Characterization of Ultrafine Particulate Matter from Traditional and Improved Biomass Cookstoves
Bibliographic record
Abstract
Biomass combustion in cookstoves has a substantial impact on human health, affects CO2 levels in the atmosphere, and black carbon (BC) and organic carbon (OC) affect the earth's radiative balance. Various initiatives propose to replace traditional fires with "improved" (nontraditional) cookstoves to offset negative local and global effects. In this laboratory study, we compared the size, composition, and morphology of ultrafine particulate emissions from a "three-stone" traditional fire to those from two improved stove designs (one "rocket", one "gasifier"). Measurement tools included a scanning mobility particle sizer, PTFE and quartz filter samples, and transmission electron microscopy. In the improved stoves, particulate mass (PM) emissions factors were much lower although median particle size was also lower: 35 and 24 nm for the rocket and gasifier, respectively, vs 61 nm for the three-stone fire. Particles from improved stoves formed clearly defined chain agglomerates and independent spheres with little evidence of volatile matter and had a higher proportion of BC to total PM, although overall BC emissions factors were fairly uniform. The 3-fold increase in quantities of sub-30 nm particles from improved cookstoves warrants further consideration by health scientists, with due consideration to the higher combustion efficiencies of improved cookstoves.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".