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From cradle-to-grave at the nanoscale : expert risk perceptions, decision-analysis, and life cycle regulation… Beaudrie, Christian Earl Henry 2013

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FROM CRADLE-TO-GRAVE AT THE NANOSCALE: EXPERT RISK PERCEPTIONS, DECISION-ANALYSIS, AND LIFE CYCLE REGULATION FOR EMERGING NANOTECHNOLOGIES by Christian Earl Henry Beaudrie  B.E.Sc., B.Sc., University of Western Ontario, 2001 M.Eng., McGill University, 2005  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY
 in
 THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  February 2013  © Christian Earl Henry Beaudrie, 2013  Abstract Engineered nanomaterials (ENMs) promise great benefits for society, yet our knowledge of potential risks and best practices for regulation are still in their infancy. High uncertainty and novel ENM properties complicate the management of risk, rendering existing regulatory frameworks inadequate. This thesis investigates the challenges that nanotechnologies pose for risk regulation, and aims to inform the development of policies and practices to address these challenges.  In chapter 2, US federal environmental, health and safety (EHS) regulations are analyzed using a life cycle framework, to evaluate their adequacy as applied to ENMs. This analysis reveals that life cycle risk management of nanomaterials under existing regulations is plagued with difficulty, and populated by myriad gaps through which ENM may escape federal oversight altogether.  Chapters 3 and 4 examine expert opinions on risks, and perceptions of regulatory agency preparedness to manage risks, using a web-based survey (N=404) of US and Canadian nanotechnology experts. Risk and preparedness perceptions were found to differ significantly across groups of experts. Nano-scientists and engineers were more than twice as likely as nano-regulators to believe that benefits from nanotechnology would greatly exceed risk. Yet, those working in regulatory agencies were far more likely to regard government agencies as unprepared than were experts outside government. These differences were explained by expert views of the novelty of benefits and risks, attitudes toward other classes of risk, preferred approaches to regulation, experts’ degree of economic conservatism, and trust in regulatory agencies.  Recognizing the myriad challenges for risk regulation, chapter 5 explores the use of decision-analytic models to cope with uncertainty. Drawing on baseline data monitoring efforts of the US EPA and California DTSC, this chapter argues for the use of novel decision-analytic tools and approaches (such as risk ranking, multi-criteria decision  ii  analysis, and “control banding”) in lieu of formal risk assessment to meet regulators’ goals in particular decision contexts.  Considered together, this thesis concludes that oversight can be improved through pending regulatory reforms, the utilization of expert opinion to inform decision-making, and the development of improved decision-analytic tools that enable the assessment and management of risks under high uncertainty.  iii  Preface Each of the chapters of this dissertation, with the exception of Chapters 1, 6, and 7, were originally written as manuscripts for publication in peer reviewed journals. Chapter 6 was written for publication as a chapter in an edited book. Details for co-authorship of Chapters 2 through 6 are outlined below.  Chapter 2. A version of this chapter was developed as a manuscript and has been submitted to an academic journal. I developed the research questions and methodology with the support of Dr. Jody Roberts (Chemical Heritage Foundation, Philadelphia), Dr. Milind Kandlikar, and Dr. Terre Satterfield, and I performed the analysis and wrote the manuscript. Dr. Kandlikar and Dr. Satterfield provided comments and edits to the manuscript.  Chapter 3. A version of Chapter 3 was developed as a manuscript, and is in the process of being submitted to an academic journal. I developed the research questions and sample methodology with the support of Dr. Kandlikar and Dr. Satterfield. Data was collected by the University of California Santa Barbara Social Science Survey Center. I analyzed the survey data and wrote the manuscript, and Dr. Kandlikar and Dr. Satterfield provided comments and edits to the manuscript. The UBC Behavioural Research Ethics Board (BREB) designated the research ‘Minimal Risk’, and certificate number H09-02121 was awarded on November 18, 2009. Renewal of the certificate was obtained on October 11, 2011.  Chapter 4. A version of Chapter 4 was developed as a manuscript, and is in the process of being submitted to an academic journal. I developed the research questions and sample methodology with the support of Dr. Kandlikar and Dr. Satterfield. Data was collected by the University of California Santa Barbara Social Science Survey Center. I analyzed the survey data and wrote the manuscript, and Dr. Kandlikar and Dr. Satterfield provided comments and edits to the manuscript. The UBC Behavioural Research Ethics Board (BREB) designated the research ‘Minimal Risk’, and certificate number H09-02121 was  iv  awarded on November 18, 2009. Renewal of the certificate was obtained on October 11, 2011.  Chapter 5. A version of this chapter has been published: Beaudrie, C. E. H.; Kandlikar, M. Horses for courses: risk information and decision making in the regulation of nanomaterials. J Nanopart Res 2011, 13, 1477–1488. The development of this chapter originated from a literature review that I completed for my comprehensive exam. My advisor Dr. Milind Kandlikar contributed to this chapter through the development of ideas and research questions, and co-wrote the section ‘Baseline Information and Nanomaterial Data Collection’. Dr. Kandlikar provided comments and edits to the manuscript. Chapter 6. Conclusion. Portions of conclusion chapter subsection 6.2, ‘Using Expert Judgment in Risk Assessment – A case for future research’, were derived from a literature review as part of a recently published book chapter: Beaudrie, C.; Kandlikar, M.; Ramachandran, G. Using Expert Judgment for Risk Assessment. In Assessing Nanoparticle Risks to Human Health; Ramachandran, G., Ed. Elsevier: Oxford, 2011; pp. 109–138. I conceived of the research question and design with the support of Dr. Kandlikar. I led the analysis and writing of the manuscript segments presented here, and Dr. Kandlikar and Dr. Ramachandran provided comments and edits to the manuscript.  v  Table of Contents Abstract .............................................................................................................................. ii Preface ............................................................................................................................... iv Table of Contents ............................................................................................................. vi List of Tables .................................................................................................................. viii List of Figures................................................................................................................... ix List of Abbreviations ....................................................................................................... xi Acknowledgements ......................................................................................................... xii Dedication ....................................................................................................................... xiv Chapter 1  Introduction ............................................................................................... 1  1.1 Overview ....................................................................................................................... 1 1.2 Risk and Regulation ..................................................................................................... 2 1.2.1 Novelty and Uncertainty ............................................................................................ 4 1.2.2 Life Cycle Paradigm .................................................................................................. 7 1.3 Expert Opinion and Risk Perceptions ........................................................................ 8 1.4 Coping with Uncertainty in Regulation of Risks ....................................................... 9 1.5 Research Objectives ................................................................................................... 10 1.6 Structure and Overview ............................................................................................ 11  Chapter 2 From Cradle-to-Grave at the Nanoscale: Gaps in US Regulatory Oversight along the Nanomaterial Life Cycle .............................................................. 16 2.1 Introduction ................................................................................................................ 16 2.2 Analyzing Regulations across the Nanomaterial Life Cycle .................................. 18 2.3 Assessing the Gaps ..................................................................................................... 20 2.3.1 ENMs versus Conventional Materials ..................................................................... 21 2.3.2 Exemptions & Thresholds ....................................................................................... 23 2.3.3 Data, Uncertainty, and the Burden of Proof ............................................................ 26 2.3.4 Risk Reassessment ................................................................................................... 28 2.3.5 Post-Market Risk Management Challenges ............................................................. 29 2.3.6 Confidential Business Information (CBI) and Limited Resources .......................... 31 2.4 Implications for Nanomaterial Oversight ................................................................ 32 2.4.1 Closing the Gaps ...................................................................................................... 37 2.4.2 Life Cycle Specific Gaps ......................................................................................... 39  Chapter 3 Scientists versus Regulators: Precaution, Novelty & Regulatory Oversight as Predictors of Perceived Risks of Engineered Nanomaterials ............... 42 3.1 Introduction ................................................................................................................ 42 3.2 Methods ....................................................................................................................... 45 3.3 Results ......................................................................................................................... 48 3.3.1 Benefits versus Risks of Nanotechnologies ............................................................. 48 3.3.2 Drivers of Perceived Risks ...................................................................................... 52 3.4 Discussion.................................................................................................................... 64 3.4.1 Characteristic Differences in Expert Group Perceptions and Attitudes................... 65 3.4.2 Perceived Novelty of Risks, Attitudes Toward Regulation, and Perceptions of Technology Risk as Drivers of Nanotechnology Risk Perceptions ...................................... 67  vi  3.5  Conclusions ................................................................................................................. 69  Chapter 4 Nanotechnology and Regulation: Experts Views on Regulatory Agency Preparedness for Managing Risks ................................................................... 71 4.1 4.2 4.3 4.4 4.5  Introduction ................................................................................................................ 71 Methods ....................................................................................................................... 73 Agency Preparedness and Regulator Concern ........................................................ 75 Drivers of Perceived Agency Preparedness ............................................................. 78 Discussion.................................................................................................................... 86  Chapter 5 Horses for Courses: Risk Information and Decision Making in the Regulation of Nanomaterials ......................................................................................... 88 5.1 Introduction ................................................................................................................ 88 5.2 Nanomaterial Risks and Regulatory Decisions ....................................................... 91 5.3 Baseline Information and Nanomaterial Data Collection ...................................... 93 5.3.1 EPA’s NMSP ........................................................................................................... 94 5.3.2 California DTSC Carbon Nanotube Information “Call-In” ..................................... 95 5.4 Risk Information and Decision Making ................................................................... 97 5.4.1 Risk Ranking and Prioritization............................................................................... 98 5.4.2 Occupational Hazards and Control Banding ......................................................... 102 5.5 Conclusions ............................................................................................................... 104  Chapter 6  Conclusions ............................................................................................ 106  6.1 Findings and Contributions .................................................................................... 106 6.1.1 Risks and Regulation ............................................................................................. 106 6.1.2 Expert Opinions and Perceptions........................................................................... 108 6.1.3 Decision-Analytic Tools ........................................................................................ 109 6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment ............................................................................................ 112 6.2.1 Thinking about Expert Judgment for Nanomaterial Risks .................................... 113 6.2.2 Challenges Likely Faced When Eliciting Expert Judgments for Nanotechnologies 115 6.2.3 What This Means for Future Expert Judgment Based Research ........................... 118  References ...................................................................................................................... 122 Appendices ..................................................................................................................... 145 Appendix A Appendix B Appendix C  Supporting Information for Chapter 2 ..................................................... 145 Supporting Information for Chapter 3 ..................................................... 148 Supporting Information for Chapter 4 ..................................................... 169  vii  List of Tables Table 2.1 Regulatory trajectories for common ENM product categories. ........................ 36 Table 3.1 Demographic and Domain of Expertise variables by expert group .................. 47 Table 3.2 Loadings from a principal components analysis over seven rating scales averaged across individuals (VARIMAX rotated solution) ..................................... 53 Table 3.3 Loadings from a principal components analysis over fourteen rating scales related to 'Regulation of Risks' and 'Regulation of Nanotechnologies', averaged across individuals (VARIMAX rotated solution) ..................................................... 55 Table 3.4 Hierarchical regression with Nano Risk Index as dependent variable ............. 57 Table 4.1 Loadings from a principal components analysis over seven rating scales averaged across individuals (VARIMAX rotated solution) ..................................... 79 Table 4.2 Descriptive statistics for control and independent variables. ........................... 82 Table 4.3 Hierarchical regression analysis with Preparedness Index as dependent variable...................................................................................................................... 83 Table A.1 Applicability triggers, thresholds and exemptions, responsibilities and requirements under US Federal EHS regulations ................................................... 145 Table B.1 One-Way Analysis of Variance (ANOVA) measuring significance of differences in mean Risk Perceptions by expert group for 14 nanotechnology scenarios.................................................................................................................. 148 Table B.2 Games-Howell post hoc analysis indicating significant differences in means between NSE-NEHS, NSE-NREG, and NEHS-NREG group pairings ................. 149 Table C.1 One-Way Analysis of Variance (ANOVA) measuring the significance of differences in 'agency preparedness' ratings by expert group for 14 nanotechnology scenarios.................................................................................................................. 169 Table C.2 Games-Howell post hoc analysis indicating significant differences in means between NSE-NEHS, NSE-NREG, and NEHS-NREG group pairings…………… 170  viii  List of Figures Figure 1.1 National Research Council 'Red Book' Risk-Assessment-Risk-Management paradigm. (National Research Council (US), 1983) ................................................... 3 Figure 1.2 Electron microscopy images of nano-ZnO. Multiple physical confirmations of nano-ZnO are possible with the same chemical composition. This illustrates the complexity and variation possible for substances at the nano-scale, which must be taken into account during risk assessment. (Wang, 2004) .......................................... 5 Figure 1.3 Dissertation overview and structure. Three main themes, ‘risk assessment and regulation’, ‘expert opinions and perceptions’, and ‘decision-making under uncertainty’ are addressed over four research chapters utilizing a number of research methods. .................................................................................................................... 12 Figure 2.1 Federal EHS regulations as they apply over the product life cycle. Solid outlines highlight the primary points for risk review and risk management decisionmaking under each regulation. Regulatory agencies responsible for enforcing these regulations are highlighted in bold text. ................................................................... 18 Figure 2.2 Federal EHS regulations over the nanomaterial life cycle considering regulatory challenges posed by ENMs. Solid outlines indicate regulations that are expected to enable comprehensive risk review and risk management for ENMs. Dashed outlines indicate regulations that are not expected to enable agencies to comprehensively review risks or impose measures for managing risks from ENMs. .................................................................................................................................. 33 Figure 3.1 "Benefit versus Risk" ratings for nanotechnologies in general. Color-coded bars indicate the proportion of respondents in each expert group (NSE, NEHS, and NREG) choosing the indicated response .................................................................. 49 Figure 3.2 'Risk Perception' ratings for NSE, NEHS, and NREG expert groups. Mean scores for each group are indicated with points on respective color-coded lines capturing 14 different nanotechnology scenarios rated between ‘almost no risk’ and ‘high risk’. Significant differences in means were determined using a one-way ANOVA with post hoc analysis, and are indicated with a, b, and c markings as outlined in the legend ................................................................................................ 50 Figure 3.3 Mean scores for the 'Novelty' and 'Attitudes toward Regulation' indices for NSE, NEHS, and NREG groups The continuum from ‘high’ to ‘low’ represents a factor score range of +/- 0.5, representing one half standard deviation in either direction from the index. a, b, and c markings indicate significant differences between groups, where a: NSE and NEHS, b: NSE and NREG, c: NEHS and NREG. Tukey HSD post hoc analysis confirms that differences in index scores are significant across all three groups for ‘Novelty’ (p < .05; NSE: N = 180, -0.29 +/0.86, NREG: N = 103, 0.39 +/- 0.88, NEHS: N = 121, 0.11 +/- 0.85), and for ‘Regulation: Preference for Precaution’ (p < .001; NSE: N = 180, -0.29 +/- 0.82; ix  NEHS: N = 121, 0.06 +/- 0.93; NREG: N = 103, 0.43 +/- 0.81). Post hoc analysis confirmed a significant difference between NSE and NREG groups only for ‘Regulation: Market-Based, Voluntary’ (p < .022; NSE: N = 180, -0.08 +/- 0.80; NREG: N = 103, -0.21 +/- 0.91).. ............................................................................. 60 Figure 3.4 a) Comparison of experts' perceptions of the novelty of benefits versus novelty of risks across groups. * indicates significant difference in means between ‘novel risks’ and ‘novel benefits’ by paired t-test, where Novel Benefits = 3.50 +/- 0.58, Novel Risks = 2.89 +/- 0.65, t(140) = 8.59 , p < .001 for NSE group; Novel Benefits = 3.3 +/- 0.62, Novel Risks: 3.16 +/- 0.67, t(90) = 2.06, p < .042 for NEHS group; and difference in means for NREG group is not significant. .................................... 62 Figure 3.5 Comparison of Tech Risk Index and Nano Risk Index scores by expert group. Paired t-test scores confirmed a significant difference in means between Tech Risk Index and Nano Risk Index for the both the NREG group (Tech Risk Index: -0.08 +/- 0.99; Nano Risk Index: 0.26 +/- 0.90; t(102)=3.822 , p < .001), and for the NSE group (Tech Risk Index: -0.04 +/- 0.82; Nano Risk Index: -0.20 +/- 0.84; t(179)=2.53 , p=.012). * indicates significant difference in means between Tech Risk Index and Nano Risk Index scores ...................................................................................... 64 Figure 4.1 'Agency Preparedness' ratings for NSE, NEHS, and NREG expert groups. Mean scores for each group are indicated with points on respective color-coded lines capturing 14 different nanotechnology scenarios. The dotted grey line indicates the mid or neutral-point between ‘strongly disagree’ and ‘strongly agree’. Significant differences in means were determined using a one-way ANOVA with GamesHowell post hoc analysis, and are indicated with a, b, and c markings as outlined in the legend .................................................................................................................. 76 Figure 5.1 Decision contexts and available decision support tools. Decision contexts (rounded rectangles) become increasingly specific from left-to-right influencing the choice of support tools (ovals) to aid in regulatory decision-making. Data requirements similarly become more specific with increasing specificity of the decision context. ....................................................................................................... 93 Figure 5.2 Control Banding matrix with risk level (RL) indicators as a function of the combination of probability and severity scores. Control bands correspond to risk levels as follows: RL 1 – General Ventilation; RL 2 – Fume hoods or local exhaust ventilation; RL 3 – Containment; RL 4 – Seek specialist advice. (Adapted from Paik et al.2008) ............................................................................................................... 103 Figure 6.1 Nano-Expertise available for use in elicitation exercises .............................. 120  x  List of Abbreviations CalEPA – California Environmental Protection Agency CBI – confidential business information
 CERCLA – Comprehensive Environmental Response, Compensation, and Liability Act (commonly known as Superfund and administered by the EPA)
 CPSA – Consumer Product Safety Act (administered by the CPSC)
 CPSC – Consumer Product Safety Commission
 DTSC – California Department of Toxic Substances Control (DTSC) ENM – Engineered Nanomaterial EPA – Environmental Protection Agency
 FCN – Food Contact Notification FDA – Food and Drug Administration
 FFDCA – Federal Food, Drug, and Cosmetic Act (administered by the FDA and by the EPA for pesticide residues on food only)
 FHSA – Federal Hazardous Substances Act (administered by the CPSC)
 FIFRA – Federal Insecticide, Fungicide, and Rodenticide Act (administered by the EPA)
 GAO – Government Accountability Office
 GRAS – generally recognized as safe
 LVE – low-volume exemption
 NMSP – Nanoscale Material Stewardship Program
 OSHA – Occupational Safety and Health Administration
 OSHAct – Occupational Safety and Health Act (administered by OSHA)
 PMN – Premanufacture Notice QSAR – Quantitative Structure Activity Relationship
 RCRA – Resource Conservation and Recovery Act (administered by the EPA)
 SNUN – Significant New Use Notification
 SNUR – Significant New Use Rule
 TSCA – Toxic Substances Control Act (administered by the EPA)  xi  Acknowledgements I would first and foremost like to thank my supervisors Dr. Milind Kandlikar and Dr. Terre Satterfield. Thank you for your mentorship, guidance, encouragement, and continued support over the last five years. I have learned much about interdisciplinary research, systems thinking, policy and decision science, and social sciences methodologies, and have expanded my horizons well beyond those defined by my engineering studies. Thank you also to Dr. Gurumurthy Ramachandran for your advice and support throughout, and for working with me to publish portions of this thesis.  I owe a great deal of gratitude to Barbara Herr Harthorn at the Center for Nanotechnology in Society at the University of California Santa Barbara (CNS-UCSB), and to Andre Nel, Hilary Godwin, Arturo Keller, and David Avery the Center for Environmental Implications of Nanotechnology at University of California Los Angeles (CEIN-UCLA). I am grateful for your encouragement and generous support, and for the many opportunities for personal and professional development that you have provided over the years. Thank you to Dr. Jody Roberts at the Chemical Heritage Foundation for the chance to explore new research topics, and for the opportunity to contribute meaningfully to the nanotechnology policy domain. And thank you to Robin Gregory (Decision Research), Greg Goss (University of Alberta), and my new colleagues at Compass Resource Management, Graham Long and Tim Wilson, for your mentorship, guidance, and encouragement in the final stages of my research.  Many thanks to Anton Pitts at UBC, and Mary Collins and Shannon Hanna at CNSUCSB for your help on all things statistics related, and to Paolo Gardinali, Indy Hurt, Tyronne Marin, and Joe Conti at CNS-UCSB for your generous assistance and advice on the experts survey research. Thank you as well to Kristen M. Kulinowski and David R. Johnson at the International Council on Nanotechnology (ICON) at Rice University, and to the numerous folks at Environment Canada, Health Canada, US EPA, and several state environmental agencies who helped make the ambitious survey sample a reality.  xii  This research was made possible by the Natural Sciences and Engineering Research Council of Canada (NSERC), a Doctoral Fellowship from the University of British Columbia, and by the generous support of the National Science Foundation (NSF) and the US Environmental Protection Agency (EPA) through CNS-UCSB and CEIN-UCLA. Thank you sincerely for your support. I would also like to acknowledge the generous awards from the Chemical Heritage Foundation (CHF Environmental History and Policy Fellowship), Society for Risk Analysis (Student Merit Award), and the Les Lavkulich Scholarship (UBC-IRES).  I want to thank all of the faculty, staff, and students from the UBC Institute for Resources, Environment and Sustainability, for making this research enjoyable and fulfilling. Special thanks to Tom Berkhout, Christina Cook, Andy Devlin, Laura Devries, Nichole Dusyk, Sara Elder, Kieran Findlater, Julia Freeman, Brian Gouge, Tom Green, Ed Gregr, Sarah Klain, Sonja Klinsky, Jana Kotaska, Reza Kowsari, Jane Lister, Megan Mach, Meg O’Shea, Conor Reynolds, Maryam Rezaei, Lucy Rodina, Gerald Singh, Jordan Tam, Paul Tehan, Nathan Vadeboncoeur, and Lisa Westerhoff. Your friendship and support has made my experience at IRES one to remember. Thank you to my mother Susan, my father Arthur, and my sisters Kelly and Chelsea – none of this would have been possible without your support, love, and understanding. It has been a long and winding road, and I am forever grateful to have had you by my side the entire way.  I especially would like to thank Jody Rechenmacher for your infinite patience and loving support – you have been my greatest supporter throughout this endeavor. Thank you so much for helping me to keep things in perspective, for your contagious love of adventure and life, for the many delicious meals that sustained my energy and focus in the final stretch, and for reminding me to take a breather from time to time.  xiii  Dedication  To my parents, and my grandfather Henry Joseph Lavoie  xiv  1.1 Overview  Chapter 1 1.1  Introduction  Overview  Nanotechnologies promise tremendous benefits for society, from targeted drug delivery and diagnostics to efficiency enhancements for water filtration, energy generation, and electronics. With the ability to manipulate matter on the atomic scale, nanoscientists and engineers are able to craft materials with custom properties, and coax novel and unusual behaviours from well-known and seemingly benign substances. Yet, the nanotechnology revolution is not without controversy. Concerns about the health and environmental risks of nanotechnologies have begun to be debated across social groups and environmental NGOs, mirroring public unease about similar technologies such as biotechnology (Savadori et al., 2004).These concerns arise from one central challenge: the same properties that make nanomaterials so promising – that they behave very differently from their bulk chemical forms – also make their health and environmental effects extremely difficult to predict. In the face of high scientific uncertainty, risk assessors and regulators have few tools available to aid in assessing and managing risks (Kandlikar, Ramachandran, Maynard, & Murdock, 2007). The result is that, if left unchecked, rapid growth in the production of ENMs and ENM-enabled products could lead to serious and yet unknown implications for society.  This thesis investigates the challenges that nanotechnologies pose for risk regulation, and seeks to inform the development of policies and practices to address these challenges. It investigates the ways in which high scientific uncertainty and novel and complex nanomaterial behavior renders existing risk assessment approaches ineffective, and thus creates a number of regulatory gaps through which ENMs may escape oversight altogether. Further, it analyzes how a cross section of experts from multiple disciplinary domains perceive nanomaterial risks and view different approaches to regulation. Finally, this thesis explores the use of decision-analytic tools to enable risk management and regulatory decision-making under high uncertainty. This introductory chapter explores these main themes and provides the necessary background and contextual detail on which the main empirical and evaluative chapters rest.  1  1.2 Risk and Regulation  1.2  Risk and Regulation  In response to growing concerns over the health and environmental implications of industrial activity in the 1960s and ‘70s, the United States Congress established a series of environmental laws authorizing science-based regulatory action to protect public health and the environment (NRC, 2009; Savadori et al., 2004). In the years following, risk assessment principles and practices were formalized to aid regulators in managing risks from pollutants (including pesticides, industrial chemicals, and wastes). In the United States, this formalization was primarily expressed in the publication of the National Research Council’s (NRC) 1983 ‘Red Book’ (National Research Council (US), 1983; NRC, 2009). The Red Book provided a framework by which the risk assessment process could be carried out. Further, it outlined methods to bridge the gap between the research activities that identify threats to human health and environment, and the risk management activities carried out by government agencies to protect the public from these same threats. Four key stages, outlined in Figure 1.1 below, capture well the volume’s risk-assessment-risk-management paradigm. The stages are: 1) Research – designed to generate relevant risk information; 2) Risk Assessment – including the evaluation of hazards, dose-response characteristics (the relationship between dose and physiological response), and exposures; 3) Risk Characterization – the synthesis of information and analysis, including uncertainties; and 4) Risk Management – the development of regulatory options, and agency decisions, based on the analysis conducted.  2  1.2 Risk and Regulation  Figure 1.1 National Research Council 'Red Book' Risk-Assessment-Risk-Management paradigm. (National Research Council (US), 1983)  To conduct analyses utilizing this framework, regulators must acquire data from a variety of sources including academic institutions, government sponsored labs, and materials manufacturers. A combination of physical and chemical characteristics, in vitro and in vivo (animal based) data, and epidemiological data, are used to assess hazards, doseresponse relationships, and exposures. The findings of such risk assessments help regulators design interventions to minimize impacts. Nonetheless, data is often not available in the scientific literature, cannot be generated by regulators due to resource constraints, or cannot be collected from industrial producers of materials due to limitations in regulatory authority. Regulators must meet high standards for adequate evidence before a product can be effectively regulated (E. C. Brown, Hathaway, Hatcher, & Rawson, 1999); data scarcity and high scientific uncertainty therefore present a significant challenge in meeting regulatory thresholds. When data is scarce, regulators can utilize alternative methodologies, including Bayesian methods (Payzan-LeNestour & Bossaerts, 2011) and quantitative structure activity relationships (QSARs). However, when rigorous quantitative risk assessment is not possible using these tools, qualitative  3  1.2 Risk and Regulation  ‘risk screening’ may be performed as an alternative using available data and decisionanalytic approaches (R. A. Howard, 1988).  Regulators face a substantial challenge in managing potential risks from emerging nanotechnologies. Despite the current availability of more than 1,300 nano-enabled consumer products (Consumer Products Inventory, 2011), and projections of more than $1 trillion in goods sold by 2015 (Lux Research, 2004), relatively little is known about the types, volumes, and uses of nanomaterials currently on the market. Furthermore, there is a high degree of scientific uncertainty in our understanding of the relationship between nanomaterial characteristics and behavior, which makes it difficult to anticipate health and environmental implications (Alderton et al., 2010). Finally, nano-scale materials often exhibit behaviours that are different from those of their bulk counterparts, rendering existing models and tools unusable (NRC National Research Council, 2012). This leaves existing risk assessment methodologies poorly suited to the task (Morris et al., 2010), and restricts the regulators’ ability to manage risks. In summary, three central factors complicate the regulation of engineered nanomaterials. They include: 1) a high degree of scientific uncertainty regarding the relationship between ENM physical-chemical characteristics and their implications for health and environment; 2) ‘novel’ behaviours that emerge as a result of size and quantum effects (Hardman, 2006); and 3) a scarcity of ENM product data, including information on production, intended use, and disposal.  