Impact of Black Carbon on Surface Ozone in the Yangtze River Delta from 2015 to 2018
Bibliographic record
Abstract
Despite the yearly decline in PM2.5 in China, surface ozone has been rapidly increasing recently, which makes it imperative to coordinate and control both PM2.5 and ozone in the atmosphere. This study utilized the data of pollutant concentrations and meteorological elements during 2015 to 2018 in Nanjing, China to analyze the daily correlation between black carbon and ozone (CBO), and the distribution of the pollutant concentrations under different levels of CBO. Besides, the diurnal variations of pollutant concentrations and meteorological elements under high positive and negative CBO were discussed and compared. The results show that the percentage of positive CBO had been increasing at the average rate of 7.1%/year, and it was 38.7% in summer on average, nearly twice of that in other seasons (19.2%). The average black carbon (BC), PM2.5 and NO2 under positive CBO was lower than those under negative CBO. It is noticeable that the surface ozone began to ascend when CBO was up to 0.2, with PM2.5 and NO2 decreasing and BC remaining steady. Under negative CBO, pollutant concentrations and meteorological elements showed obvious diurnal variations: BC showed a double-peak pattern and surface ozone, PM2.5, SO2 and CO showed single-peak patterns and NO2 showed a trough from 10:00 to 19:00. Wind speed and visibility showed a single-peak pattern with little seasonal difference. Relative humidity rose first, then it lowered and then it rose. Under positive CBO, the patterns of diurnal variations became less obvious, and some of them even showed no patterns, but just fluctuated at a certain level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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 teacher head, 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".