Classifying Nanomaterial Risks Using Multi-Criteria Decision Analysis

Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

Abstract

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.1,2

Keywords

Product Life Cycle Multiple Criterion Decision Analysis Preference Threshold Acceptability Index Indifference Threshold 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Auffan, M., J. Rose, T. Orsiere, M. De Meo, W. Achouak, C. Chaneac, J.-P. Joliver, A. Thill, O. Spalla, O. Zeyons, A. Maison, J. Labille, J.-L. Hazeman, O. Proux, V. Briois, A.-M. Flank, A. Botta, M.R. Wiesner, and J.-Y. Bottero, 2008. Surface Reactivity of Nano-Oxides and Biological Impacts Nanoparticles in the Environment: Implications and Applications. Centro Stefano Fracnscini, Monte Verita, Ascona, Switzerland.Google Scholar
  2. 2.
    Belton, V., and T.J. Stewart, 2002. Multiple Criteria Decision Analysis — An Integrated Approach. Kluwer, Dordrecht.Google Scholar
  3. 3.
    Biswas, P. , and C.-Y. Wu, 2005. Nanoparticles and the environment. Journal of the Air&Waste Management Association 55, 708–746.Google Scholar
  4. 4.
    Braydich-Stolle, L., S. Hussain, J.J. Schlager, and M. Hofmann, 2007. In vitro cytotoxicity of nanoparticles in mammalian germline stem cells. Toxicological Sciences 88(2), 412–419.CrossRefGoogle Scholar
  5. 5.
    Borm, P., and D . Müller-Schulte, 2006. Nanoparticles in drug delivery and environmental exposure: same size, same risks? Nanomedicine 1(2), 235–249.PubMedCrossRefGoogle Scholar
  6. 6.
    Borm, P., D. Robbins, S. Haubold, T. Kuhlbusch, H. Fissan, K. Donaldson, R. Schins, V . Stone, W . Kreyling, J. Lademann, J . Krutmann, D . Warheit, and E. Oberdorster, 2006. The potential risks of nanomaterials: a review carried out for ECETOC. Particle and Fibre Toxicology 3(11).Google Scholar
  7. 7.
    Bortoleto, G.G., S.S.O. Borges, and M.I.M.S. Bueno, 2007. X-ray scattering and multivariate analysis for classification of organic samples: a comparitive study using Rh tube and synchroton radiation. Analytica Chimica Acta 595(1–2), 38–42.PubMedCrossRefGoogle Scholar
  8. 8.
    Bowden, J.W., A.M. Posner, and J.P. Quirk, 1977. Ionic adsorption on variable charge mineral surfaces. Theoretical-charge development and titration curves. Australian Journal of Soil Research 15, 121.Google Scholar
  9. 9.
    Chappell, M.A., A.J. George, B.E. Porter, C.L. Price, K.M. Dontsova, A.J. Kennedy, and J.A. Steevens. 2008. Surfactive Properties of Dissolved Soil Humic Substances for Stabilizing Multi-Walled Carbon Nanotubes Dispersions Nanoparticles in the Environment: Implications and Applications. Centro Stefano Franscini, Monte Verita, Ascona, Switzerland.Google Scholar
  10. 10.
    Chen, Q., C. Saltiel, S. Manickavasagam, L.S. Schadler, R.W. Siegel, and H. Yang. 2004. Aggregation behavior of single-walled carbon nanotubes in dilute aqueous suspension. Journal of Colloid Interface Science 280, 91–97.CrossRefGoogle Scholar
  11. 11.
    Derfus, A.M., W.C. Chan, and S.N. Bhatia, 2004. Probing the cytotoxicity of semiconductor quantum dots. Nano Letters 4, 11–18.CrossRefGoogle Scholar
  12. 12.
    Erbs, J.J., T.S. Berquo, B. Gilbert, T.L. Jentzsch, S.K. Banerjee, and R.L. Penn, 2008. Reactivity of Iron and Iron Oxide Nanoparticles in the Environment: Implications and Applications. Centro Stefano Franscini, Monte Verita, Ascona, Switzerland.Google Scholar
  13. 13.
    Evangelou, V.P., 1998. Environmental Soil and Water Chemistry: Principles and Applications. Wiley, New York.Google Scholar
  14. 14.
    Figueira, J., S. Greco, and M. Ehrgott (Eds.), 2005a. Multiple Criteria Decision Analysis: State of the Art Surveys. Springer Science+Business Media, New York.MATHGoogle Scholar
  15. 15.
    Figueira, J., V. Mousseau, and B. Roy, 2005b. ELECTRE methods. In: J. Figueira, S. Greco, and M. Ehrgott (Eds.), Multiple Criteria Decision Analysis: State of the Art Surveys. Springer Science+Business Media, New York, Ch. 4.Google Scholar
  16. 16.
    Grabinski, C., S. Hussain, K. Lafdi, L. Braydich-Stolle, and J. Schlager. 2007. Effect of particle dimension on biocompatibility of carbon nanomaterials. Carbon 45, 2828– 2835.CrossRefGoogle Scholar
  17. 17.
    Gwinn, M., and V. Vallyathan, 2006. Nanoparticles: health effects — pros and cons. Environmental Health Perspectives 114(2), 1818–1825.PubMedGoogle Scholar
  18. 18.
    Hofstetter, T.B., R.P. Schwarzenbach, and S.B. Haderlein, 2003. Reactivity of Fe(II) species associated with clay minerals. Environmental Science & Technology 37, 519– 528.CrossRefGoogle Scholar
  19. 19.
