Removal of ultrafine particles in indoor air: Performance of various portable air cleaner technologies
Bibliographic record
Abstract
Ultrafine particle (UFP) exposures have been associated with human morbidity and mortality. The removal of UFP from indoor air using portable air cleaners (PACs) of various technologies has not been studied in detail. In this study, 12 devices representing different PAC technologies were tested with an UFP challenge in a full-scale stainless-steel chamber. UFP generation and measurements were conducted using a six-jet atomizer and scanning mobility particle sizer, (SMPS) respectively. It was found that high-efficiency particulate air (HEPA) and electrostatic precipitator (ESP) PACs have the best performance in terms of UFP removal rate, with an electret-based PAC also showing comparably high removal rates. Using modeling based on the experimental findings, some PAC technologies were shown to be effective in reducing indoor UFP concentrations in a typical Quebec City residential room by a factor of about 90%. Negative and bi-polar ion generators were found to have mediocre UFP removal performance, while photocatalytic oxidation-, ozone generation- and ultraviolet germicidal irradiation (UVGI)-based PACs had very limited or no UFP removal capabilities. Estimates of costs per performance index (Capital + Operating Costs/Calculated Clean Air Delivery RateCADR) showed that the HEPA-1, ESP- and electrets (FEF)based PACs provided the highest value for money in terms of total UFP removal performance.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".