Long-term trends in urban NO2 concentrations and associated pediatric asthma cases: estimates from global datasets
Bibliographic record
Abstract
Background: Combustion-related nitrogen dioxide (NO2) air pollution is associated with pediatric asthma incidence. We estimated global surface NO2 concentrations consistent with the Global Burden of Disease Study for 1990-2019 at 1km resolution, and concentrations and attributable pediatric asthma incidence trends in 13,189 cities from 2000-2019. Methods: We scaled an existing surface annual average NO2 concentrations dataset for 2010-2012 from a land use regression model (based on 5,220 NO2 monitors in 58 countries and land use variables) to other years using NO2 column densities from satellite and reanalysis datasets. We applied these concentrations to epidemiologically-derived concentration-response factors, population, and baseline asthma rates to estimate NO2-attributable pediatric asthma incidence. Findings: We estimated that 1.85 million (95% uncertainty interval: 0.93 – 2.8 million) new pediatric asthma cases were attributable to NO2 globally in 2019, two-thirds of which occurred in urban areas. The fraction of pediatric asthma incidence that is attributable to NO2 in urban areas declined from 20% in 2000 to 16% in 2019. Urban attributable fractions dropped in High-income (-41%), Latin America/Caribbean (-16%), Central Europe, Eastern Europe, and Central Asia (-13%), and Southeast Asia, East Asia, and Oceania (-6%), and rose in South Asia (+23%), Sub-Saharan Africa (+11%), and North Africa and Middle East (+5%) regions. The importance of NO2 concentrations, pediatric population size, and asthma incidence rates in driving these changes differs regionally. Interpretation: Despite improvements in some regions, combustion-related NO2 pollution continues to be an important contributor to pediatric asthma incidence globally, particularly in cities. Funding: Health Effects Institute, NASA
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".