A Systematic Review of Reported Exposure to Engineered Nanomaterials
Notice bibliographique
Résumé
BACKGROUND: Engineered nanomaterials (ENMs) have a large economic impact in a range of fields, but the concerns about health and safety of occupational activities involving nanomaterials have not yet been addressed. Monitoring exposure is an important step in risk management. Hence, the interest for reviewing studies that reported a potential for occupational exposure. METHODS: We systematically searched for studies published between January 2000 and January 2015. We included studies that used a comprehensive method of exposure assessment. Studies were grouped by nanomaterial and categorized as carbonaceous, metallic, or nanoclays. We summarized data on task, monitoring strategy, exposure outcomes, and controls in a narrative way. For each study, the strength of the exposure assessment was evaluated using predetermined criteria. Then, we identified all exposure situations that reported potential occupational exposure based on qualitative or quantitative outcomes. Results were synthesized and general conclusion statements on exposure situations were formulated. The quality of evidence for the conclusion statements was rated as low, moderate, or high depending on the number of confirmed exposure situations, the strength of the exposure assessment, and the consistency of the results. RESULTS: From the 6403 references initially identified, 220 were selected for full-text screening. From these, 50 studies describing 306 exposure situations in 72 workplaces were eligible for inclusion (27 industrial-scale plants and 45 research or pilot-scale units). There was a potential for exposure to ENMs in 233 of the exposure situations. Exposure occurred in 83% (N = 107) of the situations with carbonaceous ENMs, in 73% (N = 120) of those with metallic ENMs and in 100% (N = 6) of those with nanoclay. Concentrations of elemental carbon in the workers' breathing zone ranged from not detected (ND) to 910 µg m(-3) with local engineering controls (LEC), and from ND to 1000 µg m(-3) without those controls. For carbon nanofibres (CNFs), particle counts ranged from ND to 1.61 CNF structures cm(-3) with LEC, and from 0.09 to 193 CNF structures cm(-3) without those controls. The mass concentrations of aluminium oxide, titanium dioxide, silver, and iron nanoparticles (NPs) were ND, 10-150, 0.24-0.43, and 32 µg m(-3) with LEC, while they were <0.35, non-applicable, 0.09-33, and 335 µg m(-3) without those controls, respectively. CONCLUSIONS: Regarding the potential of exposure in the workplace, we found high-quality evidence for multiwalled carbon nanotubes (CNTs), single-walled CNTs, CNFs, aluminium oxide, titanium dioxide, and silver NPs; moderate-quality evidence for non-classified CNTs, nanoclays, and iron and silicon dioxide NPs; low-quality evidence for fullerene C60, double-walled CNTs, and zinc oxide NPs; and no evidence for cerium oxide NPs. We found high-quality evidence that potential exposure is most frequently due to handling tasks, that workers are mostly exposed to micro-sized agglomerated NPs, and that engineering controls considerably reduce workers' exposure. There was moderate-quality evidence that workers are exposed in secondary manufacturing industrial-scale plants. There was low-quality evidence that workers are exposed to airborne particles with a size <100nm. There were no studies conducted in low- and middle-income countries.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».