Heatwaves amplify air pollution risks in Sub-Saharan Africa
Bibliographic record
Abstract
Mounting evidence demonstrates that heatwaves aggravate urban air pollution, with substantial impacts on public health, but comparatively little research has addressed Sub-Saharan African contexts. In this study, we focused on Kigali, Rwanda, to assess the relationship between extreme heat events and concentrations of fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and ozone (O 3 ) from 2021 to 2024. Using low-cost sensors for dense spatiotemporal coverage, our analysis finds that O 3 concentrations increased significantly during 6 heatwave events with peak values up to 40% higher during heatwaves. Heatwaves also resulted in spikes in PM 2.5 and NO 2 , however the diurnal and seasonal analyses showed that PM 2.5 and NO 2 dynamics were shaped more by local emissions than temperature alone. These results highlight the compound risks of heat and air pollution in sub-Saharan African cities, underscoring the importance of early-warning systems and robust urban policies that account for both heat and air pollution. In addition, the atmospheric dynamics identified in this research differ from those observed in many high-income countries, highlighting a critical need for more research exploring the intersection of heat and air pollution in Sub-Saharan Africa.
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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 | 0.000 | 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) | 0.002 | 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".