Emission of airborne nanoparticles from electric motors of household appliances
Bibliographic record
Abstract
Abstract Nanoparticulate (ultrafine particle) indoor air pollution is an emerging concern. Evidence points to airborne nanoparticles’ potential adverse effects, including the impact on blood pressure, the pulmonary and cardiovascular systems, cognitive performance, oxidative stress, allergen sensitization, and inflammation. Nanoparticles originate from various sources. However, no study to date investigated emissions of nanoparticles and fine particles from electric motors in household appliances, ubiquitous indoors. This study fills this knowledge gap with an investigation of incidental emission of aerosol particles from seven electric motors taken from household appliances. The appliances were made by several different manufacturers and tested at the respective appliance’s maximum and minimum power settings as intended for use by the consumer. Aerosols were continuously measured and characterized during 24-hour measurement periods using a NanoScan scanning mobility particle sizer (SMPS). Two of the seven motors (AP B and AP G) emitted very few particles, with an average total number concentration below 10 cm − 3 . One electric motor (AP E) emitted over 170 times more aerosol particles at the maximum power setting compared to the minimum power setting. Another motor (AP A) had the highest emission of all motors at both the minimum and the maximum power settings. The total number concentration of aerosol particles exceeded 2700 cm − 3 and 3900 cm − 3 when operating two of the investigated electric motors (AP A and AP E, respectively) at the maximum power setting. We recommend that manufacturers of electric motors and household appliances test fine and ultrafine aerosol particle emissions from their products and address the problem. The design of household appliances equipped with electric motors should consider low-aerosol-emission motors and/or the installation of high-efficiency air filters in the motor air cooling duct(s) downstream of the electric motors.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".