Estimating methane emissions from the waste sector in Southern Ontario using atmospheric measurements
Bibliographic record
Abstract
We estimate methane emissions rates for urban waste treatment facilities from mobile in situ atmospheric concentration measurements using an inverse Gaussian plume methodology at facilities in Southern Ontario, Canada. We use these estimated emissions rates to investigate, update, and improve the existing high resolution methane inventories at the facility level for waste sources throughout the Greater Toronto Area and Southwestern Ontario. Our measurements encompass tens of thousands of kilometers worth of mobile survey data collected over 7 years, encompassing more than 650 downwind transects where we surveyed 14 active landfills, 11 closed landfills, 2 organic waste processing facilities, 3 open air windrow compost facilities, and 11 water resource recovery facilities across our study region. These sources account for 77% of the active landfills within Southern Ontario, which is estimated in inventories to be the largest source of methane emissions in the region. Within the Greater Toronto Area (GTA) megacity, the measured facilities represent about 52% of the total inventoried non-wetland methane emissions. We find that emissions from closed landfills are lower than inventory estimates, with significant implications for the methane budget in the GTA. We update the Facility Level and Area Methane Emissions for the GTA inventory with our measured emissions rates, which results in a 54% decline in the solid waste emissions, effecting a 35% lower estimate for the total anthropogenic methane emissions in the region. We attribute the bulk of this difference to a single facility: the Keele Valley landfill. Our atmospheric measurements also serve as a novel metric for evaluating the discrepancies between four facility level, and two high resolution gridded methane emissions inventories. Based on linear regressions of our measured emissions versus inventoried values, we find that the facility level first order decay model maintained by Environment and Climate Change Canada (ECCC) to be the most consistent with our measured emissions rates at landfills and the self-reported emissions to the Greenhouse Gas Reporting Program of ECCC to be the least consistent with our measurements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".