Assessing Exposure to Household Air Pollution: A Systematic Review and Pooled Analysis of Carbon Monoxide as a Surrogate Measure of Particulate Matter
Bibliographic record
Abstract
Background: Household air pollution from solid fuel burning is a leading contributor to disease burden globally. Fine particulate matter (PM2.5) is thought to be responsible for many of these health impacts. A co-pollutant, carbon monoxide (CO) has been widely used as a surrogate measure of PM2.5 in studies of household air pollution. Objective: The goal was to evaluate the validity of exposure to CO as a surrogate of exposure to PM2.5 in studies of household air pollution and the consistency of the PM2.5–CO relationship across different study settings and conditions. Methods: We conducted a systematic review of studies with exposure and/or cooking area PM2.5 and CO measurements and assembled 2,048 PM2.5 and CO measurements from a subset of studies (18 cooking area studies and 9 personal exposure studies) retained in the systematic review. We conducted pooled multivariate analyses of PM2.5–CO associations, evaluating fuels, urbanicity, season, study, and CO methods as covariates and effect modifiers. Results: We retained 61 of 70 studies for review, representing 27 countries. Reported PM2.5–CO correlations (r) were lower for personal exposure (range: 0.22–0.97; median=0.57) than for cooking areas (range: 0.10–0.96; median=0.71). In the pooled analyses of personal exposure and cooking area concentrations, the variation in ln(CO) explained 13% and 48% of the variation in ln(PM2.5), respectively. Conclusions: Our results suggest that exposure to CO is not a consistently valid surrogate measure of exposure to PM2.5. Studies measuring CO exposure as a surrogate measure of PM exposure should conduct local validation studies for different stove/fuel types and seasons. https://doi.org/10.1289/EHP767
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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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.021 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".