Public health burden of polycyclic aromatic hydrocarbons in the East African environment: a systematic review
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAHs) are toxic substances formed during the incomplete burning of organic matter, and they pose major threats to human health and the environment. This systematic review assesses the public health burden of PAH exposure in East Africa, focusing on sources, health effects, and mitigation strategies. East Africa is experiencing rapid urbanization, industrial growth, and increasing reliance on biomass fuels, all of which contribute to elevated environmental PAH levels. Despite these developments, the region remains underrepresented in global PAH risk assessments, with limited localized data guiding policy and public health responses. This geographic focus is thus critical to identify context-specific exposure sources, assess the unique vulnerabilities of East African populations, and support targeted mitigation strategies aligned with regional socioeconomic and environmental realities. Using the PRISMA framework, studies were screened for quality and bias via the Newcastle–Ottawa Scale and JBI checklists, with 20 out of 183 articles meeting the inclusion criteria. Key exposure sources include biomass and fossil fuel combustion, urban air pollution, industrial emissions, occupational hazards, and dietary intake. Vulnerable groups, particularly women, children, and low-income urban dwellers, face heightened risks, including the risk of respiratory diseases, cardiovascular disorders, cancer, adverse birth outcomes, and neurodevelopmental impairments. Despite growing concerns, policy gaps, weak enforcement of air quality standards, and limited public awareness hinder effective mitigation. Therefore, urgent interventions are needed, including clean energy adoption, urban air pollution control, industrial regulations, and stronger public health policies. To address PAH exposure in East Africa, a multi-sectoral approach integrating policy reforms, community engagement, and sustainable environmental practices to protect public health is imperative.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".