Estimating methane emissions from surface coal mines using satellite observations
Bibliographic record
Abstract
Monitoring and mitigating methane emissions from super-emitting sources is critical for addressing climate change. The TROPOMI instrument onboard Sentinel-5P provides daily global coverage of methane concentrations at 5.5 × 7 km² resolution, enabling the detection of methane super-emitters (>~8 t hr⁻¹). These data are instrumental in identifying hotspots that can be further investigated using high-resolution (~25 m) satellite instruments to pinpoint facility-level emissions. In support of the UNEP-IMEO Methane Alert and Response System (MARS), we have identified over 250 super-emitter hotspots. These hotspots include oil and gas production sites and urban landfills, while a third are associated with coal mining operations, including unexpected sources like surface coal mines. Given the crucial role of coal in the global energy landscape and steel production, it is essential to monitor and accurately estimate the associated methane emissions.This work highlights the synergy between TROPOMI and high-resolution instruments through an analysis of surface coal mine clusters in Kazakhstan, Russia, and India. We estimate 2021-2023 annual methane emissions from these three clusters using TROPOMI data in a Bayesian inversion approach. Our results align with emissions calculated using UNFCCC emission factors and mine-level production data, except in India, where significantly lower emissions are observed. Comparisons with bottom-up gridded emission inventories EDGAR v7 & GFEI v2 reveal notable discrepancies, primarily due to inaccuracies in spatial disaggregation. In Kazakhstan, methane emissions increase substantially between 2021 and 2023 despite stable coal production, suggesting that coal seam characteristics and other factors influence emission dynamics. Our emission estimates align closely with GHGSat-based estimates across all mines and years where a sufficient number of GHGSat observations are available. Moreover, spatial correlations are identified between GHGSat-detected methane enhancements and mining activities within the mine. Additionally, atmospheric temperature inversions are found to significantly contribute to the accumulation of methane within the mine pit, complicating emission quantifications based on high-resolution observations. The findings of this study underscore the importance of combining TROPOMI data with high-resolution satellite data to refine methane emission estimates from complex sources like surface coal mines.
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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 | 0.000 | 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".