The Impact of Climate Conditions and Traffic Emissions on the Pms Variations in Rhodes City during the Summer of 2021
Bibliographic record
Abstract
The increased traffic and human activities worsen the ambient air quality by affecting human's health and environment. Additionally, climate conditions are considered as one of the major factors that affect the concentration of the most pollutants. The aim of this study is to investigate the impact of climate conditions and traffic emissions on the concentration of PM2.5 and PM10 (Particulate Matter) variability in the center of Rhodes city during the summer of 2021. For the analysis, a series of recordings from a mobile air quality monitoring system located in the city center as well as climatological parameters from the 5 th generation ECMWF reanalysis (ERA5) are analysed. The analysis was performed during the period from July 17 to August 31, 2021. In order to investigate the effect of climate conditions on the concentration of PMs, maps of mean wind speed, relative humidity and temperature at 2m are constructed.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".