Classifying Nanomaterial Risks Using Multi-Criteria Decision Analysis
| Autor: | Linkov, Igor, Steevens, J., Chappell, M., Tervonen, T., Figueira, J.R., Merad, Myriam |
|---|---|
| Přispěvatelé: | US Army Research and Development Center, Faculty of Economics and Business, University of Groningen [Groningen], Centre for Management Studies, Instituto Superior Técnico, Technical University of Lisbon, Institut National de l'Environnement Industriel et des Risques (INERIS), LINKOV, I., STEEVENS, J. |
| Rok vydání: | 2009 |
| Předmět: |
Decision support system
Engineering 02 engineering and technology QUANTUM DOTS 010501 environmental sciences 01 natural sciences Product lifecycle HEALTH IMPLICATIONS NANOPARTICLES MANAGEMENT Production (economics) CYTOTOXICITY EXPOSURE Robustness (economics) Risk management 0105 earth and related environmental sciences Management science business.industry 021001 nanoscience & nanotechnology Multiple-criteria decision analysis SIZE Risk analysis (engineering) [SDE]Environmental Sciences 0210 nano-technology Risk assessment business Decision analysis |
| Zdroj: | Nanomaterials: Risks and Benefits ISBN: 9781402094903 Nanomaterials : risks and benefits NATO Advanced Research Workshop "Nanomaterials : Environmental Risk and Benefits" NATO Advanced Research Workshop "Nanomaterials : Environmental Risk and Benefits", Apr 2008, Faro, Portugal. pp.179-191, ⟨10.1007/978-1-4020-9491-0_13⟩ |
| DOI: | 10.1007/978-1-4020-9491-0_13 |
| Popis: | International audience; There is rapidly growing interest by regulatory agencies and stakeholders in the potential toxicity and other risks associated with nanomaterials throughout the different stages of the product life cycle (e.g., development, production, use and disposal). Risk assessment methods and tools developed and applied to chemical and biological material may not be readily adaptable for nanomaterials because of the current uncertainty in identifying the relevant physico-chemical and biological properties that adequately describe the materials. Such uncertainty is further driven by the substantial variations in the properties of the original material because of the variable manufacturing processes employed in nanomaterial production. To guide scientists and engineers in nanomaterial research and application as well as promote the safe use/handling of these materials, we propose a decision support system for classifying nanomaterials into different risk categories. The classification system is based on a set of performance metrics that measure both the toxicity and physico-chemical characteristics of the original materials, as well as the expected environmental impacts through the product life cycle. The stochastic multicriteria acceptability analysis (SMAA-TRI), a formal decision analysis method, was used as the foundation for this task. This method allowed us to cluster various nanomaterials in different risk categories based on our current knowledge of nanomaterial's physico-chemical characteristics, variation in produced material, and best professional judgement. SMAA-TRI uses Monte Carlo simulations to explore all feasible values for weights, criteria measurements, and other model parameters to assess the robustness of nanomaterial grouping for risk management purposes. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|