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.To study the impact of the concentration of PMs on air quality, the Common Air Quality Index (CAQI) is calculated.During the summer of 2021, a number of wildfire events over southwest Turkey and Rhodes island affect the air quality in the eastern Mediterranean region.Composite maps of climate conditions between the wildfires season and the fire-free season, as well as the regression maps between a non-linear fire danger index (Fosberg Fire Weather Index; FFWI) and PMs variability are constructed in order to investigate the effect of wildfires on the air quality in the city of Rhodes.Findings show that the climatic conditions and traffic emissions are driving factors for the variation of PMs concentration.Finally, the current study highlights the importance of the development of green and sustainable technologies to improve the air quality of the cities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".