Household and personal air pollution exposure measurements from 120 communities in eight countries: results from the PURE-AIR study
Bibliographic record
Abstract
Background Approximately 2·8 billion people are exposed to household air pollution from cooking with polluting fuels. Few monitoring studies have systematically measured health-damaging air pollutant (ie, fine particulate matter [PM 2·5 ] and black carbon) concentrations from a wide range of cooking fuels across diverse populations. This multinational study aimed to assess the magnitude of kitchen concentrations and personal exposures to PM 2·5 and black carbon in rural communities with a wide range of cooking environments. Methods As part of the Prospective Urban and Rural Epidemiological (PURE) cohort, the PURE-AIR study was done in 120 rural communities in eight countries (Bangladesh, Chile, China, Colombia, India, Pakistan, Tanzania, and Zimbabwe). Data were collected from 2541 households and from 998 individuals (442 men and 556 women). Gravimetric (or filter-based) 48 h kitchen and personal PM 2·5 measurements were collected. Light absorbance (10 −5 m −1 ) of the PM 2·5 filters, a proxy for black carbon concentrations, was calculated via an image-based reflectance method. Surveys of household characteristics and cooking patterns were collected before and after the 48 h monitoring period. Findings Monitoring of household air pollution for the PURE-AIR study was done from June, 2017, to September, 2019. A mean PM 2·5 kitchen concentration gradient emerged across primary cooking fuels: gas (45 μg/m 3 [95% CI 43–48]), electricity (53 μg/m 3 [47–60]), coal (68 μg/m 3 [61–77]), charcoal (92 μg/m 3 [58–146]), agricultural or crop waste (106 μg/m 3 [91–125]), wood (109 μg/m 3 [102–118]), animal dung (224 μg/m 3 [197–254]), and shrubs or grass (276 μg/m 3 [223–342]). Among households cooking primarily with wood, average PM 2·5 concentrations varied ten-fold (range: 40–380 μg/m 3 ). Fuel stacking was prevalent (981 [39%] of 2541 households); using wood as a primary cooking fuel with clean secondary cooking fuels (eg, gas) was associated with 50% lower PM 2·5 and black carbon concentrations than using only wood as a primary cooking fuel. Similar average PM 2·5 personal exposures between women (67 μg/m 3 [95% CI 62–72]) and men (62 [58–67]) were observed. Nearly equivalent average personal exposure to kitchen exposure ratios were observed for PM 2·5 (0·79 [95% 0·71–0·88] for men and 0·82 [0·74–0·91] for women) and black carbon (0·64 [0·45–0·92] for men and 0·68 [0·46–1·02] for women). Interpretation Using clean primary fuels substantially lowers kitchen PM 2·5 concentrations. Importantly, average kitchen and personal PM 2·5 measurements for all primary fuel types exceeded WHO's Interim Target-1 (35 μg/m 3 annual average), highlighting the need for comprehensive pollution mitigation strategies. Funding Canadian Institutes for Health Research, National Institutes of Health.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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, 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".