Leaving No City Behind: An Integrated Approach for Air Pollution Control
Bibliographic record
Abstract
Air pollution control and climate action are interlinked and have multifaceted impacts on public health in urban cities. With 6.7 million deaths in 2019, air pollution takes the lead as the foremost environmental health risk, incurring annual health costs of USD 8.1 trillion, as reported by the World Bank. This article highlights the importance of addressing air pollution in cities for healthier environments and accelerating climate action. An overview of air quality variations across seven select cities over time will demonstrate the unequal prioritization of air pollution control, hindering progress toward both climate action goals and the health benefits of urban environments with clean air. City-specific disparities to measure and manage air pollution are discussed, as well as successful city initiatives aimed at curbing emissions. This commentary also addresses the influential role of governance in shaping effective air pollution control measures. Policymakers can shape regional and international initiatives through regulatory frameworks and treaties to combat the shared challenge of urban air pollution. By the end of this commentary, the authors aim to construct a case for air pollution control encompassing health, economic, environmental, and social dimensions. Actionable insights are presented for policymakers to propel the transition toward healthier cities to leave no city behind.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.027 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.006 | 0.012 |
| Research integrity | 0.027 | 0.032 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".