An Investigation on Methane Flux in Landfills and Correlation with Surface Methane Concentration
Bibliographic record
Abstract
With growing concerns over greenhouse gas emissions increase on one hand, and methane’s high global warming potential on the other, direct methane emission measurement techniques from area sources such as landfills are receiving increased scrutiny. The static enclosure chamber method is the only technique that allows direct measurement of landfill gas fluxes. However, due to the large footprint of landfills, as well as the temporal and spatial variability of landfill methane emissions, the static enclosure method may not be the best option under some situations because it is time-consuming and labor-intensive. Collecting surface methane concentration (SMC) data through the instantaneous emission measurement (IEM) technique is relatively easy and inexpensive, however it is merely a qualitative means of evaluating surface methane emissions. This study investigated the development of a relationship between SMCs and methane flux across the soil-atmosphere boundary in a small-scale test cell under a partially controlled environment, in an attempt to translate SMC data into quantitative estimates. In addition, the study investigated the effect of wind speed on surface and flux measurements and the correlation between the two. The results demonstrated a significant positive correlation between SMCs and surface flux measurements. However, a better correlation was achieved when the analysis was performed under calm wind conditions and mild-to-moderate wind conditions separately. Under calm wind conditions, a linear correlation was found between SMCs and flux measurements with a resulting R2 of 0.94 and 0.90 for regression through origin and regression with intercept, respectively. These findings were in agreement with those of various researchers who have suggested surface flux has a positive and, in some cases, strong relationship with methane concentrations. The results also suggested that the presence of wind caused a decrease in average measured flux for the majority of inlet flowrates. It also significantly decreased concentrations measured in the test cell, while shifting the gas to defuse from areas that are further away from the wind source. Under windy conditions, the results of statistical analysis showed that SMCs have a linear correlation with flux divided by wind speed with a resulting R2 of 0.88, and other independent variables were found to be statistically insignificant. This finding was in agreement with the findings of researchers who observed an inverse relationship between SMCs and wind speeds. It is also in line with the Gaussian steady-state dispersion model which shows a direct relationship between SMC and emission rate divided by wind velocity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".