Socioeconomic differences in nitrogen dioxide ambient air pollution exposure among children in the three largest Canadian cities.
Bibliographic record
Abstract
BACKGROUND: Nitrogen dioxide (NO₂) is a marker for traffic-related air pollution, which exhibits strong spatial gradients in large cities. Previous studies have shown that in Canadian cities, exposure to ambient NO₂ is greater in neighbourhoods of low socioeconomic status (SES). As a result of these differences in exposure, air pollution-related health problems may be more prevalent among children in lower SES urban neighbourhoods. DATA AND METHODS: Children younger than age 18 enumerated in the 2006 Census who lived in Toronto, Montreal or Vancouver were linked to published air pollution exposure land use regression models to assign exposure at the Dissemination Area (DA) level. Associations between both socioeconomic and visible minority status and exposure to ambient NO₂ among children in these three cities were examined in a series of regression models (OLS and simultaneous autoregressive models that account for spatial autocorrelation). RESULTS: Children in lower income DAs in all three cities were exposed to higher NO₂ concentrations than were children in higher income DAs (mean difference of 2 ppb between lowest and highest income quintiles). In some cities, DAs with larger percentages of children in lone-parent families and visible minority children were characterized by greater NO₂ exposure. INTERPRETATION: The relatively high incidence of air pollution-related diseases (for example, asthma) among children in lower SES neighbourhoods may be attributable, at least in part, to variations in NO₂ air pollution exposure within the same city.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".