Assessment of a Woodstove Changeout Program on PM2.5 Levels in Keene, New Hampshire, U.S.A.
Bibliographic record
Abstract
In local airsheds, wood smoke from residential woodstoves is a major source of PM2.5 pollution. Exposure to PM2.5 can cause a variety of health problems and complications. Communities situated in valleys that experience cold winters are especially susceptible to poor air quality during inversion events on calm winter nights. Keene, New Hampshire, USA is one such community where the widespread use of outdated residential woodstoves frequently resulted in PM2.5 exceeding national standards. Seeking to improve air quality, the City of Keene partnered with the New Hampshire Department of Environmental Services from 2009-2010 to facilitate a woodstove changeout program which replaced 86 inefficient woodstoves with newer or alternate heating appliances. Despite the fact that many U.S. communities have enacted similar programs, research on their effectiveness is limited. This research assessed Keene’s program and determined that Keene has experienced a significant reduction in PM2.5 on calm winter nights. When winds are below 2 miles per hour (3.22 kilometers per hour), PM2.5 dropped 7% to 52% (1.28 to 7.30 µg/m3) after the woodstove changeout; a mean decrease of 23%. It therefore appears that Keene’s woodstove changeout program successfully improved air quality on the nights that are most likely to violate national air quality standards. This provides evidence that such programs can be an effective means to moderating the effects of wood heating in communities susceptible to inversions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".