Long-term trends in urban NO2 concentrations and associated pediatric asthma cases: estimates from global datasets
Bibliographic record
Abstract
Background: Levels of nitrogen dioxide (NO2), a combustion-related air pollutant largely associated with traffic in urban areas, have been changing rapidly due to competing influences of regulation and population and fossil fuel-powered economic expansion. Traffic-related NO2 is associated with pediatric asthma incidence in epidemiological studies around the world. We aim to assess long-term trends in NO2 concentrations and NO2-attributable pediatric asthma incidence in cities globally. Methods: We estimate global annual average surface NO2 concentrations at 1km resolution for 1990-2019 by combining land use regression model predictions with NO2 column densities from the Ozone Monitoring Instrument satellite sensor. We use these concentrations with an epidemiologically-derived concentration-response factor, population, and baseline disease rates to estimate NO2-attributable pediatric asthma incidence. We explore trends over the last two decades. Findings: We found diverging regional trends leading to an emerging global convergence in urban NO2 concentrations globally from 2000-2019. Concentrations are high but declining in high-income countries and low but rising elsewhere. Estimated NO2-attributable pediatric asthma incidence shows similar trends with decreases of 28-56% in North America, Western and Central Europe, and Australasia, but increases of >50% in Central and South Asia and >100% in Sub-Saharan Africa. Interpretation: Traffic-related air pollution continues to be an important contributor to pediatric asthma incidence in cities in both developed and developing countries. Divergent experiences of different world regions show that while population growth is worsening NO2 levels with substantial implications for children’s health in Asia and Africa, rapid and substantial NO2 declines are possible with effective regulations. Funding: Health Effects Institute and 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".