Predicting accidental release of engineered nanomaterials to the environment.

Autor: Gottschalk F; ETSS AG, Engineering, Technical and Scientific Services, Strada, Switzerland., Debray B; Institut national de l'environment industriel et des risques, Verneuil-en-Halatte, France., Klaessig F; Pennsylvania Bio Nano Systems, Doylestown, PA, USA., Park B; GBP Consulting Ltd, Purton, UK., Lacome JM; Institut national de l'environment industriel et des risques, Verneuil-en-Halatte, France., Vignes A; Institut national de l'environment industriel et des risques, Verneuil-en-Halatte, France., Portillo VP; Leitat-Technological Center de la Innovació, Terrassa, Spain., Vázquez-Campos S; Leitat-Technological Center de la Innovació, Terrassa, Spain., Hendren CO; Center for the Environmental Implications of Nano Technology (CEINT), Duke University, Durham, NC, USA., Lofts S; UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Bailrigg, UK., Harrison S; UK Centre for Ecology & Hydrology, Lancaster Environment Centre, Bailrigg, UK., Svendsen C; UK Centre for Ecology & Hydrology, Wallingford, UK., Kaegi R; Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland. ralf.kaegi@eawag.ch.
Jazyk: angličtina
Zdroj: Nature nanotechnology [Nat Nanotechnol] 2023 Apr; Vol. 18 (4), pp. 412-418. Date of Electronic Publication: 2023 Feb 02.
DOI: 10.1038/s41565-022-01290-2
Abstrakt: Challenges in distinguishing between natural and engineered nanomaterials (ENMs) and the lack of historical records on ENM accidents have hampered attempts to estimate the accidental release and associated environmental impacts of ENMs. Building on knowledge from the nuclear power industry, we provide an assessment of the likelihood of accidental release rates of ENMs within the next 10 and 30 years. We evaluate risk predictive methodology and compare the results with empirical evidence, which enables us to propose modelling approaches to estimate accidental release risk probabilities. Results from two independent modelling approaches based on either assigning 0.5% of reported accidents to ENM-releasing accidents (M1) or based on an evaluation of expert opinions (M2) correlate well and predict severe accidental release of 7% (M1) in the next 10 years and of 10% and 20% for M2 and M1, respectively, in the next 30 years. We discuss the relevance of these results in a regulatory context.
(© 2022. The Author(s), under exclusive licence to Springer Nature Limited.)
Databáze: MEDLINE
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