Estimating the size of a methane emission point source at different scales: from local to landscape

Riddick, Stuart N.; Connors, Sarah; Robinson, A. D.; Manning, Alistair J.; Jones, Pippa S. D.; Lowry, David; Nisbet, E. G.; Skelton, Robert L.; Allen, Grant; Pitt, Joseph; Harris, Neil

doi:10.5194/acp-17-7839-2017

Cited by 37 publications

(35 citation statements)

References 32 publications

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“…Methane emissions from hard-to-mitigate sources, such as enteric fermentation, are very large (Rogelj et al, 2015;Reisinger & Clark, 2018), too important to ignore as intractable. For example, high methane mole fractions, often 10-100 ppm can occur in air around feed lots (Figure 19), cattle pens (Grainger et al, 2007), manure heaps, agricultural biodigesters (Flesch et al, 2011), and near active faces of landfills (Riddick et al, 2017;Zazzeri et al, 2015). Methane yield from manure is highly variable, depending on the diet fed to the cattle (Amon et al, 2007).…”

Section: Practical Mitigation Of Methane Emissions From Farm Animalsmentioning

confidence: 99%

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

Nisbet

Fisher

Lowry

et al. 2020

Reviews of Geophysics

212

131

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The atmospheric methane burden is increasing rapidly, contrary to pathways compatible with the goals of the 2015 United Nations Framework Convention on Climate Change Paris Agreement. Urgent action is required to bring methane back to a pathway more in line with the Paris goals. Emission reduction from “tractable” (easier to mitigate) anthropogenic sources such as the fossil fuel industries and landfills is being much facilitated by technical advances in the past decade, which have radically improved our ability to locate, identify, quantify, and reduce emissions. Measures to reduce emissions from “intractable” (harder to mitigate) anthropogenic sources such as agriculture and biomass burning have received less attention and are also becoming more feasible, including removal from elevated‐methane ambient air near to sources. The wider effort to use microbiological and dietary intervention to reduce emissions from cattle (and humans) is not addressed in detail in this essentially geophysical review. Though they cannot replace the need to reach “net‐zero” emissions of CO2, significant reductions in the methane burden will ease the timescales needed to reach required CO2 reduction targets for any particular future temperature limit. There is no single magic bullet, but implementation of a wide array of mitigation and emission reduction strategies could substantially cut the global methane burden, at a cost that is relatively low compared to the parallel and necessary measures to reduce CO2, and thereby reduce the atmospheric methane burden back toward pathways consistent with the goals of the Paris Agreement.

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Section: Practical Mitigation Of Methane Emissions From Farm Animalsmentioning

confidence: 99%

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

Nisbet

Fisher

Lowry

et al. 2020

Reviews of Geophysics

212

131

View full text Add to dashboard Cite

show abstract

“…Examples of previously used methane in-situ measurement-based techniques include eddy covariance [18], mass balance box models [17,[19][20][21][22][23][24], and the tracer dispersion method (TDM), which compares the downwind ratio of methane concentration to an inert tracer [13,[25][26][27][28][29][30][31]. Alternatively, inversion dispersion models may be used for flux quantification on a facility scale [17,21,[32][33][34]. For example, Gaussian plume inversions have been applied using middle distance (~1 km from the source) [CH 4 ] sampling [33,[35][36][37][38].…”

Section: Introductionmentioning

confidence: 99%

“…Alternatively, inversion dispersion models may be used for flux quantification on a facility scale [17,21,[32][33][34]. For example, Gaussian plume inversions have been applied using middle distance (~1 km from the source) [CH 4 ] sampling [33,[35][36][37][38]. Foster-Wittig et al [39] used near-field [CH 4 ] measurements from a stationary position, downwind of the source, to derive fluxes using a Gaussian plume inversion.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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“…For the Ginninderra data we employ the more traditional Gaussian plume model, which can be seen as a surrogate for a full-blown transport model. While known to work well in the small domain (an area of approximately 100 × 100 m) setting we consider (e.g., Riddick et al, 2017), 35 2 Atmos. Meas.…”

mentioning

confidence: 99%

Bayesian atmospheric tomography for detection and quantification of methane emissions: Application to data from the 2015 Ginninderra release experiment

Cartwright¹,

Zammit‐Mangion²,

Bhatia³

et al. 2019

Preprint

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Detection and quantification of greenhouse-gas emissions is important for both compliance and environment conservation. However, despite several decades of active research, it remains predominantly an open problem, largely due to model errors and misspecifications that appear at each stage of the inversion processing chain. In 2015, a controlled-release experiment headed by Geoscience Australia was carried out at the Ginninderra Controlled Release Facility, and a variety of instruments and methods were employed for quantifying the release rates of methane and carbon dioxide from a point source. 5This paper proposes a fully Bayesian approach to atmospheric tomography for inferring the methane emission rate of this point source using data collected during the experiment from both point-and path-sampling instruments. The Bayesian framework is designed to account for uncertainty in the parametrisations of measurements, the meteorological data, and the atmospheric model itself when doing inversion using Markov chain Monte Carlo (MCMC). We apply our framework to all instrument groups using measurements from two release-rate periods. We show that the inversion framework is robust to instrument type 10 and meteorological conditions. The worst median emission-rate estimate we obtain from all the inversions is within 36% of the true value, while the worst posterior 95% credible interval has a limit within 11% of the true value.

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Estimating the size of a methane emission point source at different scales: from local to landscape

Cited by 37 publications

References 32 publications

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

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

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

Bayesian atmospheric tomography for detection and quantification of methane emissions: Application to data from the 2015 Ginninderra release experiment

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