Regional Transport of PM<sub>2.5</sub> and O<sub>3</sub> Based on Complex Network Method and Chemical Transport Model in the Yangtze River Delta, China
Bibliographic record
Abstract
Abstract Ground‐level ozone (O3) and atmospheric fine particulate matter (PM2.5) pollution are the major challenges for continually improving air quality in the Yangtze River Delta (YRD) region of China. Understanding regional transport patterns of PM2.5 and O3 pollution is essential for the development of regional cooperative prevention strategies. This study shows the annual concentration of PM2.5 in the YRD decreased by 18.5% from 2015 to 2018, while the mean values of the daily maximum 8‐hr average (MDA8) O3 concentration from March to October increased by 16.3%. A complex network method is utilized to investigate the regional transport of PM2.5 and O3 in different grid cells (nodes). The source apportionment method within the chemistry transport model is applied to verify the reliability of the complex network method. Interregional and intraregional transport play an important role in both PM2.5 and O3 over the YRD. The northern part of the YRD contributes much more than other areas, while the central part of the YRD, especially the southern part of Jiangsu, is the largest contributor of O3 in the YRD in the summer, accounting for about 70%. Intraregional transport plays more of a major role in increasing PM2.5 pollution than O3 pollution. This study not only verifies the transport patterns of heavy pollution through the complex network method and traditional source apportionment technology, it also reveals that both methods provide great potential in understanding transport patterns and air pollution relationships, which are the solid foundation for emission mitigation in the YRD region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".