Validation and error assessment of the mobile tracer gas dispersion method for measurement of fugitive emissions from area sources

Fredenslund, Aage; Rees-White, T.; Beaven, R.P.; Delre, Antonio; Finlayson, Andrew; Helmore, Jonathan; Allen, Grant; Scheutz, Charlotte

doi:10.1016/j.wasman.2018.10.036

Cited by 32 publications

(18 citation statements)

References 14 publications

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“…Misplacing the tracer gas can result in significant under-or overestimation of the emission, while the required measuring distance depends on the physical size of the source, atmospheric conditions and the topography between the landfill and the measurement/sampling point (e.g. Fredenslund et al, 2019b;Rees-White et al, 2019;Delre et al, 2018;Taylor et al 2016).…”

Section: The Stationary Tracer Gas Dispersion Methodsmentioning

confidence: 99%

Methodologies for measuring fugitive methane emissions from landfills – A review

2019

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Section: The Stationary Tracer Gas Dispersion Methodsmentioning

confidence: 99%

Methodologies for measuring fugitive methane emissions from landfills – A review

2019

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“…2 shows total CH4 emissions measured for each plume traverse at biogas plant C using the TDM, suggesting that emissions varied between ~5 and 35 kg CH4 h -1 on the measurement day. Note that measurement uncertainty for the emission rates determined using TDM for single-plume traverses should be considered higher compared to TDM measurements reporting average values of several plume traverses (Fredenslund et al, 2018;Mønster et al, 2014).…”

Section: Methane Emissions From Biogas Plant Cmentioning

confidence: 99%

On-site and ground-based remote sensing measurements of methane emissions from four biogas plants: A comparison study

Fredenslund

Hinge

Holmgren

et al. 2018

Bioresource Technology

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Methods for quantifying methane (CH) emissions from biogas plants are needed, in order to ensure that emissions are within acceptable levels and to identify options for emission mitigation. Two emission measuring approaches were used at four biogas plants: an on-site approach, whereby emission sources were identified and subsequently quantified one at a time, and a ground-based remote sensing approach, which was applied to measure total CH emissions. The emissions were between 5.5 and 13.5 kg CH h from the four plants, measured using ground-based remote sensing. Even though the measurements were performed on the same days at each facility, the sum of on-site emission rates varied between the remote sensing measurements (up to ∼100%). Several factors may have caused this difference: emission sources not measured using an on-site approach and short-time emission variation. On-site measurements showed that the majority of the emissions often occurred from just a few sources.

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“…Seven of the eight TDM flux estimates which were acquired simultaneously during this study (see Table S1) agreed with the CRF, within uncertainty, with an average bias of (−2 ± 10)%. The average uncertainty in individual TDM fluxes (through comprehensive uncertainty budgeting) was ±21% [75]. In a different study using mass balance box modelling, Krautwurst et al [42] derived the emission flux from a single landfill site based on both remote sensing column-concentration aircraft measurements and in-situ aircraft concentration measurements, with average flux uncertainties of ±34.5% and ±19.7%, respectively.…”

Section: Results and Future Guidancementioning

confidence: 99%

“…The refinement of flux quantification techniques using known controlled emission fluxes, such as those provided by the NPL in this study, is crucial in order to legitimise the integrity of further work [21,25,34,35,74]. The NPL fluxes were also used to test simultaneously-derived TDM fluxes (described in detail in Fredenslund et al [75]) by two TDM teams operating independently of the UAV team and each other. In Section 3, we then test the NGI method and account for inherent flux biases associated with the method, using simulated measurements acquired from a random walk simulation sampling a static plume.…”

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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Validation and error assessment of the mobile tracer gas dispersion method for measurement of fugitive emissions from area sources

Cited by 32 publications

References 14 publications

Methodologies for measuring fugitive methane emissions from landfills – A review

Methodologies for measuring fugitive methane emissions from landfills – A review

On-site and ground-based remote sensing measurements of methane emissions from four biogas plants: A comparison study

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

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