Surface Ozone in the Yangtze River Delta, China: A Synthesis of Basic Features, Meteorological Driving Factors, and Health Impacts
Bibliographic record
Abstract
Abstract Ozone (O3) is of great importance to air quality in the Yangtze River Delta (YRD) region, China. Focusing on surface O3 in the YRD, its spatiotemporal characteristics, meteorological driving factors, and health impacts are investigated based on surface O3 observations in this study. We find that from 2015 to 2019, most cities failed to meet the national standards and O3 pollution mainly occurred in warm seasons (April‐September). Surface O3 in the YRD increased in the recent years, so did tropospheric column O3. Spatially, surface shows a significant positive autocorrelation over the YRD except for 2019, and high O3 values mainly gather in the central of the YRD. The first two empirical orthogonal functions (EOFs) of O3 accounted for 37.4% and 20.0% of the total variance in O3, respectively, with their spatial patterns characterized by the same phase and west and east contrast, respectively. The EOF1 is related to the change in radiation and the EOF2 is attributed to regional transport by prevailing westerly wind. Downward UV radiation, temperature and u wind are crucial factors. The premature mortality caused by O3 for respiratory disease in the YRD is estimated to be 5,889 cases per year. Furthermore, we find that premature mortality is more sensitive to O3 concentration than population, suggesting that controlling peak O3 concentration can bring great health benefits.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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".