1.2.1 Novelty and Uncertainty Nanomaterials exhibit interesting and useful properties due to a number of phenomena that emerge when materials are engineered with dimensions on the nano-scale (i.e., particles with one or more dimensions on the order of 100nm) (Auffan et al., 2010). These include increased reactivity due to the dramatic increase in the ratio of surface area to mass (Lison, Lardot, Huaux, Zanetti, & Fubini, 1997), and quantum effects that emerge with shrinking particle size, including mechanical, optical, electrical, and semiconducting properties (Roduner, 2006). Additionally, particle characteristics become important in determining chemical, physical, and biological activity (Puzyn, Leszczynska, & Leszczynski, 2009). Due to their small size, ENMs can get into places  4  1.2 Risk and Regulation  that larger particles cannot (e.g., crossing the blood-brain barrier) (Oberdörster et al., 2005), and surface characteristics (e.g., coatings, irregularities, functional groups) may have a significant impact on ENM activity (G. Oberdorster, Oberdorster, & Oberdorster, 2005; Warheit et al., 2004). Further, particle shape becomes very important since a number of physical conformations may be possible for the same chemical substance (Mossman, Bignon, Corn, Seaton, & Gee, 1990; Royal-Society, 2004) (Figure 1.2). For example, several studies have found evidence that carbon nanotubes (CNTs) with an aspect ratio similar to asbestos fibers exhibit ‘asbestos-like pathogenicity’, despite their vastly different chemical composition (Poland et al., 2008). Yet, shorter carbon nanotubes do not exhibit this effect (Donaldson, Murphy, Duffin, & Poland, 2010).  Figure 1.2 Electron microscopy images of nano-ZnO. Multiple physical confirmations of nano-ZnO are possible with the same chemical composition. This illustrates the complexity and variation possible for substances at the nano-scale, which must be taken into account during risk assessment. (Wang, 2004)  5  1.2 Risk and Regulation  While the effects exhibited by nanomaterials are not new physical phenomena, what is novel is that nanomaterials can be engineered to take advantage of such phenomena in a way that was not previously possible. Novel properties and behaviours can thus be seen in engineered structures such as CNTs when compared with more common forms (allotropes) of carbon (e.g., graphite, diamonds). Novel properties are also observed when ordinary bulk materials are manufactured into nano-scale particles. For example, titanium dioxide is opaque white in bulk form, but becomes invisible when manufactured into nano-scale particles for use in sunscreen (Sadrieh et al., 2010). Similarly, aluminum is typically stable, but becomes a powerful rocket propellant when nano-aluminum powder is mixed with ice crystals (Wood, 2011). Further, any number of the above characteristics can be tweaked with relative ease to create new materials with entirely different properties. Thus, potentially thousands of different combinations (of size, shape, surface coating, and other parameters) are possible for any given chemical substance. This creates a combinatorial problem for risk assessors: if ENM properties may change with adjustments to any of a number of parameters, then it may be impossible to estimate the biological activity of ENMs based on chemical composition alone. Novel nanomaterial properties and behaviours are therefore a central source of uncertainty in the assessment and management of potential risks.  In short, there is a high degree of uncertainty regarding the relationship between ENM physical-chemical characteristics and observed behaviour. This has implications for every stage of the risk assessment paradigm, where ‘extreme’ uncertainty threatens to render existing risk assessment tools inoperable (Kandlikar et al., 2007). It is difficult to calculate dose-response relationships when there is deep uncertainty about whether dose should be measured based on mass or particle surface area (due to the increased surface reactivity of ENMs) (D. Brown, 2001; D. M. Brown, Stone, Findlay, MacNee, & Donaldson, 2000; Cullen et al., 2000; Dick, Brown, Donaldson, & Stone, 2003; Donaldson, 2000; Lison et al., 1997). Similarly, existing models for hazard assessment that relate bulk chemical characteristics to health and environmental effects are largely not applicable to ENMs given that ‘particle effects’ are not taken into account in current models (Oberdörster et al., 2005). Finally, there is a scarcity of ENM product-centric  6  1.2 Risk and Regulation  data, making it difficult to estimate human exposure and release to the environment. This is increasingly problematic in light of growing support for the use of ‘life-cycle’ approaches in understanding technological risk. Thus, novel behaviour, data scarcity, and high scientific uncertainty pose significant challenges for the assessment and management of nanomaterial risks.  1.2.2 Life Cycle Paradigm In regulatory risk assessment, risks to human health and the environment are typically calculated for very specific contexts (i.e., a single stage in the product’s life cycle). For example, one regulatory body may assess risks to the health of workers in a drug production facility, while another may investigate the safe use of drugs or supplements. Yet another may estimate the environmental implications of the disposal of materials at the end of their useful life. The importance of integrating risk assessment studies across a product’s full life cycle is gaining prominence. Such frameworks are already integrated into assessment procedures carried out under some federal regulations – for example by the US Environmental Protection Agency (EPA) under the Toxic Substances Control Act (TSCA) (NRC, 2009).  While formal Life Cycle Assessment (LCA) approaches enable the evaluation of health and environmental impacts throughout the life of a product or material (from initial production to final disposal) (Curran, 1996), the suitability of LCA for emerging nanotechnologies has yet to be fully demonstrated (Hischier & Walser, 2012). Furthermore, LCA techniques do not incorporate specific ‘risk’ measures based on inherent ENM hazards, dose-response relationships, and the potential for exposure. As a result, LCA is of limited use to regulators who seek to clearly understand risks associated with ENMs and nano-enabled products. A combination of the two approaches, LCA and Risk Analysis, is necessary to more fully understand the human health and environmental implications of ENMs throughout a product’s life cycle (Grieger et al., 2012; Shatkin, 2008). Nonetheless, incorporating life cycle concepts into the risk assessment framework greatly increases the complexity of the analysis, and requires a sizeable amount of data.  7  1.3 Expert Opinion and Risk Perceptions  While life cycle approaches are increasingly used in risk assessment, there is little evidence that these concepts are reflected in the regulation of risks. That is, US regulatory bodies do not effectively coordinate regulatory efforts, and existing environment, health, and safety (EHS) regulations are not clearly designed to provide seamless oversight from initial production to end-of-life. However, few studies have evaluated regulatory coverage from a life cycle perspective (Beaudrie, 2010; Monica & Van Calster, 2010; Wardak & Gorman, 2006; Wardak, Swami, & Gorman, 2006), leaving a large gap in our understanding of how the existing regulatory system in the United States will deal with ENMs.  In summary, novel ENM properties, a high degree of scientific uncertainty, and a lack of information on ENM production and exposure make regulation exceedingly difficult. Further, the increasing integration of life cycle concepts into risk assessment and regulation enable a more holistic assessment, yet dramatically increase data needs and analytical complexity. In light of such complexity, expert opinion may provide some insight.  1.3  Expert Opinion and Risk Perceptions  Continuing uncertainty about the potential risks of ENMs means that expert opinion will play an important role in assessing and regulating risk. In situations where scientific uncertainty is high, or when new technologies emerge, experts are often consulted to help decision makers form opinions and strategies (Cooke, 1991). Experts can advise decision-makers on potential risks, and help to prioritize research and regulatory efforts (NRC National Research Council, 2012). Nanotechnology experts regularly work with nanomaterials, forming risk judgments that can provide valuable insight for regulatory decision-making. Experts’ assessments of risk may hinge on their perceptions of particular characteristics, such as novel behaviours or high uncertainty. An understanding of the reasons for their concerns can help identify areas for further study. Nanotechnology experts’ (nano-experts) views on risk and regulation can therefore provide valuable insight for risk management.  8  1.4 Coping with Uncertainty in Regulation of Risks  Yet expert perceptions of risk are known to vary based on a number of factors, including disciplinary backgrounds and institutional affiliations (Kraus, Malmfors, & slovic, 1992; Slovic, Malmfors, Krewski, Mertz, & Neil, 1995). In addition, while expert opinion is widely believed to be more objective than that of laypersons, it is still subject to bias based on personal values and beliefs, especially as judgments stray further from the expert’s primary domain (Burgman et al., 2011; Krinitzsky, 1993; Slovic et al., 1995). Nano-experts have recently been involved in several research priority-setting exercises to date (EPA, 2010a; 2010b; NRC National Research Council, 2012). However, little is known about how experts whose work focuses on different stages of the ENM life cycle differ on their views of risk, and by extension, their perceptions of nanotechnology benefits and risks, novel behaviours, and suitable approaches to risk assessment and regulation. Further, despite wide agreement among nano-scientists that nanomaterials display novel behaviours, some disagree that nanomaterials are any different than conventional or bulk (non-nanoscale) materials. Some scientists view nanotechnology as a simple ‘re-branding’ of science that has been conducted for decades, or as a ‘buzzword’ used to generate hype and attract research funding (Harthorn & Bryant, 2007). Given widely varied opinions among experts in the nanotechnology domain, further research is necessary to understand perceptions of risk and of regulatory preparedness, and the factors that drive these perceptions. The identification of biasing factors is crucial to the development of protocols and expert-selection procedures that minimize bias and elicit opinions from a diverse sample of experts.  1.4  Coping with Uncertainty in Regulation of Risks  Decision-analytic techniques are often useful to assist with difficult decisions under conditions of high uncertainty (R. A. Howard, 1988; Keeney & Raiffa, 1993). Regarding risk, decision analysis can help regulators structure problems, screen for risks, and prioritize materials for further research (Huang, Keisler, & Linkov, 2011; Linkov, Satterstrom, Steevens, & Ferguson, 2007). Decision-analytic techniques are most useful when the data requirements are well specified and limited in scope. For instance, such techniques have been successfully incorporated into risk analyses for climate-change impacts on fisheries, invasive-species threats, and medical risks (Gregory et al., 2012).  9  1.5 Research Objectives  Several approaches have been proposed for nanotechnology risks, including the development of influence diagrams to elucidate the causal relationships between ENM properties and biological behaviours (K. Morgan, 2005). Decision-analytic techniques have also been used to identify ENM properties and behaviours of concern (Berube, Cummings, & Cacciatore, 2011; Fauss, Gorman, & Swami, 2009), to enable the prediction of behaviours based on physical-chemical properties (Flari, Chaudhry, Neslo, & Cooke, 2011; Tervonen et al., 2009), and to provide guidance for the selection of occupational exposure controls (S. Paik, Zalk, & Swuste, 2008). These approaches facilitate the screening of risks and inform decision-making under high uncertainty.  Faced with demands for improved approaches to ENM assessment under high uncertainty, regulators can choose from a number of tools and decision-analytic approaches. Yet, given the variety of available tools and the variability in specific decision support needs for different contexts, there are gaps both in the evaluation of the utility of existing tools and in guidance on the selection of decision-analytic techniques to suit specific needs. Several decision-analytic techniques are explored here in detail to evaluate their utility and to identify best practices for use with engineered nanomaterials. Given the high level of uncertainty and the poorly understood relationships between physical-chemical properties and biological activity, decision-analytic tools can be used to enable risk screening, to identify hot spots and areas of concern, to compare and rank risks from various nanomaterials, and to identify opportunities to minimize risks from reengineered materials and products.  1.5  Research Objectives  This research addresses pressing regulatory challenges resulting from the rapid development of nanotechnologies. It is policy-relevant, designed to inform the development of tools and approaches to assess and manage potential nanomaterial risks under high uncertainty, and to inform the creation of policies and practices suitable for nanomaterial risk management. It is also interdisciplinary, drawing from multiple fields to enable a comprehensive analysis, including those of risk analysis (risk assessment, management, and perceptions), decision analysis, and life cycle analysis.  10  1.6 Structure and Overview  Broadly stated, the objective of this research is to investigate the challenges that nanotechnologies pose for risk regulation along the life cycle, and to inform the development of policies and practices to address these challenges. There are additionally three specific sub-objectives:  a) To identify the challenges and regulatory fault-lines that nanomaterials pose for US federal environment, health, and safety (EHS) regulations using the life cycle paradigm (Chapter 2) b) To survey expert opinion on risks and regulatory preparedness, to identify areas of concern, drivers of opinion, and variations in judgment across expert groups (Chapters 3 & 4) c) To investigate the use of decision-analytic tools to aid in risk assessment and risk management (including regulatory decision-making) under high uncertainty (Chapter 5)  1.6  Structure and Overview  My main dissertation objective – to investigate the challenges that nanotechnology poses for risk regulation along the product life cycle – was accomplished through four studies (Chapters 2 to 5). As this is a paper-based thesis, it is comprised of an introductory chapter, four research chapters, and a concluding chapter. All research chapters were developed as manuscripts for publication in academic journals. The research chapters span the three main themes described above (‘risk assessment and regulation’, ‘expert opinions and perceptions’, and ‘dealing with uncertainty’) and utilize various methods to achieve their objectives, including qualitative case study analysis, a synthesis on literatures pertaining to risk law and policy, and regulation; and quantitative results given a survey of the expert perceptions of three groups operating in the nanotechnology field: nano-scientists and engineers, nano-environmental health and safety scientists, and nanoregulators. This work includes a novel methodological contribution, expanding upon existing methodologies through the integration of life-cycle concepts in regulatory policy analysis (Chapter 2). Figure 1.3 provides an overview of the themes covered in each  11  1.6 Structure and Overview  chapter and the methods utilized in exploring each of these themes, followed by a brief description of each substantive chapter.  Figure 1.3 Dissertation overview and structure. Three main themes, ‘risk assessment and regulation’, ‘expert opinions and perceptions’, and ‘decision-making under uncertainty’ are addressed over four research chapters utilizing a number of research methods.  The first substantive chapter, chapter 2, reveals the regulatory challenges nanomaterials pose by evaluating the primary gaps in US federal environmental, health, and safety (EHS) regulations as they apply to this new class of risks (albeit a ubiquitous class, a point that will become evident shortly). Using a life cycle framework, this study maps regulatory coverage along the ENM life, from initial production to final disposal, to identify gaps through which emerging nanomaterials may escape regulation. This research finds that high scientific uncertainty, a lack of EHS and product data, inappropriately designed exemptions and thresholds, and limited agency resources are a challenge to both the applicability and adequacy of current regulations. Together these challenges produce many gaps in regulatory coverage along the ENM life cycle, with the largest occurring at the post-market stages and/or the release of ENMs to the environment. What becomes evident in conducting a life cycle analysis as superimposed onto existing regulatory frameworks is that pending regulatory reforms and investments in research are highly necessary. These include but are not limited to: expedited research to develop quantitative structure-activity-relationship models (QSARs), development of monitoring and control technologies, and periodic re-evaluation of risks as analytical tools and data availability improves. Each would help to close some of the largest gaps in oversight. Finally, the development of expert judgment based ‘risk screening’ tools  12  1.6 Structure and Overview  would improve regulatory assessment and decision-making in the interim until more robust quantitative assessment tools are available, a topic considered in some detail in this chapter, chapter 5, and the conclusion. Ultimately, chapter two also finds that a life cycle approach to regulatory policy analysis provides fresh insights into the complex challenges that nanomaterials pose for existing regulatory regimes, and highlights several gaps that, if left un-attended, will enable a large proportion of nanomaterials to escape risk assessment and regulatory oversight.  Chapter 3 utilizes a web-survey (N=404) of three groups of US-based and Canada-based nanotechnology experts: nano-scientists and engineers, nano-environmental health and safety scientists, and nano-regulators. It explores the perceptions and attitudes of three groups of nanotechnology experts (nano-scientists and engineers, nano-environmental health and safety scientists, and nano-regulators) to identify practices and products of greatest concern, and to determine whether nanotechnology risk perceptions differ between groups. In doing so, it aims to anticipate the different concerns of experts depending on their positions in research and development, toxicology and assessment, versus regulatory responsibility. It also identifies more fundamental drivers of risk perceptions. The survey’s investigation of expertise and its influence on perceived risks finds that perceptions of the novelty of nanotechnology benefits and risks; preferences for precautionary, market-based, and voluntary approaches to regulation; socio-political values; and perceptions of risk from other (non-nano) technologies are all key factors in risk judgments. The survey also revealed substantial agreement between expert groups on the relative ranking of nanotechnology scenarios. This is so even though significant differences exist in the overall magnitude of risk perceptions between groups. Nanoregulators consistently perceived the greatest risk, while nano-scientists and engineers perceived the least. Perceived novelty of risks and preference for regulatory precaution were strong indicators of perceived nanotechnology risk. Perceptions of risk from other (non-nano) technologies was also a strong predictor, suggesting its utility as a baseline measure of expert views of risk from technologies in general. Expert groups also demonstrated sizable differences in novelty perceptions and regulatory preferences, indicating that these traits are characteristic of each group. Ultimately, these findings  13  1.6 Structure and Overview  indicate the need to be aware of inherent predispositions among experts from different domains of research and responsibility, which might in turn influence decisions on which (nano)technologies get more attention and how. It is therefore important to draw upon diverse expertise to find appropriate approaches to risk management.  A third study, carried out in chapter 4, investigates nano-experts’ perceptions of regulatory agency preparedness to manage risks from emerging nanotechnologies, and the factors that drive these perceptions. It is based on the survey employed in chapter 3, with analysis limited to US-based nanotechnology experts only (N=254). The survey assessed experts’ views on the novelty of nanotechnology risks and benefits, trust in regulatory agencies, views on stakeholder responsibility, and socio-political values as drivers of preparedness perceptions. The results demonstrate that all three expert groups view regulatory agencies as unprepared to manage risks, while nano-regulators most strongly view regulatory agencies as unprepared. The results indicate that the perceived novelty of risks is the strongest driver of preparedness perceptions, followed by trust in regulatory agencies, with minor contributions from socio-political values and views on stakeholder responsibility. These findings indicate that novel nanomaterial behaviours and risks are believed to pose a significant challenge for regulators. The observed relationship between trust and novelty of risks indicates that preparedness perceptions are based, in part, on the perceived inadequacy of resources and regulatory mechanisms to manage nanomaterial risks. This research indicates a need for tools and methodologies that enable regulators to assess and manage risks under scientific uncertainty and with scarce risk information.  The final substantive chapter, chapter 5, systematically examines the challenges that scientific uncertainty poses for risk assessment and regulation of engineered nanomaterials, and evaluates the use of ‘decision-analytic’ approaches to address these challenges. The first challenge revealed is the need for adequate product information and nanomaterial testing data (characterization and hazard evaluation) to perform risk assessment. The second challenge is that of institutional barriers – regulatory agencies are under-resourced and are therefore unable to generate or acquire the rapidly expanding  14  1.6 Structure and Overview  amount of information needed to regulate ENMs. The third challenge is posed by novel behaviours of nanomaterials themselves, for which existing evaluation tools and models are not appropriate for evaluating their health or environmental implications. Using a case-study approach and literature review, this chapter considers three decision contexts: 1) the development of baseline information on ENM production and releases; 2) the establishment of priorities for risk related research; and 3) the management of occupational risks in the workplace. Existing decision-analytic approaches are reviewed for each context to evaluate their usefulness in enabling data collection, prioritization and risk ranking, and providing occupational safety guidance in the absence of adequate data and tools to conduct rigorous quantitative risk assessment. The results of this study indicate that decision-analytic approaches, including the use of expert judgment, show promise for assessing and managing nanomaterial risk until quantitative tools can be developed to enable formal risk assessment. Alongside the concluding chapter’s (6) summary of findings and contributions, the overall conclusion also includes careful consideration of the strengths and limitations of this work and one set of possible options for a path forward, be that for current regulatory dilemmas and/or for proposed future research.  15  2.1 Introduction  Chapter 2 From Cradle-to-Grave at the Nanoscale: Gaps in US Regulatory Oversight along the Nanomaterial Life Cycle 2.1  Introduction  Nanotechnology has taken the leap from the realm of research into a growing number of applications and consumer products, yet knowledge of potential health and environmental risks has lagged. One promising method for negotiating the complexity of interactions between emerging technologies, society, and the environment involves a systems, or life cycle, approach (Kuczenski, Geyer, & Boughton, 2011; Renn & Roco, 2006). From the early stages of product design and production to later use, recycling, and disposal, different risks may be present for both humans and the environment. A nanomaterialcoated garment, for example, might be safe for use by consumers who come in contact with the clothing, but could pose risks to the environment when the garment is washed and nanomaterials are released in wastewater. Performing a risk assessment for one life cycle stage in this case will miss the potential risks posed at another. Integrating life cycle thinking into risk assessment can also help identify opportunities to manage risks proactively. By recognizing ‘hot spots’ along the life cycle, stakeholders can redesign products for safety (green nano) (Hutchison, 2008), utilize control measures to minimize exposure (in occupational settings) (Kuczenski et al., 2011; Renn & Roco, 2006; Schulte, Geraci, Zumwalde, Hoover, & Kuempel, 2008), or limit applications to avoid adverse effects (material or product regulation).  The life cycle paradigm has been increasingly incorporated into risk-assessment procedures carried out by governments, academia, nongovernmental organizations, and industry over the past decade (J. M. Davis, 2007; Environmental DefenseDupont, 2007; Shatkin, 2008; Sweet & Strohm, 2006). However, while the assessment of risks continues to advance within the constraints of current knowledge of nanomaterial safety (Hischier & Walser, 2012; Savolainen et al., 2010), the management of risks over a product’s life cycle has relied on a patchwork of regulatory oversight (Council of Canadian Academies, 2008). Opinions are divided over whether existing statutes provide adequate authority for administrative agencies to regulate engineered nanomaterials (ENMs) (C. L. Bell, Duvall, Chen, & Votaw, 2006; L. L. Bergeson, 2012; Breggin & Pendergrass, 2007; Breggin, 16  2.1 Introduction  Falkner, Jaspers, Pendergrass, & Porter, 2009; Council of Canadian Academies, 2008; Felcher, 2008; Hester, 2006; Lin, 2007; Mandel, 2008; Powell, Griffin, & Tai, 2008; Ternes, 2006; US Food and Drug Administration, 2007). More important, most analyses to date have conducted a ‘regulation-by-regulation’ review without giving careful consideration to the relationship between regulations. There are thus few studies that assess US environmental, health, and safety regulations in an integrated manner to identify gaps in oversight that occur as a nanomaterial moves along its life cycle from initial production to end-of-life (Beaudrie, 2010; Monica & Van Calster, 2010; Wardak et al., 2006; Wardak & Gorman, 2006), and so across regulatory jurisdictions. Furthermore, few analyses have explored the ways in which engineered nanomaterials differ from their conventional counterparts along their life cycle, thus posing unique challenges for risk assessment and regulation (Hischier & Walser, 2012).  This paper investigates the viability of extending life cycle thinking to the case of ENM risk and regulation, to understand the unique challenges that ENMs pose, and to investigate the suitability of the US regulatory system as a comprehensive package addressing multiple types and uses of ENMs over their life cycle. Specifically, the analysis examines whether current regulatory regimes are designed to trigger formal risk reviews for novel nanomaterials at each life cycle stage, and whether regulatory agencies charged with enforcing regulations have the appropriate tools, resources, and authority to manage potential risks. Each regulation is reviewed in the context of the collection of EHS regulations to understand how existing shortcomings and new challenges posed by ENMs create gaps both within each regulation, and collectively over the entire set of regulations. Analysis involved a rigorous review of relevant federal regulatory statues and governmental, non-governmental, and academic reports that investigate federal health, safety, and environmental regulations and their applicability to nanomaterials. Each regulation was assessed for (a) its coverage at each stage of the product life cycle, and (b) its adequacy for managing ENM risks. Mapping of regulatory coverage and gaps is then used to identify oversight improvements such that ENM risks are better managed from initial production to final disposal.  17  2.2 Analyzing Regulations across the Nanomaterial Life Cycle  2.2  Analyzing Regulations across the Nanomaterial Life Cycle  In the US, federal oversight of potentially toxic materials and products spans several federal agencies. These include the Environmental Protection Agency (EPA), the Food and Drug Administration (FDA), the Occupational Safety and Health Administration (OSHA), and the Consumer Product Safety Commission (CPSC). Each agency is charged with enforcing regulations to control risks from specific types or uses of substances (i.e., chemicals, pharmaceuticals, and pesticides), or from potentially harmful releases in the workplace or into the environment. Together the regulations that they enforce cover every stage of the nanomaterial life cycle. Figure 2.1 illustrates the collection of regulations that apply to occupational and environmental releases across the entire nanomaterial life cycle, and regulations that apply to specific product types at the 1) rawmaterials processing and nano-product fabrication, 2) product use, consumption and maintenance, and 3) recycling, disposal and incineration (End-Of-Life) stages.  Figure 2.1 Federal EHS regulations as they apply over the product life cycle. Solid outlines highlight the primary points for risk review and risk management decision-making under each regulation. Regulatory agencies responsible for enforcing these regulations are highlighted in bold text.  The first stage of the product life cycle includes ENM production, from the transformation of raw materials into nanomaterials (e.g., manufacturing bulk silver into 18  2.2 Analyzing Regulations across the Nanomaterial Life Cycle  nanoscale silver particles) to the incorporation of nanomaterials as a component of other products (e.g., nano-silver antimicrobial textile coatings). Three key statutes that come into effect at this stage (as signaled in Figure 2.1) depend on the intended application of the nanomaterial. Chemical substances and pesticides are regulated under the Toxic Substances Control Act (TSCA, 1976) and the Federal Insecticide, Fungicide, and Rodenticide Act (FIFRA, 1972), respectively, and the EPA administers both acts. Food additives and drugs are regulated under the Federal Food, Drug, and Cosmetic Act (FFDCA, 2002), which is administered by the FDA. Together, TSCA, FIFRA, and FFDCA apply to chemical substances, pesticides, food additives, and drugs primarily through a ‘pre-market’ risk-assessment, registration, and management approach. With this approach, each substance is evaluated and risk-management decisions are typically made before a product is released for use on the market.  The second stage of the product life cycle is the point at which a material or product is marketed and sold, and its intended function realized. At this stage, ENMs may be a component of a number of products or technologies including cleaning products, clothing, food packaging, electronic devices, and sports equipment (Consumer Products Inventory, 2011). While the FFDCA applies solely to drugs and food additives in the previous life cycle stage, the act also includes FDA oversight of dietary supplements and cosmetics at this stage. All other consumer products are regulated under the Consumer Product Safety Act (CPSA, 1972), which is enforced by the Consumer Product Safety Commission (CPSC). Regulations for supplements and cosmetics under FFDCA, and for consumer products under the CPSA operate through ‘post-market’ mechanisms whereby producers are responsible for reviewing and ensuring the safety of products, and regulatory agencies have the ability remove products from the market that are demonstrably ‘unsafe’.  The third stage represents the endpoint of a product life cycle wherein the nanomaterial is reclaimed for use in a new product, is destroyed by incineration, or is otherwise disposed. At this stage ENMs may already be incorporated into consumer products that have reached the end of their useful life, or may constitute a by-product solid or liquid waste  19  2.3 Assessing the Gaps  from industrial or consumer use. One main waste-related regulation comes into play at this stage: the Resource Conservation and Recovery Act (RCRA, 1976). Under RCRA, hazardous wastes must be tracked from their initial production to the time of their final disposition to ensure they are handled and disposed of safely. While an additional statute, the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA, 1980) may also apply to waste products1, it deals with accidental releases into the environment not otherwise controlled under RCRA.  Finally, releases of nanomaterials in occupational settings or into air, water, or soil can occur at any stage of the life cycle. The three statutes that apply across all stages of an ENM’s life cycle are the Occupational Safety and Health Act (OSHAct, 1970), the Clean Air Act (CAA, 1970), and the Clean Water Act (CWA, 1972), administered by OSHA and the EPA respectively. Under the OSHAct, workplaces must ensure that exposure levels for hazardous materials do not exceed ‘Permissible Exposure Limits’ (PELs), and take measures to ensure a safe workplace. The CWA and CAA are ‘end-of-pipe’ statutes, and aim to prevent and control discharges of pollutants (Powell et al., 2008). The EPA is charged with setting standards for pollutant levels in ambient emissions, and regulates site-specific releases into the air and water based on facility-control permits.  2.3  Assessing the Gaps  The above federal regulations are complex and nuanced, and the rapidly evolving regulatory environment makes evaluation of regulatory coverage for ENMs difficult (L. L. Bergeson, 2012). Also complex are nanomaterial behaviours and potential health and environmental implications, which are difficult to predict using conventional analytic tools. Assessments of the adequacy of current regulations as applied to ENMs need to carefully account for these complexities. In theory, nanomaterial risks can be assessed and managed under the existing regulations. In practice, however, there are many ways that nanomaterials can avoid regulation as they move from one life cycle stage to another.  1 Since CERCLA’s ‘contaminated sites’ regulation is not likely to come into effect until long after a hazardous nanomaterial has been released into the environment, it is not reviewed here in depth.  