Aquatic toxicity of manufactured nanomaterials: challenges and recommendations for future toxicity testing
Notice bibliographique
Résumé
Environmental context The increased use of nanomaterials in industrial and consumer products requires robust strategies to identify risks when they are released into the environment. Aquatic toxicologists are beginning to possess a clearer understanding of the chemical and physical properties of nanomaterials in solution, and which of the properties potentially affect the health of aquatic organisms. This review highlights the main challenges encountered in aquatic nanotoxicity testing, provides recommendations for overcoming these challenges, and discusses recent studies that have advanced our understanding of the toxicity of three important OECD nanomaterials, titanium dioxide, zinc oxide and silver nanomaterials. Abstract Aquatic nanotoxicologists and ecotoxicologists have begun to identify the unique properties of the nanomaterials (NMs) that potentially affect the health of wildlife. In this review the scientific aims are to discuss the main challenges nanotoxicologists currently face in aquatic toxicity testing, including the transformations of NMs in aquatic test media (dissolution, aggregation and small molecule interactions), and modes of NM interference (optical interference, adsorption to assay components and generation of reactive oxygen species) on common toxicity assays. Three of the major OECD (Organisation for Economic Co-operation and Development) priority materials, titanium dioxide (TiO2), zinc oxide (ZnO) and silver (Ag) NMs, studied recently by the Natural Sciences and Engineering Research Council of Canada (NSERC), National Research Council of Canada (NRC) and the Business Development Bank of Canada (BDC) Nanotechnology Initiative (NNBNI), a Canadian consortium, have been identified to cause both bulk effect, dissolution-based (i.e. free metal), or NM-specific toxicity in aquatic organisms. TiO2 NMs are most toxic to algae, with toxicity being NM size-dependent and principally associated with binding of the materials to the organism. Conversely, dissolution of Zn and Ag NMs and the subsequent release of their ionic metal counterparts appear to represent the primary mode of toxicity to aquatic organisms for these NMs. In recent years, our understanding of the toxicological properties of these specific OECD relevant materials has increased significantly. Specifically, researchers have begun to alter their experimental design to identify the different behaviour of these materials as colloids and, by introducing appropriate controls and NM characterisation, aquatic nanotoxicologists are now beginning to possess a clearer understanding of the chemical and physical properties of these materials in solution, and how these materials may interact with organisms. Arming nanotoxicologists with this understanding, combined with knowledge of the physics, chemistry and biology of these materials is essential for maintaining the accuracy of all future toxicological assessments.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,000 | 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 ».