Volatile organic compound and particulate matter emissions from an ultrasonic essential oil diffuser
Bibliographic record
Abstract
Ultrasonic essential oil diffusers (EODs) are a popular type of indoor scenting source. We performed a chamber study in which we measured the emissions from EODs used with lemon, lavender, eucalyptus, and grapeseed oils. Over the course of 15 min, the most abundant VOCs released from lemon, lavender, eucalyptus, and grapeseed oils were 2.6 ± 0.7 mg of d-limonene, 3.5 ± 0.4 mg of eucalyptol, 1.0 ± 0.1 mg of linalyl acetate, and 0.2 ± 0.02 mg of linalyl acetate, respectively. Each oil had a unique particulate matter (PM) emission profile in terms of size, number density, and rate. The dominant size ranges of the PM were 10–100 nm for lemon oil, 50–100 nm for lavender oil, 10–50 nm for lemon oil, and above 200 nm for grapeseed oil. PM1 emission rates of approximately 2 mg/h, 0.1 mg/h, and 3 mg/h, were observed for lemon, lavender/eucalyptus, and grapeseed oils, respectively. A fivefold increase in PM1 emission was measured when the EOD with eucalyptus oil was filled with tap water as opposed to deionized water. Modeling suggests that reasonable use cases of EODs can contribute substantially to primary and secondary PM in indoor environments, but this potential varies depending on the oil and water types 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 | 0.000 | 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) | 0.001 | 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".