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Record W7133034015

Measuring Methane Emissions in the Urban Environment

2025· dissertation· W7133034015 on OpenAlexaboutno aff
Lawson D Gillespie

Bibliographic record

VenueTSpace · 2025
Typedissertation
Language
FieldEnvironmental Science
TopicLandfill Environmental Impact Studies
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasMethaneMethane emissionsTransectFugitive emissionsMunicipal solid wasteEmission inventory
DOInot available

Abstract

fetched live from OpenAlex

Mitigating methane (CH4 ) emissions is a necessary intervention to address the immediate impacts of anthropogenic climate change. Bottom-up inventories and models are used to predict the quantity of CH4 emissions, but these estimates are poorly constrained, especially on a source-by-source basis. Measuring CH4 atmospheric concentrations enables direct estimation of emissions rates, and allows for the direct monitoring of the efficacy of emissions mitigation interventions. Within the GreaterToronto Area (GTA), high resolution CH4 inventories predict that anthropogenic CH4 comprises the majority of emissions, primarily from landfills. Atmospheric measurements of CH4 were conducted to quantify emissions from sources in the GTA and throughout Southern Ontario. Within the GTA, a network of solar-viewing Fourier transform infrared (FTIR) spectrometers was used to quantify the total column dry air mole fractions of CH4 and other greenhouse gases (GHG). This network was expanded to five semi-permanent observatory sites and operated to measure emissions coming from the GTA. In order to confirm, quantify, and discover new sources of CH4 emissions in the GTA, a bicycle-based mobile in situ measurement laboratory was deployed to measure CH4 concentrations downwind of sources in the city. These measurements were used to quantify emissions from various urban CH4 sources. Over 650 downwind transects of solid waste and water resource recovery facilities (WRRFs) were used to quantify CH4 emissions from the waste sector in Southern Ontario. From these measurements, solid waste emissions in the GTA in bottom-up inventories were shown to be overestimated. These data were used to investigate correlations between emissions and predictive variables such as meteorological variability for landfills, and volume of treated water for WRRFs. At a large, active landfill facility in Southern Ontario, six different emissions quantification technologies were compared. This study demonstrates that each technology was capable of quantifying emissions from this facility, and that their average measured emission rates all agreed within uncertainty. Lower measured emissions rates were observed from the ground-based in situ methodologies, and possible explanations for these differences are explored. Implications for monitoring Canadian landfill emissions are discussed with respect to the demonstrated detection limits of these technologies.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score0.117

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.304
Teacher spread0.262 · 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 designObservational
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
Published2025
Admission routes1
Has abstractyes

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