversion flux quantification method, applied to unmanned aerial vehicle sampling

Adil Shah*, Grant Allen, Joseph R. Pitt, Hugo Ricketts, Paul I. Williams, Jonathan Helmore, Andrew Finlayson, Rod Robinson, Khristopher Kabbabe, Peter Hollingsworth, Tristan C. Rees-White, Richard Beaven, Charlotte Scheutz, Mark Bourn

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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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.
Original languageEnglish
Article number396
Issue number7
Number of pages25
Publication statusPublished - 2019


  • Methane
  • Flux quantification
  • Gaussian plume
  • UAV

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