Residential wood smoke: perceptions, health risks, and mitigating exposures
Bibliographic record
Abstract
Almost one in five (19%) Canadian households rely on wood stoves or fireplaces as primary or supplemental sources of heating and cooking, and the emissions from these devices are an often overlooked source of air pollution. Residential wood burning produces the same amount of fine particulate matter (PM 2.5 ) as the transportation and industrial sectors combined across Canada. Exposure to wood smoke can have serious acute and chronic health effects, ranging from symptoms such as headaches, nausea, dizziness, and irritation of the eyes, nose, throat, and lungs, to an increased risk of developing chronic conditions like heart and lung diseases. Although efforts to reduce exposure by replacing old, high-emission wood stoves through education and incentives have been implemented in many jurisdictions across Canada, many of these heavily polluting devices are still regularly in use. In British Columbia alone, only 67% of fireplace inserts and 65% of wood-burning stoves are certified as low-emission. This paper examines the evidence on the disconnect between what people believe about residential wood smoke, and what is actually measured, along with the resulting health risks. We also examined current interventions, good burn practices, and jurisdictional regulations aimed at reducing indoor and outdoor wood smoke exposures. Perceptions of wood smoke were found to be inconsistent with estimated health burdens, and implementation of interventions to reduce wood smoke emissions varied greatly across jurisdictions. However, wood burning still represents the only source of energy for many Canadian households, and adaptive strategies are needed to mitigate health risks.
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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 | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".