Bibliographic record
Abstract
Abstract. Increasing surface ozone (O3) concentrations have long been a significant environmental issue in China, despite the Clean Air Action Plan launched in 2013 by the government. In this study, we assessed the effect of anthropogenic emissions, meteorological factors, and CO2 changes on the summer surface O3 from 2008 to 2018 in China using an improved regional climate-chemistry-ecology model (RegCM-Chem-YIBs). The model was improved regarding the photolysis of O3 and the radiation effect of CO2 and O3. The investigations showed anthropogenic emissions dominated the O3 increase in China, contributing 4.08–18.51 ppb a−1 in the North China Plain. The meteorological conditions decreased O3 over China and could be more significant than anthropogenic emissions in some regions. In Pearl River Delta, for example, the contributions of meteorological conditions and anthropogenic emissions on O3 were −1.29 and 0.81 ppb in 2013, respectively. CO2 was critical in O3 variations, especially in southern China, inducing an increase in O3 on the southeast coast of China (0.28–0.46 ppb a−1) and a decrease in the southwest and central China (−0.51–−0.11 ppb a−1). Our study comprehensively analyzed O3 variation across China from various perspectives and highlighted the importance of considering CO2 variations when designing long-term O3 control policies, especially in high vegetation coverage areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.025 | 0.013 |
| Insufficient payload (model declined to judge) | 0.446 | 0.370 |
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".