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.

Highlights

  • Methane is the second most important greenhouse gas in terms of radiative forcing [1], with a direct radiative forcing of +0.61 W m−2 [2]

  • The near-field Gaussian plume inversion (NGI) fluxes for each of eight unmanned aerial vehicle (UAV) flights are displayed in Figure 4, along

  • The sample data used to develop the NGI flux quantification method described here was with flux uncertainty bounds, expressed a percentage of theby controlled release facility (CRF)

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Summary

Introduction

Methane is the second most important greenhouse gas in terms of radiative forcing [1], with a direct radiative forcing of +0.61 W m−2 [2]. Though a global mean methane concentration up to. Though most methane emission source types are known, the quantification of source emission contributions to the global methane budget may be poor [12], especially on a facility-scale. This prioritises the retrieval of facility-scale methane emission fluxes using near-field (~100 m from the source) sampling to help constrain and improve emission inventories by top-down validation and their global extrapolation [6]. By carefully targeting potent anthropogenic methane emission sources, accurate facility scale flux quantification is crucial in efforts to mitigate climate change

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