RISK ANALYSIS AND MANAGEMENT: NANOSCIENCE David M. Berube Professor of Science Communication, STS, and CRDM (Communication, Rhetoric and Digital Media), North Carolina State University Director: NCSU Public Communication of Science and Technology (PCOST) Project. Manager, Center for Converging Technologies, LLC – social media consultancy (trade assns and food industry). PI: NSF NIRT # – Intuitive Toxicology and Public Engagement, Society for Risk Analysis© Berube December 7, 2010 – Salt Lake City, UT

 Status Quo – regulate under current rules (EPA).  Enhanced regulation  Occup. EHS – promulgate rules for types of nanoparticles.  Product related – promulgate rules by agency mandate – USDA, FDA & CPSC. NANO-GOVERNANCE (1)

 Stipulated regulation.  Safety Data Sheet.  Labeling.  Restricted regulation.  Import-export restrictions by application (WTO).  Outright bans on some or all applications. NANO-GOVERNANCE (2)

1. Hyper-acceleration. 2. Public complexity. 3. Digital obfuscation. 4. Suspect experts/expertise. 5. Fact/fictionality. UNCERTAINTY: OVER-ARCHING VARIABLES

CHARACTERISTICS AND CLASSIFICATION OF NANOPARTICLES: EXPERT DELPHI SURVEY (Nanotoxicology 30 Sep doi: / ).

EXPERT DELPHI SURVEY (Nanotoxicology 30 Sep doi: / )

 RAND – cross-impact matrix (Dalkey 1971)  IRGC – International Risk Governance Council.  CBEN (Rice) and ICON.  CET - Return on Investment. ORIGINS Future assessment tool used in identifying main forces in a firm's environment, and in estimating their collective impacts. Each force or factor is assigned a score (usually between -10 and +10) in a table (matrix) based on its own strength and the strength of its interactions. On adding up, the scores separate the 'driving' forces from 'inhibiting' forces.

 High Uncertainty/High Risk – ETC/Soil Association/FoE-A.  Some Uncertainty/Some Risk – Voluntary Stewardship.  Some Uncertainty/Low Risk – Best/Common/Good Practice.  Low Uncertainty/Low Risk – Self- regulation/Asilomar Model. SCENARIO MODELING High Uncertainty/High Risk – ETC/Soil Association/FoE-A.

R(θ,δ(x)) = ∫ L(θ, δ(x)) f(x|θ) dx  δ(x) = estimator.  Θ = parameter.  L = loss function.  x = observables.  What constitutes data?  How do we collect data?  Are some data privileged over others?  How do we resolve contradictions?  When do we have enough data? ON FUZZINESS finite data

 IF we defined C = ~V then  Violating the law of the Excluded Middle IF C = ~V CV y ON FUZZINESS

I. I. Nomenclature. II. II. Classification. III. III. Monitoring. IV. IV. Characterization. 1.size, 2.composition, 3.surface area to volume, 4.surface coating, 5.shape and surface curvature, 6.surface charge, 7.contaminants and impurities, 8.surface hydrophobicity, 9.surface defects, 10.fibrosity, 11.porosity, and/or 12.density UNCERTAINTY: NANOPARTICLES SETS

 Multiple sets.  About membership among/between sets.  Value of membership from none to total.

 Landfill pollution.  Soil and sediments pollution.  Groundwater contamination.  River water quality management.  Hydrocarbon contamination. ON FUZZINESS  Cadmium in surficial soils.  Dredging.  Flood control.  Nitrate contamination.  Highway bridge safety.

Merge with cross-impact matrix (multi- dimensional) (1)Scientific reasoning model. (2)Human reasoning model. (Tesfemariam & Sadiz 2006)

ON FUZZINESS - PROJECTS  Decide on the sets appropriate for data management.  Decide on the dimensions – characteristics, exposure, etc.  Decide on the method(s) – hybrid approaches of fuzzy set theory in conjunction stochastic techniques. (Maybe, crossing fuzzy set theory with cross-impact matrices.)  Experiment with rounds of estimations.

Society for Risk Analysis© Berube December 7, 2010 – Salt Lake City, UT RISK ANALYSIS AND MANAGEMENT: NANOSCIENCE This work was supported in part by grants from the National Science Foundation, NSF , Nanotechnology Interdisciplinary Research Team (NIRT): Intuitive Toxicology and Public Engagement. NCSU, U Wisconsin, U Minnesota, U South Carolina, & Rice U. (6 grad. students). THANKS