Spatiotemporal patterns in air pollution and sound in Dhaka, Bangladesh
Bibliographic record
Abstract
Abstract High air pollution and sound in Dhaka pose major health risks, yet limited high-resolution data hinders epidemiologic assessments and interventions. We measured fine particulate matter (PM₂.₅), black carbon (BC), and sound (dBA) at 70 locations across Dhaka during the 2023 dry (Jan–Mar) and wet (Jul–Sep) seasons, with continuous monitoring at 8 fixed sites and short-term sampling at 62 rotating sites (3 days/season). Over 1.7 million minutes of PM2.5 and 1.2 million minutes of sound were collected, enabling detailed spatial (land use features) and temporal (seasonal, weekly, daily, and diurnal) analyses. Air pollution was four times higher in the dry season, with commercial/industrial areas and transportation corridors having the highest levels, particularly at night. Sound levels varied less temporally, and were highest in transportation corridors, mixed-use areas, and commercial hubs. Air pollution and sound across the city exceeded international guidelines. These findings provide critical evidence to inform targeted policy and interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".