Elevated Toxic Element Emissions from Popular Disposable E-Cigarettes: Sources, Life Cycle, and Health Risks
Bibliographic record
Abstract
The rapidly evolving market of disposable e-cigarettes poses unknown health risks to adolescents and young adults. We report excessive emissions of toxic metallic elements in aerosols from flavored and "clear" versions of three popular products (Esco Bar, Flum Pebble, and ELF Bar), orders of magnitude higher in concentration than traditional cigarettes and other e-cigarettes. Heating coil elements (chromium (Cr), nickel (Ni)) likely leached into e-liquids and aerosols from coil degradation during use, increasing up to 1000-fold in concentration over the device life. In Esco Bar devices, high concentrations of lead (Pb, ≤175 ppm), Ni (≤38 ppm), copper (Cu, ≤546 ppm), and zinc (Zn, ≤462 ppm) were observed in both e-liquids and aerosols. We identified the illicit use of leaded bronze in nonheating device components in contact with e-liquid as the source of Pb. Elevated antimony (Sb) in Flum Pebble and Esco Bar samples had unknown origins. Analyses showed Cr was present as nontoxic Cr-(III), while Sb was a mixture of nontoxic Sb-(V) and carcinogenic Sb-(III). Risk assessments revealed cancer risks from Ni and Sb-(III) and noncancer toxicity risks from Pb and Ni exceeded safety thresholds. These findings highlight critical gaps in e-cigarette regulation, characterization, and enforcement, with implications for public health.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".