Abstract
Abstract. Methane emission fluxes from many facility-scale sources may be poorly quantified, potentially leading to uncertainties in the global methane budget. Accurate atmospheric measurement-based flux quantification is urgently required to address this. This paper describes the first test (using unbiased sampling) of a near-field Gaussian plume inversion (NGI) technique, suitable for facility-scale flux quantification, using a controlled release of methane gas. Two unmanned-aerial-vehicle (UAV) platforms were used to perform 22 flight surveys downwind of a point-source methane gas release from a regulated cylinder with a flowmeter. One UAV was tethered to an instrument on the ground, while the other UAV carried an on-board prototype instrument (both of which used the same near-infrared laser technology). Both instruments were calibrated using certified standards to account for variability in the instrumental gain factor, assuming fixed temperature and pressure. Furthermore, a water vapour correction factor, specifically calculated for the instrument, was applied and is described here in detail. We also provide guidance on potential systematic uncertainties associated with temperature and pressure, which may require further characterisation for improved measurement accuracy. The NGI technique was then used to derive emission fluxes for each UAV flight survey. We found good agreement of most NGI fluxes with the known controlled emission flux, within uncertainty, verifying the flux quantification methodology. The lower and upper NGI flux uncertainty bounds were, on average, 17 %±10(1σ) % and 227 %±98(1σ) % of the controlled emission flux, respectively. This range of conservative uncertainty bounds incorporate factors including the variability in the position of the time-invariant plume and potential for under-sampling. While these average uncertainties are large compared to methods such as tracer dispersion, we suggest that UAV sampling can be highly complementary to a toolkit of flux quantification approaches and may be a valuable alternative in situations where site access for tracer release is problematic. We see tracer release combined with UAV sampling as an effective approach in future flux quantification studies. Successful flux quantification using the UAV sampling methodology described here demonstrates its future utility in identifying and quantifying emissions from methane sources such as oil and gas extraction infrastructure facilities, livestock agriculture, and landfill sites, where site access may be difficult.
Highlights
Methane is the second most important anthropogenic greenhouse gas (Etminan et al, 2016), with an important role in atmospheric chemistry processes (Ehhalt et al, 1972)
Of the controlled emission flux, respectively. This range of conservative uncertainty bounds incorporate factors including the variability in the position of the time-invariant plume and potential for under-sampling. While these average uncertainties are large compared to methods such as tracer dispersion, we suggest that UAV sampling can be highly complementary to a toolkit of flux quantification approaches and may be a valuable alternative in situations where site access for tracer release is problematic
Manual UAV piloting was employed in this previous study to actively pursue the position of the time-invariant emission plume on the sampling plane using mid-flight knowledge of its position. This resulted in calculated emission fluxes that were significantly positively biased compared to known emission fluxes; this represents a source of vulnerability in fully manual UAV sampling, which we address in this work
Summary
Methane is the second most important anthropogenic greenhouse gas (Etminan et al, 2016), with an important role in atmospheric chemistry processes (Ehhalt et al, 1972). Nisbet et al, 2019), from inventory uncertainty in facility-scale sources such as landfill sites (Scheutz et al, 2009), herds of cattle (Blaxter and Clapperton, 1965), and oil and gas extraction infrastructure (Brantley et al, 2014), which collectively contribute significantly to global methane emissions (Dlugokencky et al, 2011; Saunois et al, 2016a) These uncertainties can be reduced through the accurate source identification and subsequent quantification of methane emission fluxes using top-down (atmospheric measurement-based) methods in order to validate bottom-up (inventory-based) emission flux estimates (Lowry et al, 2001; Nisbet and Weiss, 2010; Allen, 2016; Desjardins et al, 2018). A UAV with an on-board in situ low-precision sensor (±0.1 ppm at 1 Hz) flew in orbits around a gas compressor station, using mass balance box modelling, with geospatial kriging for interpolation, to derive the emission flux This method was not tested for UAV sampling with an accurate known (controlled) methane flux rate.
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