Journal of Nanoparticle Research

, Volume 11, Issue 4, pp 757–766 | Cite as

Risk-based classification system of nanomaterials

  • Tommi Tervonen
  • Igor Linkov
  • José Rui Figueira
  • Jeffery Steevens
  • Mark Chappell
  • Myriam Merad
Research Paper


Various stakeholders are increasingly interested in the potential toxicity and other risks associated with nanomaterials throughout the different stages of a product’s life cycle (e.g., development, production, use, disposal). Risk assessment methods and tools developed and applied to chemical and biological materials 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 due to variable manufacturing processes employed in nanomaterial production. To guide scientists and engineers in nanomaterial research and application as well as to promote the safe handling and use 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. 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 ecological risk categories based on our current knowledge of nanomaterial physico-chemical characteristics, variation in produced material, and best professional judgments. 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.


Nanotechnology Risk assessment Toxicology Decision analysis Governance 



Comments provided by F. Kyle Satterstrom, Jacob Stanley, and David Johnson were very helpful in the preparation of this manuscript. Permission was granted by the Chief of Engineers to publish this information. The studies described and the resulting data presented herein were obtained from research supported by the Environmental Quality Technology Program of the US Army Engineer Research and Development Center (Dr. John Cullinane, Technical Director). The views and opinions expressed in this article are those of the individual authors and not those of the US Army or other sponsor organizations.


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Copyright information

© US Government 2008

Authors and Affiliations

  • Tommi Tervonen
    • 1
  • Igor Linkov
    • 2
  • José Rui Figueira
    • 3
    • 4
  • Jeffery Steevens
    • 5
  • Mark Chappell
    • 5
  • Myriam Merad
    • 6
  1. 1.Faculty of Economics and BusinessUniversity of GroningenGroningenThe Netherlands
  2. 2.US Army Research and Development CenterBrooklineUSA
  3. 3.CEG-IST, Centre for Management Studies, Instituto Superior TécnicoTechnical University of LisbonPorto SalvoPortugal
  4. 4.LAMSADEUniversité ParisParisFrance
  5. 5.US Army Research and Development CenterVicksburgUSA
  6. 6.Societal Management of Risks Unit/Accidental Risks DivisionINERIS BP 2Verneuil-en-HalatteFrance

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