Deep learning-derived anthropogenic and meteorological drivers of surface ozone change in China
Bibliographic record
Abstract
Urban air pollution continues to pose a significant health threat, despite regulations to control emissions. Here we present a comparative analysis of the anthropogenic and meteorological drivers of surface ozone (O3) change in China by integrating deep learning (DL) and chemical transport model (CTM) methods. The DL method suggests volatile organic compound (VOC)-limited regimes in urban areas over northern inland China in contrast to strong nitrogen oxides (NOx)-limited regimes in GEOS-Chem simulations. Sensitivity analysis indicates that the inconsistent O3 responses are partially caused by the inaccurate representation of O3 precursor concentrations at the locations of urban air quality stations in the simulations. The DL method exhibits possible weakened anthropogenic contributions to surface O3 rise in the North China Plain, for example, 1.53 and 0.54 ppb/y 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. Furthermore, both DL and GEOS-Chem models suggest the reverse of meteorological contributions to the observed O3 change in the North China Plain in 2019-2021, which is mainly resulted from the reversed changes in meteorological variables in surface air temperature and relative humidity. This work highlights the importance of DL as a supplement to CTM-based analysis. The derived O3 drivers are helpful for making effective regulatory policies to control 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".