Global CO emissions and drivers of atmospheric CO trends constrained by MOPITT satellite observations
Bibliographic record
Abstract
Abstract. Carbon monoxide (CO), an important atmospheric pollutant produced from incomplete combustion and hydrocarbon oxidation, significantly influences atmospheric chemistry and air quality. Accurate quantification of its global emissions and the underlying drivers of atmospheric trends is essential for understanding and improving global environmental conditions. Using 20 years (2003–2022) of satellite observations from the Measurement of Pollution in the Troposphere (MOPITT) instrument, here we analyze changes in global CO emissions and atmospheric concentrations. The a posteriori simulations show improved consistency with independent surface and aircraft measurements compared to the a priori simulations. Sensitivity analyses further confirm that inferred emissions remain robust against uncertainties associated with satellite vertical sensitivity and variations in hydroxyl radical (OH) concentrations. Our results indicate a substantial decline in global anthropogenic CO emissions of 14–17 % (approximately 85–110 Tg) over the two-decade period, largely driven by reductions in the United States, Europe, and eastern China. In contrast, biomass burning emissions exhibited strong interannual variability, with recent increases in Northern Hemisphere high-latitude forests. A key finding is that rising biomass burning emissions have offset about 37 % of the global anthropogenic emission reduction (47 % in the Northern Hemisphere alone), underscoring the considerable moderating influence of wildfires on atmospheric composition trends. This study provides a comprehensive assessment of global CO emissions and the mechanisms governing atmospheric CO trends, offering a scientific basis for integrated policies addressing both climate change and air pollution.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".