Spatial and seasonal variations and trends in carbon monoxide over China during 2013–2022
Bibliographic record
Abstract
Carbon monoxide (CO) pollution is one of the unsolved atmospheric environmental issues over countries worldwide. Here, we comprehensively assess the spatial-seasonal variations and trends in CO over China during 2013–2022, by site categorization and cause analysis, which have not been reported in previous studies. Our analysis is based on surface observations, statistical classification techniques, and sensitivity simulations from a global chemical transport model, GEOS-Chem. The results show that CO concentrations and their seasonal variation are higher over the North China Plain than other areas of China during 2013–2022. CO concentrations have decreased at −38.2±31.4 ppbv yr −1 (−3.7%±1.8% yr −1 ) over China during 2013–2022. The declining trends in CO concentrations vary with space, stronger over larger, more polluted, and more industralized cities. GEOS-Chem simulations suggest that the decrease in CO anthropgenic emissions in China would be the main driver for the decreasing trends of CO over China, with a contribution of 80% to the trend. Meteorology, CO anthropgenic emissions over foreign regions, and emissions from biomass burning are the remaining drivers. Our results suggest remarkable benefits of emission reductions in China in abating CO pollution and enhance our understanding on trends of long-term CO pollution over China during 2013–2022. • CO concentrations and their seasonal variation are higher over the North China Plain than other areas of China during 2013–2022. • CO concentrations decreased at −38.2±31.4 ppbv yr −1 (−3.7%±1.8% yr −1 ) over China during 2013–2022 and the declining trends are stronger over larger, more polluted, and more industralized cities. • CO anthropogenic emissions over China are the major driver for the CO declining trends over China during 2013–2022, and meteorology, CO anthropogenic emissions over foreign regions, and emissions from biomass burning dominate the left.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".