Controlling the risks of nano-enabled products through the life cycle: The case of nano copper oxide paint for wood protection and nano-pigments used in the automotive industry.


Journal

Environment international
ISSN: 1873-6750
Titre abrégé: Environ Int
Pays: Netherlands
ID NLM: 7807270

Informations de publication

Date de publication:
10 2019
Historique:
received: 25 03 2019
revised: 31 05 2019
accepted: 05 06 2019
pubmed: 8 7 2019
medline: 28 2 2020
entrez: 8 7 2019
Statut: ppublish

Résumé

The widespread use of engineered nanomaterials (ENMs) in consumer products and the overwhelming uncertainties in their ecological and human health risks have raised concerns regarding their safety among industries and regulators. There has been an ongoing debate over the past few decades on ways to overcome the challenges in assessing and mitigating nano-related risks, which has reached a phase of general consensus that nanotechnology innovation should be accompanied by the application of the precautionary principle and best practice risk management, even if the risk assessment uncertainties are large. We propose a quantitative methodology for selecting the optimal risk control strategy based on information about human health and ecological risks, efficacy of risk mitigation measures, cost and other contextual factors. The risk control (RC) methodology was developed in the European FP7 research project SUN and successfully demonstrated in two case studies involving real industrial nano-enabled products (NEPs): nano-scale copper oxide (CuO) and basic copper carbonate (Cu

Identifiants

pubmed: 31279910
pii: S0160-4120(19)30986-9
doi: 10.1016/j.envint.2019.06.011
pii:
doi:

Substances chimiques

Anti-Bacterial Agents 0
Carbonates 0
Coloring Agents 0
Fungicides, Industrial 0
Soot 0
Copper 789U1901C5
copper carbonate 9AOA5F11GJ
cupric oxide V1XJQ704R4

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

104901

Informations de copyright

Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

Auteurs

Elena Semenzin (E)

Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30170 Mestre-Venezia, Italy.

Vrishali Subramanian (V)

Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30170 Mestre-Venezia, Italy.

Lisa Pizzol (L)

Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30170 Mestre-Venezia, Italy.

Alex Zabeo (A)

Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30170 Mestre-Venezia, Italy.

Wouter Fransman (W)

TNO, Utrechtseweg 48, NL-3704 HE Zeist, the Netherlands.

Ceyda Oksel (C)

Institute of Particle Science and Engineering, School of Process, Environmental and Materials Engineering, University of Leeds, Leeds, UK.

Danail Hristozov (D)

Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30170 Mestre-Venezia, Italy.

Antonio Marcomini (A)

Department of Environmental Sciences, Informatics and Statistics, Ca' Foscari University of Venice, Via Torino 155, 30170 Mestre-Venezia, Italy. Electronic address: marcom@unive.it.

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Classifications MeSH