Reductions in Indoor Black Carbon Concentrations from Improved Biomass Stoves in Rural India
Bibliographic record
Abstract
Deployment of improved biomass burning cookstoves is recognized as a black carbon (BC) mitigation measure that has the potential to achieve health benefits and climate cobenefits. Yet, few field based studies document BC concentration reductions (and resulting human exposure) resulting from improved stove usage. In this paper, data are presented from 277 real-world cooking sessions collected during two field studies to document the impacts on indoor BC concentrations inside village kitchens as a result of switching from traditional stoves to improved forced draft (FD) stoves. Data collection utilized new low-cost cellphone methods to monitor BC, cooking duration, and fuel consumption. A cross sectional study recorded a reduction of 36% in BC during cooking sessions. An independent paired sample study demonstrated a statistically significant reduction of 40% in 24 h BC concentrations when traditional stoves were replaced with FD stoves. Reductions observed in these field studies differ from emission factor reductions (up to 99%) observed under controlled conditions in laboratory studies. Other nonstove sources (e.g., kerosene lamps, ambient concentrations) likely offset the reductions. Health exposure studies should utilize reductions determined by field measurements inside village kitchens, in conjunction with laboratory data, to assess the health impacts of new cooking technologies.
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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 | 0.001 |
| 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) | 0.001 | 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".