Children's exposure to indoor and outdoor black carbon and particulate matter air pollution at school in Rwanda, Central-East Africa
Bibliographic record
Abstract
Schoolchildren are a high-risk population, and their exposure to air pollution at school has been documented in high-income countries. As a result, school siting policies to protect children from air pollution have been established in those countries. Such policies are, however, non-existent in Africa where the air pollution problem is growing and gravely under-studied due to the lack of funding to install reliable ground-level monitoring networks and lack of air quality standards. The present study, the first of its kind to be conducted in Africa, measured Black Carbon (BC) and its related sources: fossil fuels (BCff) and biomass burning (BCbb). BC and fine particles (PM2.5) were measured simultaneously indoors and outdoors at schools over a 12-month period in Rwanda. The annual concentrations of BC in classrooms (8.15 µg/m3) and outdoor schoolyards (9.22 µg/m3) were higher than simultaneous concentrations recorded at Kigali urban background site (7.78 μg m3). The annual mean PM2.5 concentrations in classrooms and outdoor school were more than eight times higher than the World Health Organization's safe limits. The results demonstrated a more than two-fold increase in the concentrations of BCff during drop-off hours compared to off-peak hours, indicating the dominant contribution of vehicles queuing on the school premises. However, higher indoor than outdoor BC levels were recorded in some instances, 2 hours after an outdoor peak during drop-off times. The exposure peaks inside the classrooms were more pronounced for BC than PM2.5.The highest reduction of 24% and 19% were observed for BCff during weekends and school holidays in classrooms compared to regular days. No reduction was observed for BCbb concentrations. This study shows that BC and PM2.5 levels are considerably higher in schoolyards, and that further investigation of air pollution exposure at schools is warranted in Africa to promote improved drop-off/pick-up behaviors at and around school environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".