Assessment of the Meteorological Impact on Improved PM<sub>2.5</sub> Air Quality Over North China During 2016–2019 Based on a Regional Joint Atmospheric Composition Reanalysis Data‐Set
Bibliographic record
Abstract
Abstract In the context of China's clean air policy, the meteorological impacts on improved particulate matter (PM2.5) air quality during 2016–2019 are investigated based on a four‐year high‐resolution atmospheric composition reanalysis data‐set, which has been produced by the Joint Data Assimilation System to resolve long‐term fine‐scale air quality variability over China. The reanalysis assimilates surface air quality observations using the Weather Research and Forecasting model coupled with Chemistry and an ensemble‐based assimilation algorithm, and simultaneous assimilations of meteorological observations, chemical initial conditions (ICs) and emissions are applied to help reduce the uncertainty in meteorology, ICs and the emissions inventory. Further, objective weather classification method is applied to quantitatively explore synoptic circulation pattern changes and associated PM2.5 variability over North China by using this unique reanalysis data‐set. PM2.5 reanalysis data are also investigated according to different circulation types, and results indicate that temporal and spatial variations of PM2.5 are found to be closely connected with weather and circulation patterns. The northerly types correspond to the lower PM2.5 levels, while the southerly and easterly types correspond to the higher PM2.5 concentration due to favorable local meteorological conditions. According to the quantitative evaluation on circulation pattern changes, meteorological contribution have played a positive role in improving air quality in the context of China's clean air policy during 2016–2019. This study serves as a basis for future retrospective assessments of air pollutant variation and emissions regulation measures.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".