20  2.3 Assessing the Gaps  Existing regulatory shortcomings make it difficult in general for federal agencies to assess and manage potential risks from conventional materials (Applegate, 2008). Nanomaterials exacerbate those shortcomings and introduce additional regulatory difficulties, creating many gaps through which ENMs can avoid comprehensive risk review and federally mandated risk management measures. These are explored here. A summary of applicability triggers, thresholds and exemptions, responsibilities for assessing and managing risk, and testing and reporting requirements under each of these federal EHS regulations is available in Table A.1 in Appendix A.  2.3.1 ENMs versus Conventional Materials Perhaps most striking and promising about nanomaterials is their ability to surprise and enable previously impossible technologies and applications. Their unique properties derive from physicochemical parameters including particle size, shape, and surface functionalization, which can be fine-tuned to suit different applications often by exploiting quantum effects, a high surface area to volume ratio, or novel semi-conductive properties (Nel, Xia, Madler, & Li, 2006; Roduner, 2006). Yet, the same characteristics render evaluation of ENM risks difficult. Risk reviews conducted under TSCA, FIFRA, or the FFDCA at the pre-market stage require tools that enable them to evaluate the potential for health and environmental implications along the product life cycle. Research is beginning to piece together how nanomaterial physicochemical characteristics, more so than chemical composition alone, contribute to toxicological responses and their fate and transport in the environment (Klaine et al., 2008; Meng, Xia, George, & Nel, 2009; Zhang et al., 2012). However, in contrast to conventional materials for which regulators have a suite of tools to predict implications based on chemical composition, too little is known about the relationship between nanomaterial physicochemical characteristics and behavior to update existing models or to create new predictive models to anticipate risks (Alderton et al., 2010; Morris et al., 2010). The result is a serious lack of predictive analytical capacity to anticipate harmful implications based on available nanomaterial information. Additionally, due to their high surface-to-volume ratio, nanomaterials tend to be more reactive than their bulk counterparts and may have larger toxic dose-response effects by equivalent mass (G. Oberdorster et al., 2005). This phenomenon makes it  21  2.3 Assessing the Gaps  difficult to directly compare ENMs to their bulk counterparts on a mass basis when comparing magnitudes of risk, calculating dose-response curves for health risk assessment (G. Oberdorster et al., 2005; Wittmaack, 2006), or defining ‘functional units’ for comparison in life cycle analysis (Hischier & Walser, 2012). Finally, given the diverse and rapidly growing nanomaterial market, the evaluation of consumer exposure to ENMs is hindered by deficient knowledge of ENM production volumes, product concentrations, and releases of ENMs from products over their life cycles (Gottschalk & Nowack, 2011; S. F. Hansen et al., 2008; Hendren, Mesnard, Dröge, & Wiesner, 2011; Hristozov, Gottardo, Critto, & Marcomini, 2012; NRC National Research Council, 2012; T. Seager & Linkov, 2009). Without adequate ENM product information, reliable modeling tools, and standardized measures for risk analysis, proactive risk assessment at the pre-market stage becomes a serious challenge.  Evaluation of risks in occupational environments under OSHA is similarly challenged by unique and complex ENM behavior once a nanomaterial is in production. Factors such as nanoparticle agglomeration can significantly influence both exposure potential and bioavailability (Helland, Wick, Koehler, Schmid, & Som, 2007), and it is currently unclear whether standard control methodology for conventional materials are suitable for limiting workplace exposure to both free and agglomerated nanoparticles (Schulte et al., 2008). There is also a shortage of adequate tools to effectively measure the concentrations of ENMs in the workplace and distinguish them from other materials, thereby severely limiting industrial hygienists’ ability to monitor exposures and enforce workplace exposure limits (Hristozov et al., 2012).  ENMs also present significant challenges for monitoring and controlling emissions during production, use, and disposal. Recent studies estimate that significant amounts of nanomaterials such as carbon nanotubes (CNTs) will be released into the environment across each stage of the life cycle (Eckelman, Mauter, Isaacs, & Elimelech, 2012; Gottschalk & Nowack, 2011). Technologies designed for monitoring and controlling conventional materials in effluents at the production stage are also not suitable for ENMs (Gottschalk & Nowack, 2011), making it difficult for regulators to prescribe control  22  2.3 Assessing the Gaps  strategies for industrial releases under the Clean Air Act and Clean Water Act (discussed further below). There is also evidence that some ENMs resist degradation in standard incineration processes, signaling the potential need for alterations to existing technologies to ensure ENM destruction at their end-of-life (Olapiriyakul & Caudill, 2009). Finally, ENMs present potentially more complex behavior in the environment than conventional materials due to a large number of intrinsic and extrinsic (environmental) variables that influence transformation, fate, transport, and toxicity (Klaine et al., 2008). It is therefore not enough to know the properties of the ENM in its original state when produced; risk assessors will need to know the state of the ENM upon release, including conditions in air, water, and soil in which the ENM will be released.  Together these novel characteristics make regulatory risk assessment and management extremely difficult at each stage of the life cycle and as compared to conventional materials. The following paragraphs investigate prominent shortcomings of existing regulations, and several ways that the unique nanomaterial properties and high uncertainty pose new risk assessment and regulatory challenges.  2.3.2 Exemptions & Thresholds Across the nanomaterial life cycle, several federal EHS regulations include applicability thresholds or provide mass-, volume-, or category-based exemptions that may allow some ENMs to escape federal oversight. At the pre-market stage, the EPA regulates chemicals by maintaining a Chemical Substances Inventory under TSCA for substances manufactured in the United States. “Existing substances” (i.e., those previously added to the inventory) are deemed safe and are authorized for use, while “new substances” undergo a risk review before being added to the inventory. The EPA has recently clarified that ENMs with the “same molecular identity” as a substance already in the inventory will be considered an ‘existing substance’ and thereby exempt from new chemical notification and risk review requirements (US Environmental Protection Agency, 2007). Under this definition, ENMs derived from existing bulk substances will not undergo a risk review despite mounting evidence that nanoscale materials with the same chemical composition but with differing morphologies may behave very differently.  23  2.3 Assessing the Gaps  For instance, gold nanoparticles can take numerous forms, each with very different hazard characteristics (Harper et al., 2011). The exemption is thus likely to affect a wide variety of ENMs that differ from their bulk counterparts in size but that do not alter their molecular identity with chemical modifications (i.e., by adding surface coatings or functional groups). Some ‘existing’ ENMs may be subject to a Significant New Use Rule (SNUR), such as the recently promulgated SNUR that requires manufacturers of multi-walled carbon nanotubes to send a notification to the EPA (US Environmental Protection Agency, 2011a). While the SNUR mechanism is flexible and enables the EPA to conduct a comprehensive 90-day risk review for ‘existing’ substances on a case-by-case basis, it is an extremely burdensome process for the agency. The EPA reports that the typical timeframe for issuing a SNUR is two years, and a total of just 700 SNURs have been issued based on more than 80,000 ‘existing’ chemicals on the inventory (Breggin et al., 2009). With an expected boom in the nanomaterial market (NRC National Research Council, 2012), it may prove impossible for the EPA to evaluate risks from nanoscale versions of ‘existing’ substances on such a case-by-case basis, under SNUR.  A second point of contention under TSCA is the Low-Volume Exemption (LVE) for substances manufactured/imported in amounts less than 10,000 kg annually (TSCA Low Volume Exemption, 1976). The LVE assumes that substances produced in lower volumes pose less overall risk than those produced in higher volumes, and thereby exempts these substances from a full 90-day review. However, since a high surface-to-volume ratio can increase nanoparticle reactivity and toxicity (by equivalent mass), much smaller volumes of ENMs may therefore pose a significant risk, making the 10 tonne per year exemption inappropriate for some ENMs. In sum, ENMs classified as ‘existing’ substances or produced in relatively low volumes (< 10 tonnes/year) may be exempt from EPA risk review under TSCA yet may pose significant risks to human and environmental health (Auffan et al., 2010; Daniel & Astruc, 2004; Jiang, Kim, Rutka, & Chan, 2008).  24  2.3 Assessing the Gaps  A similar category-based exemption exists at the pre-market stage for food additives under FFDCA. Premarket approval is not required if a food additive is “generally recognized as safe” (GRAS), a determination made by the manufacturer rather than the FDA (US Food and Drug Administration, 2007). Consequently, the FDA may not be aware that particular nanomaterials used in food additives are considered GRAS, and may therefore be unable to assess the potential for risk. Manufacturers can also bypass the premarket review by submitting a food contact notification (FCN) for substances believed to migrate into food in small amounts (altogether the FDA has a 120-day window to approve/disapprove the FCN). For pesticides one sizable regulatory gap is that FIFRA only applies to materials that claim to be pesticides (FIFRA Pesticide Registration and Classification Procedures, 2003). This makes it possible for manufacturers to utilize nanomaterials for their pesticidal properties (e.g., nanosilver to reduce odor in sports garments) but not claim use as pesticides, thus avoiding review under FIFRA. In an attempt to reduce avoidance of the risk review processes, the EPA recently released a fact sheet to clarify requirements for pesticide registration under FIFRA (Determining if a Cleaning Product is A Pesticide Under FIFRA, 2012). At end-of-life, nanomaterials that exhibit hazardous properties or are listed as ‘hazardous materials’ would be subject to regulation under RCRA. However, small-quantity manufacturers of nanomaterials (those who produce less than 100 kilograms annually) are not required to report their activities or waste-storage plans to the EPA (Powell et al., 2008). In addition to this mass-based threshold, ‘household hazardous wastes’ such as discarded consumer products containing hazardous nanomaterials, are exempted from RCRA requirements (Breggin & Pendergrass, 2007). These small volumes of household wastes may not pose a direct risk to the household or waste collector, but may pose significant health and environmental risk when incinerated or deposited in a landfill in the aggregate. With more than 1,300 ENM-enabled consumer products already on the market (Consumer Products Inventory, 2011), and an increasing volume of nanomaterial products expected in coming years, this gap could prove significant.  25  2.3 Assessing the Gaps  2.3.3 Data, Uncertainty, and the Burden of Proof The burden of proof for demonstration of risk rests with federal regulatory agencies rather than manufacturers (drugs, food additives, and pesticides excepted). Agencies generally operate on the principle of ‘safe until proven harmful’, which limits their options under conditions of high uncertainty. Yet, high uncertainty is a defining feature of ENMs - it pervades nearly every aspect of the risk assessment framework for nanomaterials, complicating and radically limiting regulators’ ability to evaluate risks (Kandlikar et al., 2007). With little information available, regulators face great difficulty in understanding and anticipating risks, in modeling potential impacts, and in meeting statutory thresholds for adequate evidence before a product can be regulated (Hanson, Harris, Joseph, Ramakrishnan, & Thompson, 2011).  At the pre-market stage, chemical manufacturers are required to submit all available substance information and EHS data to the EPA (through a Pre-Manufacture Notice or Significant New Use Notice under TSCA) to allow for the assessment of potential health and environmental implications of chemical substances. However, manufacturers are not required by TSCA to test new chemicals before they are submitted for review, and a recent Government Accountability Office (GAO) report found that companies generally do not perform voluntary testing (Jeffords, Lautenberg, & Leahy, 2005a). Given the scarcity of substance specific in vivo or in vitro toxicity testing data, regulators must either rely solely on less-precise analytical tools (QSARs, ‘read-across’ methods) to estimate risks (Morris et al., 2010) or they must promulgate a test rule (TSCA Section 4 Test Rules, 1972). Yet the test rule itself depends on the EPA demonstrating that a substance may present an unreasonable risk of injury to health or environment. This catch-22 is not easily overcome (Davies, 2005). As of 2005, formal “test rules” have been issued for just 185 of the 80,000 chemicals on the TSCA inventory (Jeffords, Lautenberg, & Leahy, 2005a). For the case of nanoscale substances under TSCA, the need for ENM specific toxicity testing data is paramount since many of the assessment tools developed for bulk chemicals are not yet suitable for nanomaterial assessment, as described above. When chemical assessment tools do not work, and toxicity testing data is not available, the EPA task of assessing and controlling potential risks from ENMs is an impossible  26  2.3 Assessing the Gaps  one. Given these challenges, the EPA may be limited to negotiating consent orders on a case-by-case basis to obtain additional information needed to assess the safety of ENMenabled products (Jeffords, Lautenberg, & Leahy, 2005a).  Dietary supplements and cosmetics regulated under the FFDCA at the post market stage present similar challenges for the FDA. They must prove that a supplement “present(s) a significant or unreasonable risk of illness or injury” before a product can be removed from the market (FFCDA Adulterated Food, 2002). However, the FDA may only become aware of ENMs used in dietary supplements if noted in ‘new dietary ingredient’ notifications or in adverse-effects reports (US Food and Drug Administration, 2007). For cosmetics, this is equally so, and no mandatory reporting requirements exist for ingredients or adverse-effects, hence, again agencies must resort to the inefficacies of voluntary reporting. In sum, the FDA’s limited ability to collect basic product information makes it difficult to both identify potentially hazardous conventional or ENM product ingredients, and to justify the removal of a product from the market (Breggin et al., 2009; US Food and Drug Administration, 2007).  Under RCRA, at the end-of-life stage, a nanomaterial must first be recognized and ‘listed’ as a hazardous waste, or shown to exhibit hazardous characteristics (Hester, 2006; Mandel, 2008; Powell et al., 2008). Relatively straightforward procedures are used to assess hazards based on ignitability, corrosivity, and reactivity characteristics. However, modeling techniques used to estimate toxicity and potential for release (from landfills) are not clearly applicable or accurate for ENMs (Hester, 2006), and little data exists to directly assess environmental implications (Powell et al., 2008). With little information available to evaluate ENM hazards, it is unlikely that nanomaterials will be specifically managed under RCRA until relevant data and nanomaterial-specific models become available. The Clean Water Act (CWA) and Clean Air Act (CAA), like TSCA and FFDCA, also require sufficient data to be available before the EPA can consider a nanomaterial a ‘pollutant’ (e.g., a Hazardous Air Pollutant) and so build a case for regulation. Complicating this challenge is the current limitation in technology for monitoring and controlling nanomaterials in air or water effluents, discussed above. In  27  2.3 Assessing the Gaps  order for the EPA to regulate a pollutant, the agency must first be able to distinguish between different types of nanomaterials to identify those that pose a risk (Ternes, 2006). However, a recent EPA report found that existing methodologies and technologies are not yet equal to the task, and fall short of adequately addressing needs for detecting and characterizing nanoparticles (Science Policy Council, 2007). Additionally, current emissions-control technologies may not be effective for reducing nanomaterial releases into the environment (Powell et al., 2008; Ternes, 2006). Ultimately, if nanomaterials cannot be detected and controlled in effluents, CAA and CWA are inoperable (Davies, 2005).  For occupational exposures to ENMs, OSHA has the authority to issue substance-specific standards such as Permissible Exposure Limits (PELs) if a significant risk of harm can be demonstrated (Balbus, Florini, Denison, & Walsh, 2006). One report suggested however that it would be “virtually impossible to [currently] meet the statutory thresholds for regulation” under the OSHAct (Lin, 2007), and it may be years before accurate data for defining nanomaterial specific standards are available (Balbus et al., 2006). Without substance-specific standards, general respiratory-protection standards for “particulates not otherwise regulated” (a.k.a. nuisance dust) would apply, with a limit of 5 mg/m3 for inhalable particles (Balbus et al., 2006). However, as discussed earlier, mass-based bulkmaterial standards for dose do not account for large surface-to-volume ratio and high potential for toxicity per unit mass of ENMs (CPSA, 1972; Oberdörster, Stone, & Donaldson, 2007). By comparison, NIOSH has recently published a draft recommended exposure limit (REL) for carbon nanotubes of 7 ug/m3, nearly one thousand times more restrictive than the nuisance dust standard (NIOSH Current Intelligence Bulletin, 2010; Wu & Janssen, 2011; Wu et al., 2009). For nanomaterials that have similar potency to CNTs when inhaled, the existing nuisance dust standard may not be sufficiently precautionary to protect the health of workers in ENM manufacturing facilities.  2.3.4 Risk Reassessment Products that undergo risk review and authorization under FIFRA and FFDCA at the ‘pre-market’ stage may be subject to a re-assessment in the future as new information and  28  2.3 Assessing the Gaps  analytical techniques emerge. FIFRA, for example, calls for the assessment of risks across the entire pesticide life cycle, and requires that pesticides be reregistered every fifteen years allowing the product to be banned if deemed harmful given new information (FIFRA, 1972; Wu et al., 2009). Conversely, under TSCA, a risk review is a one-time event. Regulators have 90-days under a Pre-Manufacture Notice to consider all available data and assess the potential for health and environmental risks along the product life cycle. However, the available data may not include substance specific in vitro or in vivo testing data, potentially limiting the accuracy of their assessment. In addition, since products are often novel and may not be on the market when this initial analysis is performed, very little real-world data from the ‘use’ and ‘end-of-life’ stages may be available.  For nanoscale materials, this assessment challenge is compounded by high uncertainty, scarce data, and a lack of suitable models for assessing health and environmental implications, as described above. Following this initial EPA review, when improved assessment tools are developed, or additional use, hazard, and exposure information becomes available, there is no automatic mechanism for the reassessment of risks (Denison, 2009; Hester, 2006; Ternes, 2006). Such a reassessment would occur only after a SNUR is issued or if the EPA is able to negotiate an agreement outside of the formal regulatory framework. These additional burdens will likely impede the reassessment of nanomaterials in the future when our understanding of ENM properties, behavior, and implications are better developed, placing tremendous pressure on EPA regulators to ‘get it right’ during their initial review. In a rapidly changing field such as ENM risks, adaptive management is clearly desirable; yet the current regulatory framework provides little room for innovation and change.  2.3.5 Post-Market Risk Management Challenges While FIFRA and TSCA enable agencies to perform risk reviews considering the nanomaterial life cycle from cradle-to-grave, CPSA and FFDCA do not explicitly address end-of-life or environmental implications. CPSC oversight, for example, is limited to health risks at the ‘use’ stage, but does not apply to environmental risks owing to a  29  2.3 Assessing the Gaps  product’s disposal (CPSA, 1972; Gottschalk & Nowack, 2011). Similarly for products regulated under FFDCA, the environmental and health implications due to routine disposal or inadvertent entry into the environment are not considered. Drug risk assessments under FFDCA, for example, focus on the harmful implications of a drug for potential users. They do not consider the implications for the broader population or the environment, such as when expired drugs are flushed and enter aquatic ecosystems or drinking water (Jeffords, Lautenberg, & Leahy, 2005a; Wu et al., 2009; Wu & Janssen, 2011). The FDA does require that pharmaceutical companies submit an Environmental Assessment (EA) under the National Environmental Protection Act (NEPA) to evaluate potential environmental impacts. However critics conclude it is unlikely that this authority would adequately restrict pharmaceuticals from the environment (Alderton et al., 2010; Breggin et al., 2009; Falkner, Breggin, Jaspers, Pendergrass, & Porter, 2009; Hanson et al., 2011; Jeffords, Lautenberg, & Leahy, 2005b; 2005a; Wu et al., 2009).  The existing regulatory regime therefore relies heavily upon CAA, CWA and RCRA to manage potential environmental and end-of-life risks from consumer products, drugs, food additives, supplements, and cosmetics once they (or their component ingredients) move beyond the ‘use’ life cycle stage. These regulations are expected to provide adequate authority to enable the EPA to regulate ENMs in instances where the potential for harm can be clearly demonstrated (Hester, 2006; Jeffords, Lautenberg, & Leahy, 2005a; Ternes, 2006). However, as described above, they face significant challenges in their ability to classify an ENM as a pollutant or hazardous substance, and may not be triggered once ENMs move beyond the ‘use’ stage of the life cycle. A recent review by Gottschalk and Nowack (Gottschalk & Nowack, 2011) estimates that sewage sludge, wastewater, and waste incineration of ENM containing products at their end-of-life will constitute the major flows through which ENMs end up in the environment. If these releases and associated risks are not anticipated at the pre-market stage under TSCA or FIFRA, and the CAA, CWA, and RCRA are not expected to come into effect until an ENM is classified ‘hazardous’ and appropriate monitoring and control technologies for ENMs are developed, then a substantial proportion of ENMs will likely enter the environment un-monitored and un-controlled. Currently there are no nanomaterial  30  2.3 Assessing the Gaps  specific regulations for ENMs in air or water emissions, and no ENM classified ‘hazardous waste’ under RCRA.  2.3.6 Confidential Business Information (CBI) and Limited Resources In addition to the regulatory challenges posed by scarce data and high uncertainty, regulators are generally limited in what information they can share outside of their agency. Under TSCA, for example, manufacturers can claim data as ‘confidential business information’ (CBI) with relative ease, thereby restricting data to review by EPA regulators only (Jeffords, Lautenberg, & Leahy, 2005a). While designed to protect proprietary information, CBI claims are widely recognized as problematic due to their overuse (Alderton et al., 2010; Breggin et al., 2009; Falkner et al., 2009; Hanson et al., 2011; Jeffords, Lautenberg, & Leahy, 2005a; 2005b). A recent GAO report, for example, found that 95% of Pre-Manufacture Notices (PMNs) submitted to the EPA contain some information that chemical companies claim as confidential (Jeffords, Lautenberg, & Leahy, 2005a). Excessive CBI claims limit the availability of valuable chemical property, use, exposure, and testing information to other regulatory agencies and stakeholders along the ENM life cycle, and thereby hamper risk assessment, mitigation, and emergency response planning efforts. They also limit the data available to researchers, and so constrain the development of new methods, analytics, and decision support tools that are urgently needed. Greater transparency and access to risk related data would remove a sizable barrier to the responsible stewardship and development of ENM products along the life cycle.  Another ongoing criticism of federal environment, health, and safety regulation is the serious limitation in resources available to agencies to manage risks from a growing number of products and practices (Breggin et al., 2009; Davies, 2005; 2008; Felcher, 2008; Hanson et al., 2011; Lin, 2007; Mandel, 2009). The CPSC for example has jurisdiction over more than 15,000 types of consumer products (Felcher, 2008; Pelley & Saner, 2009), yet is hampered by a small number of staff and extremely limited budget (Breggin et al., 2009; Lin, 2007). With the expected growth of nano-enabled consumer products in the coming decade (Lux Research, 2004), regulatory agencies are expected to  31  2.4 Implications for Nanomaterial Oversight  face a significant challenge managing products containing nanomaterials given the limited resources at their disposal (Davies, 2009; Felcher, 2008; Lin, 2007). The result may be a greater reliance on screening-level assessments, and voluntary, guidance-based approaches, rather than in-depth review and comprehensive management of risks (International Council on Nanotechnology, 2011; Department of Energy, 2007; ISO 12885, 2008; Zumwalde, 2009). While increases in resources are essential for some agencies, these resources will be most effectively utilized when problematic substances can be identified among a growing sea of largely benign products. New assessment tools designed to rapidly identify materials worthy of in-depth review can help regulators focus precious resources on understanding health and environmental implications of the fewer bad apples in the bunch (Beaudrie & Kandlikar, 2011). Increases in agency resources should be matched by increased support for the development of new structure-activity relationships tools that allow regulators to quickly recognize nanomaterial characteristics that indicate the potential for harmful or environmental implications (NRC National Research Council, 2012).  2.4  Implications for Nanomaterial Oversight  It is clear that under the current system a number of nano-products will be exempt from regulations, will not trigger thresholds for applicability, and may not be managed until they are categorized as ‘hazardous’ (under RCRA, OSHAct), determined to be unsafe (CPSA, FFDCA), or can be specifically monitored and controlled (CWA/CAA). For those ENMs that do undergo assessment, limitations in existing models and methods, and a general lack of risk relevant data limit the scope and depth of the risk review. Further, many of these materials will undergo a one-time assessment, with no automatic reevaluation when new information becomes available and our understanding of nanomaterial behavior improves. As a result, a multitude of nanomaterial products will make it through their life from cradle-to-grave with minimal regulatory oversight. Figure 2.2 highlights the regulations that face the most serious challenges, and illustrates the many points along the life cycle where ENMs can fall through regulatory gaps.  32  2.4 Implications for Nanomaterial Oversight  Figure 2.2 Federal EHS regulations over the nanomaterial life cycle considering regulatory challenges posed by ENMs. Solid outlines indicate regulations that are expected to enable comprehensive risk review and risk management for ENMs. Dashed outlines indicate regulations that are not expected to enable agencies to comprehensively review risks or impose measures for managing risks from ENMs.  To better understand the implications of these challenges for existing regulations, consider the case of carbon nanotubes (CNTs). A 2011 analysis identified nineteen CNT producing companies in the United States, with estimated production volumes for eleven large companies between 5 and 100 tonnes/yr, and for eight small companies between 10 and 100 kg/yr (Hendren et al., 2011). While many have issued concerns that EMNs may escape full EPA review under TSCA as ‘existing’ substances (i.e., CNTs are technically allotropes of carbon already listed on the TSCA Inventory), the EPA currently recognizes that specific structural characteristics differentiate CNTs from other forms of carbon, and hence require producers to submit a premanufacture notice (US Environmental Protection Agency, 2008). Carbon nanotubes are thus reviewed on a case-by-case basis at the ‘production’ stage of the life cycle, and a number of SNURs have been issued or proposed for different types and uses of CNTs (US Environmental Protection Agency, 2010; 2011b; 2011a; 2012). However, this case-by-case approach may set an unsustainable path for the evaluation of CNTs – there are over 50,000 possible permutations of single-walled CNTs alone (Schmidt, 2007), each with potentially  33  2.4 Implications for Nanomaterial Oversight  different properties. The EPA faces an increasing assessment burden with the expected growth of the CNT market. Furthermore, since many large and small CNT producers are estimated to manufacture less than 10 tonnes per year, several variations of CNTs may escape a comprehensive risk review under TSCA’s heretofore mentioned Low Volume Exemption. Under the FFDCA, food and drug products that utilize CNTs will likely undergo a full FDA review at the ‘production’ stage, though CNT enhanced food packaging may avoid a full review under the Food Contact Notification (FCN). CNTs used in pesticide applications would also be subject to registration and full EPA review under FIFRA if the manufacturer claims its use for pesticidal purposes.  Basic protective practices are required for occupational risks under OSHAct, and a growing body of guidance material for safe practices is currently available, including a NIOSH Recommended Exposure Limit for CNTs (NIOSH Current Intelligence Bulletin, 2010). However there are no enforceable carbon-nanotube-specific exposure limits to date (Schulte, Murashov, Zumwalde, Kuempel, & Geraci, 2010), leaving management of workplace risks largely voluntary. At the ‘use’ stage, while the CPSC is involved in EHS research funded by the National Nanotechnology Initiative to develop protocols to evaluate human exposure (NNI, 2011), without evidence of risk or harm caused by a specific CNT enabled product, no formal risk assessment or management will be triggered at this stage. Further, CNTs used in cosmetics do not require pre-market approval or FDA notification for ingredients or adverse effects at this stage. They will rely instead on manufacturers to voluntarily assess, manage, and report risks to human health. At the ‘end-of-life’ stage, RCRA would apply to hazardous wastes generated by CNT manufacturing, but only if the CNTs clearly demonstrate acutely hazardous properties (i.e., ignitability, corrosivity, reactivity), or are specifically classified as ‘hazardous’ by the EPA. Consumer wastes containing CNTs deemed hazardous will escape oversight under RCRA as ‘household hazardous wastes’, and will end up in landfills or waste incinerators. Finally, without nanomaterial specific requirements, and a lack of appropriate technologies for characterizing, monitoring, and controlling ENM emissions,  34  2.4 Implications for Nanomaterial Oversight  environmental releases of CNTs to air and water along the life cycle would be subject only to general requirements under CAA & CWA, allowing CNTs to enter the environment through industrial effluents.  The CNT example illustrates a lack of comprehensive and consistent oversight along the life cycle. Many ENM products are likely to avoid risk review and management at the post-market stage (during consumer use, when discarded as waste, or when released to air or water), highlighting the importance of capturing ENM products under the full regulatory review process before being brought to market. However, a collection of categorical and threshold exemptions under TSCA, FIFRA, and FFDCA mean that a number of ENMs will avoid rigorous review at the pre-market stage as well. Without premarket assessment, and without appropriate technologies and adequate risk information to trigger environmental and end-of-life regulations, many ENMs will avoid rigorous risk review and management across each stage of their life cycle. Table 2.1 summarizes applicable regulations and gaps for several ENM product categories across their life cycle.  35  2.4 Implications for Nanomaterial Oversight  Table 2.1 Regulatory trajectories for common ENM product categories. Color-coding indicates the adequacy of current regulations for enabling the assessment and management of risks from ENMs across product categories and life cycle stages. Green indicates that the regulation provides an adequate basis for managing ENM risks and few exemptions or gaps exist (as discussed above). Yellow indicates that regulations provide an adequate basis for managing ENM risks, but exemptions and other gaps limit their effectiveness. Red indicates that significant challenges and gaps render the regulation inadequate and practically ineffective for managing potential ENM risks Life Cycle Stage ENM Product Category ENMs as 'NEW' Chemical Substances, or a 'new use' of an existing substance ENMs based on 'EXISTING' Chemical Substances  Product Fabrication (Pre-Market)  End-of-Life  Environment  TSCA - No risk review for ENMs based on substances currently on the TSCA inventory ('Existing Substances') FIFRA - Risk review considers implications for human health and environment across the life cycle of a ENM pesticide. Regulations apply only to substances 'claimed' to be pesticdes FFDCA - Risk review considers implications for human health only Food additives considered 'GRAS' exempt from review Exemption available under a Food Contact Notification (FCN)  Food Additives or Drugs  FFDCA - Authority to recall unsafe FFDCA - No risk review or authority to products. Reporting of ingredients is restrict supplements at the 'pre-market' voluntary except for 'new dietary stage ingredients' and adverse-effect reports. FFDCA - No risk review or authority to  Cosmetics restrict cosmetics at the 'pre-market' stage  FFDCA - Reporting of ingredients and product recalls are voluntary.  