Assessment of the Air-Quality Over Urban Areas by Means of Biometeorological Indices. The Case of Athens, Greece
Bibliographic record
Abstract
This article deals with the part of air pollution, which has a particular relevance to the objective assessment of the quality of urban air. The correct understanding of the pollution levels over an urban region is of great importance to both authorized government services and to the community. This is particularly true for high polluted urban regions such as the Athens basin; so, it is important to recognize the levels of atmospheric quality by means of an easily understandable manner even for non-specialists. Thus, in this study an attempt is made for the application of two different groups of air quality indices (AQI) (statistical and biometeorological) by utilizing air pollutants measured into Athens basin, in a network of 17 measuring stations, during the period 2001-2002. The calculations of the (AQI) are referred to data of all 17 measuring stations and concern levels of air-pollution concentrations to both short (daily) and long time periods. Then comparisons were made between the obtained statistical and biometeorological indices in order to identify whether or not there is any existence of consistency between them. The compositions of the calculated indices were also determined, as well as, the most important air-pollutant for them. This procedure was applied for each day of the week in order to reveal the weekly cycle of indices and perhaps to isolate air-quality differences between weekdays and weekends. Finally, the varying forms of both frequency distributions are mainly caused by the impact related concentrations ranges of single air-pollutants which are typical of air-quality indices. Especially, PM10 and O3 seem to have a stronger influence on the determination of values of air quality indices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".