Individual Source Measurements of Methane Emissions from Anthropogenic Sources - Canada and the United States
Bibliographic record
Abstract
Anthropogenic methane emissions have been identified as a strategic greenhouse gas emission reduction target. The scientific consensus is that the majority of cumulative methane emissions are emitted by a small percentage of high-emitting sites (i.e., super-emitters). However, cumulative methane emissions from lower-emitting sources can be significant if the site counts (i.e. activity data) are high. Accurately quantifying methane emissions from all sources, including low-emitting sources, is a critical component of tracking progress towards reducing emissions. In this thesis, data analysis and field measurements are used to study methane emissions from abandoned oil and gas wells, historic landfills, manholes, and natural gas distribution systems, all sources that have relatively low site level emission rates and require direct on-site measurement methods with to accurately capture the full range of emissions distributions.The static chamber methodology, a direct measurement method, was the primary method used in the field measurements of methane emissions presented in this thesis. We conducted controlled release experiments and explored the role of chamber design parameters. We found that static chambers can quantify methane flowrates ranging from 1 to 500 g/h, which represents the lower range of emission rates when compared to other methane sources such as active oil and gas wells, with an accuracy of ±14%.Within the oil and gas sector, abandoned oil and gas (AOG) wells have the largest activity data, with >4 million wells in the U.S. and >370,000 in Canada. We analyzed methane emissions from 598 published measurements, including previously unpublished measurements of methane emissions we made from 54 AOG wells in Oklahoma and 17 in British Columbia using a static chamber methodology. We developed attribute- and region-specific emission factors of wells which ranged from 1.8x10-3 to 48 g/h of methane for AOG wells in the U.S. and Canada. We estimated that, as of 2020, the annual methane emissions from AOG wells are 20% higher than inventory estimates for the U.S. and 150% higher for Canada.We quantified methane emissions from wastewater utility holes (WUHs) and historic landfills in Montreal (Canada), two sources with high population counts and little direct measurement data. In addition, we quantified emissions from natural gas (NG) distribution systems within the city, which is recognized as a significant methane source for cities. We extrapolated the methane emissions to city-wide estimates and performed a cost-benefit analysis of mitigation strategies. We estimated that historic landfills and WUHs were the second and third highest methane sources in Montreal. We found that historic landfills have high potential for methane reductions at high mitigation costs, methane mitigation from WUHs is low-cost but the methods require further research, and increasing repair rates of NG distribution leaks are a cost-effective mitigation strategy.Recent studies have shown that biogenic sources of methane (e.g., WUHs and urban water bodies) were significant sources in cities. Therefore, we directly measured methane emissions from WUHs and urban water bodies in the Greater Toronto Area (GTA), the largest urban agglomeration in Canada. We found that annual methane emissions from urban water bodies totaled 2,737 t/yr of methane, or 26.4% of emissions from agriculture and wetlands, and that emissions from WUHs totaled 9,122 t/yr of methane, which is more than 10% of the total GTA methane budget.Our findings address several knowledge gaps in methane emissions quantification of sources requiring direct on-site measurements, which are needed to improve greenhouse gas inventories and guide mitigation strategy development. Overall, multi-scale measurements including direct measurements are needed to fill gaps in current inventories and improved data sharing can reduce current limitations and uncertainties
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".