FFDCA - Risk review does not consider environmental or human health implications from discarded products NEPA - manufacturers may be required to submit an Environmental Assessment for end-of-life and environmental implications of discarded products  FFDCA -Does not consider environmental or human health implications from product manufacturing, environmental releases, or discarded supplements  FFDCA -Does not consider environmental or human health implications from product manufacturing, environmental releases, or discarded cosmetics  CPSA - No risk review or authority to  Consumer Products restrict consumer products at the 'pre-  CPSA - Case-by-case risk review and product recalls, limited by agency resources  market' stage  Regulations applicable to ALL ENM Product Categories  Occupational  TSCA - Risk review considers implications for human health and environment across the life cycle of an ENM chemical substance. Significant New Use Rule (SNUR) utilized on a case-by-case basis, resource intensive Low Volume Exemption (LVE) for substances produced < 10 tonnes/year may limit review of ENMs  Pesticides  Dietary Supplements  Product Use (Post-Market)  See TSCA, FFDCA, and FIFRA above  CPSA - Risk review and recalls for consumer products containing ENMs is limited by agency resources  CPSA - Does not consider occupational health and safety implications from consumer products OSHA - Currently no substance specific standards for ENMs. General Respiratory Protection Standards may not be appropriate.  RCRA - Limited to ENMs classified as 'hazardous'  CWA/CAA - ENMs must first be classified as a pollutant  Does not apply to household hazardous waste, or to small-scale industrial generators (< 100 kg ENMs/yr)  Monitoring and Control technologies necessary for regulations to be Resource constraints, and challenges enforced, but not currently adequate for regulating substances under high for ENMs. uncertainty  36  2.4 Implications for Nanomaterial Oversight  2.4.1 Closing the Gaps Proposed legislation (Lautenberg, 2011; Rush & Waxman, 2010) to reform TSCA aims to address many of the shortcomings described above, including limiting confidential business information claims, requiring manufacturers to produce minimum data sets, improving authority to request additional data, and increasing public disclosure of product details. There are also proposals for the inclusion of a definition for ‘special substance characteristics’ for size or size distribution; shape and surface structure; reactivity; and any other property that may significantly affect the risks posed (Lautenberg, 2011). This will provide the EPA with greater latitude in determining whether a nanoscale version of an ‘existing’ substance on the TSCA inventory constitutes a ‘new’ substance, closing a sizable regulatory gap (as demonstrated in Table 2.1). Similarly, proposed cosmetics regulation reform would allow for FDA recalls and would require full ingredient disclosure and improved data sharing between agencies while phasing out toxic materials (Schakowsky, 2011). Until these proposed reforms are adopted, the FDA has released draft guidelines for industry and has proposed that nanomaterials used in food contact surfaces be classified as food additives requiring a pre-market review (US Food and Drug Administration, 2012). Similarly, the EPA has focused on developing an information gathering rule, an additional SNUR to require notification for nanoscale materials, and a test rule to require testing for certain classes of nanomaterials already in commerce (Control of Nanoscale Materials under TSCA, 2011). For nanoscale pesticides under FIFRA, the EPA published a proposed rule in 2011 to allow the agency to collect information on engineered nanoscale pesticide ingredients through either a general data collection provision, or through data call-in notices aimed at specific registrants (US Environmental Protection Agency, 2011c).  However, progress on these reforms and proposed regulations have stalled. The Safe Chemicals Act of 2011 and Safe Cosmetics Act of 2011 have yet to be taken up for a vote in the Senate or House respectively, and are not expected to do so in this election year (Bergeson and Campbell, 2012). Additionally, since the EPA regulatory proposals under TSCA and FIFRA in 2010 and 2011, the White House Office of Budget and Management (OMB) have approved none of the proposed rules, and officials have remained silent on  37  2.4 Implications for Nanomaterial Oversight  their status. Some critics argue that this may be an indication of the OMB’s desire to continue with voluntary approaches to avoid the stigmatization of new nanotechnologies (Ambrosio & Rizzuto, 2012). Yet these proposed reforms are urgently needed to improve regulatory oversight and responsible management for both conventional products and engineered nanomaterials. By establishing clear regulations for ENMs, regulators can also reduce ambiguity and encourage innovation and investment in this promising technology. Regulatory uncertainty and delay has been shown to hinder innovation and limit availability of new and potentially transformative products (Hoerr, 2011).  In the absence of regulatory reform, and without the proposed regulations under TSCA, FIFRA, and FFDCA, regulators would have to rely primarily on current research efforts and voluntary approaches to fill data gaps and assess risks. Regulatory agencies can play a strong role in promoting ENM product stewardship and encouraging proactive ‘bottomup’ risk management and shared responsibility in the oversight of health and environmental risks. Such voluntary stewardship-based approaches have been modestly effective in the past, but are subject to a number of challenges that must be addressed to ensure their effectiveness (Bowman & Hodge, 2008a; Helland et al., 2008; Kuzma & Kuzhabekova, 2011; Malloy, 2012; Marchant, Sylvester, & Abbott, 2009). The failure of the EPA Nanomaterial Stewardship Program to collect ENM data voluntarily from industry illustrates this point with a lack of clear incentives for cooperation and perceived disadvantages to cooperating firms if competitors do not follow suit (Beaudrie & Kandlikar, 2011). To overcome these shortcomings, legal scholars have proposed multifaceted governance approaches utilizing a variety of legal, policy, and regulatory tools to address risks in a comprehensive manner (Bowman & Hodge, 2008b; Malloy, 2012; Monica & Van Calster, 2010). The need is clear for innovative approaches to address novel ENM products and novel risks as nanotechnologies gain in complexity and ubiquity (Roco, Harthorn, Guston, & Shapira, 2011). However, without an effective topdown regulatory framework legally obligating producers to comply with information requests, voluntary programs are unlikely to provide regulators with the information they seek.  38  2.4 Implications for Nanomaterial Oversight  2.4.2 Life Cycle Specific Gaps With the gaps identified in this analysis at the pre-market stage, several challenges remain. A serious lack of data, assessment tools, and appropriate thresholds for ENMs, and the exclusion of ENMs based on ‘existing’ substances under current regulations, mean that a significant proportion of ENMs are likely to escape rigorous risk review and oversight. To overcome these obstacles, regulators need access to minimal nanomaterial characterization data (Klaine et al., 2012) and detailed production and use information at the pre-market stage to enable thorough and precautionary screening of ENM risks. It is also prudent to revise inappropriate threshold levels for the triggering of regulations, including reducing the minimum production volume for the Low Volume Exemption for nanomaterials until more is known about the potency of ENMs by mass. The US could follow France, which recently implemented a compulsory declaration scheme with minimal product information for ENMs produced over 100 grams (Verdant Law Blog, 2012). A similar reporting scheme is also in the works in Canada (OECD, 2010), and the California Department of Toxic Substances Control (DTSC) is engaging ENM producers and users in that state through mandatory call-ins, an encouraging sign that regulatory agencies and industry can take a collaborative approach to filling information gaps (Beaudrie & Kandlikar, 2011). Further, the findings in this analysis mirror and lend further weight to calls in the US (Maynard, Bowman, & Hodge, 2011), UK (Frater, Stokes, Lee, & Oriola, 2006; Royal-Society, 2004), and Australia (Bowman & Hodge, 2006; Ludlow, Bowman, & Hodge, 2007), to treat ENM’s based on ‘existing’ substances as new, and to revise regulatory thresholds and exemptions to reflect increased ENM activity at smaller volumes than their bulk counterparts. Finally, the EPA can further instill confidence in their management of ENM risks under TSCA by proposing an automatic re-assessment of risks at a time interval comparable to that used under FIFRA. Regulatory decision making under the currently high level of uncertainty, and with poorly suited tools for assessment, presents a dangerous gap if these nanomaterials are not slated for reassessment when more information becomes available.  It is also imperative that regulators address gaps in environmental and end-of-life oversight, which severely limit the consideration of routine releases of ENMs from  39  2.4 Implications for Nanomaterial Oversight  industrial facilities and in household wastes. This will require both the acceleration of research efforts to develop new ENM monitoring and control technologies for releases to air and water (Science Policy Council, 2007), and the adoption of a proactive risk mitigation approach to handling ENM wastes as ‘hazardous’ until they can be effectively screened using techniques suitable for assessing ENM behavior in the environment. Increases in funding for Life Cycle Analysis of nanomaterial products is also vitally important, including the development of an ENM specific inventory database, and techniques and tools to enable full assessment of ENM releases during production, use, and end-of-life. With a majority of ENM releases expected in wastewater and solid waste streams, a thorough understanding of these releases and development of robust mitigation strategies to minimize harmful implications is urgently needed.  Finally, there is a growing need for risk research funding to keep up with the pace of nanotechnology R&D and nano-product proliferation. The recent President’s Council of Advisors on Science and Technology (PCAST) 4th assessment of the National Nanotechnology Initiative (NNI) states that while funding for nano-EHS research has been growing, NNI spending on EHS research is still $20-25 million shy of National Research Council recommendations (NRC, 2012). Given limited regulator resources, the high costs of in vivo toxicity testing, and sheer numbers of new types and variations of ENMs, a conventional chemical toxicity testing paradigm is impractical for nanomaterials (Choi, Ramachandran, & Kandlikar, 2009). Further investments in research supporting the development of screening level assessment and decision support tools (NNI, 2011), nano-specific models for risk assessment and life cycle analysis (LCA) (Klöpffer, Curran, Frankl, & Heijungs, 2006), and high throughput assays and computational toxicology approaches (Dix et al., 2007; Godwin et al., 2009), will help regulators prioritize actions aimed at managing risks from engineered nanomaterials.  Nanotechnologies promise tremendous benefits for society, but these benefits cannot be fully realized unless the risks are understood and effectively managed along a product’s life cycle. With timely improvements in regulatory oversight and advances in risk  40  2.4 Implications for Nanomaterial Oversight  research for nanomaterials, current and future nanotechnology products can be developed responsibly.  41  3.1 Introduction  Chapter 3 Scientists versus Regulators: Precaution, Novelty & Regulatory Oversight as Predictors of Perceived Risks of Engineered Nanomaterials 3.1  Introduction  Rapid advances in promising new nanotechnologies have been accompanied by mounting concerns over their human health and environmental risks – concerns that are exacerbated by the uncertainties inherent in this still-emerging domain (Kandlikar et al., 2007). Despite growing support for environment, health, and safety (EHS) research (NNI, 2012), decision makers in industry and government are in the very early stages of understanding and managing potential risks. Primary to regulatory conundrums is the question of whether and by whom nanotechnologies are seen as novel and as posing new kinds of risk, positions that many though not all accept (see chapter 2). In situations of high uncertainty, expert opinion plays an important role in informing policy (Cooke, 1991). Still, expert perceptions of nanotechnologies’ benefits and risks have received limited attention. Further, despite the inherently interdisciplinary nature of the nanotechnology enterprise, little is known about how expert opinion on nanomaterial risks differs from one expert group to the next, and what drives those differences. Experts’ perceptions of risk have been studied in many other domains including genetically modified organisms (Gaskell, Allum, Wagner, & Kronberger, 2004; Savadori et al., 2004), chemicals and toxics (Kraus et al., 1992; Mertz, Slovic, & Purchase, 1998; Neil, Malmfors, & slovic, 1994; Slovic et al., 1995), and ecological risks (Lazo, Kinnell, & Fisher, 2000; McDaniels, Axelrod, & Slovic, 2006). Results generally indicate that disciplinary field (e.g., physical versus biological sciences) (Gaskell et al., 2004; Slovic et al., 1995), institutional affiliation (e.g., university versus industry scientist) (Kraus et al., 1992; Slovic, Malmfors, Mertz, & Neil, 1997), demographic position (e.g., gender, age, etc.) (Flynn, Slovic, & Mertz, 1994; Gaskell et al., 2004; Kraus et al., 1992; Slovic et al., 1997), and social-political values (e.g., social or economic conservatism) (Burgman et al., 2011; Krinitzsky, 1993; Slovic et al., 1995) are all strongly predictive of perceived risk (regardless of the technological domain examined). In the nanotechnology case, a few recent studies have begun to identify some key factors behind risk judgments among  42  3.1 Introduction  nanoscientists. Besley et al. found that an experts’ disciplinary field influenced their perceptions of risk (Besley, Kramer, & Priest, 2008), while Corley et al. found discipline, gender, and socio-political values to be significant drivers of experts’ support for risk regulation (Corley, Scheufele, & Hu, 2009). Siegrist et al. discovered that experts’ trust in government agencies was a significant predictor of risk judgments, while Ho et al. similarly found that gender and trust in scientists and government drive perceived risk (Ho, Scheufele, & Corley, 2011; Siegrist, Keller, Kastenholz, Frey, & Wiek, 2007). The perceived adequacy of existing regulations was also found to be inversely correlated with the perceived risk of nanotechnologies among experts (Besley et al., 2008). Other studies suggest a connection (untested empirically) between novelty of nanomaterial benefits and risks and perceived risk of nanotechnologies (Powell, 2007). Virtually all expert studies have focused on investigating the drivers of perceived risk in reference to or within particular expert groups (Y. Kim, Corley, & Scheufele, 2012; Siegrist et al., 2007). These findings suggest that a combination of factors: expert demographics, fields of expertise, and opinions about the risk object as well as the existing regulatory regime are all correlated with perceived risks from nanomaterials. Critically, what we do not know is: What kinds of regulatory approaches at this early stage of nanotechnology development experts support or prefer, how that corresponds to their risk attitudes, and/or how that is also a function of their domain of expertise. This study examines what approaches to regulation experts’ deem most suitable for nanotechnologies, how those viewpoints and perceptions of novelty influence their perceptions of risk, and how perceptions vary due to the particular ‘class’ of expertise to which experts belong. In order to operationalize the influence of different domains of expertise, we examined the perceptions of risks and attitudes toward regulation of three distinct groups of nanotechnology experts: nano-scientists and engineers (NSE, n=180), nano-environmental health and safety scientists (e.g., toxicologists) (NEHS, n=121), and nano-regulators including those who assess and manage risks (NREG, n=103). Based on previous findings that expert perceptions of benefits and risks vary across research and development domains, and that each of these domains suggest different investigatory responsibilities and interests viz. nanotechnologies, our main hypothesis is as follows:  43  3.1 Introduction  1) Nanotechnology experts working on research and development versus EHS implications versus risk regulation will differ significantly on their perceptions of benefits and risks from nanotechnologies.  Second, in considering debates about whether nanotechnologies are new or different from existing technologies, and given previous findings that variation in the perceived novelty of nanotechnologies is evident across experts, we propose two additional hypotheses:  2a) Experts who see nanotechnology benefits as novel (i.e., as a new class of materials or objects with novel properties) will see less overall risk from nanotechnologies compared to those who see nanotechnology benefits as not new (i.e., as little different from their bulk form); and  2b) Experts who see nanotechnology risks as novel (i.e., as a new class of materials or objects with new risks) will see more overall risk from nanotechnologies compared to those who see nanotechnology risks as not new;  Third, given earlier studies of expert perceptions of technologies’ risks and benefits (i.e., those explored in non-nanotechnology domains such as chemical, biotech, or ecological risks), we also expect that:  3) Experts who assign higher levels of perceived risk overall (that is, across other technological domains such as nuclear power and GM foods) will see more risk from nanotechnologies as well, versus those who see less risk from other studied technologies Finally, given findings that experts’ support for regulation and perceived adequacy of regulation are correlated with nanotechnology risk perceptions, we propose a fourth hypothesis:  44  3.2 Methods  4) Experts who prefer more government regulation (of nanotechnologies) and a more precautionary approach to risk management will see greater risk from nanotechnologies compared to those who view regulations as adequate, and who prefer market-based approaches to risk management.  3.2  Methods  The data reported here were collected through a web-based survey (N=404), designed to assess US & Canadian nanotechnology experts’ perceptions of risks and regulation. The survey was conducted by the University of California Santa Barbara Social Science Survey Center for the UCSB Center for Nanotechnology in Society between June 2nd and November 8th, 2010. To construct the sample frame, we compiled names and detailed contact information for 2,100 experts within three pools of US and Canadian experts: nano scientists and engineers (NSE), nano-EHS scientists and toxicologists (NEHS), and scientists and regulators in government agencies (NREG). Subjects were contacted by email in a three-step process, including initial contact and two reminders at two-week intervals. Respondents received an ‘A’ or ‘B’ version of the survey at random, where the wording of several survey questions were modified to reverse the meaning of the question. Questions with alternate wording were reversed-coded during analysis to enable direct comparison of responses. Where appropriate the sequence of questions was also varied to minimize order effects.  For the NSE group, experts were selected using a rigorous sampling design, based on a bibliometric analysis methodology developed by Porter et al. (A. L. Porter, Youtie, Shapira, & Schoeneck, 2008) to identify nanotechnology publications using ISI Web Of Science. We excluded papers with the following terms to remove publications that would fall under our NEHS sampling strategy: toxic* or genotoxic* or ecotoxic* or (oxidative stress) or safety or pollution or (environmental health) or (human health) or (animal health) or (public health) or (occupational health). Results were limited to articles and review papers by authors in the US and Canada. 1,200 subjects were selected at random from a pool of over 5,700 first or corresponding authors who published five or more nanotechnology articles that were cited five or more times between 2000 and 2009 (a  45  3.2 Methods  method utilized by Scheufele et al. (2007)), with at least one article newer than 2006. Database searches were conducted between August and September 2009.  NEHS experts were selected from first or corresponding authors of 1,600 articles entered into the International Council on Nanotechnology (ICON) Environment, Health and Safety Database between early 2007 and spring 2010. Due to the relatively small domain of nano EHS research, we could not apply the same rigorous NSE standard of selecting authors with five or more publications, and instead selected 500 experts at random from a list of over 1,600 authors. International contacts were removed from the list, and several authors listed with .gov email suffixes were cross-referenced with the NREG group for duplications, and removed from the NEHS group.  NREG experts were identified from nanotechnology conference attendance lists, referrals, and website searches of employees in nanotechnology groups in US and Canadian Federal Regulatory agencies (including EPA, OSHA, FDA, CPSC, Health Canada, Environment Canada) and within Federal research institutes (NIOSH, NIH, national labs), as well as US State regulatory agencies (including Massachusetts Department of Environmental Protection, New York Department of Environmental Conservation, California EPA, North Carolina Department of Environmental and Natural Resources, and Washington Department of Ecology). Contact information and agency affiliation were compiled for 400 NREG experts in spring 2010. A full list of agencies is available in Appendix B.  A total of 404 responses were analyzed, for an overall response rate of 23% (AAPOR RR-3: 23%). In total 255 participants specified their residence in the US, while 55 reside in Canada, and 94 did not disclose their country of residence, and so might belong to either country. Individual group response rates were: NSE: N=180, RR=16%; NEHS: N=121, RR=33%; NREG: N=103, RR=32%. We believe the relatively low response rate of the NSE group is due to a large number of outdated mail and email addresses (our search criteria includes publications since 2000). Contacts may have moved institutions or changed email addresses since the date of publication, and therefore were not  46  3.2 Methods  measured as ‘bounced’ or ‘out-of-scope’. Separate response rates for the US and Canadian groups were not possible since not all respondents indicated their country of residence in their survey responses. Statistics were calculated using the SPSS software package (IBM, 2012). Table 3.1 outlines a breakdown of demographic and domain of expertise variables across the three expert groups. Table 3.1 Demographic and Domain of Expertise variables by expert group NSE  NEHS  NREG  (N=171)  (N=143)  (N=110)  1990.1 (11.3) 89.1%  1994.0 (10.4) 60.2%  1992.3 (10.2) 64.9%  PhD degree (or professional degree e.g., MD, DVM, Doc of Law)  99.3%  98.9%  48.7%  Masters degree  0.7%  0.0%  35.9%  Bachelors degree  0.0%  1.1%  15.4%  0.64 (0.28)  0.57 (0.30)  0.34 (0.34)  Academic  81.9%  89.4%  0.0%  Government  8.0%  1.1%  97.4%  Other (private sector, NGO, or specified response)  10.1%  9.6%  2.6%  Physical Sciences (only)  85.0%  13.7%  6.4%  Biological, Environmental, and Health Sciences (only)  6.4%  60.0%  50.0%  Policy, Management, and Social Sciences (only)  0.7%  7.4%  17.9%  Phys and Bio Sciences (both indicated)  7.9%  15.8%  7.7%  Bio and Policy (both indicated)  0.0%  3.2%  16.7%  Involvement in Research  99.3%  94.7%  43.6%  Demographic Variables  Year of highest degree (mean (SD)) Gender (% Male) Education  Domain Of Expertise Variables  Proportion of time working on nano (mean (SD)) Affiliation  Disciplinary Field  Research Notes: All values (except for ‘year of highest degree’ and ‘proportion of time working on nano’) indicate the distribution of respondents by group for each variable (out of a total of 100%). Figures for the ‘year of highest degree’ and ‘proportion of time working on nano’ scales indicate mean scores and standard deviations.  47  3.3 Results  Questions used to examine the above hypotheses are detailed in each relevant findings section. In brief, however, we relied upon two relevant question sets: 1) Those addressing assessments of perceptions of nanotechnologies’ newness, and their benefits, properties, and risks; and we elicited evaluations of uncertainty and the suitability of existing methods for testing risks. 2) A second set of questions looked at preferences for regulatory approaches, judgments about the suitability of existing regulations and tools for managing risks from technologies in general, and nanotechnologies in particular.  3.3  Results  3.3.1 Benefits versus Risks of Nanotechnologies In order to understand comparative risk judgments across different expert groups, we asked respondents to evaluate overall benefits versus risks of nanotechnologies both in general, and across multiple different NT applications. On the first point, participants were asked: “Overall, do you think that: ‘1 - the risks of nanotechnology will greatly outweigh its benefits’, ‘2 - risks will somewhat outweigh its benefits’, ‘3 - risks will equal its benefits’, ‘4 - the benefits of nanotechnology will somewhat outweigh its risks’, ‘5 benefits will greatly outweigh its risks’. Respondents were also given the option to choose ‘Don’t know / not sure’. Figure 3.1 provides a summary of the results across expert groups. All three groups see that benefits somewhat or greatly outweigh risks, while for a small minority risks equal or outweigh benefits. NSE respondents most strongly supporting the stance that benefits somewhat or greatly outweigh risks (NSE – 81%, NEHS – 66%, NREG – 58%). The largest difference between groups is observed for the ‘benefits greatly exceed risks’ response chosen by 61% of the NSE group, compared to 44% for the NEHS group, and 28% for the NREG group. Strikingly, the highest rate of ‘Don’t know’ responses came from the NREG group at 23%, followed by NEHS at 16% and NSE at 11%. Taken as a proxy measure for confidence in their judgment, this indicates that NREG respondents are more hesitant to make a judgment than their counterparts when evaluating risks versus benefits. In summary, NSE respondents as a group view that benefits predominantly outweigh risks, demonstrate great confidence in their stance, and have relatively few undecided 48  3.3 Results  responses. Fewer experts whose research focuses on the risk implications of nanotechnologies (NEHS, NREG) demonstrate the combination of high benefit to risk ratio and low rate of ‘don’t know’ responses.  Figure 3.1 "Benefit versus Risk" ratings for nanotechnologies in general. Color-coded bars indicate the proportion of respondents in each expert group (NSE, NEHS, and NREG) choosing the indicated response  We calculated mean ‘benefit vs. risk’ scores for each expert group, and found a significant difference between groups using a one-way ANOVA (F(2, 298) = 10.328, p .004). A Tukey HSD post hoc analysis revealed that the ‘Benefit vs. Risk’ score was significantly lower for both NEHS (N=92, 4.16 +/- 1.1; p = .02) and NREG (N=67, 3.87 +/- 1.2; p < .001) groups than for NSE (N=142, 4.53 +/- 0.86). However, there was no statistically significant difference between NEHS and NREG groups (p = .166). This result partially supports our first hypothesis that perceptions of benefits and risks might differ significantly across groups, though no significant difference was found between the NEHS and NREG groups.  49  3.3 Results  3.3.1.1 Differences in Risk Perceptions of Nanotechnology Scenarios by Expert Group On the 2nd point, comparing experts across multiple NT applications, participants were presented with 14 nanotechnology scenarios and asked to rate each using the following question: “From the following list of nanomaterial applications and situations, please indicate whether you think they pose almost no risk, slight risk, moderate risk, or high risk to society”. This four-point scale indexes ‘1’ as ‘almost no risk’ through ‘4’ as ‘high risk’; also provided was the option: ‘don’t know / not sure’. These scenarios include situations in which nanomaterials may be encountered (e.g., in occupational settings) or released (e.g., in air or water emissions during production), and applications such as nanomaterial use in cosmetics or fuel additives. A full description of each scenario can be found in Appendix B. Figure 3.2 illustrates the results for the four scenarios, where points on color-coded lines indicate the mean risk score for each expert group (NSE, NEHS, and NREG).  Figure 3.2 'Risk Perception' ratings for NSE, NEHS, and NREG expert groups. Mean scores for each group are indicated with points on respective color-coded lines capturing 14 different nanotechnology scenarios rated between ‘almost no risk’ and ‘high risk’. Significant differences in means were determined using a one-way ANOVA with post hoc analysis, and are indicated with a, b, and c markings as outlined in the legend  50  3.3 Results  We find small but consistent differences in risk judgments between expert groups for a majority of scenarios, and a uniform trend in risk ratings across scenarios, creating roughly parallel response patterns for each group. The similarity in relative ratings of scenarios suggests a high degree of agreement between expert groups over the risk posed by one scenario relative to another. However the data illustrate that the NSE respondents perceive less risk for each scenario, while NREG respondents see the most risk, with NEHS respondents in the middle. This finding illustrates clear differences in risk perceptions between groups, which is most pronounced for the case of nanomaterials in occupational settings. Nanomaterial based computer chips receive the lowest risk rating of all scenarios.  To confirm that the observed differences in risk perceptions were significant across all 14 scenarios, we conducted a one-way between subjects Analysis of Variance (ANOVA). In this analysis, each of the fourteen scenarios was used as a dependent variable with expert group (NSE vs. NEHS vs. NREG) as the independent factor, followed by Levene’s test of homogeneity of variance. We found that the assumption of homogeneity of variances of groups was maintained for 12 of 14 scenarios, and a separate Welch test was conducted in place of the ANOVA test for the two scenario variables with non-homogeneous variances. ANOVA and Welch test results indicated significant differences in means at the p<0.05 level for 9 of 14 scenarios. A Games-Howell post hoc analysis was then conducted, with significant differences found between NSE and NREG groups on 8 of 14 scenarios, between NSE and NEHS on three scenarios, and between NEHS and NREG on just one scenario, as indicated in Figure 3.2. For complete results see Tables B.1 and B.2 in Appendix B. An additional one-way ANOVA test found no significant difference in risk perceptions between the US and Canadian respondents for each of the 14 nanotechnology scenarios. All remaining analyses performed use an aggregated sample of Canadian and US respondents within each expert category.  3.3.1.2 Differences in Composite Nano Risk Index by Expert Group To determine whether the difference in means by expert group was still significant when considering all 14 nanotechnology scenarios together, we created a composite index 51  3.3 Results  (hereafter referred to as ‘Nano Risk Index’) using a principal component analysis (PCA) with orthogonal rotation (varimax). Based on a scree test we confirmed that only one component accounting for 47% of the variance was adequate to explain the correlations among the 14 nanotechnology scenarios. Cronbach’s alpha (α = 0.92) is evidence that the scale is internally consistent and highly reliable. Nano Risk Index factor scores were calculated using the Anderson-Rubin method, producing scores with an overall mean of zero and standard deviation of 1. Using a one-way ANOVA test we found a statistically significant difference in mean Nano Risk Index scores between groups (F(2, 401) = 9.166, p < .0001). A Tukey HSD post hoc analysis revealed that the Nano Risk Index score was significantly higher for both NEHS (N=121, 0.07 +/- 0.97; p = .03) and NREG (N=103, 0.26 +/- 0.90; p < .001) groups than for NSE (N=180, -0.20 +/- 0.84). However, there was no statistically significant difference between NEHS and NREG groups (p = .255). This result partially supports the hypothesis that risk perceptions differ significantly between NSE and NEHS groups and between NSE and NREG groups. However, our hypothesis was not supported regarding the difference in risk perceptions between NEHS and NREG groups. 3.3.2 Drivers of Perceived Risks 3.3.2.1 Novelty, Regulatory Preferences, and Technology Risk Indices We considered several hypotheses to explain the observed differences in nanotechnology risk perceptions across expert groups. To facilitate hypothesis testing and analysis we developed three sets of index variables based on survey responses: ‘Perceived Novelty of Benefits and Risks’; ‘Perceived Technology Risks’; and ‘Preferences for Regulation’. To test hypothesis 2, that a) experts who see benefits as novel will perceive less risk, and that b) experts who see risks as novel will perceive more risk from nanotechnologies, we developed two indices based on survey questions measuring seven dimensions of novelty. For each novelty item, the following Likert scale was used: 1 – Strongly Disagree, 2 – Disagree, 3 – Agree, 4 – Strongly Agree. Table 3.2 shows the factor loadings for two orthogonal components based on a principal component analysis (PCA) with varimax rotation. These are measured as ‘New and Uncertain Risks‘ (5 items, α = 0.65)’ and ‘Novel Benefits and Properties’ (2 items, α = 0.74). Together these two components  52  3.3 Results  account for 53% of the variance. Factor scores were calculated using the Anderson-Rubin method to create orthogonal factor scores with a mean of 0 and standard deviation of 1. Both factors were included in the regression analysis below to examine their influence on nanotechnology risk perceptions.  Table 3.2 Loadings from a principal components analysis over seven rating scales averaged across individuals (VARIMAX rotated solution) Factor 1: New and Uncertain Risks (31.9% of var.)  Factor 2: Novel Benefits and Properties (20.8% of var.)  .10  .87  .08  .87  Properties Cannot be Anticipated *  .54  .17  New Risks4  .56  .24  Risks are Not Well Known5*  .76  -.16  .73  -.02  Rating Scale New Benefits1 Novel Properties  2 3  6  Risks Cannot be Determined * 7  More Uncertainty .56 * Items are reverse coded to facilitate comparison  .16  Notes: Loadings exceeding 0.3 are in boldface. 