PM2.5 in Beijing – temporal pattern and its association with influenza
Bibliographic record
Abstract
BACKGROUND: Air pollution in Beijing, especially PM2.5, has received increasing attention in the past years. Despite Beijing being one of the most polluted cities in the world, there has still been a lack of quantitative research regarding the health impact of PM2.5 on the impact of diseases in Beijing. In this study, we aimed to characterize temporal pattern of PM2.5 and its potential association with human influenza in Beijing. METHODS: Based on the data collected on hourly ambient PM2.5 from year 2008 to 2013 and on monthly human influenza cases from 2008 and 2011, we investigated temporal patterns of PM2.5 over the five-year period and utilized the wavelet approach to exploring the potential association between PM2.5 and influenza. RESULTS: Our results found that ambient PM2.5 pollution was severe in Beijing with PM2.5 concentrations being significantly higher than the standards of the World Health Organization, the US EPA, and the Chinese EPA in the majority of days during the study period. Furthermore, PM2.5 concentrations in the winter heating seasons were higher than those in non-heating seasons despite high variations. We also found significant association between ambient PM2.5 peak and human influenza case increase with a delayed effect (e.g. delayed effect of PM2.5 on influenza). CONCLUSIONS: Ambient PM2.5 concentrations were significantly associated with human influenza cases in Beijing, which have important implications for public health and environmental actions.
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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 | 0.001 |
| 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".