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Society For Risk Analysis Annual Meeting 2009
Risk Analysis: The Evolution of a Science
Session Schedule & Abstracts
* Disclaimer: All presentations represent the views of the authors, and not the organizations that support their research. Please apply the standard disclaimer that any opinions, findings, and conclusions or recommendations in abstracts, posters, and presentations at the meeting are those of the authors and do not necessarily reflect the views of any other organization or agency. Meeting attendees and authors should be aware that this disclaimer is intended to apply to all abstracts contained in this document. Authors who wish to emphasize this disclaimer should do so in their presentation or poster. In an effort to make the abstracts as concise as possible and easy for meeting participants to read, the abstracts have been formatted such that they exclude references to papers, affiliations, and/or funding sources. Authors who wish to provide attendees with this information should do so in their presentation or poster.
Common abbreviations
T2-I |
| Chair(s): Elizabeth Casman |
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T2-I.1 10:30 Estimating Environmental Nanomaterial Emissions from Production Data. Robichaud C.*, Wiesner M.R., Casman E.; C.O.R. and M.R.W. - Duke University; E.C. - Carnegie Melon University cor@duke.edu Abstract: Meaningful assessments of the environmental impact of nanomaterials will require quantitative information about the potential volumes being released. This work presents a methodology for estimating the emission of nanomaterials to the environment based on production. It is inevitable that there will be potential leakage of nanomaterials and their byproducts to the environment from each life cycle phase, with the total source of leakage to the environment being the summation of releases from material production, nano-enabled product fabrication, product use, recycling, and disposal. Our work is based on the assertion that the production volume of a nanomaterial – a first step in the life cycle - will be proportional to the true release and more attainable than a more detailed summation of releases from all stages. Although it is an overly simplified parameter, using material production as a starting point is advantageous because it avoids some layers of assumptions and sources of uncertainty that would be entailed in estimating each of the separate flows and all of their potential permutations. Every successive phase might be expected have an increasing number of different variations as the materials are fabricated and incorporated into a variety of products, each of which may then have a few different applications, each of which may be disposed of and reach the environment in a myriad of ways. By focusing primarily on the production of the nanomaterials themselves and seeking to scale the emissions to the environment as a function of that initial number, we aim to minimize these assumptions and thus arrive at a reasonable estimate of potential exposure. Production volume can be compared with environmental concentration data to arrive at an "emission factor" describing the fraction of produced nanomaterials that are expected to be released. |
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T2-I.2 10:50 A probabilistic network modeling approach for nanoparticle risk assessments. Money ES*, Reckhow KH; Center for the Environmental Implications of NanoTechnology @ Duke University eric.money@duke.edu Abstract: Nanoparticles have become a ubiquitous component of many manufacturing and consumer products over the last decade and the number and type of nanomaterials being used continues to increase. Due to the unique properties of these materials, it has been difficult to accurately assess their potential risks after being released into the environment. The characterization of these particles and their subsequent fate, transport, uptake, and exposure in various environmental media is continuously being investigated, but at the same time government and public stakeholders are becoming increasingly concerned with the potential impacts of these materials and are calling for the development of risk frameworks that can eventually inform decision-makers. Given the amount of uncertainty and the relative infancy of nanoparticle research, there is a need for an adaptable risk assessment framework that can account for this uncertainty and be easily modified as new evidence becomes available. One such framework employs the use of Bayesian networks. This presentation introduces the concepts of Bayesian probabilistic networks and how they can be used within the context of nanoparticle risk assessments. With a combination of expert elicitation and experimental data, we can construct a probabilistic based model that retains the necessary complexity of the system and which is updateable as new data become available. Using Nano-silver as an example, we illustrate both the diagnostic and predictive capabilities of the Bayesian network model. |
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T2-I.3 11:10 Using Risk Ranking and Reasoning by Analogy for Nanoparticle Risk Assessment and Standard Setting. Christian Beaudrie CB*, Milind Kandlikar MK, Terre Satterfield TS, Gurumurthy Ramachandran GR; University of British Columbia christian.beaudrie@gmail.com Abstract: Nanoparticles present a difficult challenge for risk assessors. The uncertainties are extreme and difficult to quantify using conventional risk assessment approaches. Relationships between physical and chemical properties of nanoparticles and their toxicity are poorly understood. Although size metrics define nanoparticles as a class (e.g. diameter < 100 nm), other particle characteristics such as shape/morphology, chemical composition, and coating have been shown to mediate the relationship between size and toxic response. Further, biologically relevant dose and exposure metrics are not known with any reasonable degree of certainty. Additionally, poorly understood physical and chemical processes influence particle concentrations in a medium and so influence exposures. Here we propose an approach to assess risks and aid standard setting for nanoparticles by merging two simple ideas - risk ranking and reasoning by analogy. The approach begins with a pre-defined set of nanoparticles (e.g. SWCNT, Silver NP, Se Quantum dots) which are ranked according to their relative toxicity. Rankings will be based on the literature, but the method could easily be expanded to use expert judgments for the ranks. Known analog particles with existing exposure limits standards are then used to anchor the ranks to absolute values. For example, SWCNTs are seen to be analogous to chrysotile asbestos and so have exposure limits that are at least as low. When more than one analog is available, ranking would need to reconcile information from multiple analogs in a consistent manner. The approach is simple, flexible and uses a "common-sense" approach to risk assessment |
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T2-I.4 11:30 Advances in Laboratory Techniques and Decision Tools to Enhance Environmental Risk Assessments of Engineered Nanomaterials. Johnson D*, Linkov I, Kennedy A, Stanley J, Coleman J, Chappell M, Bednar T, Kirgan R, Steevens J; US Army Engineer Research and Development Center, Vicksburg, MS igor.linkov@usace.army.mil Abstract: Modeling the fate and effects of engineered nanomaterials in the environment is challenging because of the complexities of aquatic and soil systems. We will summarize new methods to identify environmentally, ecologically, and biologically-relevant factors that influence nanomaterial fate and effects. For example, scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX) and synchrotron x-ray tomography can determine the agglomeration state and chemical composition/speciation of nanomaterials in sediments and soils. Simulation of different environmental systems using different exposure scenarios are being used in aquatic, benthic, and terrestrial studies to determine biologically relevant routes of exposure. We will also discuss analytical equipment and methodologies implemented to determine the uptake and distribution of nanomaterials in plants and animals. Furthermore, we are exploring discovery tools used in human drug development to assess effects of nanomaterials in ecological receptors. This presentation will also introduce multi-criteria decision analysis methods and tools for combining interdisciplinary sets of data that cover broad spectra of environmental factors (i.e., geology, soil science, chemistry, ecology, and biology) to classify nanomaterials in different risk groups. Integration of solid environmental data with quantitative classification approaches could reduce uncertainty and support nanomaterial risk assessments. |
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