Spatiotemporal Assessment of PM2.5 Exposure of a High-risk Occupational Group in a Southeast Asian Megacity
Bibliographic record
Abstract
Drivers of open-air public utility jeepneys (PUJs) in the Philippines are regularly exposed to severe levels of fine particulate pollution (PM2.5), making them the appropriate sub-population for investigating the health impacts of PM2.5 on populations chronically exposed to these kinds of unique sources. Real-time PM2.5 exposures of PUJ drivers for a high-traffic route in Metro Manila, Philippines were assessed using Academia Sinica-LUNG (AS_LUNG) portable sensing devices. From all 15-second measurements obtained, the mean concentration of PM2.5 is 36.4 µg m–3, seven times greater than the mean annual guideline value (5.0 µg m–3) set by the World Health Organization (WHO). Elevated levels of PM2.5 were observed at key transportation microenvironments (TMEs) such as a transport terminal and near a shopping mall. The occurrence of hotspots along the route is mainly attributed to traffic-promoting factors like stoplights and traffic rush hours. Multiple linear regression (MLR) analysis revealed that the area by the shopping mall had the highest contribution (β = 52 µg m–3) to PUJ driver exposure. To the best of our knowledge, this study is the first in the country to perform a detailed characterization of the exposure of a high-risk occupational group to PM2.5. These results reveal information that is normally undetected by fixed site monitoring (FSM), underscoring the importance of mobile measurements as a complement to FSM in assessing the exposure of urban populations to air pollution more extensively. Furthermore, this study demonstrates the heavy influence of traffic-promoting factors on air pollution, and the feasibility of high-resolution mobile sensing for quantifying pollution characteristics in rapidly developing nations with unique air pollution sources. Gaps in our knowledge of their health impacts may be closed through quantifying exposure using reliable sensing devices and methods presented in this work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".