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Record W4322212589 · doi:10.5194/egusphere-egu23-15766

Fugitive Methane Across the UK Gas Distribution Network from Terminals to Cities: Characterisation and Methodology Development

2023· preprint· en· W4322212589 on OpenAlexaff
David Lowry, James L. France, Julianne M. Fernandez, Aliah al-Shalan, Rebecca Fisher, Felix Vogel, Euan G. Nisbet

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsEnvironment and Climate Change Canada
FundersNatural Environment Research CouncilSight Research UK
KeywordsFugitive emissionsMethaneNatural gasEnvironmental scienceGreenhouse gasFossil fuelMethane emissionsBayEnvironmental engineeringWaste managementChemistryEngineeringGeologyOceanography

Abstract

fetched live from OpenAlex

Fugitive emissions from gas distribution are a top target for reduction of CH4 emissions to atmosphere with the UN considering that emissions from fossil fuel activities can be reduced by 61% (UN, 2021). Once the emissions are identified there are mitigation solutions to stop the leaks far more easily than emissions from waste and agricultural sectors. The RHUL group has identified fugitive methane from UK sources using a mobile survey vehicle since 2013, initially to identify plumes and characterise the emissions by source category using isotopic signatures (δ13C) and ethane to methane ratios (C2:C1). More recent measurements have focussed on CH4 emissions from buried city gas pipelines, primarily London, and development of methodology for interpreting data from a range of different high-precision instruments. Much of the gas in the UK distribution system has very similar charactersistics once mixing downstream of terminals has taken place. This is typically characterised by δ13C signature of -39 ± 1 ‰ and C2:C1 of 0.055 ± 0.015, which make it distinct from agricultural, waste and combustion CH4 sources. The small proportion of gas coming from the Southern North Sea and Morecambe Bay fields (now <20%) is more enriched in 13C (-34 to -28 ‰) and terminals receiving gas from these locations have different emission signatures; that for the Bacton terminal can be traced downstream toward London. City measurements by Picarro 2301 and LGR UMEA of London and Birmingham pipeline gas leaks in 2019 have been used to quantify emissions using methodology developed by Weller et al. (2019) and refined by Maazallahi et al. (2020). A total estimated emission for the Greater London area of 2.2 kT (Fernandez et al., in prep.), is much lower than the inventory suggests and lower than estimates and from aircraft or fixed site measurements. While fugitive gas emissions from rural areas (pipelines and above-ground infrastructure) are much larger than the inventory suggest, lowering expected urban emissions, and small peaks of <200 ppb cannot be definitively characterised as gas leaks, leading to underestimation, the methodology for leak emissions estimation needs further refinement for dense urban environments. A range of instruments measuring at 0.3 to 10Hz and different emissions methodologies are currently being assessed through repeat surveys of some London boroughs. Maazallahi et al., 2020, Atmos. Chem. Phys., 20, 14717–14740, https://doi.org/10.5194/acp-20-14717-2020United Nations Environment Programme, 2021, Emissions Gap Report 2021: The Heat Is On – A World of Climate Promises Not Yet Delivered, NairobiWeller et al., 2019, PLoS One 14, e0212287, https://doi.org/10.1371/journal.pone.0212287

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.038
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.005
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.060
GPT teacher head0.302
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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