1. Nano-scale materials promise benefits for society that are not possible with bulk (non nano-scale) materials 2. Nano-scale materials possess novel properties that are not expressed in their corresponding bulk forms 3. The novel properties of nano-scale materials cannot be anticipated by knowing the properties of the same material in its bulk form 4. Nano-scale materials pose risks for society that are not present with bulk (non nano-scale) materials 5. The health and environmental risks from nano-scale materials are not well known to scientists 6. The existing methods for assessing health and environmental risks from bulk materials are not suitable for determining risks from nano-scale materials 7. There is more uncertainty about the risks from nano-scale materials than the risks from bulk forms  To test hypothesis 3, that experts’ attitudes towards risks from technologies in general influence their perceptions of risk for nanotechnologies specifically, we developed a comprehensive technology risk index (hereafter referred to as Tech Risk Index). Respondents were presented with 10 technologies commonly investigated in the risk perceptions literature, and asked to rate each scenario on the following scale: 1 – Almost No Risk, 2 – Slight Risk, 3 – Moderate Risk, 4 – High Risk. Technology scenarios 53  3.3 Results  included GM crops, cell phone communications, nuclear power plants, food additives and preservatives, prescription drugs, pesticides and herbicides, biofuels, vaccines, lead in paint or dust, and non-prescription vitamins and supplements. A principal component analysis (PCA) with orthogonal (varimax) rotation was performed with all ten scenarios. Based on a scree test we found that one component explained correlations between all ten scenarios, accounting for 30% of the variance. Cronbach’s alpha suggests a highly reliable scale (α = 0.74). Tech Risk Index scores were calculated using the AndersonRubin method to create orthogonal factor scores with a mean of 0 and standard deviation of 1. Given that the Tech Risk Index measures risk perceptions across a comprehensive set of technologies, we expect the index to provide a baseline measure of an expert’s perceptions of technology risks. To test hypothesis 4, that ‘attitudes toward regulation’ influence risk perceptions, we developed composite indices based on a series of survey questions related to the ‘regulation of risks’ and ‘regulation of nanotechnology’, as shown in Table 3.3. Responses were measured using a four point Likert scale: 1 – Strongly Disagree, 2 – Disagree, 3 – Agree, 4 – Strongly Agree. A principal component analysis (PCA) of the aggregated data for these thirteen dimensions of attitudes toward regulation was conducted with orthogonal rotation (varimax). Based on a scree test we concluded that two orthogonal components were necessary to explain the correlations among the thirteen variables, accounting for 51% of the variance. The first component of the rotated factor loadings shown in Table 3.3 is highly correlated with scales indicating that current regulations are sufficient, and indicating confidence in voluntary and market-based approaches to regulation. This factor is labeled “Confidence in Markets and Voluntary regulation” (α = 0.81). The second component is associated with the perception of inadequacy of current regulations, and preference for a precautionary approach to regulation. This factor is labeled “Preference for Precaution” (α = 0.79). Index scores were calculated using the Anderson-Rubin method. Both factors were included in the regression analysis below to examine their influence on the dependent variable, Nano Risk Index.  54  3.3 Results  Table 3.3 Loadings from a principal components analysis over fourteen rating scales related to 'Regulation of Risks' and 'Regulation of Nanotechnologies', averaged across individuals (VARIMAX rotated solution) Factor 1: Confidence in Markets and Voluntary Regulation (33.7% of var.)  Factor 2: Preference for Precaution (12.2% of var.)  The government should err on the side of precaution to protect the public from the risks from technology  -.21  .66  Regulations unduly prevent society from reaping the benefits of technology  .42  -.33  Chemical risks are sufficiently regulated in this country  .61  -.29  Voluntary approaches for risk management are effective for protecting human health and the environment.  .73  -.16  Market-based approaches are an effective means of managing health and environmental risks from technology  .69  -.08  Consumers should be provided with more product information to allow them to better understand a product’s risks and benefits  .01  .69  Traditional government regulation too frequently determines that a product is dangerous when it is really safe.  .29  -.53  Because current regulations do not take into account novel (size-dependent) properties of nano-scale materials, they are inadequate for protecting society from risks  -.29  .60  Government should restrict commercial development of nanotechnology until studies have been done on how to control risks  -.12  .74  Companies utilizing nano-materials in their products should be required to perform more stringent toxicity testing for the products they create  -.07  .64  Consumers, through their purchasing decisions, are able to avoid products containing nano-scale materials if they deem them to be too risky  .65  .07  Rating Scale Regulation of Risks  Regulation of Nanotechnology  55  3.3 Results  Government regulations, as they currently exist, will do a good job of managing risks across the entire life-cycle of nanomaterials (from initial production to end-of-life)  .60  -.37  Government should focus on developing voluntary programs rather than mandatory programs to manage risks from nanotechnology  .70  -.20  Note: Loadings exceeding 0.4 are in boldface.  3.3.2.2 Factors Influencing Experts’ Perceptions of Nanotechnology Risks To investigate the relationship between each independent variable and the dependent variable ‘Nano Risk Index’, we conducted a hierarchical ordinary least squares (OLS) multivariate regression, shown in Table 3.4. Variables were entered in six steps. Steps 1 through 3 introduce ‘expert group’ variables along with commonly measured demographic and domain of expertise control variables. Steps 4 through 6 introduce the ‘nanotechnology novelty’, ‘attitudes toward regulation’, and ‘Tech Risk Index’ variables respectively. Other variables including ‘proportion of time working on nanotechnology’, and ‘involvement in research’, as well as ‘social and political values’, were evaluated but ultimately omitted due to non-significance in the final model. ‘Trust in government agencies’ was also tested and found to be not significant, but was a key finding in another paper (see Chapter 4).  56  3.3 Results  Table 3.4 Hierarchical regression with Nano Risk Index as dependent variable I  II  III  IV  V  VI  Group NSE vs. NEHSa  0.14*  0.08  0.07  0.03  0.03  0.02  NSE vs. NREG  0.22***  0.18**  0.06  0.02  0.00  0.04  0.16**  0.15**  0.12*  0.08  0.02  0.00  0.05  0.08  0.04  0.04  0.11*  0.10*  0.08  0.08  0.09*  0.16*  0.13  0.06  0.07  Affiliation (Academic vs Government)f  0.00  0.01  0.01  0.01  Affiliation (Academic vs Other)  -0.06  -0.02  0.03  0.00  Novelty: New and Uncertain Risksg  0.33***  0.2***  0.21***  Novelty: Novel Benefits and Propertiesh  0.00  0.01  0.04  Regulation: Market-Based, Voluntaryi  -0.10*  -0.10*  Regulation: Precautionj  0.33***  0.19***  Demographics Genderb Education  c  Year of Degree  d  Domain of Expertise Disciplinary Fielde  Nanotechnology Novelty  Attitudes Toward Regulation  Technological Risk Tech Risk Indexk  0.41***  Incremental R2 (%) Total R2 (%) *p < .05. **p < .01. ***p < .001  3.9%  3.5%***  0.5%  9.0%***  7.7%***  14.7%***  7.4%  7.9%  16.9%  24.6%  39.3%  Notes: N = 404. Independent variables were entered in six steps, where I through VI indicate model steps, and cell entries are standardized (β) regression coefficients. Diagnostics indicate no evidence of multicollinearity (VIF < 10), and that none of the four principal assumptions for linear regressions have been violated (Field, 2005). a  Paired dummy variables, where ‘NSE vs NEHS’ is coded as NSE = 0, NEHS =1, and ‘NSE vs NREG’ is coded as NSE = 0, NREG = 1. b 1 = female, 0 = male c 1 = PhD, 0 = Bachelors/Masters d Standardized continuous variable e 1 = physical sciences, 0 = other, where ‘physical sciences’ includes chemistry, physics, materials science, chemical engineering, electrical engineering, and mechanical engineering f Paired dummy variables, where ‘academic vs government’ is coded as academic = 0, government = 1, and ‘academic vs other’ is coded as academic = 0, other = 1 g-k Continuous index variables, described above  57  3.3 Results  We found that the perception that risks are ‘new and uncertain’ is positively correlated (β = 0.21, p < .001) with Nano Risk Index in our final model (step VI), after controlling for the effects of demographics (gender, education, year of degree) and domain of expertise (disciplinary field and affiliation). This indicates that those individuals who perceive that risks from nanotechnologies are new and dissimilar to risks from bulk (non-nano) materials, and who perceive greater uncertainty and less ability to anticipate risks given available risk assessment methods, also see more risk overall. We also found that both ‘preference for regulatory precaution’ and Tech Risk Index are positively correlated with Nano Risk Index (β = 0.19, p < .001 and β = 0.41, p < .001 respectively). Those who see more risk from other technologies, and who prefer precautionary approaches to risk management also perceive greater risks from nanotechnologies. Risk perceptions are however negatively correlated with the measure of confidence in market-based and voluntary approaches for regulation (β = -0.10, p < .05). This finding suggests that those with greater confidence in voluntary programs and market-based approaches for managing risk also perceive less overall risk. The measure of perceived ‘novelty of benefits and properties’ was not significant. The year of graduation for participants’ most recent degree also explains a small proportion of variance in the model, where more recent graduates perceive greater risk. Included as a proxy for participant’s age, this finding suggests that younger participants see more risk from nanotechnologies than older participants. However the contribution to the model is small in comparison to the comprehensive index variables. Overall the model fit is good with R2 = 39%. Considering the contribution of the ‘expert group’ variables in the regression model, their descriptive power diminishes and becomes statistically insignificant once the demographic and domain of expertise variables are entered in steps II and III. The variance explained by the ‘NSE vs NEHS’ component of the dummy variable pair (indicating the distinction between the NSE and NEHS groups) becomes insignificant with the addition of the demographic variables in step II, while ‘NSE vs NREG’ drops below the p < .05 level with the addition of the domain of expertise variables in step III. Further, ‘expert group’ variables account for just 4% of the variance in the model, with the domain and demographics variables similarly contributing only 4%. This regression  58  3.3 Results  analysis therefore suggests that the mean differences between groups observed in section 3.3.1 are better explained by the perceptions and attitude characteristics of individuals within each expert group than by group distinction itself. These findings support our hypotheses that experts’ perceptions of the novelty of risks, perceptions of risk from other technologies, and attitudes toward regulation constitute distinct factors affecting perceptions of nanotechnology risks. We find that together these factors diminish the power of group, domain of expertise, gender, and education variables in describing observed nanotechnology risk perceptions. However, our hypothesis that perceived novelty of benefits would decrease perceived risk was rejected.  3.3.2.3 Novelty, Precaution, and Voluntary regulation as Characteristics of Expert groups To further characterize the link between observed differences in risk perceptions by expert groups, and the independent variables explored above, we calculated and compared mean scores for each index across expert groups (Nano Risk Index), illustrated in Figure 3.3. This figure represents the relative difference between groups for each index, rather than absolute scores on the Likert ‘agreement’ scale. ‘High’ and ‘Low’ scores on this scale are defined as index scores of +/- 0.5, representing one half standard deviation from the index mean for the Anderson-Rubin calculated indices. Here we see for the index ‘Novelty: New and Uncertain Risks’ that the NSE group on average scores the lowest, while the NREG group scores the highest. The NEHS group is also above the mean score for the index. A One-Way ANOVA analysis confirms that the observed difference in means is significant (F(2, 401) = 22.17, p < .001), and a Tukey HSD post hoc analysis confirms that mean scores are significantly different across all three groups. This indicates a larger difference in perceptions of the novelty of nanotechnology risks between the NSE and NREG groups, than between NSE and NEHS groups. For the ‘Regulation: Preference for Precaution’ index in Figure 3.3 we see a pattern similar to the novelty index with NREG scoring highest on the index, NSE on the opposite end of the spectrum, and NEHS roughly at the center point. A One-Way  59  3.3 Results  ANOVA confirms that the mean scores are significantly different (F(2, 401) = 24.23, p < .001), and a Tukey HSD post hoc analysis confirms significant differences between all three groups. As a whole, NREG respondents most strongly prefer precautionary approaches to regulation, while NSE respondents prefer precaution the least. For the ‘Regulation: Market-Based, Voluntary’ index, the NSE and NEHS groups reflect the average score for the index, while NREG indicates relatively less support for current regulations and market-based or voluntary approaches. A One-Way ANOVA confirms that the mean scores are significantly different (F(2, 401) = 3.89, p < .001), and a Tukey HSD post hoc analysis confirms significant differences between the NSE and NREG group only.  Figure 3.3 Mean scores for the 'Novelty' and 'Attitudes toward Regulation' indices for NSE, NEHS, and NREG groups The continuum from ‘high’ to ‘low’ represents a factor score range of +/- 0.5, representing one half standard deviation in either direction from the index. a, b, and c markings indicate significant differences between groups, where a: NSE and NEHS, b: NSE and NREG, c: NEHS and NREG. Tukey HSD post hoc analysis confirms that differences in index scores are significant across all three groups for ‘Novelty’ (p < .05; NSE: N = 180, -0.29 +/- 0.86, NREG: N = 103, 0.39 +/- 0.88, NEHS: N = 121, 0.11 +/- 0.85), and for ‘Regulation: Preference for Precaution’ (p < .001; NSE: N = 180, -0.29 +/- 0.82; NEHS: N = 121, 0.06 +/- 0.93; NREG: N = 103, 0.43 +/- 0.81). Post hoc analysis confirmed a significant difference between NSE and NREG groups only for ‘Regulation: Market-Based, Voluntary’ (p < .022; NSE: N = 180, -0.08 +/- 0.80; NREG: N = 103, -0.21 +/- 0.91)..  60  3.3 Results  To evaluate scores in absolute terms, we compared responses for several survey questions based on the Likert ‘agreement’ scale. For the individual ‘novelty’ survey items, we compared two questions to gauge the difference in agreement between groups on the novelty of benefits and the novelty of risks. Participants were asked to answer the following questions using a four point likert agreement scale: i) Novel Benefits: “Nanoscale materials promise benefits for society that are not possible with bulk (non nanoscale) materials”; and ii) Novel Risks: “Nano-scale materials pose risks for society that are not present with bulk (non nano-scale) materials”. Figure 3.4a) shows that while all three groups on average agree that nanotechnologies pose both novel benefits and novel risks, there is a consistent difference in agreement between these two items across groups, where risks are seen as less novel than are the benefits. This difference in novelty perceptions is most pronounced for the NSE group, where a paired t-test finds a significant difference of 0.61 between ‘novel benefits’ and ‘novel risks’ compared to 0.17 for the NEHS group and 0.14 for the NREG group (not significant). NSE respondents on average see far less ‘novel risk’ from nanotechnologies, yet view a similar level of ‘novel benefits’ compared to other groups.  61  3.3 Results  Figure 3.4 a) Comparison of experts' perceptions of the novelty of benefits versus novelty of risks across groups. * indicates significant difference in means between ‘novel risks’ and ‘novel benefits’ by paired t-test, where Novel Benefits = 3.50 +/- 0.58, Novel Risks = 2.89 +/0.65, t(140) = 8.59 , p < .001 for NSE group; Novel Benefits = 3.3 +/- 0.62, Novel Risks: 3.16 +/- 0.67, t(90) = 2.06, p < .042 for NEHS group; and difference in means for NREG group is not significant. b) Comparison of average scores for survey questions related to ‘Confidence in Markets and Voluntary Regulation’, and ‘Preference for Precaution’  For the attitudes towards regulation indices, absolute scores were calculated by averaging responses across survey items for each of factors 1 and 2 (listed in Table 3.3 above) to provide scores on the 4-point ‘agreement’ scale. Figure 3.4b) shows that the mean score for each group is less than 2.5 for the ‘Confidence in Markets and Voluntary Regulation’ index, indicating disagreement with questions on the sufficiency of current regulations and support for market-based or voluntary approaches to regulation. However, respondents in the NREG disagree relatively most strongly with all items. Conversely, average ‘Preference for Precaution’ scores indicated agreement with questions related to precautionary approaches to regulation, and greater restriction of nanotechnology development. 62  3.3 Results  In order to compare risk perceptions between nanotechnology risk scenarios and (nonnano) technology risk scenarios, we compared Tech Risk Index and Nano Risk Index scores in Figure 3.5. Here we see that all three groups score at or near the mean Tech Risk Index score. A One-Way ANOVA finds no significant difference in means between groups for Tech Risk Index. However, the mean Nano Risk Index scores were found to differ significantly between NSE and the NREG and NEHS groups as previously described in section 3.3.1.2. In terms of within group differences, we see that the mean Nano Risk Index score is greater than the mean Tech Risk Index score for the NREG group. A paired t-test confirmed that the difference in mean scores is significant (p < .05). Conversely, the mean Nano Risk Index score for the NSE group was found to be significantly less by paired t-test than the Tech Risk Index score. The mean Nano Risk Index score for the NEHS group was slightly lower but not significantly different than the corresponding Tech Risk Index score. This finding suggests that those in the NREG group see nanotechnology risks differently than the other groups, perceiving greater risk from nanotechnologies than other technologies compared to the NSE and NEHS groups who see less.  63  3.4 Discussion  Figure 3.5 Comparison of Tech Risk Index and Nano Risk Index scores by expert group. Paired t-test scores confirmed a significant difference in means between Tech Risk Index and Nano Risk Index for the both the NREG group (Tech Risk Index: -0.08 +/- 0.99; Nano Risk Index: 0.26 +/- 0.90; t(102)=3.822 , p < .001), and for the NSE group (Tech Risk Index: -0.04 +/- 0.82; Nano Risk Index: -0.20 +/- 0.84; t(179)=-2.53 , p=.012). * indicates significant difference in means between Tech Risk Index and Nano Risk Index scores  3.4  Discussion  This research demonstrates that perceptions of risk from nanotechnologies differ significantly across expert groups, a finding that holds true between the NSE group and both the NEHS and NREG groups. Nanoscientists and engineers at the upstream end of the life cycle were found to perceive significantly less risk from nanotechnologies compared to those who are responsible for the downstream assessment and regulation. This finding partially supports our first hypothesis: that nanotechnology risk perceptions will differ between groups. We did not however find a significant difference between NEHS and NREG groups on this measure. In terms of characteristic qualities of each expert group, further analysis revealed significant differences between groups on several index measures. Perceptions of the novelty of risks, preference for precaution, and confidence in market and voluntary regulation varied significantly between the NSE, 64  3.4 Discussion  NEHS, and NREG groups, as discussed below. Nanomaterials were also perceived differently compared to their bulk counterparts for the NSE and NREG groups, albeit with opposite trends. However, in absolute terms, we found that precautionary approaches to regulation are preferred overall, and all three expert groups exhibited low confidence in existing regulations, and low support for market-based or voluntary approaches.  In addition to the finding of characteristic differences between expert groups, we found by multivariate regression that four factors are significant drivers of perceived nanotechnology risks. These results support our second and fourth hypotheses, that perceived novelty of nanotechnologies, and attitudes towards regulation influence perceived nanotechnology risks. In addition, perceived risks for non-nano technologies were also found to be predictive of nanotechnology risk perceptions, thus supporting our third hypothesis. The influence of these perceptions and attitudes on perceived nanotechnology risks are explored further below. 3.4.1 Characteristic Differences in Expert Group Perceptions and Attitudes Considering the four composite indices (novelty, precaution, market-based/voluntary regulation, technology risk) tested here, the observed differences in mean index scores provide insight into the characteristic attitudes of each expert group. NSE respondents viewed nanotechnologies to pose significantly less risk than other technologies. NSE respondents also scored the lowest on precaution and novelty of risks on average, corresponding with their low mean Nano Risk Index scores. This mirrors findings by Powell et al. (2007) and Harthorn & Bryant (2007) who found that NSE experts more frequently express reservations about nanotechnology being new or different than other technologies, and thus not any more risky. However our research also finds a sizable disparity between individual ‘Novelty of Benefits’ and ‘Novelty of Risks’ survey items (Figure 3.4a) for the NSE group, indicating that benefits are seen as new yet risks are much less so. These findings together strongly suggest that NSE experts are more optimistic in their views (this is similar to what is referred to as ‘optimism bias’  65  3.4 Discussion  (Weinstein & Klein, 1996) in the risk perceptions literature). This effect was also noted in recent expert interviews (Harthorn & Bryant, 2007). The finding of NSE respondents’ high confidence (with relatively few undecided in their response) that nanotechnology benefits will strongly outweigh risks further supports the hypothesis that optimism plays a significant role in NSE experts’ perceptions of risk. It is not surprising to see that the NSE respondents demonstrate this optimism considering their close proximity to the design and development of new technologies at the ‘upstream’ end of the nanotechnology life-cycle. Finally, NSE respondents also demonstrated the greatest support among groups for a hands-off, free-market approach to managing nanotechnology risks. This suggests that, consistent with their optimistic views, NSE experts are also more likely to perceive top-down, or precautionary regulation as threatening, with the potential to limit opportunities for the development of new nanomaterials, and thus to prevent society from reaping the benefits.  In contrast with the NSE group, NREG respondents perceived the greatest novelty of risks, on par with their perceptions of novel benefits (Figure 3.4a.). They also averaged the lowest among groups on the benefits versus risks rating for nanotechnologies in general (Figure 3.1). This suggests that NREG respondents recognize that novel nanomaterial properties may pose both benefits and risks equally. NREG respondents on average also scored the highest on precaution, and the lowest on the market/voluntary regulation index, a trend that correlates closely with the high Nano Risk Index score observed for the NREG group. Further, comparison of nano risk perceptions with the comprehensive technology risk index (Tech Risk Index) shows that NREG respondents on average see more risk from nanotechnologies than from other technologies, while NSE and NEHS respondents see less. Together these findings suggest that NREG respondents are more likely to see nanotechnology as new and risky, and prefer precautionary top-down regulatory approaches to manage risks rather than to leave regulation to market-based mechanisms. Hence, compared to NSE respondents, NREG respondents display a tendency towards negativity or worry with respect to nanotechnology risks and benefits. These ‘cautious regulators’ are likely highly attuned  66  3.4 Discussion  to the challenges of assessing and managing risks, directly face the challenge of regulating nanotechnologies on a day-to-day basis, and have first-hand experience with the limitations of market-based and voluntary approaches to regulation (Conti & Becker, 2011). Together these experiences are likely responsible for the observed pattern of precaution, the belief that nanotechnology is new and more uncertain, and the attention to risk that is not seen with other groups. NEHS respondents’ perceptions of novelty of risk, preference for precaution, and confidence in market and voluntary regulation were found to lie consistently between the NSE and NREG groups. However, the differences between the NEHS respondents and NSE and NREG groups were only significant for their perception of the novelty of risks and preference for precautionary regulation. For perceptions of nanotechnology risk (Nano Risk Index), no difference is noted between the NEHS and NREG groups. Given the NEHS experts’ focus on assessment of risks and direct experience with the use and limitations of risk assessment methodologies, it is understandable that NEHS experts would be more attuned to the limitations of risk assessment methodologies for nanotechnologies than would NSE respondents, though perhaps less so than NREG respondents. 3.4.2 Perceived Novelty of Risks, Attitudes Toward Regulation, and Perceptions of Technology Risk as Drivers of Nanotechnology Risk Perceptions The results of a multivariate regression analysis confirmed that ‘expert group’ variables were not a significant driver of the observed Nano Risk Index scores in the final regression model. Rather, a large proportion of the variance was described by the four indices. The implications of this finding are explored below for each such comprehensive index variable.  There is continued disagreement between experts on whether or not nanotechnology is indeed a new and distinct domain of science and engineering, and whether nanotechnologies pose new or different risks than their bulk (non-nano) counterparts (Harthorn & Bryant, 2007; Powell, 2007). We found that the perceived novelty of risks  67  3.4 Discussion  was a significant driving factor, whereby novelty perceptions were positively correlated with nanotechnology risk perceptions. These findings echo similar results based on the psychometric paradigm (Kraus et al., 1992; Siegrist et al., 2007; Slovic, 1987) in which perceived uncertainty, and judgments of whether risks are ‘known’, were found to be drivers of overall risk perceptions (Bostrom, 1997; Fischhoff, Slovic, Lichtenstein, & Read, 1978; Slovic, 1987). Our ‘novelty of risk’ index explored whether experts believed that risks were different than conventional (non-nano) materials (and hence uncertain), whether the uncertainty was greater than for non-nano materials, and whether their properties can be anticipated by knowing the properties of the bulk (non-nano) material. In this sense, ‘Novelty: New and Uncertain Risks’ indicates an overall uncertainty in both the types of risks and magnitude of risks posed by nanotechnologies. The judgment that current methods are not suitable to assess these risks may reinforce an experts’ sense of uncertainty, further contributing to their perceptions of risks.  We also found that perceptions of risk from other technologies, measured here with a comprehensive set of technologies frequently studied in the risk literature, proved to be a good predictor of risk perceptions for nanotechnologies. We found that experts who see more risk overall from technologies are more likely to see greater risk from nanotechnologies as well. Given the diverse set of technologies used in the creation of this index, we expect this result is robust. This approach is nonetheless a methodology worthy of further exploration in future research.  Attitudes towards regulation were assessed along two dimensions, including preference for precaution in regulation and preference for market-based and voluntary approaches to risk management. Together these dimensions reflect a measure similar to support for regulation, measured by Besley et al. (2008), or need for regulation, by Corley et al. (2009), confirming that the expert groups studied here would prefer more government regulation as a precaution (though the NSE group scored lowest on this index, Fig 4b). However the ‘precaution’ index is a complex measure of experts’ attitudes and indicates both dissatisfaction with current levels of regulation, and preference for precautionary actions including measures to restrict commercial development, to require additional  68  3.5 Conclusions  testing, and to provide consumers with additional product information. The relationship between the ‘precaution’ index and nano risks perceptions (in the regression Table 3.4) demonstrates that experts’ generalized attitudes toward precautionary regulation color their perceptions of risk, whereby those with more precautionary predispositions see more overall risk than those who favor less precaution. Like the ‘Preference for Precaution’ measure, the ‘market/voluntary regulation’ index is also reflective of experts’ attitudes toward regulation in general, and their preference for less government regulation and a free-market approach. However in absolute terms (Fig 4b), this measure did not receive much overall support, with experts on average disagreeing with the survey items composing the index. This index also played a minor role in the regression, indicating little influence overall on perceived nanotechnology risks. 3.5  Conclusions  This research shows that differences in nanotechnology risk perceptions across groups are not driven by the group distinction per se, but rather are the result of characteristic perceptions and attitudes of the experts within each group. These characteristics are reflective of where the experts are situated along the nanotechnology life cycle, their focus on creation, testing, or regulation of nanotechnologies, and their familiarity with the challenges corresponding to risk assessment and regulation. Together these factors account for the observed predispositions toward optimism at the upstream, creationoriented end of the life cycle, or caution at the downstream, risk regulation end. While the differences in perceptions and attitudes are nuanced, the ‘expert group’ distinction provides insight into the collection of risk perceptions, opinions, and regulatory attitudes that can be expected from experts in each group. While all experts surveyed here are involved in the multidisciplinary nanotechnology enterprise, they each constitute different and distinct points of view, drawn closely from experiences in nanotechnology development, risk evaluation, and regulation. These opinions are also reflective of predominant opinions and attitudes that derive from institutional cultures, and are a function of training, affiliation, and experience. As such these opinions may reflect  69  3.5 Conclusions  optimistic attitudes such as in the NSE group, and a tendency toward caution in the NREG group. These findings reinforce the need to be aware of inherent biases and predispositions with experts from different groups, which can lead to possible attenuation or amplification of risk signals, and can influence decisions on which (nano)technologies get more attention and how. It is important therefore to consult experts from across the life cycle, from upstream development to downstream testing and regulation, to ensure a cross sample of opinions and to draw upon diverse expertise to find appropriate approaches for managing risks.  In addition to these findings, it is important to note that all three expert groups believe current regulations to be insufficient for managing nanomaterial risks, and support the use of precautionary approaches to regulation over market-based or voluntary programs, albeit at varying levels within each group.  70  4.1 Introduction  Chapter 4 Nanotechnology and Regulation: Experts Views on Regulatory Agency Preparedness for Managing Risks 4.1  Introduction  Knowledge of the health and environmental risks from nanoscale materials across product lifecycles could provide a scientific basis upon which nanomaterials might be regulated. However, such knowledge is emerging at a slow pace (Renn & Roco, 2006), and significant scientific uncertainties on both the toxicity and exposure characteristics of existing nanomaterials remain. Thus, nanomaterial regulations are being considered prior to a more complete understanding of nanomaterial risks. Because of this ‘upstream’ nature expert opinions on the regulation of nanomaterials take on greater significance. An early study indicated that, overall, nano-experts are more worried about the risks of engineered nanomaterials than are lay or public groups (Scheufele et al., 2007). Yet subsequent studies are rare, and none have compared the perspectives of the different groups of scientists in this inter-disciplinary field on whether and how nanomaterials should be regulated. In this work we investigate the opinions of three expert groups (nano-scientists and engineers (NSE), nano-environmental health and safety scientists (NEHS), and regulatory scientists (NREG)) on how nanomaterials should be regulated as new products, and whether regulatory agencies are sufficiently prepared to manage risks they pose.  Studies of expert opinion have been conducted in other domains, including expert evaluations of chemical risks (Kraus et al., 1992; Mertz et al., 1998; Neil et al., 1994; Slovic et al., 1995), climate change detection and impacts (G. Morgan & Keith, 1995; Risbey, Kandlikar, & Karoly, 2001; Risbey & Kandlikar, 2002), genetically modified organisms (GMOs) (Gaskell et al., 2004; Savadori et al., 2004), and ecological risks (Lazo et al., 2000; McDaniels et al., 2006). Differences of opinion have been found in experts across disciplinary fields (Gaskell et al., 2004; Slovic et al., 1995), including those attributed to different institutional affiliations (e.g., toxicologists in industry versus academia) (Kraus et al., 1992; Slovic et al., 1997). In the nanotechnology domain, both Besley et al. (2008) and Corely et al. 2009 demonstrate significant differences in perceived need and support for nanotechnology regulation, respectively, based on 71  4.1 Introduction  experts’ disciplinary fields. Similarly, Powell (Powell, 2007) found significant differences in opinion between ‘upstream’ and ‘downstream’ researchers; that is, experts involved in the creation of nanotechnologies, versus those engaged in evaluating the health and environmental implications of ENMs. Such disparity in opinion might reflect the inherent benefit versus risk focus and optimism (Harthorn & Bryant, 2007) of R&D researchers in the former case, versus health and safety scientists (e.g., toxicologists) and risk assessors in the latter. Accompanying this trend, however, was a marked difference in perceptions of the ‘novelty’ of nanotechnology benefits and risks (Powell, 2007). Perceived ‘novelty’ may therefore be a (relatively untested) driver of risk and preparedness perceptions, where perceived novelty of nanomaterial properties and risks diminish experts’ estimations of agencies’ ability to manage such risks.  