Advancing indoor environmental quality in African countries: A call to action for awareness, research, and policy
Bibliographic record
Abstract
Indoor Environmental Quality (IEQ), including indoor air quality (IAQ), thermal comfort, lighting, and noise, is a critical determinant of health, well-being, and productivity. However, African countries remain underrepresented in IEQ research, policy, and advocacy, despite facing unique challenges such as energy poverty, reliance on biomass fuels, inadequate building practices, poor ventilation, overheating, inadequate lighting, and pervasive noise pollution. These conditions increase health risks and compromise learning, working, and living environments. This paper highlights the urgent need for a comprehensive approach to IEQ in Africa, addressing not only indoor air pollution but also thermal discomfort from rising temperatures, insufficient indoor lighting, and chronic exposure to harmful noise levels. It introduces the "Promoting IEQ and IAQ in Africa" initiative launched by the International Society of Indoor Air Quality and Climate (ISIAQ), which aims to foster research collaboration, raise awareness, support context-specific solutions, and influence policy development tailored to Africa’s diverse climates and socio-economic realities. By aligning with the United Nations Sustainable Development Goals (SDGs), this initiative advocates healthier and more sustainable indoor environments across the continent. This paper serves as a call to action for researchers, policymakers, and practitioners to work together to advance IEQ research, innovation, and advocacy for African communities. • Indoor Environmental Quality (IEQ) studies are underrepresented in African research, despite their major impacts on health, learning, and productivity. • Unique regional challenges such as energy poverty, biomass reliance, and poor infrastructure demand context-specific IEQ solutions in Africa. • A comprehensive approach to IEQ in Africa must simultaneously address indoor air quality, thermal comfort, lighting, and noise. • There is an urgent need for studies on scalable, cost-effective IEQ solutions using locally available materials tailored to Africa’s diverse climates. • The ISIAQ Innovation Network's ‘Promoting IEQ/IAQ in Africa’ initiative calls on researchers, policymakers, and practitioners to collaborate for lasting impact.
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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 | 0.051 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.016 |
| Scholarly communication | 0.015 | 0.027 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".