Air pollution exposure and its impacts on everyday life and livelihoods of vulnerable urban populations in South Asia
Bibliographic record
Abstract
Abstract Urban populations in South Asia are regularly exposed to poor air quality, especially elevated concentrations of fine particulate matter (PM2.5). However, the potential differential burden for the urban poor has received little attention. Here, we evaluate the links between occupation, patterns of exposure to PM2.5, and the impacts at an individual and household level for vulnerable populations in Lahore (Pakistan), Kathmandu (Nepal), and Mandalay (Myanmar). We conduct personal exposure measurements and detailed interviews, identifying a wide range of impacts at individual and household levels. Low-income populations are concentrated in occupations that expose them to higher concentrations. Individuals report a range of adverse health impacts and limited capacities to reduce exposure. The lost income, compounded with the costs of managing these health impacts and limited opportunities for alternative employment, can deepen the socioeconomic vulnerability for the household. Reducing these risks requires targeted interventions such as improved social safety nets.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".