Expert opinion has also been found to vary significantly with attitudes, perceptions, and values. Several scholars have found that similar to non-experts, scientists often use norms or values when making judgments about risk under high uncertainty (Burgman et al., 2011; Corley et al., 2009; Krinitzsky, 1993; Slovic et al., 1995). For instance, economically conservative nanoscientists were found by Corley et al. to show less support for regulation (Corley et al., 2009). Similarly, trust (in scientists, government) has been found to correlate closely with risk perceptions, with attenuation in perceived risk accompanying higher levels of trust (Ho et al., 2011; Siegrist et al., 2007). Prominent examples of this effect have also been demonstrated in chemical and GMO studies (Savadori et al., 2004; Slovic, 1999). The effect of attributed stakeholder responsibility, that is, the degree of responsibility assigned to various stakeholders to mitigate or manage risk, has received relatively less attention in the nanotechnology domain. Yet a growing body of literature in the public health domain suggests a link between attributions of responsibility and support for government policy (Iyengar, 1989; Rickard, Scherer, & Newman, 2011; Weiner, 1993; 2005). This suggests that an association between attributed responsibility and perceived agency preparedness is plausible.  This study is a web-based survey of regulatory agency preparedness for the management of nanomaterials (response rate: AAPOR RR-3: 23%), as perceived by 254 US experts  72  4.2 Methods  responsible for the development, assessment, and regulation of nanotechnologies. They are, respectively, nano-scientists and engineers (NSE, n=114), nano-environmental health and safety scientists (e.g., toxicologists) (NEHS, n=86), and nano-regulators including those who work for federal and state agencies with the responsibility to assess and manage risks (NREG, n=54). Given previous findings that expert opinion differs across domains, we tested the following hypotheses:  (1) Expert views on whether US federal agencies are sufficiently prepared for managing any risks posed by nanotechnologies will differ significantly across expert classes (NSE vs. NEHS. Vs. NREG). (2) Experts who see nanotechnologies as novel (i.e., as a new class of materials or objects) will view US federal regulatory agencies as unprepared for managing risks as compared to those who see nanotechnologies as not new (i.e., as little different from their bulk form) (3) Experts who deem US federal regulatory agencies as less trustworthy will also view agencies as less prepared compared to those with more trust in agencies (4) Experts who attribute greater collective stakeholder responsibility will see agencies as less prepared compared to those who attribute less responsibility. (5) Experts who are more socially and economically conservative will see regulatory agencies as more prepared compared to those with a more liberal orientation.  To ensure that measured differences in perceptions of preparedness were not the result of unmeasured differences in demographics and domain of expertise (Rowe & Wright, 2001), gender, highest degree achieved, year awarded, disciplinary field, and institutional affiliation are included as control variables in this analysis.  4.2  Methods  The US & Canadian survey was conducted by the University of California Santa Barbara Social Science Survey Center for the UCSB Center for Nanotechnology in Society between June 2nd and November 8th, 2010. Data was collected using a web-based instrument with a total sample size of n = 404 and an overall response rate of 23%.  73  4.2 Methods  To construct the sample, we compiled names and detailed contact information for 2,100 experts within three pools of US and Canadian experts: nano scientists and engineers (NSE), nano EHS scientists and toxicologists (NEHS), and scientists and regulators in government agencies (NREG). For the NSE group, experts were selected using a rigorous sampling design, based on a bibliometric analysis methodology developed by Porter et al. (2008), to identify nanotechnology publications using ISI Web Of Science. We excluded papers with the following terms to remove publications that would fall under our NEHS sampling strategy: toxic* or genotoxic* or ecotoxic* or (oxidative stress) or safety or pollution or (environmental health) or (human health) or (animal health) or (public health) or (occupational health). Results were limited to articles and review papers by authors in the US and Canada. 1,200 subjects were selected at random from a pool of over 5,700 first or contact authors who published five or more nanotechnology articles that were cited five or more times between 2000 and 2009 (a method utilized by Scheufele et al. (2007), with at least one article newer than 2006. Database searches were conducted between August and September 2009.  NEHS experts were selected from first or contact authors of 1,600 articles entered into the International Council on Nanotechnology (ICON) Environment, Health and Safety Database between early 2007 and spring 2010. Due to the relatively small field of nano EHS research, we could not apply the same rigorous NSE standard of selecting authors with five or more publications, and instead selected 500 experts at random from list of over 1,600 authors. The publication list included international contacts, and was extensively cleaned to remove email addresses with suffixes outside of the US and Canada (according to http://ftp.ics.uci.edu/pub/websoft/wwwstat/country-codes.txt), and from international email providers (e.g., 126.com, 163.com, sh163.net, 263.net located in China). Several authors listed with .gov email suffixes were cross-referenced with the NREG group for duplications, and moved to the NREG group.  NREG experts were identified from nanotechnology conference attendance lists, referrals, and website searches of employees in nanotechnology groups in US and  74  4.3 Agency Preparedness and Regulator Concern  Canadian Federal Regulatory agencies (including EPA, OSHA, FDA, CPSC, Health Canada, Environment Canada) and within Federal research institutes (NIOSH, NIH, national labs), as well as US State regulatory agencies (including Massachusetts Department of Environmental Protection, New York Department of Environmental Conservation, California EPA, North Carolina Department of Environmental and Natural Resources, and Washington Department of Ecology). Contact information and agency affiliation were compiled for 400 NREG experts in spring 2010. A full list of agencies is available in Appendix C.  Subjects were contacted by email in a three-step process, including initial contact and two reminders at two-week intervals. NSE experts received a mailed letter invitation in addition to the initial email. Individual group response rates include both US and Canadian respondents. NSE: N=180, RR=16%; NEHS: N=121, RR=33%; NREG: N=103, RR=32%. We believe the relatively low response rate of the NSE group is due to a large number of outdated email addresses (our search criteria includes publications since 2000). Contacts may have moved institutions or changed email addresses since the date of publication, and therefore were not measured as ‘bounced’ or ‘out-of-scope’. A total of 404 responses were analyzed, for an overall response rate of 23% (AAPOR RR-3: 23%). Separate response rates for the US and Canadian groups were not possible since not all respondents indicated their country of residence in their survey responses. In total 254 participants specified their residence in the US, while 55 reside in Canada and 95 did not disclose their country of residence. Only the US responses were included in this analysis, Canadian results will be reported in a future publication. For the data reported in this paper, the US sample sizes were: NSE = 114, NEHS = 86, and NREG = 54. Statistics were calculated using the SPSS software package (IBM, 2012).  4.3  Agency Preparedness and Regulator Concern  In testing the first hypothesis, participants were presented with 14 nanotechnology scenarios and asked to rate each using the following scale: “Please indicate whether you strongly disagree, disagree, agree, or strongly agree that current US government agencies are adequately prepared for controlling risks from nanotechnologies in the  75  4.3 Agency Preparedness and Regulator Concern  following categories”. This four-point likert scale indexed 1 as ‘strongly disagree’ through 4 as ‘strongly agree’; also provided were the options: “not familiar with relevant agency or its regulations / can’t answer” and “don’t know / not sure”. Figure 4.1 illustrates the results for each of 14 scenarios, where points on color- coded lines indicate the mean score on agency preparedness for each expert group (NSE, NEHS, and NREG).  Figure 4.1 'Agency Preparedness' ratings for NSE, NEHS, and NREG expert groups. Mean scores for each group are indicated with points on respective color-coded lines capturing 14 different nanotechnology scenarios. The dotted grey line indicates the mid or neutral-point between ‘strongly disagree’ and ‘strongly agree’. Significant differences in means were determined using a one-way ANOVA with Games-Howell post hoc analysis, and are indicated with a, b, and c markings as outlined in the legend  Across 10 of 14 scenarios, the mean scores for all three groups lie to the left of the centerline, demonstrating consistent disagreement with the claim that federal agencies are ‘adequately prepared’ to control risks from nanotechnologies. Agreement is demonstrated in one case: computers and electronic devices, by just one group (NSE). This result indicates a prevailing trend towards disagreement (agencies are not prepared for controlling risks) for a majority of the 14 scenarios presented. The NSE and NEHS  76  4.3 Agency Preparedness and Regulator Concern  groups also visibly vary from scenario to scenario in much closer agreement with one another than with NREG, and differ significantly from one another on only one scenario: ‘Computers and Electronic Devices’. The NSE and NEHS groups are also proximate to the neutral center for several items (medical devices and treatments, pharmaceuticals, industrial workplaces, chemicals and product additives). More striking, however, are the low mean scores for the NREG group, all of which lie consistently to the left of the NEHS and NSE groups, and largely to the left of the ‘disagree’ point on scale. This suggests that those most fully responsible for managing the risks of nanotechnologies regard government agencies as unprepared, more so than their counterparts outside of government regulatory and research agencies. These results are consistent with Besley et al, who similarly found that on average nanoscientists and engineers also find existing regulations inadequate (Besley et al., 2008).  To confirm that the differences in opinion observed between expert groups are statistically significant, we conduct a one-way between subjects Analysis of Variance (ANOVA) . Here each of the fourteen scenarios is used as a dependent variable with the expert group (NSE vs NEHS vs NREG) as the independent factor. We find that the assumption of homogeneity of variances of groups was maintained for 10 of 14 scenarios (Levene’s test). A Welch test is conducted in place of the ANOVA results for the four scenario variables with non-homogeneous variances. ANOVA and Welch tests indicate significant differences in means for 12 of 14 items (p<0.05; detailed results provided in Table C.1 in Appendix C. A Games-Howell post hoc analysis found significant differences between: NSE and NREG groups on 12 of 14 items; NEHS and NREG on 7 of 14 items; and NSE and NEHS on one item only. This is indicated in Figure 4.1 (detailed post hoc results provided in Table C.2 in Appendix C). This finding confirms that NREG and NSE groups are most dissimilar in their opinions on a majority of items. Differences in opinion are also observed between the NREG and NEHS groups with fewer significant differences across scenarios, and smaller difference in the magnitude of their mean responses.  77  4.4 Drivers of Perceived Agency Preparedness  To determine whether the difference in mean responses across groups is significant when all 14 scenarios are aggregated, we created a composite measure (hereafter referred to as ‘Preparedness Index’) using Principal Component Analysis (PCA) with orthogonal rotation (Varimax). We used a scree test (Field, 2005) in deciding that that only one component accounting for 56% of the variance was necessary to explain the correlations among the 14 scenarios. Preparedness Index scores were calculated using the AndersonRubin method, producing scores with an overall mean of zero and standard deviation of 1. A Cronbach’s alpha measure found that the 14 items form a consistent and highly reliable scale (α = 0.98). Using a second ANOVA test we find a statistically significant difference between groups (F(2, 251) = 10.216, p = < .001). The observed differences between expert groups using the composite Preparedness Index are consistent with the findings from the individual nanotechnology scenarios. A Tukey HSD post hoc analysis revealed that the Preparedness Index score was significantly higher for NSE (N=114, 0.21 +/- 0.91; p < .001) and NEHS (N=86, 0.02 +/- 0.85; p < .001) groups than for the NREG (N=54, -0.47 +/- 0.99) group. However, there was no statistically significant difference between NSE and NEHS groups (p = .33). Thus preparedness judgments differ significantly across NSE and NREG, and NEHS and NREG group pairings only.  4.4  Drivers of Perceived Agency Preparedness  Several competing hypotheses (2 through 5) were posed for why experts might differ on their views of agency preparedness. Agency preparedness might be driven by the degree to which nanotechnologies were seen as a ‘new’ or ‘novel’ set of materials and applications, thereby warranting additional scrutiny (Hypothesis 2). To test ‘nanotechnology novelty,’ we developed an index measuring seven dimensions of novelty listed in Table 4.1 using survey responses. Survey questions were developed using face-to-face interviews with US and Canadian nanotechnology experts whose findings are reported elsewhere (Harthorn & Bryant, 2007), and are shown in the table legend. For each novelty item, the following Likert scale was used: 1 – Strongly Disagree, 2 – Disagree, 3 – Agree, 4 – Strongly Agree. PCA with Varimax rotation followed by a scree test were used to isolate two orthogonal components. Factor loadings from the PCA are also shown in Table 4.1. The first component labeled “New and  78  4.4 Drivers of Perceived Agency Preparedness  Uncertain Risks” (Cronbach’s α = 0.65) is highly correlated with ‘Properties Cannot be Anticipated’, ‘New Risks’, ‘Risks are Not Well Known’, and ‘Risks Cannot be Determined’, and ‘More Uncertainty’. The second component is associated with ‘New Benefits’ and ‘Novel Properties’, and is labeled accordingly as “Novel Benefits and Properties” (Cronbach’s α = 0.74). Together these two components account for 53% of the variance. The Anderson-Rubin method was used to create orthogonal factor scores with a mean of 0 and standard deviation of 1. Both factors are included in the regression analysis (Table 4.3) below to examine their influence on perceptions of ‘Agency Preparedness’. Table 4.1 Loadings from a principal components analysis over seven rating scales averaged across individuals (VARIMAX rotated solution) Factor 1: New and Uncertain Risks (31.9% of var.)  Factor 2: Novel Benefits and Properties (20.8% of var.)  .10  .87  .08  .87  Properties Cannot be Anticipated *  .54  .17  New Risks4  .56  .24  Risks are Not Well Known5*  .76  -.16  Risks Cannot be Determined6*  .73  -.02  .56  .16  Rating Scale New Benefits1 Novel Properties  2 3  More Uncertainty  7  Note: Loadings exceeding 0.3 are in boldface. * Items are reverse coded to facilitate comparison 1. Nano-scale materials promise benefits for society that are not possible with bulk (non nano-scale) materials 2. Nano-scale materials possess novel properties that are not expressed in their corresponding bulk forms 3. The novel properties of nano-scale materials cannot be anticipated by knowing the properties of the same material in its bulk form 4. Nano-scale materials pose risks for society that are not present with bulk (non nanoscale) materials 5. The health and environmental risks from nano-scale materials are not well known to scientists 6. The existing methods for assessing health and environmental risks from bulk materials are not suitable for determining risks from nano-scale materials 7. There is more uncertainty about the risks from nano-scale materials than the risks from bulk forms  79  4.4 Drivers of Perceived Agency Preparedness  To test hypothesis 3, i.e., trust in regulatory agencies is predictive of preparedness perceptions, we develop a comprehensive index based on responses to the question: “Please indicate how trustworthy you feel the following government agencies are for effectively managing nano-specific environmental health and safety risks from: very untrustworthy, somewhat untrustworthy, somewhat trustworthy, very trustworthy”. Federal regulatory agencies presented included those expected to play a central role in nanotechnology regulation (Beaudrie, 2010), including: US Environmental Protection Agency (EPA), Food and Drug Administration (FDA), Occupational Safety and Health Administration (OSHA), Consumer Product Safety Commission (CPSC). By evaluating trustworthiness of several regulatory agencies in the aggregate this variable provides a comprehensive measure of trust in regulatory agencies in general. As before, a ‘Trust’ index was created with all four items using a PCA with orthogonal (varimax) rotation and index scores were calculated using the Anderson-Rubin method. Only one component is needed to explain the correlations between all four items, accounting for 69% of the variance (Cronbach’s α = 0.86).  To test hypothesis 4, i.e., attribution of stakeholder responsibility influences preparedness perceptions, we developed a comprehensive index based on responses to the question: “For the following list of groups or stakeholders, please indicate whether they should: Bear none of the responsibility, some of the responsibility, most of the responsibility, or all of the responsibility, for managing risks that emerge from nanotechnologies.” Stakeholder groups included:   Academic basic sciences and R&D laboratories (i.e. Physics, chemistry, engineering)    Academic environmental and health sciences laboratories (i.e. toxicology, epidemiology)    Private research and development laboratories    Smaller companies developing nanotechnology products    Larger companies developing nanotechnology products    Government agencies (eg. EPA, FDA)  80  4.4 Drivers of Perceived Agency Preparedness    Environmental groups and non-governmental organizations (NGOs)    Consumers, through their product purchasing decisions  Evaluating assigned responsibility ratings for the above stakeholder groups provides a comprehensive measure of the scope of attributed responsibility. That is, the ‘responsibility’ index provides a measure of whether responsibility is attributed to a single or narrow set of stakeholders, or whether it is widely attributed to many or all stakeholder groups. This ‘responsibility’ index was created with all eight items using a principal component analysis (PCA) with orthogonal (varimax) rotation; index scores were calculated using the Anderson-Rubin method. Only one component was needed to explain the correlations between all eight items, accounting for 42% of the variance (Cronbach’s α = 0.79). Finally, hypothesis 5 includes the influence of ‘Socio-political values’, measured as social and economic conservatism. To test the role of ‘socio-political values’ in driving views on agency preparedness, we used the following two questions: “The terms ‘liberal’ and ‘conservative’ may mean different things to different people, depending on the kind of issue one is considering. In terms of economic issues, would you say you are: 1- Very Liberal, 2 – Somewhat Liberal, 3 – Somewhat Conservative, 4- Very Conservative, 5 – Don’t Know/Not sure”. The question was then repeated, using social issues. A ‘Social/Economic Conservatism’ index was created based on the standardized z-score of the combined mean responses for these two questions (Cronbach’s α= 0.64). Table 4.2 summarizes descriptive statistics for each of these variables and controls across expert groups.  81  4.4 Drivers of Perceived Agency Preparedness  Table 4.2 Descriptive statistics for control and independent variables. All values for Demographics and Domain of expertise variables indicate the distribution of respondents by group (out of a total of 100%), while figures for the ‘Graduation Year’ indicate mean scores and standard deviations. Values for independent variables trust, responsibility, conservatism, and novelty represent mean index scores and standard deviations. NSE  NEHS  NREG  Demographics Sample Size Gender  Education  Graduation Year  N = 114  N = 86  Male  Female  89.30%  10.70%  Bachelor s  Masters  0.00%  0.90%  N = 54  Male  Female  60.00%  40.00%  PhD  Bachelor s  Masters  99.10 %  1.20%  0.00%  Male  Female  68.50%  31.50%  PhD  Bachelor s  Masters  PhD  98.80 %  18.20%  30.90%  50.90 %  Mean  SD  Mean  SD  Mean  SD  1990  11.70  1994  10.80  1992  8.90  Domain of Expertise Discipline  Affiliation  Phys Sci  Other  Phys Sci  Other  Phys Sci  Other  92.10%  7.90%  30.20%  69.80%  16.40%  83.60%  Academi a  Governmen t  Other  Academi a  Governmen t  Other  Academi a  Governmen t  Other  82.30%  7.10%  10.60 %  90.60%  0.00%  9.40%  0.00%  96.40%  3.60%  Trust in Regulatory Agencies Trust  Mean  SD  Mean  SD  Mean  SD  -0.08  0.95  -0.06  0.92  -0.08  1.16  Attributed Stakeholder Responsibility Responsibilit y  Mean  SD  Mean  SD  Mean  SD  -0.10  0.93  -0.02  0.95  0.25  1.33  Socio-Political Values Conservatis m  Mean  SD  Mean  SD  Mean  SD  1.42  0.39  1.47  0.37  1.52  0.43  Novelty Novelty Risks  Mean  SD  Mean  SD  Mean  SD  0.33  0.91  -0.19  0.95  -0.40  1.00  Novelty Benefits  Mean  SD  Mean  SD  Mean  SD  0.08  0.97  -0.09  0.97  -0.01  1.10  A hierarchical ordinary least squares (OLS) multivariate regression was conducted to investigate the relationship between constructed independent variables in Table 4.3 and the dependent variable ‘Preparedness Index’. Variables were entered in six steps, where  82  4.4 Drivers of Perceived Agency Preparedness  step 1 introduces ‘expert group’ along with demographic variables gender, education, and year of highest degree (as a proxy for age) as control variables. Step 2 introduces ‘domain of expertise’ control variables, including disciplinary field, and Institutional Affiliation. Steps 3 through 6 introduce the socio-political values, trust, responsibility, and novelty variables respectively. Table 4.3 presents the results of the hierarchical regression. Table 4.3 Hierarchical regression analysis with Preparedness Index as dependent variable. N = 254. Cell entries for Steps 1 through 6 are final unstandardized (B) and standardized (β) regression coefficients. Diagnostics indicate no evidence of multicollinearity (VIF < 10), and that none of the four principal assumptions for linear regressions have been violated (Field, 2005). S.E. 0.24  β  (Constant)  B 0.25  Step 1. Demographics and Group NSE vs. NEHSa  0.09  0.15  0.05  NSE vs. NREG  -0.27  0.30  -0.12  0.03  0.12  0.01  -0.31  0.21  -0.10  0.02  0.06  0.02  -0.13  0.27  -0.06  0.17  0.18  0.05  Affiliation (Academic vs Other)  0.18  0.14  0.10  Step 3. Socio-Political Values Social/Economic Conservatismg  0.16**  0.06  0.15**  Step 4. TRUST TRUSTh  0.20***  0.05  0.21***  Step 5. RESPONSIBILITY RESPONSIBILITYi  -0.13**  0.05  -0.14**  -0.40***  0.06  -0.40***  -0.03  0.05  -0.04  Gender  b  Education  c  Year of Degree  d  Step 2. Domain of Expertise Disciplinary Fielde Affiliation (Academic vs Government)  f  Step 6. Nanotechnology Novelty Novelty: New and Uncertain Risksj Novelty: Novel Benefits, Novel Properties 2  2  k  2  Note: N=254, R = .06 for Step 1; ΔR = .02 for Step 2 (p = .11); ΔR = .02 for Step 3 (p = 0.02); ΔR2 = .06 for Step 4 (p < .001); ΔR2 = .03 for Step 5 (p < .01); ΔR2 = .14 for Step 6 (p < .001) . Total adjusted R2 = 0.32  a  *p <.05. **p <.01. ***p <.001 Paired dummy variables, where ‘NSE vs NEHS’ is coded as NSE = 0, NEHS =1, and ‘NSE vs NREG’ is coded as NSE = 0, NREG = 1.  83  4.4 Drivers of Perceived Agency Preparedness  b  1 = female, 0 = male 1 = PhD, 0 = Bachelors/Masters d Standardized continuous variable e 1 = physical sciences, 0 = other, where ‘physical sciences’ includes chemistry, physics, materials science, chemical engineering, electrical engineering, and mechanical engineering f Paired dummy variables, where ‘academic vs government’ is coded as academic = 0, government = 1, and ‘academic vs other’ is coded as academic = 0, other = 1. g, h, i, j, k Continuous index variables, described above c  The resulting final model explained 32% of the variance (adjusted R2) and revealed significant contributions (at the p < .05 level) from four variables: “Novelty: New and Uncertain Risks” (β = -0.40; p < .001, ΔR2 = 14%), “Trust” (β = 0.21; p < .001, ΔR2 = 6%), “Responsibility” (β = -0.14; p < .01, ΔR2 = 3%), and “Social/Economic Conservatism” (β = 0.15; p = .02, ΔR2 = 2%). We see that respondents judged agencies are more prepared when they were more conservative, and when they had more trust in regulatory agencies. Conversely, respondents judged agencies are less prepared when they attributed responsibility more uniformly across stakeholder groups, and when they perceived nanotechnology risks as new and more uncertain. Experts relied strongly upon framing of risks (as novel) as a heuristic cue, echoing findings in recent interview-based research showing substantial differences in experts’ framing of the novelty of nanotechnology risks (Powell, 2007). This suggests that nanotechnology risks are seen as new and more uncertain, and existing knowledge and tools for anticipating risks are viewed as unsuitable. Experts tend to view regulatory agencies as less prepared for controlling more novel risks. The framing of benefits and properties as novel however was not utilized as a heuristic cue, and little variation in views of the novelty of benefits were found between groups (see Table 4.2). Thus hypothesis 2, that perceptions of novelty significantly affect preparedness perceptions, is supported for the novelty of nanomaterial risks, but not for novelty of properties and benefits.  Trust in regulatory agencies was also a strong driver of preparedness perceptions, supporting hypothesis 3, and reinforcing findings in the risk literature that demonstrate a significant inverse relationship between trust and perceived risk (Slovic, 1999). However, no significant difference in means was observed across groups for this variable (see Table 4.2). This suggests that trust is limited to ‘within-group’ variation and does not drive 84  4.4 Drivers of Perceived Agency Preparedness  observed differences in preparedness perceptions between groups. ‘Trust in regulatory agencies’ can be understood to reflect several possible trust judgments, so it is important to assess what aspect of trust is being invoked. Trust in regulatory agencies to manage risks may reflect, among others, 1) trust in regulatory agencies’ intent to manage risks, 2) trust that regulatory authority and regulatory mechanisms are adequate for the task, or 3) trust that regulators have adequate evidence and a sound scientific basis to take action. A strong inverse correlation between trust and novelty of risks would provide evidence for cases 2 and 3, where the novelty of nanotechnology risks challenge the adequacy of evidence, or appropriateness of existing regulatory mechanisms and authority. We found a small but significant negative correlation between the aggregate metrics of trust and novelty of risk (Pearson’s r = -.128, p < .05, 2-tailed), suggesting that our trust metric is based in part on judgments of regulatory adequacy for managing nanomaterial risks.  Attribution of collective stakeholder responsibility was also found to relate significantly to views on preparedness, supporting hypothesis 4. The attributed responsibility index provides insight into expert’s expectations for stakeholders to manage risks. A high score on the attributed responsibility scale indicates that a greater degree of responsibility is expected from stakeholders overall. It also reflects the judgment that a wide range of stakeholders should play a role in the management of nanotechnologies, rather than one or a narrow set of stakeholders. Attributed responsibility can thus be seen as a proxy measurement for perceived magnitude or complexity of the risk management challenge, where a greater challenge requires greater attention from a number of stakeholders. Hence, when attributed responsibility is high, the management challenge is seen as great, and regulatory agencies (among other stakeholder groups) are perceived as less prepared for managing those risks on their own. Nonetheless, attributed responsibility played only a minor role in overall variance explained by the model (ΔR2 = 3%).  The finding on the significant role of social-political views (conservatism) is somewhat contrary to theory suggesting that experts draw upon their expertise and experience, and not upon heuristic cues and value predispositions, when making judgments on risk and regulatory policy (Ho, Scheufele, & Corley, 2011; Siegrist et al., 2007). The range of  85  4.5 Discussion  socio-political differences across the three groups is small with mean responses roughly half-way between ‘very liberal’ and ‘liberal’. Nonetheless, the regression results weakly support hypothesis 5, and reflect longstanding findings that cultural worldviews, including political ideology, influence expert judgment (Slovic et al., 1995). Our results also echo recent findings in the nano-risk perceptions literature, where Corley et al. found economic conservatism is inversely related to experts’ support for regulation of nanotechnology (Corley et al., 2009).  4.5  Discussion  This research demonstrates that consistent differences in perceptions of agency preparedness exist between expert groups. What is striking however is that while all three groups perceive regulatory agencies as unprepared for managing risks, NREG experts see agencies as considerably less prepared than their counterparts do. However, drivers of these perceptions and experts’ concerns over regulator preparedness for managing nanotechnology risks tell a more nuanced story. After accounting for other differences, the ‘expert group’ classification per se does not drive the observed differences in perceptions of agency preparedness. Rather a substantial portion of this difference results from differing assessments of the perceived novelty of risks across expert groups. Of the remaining variables, trust in regulators is a small but significant driver, and our findings suggest a link between concerns over the novelty of nanomaterials and the adequacy of regulatory design. Experts’ views on stakeholder responsibility are not particularly surprising since greater reliance on a collective responsibility model would need the burden to move away exclusively from regulatory bodies to other groups, and result presumptively in a reduced sense of preparedness. Experts’ reliance in part upon socio-political values indicates that personal values also play a minor role in preparedness judgments. This might indicate some difficulty with the evaluation task, where a greater reliance upon personal values can be expected for experts who make judgments that span beyond their specific area of expertise (Burgman et al., 2011; Corley et al., 2009; Krinitzsky, 1993; Slovic et al., 1995). For instance, experts outside of regulatory agencies may have less direct knowledge and experience with the  86  4.5 Discussion  challenges of regulation and hence may rely in part upon personal values and experiences when making an assessment (and vice versa) thus accounting for some of the observed variance in preparedness judgments.  While these four factors (novelty of risks, socio-economic views, trust, and attributed responsibility) provide insight into the drivers of preparedness perceptions, together they account for less than one-third of the observed variance in preparedness perceptions. The observed differences in mean preparedness judgments between NREG, NSE, and NEHS groups (in Figure 4.1) can likely be explained by a combination of the above factors, optimism bias owing to an experts’ proximity to the development of new technologies, and other unmeasured factors including an experts’ depth of understanding of the limitations of risk assessment methodologies and regulatory challenges in general. For instance, experts in regulatory agencies may be more keenly aware of historical successes and failures in managing risks under uncertainty, as well as the new challenges inherent in regulating emerging (and highly uncertain) nanotechnologies, than other expert groups. Recent interviews conducted with experts in US Federal regulatory agencies (Conti & Becker, 2011), indeed point to limited scientific knowledge and uncertainty surrounding nanomaterial behavior as perceived complicating factors for risk assessors and regulators. Given their close familiarity with matters of regulation, NREG participants may be better suited to judge regulatory agency preparedness. Conversely, their close proximity to regulatory matters may also result in its own bias, whereby NREG experts may focus too narrowly on risk and ‘miss the forest for the trees’. These findings also point to a need for decision makers to solicit opinions from a wide range of experts along the nanomaterial life cycle, from upstream research to downstream management, in matters of risk regulation.  87  5.1 Introduction  Chapter 5 Horses for Courses: Risk Information and Decision Making in the Regulation of Nanomaterials 5.1  Introduction  The growth of nanotechnologies in industry sectors ranging from pharmaceuticals and chemicals to energy and environment has been rapid. An ever-increasing number of unique nanomaterials are created every year, each engineered to take advantage of the properties that emerge when materials are manipulated at the nanoscale (Maynard, 2007). Nanomaterials are already in use in scores of consumer products (Consumer Products Inventory, 2011; Berube, Searson, Morton, & Cummings, 2010), and hundreds of distinct types of nanomaterials are in production in the United States (Nanowerk, 2012). With their growing prevalence, nanomaterials are expected to be released in occupational settings (Johnson, Methner, Kennedy, & Steevens, 2010), during product use (Colvin, 2003; Felcher, 2008), and into wastewater and landfills at the end of their useful life (Benn & Westerhoff, 2008; Breggin & Pendergrass, 2007). With these expected releases, human and environmental health may be negatively impacted, and such impacts will have to be understood and managed if we are to safely enjoy the benefits of nanotechnology.  