Quantifying Space Heating Stove Emissions Related to Different Use Patterns in Mongolia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A major source of particulate matter pollution in Mongolia’s capital, Ulaanbaatar, is emissions from traditional coal-burning space-heating stoves. Significant investment has been made to replace traditional highly polluting heating stoves with improved low-emission high-efficiency stoves. Performance testing that has been undertaken to support the selection of replacement stoves is typically based on manufacturers’ recommended operating procedures, which may not be representative of the operating procedures used in homes. The objective of this research is to evaluate factors that influence stove emissions under typical field operating conditions. A highly-instrumented stove testing facility was constructed to allow for rapid and precise adjustment of factors influencing stove performance. Tests were performed using one of the improved stove models currently available in Ulaanbaatar. Complete burn cycles were conducted with coal from the Ulaanbaatar region using various startup parameters, refueling conditions, and fuel characteristics. Measurements were collected simultaneously from undiluted chimney gas, diluted chimney gas, and plume gas drawn from a dilution tunnel above the chimney. Ignition events lead to increased PM emissions with more than 98% of PM mass emitted during the startup and refueling process. However, emissions during refueling are of particular interest, both because refueling is common and because refueling associated emissions appear to be very high. CO emissions are distributed more evenly over the burn cycle, peaking during ignition and late in the burn cycle. We anticipate these results being useful, in combination with behavioral surveys, for quantifying public health outcomes related to the distribution of improved stoves and to identify opportunities for improving and sustaining performance of the new stoves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it