Quantification of Methane Emissions by Surface Mass Balance Method
Bibliographic record
Abstract
This thesis presents a surface mass balance method as a cost-effective top-down technique to conveniently validate the bottom-up inventories. Mobile methane measurements were performed for two large landfills, Keele Valley Landfill and Greenlane Landfill and the city of Sarnia which included petrochemical industries and residential areas by employing a Cavity Ringdown Spectrometer (CRDS) mounted in a vehicle to capture downwind enhancements of methane. Methane emission from the Greenlane landfill was estimated to be 3300 ± 730 kg h-1 by a mass balance approach. An estimation by a gaussian dispersion model provided a similar emission rate of 3320 ± 250 kg h-1. The regression analysis of the mixing ratios of CO2 and CH4 showed positive correlation with an average molar ratio of 0.99 ± 0.04 mole mole-1 which was used to estimate CO2 emission to be 7600 ± 1700 kg h-1. The city of Sarnia including its industrial complex and residential areas showed a total methane emission rate of 2450 ± 560 kg h-1. It is estimated the city emits 21.5 ± 4.9 kt CH4 annually accounting for 45% of Ontario’s oil and gas methane emission. These estimated source rates from facilities were consistently 9-10 times greater than the GHGRP estimates. The discrepancies confirmed in the study emphasizes that it is significant to reconcile top-down measurements with the bottom-up inventories to provide a more accurate understanding of methane sources and sinks in Canada.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".