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.

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.

Abstract. In efforts to improve methane source characterisation, networks of cheap high-frequency in situ sensors are required, with parts-per-million-level methane mole fraction ([CH4]) precision. Low-cost semiconductor-based metal oxide sensors, such as the Figaro Taguchi Gas Sensor (TGS) 2611-E00, may satisfy this requirement. The resistance of these sensors decreases in response to the exposure of reducing gases, such as methane. In this study, we set out to characterise the Figaro TGS 2611-E00 in an effort to eventually yield [CH4] when deployed in the field. We found that different gas sources containing the same ambient 2 ppm [CH4] level yielded different resistance responses. For example, synthetically generated air containing 2 ppm [CH4] produced a lower sensor resistance than 2 ppm [CH4] found in natural ambient air due to possible interference from supplementary reducing gas species in ambient air, though the specific cause of this phenomenon is not clear. TGS 2611-E00 carbon monoxide response is small and incapable of causing this effect. For this reason, ambient laboratory air was selected as a testing gas standard to naturally incorporate such background effects into a reference resistance. Figaro TGS 2611-E00 resistance is sensitive to temperature and water vapour mole fraction ([H2O]). Therefore, a reference resistance using this ambient air gas standard was characterised for five sensors (each inside its own field logging enclosure) using a large environmental chamber, where logger enclosure temperature ranged between 8 and 38 ∘C and [H2O] ranged between 0.4 % and 1.9 %. [H2O] dominated resistance variability in the standard gas. A linear [H2O] and temperature model fit was derived, resulting in a root mean squared error (RMSE) between measured and modelled resistance in standard gas of between ±0.4 and ±1.0 kΩ for the five sensors, corresponding to a fractional resistance uncertainty of less than ±3 % at 25 ∘C and 1 % [H2O]. The TGS 2611-E00 loggers were deployed at a landfill site for 242 d before and 96 d after sensor testing. Yet the standard (i.e. ambient air) reference resistance model fit based on temperature and [H2O] could not replicate resistance measurements made in the field, where [CH4] was mostly expected to be close to the ambient background, with minor enhancements. This field disparity may have been due to variability in sensor cooling dynamics, a difference in ambient air composition during environmental chamber testing compared to the field or variability in natural sensor response, either spontaneously or environmentally driven. Despite difficulties in replicating a standard reference resistance in the field, we devised an excellent methane characterisation model up to 1000 ppm [CH4] by using the ratio between measured resistance with [CH4] enhancement and its corresponding reference resistance in standard gas. A bespoke power-type fit between resistance ratio and [CH4] resulted in an RMSE between the modelled and measured resistance ratio of no more than ±1 % Ω Ω−1 for the five sensors. This fit and its corresponding fit parameters were then inverted and the original resistance ratio values were used to derive [CH4], yielding an inverted model [CH4] RMSE of less than ±1 ppm, where [CH4] was limited to 28 ppm. Our methane response model allows other reducing gases to be included if necessary by characterising additional model coefficients. Our model shows that a 1 ppm [CH4] enhancement above the ambient background results in a resistance drop of between 1.4 % and 2.0 % for the five tested sensors. With future improvements in deriving a standard reference resistance, the TGS 2611-E00 offers great potential in measuring [CH4] with parts-per-million-level precision.