Heavy metals in ENDS: a comparison of open versus closed systems purchased from the USA, England, Canada and Australia
Bibliographic record
Abstract
INTRODUCTION: Electronic nicotine delivery systems (ENDS) are known to contain heavy metals such as lead (Pb), nickel (Ni) and chromium (Cr). The presence of heavy metals in ENDS may be due to contamination of e-liquids or leaching from elements of the ENDS device. This study investigates differences in ENDS metal concentrations between product type, year of purchase, country of purchase and e-liquid flavour. METHODS: Various open-system (refill e-liquids; n=116) and closed-system (prefilled with e-liquid; n=120) products were purchased in 2017 and 2018 from the USA, England, Canada and Australia. Electrothermal atomic absorption spectroscopy was used to analyse each product for Pb, Ni and Cr. Multiple linear regression and Kruskal-Wallis non-parametric statistical tests were conducted using GraphPad. RESULTS: Linear regression showed system type, year of purchase (not supported by Kruskal-Wallis), country of purchase and flavour type each had significant impacts on heavy metal concentrations. Open-system e-liquid samples showed no quantifiable levels of heavy metals. Closed-system samples contained concerningly high concentrations of Pb, Ni and Cr. Closed-system samples from the USA commonly displayed higher average heavy metal concentrations than those from England. Some fruit and mint-flavoured closed-system products showed higher heavy metal concentrations than tobacco-flavoured products. CONCLUSION: The presence of heavy metals only in closed-system products suggests that metals may be leaching from ENDS device parts. Highly variable heavy metal concentrations between ENDS products demonstrate that various product characteristics may affect the degree of leaching and that there is a need for further regulation of these products.
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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 | 0.001 | 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".