    Joo, S.H., A.J. Feitz, and T.D. Waite, 2004. Oxidative degradation of the carbothioate herbicide, monlinate, using nanoscale zero-valent iron. Environmental Science & Technology 38, 2242–2247.CrossRefGoogle Scholar
  20. 20.
    Kashiwada, S., 2006. Distribution of nanoparticles in the see-through medaka (Oryzias latipes). Environmental Health Perspectives 114, 1697–1702.PubMedGoogle Scholar
  21. 21.
    Kreyling, W., M. Semmler-Behnke, and W. Möller, 2006. Health implications of nanoparticles. Journal of Nanomaterial Research 8, 543–562.CrossRefGoogle Scholar
  22. 22.
    Linkov, I., K. Satterstrom, G. Kiker, C. Batchelor, and T. Bridges, 2006. From comparative risk assessment to multi-criteria decision analysis and adaptive management: recent developments and applications. Environment International 32, 1072–1093.PubMedCrossRefGoogle Scholar
  23. 23.
    Medina, C., M. Santos-Martinez, A. Radomski, O. Corrigan, and M. Radomski, 2007. Nanoparticles: pharmacological and toxicological significance. British Journal of Pharmacology 150, 552–558.PubMedCrossRefGoogle Scholar
  24. 24.
    Medinsky, M.A., and J.L. Valentine, 2001. Chapter 7. Toxicokinetics. In Casarett and Doull's Toxicology, 6th Edition. Klaassen, C.D. Ed.; McGraw-Hill. New York. 230 pp.Google Scholar
  25. 25.
    Merad, M.M., T. Verdel, B. Roy, and S. Kouniali, 2004. Use of multi-criteria decision-aids for risk zoning and management of large area subjected to mining-induced hazards. Tunnelling and Underground Space Technology 19(2), 125–138.CrossRefGoogle Scholar
  26. 26.
    Murr, L.E., K.M. Garza, K.F. Soto, A. Carrasco, T.G. Powell, D.A. Ramirez, P.A. Guerero, D.A. Lopez, and J. Venzor, 2005. Cytotoxicity assessment of some carbon nanotubes and related carbon nanoparticle aggregates and the implications for anthropogenic carbon nanotube aggregates in the environment. International Journal of Environmental Research and Public Health 2(1), 31–42.PubMedCrossRefGoogle Scholar
  27. 27.
    Nel, A., T. Xia, L. Mädler, and N. Li, 2006. Toxic potential of materials at the nanolevel. Science 311, 622–627.PubMedCrossRefADSGoogle Scholar
  28. 28.
    Oberdörster, G., V. Stone, and K. Donaldson, 2007. Toxicology of nanoparticles: a historical perspective. Nanotoxicology 1(1), 2–25.CrossRefGoogle Scholar
  29. 29.
    Powers, K., M . Palazuelos, B . Moudgil, and S . Roberts, 2007. Characterization of the size, shape, and state dispersion of nanoparticles for toxicological studies. Nanotoxicology 1(1), 42–51.CrossRefGoogle Scholar
  30. 30.
    Ryman-Rasmussen, J.P., J.E. Riviere, and N.A. Monterio-Riviere, 2006. Penetration of intact skin by quantum dots with diverse physicochemical properties. Toxicological Sciences 91, 159–165.PubMedCrossRefGoogle Scholar
  31. 31.
    Tervonen, T., 2007. New directions in stochastic multicriteria acceptability analysis. Ph.D. thesis, Annales Universitatis Turkuensis AI:376, University of Turku, Finland.Google Scholar
  32. 32.
    Tervonen, T., J.R. Figueira, R. Lahdelma, J. Almeida Dias, and P. Salminen, 2009. A stochastic method for robustness analysis in sorting problems. European Journal of Operational Research 191(1), 236–242.CrossRefGoogle Scholar
  33. 33.
    Tervonen, T., and R. Lahdelma, 2007. Implementing stochastic multicriteria acceptability analysis. European Journal of Operational Research 178 (2), 500–513.MATHCrossRefGoogle Scholar
  34. 34.
    Tervonen, T., and J.R. Figueira, 2008. A survey on stochastic multicriteria acceptability analysis methods. Journal of Multi-Criteria Decision Analysis 15(1–2), 1–14.CrossRefGoogle Scholar
  35. 35.
    Thomas, K., and P. Sayre, 2005. Research strategies for safety evaluation of nano-materials, part I: evaluating the human health implications of exposure to nanoscale materials. Toxicological Sciences 87(2), 316–321.PubMedCrossRefGoogle Scholar
  36. 36.
    Uehara, G., and G. Gillman, 1981. The Mineralogy, Chemistry, and Physics of Tropical Soils with Variable Charged Clays. Westview Press, Boulder, CO.Google Scholar
  37. 37.
    Unfried, K., C. Albrecht, L. Klotz, A. Von Mikecz, S. Grether-Beck, and R.P.F. Shins, 2007. Cellular responses to nanoparticles: target structures and mechanisms. Nanotoxicology 1, 52–71.CrossRefGoogle Scholar

Copyright information

© Springer Science + Business Media B.V. 2009

Authors and Affiliations

  1. 1.US Army Research and Development CenterCEERD-EP-R VicksburgUSA
  2. 2.Faculty of Economics and BusinessUniversity of GroningenAV GroningenThe Netherlands
  3. 3.CEG-IST, Centre for Management Studies Instituto Superior TécnicoTechnical University of LisbonPorto SalvoPortugal
  4. 4.Societal Management of Risks Unit/Accidental Risks Division —France

Personalised recommendations