Indoor levels and personal exposures to traffic pollution and environmental noise among retired adults in urban Colombia
Bibliographic record
Abstract
Background: Exposures to traffic-related air pollution and urban environmental noise are poorly understood in low and middle-income countries, where vehicle fleets, road conditions, and housing differ from high-income settings. We characterized exposures to air pollutants and noise among retired adults in urban Bucaramanga, Colombia. Methods: We selected 4 neighbourhoods that represented a range of traffic settings: high traffic/high diesel, high traffic/low diesel, low traffic/low diesel, and low traffic/low diesel/high braking. In each neighbourhood, we measured outdoor PM2.5 and black carbon (BC) for 6 days and selected ~20 homes with retired adults and measured indoor levels and exposures to PM2.5 and black carbon (BC), and indoor noise. Results: Indoor concentrations and exposures to PM2.5 and BC ranged from (in ug/m3) 10.2-17.7 and 2.2-3.0, respectively, and BC comprised 12-29% of PM2.5. The high traffic/high diesel neighbourhood had the highest PM2.5 levels (ug/m3): outdoor=15.1 (± 7.1), indoor=17.3 (± 6.8), personal=17.7 (± 5.6). The low traffic/low diesel neighbourhood had the lowest indoor PM2.5 and exposures but the highest BC. Indoor noise levels did not very much between neighbourhoods and ranged from 54.5-57.1 dBa. Correlations between indoor concentrations and exposures to PM2.5 were moderate-to-strong and positive in all neighbourhoods, while for BC, correlations varied from near 0 to moderate and positive. Weak inverse correlations were observed between air pollution (indoor and exposure) and noise. Conclusion: Average exposures to PM2.5 were lower than the WHO’s 24-h guideline (25 ug/m3), though indoor noise levels exceeded the WHO Europe’s guideline (35 dBa). The low correlations between indoor and exposure levels of BC may indicate additional sources of BC exposure. Similarly, while traffic is a major source air pollutants and noise in urban areas, we observe that these variables are weakly (and inversely in some cases) related, which may be related to other local sources of these pollutants.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".