Despite the wide use of nanomaterials in commerce in the United States, regulators currently have limited access to information required for characterizing risks (Linkov, F Kyle Satterstrom, Monica, Hansen, & Davis, 2009a). This lack of information has hampered regulators’ ability to assess and manage potential risks (US-GAO, 2010). In addition to a lack of information, there are at least three sets of barriers to the effective regulation of nanomaterial production, use, and release. The first set of barriers are institutional, particularly in the United States where environmental and non-occupational human health risks are primarily the responsibility the U.S. Environmental Protection Agency (EPA) and occupational risks are under the purview of the Occupational Safety and Health Administration (OSHA). In both cases, the regulatory agencies (EPA and OSHA) are under-resourced and are structurally unable to generate or acquire the rapidly expanding amount of risk information required to regulate nanomaterials and, more generally, chemicals (L. Bergeson, Campbell, & Rothenberg, 2000; Powell et al., 2008). For instance, EPA can require testing of a new chemical, but it must first show the 88  5.1 Introduction  chemical could pose a risk – this puts the agency in a catch-22 since it does not have hazard data in the first place (Choi et al., 2009; Davies, 2005). The burden of data collection and risk assessment is placed on these agencies that do not have budgetary means to carry out this mandate, while nanotechnology firms have little or no incentive to reveal or generate risk-relevant information under the existing regulatory regime (Choi et al., 2009; Choi & Ramachandran, 2009). Institutional difficulties are compounded by a second set of challenges – those posed by nanomaterials to existing methods for assessing and characterizing risks. For many environmental contaminants, there is a lack of sufficient information for analyzing 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 (Cooke, 2010). Nanoparticles, however, pose an additional novel form of risk assessment challenge. As noted there is deep scientific uncertainty regarding every aspect of the risk assessment framework. These include uncertainties about particle characteristics that may affect toxicity, fate, and transport through the environment, routes of exposure and the metrics by which exposure ought to be measured, the mechanisms of translocation to different parts of the body, and the mechanisms of toxicity and disease (Kandlikar et al., 2007). In each case, there are multiple and competing models and hypotheses. Further compounding this risk assessment challenge is the emerging paradigm of life-cycle risk assessment (Beaudrie, 2010; Owens, 1997; Shatkin, 2008; Sweet & Strohm, 2006), whereby regulators are expected to investigate potential impacts at every stage of a material or product’s life. Consequently, uncertainties in estimating risks due to nanoparticle exposures are extreme and not yet easily amenable to the sorts of risk assessments that form the basis for current regulatory activities (Tsuji, Maynard, Howard, James, & Lam, 2006).  A final consideration in the regulation of nanomaterial risks is the regulatory impact on innovation in an emerging sector. Like other new technological domains, nanotechnology innovations are often made by small companies and startups. These firms have neither the expertise nor the resources to adequately assess the health and  89  5.1 Introduction  environmental risks of nanomaterials. Consequently, regulations that do not recognize this run the risk of slowing down the pace of innovation or increasing costs. This results in a “regulator’s dilemma” (Weinberg, 1986) where the uncertain costs of doing too much (chilling effects on innovation, increasing product costs) need to be weighed against the costs of doing too little (eroding trust in regulatory institutions, causing undue harm) to manage emerging risks.  The early U.S. regulatory response to nanomaterials in the face of institutional barriers and uncertain science was one based on voluntary measures. In early 2007, the EPA implemented a voluntary Nanomaterial Stewardship Program (NMSP). Like prior voluntary programs under Toxic Substances Control Act (TSCA) aimed at persuading chemical manufacturers to reveal screening level data, the NMSP has also been limited in its ability to generate risk information (Breggin et al., 2009). The scantiness of data gathered makes it evident that a compulsory reporting regime might be required (S. Brown, 2009). Other North American jurisdictions have begun to mandate reporting through information “call-ins,” such as the one issued by the California Department of Toxic Substances Control (DTSC), formally requesting "information regarding analytical test methods, fate and transport in the environment, and other relevant information from manufacturers of carbon nanotubes" (Wong, 2009). Similarly, Environment Canada (EC) decided in 2007 to treat nanomaterials as “new substances” under the Canadian Environmental Protection Act; this requires manufacturers and importers to submit risk related information to regulators (Proposed regulatory framework for nanomaterials under the Canadian Environmental Protection Act, 2007).  Data requests can provide much needed baseline information on nanomaterial manufacture and use activities. However, baseline data is just one of many pieces of information that regulators might need. As nanomaterial use continues apace, regulators will face various decision contexts when dealing with the regulation of potential environmental pollutants. Agencies are responsible for, among other things, developing an understanding of the scope of a regulatory challenge, investigating and managing potential risks, and providing guidance for the safe production, use, and disposal of  90  5.2 Nanomaterial Risks and Regulatory Decisions  materials or technologies. In each of these contexts, regulators face decision-making and data challenges that are complicated by limitations in existing risk assessment tools. Until the science of nanomaterial risk assessment matures, regulators will need to explore the use of alternative approaches to aid in near-term decision-making (Grieger, Baun, & Owen, 2010; Kandlikar et al., 2007). This paper focuses on such challenges and explores some possible solutions.  The remainder of the chapter is structured as follows: in section 5.2 we focus on how the decision context can determine data needs; in section 5.3 we examine, based on recent experiences of U.S. regulators, what data/information can actually be obtained from firms; in section 5.4 we examine how available information- and expert judgment-based decision support tools (both existing and novel) might help regulatory bodies manage nanomaterial risks. We conclude in section 5.5 with a discussion of data needs for supporting near-term regulatory decision-making.  5.2  Nanomaterial Risks and Regulatory Decisions  Quantitative risk assessment for environmental pollutants relies on mathematical models with input parameters relating to concentrations of pollutants in the environment, extent and duration of exposure, and toxicological effects from exposure. In conventional chemical-based risk assessment models, uncertainties in the values of each of these model parameters are parametric and analyzed using Monte Carlo simulations. In the case of nanomaterials, assessing and quantifying health risks is further complicated by lack of data and deep scientific uncertainty regarding every aspect of the risk assessment framework: (a) particle characteristics that may affect toxicity; (b) the persistence of manufactured nanoparticles in the environment which, in turn, influences the probability of exposure; (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. These are not merely uncertainties in the value of model parameters but rather uncertainties about the choice of the causal mechanisms themselves and the proper model variables to be used. Consequently, uncertainties in estimating risks due to nanoparticle exposures may be characterized as “extreme.” The  91  5.2 Nanomaterial Risks and Regulatory Decisions  central challenge in quantifying nanoparticle risks is the presence of extreme uncertainty as manifested in difficulties of choosing appropriate model variables and the presence of multiple and competing models (Kandlikar et al., 2007).  Due to these extreme uncertainties, developing the information base needed to support regulatory action for nanomaterials using traditional risk assessment techniques is more challenging than it is for conventional chemicals. It is unlikely that traditional risk assessment tools can be used in the near future (IRGC, 2007; Marchant, Sylvester, & Abbott, 2008), and regulators will be faced with understanding and managing the growing field of nanomaterials by utilizing alternative assessment tools and approaches (Owen & Handy, 2007). Professional or expert judgment can be useful in identifying relevant variables, assessing the strengths of competing mechanisms and models, and in estimating uncertainties in parameters (M. G. Morgan & Henrion, 1992). Expert judgment also lends itself naturally to the development of tools for decision-making under uncertainty (Cooke & Probst, 2006; Knol, Slottje, van der Sluijs, & Lebret, 2010).  In what follows, we will explore three regulatory scenarios that highlight the difficulty of collecting risk-relevant information and that demonstrate how decision-support methods can aid in regulatory decision making while the science of nanomaterial risk assessment is developed further. The scenarios include:   developing baseline information for production and releases of nanomaterials;    establishing priorities for risk related research; and    managing occupational risks in the workplace.  Figure 5.1 illustrates these three scenarios and highlights the relationship between the increasing specificity of the decision context and the different tools and data needs required to meet regulatory goals. As a decision context becomes more specific (moving from left-to-right), the data needs become more apparent, and the requirements for decision support methodologies become clear (i.e., selecting control methods for a specific nano-process). For less specific contexts in which decisions are broad based  92  5.3 Baseline Information and Nanomaterial Data Collection  (moving from right-to-left), greater clarity in decision goals is needed to improve selection from a menu of support tools. These following sections will investigate this spectrum of regulatory decision contexts in greater detail.  Figure 5.1 Decision contexts and available decision support tools. Decision contexts (rounded rectangles) become increasingly specific from left-to-right influencing the choice of support tools (ovals) to aid in regulatory decision-making. Data requirements similarly become more specific with increasing specificity of the decision context.  5.3  Baseline Information and Nanomaterial Data Collection  Since many nanomaterials are largely unregulated (Beaudrie 2010), information about risks from their current use is scarce. Publicly available information can be accessed primarily through two sources – the Nanowerk database (Nanowerk 2010) and the Project on Emerging Nanotechnologies’ Consumer Products Inventory (2011). The Nanowerk 93  5.3 Baseline Information and Nanomaterial Data Collection  database contains information on nanoscale materials that are available for commercial and research sale. While a useful tool, the database does not distinguish between research and commercial use, nor does it have the means to check the accuracy of information provided. The PEN database catalogs consumer products on the basis of producer claims about the presence of nanomaterials. The PEN database also suffers similar shortcomings related to verification of the presence of nanomaterials and their molecular identity (Berube et al., 2010). The paucity of reliable data on nanomaterial production and use is one motivation for data collection efforts of regulatory agencies such as EPA, DTSC, and Environment Canada. In what follows, we will summarize the goals behind the EPA and DTSC efforts for baseline data monitoring and will briefly comment on the outcomes and their implications for managing and regulating nanomaterials.  5.3.1 EPA’s NMSP The NMSP’s data collection efforts are part of an ambitious voluntary plan to promote environmental stewardship of nanomaterials. Of the four explicitly stated goals of NMSP only one is aimed at collecting data about existing nanoscale materials from manufacturers and is the focus of this section. The other goals pertain to identification and promotion of risk management practices, development of test data, and encouragement of “responsible development” and are not examined here. Under the “Basic” program of the NMSP2, the EPA developed a data submission form modeled after TSCA’s Pre-Manufacture Notification (PMN). Firms were encouraged (but not required) to use this form in responding to the program. In addition to general identification information about the substance (i.e., chemical name, molecular formula, CAS number), the form also asks for data on amounts, chemical and physical properties in the standard PMN format, properties specific to nanomaterials (e.g., size-dependent properties) not included in the PMN, and hazard information such as health and environmental effects, bioaccumulation, and biodegradation.  2 An advanced program was also envisaged, but as of December 2009 only four companies had signed on.  94  5.3 Baseline Information and Nanomaterial Data Collection  The data collection phase of NMSP lasted for six months, and EPA issued an interim report in July 2008 (US EPA NMSP, 2009). While the Nanowerk database had over 1800 distinct entries for nanoscale materials and the PEN database over 600, the NMSP reported 106 distinct nanoscale materials, which is a relatively low yield rate. Of these almost two-thirds of the nanomaterials (63) were reported by a single company, and onesixth of nanomaterials were not named due to claims of confidentiality (Chatterjee, 2008). While the agency had relative success in collecting information on basic physical and material characteristics (this type of information was obtained for between 60% and 80% of nanomaterials), risk information was largely missing from the submission. Data for acute toxicity was provided for about 20% of the materials, while chronic toxicity was provided for less than 5% of materials. Data collection under the NMSP was ambitious, and the categories of data requested went beyond those expected of other new chemicals. However, the voluntary nature of the program meant that the yield rate was low, as was the quality of risk information obtained. A common critique of the NMSP is that companies were given little incentive to cooperate in the program (Breggin et al., 2009; Pelley & Saner, 2009; Linkov et al. 2009a). It is also possible that the companies with little experience working collaboratively with EPA might have had concerns about the implications of voluntary disclosures, including those related to confidentiality, and refrained from complying with the NMSP request (Lockard, 2012). Clearly, there was a mismatch between the expectations and goals of EPA and the eventual outcome of the NMSP data collection effort.  5.3.2 California DTSC Carbon Nanotube Information “Call-In” In January 2009, the California DTSC issued a letter “requiring information regarding analytical test methods, fate and transport in the environment, and other relevant information from manufacturers of carbon nanotubes” from all California-based producers and importers carbon nanotubes (CNTs) (Wong, 2009). DTSC used its authority under the California Health and Safety Code in issuing a mandatory “call-in.” The six call-in questions were general in nature and asked each firm about its position in the value chain, sampling and monitoring methods used in the workplace, knowledge about the firm’s product in the environment, knowledge about CNT risks, and methods  95  5.3 Baseline Information and Nanomaterial Data Collection  used to protect workers. Questions were aimed more at discovering the types of work in which the firms engaged and less about technical details related to material properties and risk.  While a comprehensive assessment of the response to DTSC is beyond the scope of this paper, the overall response to the call-in was mixed. Of the twenty-two respondents to the DTSC call-in, eleven research organizations (universities and national research labs) and six private firms provided substantive responses. Additionally, two firms were out of business and three other respondents stated that their work did not involve CNTs. Strikingly, half of the six private firms provided very brief responses – these are likely small venture-capital based companies lacking resources to respond fully to questions. Among the universities and research organizations, there was substantial variation. Some groups (such as the California State University system) reported no CNT usage, while others provided detailed responses. The specific responses to questions might (at least at the current stage) be less useful than the process and dialogue in which DTSC has begun to engage manufacturers of nanomaterials. DTSC is signaling to users and producers of nanomaterials that there is need for information disclosure and is thereby raising awareness about the potential health and safety consequences. The DTSC is also engaging in dialogue via site visits3 and information sessions. The DTSC call-in and EPA NMSP provide interesting contrasts. EPA’s program was voluntary, while DTSC’s call-in was mandatory and required firms to respond. EPA’s data collection efforts were comprehensive and based on a standardized data collection form; they also went beyond what is required for new chemicals under TSCA. DTSC’s call-in, on the other hand, included open-ended questions that accommodated a range of qualitative responses. In particular, it appears that an explicit decision was made by DTSC to avoid asking for risk information that needed expensive (and potentially mandatory) bioassays (Lockard, 2012). The response to both initiatives was mixed, suggesting that improvements could be made. There may also be inherent limits to  3  Survey questionnaires administered to CEOs during the site visits also provided DTSC with additional information about activities of ten companies  96  5.4 Risk Information and Decision Making  obtaining useful information from such efforts in the face of confidentiality claims. Neither of these approaches has been successful in acquiring the full range of nanomaterial property and toxicology data required to permit a full risk assessment in the near-term. However, at this early stage, the collected data can help regulators to better understand the scope of the challenge and to assess their needs for future calls for information.  Baseline information call-ins provide regulators and risk managers with preliminary data on the types and amounts of nanomaterials being created, used, and released. As the EPA and DTSC experience shows, this data will initially be scant, and procedures for collection will need to be improved. However, as more complete data becomes available, regulators will be faced with the greater challenge of assessing the implications of a variety of nanomaterials used in a wide range of applications. We turn to this challenge in the next section.  5.4  Risk Information and Decision Making  As more risk information becomes available for nanomaterials, regulators will face a challenge in deciding how to utilize limited resources to best manage potential risks. Nanomaterials, nano-applications, and nano-products will have to be analyzed to determine which may pose the greatest harm (if any) along its lifecycle, and regulators will have to prioritize them accordingly for further scrutiny. Additionally, regulators will be required to provide guidance and advice to manufacturers of nanomaterials so they may protect workers and make products that are safe. It is unlikely, however, that traditional risk assessment tools can guide this decision-making process in the near-term (Grieger et al., 2010; Kuempel, Geraci, & Schulte, 2007). While research continues on developing nano-specific risk assessment models (Tsuji et al., 2006; Warheit, Sayes, Reed, & Swain, 2008), regulators will be required to make complex risk-benefit tradeoffs. This task will require tools that allow regulators to make best judgments given available information. In contexts where complexity is endemic, uncertainties are large, and optimal decisions are not obvious, formal decision-analytic methods can help  97  5.4 Risk Information and Decision Making  (Grieger et al., 2010; Kuempel et al., 2007; Kuzma et al., 2008; Linkov & Satterstrom, 2007; Linkov et al., 2009b).  Decision-analytic methods can synthesize both available information and expert judgment into an integrated framework (Tervonen et al., 2009). Rather than providing an absolute measure of risk, decision-analytic methods can be used to provide a measure that allows regulators to rank the relative risks of nanomaterials (B. Hansen, van Haelst, van Leeuwen, & van der Zandt, 1999). Organizing a multitude of potential risk sources into a ranked list might help regulators focus their attention on those with the greatest potential for harm. Similarly, risk-ranking tools can be used to provide guidance on the selection of safety measures to limit exposure or to anticipate and plan for risk events (Fauss et al., 2009; B. Hansen et al., 1999; Owen et al., 2009).  There are numerous decision contexts for which regulators must begin to investigate potential harm from nanomaterials, and each context brings with it a specific set of data needs and support tools. Below we provide two examples to illustrate the information and assessment challenge that regulators are likely to face as nanomaterials proliferate.  5.4.1 Risk Ranking and Prioritization Risk managers and regulators are currently faced with a growing problem. If conventional risk assessment is to be used as the standard for making decisions, then many questions about nanomaterial risk management could go unanswered until adequate information becomes available. As noted above, however, decision-analytic tools and expert judgment can be used to enable a preliminary assessment and ranking of risks, and several examples of such methodologies have been demonstrated for nanomaterials in recent years (S. F. Hansen et al., 2008; Robichaud, Tanzil, Weilenmann, & Wiesner, 2005; Wardak, Gorman, Swami, & Deshpande, 2008).  Risk ranking methodologies can involve qualitative or quantitative estimations of hazards and/or exposures and can be applied to materials, products, applications, or lifecycle stages. As the examples below indicate, these methodologies are flexible and can be  98  5.4 Risk Information and Decision Making  useful in many different decision contexts. Hansen et al. (2008) conducted a risk ranking analysis by utilizing scenarios for exposure from consumer products containing nanomaterials. Exposure was rated from “expected exposure” to “possible exposure” or “no exposure” based on the location of nanomaterials and product use, and the researchers were able to identify classes of nanomaterials and products currently on the market that are likely to pose the highest exposure. Robichaud et al. (2005) investigated a similar problem involving a qualitative assessment of risks from the production of nanomaterials. Their analysis involved expert judgment on five factors related to both hazard and exposure potential: emissions, flammability, log KOW (bioaccumulation), water solubility, and toxicity. While the analysis investigated comparative risks from the chemicals used in the production process, and not risks from nanomaterials per se, their work illustrates how comparative estimates of risk might be made. Wardak et al. (2008) similarly used expert judgment as input to the “probability” and “severity” estimates of possible “risk triggers” (inherent nanoparticle properties that trigger a higher level of risk) for a variety of nanomaterials across their lifecycle. Risk triggers were identified for two lifecycle stages (use and disposal) and three exposure pathways (inhalation, ingestion, and skin absorption). Expert judgment was used to determine subjective scores (scale of 1 to 5) for each risk trigger, and the scores were combined to map the relative risks of different nanomaterials for each combination of life cycle stage and exposure pathway. These three approaches have many similarities in methodology that can be formalized using multi criteria decision analysis (MCDA), which we turn to next.  MCDA is a widely discussed approach to nanomaterial risk assessment with a long history of use in various decision contexts (Linkov et al., 2007). The first stage of MCDA typically involves the development of criteria by which the “utility” of each nanomaterial under consideration is characterized. Each of these criteria is then given weight based on its importance for the decision maker. Several tools are available to help the decision maker in this weighting task (Linkov et al., 2007; Linkov & Tervonen, 2009). In a final step, nanomaterial performance can be compared across decision criteria and a weighted aggregate performance measure defined. Because MCDA is an inherently subjective process, it requires the use of judgment at every step of the analysis.  99  5.4 Risk Information and Decision Making  Consequently, experts are able to weigh available evidence and make best judgments when data are not available. As such, the MCDA framework is well suited to analysis under high uncertainty (Kiker, Bridges, Varghese, Seager, & Linkov, 2005; T. P. Seager & Linkov, 2008; Tervonen & Lahdelma, 2007).  Tervonen et al. (2009) use MCDA-type analysis called SMAA-TRI that classifies five nanomaterials into five different risk categories: extreme risk, high risk, medium risk, low risk, and very low risk. The analysis is based on a set of performance metrics that measure both the toxicity and physicochemical characteristics of nanomaterials, along with expected environmental impacts through the lifecycle. The approach can incorporate available data (i.e., particle size) as well as subjective probabilities for variables that are not available from the literature (i.e., measures for bioavailability). The result is the assignment of each nanomaterial to different risk classes along with an associated measure of confidence in the assignment.  A benefit of MCDA is its adaptability to various decision contexts. Whether the goal is risk ranking, prioritization, or identification of high-risk lifecycle stages, the MCDA approach can be applied. Further, MCDA can draw input from various groups of experts and members of the public, and analyses can be made even with limited data. The MCDA framework is flexible enough to incorporate criteria such as “social importance” and “stakeholder preference” in addition to traditional risk measures, allowing a much broader analysis of the benefits and risks of emerging nanomaterials. MCDA is also adaptive because it enables the implementation of near-term solutions. Subsequent management modifications can be made as new scientific data becomes available or regulatory policy evolves (Linkov et al., 2007).  While MCDA is a useful tool, the simple modeling techniques that underlie it (linear, additive response models) can miss the actual complexity of the risk phenomena. Mechanism-based models that characterize the relevant physical and biological variables and their interactions provide more accurate representations and are, therefore, more scientifically defensible. Work by Morgan et al. (2005) using influence diagrams  100  5.4 Risk Information and Decision Making  demonstrates how physical and biological variables can be systematically mapped in an influence hierarchy that characterizes nano-toxicology. Influence diagrams are a generalized representation of probabilistic networks such as Bayes Belief Networks (BBNs) (Pearl, 1986) that can be used to model variables and their influences in a probabilistic manner. BBNs have been used in myriad fields including ecology, resource management, and technology forecasting (Heckerman, Mamdani, & Wellman, 1995; Vans, 1998). While there are no current examples of the use of BBNs in the nanomaterial risk domain, the field is well suited for the use of this approach, particularly for calculating the value of different types of information and so suggesting directions for new research and data monitoring efforts.  Risk ranking and other decision-analytic tools are largely illustrative at this point; however the works described above provide preliminary evidence that regulatory decisions could benefit from their use. Such tools could be utilized in the near-term to provide guidance for action, to prioritize for data collection and further research, or to limit the use of certain nanomaterials or applications. As more data and a better scientific understanding of the relationship between nanomaterial properties and toxicity/exposure become available, these methodologies can develop into more robust risk assessment tools.  Expert judgment will likely play a significant role both in the selection of variables and their weighting when developing or fine-tuning frameworks, as well as in estimating values when utilizing frameworks for specific decisions. In the case of occupational health, expert judgment is currently used to estimate both prospective risks in operational settings (Ramachandran, 2003; Walker, Evans, & MacIntosh, 2001) as well as retrospective exposures (Ramachandran, 2001) in historical workplace settings. It is therefore not surprising that occupational exposure assessment has made the greatest progress towards developing risk management and mitigation tools for nanomaterials. We describe these efforts below.  101  5.4 Risk Information and Decision Making  5.4.2 Occupational Hazards and Control Banding Occupational health is at the forefront of concern for nanomaterial safety. However, recent research by Engeman et al. (2010) describes that only 45% of companies surveyed in North America, Europe, and Asia report having a nano- environment, health, and safety (EHS) program in place. The top three reasons for not developing a nano-EHS program include “a lack of information,” “a lack of guidance/regulation,” and “budget constraints” (Reported practices and perceived risks related to health, safety and environmental stewardship in nanomaterials industries, 2010). A similar survey of companies working with nanomaterials in Germany and Switzerland in 2008 indicated that 65% of companies did not conduct risk assessments on materials that they produce (Helland et al., 2008). These figures illustrate a significant gap in occupational safety programs that can protect workers from potential risks associated with nanomaterial production. Further, this gap signals a growing need for nanomaterial safety guidance for the workplace.  Occupational risks pose a different challenge than the risk-ranking scenarios described in the previous section. Given the very specific context of risk in a laboratory or production facility, it is likely that more information is available to a risk manager, especially in cases where nano-EHS programs are in place. First, the basic characteristics of materials such as composition and size distribution will likely be known. Other physical/chemical properties of materials may also be known in some instances, and some assay or toxicological data may be available. It is also very likely that the exposure scenarios under consideration can be clearly defined, and a menu of mitigation options is available. In other words, in occupational settings the decision context is well mapped, and the decision problem is more manageable than the open-ended ranking exercise of the previous section. Decision-making often boils down to a single question: how can particular hazard/exposure combinations linked to different workplace tasks be associated with specific measures for exposure control? In these instances, decision support tools are useful (Maynard, 2007; D. Zalk & Nelson, 2008). In particular, a recent approach known as “control banding” can aid in choosing appropriate exposure control methods.  102  5.4 Risk Information and Decision Making  Control banding is a methodology that has served as a support tool for occupational safety for a number of years and has been used extensively by the pharmaceutical industry for categorizing exposure controls in the workplace (Maynard, 2007; D. Zalk & Nelson, 2008). The idea is to develop “control bands” that can be mapped to particular sets of exposure/hazard combinations so that health risks for workers involved in particular operational tasks are minimized. Each control band corresponds to a particular control technology or action that is suitable for the given hazard and exposure scenario. A nanomaterial-specific control banding methodology (“CB Nanotool”) was developed by Paik et al. (2008) through extensive expert input, review, and testing. The CB Nanotool involves a basic matrix with “severity” (i.e., hazard) and “probability” (i.e., exposure) indices as the X and Y axes and utilizes nanomaterial physical/chemical properties (shape, size, surface area, and surface activity), available toxicology information (carcinogenicity, mutagenicity), and exposure availability (volume produced, dustiness) to link the indices to one of four control bands (Paik et al., 2008). The control bands correspond with increasingly stringent control methodologies from “general ventilation” up to “seek specialist advice.” An example of the CB Nanotool control banding matrix can be seen in Figure 5.2.  Figure 5.2 Control Banding matrix with risk level (RL) indicators as a function of the combination of probability and severity scores. Control bands correspond to risk levels as follows: RL 1 – General Ventilation; RL 2 – Fume hoods or local exhaust ventilation; RL 3 – Containment; RL 4 – Seek specialist advice. (Adapted from Paik et al.2008)  103  5.5 Conclusions  Analysts can input known “severity” factors for each nanomaterial in use and estimate “probability” factors based on specific exposure characteristics of each occupational task under consideration. The resulting combination of severity and exposure scores for each task relates to one of the four control bands that provide control advice for minimizing occupational risk.  One strength of such a tool lies in its ability to utilize basic data without the need for expensive testing. Additionally, control banding offers the advantage of focusing on a small number of risk management/mitigation decisions to help in managing the “extreme uncertainty” problem. Furthermore, where nanomaterial specific information on factors related to “severity” are unavailable, “unknown” values can be set to a default of “high” to enable a precautionary approach to the selection of exposure control measures. The tool is more risk averse at the start but can be modified to reflect new scientific findings as and when better risk information becomes available. Not surprisingly, the CB Nanotool was demonstrated to provide recommendations that were equal to or more conservative than industrial hygiene experts’ opinions for adequate controls in 27 of 31 trials (D. M. Zalk, Paik, & Swuste, 2009). Since it can provide guidance for selecting control measures in the workplace without extensive workplace specific research, the CB Nanotool has been adopted as part of the Lawrence Livermore National Laboratory Nanotechnology Safety Program (D. M. Zalk et al., 2009) and is under consideration for use by Safe Workplace Australia, which is the Australian regulatory body for worker health and safety (Australia considers control banding, 2010).  