Local and synoptic meteorological influences on daily variability ofsummertime surface ozone in eastern China
Bibliographic record
Abstract
Abstract. Ozone pollution in China is influenced by meteorological processes on multiple scales. Using multiple linear regression and weather classification, we statistically assess the impacts of local and synoptic meteorology on daily variability of surface ozone in eastern China in summer during 2013–2018. In this period, summertime surface ozone in eastern China (110–130° E, 20–42° N) is among the highest in the world with regional means of 73.1 and 114.7 µg m−3, respectively, in daily mean and daily maximum 8-hour average. By developing a multiple linear regression (MLR) model driven by local and synoptic weather factors, we establish a quantitative linkage between the daily ozone concentrations and meteorology in the study region. The meteorology described by the MLR model can explain ~ 46 % of the daily variability in summertime surface ozone across eastern China. The model shows that synoptic factors contribute to ~ 37 % of the overall meteorological effects on daily variability of surface ozone in eastern China. Among local meteorological factors, relative humidity is the most influential variable in the center and south of eastern China including the Yangtze River Delta and the Pearl River Delta regions, while temperature is the most influential variable in the north covering the Beijing-Tianjin-Hebei region. To further examine the synoptic influence of weather conditions explicitly, six predominant synoptic weather patterns (SWPs) over eastern China in summer are objectively identified using the self-organizing map clustering technique. The six SWPs are formed under the integral influence of the East Asian summer monsoon, the western Pacific subtropical high, the Meiyu front, and the typhoon activities. The results show that the impacts of each of the SWPs on the daily variability of surface ozone vary largely inside the study area. The maximum impact can reach ±8 µg m−3 or ±16 % of the daily mean over some subregions in eastern China. A combination of the regression and the clustering approaches suggests a strong performance of the MLR model in predicting the sensitivity of surface ozone in eastern China to the variation of synoptic weather. Our assessment highlights the important role of meteorology in modulating ozone pollution over China.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".