Multi-criteria decision analysis and environmental risk assessment for nanomaterials View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2007-08

AUTHORS

Igor Linkov, F. Kyle Satterstrom, Jeffery Steevens, Elizabeth Ferguson, Richard C. Pleus

ABSTRACT

Nanotechnology is a broad and complex discipline that holds great promise for innovations that can benefit mankind. Yet, one must not overlook the wide array of factors involved in managing nanomaterial development, ranging from the technical specifications of the material to possible adverse effects in humans. Other opportunities to evaluate benefits and risks are inherent in environmental health and safety (EHS) issues related to nanotechnology. However, there is currently no structured approach for making justifiable and transparent decisions with explicit trade-offs between the many factors that need to be taken into account. While many possible decision-making approaches exist, we believe that multi-criteria decision analysis (MCDA) is a powerful and scientifically sound decision analytical framework for nanomaterial risk assessment and management. This paper combines state-of-the-art research in MCDA methods applicable to nanotechnology with a hypothetical case study for nanomaterial management. The example shows how MCDA application can balance societal benefits against unintended side effects and risks, and how it can also bring together multiple lines of evidence to estimate the likely toxicity and risk of nanomaterials given limited information on physical and chemical properties. The essential contribution of MCDA is to link this performance information with decision criteria and weightings elicited from scientists and managers, allowing visualization and quantification of the trade-offs involved in the decision-making process. More... »

PAGES

543-554

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11051-007-9211-0

DOI

http://dx.doi.org/10.1007/s11051-007-9211-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1049818332


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