Weather Condition Dominates Regional PM2.5 Pollutions in the Eastern Coastal Provinces of China during Winter
Bibliographic record
Abstract
China has suffered from severe particulate matter (PM) pollution in recent years. Both pollution areas and levels are increasing gradually. The PM pollution episodes not only occur in the traditional developed areas like the Yangtze River Delta (YRD) and the Beijing-Tianjin-Hebei (BTH) region, but also frequently happen in the eastern coastal provinces (ECPs) of China. Based on hourly fine-PM (PM2.5) concentrations during December 2013 to February 2014 of 55 cities located in the ECPs, we investigated the spatial and temporal variabilities of PM2.5 concentration and the corresponding meteorological conditions during winter. The results generally showed that the winter mean concentrations over all ECPs exceeded China’s national standard of 75 µg m–3, and the most polluted areas with mean concentrations exceeding 150 µg m–3 were in the southwest of Hebei and the west of Shandong Province. The PM2.5 concentrations in February were lower than December in most areas, especially in the YRD, but they were higher over the north of Hebei Province. The spatial distributions and monthly variations were strongly related to weather conditions. Overall, severe PM pollution corresponded with stable weather conditions: small Sea Level Pressure gradient, lower Planetary Boundary Layer (PBL) height and weaker winds. Statistics showed that the changes of the mean PM2.5 concentration over the ECP region lagged behind the variations in the PBL height and wind speeds by about 12–18 h, and the variations in weather conditions could explain about 71% (R2) of the overall changes in PM2.5 concentrations, indicating that regional PM2.5 pollution was dominated by weather conditions in the ECPs. This study gives insight into the PM2.5 pollution in the ECPs of China during winter, which would be helpful to predict and control the PM2.5 pollution for this area in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".