WHO Air Quality, Energy Access and Health Science and Policy Summaries
Bibliographic record
Abstract
F or nearly 70 years, the World Health Organization (WHO) has been at the forefront of global efforts to advance clean air for better health.Through its leadership in setting evidence-based air quality guidelines, convening multisectoral stakeholders, collecting relevant data and supporting countries in implementing effective policies, WHO has played a central role in protecting populations from the health risks of air pollution.The recent Second WHO Global Conference on Air Pollution and Health, held in Colombia in March 2025, built upon this legacy.It convened ministers of health, environment and energy, and key stakeholders from across sectors, catalysing the global momentum to accelerate action on air pollution, energy access and climate change. An introductionAt the heart of this collective effort is the WHO Science and Policy Summaries (SPS) series.These concise, evidencebased snapshots synthesize the latest scientific knowledge, highlight pressing challenges and identify vulnerable groups and sector-specific solutions to reduce air pollution and promote health.They provide a powerful evidence-based risk communication mechanism to develop a common understanding of the priorities for action among diverse stakeholders.By focusing on sectoral solutions -ranging from clean household energy, sustainable transport, agriculture and green spaces, to land use planning, power generation, industry and waste management -the SPS provide practical pathways for governments and stakeholders to act decisively and inclusively.Notably, the series also addresses key policy instruments such as transboundary conventions and air quality legislation, essential levers for achieving clean air and public health gains across borders and jurisdictions.Furthermore, the series explores critical intersections with climate change, gender, equity and regional dynamics, ensuring that no one is left behind.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".