Concentration, source identification and risk assessment of atmospheric polycyclic aromatic hydrocarbons at a kindergarten school in Kigali, Rwanda
Bibliographic record
Abstract
• We characterize PAHs at Kindergarten School in Sub-Saharan Africa. • PAHs exceeded the levels reported in kindergarten schools in high-income countries. • Wood burning and diesel traffic emissions were the primary sources of PAHs. • Cancer risk level significantly exceeded the WHO safe limit. • Policies to address air quality at African schools are urgently required. Africa has the world’s youngest population, with 40 % aged between 0 and 15 years, and it remains the region most affected by air pollution. In many African cities, kindergarten schools are often located in high-density and polluted urban environments with high levels of hazardous pollutants, including atmospheric polycyclic aromatic hydrocarbons (PAHs). Compared to the rest of the world, limited studies have quantified the exposure to and associated health risks of PAHs in schools in Africa. This study first characterizes and identifies sources of atmospheric PAH and assesses the health risks at a kindergarten school in Rwanda. Fine particulate matter (PM 2.5 ) was collected during the dry and wet seasons using a mini-volume air sampler, and 16 PAHs were characterized using gas chromatography. The total mean concentration of 16 PAHs was higher in the dry season (52.7 ± 21.9 ng/m 3 ) than in the wet season (49.5 ± 14.7 ng/m 3 ) and exceeded the levels reported in kindergarten schools in high-income countries. Source analysis using PAH diagnostic ratios and correlation analysis of PAHs with black carbon from biomass burning and fossil fuel showed that wood burning for cooking fuel and diesel traffic emissions were the main sources of PAHs at the kindergarten school. The estimated cancer risk level significantly exceeded the World Health Organization’s safe limit and indicates that children at kindergarten in Rwanda are exposed to high levels of toxic PAHs. Immediate attention is required, and schools should consider implementing policies and interventions to reduce children’s exposure to air pollution at kindergarten schools.
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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 | 0.000 |
| 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 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".