Meteorological and anthropogenic drivers of surface ozone change in the North China Plain in 2015–2021
Bibliographic record
Abstract
Surface ozone (O3) concentrations in China have increased largely in the past decade. An accurate understanding of O3 pollution evolution is critical for making effective regulatory policies. Here we integrate data- and process-based models to explore the drivers of the observed summertime surface O3 change in the North China Plain (NCP) over 2015–2021. The data-based model by the deep learning (DL) suggests the reverse of meteorological contributions to the observed O3 change, i.e., 0.14 ppb/y in 2015–2019 and − 1.74 ppb/y in 2019–2021. This is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. The simulations from a global chemical transport model, GEOS-Chem, also support those results, i.e., the meteorological contribution to O3 changes are 0.26 ppb/y in 2015–2019 and − 0.74 ppb/y in 2019–2021. Furthermore, our analysis exhibits possible weakened anthropogenic contributions to surface O3 rise, for example, 1.53 and 0.54 ppb/y by DL in 2015–2019 and 2019–2021, respectively. Similarly, GEOS-Chem simulations suggest an accelerated decrease in surface O3 concentrations driven by the decline in nitrogen dioxide (NO2) concentrations, i.e., approximately 0.4 and 1.2 ppb in 2015–2019 and 2019–2021, respectively. The combined effects of meteorological and anthropogenic contributions led to a significant decrease in surface O3 concentrations by −1.20 ppb/y in 2019–2021. The findings in this work offer valuable insights to mitigate O3 pollution in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".