Familial Differences in Personal PM<sub>2.5</sub> Exposure within a Rural African Community Explained with Spatiotemporal Exposure Apportionment
Bibliographic record
Abstract
Exposure to fine particulate matter (PM 2.5 ) from solid-fuel combustion is a major determinant of global morbidity and mortality. However, variations in exposure remain uncertain across many high-risk populations. This work describes personal PM 2.5 exposures among household members (adult men, adult women, and children) in rural sub-Saharan Africa, where biomass fuel is the primary household energy source. We assessed personal PM 2.5 exposures using wearable monitors that combined real-time sensing, time-integrated (gravimetric filter) sampling, and continuous location-activity tracking over 48 h periods. A total of 1280 samples were collected from 579 Rwandan homes over a 15-month period comprising 304 men (aged 23–84 years), 495 women (aged 20–84 years), and 481 children (aged 8–17 years). Linear mixed models, controlling for household, suggested that children were exposed to 14% (CI: 6, 22%) more PM 2.5 than their mothers and 100% (CI: 85, 117%) more than their fathers. Spatiotemporal analyses, aggregated into various microenvironments (e.g., home, school, transit, agricultural fieldwork), reveal that children bore a disproportionate exposure burden from in-home cooking activities compared with their parents. Results from this work indicate that interventions for household energy systems, in conjunction with familial lifestyle and behavior modifications, are necessary to reduce personal PM 2.5 exposures in rural Rwanda, especially among children.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".