Statistical classification of synoptic weather patterns associated with Tehran air pollution
Bibliographic record
Abstract
Introduction: Poor air quality in the heavily polluted cities like Tehran is often the main city problem that influences people health and comfort. The main goals of this study are summarized as: 1) Seasonal pollutants mean variations during 2005, meteorological conditions effects on pollutant concentration; 2) Meteorological conditions case study and pollution spatial distribution for three determining synoptic patterns (MET1, MET4, MET5); 3) Further analysis of the episode from 30th November to 13th December 2005 (MET4); 4) Episode analysis from 30th November to 13th December 2005 (MET4) and 5) Episode analysis from 12th-22th of September 2005 (MET5). These are systematic weather patterns that usually affect the air pollution levels in Tehran. Materials and methods: Concentration changes of CO, PM10, SO2 and O3, as the relationship between the air pollution extreme events and atmospheric conditions in Tehran have been investigated. The hourly air pollution data from 11 representative monitoring sites were used. To understand the relationship between local meteorological synoptic patterns and air pollution, the principal component analysis (PCA) method has been applied to meteorological data. Then for minimizing the data complication the varimax rotations (VR) was used and five synoptic perspectives weather patterns have resulted for highly polluted periods. Results: Pollutants correlation investigation of the five patterns showed that air quality was highly dependent on middle tropospheric high geopotential ridge development, local southerly wind with strong static stability. Conclusion: The most polluted periods were associated with a weak pressure gradient, a weak wind, severe air descent, and radiation inversion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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".