Global Estimation of Exposure to PM2.5 from Household Air Pollution
Bibliographic record
Abstract
Background: Household air pollution (HAP) exposure from cooking with dirty fuels is a major global health risk factor. Epidemiological studies have demonstrated significant variation in particulate matter concentrations of diameter ≤ 2.5 micrometers (PM2.5), an important metric for using integrated exposure–response functions to assess risks. To characterize global HAP-PM2.5 exposures, novel estimation methods are needed, as financial/resource constraints render it difficult to monitor exposures in all relevant areas.Methods: A Bayesian, hierarchical HAP-PM2.5 global exposure model was developed using kitchen and female HAP-PM2.5 exposure data available in published, peer-reviewed studies. Cooking environment characteristics and quantitative HAP-PM2.5 measurements from 47 studies were used to model urban and rural, fuel- and country-specific (traditional wood, improved biomass, coal, dung and gas/electric stoves) 24-hour HAP-PM2.5 kitchen concentrations and male, female and child exposures for 106 countries in Asia, Africa and Latin America.Results: A model incorporating fuel/stove type, urban/rural location and the socio-demographic index resulted in a Bayesian R2 of 0.57. Estimated global average 24-hour HAP-PM2.5 concentrations in rural kitchens using traditional, improved biomass, animal dung, and coal stoves were 320 μg/m3, 180 μg/m3, 1,760 μg/m3 and 400 μg/m3, respectively, higher than in rural kitchens using gas/electricity. Modeled female exposures from traditional wood stoves varied from 90–260 across countries, on average, with urban area exposures 40 μg/m3 less than those in rural areas. Male and child rural area exposures from traditional wood stoves ranged from 60-190 and 80-230 μg/m3, respectively; urban area exposures were 10 μg/m3 less than rural area exposures, among both sub-groups.Conclusions: A global exposure model incorporating type of fuel-stove combinations adds specificity and reduces exposure misclassification for estimation of HAP risk.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Simulation or modeling | high |
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".