Vacuum Cleaner Emissions as a Source of Indoor Exposure to Airborne Particles and Bacteria
Bibliographic record
Abstract
Vacuuming can be a source of indoor exposure to biological and nonbiological aerosols, although there are few data that describe the magnitude of emissions from the vacuum cleaner itself. We therefore sought to quantify emission rates of particles and bacteria from a large group of vacuum cleaners and investigate their potential determinants, including temperature, dust bags, exhaust filters, price, and age. Emissions of particles between 0.009 and 20 μm and bacteria were measured from 21 vacuums. Ultrafine (<100 nm) particle emission rates ranged from 4.0 × 10(6) to 1.1 × 10(11) particles min(-1). Emission of 0.54-20 μm particles ranged from 4.0 × 10(4) to 1.2 × 10(9) particles min(-1). PM(2.5) emissions were between 2.4 × 10(-1) and 5.4 × 10(3) μg min(-1). Bacteria emissions ranged from 0 to 7.4 × 10(5) bacteria min(-1) and were poorly correlated with dust bag bacteria content and particle emissions. Large variability in emission of all parameters was observed across the 21 vacuums, which was largely not attributable to the range of determinant factors we assessed. Vacuum cleaner emissions contribute to indoor exposure to nonbiological and biological aerosols when vacuuming, and this may vary markedly depending on the vacuum used.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".