Spatio-Temporal Patterns of Methane Emissions from 2019 Onwards: A Satellite-Based Comparison of High- and Low-Emission Regions
Bibliographic record
Abstract
Methane (CH4) is a potent greenhouse gas with a significant impact on short- and medium-term climate forcing, and its atmospheric concentration has been increasing rapidly in recent decades. This study aims to analyze spatio-temporal patterns of atmospheric methane concentrations between 2019 and 2025, focusing on comparisons between regions characterized by high and low emission intensities. Level-3 XCH4 data from the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite were used, which were aggregated into seasonal and annual composites. High-emission regions, such as the Mekong Delta, Nile Delta, Eastern Uttar Pradesh and Bihar, Central Thailand, Lake Victoria Basin, and Eastern Arkansas, were contrasted with low-emission areas including Patagonia, the Mongolian Steppe, Northern Scandinavia, the Australian Outback, the Sahara Desert, and the Canadian Shield. The results show that high-emission regions exhibit substantially higher seasonal amplitude in XCH4 concentrations, with an average seasonal variation of approximately 30.00 ppb, compared to 17.39 ppb in low-emission regions. Methane concentrations generally peaked at the end of the year (Q4) and reached their lowest levels during the first half of the year (Q1 or Q2), particularly in agriculturally dominated regions. Principal component and cluster analyses further confirmed a strong spatial differentiation between high- and low-emission regions based on both temporal trends and seasonal behavior. These findings demonstrate the potential of satellite remote sensing to monitor regional methane dynamics and highlight the need for targeted mitigation strategies in major agricultural and wetland zones.
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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) | 0.001 | 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 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".