Removal of particles by portable air cleaners: Implications for residential infection transmission
Bibliographic record
Abstract
Reducing indoor exposure to influenza particles associated with flu can be an important strategy to manage residential infections. Many portable air cleaning (PAC) technologies are currently employed in residential environments but very little research has been performed to evaluate and compare their performance in terms of particle removal associated with influenza. This study evaluates the effectiveness of portable air cleaners at removing airborne NaCl particles as an analogue to the influenza virus and applies the results to an IAQ mass balance model to evaluate the performance in controlling residential exposures and mitigating infection risks. Various devices representing different PAC technologies were tested using a pull down particle challenge in a full scale stainless steel chamber. Particle generation and measurement were conducted using a 6-jet atomizer and a paired aerodynamic particle sizer (APS)-scann ing mobility particle size r (SMPS), respectively. PAC incorporating HEPA filt ration, electrostatic precipitation, ion generation and electret filtration were tested. We found that particle exposures released during a cough or sneeze event in a typical Canadian residential room can significantly be reduced using HEPA, electrostatic precipitation and electret filtration PACs when compared with a situation where no PAC is being used. Modelling analysis demonstrates that the use of these PACs can mitigate the risks of influenza infection via airborne route for a caregiver or a spouse sharing the same room. The implications of this study is significant considering low. © 2011 ASHRAE.
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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".