Characteristics and Meteorological Factors of Severe Haze Pollution in China
Bibliographic record
Abstract
A severe haze pollution incident caused by unfavorable weather conditions and a northern air mass occurred in eastern, northern, northwestern, and southwestern China from January 15 to January 22, 2018. To comparatively analyze variations in PM2.5 pollution, hourly monitoring data and 24 h meteorological data were collected. Air quality observations revealed large spatiotemporal variation in PM2.5 concentrations in Handan, Zhengzhou, Xi’an, Yuncheng, Chengdu, Xiangyang, and Jinan. The daily mean PM2.5 concentrations ranged from 111.35 to 227.23 μg·m−³, with concentration being highest in Zhengzhou. Hourly mean PM2.5 concentration presented multiple U-shaped curves, with higher values at night and lower values during the day. The ratios of PM2.5 to PM10 were large in target cities and the results of multiscale geographic weighted regression model (MGWR) and Pearson correlation coefficients showed that PM2.5 had a significant positive or negative correlation with PM10, CO, NO2, and SO2. The concentration of PM2.5 was closely related to the combustion of fossil fuels and other organic compounds, indicating the large contribution of secondary aerosols to PM2.5 concentrations. The analysis of meteorological conditions showed that low temperature, low wind speed, and high relative humidity could aggravate the accumulation of regional pollutants in winter. Northwestern trajectory clusters were predominant contributions except in Jinan, and the highest PM2.5 concentrations in target cities were associated with short trajectory clusters in winter. The potential sources calculated by Weight Potential Source Contribution Function (WPSCF) and Weight Concentration-Weighted Trajectory (WCWT) models were similar and the highest values of the WPSCF (>0.5) and the WCWT (>100 μg·m−³) were mainly distributed in densely populated, industrial, arid, and semiarid regions.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".