The effect of anthropogenic emission, meteorological factors, and carbon dioxide on the surface ozone increase in China from 2008 to 2018 during the East Asia summer monsoon season
Bibliographic record
Abstract
Despite the implementation of the Clean Air Action Plan by the Chinese government in 2013, the issue of increasing surface ozone (O 3 ) concentrations remains a significant environmental concern in China. In this study, we used an improved regional climate–chemistry–ecology model (RegCM-Chem-YIBs) to investigate the impact of anthropogenic emissions, meteorological factors, and CO 2 changes on summer surface O 3 levels in China from 2008 to 2018. Compared to its predecessor, the model has been enhanced concerning the photolysis of O 3 and the radiative impacts of CO 2 and O 3 . The investigations showed anthropogenic emissions were the primary contributor to the O 3 increase in China, responsible for 4.08–18.51 ppb in the North China Plain. However, changed meteorological conditions played a crucial role in decreasing O 3 in China and may have a more significant impact than anthropogenic emissions in some regions. Changed CO 2 played a critical role in the variability of O 3 through radiative forcing and isoprene emissions, particularly in southern China, inducing an increase in O 3 on the southeast coast of China (0.28–0.46 ppb) and a decrease in southwest and central China (−0.51 to −0.11 ppb). Our study comprehensively analyzed O 3 variation across China from various perspectives and highlighted the importance of considering CO 2 variations when designing long-term O 3 control policies, especially in high-vegetation-coverage areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".