Household Cooking with Solid Fuels Contributes to Ambient PM <sub>2.5</sub> Air Pollution and the Burden of Disease
Bibliographic record
Abstract
BACKGROUND: Approximately 2.8 billion people cook with solid fuels. Research has focused on the health impacts of indoor exposure to fine particulate pollution. Here, for the 2010 Global Burden of Disease project (GBD 2010), we evaluated the impact of household cooking with solid fuels on regional population-weighted ambient PM2.5 (particulate matter ≤ 2.5 μm) pollution (APM2.5). OBJECTIVES: We estimated the proportion and concentrations of APM2.5 attributable to household cooking with solid fuels (PM2.5-cook) for the years 1990, 2005, and 2010 in 170 countries, and associated ill health. METHODS: We used an energy supply-driven emissions model (GAINS; Greenhouse Gas and Air Pollution Interactions and Synergies) and source-receptor model (TM5-FASST) to estimate the proportion of APM2.5 produced by households and the proportion of household PM2.5 emissions from cooking with solid fuels. We estimated health effects using GBD 2010 data on ill health from APM2.5 exposure. RESULTS: In 2010, household cooking with solid fuels accounted for 12% of APM2.5 globally, varying from 0% of APM2.5 in five higher-income regions to 37% (2.8 μg/m3 of 6.9 μg/m3 total) in southern sub-Saharan Africa. PM2.5-cook constituted > 10% of APM2.5 in seven regions housing 4.4 billion people. South Asia showed the highest regional concentration of APM2.5 from household cooking (8.6 μg/m3). On the basis of GBD 2010, we estimate that exposure to APM2.5 from cooking with solid fuels caused the loss of 370,000 lives and 9.9 million disability-adjusted life years globally in 2010. CONCLUSIONS: PM2.5 emissions from household cooking constitute an important portion of APM2.5 concentrations in many places, including India and China. Efforts to improve ambient air quality will be hindered if household cooking conditions are not addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".