Characteristic and Spatiotemporal Variation of Air Pollution in Northern China Based on Correlation Analysis and Clustering Analysis of Five Air Pollutants
Bibliographic record
Abstract
Abstract Air quality in Northern China has become a global hot spot issue due to a series of air pollution events in the recent years. In this study, five representative air pollutants (PM 2.5 , SO 2 , NO 2 , CO, and O 3 ) were employed to reveal the spatial and temporal distribution of air pollution in Northern China. Periodic decline in PM 2.5 , SO 2 , CO, and NO 2 from 2016 to 2018 indicated that air pollution control measures have achieved desired results. In addition, PM2.5 was significantly positively correlated with SO 2 , CO, and NO 2 ( p < 0.001), and O 3 had negative correlations with the other four pollutants. Furthermore, the heavy pollution phenomenon in Shijiazhuang, Anyang, Xingtai, and Handan was attributed to their industrial structures (e.g., steel industry) and geographical location based on clustering analysis. Contrary to above four pollutants, the annual average concentrations of O 3 increased in all the nine cities (1.4–35.9%) from 2016 to 2018. It is necessary to plan mitigation strategy for O 3 based on further investigation of source and formation mechanism of O 3 .
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".