Research Paper

Journal of Nanoparticle Research

, Volume 11, Issue 4, pp 757-766

First online:

Risk-based classification system of nanomaterials

  • Tommi TervonenAffiliated withFaculty of Economics and Business, University of Groningen
  • , Igor LinkovAffiliated withUS Army Research and Development Center Email author 
  • , José Rui FigueiraAffiliated withCEG-IST, Centre for Management Studies, Instituto Superior Técnico, Technical University of LisbonLAMSADE, Université Paris
  • , Jeffery SteevensAffiliated withUS Army Research and Development Center
  • , Mark ChappellAffiliated withUS Army Research and Development Center
  • , Myriam MeradAffiliated withSocietal Management of Risks Unit/Accidental Risks Division, INERIS BP 2

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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