Exposure to PM2.5 Related to Road Traffic: Comparison between Crossroads and Outside of Crossroads at Cotonou, Benin
Bibliographic record
Abstract
Background: Several studies have analysed the pollution issues owing to road traffic in Cotonou, Benin. Concentration levels of particles are higher on high traffic than a low traffic. The exposure of human populations to air pollution is more intense on the roads. In Benin, the density of traffic on the crossroads is indeed more important. Are traffic locations such as crossroads, areas where the level of exposure PM2.5 is increased? Methods: This study was conducted along the 5 km high-traffic road in the city of Cotonou. It is a high traffic lane with two crossroads. Sampling and measurements were carried out in dry sea-son (January and February) and rainy season (June and July). For each season the measurements were made over two months from 7 am to 9 pm. PM2.5 measurements were made at different locations at crossroads and also along the track. To compare concentrations of PM2.5 at crossroads and outside of roundabout, we used the Generalized Linear Mixed Model. Results: In the rainy season the PM2.5 hourly concentrations ranged between 400 μg/m3 and 500 μg/m3 while in the dry season 100 μg/m3 and 300 μg/m3. In the rainy season, the average of PM2.5 concentration was 463.25 ± 66.21 μg/m3 at crossroads and 264.75 ± 50.97 μg/m3 outside of crossroads. In the dry season, the average of PM2.5 concentration was 232.75 ± 97.29 μg/m3 at crossroads and 123.31 ± 63.79 μg/m3 outside of crossroads. Both in dry and rainy seasons, PM2.5 concentration level peaks are observed from 7 am to 9 am and from 7 pm to 9 pm. The Generalized Linear Mixed Model showed that there is high significant difference between concentrations of PM2.5 at crossroads compared to outside of crossroads. Occupation of the roadside (in particular crossroads) for various economic activities is common practice in Cotonou thus health risk for people working around crossroads increases. Conclusion: Locations such as crossroads are areas where the level of exposure PM2.5 is highest on road traffics.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".