Chemical analysis and hazard identification of the most common electronic cigarette liquids in nine European countries
Notice bibliographique
Résumé
Background We aimed to detect the composition and reported chemical health hazards of the most common electronic cigarette liquids (e-liquids) in nine European Union (EU) Member States (MS) prior to adoption of the Tobacco Product Directive (TPD). Material and Methods Within the Horizon2020, EUREST-PLUS study, 122 of the most commonly used e-liquids were purchased from 9 EU MS. Chromatography - mass spectrometry and liquid chromatography - mass spectrometry methods were used to analyze the samples. Among the most frequently detected compounds (detected ≥4 times), Danger Globally Harmonized System of Classification and Labelling of Chemicals (GHS) and Warning GHS codes were identified. Results During the samples analysis, several discrepancies in nicotine concentration were detected among the samples from the 9 EU MS. French samples contained an average of 21.9% more nicotine than labelled, while Romanian samples contained an average of 22.5% less nicotine than labelled. In addition, in the 9.8% of the samples, the nicotine concentration exceeded the limit of 20 mg/ml. With regards to the samples’ composition, 171 different compounds were identified and detected 750 times in total while we did not identify samples positive for PAHs or nitrosamines. Finally from the 171 substances, only 5 (10.4%), (Oxime-, methoxy-phenyl, +/-.-.alpha.-Methylbenzyl acetate, 1,3-Dioxolane, 2-butyl-4-methyl-, Melonal and l-Menthyl acetate) were not associated with a Danger GHS and Warning GHS codes. Conclusions As large number of potential harmful compounds was identified, the systematic monitoring and chemical evaluation of e-liquids is necessary in order to protect the consumers’ health. Acknowledgements EUREST-PLUS is a Horizon2020 project conducted by researchers throughout Europe from both the six participating countries as well as other institution partners within Europe and abroad. Partnering organizations include the European Network on Smoking Prevention (Belgium), Kings College London (United Kingdom), German Cancer Research Centre (Germany), University of Maastricht (The Netherlands), University of Athens (Greece), Aer Pur Romania (Romania), European Respiratory Society (Switzerland), the University of Waterloo (Canada), the Catalan Institute of Oncology (Catalonia, Spain), Smoking or Health Hungarian Foundation (Hungary), Health Promotion Foundation (Poland), University of Crete (Greece), and Kantar Public Brussels (Belgium). Funding The EUREST-PLUS Project takes place with the financial support of the European Commission, Horizon 2020 HCO-6-2015 program (EUREST-PLUS: 681109; C. Vardavas) and the University of Waterloo (GT. Fong). Additional support was provided to the University of Waterloo by the Canadian Institutes of Health Research (FDN-148477). GT. Fong was supported by a Senior Investigator Grant from the Ontario Institute for Cancer Research. E. Fernández is partly supported by Ministry of Universities and Research, Government of Catalonia (2017SGR139) and by the Instituto Carlos III and co-funded by the European Regional Development Fund (FEDER) (INT16/00211 and INT17/00103), Government of Spain.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».