Health risk assessment for nanoparticles: a case for using expert judgment.
Kandlikar-M; Ramachandran-G; Maynard-A; Murdock-B; Toscano-WA
J Nanoparticle Res 2007 Sep; 9(1):137-156
Uncertainties in conventional quantitative risk assessment typically relate to values of parameters in risk models. For many environmental contaminants, there is a lack of sufficient information about multiple components of the risk assessment framework. In such cases, the use of default assumptions and extrapolations to fill in the data gaps is a common practice. Nanoparticle risks, however, pose a new form of risk assessment challenge. Besides a lack of data, there is deep scientific uncertainty regarding every aspect of the risk assessment framework: (a) particle characteristics that may affect toxicity; (b) their fate and transport through the environment; (c) the routes of exposure and the metrics by which exposure ought to be measured; (d) the mechanisms of translocation to different parts of the body; and (e) the mechanisms of toxicity and disease. In each of these areas, there are multiple and competing models and hypotheses. These are not merely parametric uncertainties but uncertainties about the choice of the causal mechanisms themselves and the proper model variables to be used, i.e., structural uncertainties. While these uncertainties exist for PM2.5 as well, risk assessment for PM2.5 has avoided dealing with these issues because of a plethora of epidemiological studies. However, such studies don't exist for the case of nanoparticles. Even if such studies are done in the future, they will be very specific to a particular type of engineered nanoparticle and not generalizable to other nanoparticles. Therefore, risk assessment for nanoparticles will have to deal with the various uncertainties that were avoided in the case of PM2.5. Consequently, uncertainties in estimating risks due to nanoparticle exposures may be characterized as "extreme". This paper proposes a methodology by which risk analysts can cope with such extreme uncertainty. One way to make these problems analytically tractable is to use expert judgment approaches to study the degree of consensus and/or disagreement between experts on different parts of the exposure-response paradigm. This can be done by eliciting judgments from a wide range of experts on different parts of the risk causal chain. We also use examples to illustrate how studying expert consensus/disagreement helps in research prioritization and budget allocation exercises. The expert elicitation can be repeated over the course of several years, over which time, the state of scientific knowledge will also improve and uncertainties may possibly reduce. Results from expert the elicitation exercise can be used by risk managers or managers of funding agencies as a tool for research prioritization.
Aerosol-particles; Aerosols; Airborne-particles; Air-contamination; Bacteria; Bacterial-disease; Bacterial-dusts; Bacterial-infections; Biological-effects; Cell-biology; Demographic-characteristics; Engineering; Engineering-controls; Environmental-exposure; Environmental-factors; Environmental-hazards; Environmental-health; Exposure-assessment; Exposure-levels; Exposure-methods; Immune-reaction; Immune-system; Inhalants; Inhalation-studies; Personal-protection; Physiological-effects; Physiological-factors; Physiological-response; Pollution; Protective-equipment; Protective-measures; Public-health; Quantitative-analysis; Risk-analysis; Risk-factors; Safety-measures; Safety-practices; Statistical-analysis; Surface-properties; Water-analysis; Work-environment; Workplace-studies; Work-practices; Nanotechnology;
Author Keywords: nanoparticle health risks; deep uncertainty; parametric uncertainty; model uncertainty; probabilistic expert judgment; degree of expert consensus; occupational health
Gurumurthy Ramachandran, Division of Environmental Health Sciences, School of Public Health, University of Minnesota, Minneapolis, MN
Journal of Nanoparticle Research
University of Minnesota Twin Cities