Air treatment technologies for shelter-in-place scenarios in response to WUI fires
Bibliographic record
Abstract
This report discusses the health impact of wildfires smoke on communities situated at the interface between wildland and urban areas, and the use of available air treatment technologies to mitigate this hazard. Wildfire smoke concerns are increasing with the increase of wildfires events frequency, which is partially attributed to climate change. Emissions from wildfires are chemically and physically complex and represent a high health hazard. Most of the health hazards from smoke are caused by the microscopic particles that can trigger heart attacks, breathing problems and other health issues. In response to wildfires events, communities are either asked to evacuate or seek a shelter-in-place. In order to protect the occupants from the smoke, high Indoor Air Quality (IAQ) should be provided within the shelter. The most commonly used technologies, for air treatment to improve the IAQ, are portable air cleaners and in-duct air filters. However, limited research is conducted to assess the efficiency of these technologies against wildfire smoke. Thus, this report suggests the development of a standard test for assessing air filters against wildfires smoke. Further, the report highly recommends developing guidelines for best practice at homes or community shelters to protect residents from wildfires induced smoke. The report is structured as follow, first types of shelter-in-place as alternative to population evacuation are presented followed by the discussion of the frequency of wildfires in Canada. Next a detailed discussion of emissions from these fires presented followed by discussing health effects of the exposure to wildfires smoke. Subsequently, current technologies for air treatment are discussed. Finally, international and provincial regulations and guidelines for reducing health risk of wildfires smoke are reported.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".