A Near-Field Gaussian Plume Inversion Flux Quantification Method, Applied to Unmanned Aerial Vehicle Sampling

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.

Methane Mitigation: Methods to Reduce Emissions, on the Path to the Paris Agreement

“…The technology is changing rapidly. Better than 10 ppb (at 1 Hz) precision instrumentation is now available and in use on commercially available UAV platforms (Shah et al, ), and 1 ppm precision is likely soon. Further marked improvement in the precision, price, and availability of UAV‐mounted sensors should be expected in the near future.…”

Unmanned aerial vehicle observations of cold venting from exploratory hydraulic fracturing in the United Kingdom

“…[CH 4 ] was plotted on a vertical plane perpendicular to θ, to ascertain whether a well-defined background mole fraction could be derived from [CH 4 ] measurements (see figure 2 for fracking surveys and figure S4 for cattle surveys). The vertical axis represents height of the air inlet above ground level and the horizontal axis represents distance along the plane perpendicular to θ (see Shah et al (2019a) for a method to project longitude and latitude onto a perpendicular plane). It is clear from figure 2 that only four fracking surveys (F1.4, F2.5, F2.6 and F2.7) from a single sampling day resulted in [CH 4 ] enhancements sufficient for use in flux calculation.…”

“…To estimate fluxes (where detectable emissions were identified), [CH 4 ] enhancements above a calculated representative background were combined with WS(z) profiles (as described in the previous section) to derive geospatially mapped methane flux density (q) in kg s −1 m −2 (see Shah et al (2019a) for further details on q; see figures S5 and S6 for q during fracking and cattle surveys, respectively). The NGI flux quantification method, described in Shah et al (2019a) and tested in Shah et al (2019b), was used to derive a range from the lower flux uncertainty bound (F − ) to the upper flux uncertainty bound (F + ). The NGI method is an adaptation of the traditional Gaussian plume model (see Turner (1994) for details).…”

Cavity Ring-Down Methane Sensor for Small Unmanned Aerial Systems

“…In particular, the effect of the downwash caused by the rotating rotor blades [35,36] can distort the shape (concentration field) of the methane plume, which could then complicate the use of algorithms to infer source mass flow based on the measured flow field. Indeed, future research will focus on inferring mass-flow (and source location) from the sUAS methane concentration maps and wind data [15,[37][38][39].…”

“…High-end analyzers of larger mass (>~15 kg), as are typically used for sensitive ground-based measurements, can be deployed on larger UAS (as opposed to sUAS), such as the NASA SIERRA aircraft (mass~250 kg) [14]. The same class of sensors can be deployed via sUAS by keeping the sensor on the ground and flying only the inlet, which passes sample air to the sensor through a tube (~100 m length), but such arrangements are logistically more complex and can introduce artifacts associated with the flow lag-time through the rather long tube [15,16].…”