Traffic Air Quality Health Index in a Selected Street, Alexandria
Bibliographic record
Abstract
Background: Air Quality Health Index (AQHI) is a scale, which was designed in Canada to help people to understand how the air quality affects their health. It measures the relationship between the air quality and health on a scale from 1 to 10+. Aim: The aim of this study was to assess traffic AQHI on one street in Alexandria, Egypt at different seasonal conditions. Methods: This was a time-series study that was conducted during the period from January 1 to December 31, 2016 at Ibrahim Sherif Street. It was accomplished by three-hour air sampling of respirable particulates (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ground-level ozone (O3), during the morning rush hours. A total of 156 samples for each pollutant covered all seasonal variations and activities. After laboratory analysis, the added health risks (%AR) and AQHI were calculated using the Hong Kong equation. Results: The uppermost median value of %AR was during April [72.9 (23.4)] and the minimum was during January [32.2 (10.0)]. The traffic AQHIs in the study setting were of the serious category 10+ in almost all sampling days. The maximum %AR was during spring [70.0 (19.7)], and the minimum was during winter [40.6 (19.0)]. Conclusion: From the results of the present study, we can conclude that; the highest %AR was during April, and the minimum was during January. The traffic AQHIs in the study setting were of the serious category 10+ in almost all sampling days. The most dominating pollutant affecting the %AR and AQHI was the PM10.
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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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".