5.5  Conclusions  The data and analysis challenges facing regulators of nanomaterials are extensive. Some of these challenges – particularly institutional ones - can only be fixed with changes to existing regulations. For instance, TSCA reform that is currently underway might make a big difference if it releases EPA from the informational catch-22 it currently faces when seeking to regulate new chemicals. Other challenges are more closely tied to nanomaterials and their properties and are common to regulatory bodies within most  104  5.5 Conclusions  jurisdictions. In particular, gathering baseline information is proving to be challenging because of the regulators dilemma and confidentiality concerns. However, progress is being made on collection of baseline monitoring information and the quality and coverage of such information is likely to increase substantially. In this paper, we have argued for a “horses for courses” approach to how information about risks (including baseline information) and related decision-analytical tools can help regulators. In particular, until such time as formal risk assessments can be performed in scientifically defensible ways, regulators could use suitably designed decision-analytic tools such as those involving risk ranking (e.g., to prioritize for further research on nanomaterials) and control banding (for workplace risk mitigation) to help them manage complexity and uncertainty.  Decision-analytic tools are currently at a preliminary stage and much research needs to be done on how they might be tailored to suit regulatory goals. New methods development is also particularly appropriate in the case of nanomaterials where the uncertainty is extreme. Consequently, there needs to be a more concerted effort to build decisionanalytic tools than is currently the case. There are several potentially fruitful areas of research including: the systematic use of subjective expert judgments, modeling using probabilistic networks and BBNs, and integrated assessment efforts that consider nanomaterial life cycles. Funding agencies and regulatory bodies should consider supporting such research in a targeted manner, because interdisciplinary research that combines the relevant sciences (physical sciences, biology/toxicology, and decision science) is unlikely to emerge organically and targeted funding can seed such collaborations.  105  6.1 Findings and Contributions  Chapter 6 Conclusions The research that comprises this dissertation contributes to an improved understanding of the challenges that novel nanomaterials pose for risk regulation, and investigates approaches that enable the management of their risks and benefits. In line with the main objective of this thesis, to investigate the challenges that nanotechnologies pose for risk regulation, this research uncovers several barriers to effective regulation. These include the problems posed by the novel properties of nanomaterials, and a number of regulatory gaps, both new and existing, that make regulatory management of engineered nanomaterials difficult. The objective, to inform the development of policies and practices to address these challenges, was achieved through expert opinion research and a study of emerging decision-analytic tools. The survey research elicited and characterized expert opinion and perceptions of nanomaterial risks and regulation, and identified the role of attitudes towards these materials as well as experts’ own social values in informing those opinions. Finally, through an investigation of decision-analytic methods for assessing and managing risk, this research found several promising approaches including the use of multi-criteria decision analysis and control-banding techniques. This thesis concludes that oversight can be improved through pending regulatory reforms, the utilization of expert opinion to inform decision-making, and the development of improved decision-analytic tools that enable the assessment and management of risks under high uncertainty. In what follows I present a summary of my research contributions and findings, and strengths and limitations, followed by a proposed direction for future research.  6.1  Findings and Contributions  6.1.1 Risks and Regulation I achieved my first objective, to explore the challenges that nanomaterials pose for US federal environment, health, and safety (EHS) regulations using the life cycle paradigm, through a rigorous review and synthesis of the applicability of existing US federal regulations on a regulation-by-regulation basis, given different materials and products across their life cycle. While several papers report on the shortcomings of existing  106  6.1 Findings and Contributions  regulations, few have attempted a synthesis of all regulations expected to apply across the EMN life cycle in a holistic manner. The research presented in Chapter 2 includes a comprehensive look at the collection of EHS regulations for nanomaterials, and conducts a rigorous analysis of both existing regulatory shortcomings and ways that novel nanomaterial properties and high uncertainty create gaps through which many ENMs are expected to fall. These findings constitute a new contribution to the field, and the life cycle regulatory analysis approach used is a new methodological contribution.  This research concludes that the most severe gaps in oversight occur for environmental releases and at the end-of-life stage, including routine releases from industrial facilities and in household wastes. To close these gaps, it is imperative that ENM monitoring and control technologies are developed rapidly, necessitating increased research spending in this area. Further, a precautionary approach could be taken for industrial ENM wastes whereby these wastes are treated as ‘hazardous’ by default until more information is available demonstrating their safety.  Proposed regulatory reforms for chemical substances and cosmetics manufactured in the US were found to be a promising approach for addressing many of the pressing concerns for ENM regulation. These reforms would require manufacturers to produce minimum data sets, and to report on ENMs created from ‘existing’ substances and/or produced in low volumes. Additionally, this research found support from leading legal scholars for a multi-faceted governance approach that use a variety of legal, policy, and regulatory tools, rather than purely voluntary stewardship-based programs given their shortcomings. However, it is unlikely that regulatory reforms will be approved by Congress in the near future in the current political environment, and evidence suggests that the White House would prefer voluntary approaches over increased regulatory oversight. To aid regulators in assessing and managing risks under the current regulatory frameworks, increased funding support is required to accelerate the medium-term development of quantitative tools (e.g., nano-QSARs), and the creation of qualitative risk-screening and decision support tools in near-term.  107  6.1 Findings and Contributions  6.1.2 Expert Opinions and Perceptions I achieved my second objective, to survey expert opinion on risk and regulation, to identify areas of concern, drivers of opinion, and variations in judgment across expert groups, through a web-based survey of nanoscientists, engineers, and regulators as reported in Chapters 3 and 4.  These chapters present significant contributions in two areas. First, the survey conducted represents the first systematic effort to compare the thinking several distinct groups of nanotechnology experts. Second, this research contributes to the risk perception literature by introducing several new indices or drivers of expert opinion than previously evident in the literature. These include the contribution of a new and previously untested index for perceived novelty of ENM benefits and risks, and measures that evaluate experts’ attitudes towards regulation, including preferences for distinct regulatory approaches (precautionary approaches, market-based and voluntary approaches).  Experts were found to differ significantly on both their perceptions of nanotechnology risks, and perceptions of agency preparedness for managing risk. Nano-regulators (those involved in research, risk-analysis, or decision-making in regulatory and research-based agencies) perceived the greatest risk from nanotechnologies, were strongest in their view that regulatory agencies are unprepared for managing risks, and strongly preferred precaution in regulatory management of ENMs. Nanoscientists and engineers on the other hand viewed the lowest levels of risk, viewed agencies more favourably than other expert groups (though still view agencies as unprepared), and showed the least support for precaution in regulatory efforts for ENMs. Further, nanoscientists demonstrated a sizeable gap between their perceptions of novel benefits and perceptions of novel risks, and demonstrated the greatest level of confidence in their views. These findings suggest a predisposition towards optimism for nanoscientists and engineers whose work is conducted primarily at the upstream or development end of the life cycle, and ‘caution’ or pessimism for nano-regulators at the risk management end of the life cycle. Additionally, this research did not find support for the longstanding assumption that demographic factors (age, gender), disciplinary field, and institutional affiliation are  108  6.1 Findings and Contributions  significant factors driving risk and regulatory perceptions. Once indices of the perceived novelty of nano-risks and attitudes towards regulation were added to the regression models, these factors were not significant. This demonstrates that both risk perceptions and perceived agency preparedness depend in greater part on the attitudes, perceptions, and values of experts sampled, than by differences in demographic, domain of expertise, or group distinction. These findings together suggest that selection of nanotechnology experts for involvement in decision-making and strategy development should include sampling from a range of expertise across the life cycle, including those involved in development, testing, and regulation of nanotechnologies, and should extend across institutional domains.  Additionally, this research found that nano-experts see moderate levels of risk from nanotechnologies overall, but overwhelmingly view regulatory agencies as unprepared for managing those risks. While the finding of moderate levels of perceived risk is not unlike what has been found in other studies, the finding of overwhelming judgment that agencies are not prepared to manage risks signals a larger problem in the regulatory environment. Through a hierarchical regression analysis I found that perceptions of regulatory preparedness were driven largely by perceived novelty of risk, and to a lesser extent by experts’ trust in regulatory agencies to effectively manage risks. This suggests that high uncertainty and novel nanotechnology risks are the largest concern, and that agency preparedness judgments are likely based on assessment of their ability to regulate under these circumstances given the existing tools for achieving regulatory responsibilities. This signals a need for measures to reduce uncertainties, to support expedited research to understand nanotechnology risks, and to facilitate the rapid development of assessment methodologies and tools to enable assessment under these conditions.  6.1.3 Decision-Analytic Tools I achieved my third objective, to investigate the use of decision-analytic tools to aid in risk assessment and regulation under high uncertainty, through chapter 5’s analysis of available options for conducting risk-analytic assessments of material safety in this early  109  6.1 Findings and Contributions  stage of research and development. The main goal of this research was to explore the suitability of decision-analytic tools to aid near-term decision-making in three specific decision contexts: for data collection, prioritization, and occupational risk management. I accomplished this by analyzing the successes and failures of two regulatory datacollection programs in the US, and by reviewing the available tools in the nanotechnology domain for risk ranking, establishing priorities for research and analysis, and choosing control measures to manage risks in the workplace.  This research also illustrates that decision-analytic tool selection depends heavily upon the decision context, with results varying significantly based on approaches chosen. Data collection schemes in the regulatory environment (e.g., the EPA stewardship program (US EPA NMSP, 2009) and the DTSC data call-in (Wong, 2009)) enabled regulators to collect basic product data for risk assessment, including information on the types and amounts of nanomaterials being created, used, and released. This information can help regulators to understand the scope of the regulatory challenge for ENMs (identifying the types of ENMs in production, the amounts produced, and potential for human exposure and release to the environment). However, these approaches have not been successful in collecting specific hazard related data, due to the expense incurred on manufacturers who are required to characterize ENM physical-chemical properties or collect in vitro or in vivo toxicity data. Such information is difficult to collect on a voluntary basis (due to costs), and some regulatory agencies may not have adequate authority to require the production of such data. This research concludes that mandatory data call-ins (such as conducted by the DTSC) show the greatest promise for ensuring that a wealth of high quality data is available to regulators. However, information will likely be limited to basic production and use data.  On the contrary, decision-analytic tools aimed at assessing hazards based on available information and expert judgment, do a poor job altogether of quantitatively assessing ENM toxicity in absolute terms. Several approaches have been developed for evaluating risks in relative terms, suitable for ranking risks or prioritizing ENMs for further review. The most promising tools involve the use of MCDA techniques (Linkov et al., 2007) and  110  6.1 Findings and Contributions  influence-diagram based models that take into account the ways that ENM physicalchemical phenomena determine complex biological behaviours (K. Morgan, 2005). Further, the case for a Control Banding approach is strong when applied in occupational situations under high uncertainty. Such an approach can enable the selection of suitable controls based on easy to evaluate criteria for ‘probability’ of exposure, and ‘severity’ of consequences. Analogous approaches can be imagined for a variety of situations where guidance is required to select the most appropriate option, such as for pollution control equipment on effluent stacks, or as a guidance tool to limit ENM use in products based on potential for human exposure.  This research contributes to the growing literature on the viability of decision-analytic approaches, by highlighting trends, best practices, and a methodology for matching the selection of available decision-analytic approaches to the decision context at hand. It also fills an important gap in the decision-analytic literature in the nanotechnology domain by proposing a ‘decision context’ basis for tool selection, and providing an overview of approaches available to risk assessors and decision-makers.  Decision-analytic tools show great promise for ENM assessment and decision support purposes. Such tools can enable risk assessors and regulators to review and manage risks in the near term, while robust quantitative risk assessment methodologies are developed for engineered nanomaterials. However, relatively little progress has been made in this domain. This area of research needs greater support from regulatory agencies and funding bodies in order to mature. Research aimed at moving decision-analytic tools beyond ‘qualitative’ and ‘screening’ applications is urgently needed.  Given what we know about the value of decision-analytic models, and the relative lack of progress in developing near-term risk assessment tools, the question is: how best to proceed? In similar situations in other domains, for example in assessments of global climate change (Risbey & Kandlikar, 2002), elicited expert judgment has been used successfully to enable assessment under high uncertainty. Expert judgment could therefore prove helpful in bridging the gap between available qualitative risk screening  111  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  approaches for nanomaterials and more robust quantitative models based on empirical data. Yet, eliciting expert judgment can be difficult. The next section reviews the potential use of expert judgment to help risk assessors and regulators deal with high uncertainty in the nanotechnology domain. It reviews some expected challenges for the nanotechnology case, and presents a case for future research to develop an adaptive and quantitative expert judgment based model suitable for nanomaterial risk assessment.  6.2  A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  Uncertainty can be found in every element of the risk assessment framework, and this uncertainty is often compounded in the case of emerging technologies. In the absence of sufficient empirical data, uncertain parameters and models can be estimated using subjective expert judgment obtained through careful elicitation processes. Subjective or Bayesian methods for handling uncertainty have a long history originating with the use of the Delphi method in technology forecasting and nuclear deterrence (Helmer, Brown, & Gordon, 1966; Kahn, Wiener, & Bell, 1967; Linstone, Turoff, & Helmer, 1975), with subsequent applications in policy analysis, engineering, and risk analysis (M. G. Morgan & Henrion, 1992). Expert judgment is most often used to quantify uncertain parameters in a probabilistic form. However, it is not solely limited to assessing model parameters. Often, and especially in the early stages of a scientific issue when uncertainty is high, expert judgment is used to structure problems, to indicate key variables, and to examine relationships between variables by building “influence diagrams” (M. G. Morgan & Henrion, 1992). These influence diagrams are useful devices for structuring problems and can be used quantitatively if sufficient data are available about the quantitative relationships between variables (K. Morgan, 2005).  Subjective uncertainty analyses do however require a significant commitment of resources, and involve the use of methods that are not typically familiar or comfortable to research scientists or policy analysts. Notwithstanding, there are substantial benefits to understanding uncertainty. In the domain of risk assessment, an informed understanding of uncertainty can enhance decisions on complex health or environmental issues, and has  112  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  been used in environmental exposure assessment (Hawkins & Evans, 1989; Ramachandran, 2001; 2003; Vincent, 1999; Walker et al., 2001), and in assessment of global climate change (G. Morgan & Keith, 1995; G. Morgan, Pitelka, & Shevliakova, 2001; Risbey et al., 2001; Risbey & Kandlikar, 2002). Careful expert assessment of uncertainty can provide improvements in choosing explicit and consistent decision criteria and policy strategies, in choosing relevant boundaries for analysis, in improving transparency in the choice of relevant variables, and in understanding further research needs (Burgman, 2005; M. G. Morgan & Henrion, 1992). Additionally, uncertainty analysis can help guide the design and refinement of a model, and can explicitly characterize technical uncertainties to clarify issues of value and of fact. Expert judgment therefore shows great promise for enabling early-stage risk assessment for nanomaterials.  6.2.1 Thinking about Expert Judgment for Nanomaterial Risks Deep uncertainty pervades every element of the exposure–response–risk paradigm for nanoparticles, and exists in part due to the wide and disparate forms that nanotechnology can take (e.g., medical nanotechnology, environmental applications, use in consumer products). Given the myriad applications and types of nanomaterials, it is difficult to understand which materials or applications may pose risks, and to what extent. Additionally, a tremendous amount of uncertainty arises due to changes in physical and chemical properties that can occur when bulk materials with known properties are manufactured at the nanoscale (A. Fairbrother & Fairbrother, 2009). Given this high level of uncertainty, researchers and policy analysts in academia, industry, and government are grappling with the challenge of risk assessment for emerging nanotechnologies (Beaudrie, 2010).  Much of the uncertainty faced in nanomaterial risk assessment can likely be addressed using expert judgment to develop predictive models. Model uncertainties in risk assessments of nanoparticles can be classified into three categories: those resulting from physical and chemical characterization of nanoparticles including the choice of an appropriate exposure metric; those resulting from uncertainty in dose and health endpoints from different exposure routes; and those resulting from a lack of understanding of  113  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  toxicity mechanisms. Substantial uncertainty surrounds the effects of various nanomaterial characteristics and their contribution to risk, and will require careful consideration for inclusion in expert judgment based quantitative models. Characteristics to keep in mind when modeling include:   Particle size o may affect total surface reactivity, agglomeration potential (Aitken, R, Creely, Tran, & Britain, 2004; M. Wiesner, Lowry, Alvarez, Dionysiou, & Biswas, 2006), and potential for inhalation and translocation through the body (Ma-Hock et al., 2007)    Particle shape o smaller diameter fibers penetrate deeper into the respiratory tract, while longer fibers are cleared more slowly (Mossman et al., 1990; RoyalSociety, 2004) o can affect the kinetics of their deposition and transport in the environment    Surface area o greater specific surface area when compared to the same mass of material in larger particles o increased reactivity and sorption behavior of the particle (Auffan et al., 2010; Tiede et al., 2008; US EPA, 2010).    Chemical composition o ENMs based on different bulk materials may exhibit different effects (Zhang et al., 2012) o surface coatings that modify the agglomeration properties of nanoparticles will have biological effects (G. Oberdorster et al., 2005; Warheit et al., 2004)    Choice of exposure metric o mass concentration may not always be appropriate for nanomaterials. o several studies have suggested that at similar mass concentrations, nanometer size particles are more harmful than micron size particles (D.  114  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  Brown, 2001; D. M. Brown et al., 2000; Cullen et al., 2000; Dick et al., 2003; Donaldson, 2000; Lison et al., 1997)  Tremendous potential for variability in nanoparticle properties exists given differences in size, shape, surface area, and surface coating from one ENM to the next. Furthermore, these properties can change for nanoparticles made with different materials (e.g., metal oxides, silver, carbon, silicon, etc.), and could be impacted by impurities and manufacturing by-products (Nel et al., 2006). Our understanding of the properties and reactivity of nanoparticles is still in the early stages, and consequently limits any early attempt at analyzing risks from emerging nanomaterials. Fortunately, this is an area where expert judgment can aid in assessment. Expert judgment is well suited for 1) estimating parameters and characterizing measures and scales, and 2) evaluating causal relationships between these parameters to create predictive models. Using expert judgment techniques, it may be possible to create predictive screening-level tools for risk assessors based on the physical-chemical characteristics listed above. However, the use of expert judgment is not without challenges, and several important criteria must be considered when designing expert judgment based research.  6.2.2 Challenges Likely Faced When Eliciting Expert Judgments for Nanotechnologies Expert judgment has been used in many contexts when uncertainty is high, and is well suited for the challenges posed by emerging nanomaterials (Kandlikar et al., 2007). However, the elicitation of expert judgments will likely be challenged by various factors including the selection of experts from a relatively young field, and the need for refinements to existing models for the case of nanomaterials. These challenges are summarized here.  6.2.2.1 Thinking about Expert Selection A fundamental challenge for nanomaterial risk assessment is the selection of appropriate experts to partake in elicitation tasks. Given the young and relatively small fields of nano-environmental health and safety (EHS) and nano-toxicology research, specific 115  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  expertise in nanotechnology risks is limited. However, expertise may be drawn from outside of the nanotechnology domain for certain risk assessment tasks. Recent research by Fauss et al. involved experts from various disciplines and institutions, including government, industry, nonprofit, and academia, to understand possible exposure routes for consumer products containing nano-silver particles. Collectively the experts came up with a much larger set of exposure pathways than any single expert (Fauss et al., 2009), and demonstrated the value of expertise from areas outside of the nanotechnology domain. Additionally, the similarities between nanoparticle research and the wellestablished PM2.5 particulate research domain mean that PM2.5 scientists may be suitable candidates for expert judgment on a variety of aspects of nanoparticle risk assessment. Specific expertise is expanding in the young fields of nano-toxicology, nanorisk, and nano-EHS research. However, it may be some time before nano-experts have the substantive expertise necessary to make accurate judgments on various aspects of nanoparticle risk.  6.2.2.2 Using Analogous Models as a Guide Another challenge for expert judgment in the field of nanotechnology is that objective models that guide judgments may not exist, and reliable feedback on the accuracy of expert judgments may not be possible. This can lead to elicitation tasks that are practically “un-learnable” (Bolger & Wright, 1994; Rowe & Wright, 2001). However, depending on the decision context, analogous models may be helpful as a proxy for proper nano-specific models (e.g., PM2.5 particulate research). When existing models are not directly appropriate for the case of nanomaterials, they may serve as a framework for developing new models. For example, the field of chemical risk assessment has developed quantitative structure–activity relationship (QSAR) models to help experts estimate hazard and exposure potential for chemicals given their physical and chemical properties (Kandlikar et al., 2007; K. Morgan, 2005). Similar nanomaterial specific QSAR models could be developed to perform the same assessments for nanomaterials. However, the fundamental relationships between physical/chemical characteristics of nanomaterials and their hazard and exposure potential are seemingly quite different from those of nonparticle-based chemicals (Puzyn, Gajewicz, Leszczynska, & Leszczynski, 116  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  2010). Therefore, the QSAR models for chemicals may better serve as a guiding framework for the creation of nanomaterial specific QSARs rather than as proxy models for estimating risks.  Expert judgment could be helpful in the creation of a nano-QSAR, and elicitation tasks would require both judgments on model structure and parameters. Considering the complexity in approaching the development of nano-QSAR models, an elicitation protocol would require careful selection of experts with appropriate expertise, and careful disaggregation into tasks that are familiar to experts. For example, model development would require scientists with expertise in physical chemistry, and hazard and exposure assessment within the nanotechnology domain. Those with expertise in structure-activity relationship modeling should guide the modeling process. The modeling process should be disaggregated into components to enable elicitations with the appropriate experts, and should define the relationship between nanomaterial physical and chemical properties, and between those properties and expected nanomaterial behavior, hazards and exposures. The resulting model would constitute a preliminary risk-screening framework whereby available nanomaterial physical and chemical property data could be utilized to estimate the magnitude of hazards and exposures. This framework could form the basis for an adaptive model that would improve over time as our understanding of nanomaterial properties and behavior improves.  6.2.2.3 Proceeding with Caution when Feedback is Limited Feedback is an important element in expert judgment, enabling experts to understand when they are in error, and providing cues to help fine-tune their estimates. In cases where uncertainty is very high and expertise in the domain is limited, it may be difficult to get empirical feedback or guidance. This is especially the case when making estimations for rare events or novel technologies. Freudenburg (1988) argues that many areas of risk assessment provide enough experience to correct errors, however with events that are truly rare, or technologies that are still new or untried, there may be too little information to permit the needed corrections (Freudenburg, 1988). As such, it is important to proceed with caution when making estimates that may have a large impact 117  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  on society for which we have little empirical information to gauge and correct errors. It is therefore necessary to exercise great care in the application of expert judgment for the assessment of nanomaterial risks.  6.2.2.4 Taking an Adaptive Approach to Managing Extreme Uncertainty As described earlier, deep uncertainty pervades every stage of the environmental health risk assessment framework. However, expert judgment techniques show great promise for enabling the formation of nanomaterial specific models, or modification of existing models to enable the use of this framework. Progress will necessarily be iterative given the high levels of uncertainty. With available knowledge and data, expert judgment can be utilized in the near-term for qualitative risk ranking or other forms of comparative risk assessment. As the understanding of ENM properties and biological behaviour develops, these models can evolve from qualitative screening-level tools to predictive quantitative tools. Careful identification of research needs and relevant areas of expertise can help guide research on the fundamentals, which in turn could enable quantitative nanomaterial specific risk assessments in the not-to-distant future.  In short, expert judgment can and will be a useful tool for enabling risk assessment for emerging technologies when data is scarce and uncertainty is high. While expert judgment is subject to many biases, methodological best practices can be employed to minimize their effect. The use of expert judgment in early nanotechnology risk research has proved to be conceptually valuable, as seen in the number of decision-analytic techniques reviewed here. Continued research utilizing expert judgment is therefore warranted for the case of nanotechnology.  6.2.3 What This Means for Future Expert Judgment Based Research Existing decision-analytic nanomaterial screening methodologies discussed in Chapter 5 suffer from several shortcomings. These include disagreement over which variables drive nanomaterial risks, inconsistent measures and scales from one model to another, and poor definition of the complex relationship between physical-chemical characteristics and risk  118  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  indicators. Much work remains before robust quantitative decision-analytic tools are available to regulators to aid in regulatory assessment and decision making for nanomaterials. Further, in order for an expert-judgment based risk-screening framework to be accepted as a reasonable tool for evaluating potential risks, there must be a sufficient level of agreement among risk experts that the model accurately captures key parameters and relationships between nanomaterial properties and risk indicators. This requires an open framework that can allow input from a sufficiently large number of experts, and an adaptive framework that can be refined as our understanding of nanomaterial behavior improves.  Future research in this domain should utilize formalized expert judgment elicitation techniques to develop a robust model suitable for supporting regulatory decision making for emerging nanomaterials. Such a model will need to be adaptable, enabling the incorporation of empirical and expert-derived data, and would originate as a qualitative ‘screening tool’ that could be refined to enable quantitative estimations as our understanding of ENM behaviour improves. To address the need for transparency and expert consensus, this research should utilize group elicitation methods based on Structured Decision Making (SDM) techniques (Gregory et al., 2012), such as MCDA (Linkov & Seager, 2011). Given the differences in perceptions and opinions found between expert groups in this thesis, and the complexity and variability of ENM behaviour over the life cycle, experts should be selected from a variety of backgrounds (e.g., nanotoxicology, human exposure, fate and transport). Figure 6.1 highlights opportunities to draw upon existing nano-expertise to aid in the screening of health and environmental risks along the nanomaterial life cycle.  119  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  Figure 6.1 Nano-Expertise available for use in elicitation exercises  Development of a simple and easy to use interface would enable stakeholders with limited experience in risk assessment to attain actionable results. To facilitate ease of use, the framework should make use of readily measurable parameters. These should include primary nanomaterial characteristics and physical-chemical properties (e.g., size, shape, surface coating), secondary characteristics (e.g., agglomeration potential, stability in solution), and product parameters (e.g., location in product, concentration) as predictive variables for indicators of toxicity (e.g., ROS generation potential) and exposure (e.g., release potential, persistence, mobility). A screening framework that integrates ENM properties as input variables, and toxicity and exposure estimates as outcome variables, would be relatively straightforward to use, and would be of benefit to a variety of stakeholders (including regulators and risk-assessors in government or industry). Further, the development of a causal model to account for complexities of ENM behaviour, rather than simplistic linear models currently in use, would represent a significant improvement over currently available decision-analytic methods.  In summary, an expert-judgment-based risk-screening framework would provide a preliminary tool for stakeholders to pinpoint areas of concern along the product life cycle,  120  6.2 A Case for Future Research: Potential Use of Expert Judgment in Early-Stage Nanomaterial Risk Assessment  and to identify opportunities to re-engineer products and minimize risks. It would also provide a foundation for further collaborative development of an adaptive, open-source screening tool. Through continual refinement, such a framework could be updated as our understanding of nanomaterial behavior develops. The result would be an easy to use, adaptive, and open-source framework for risk screening, and a powerful tool for decisionmakers in regulatory agencies and industry. In precisely this context it may be a decade or more before the ideal of comprehensive quantitative risk assessment will be possible for nanomaterials.  121  References  References Aitken, R., R, Creely, K., Tran, C., & Britain, G. 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Retrieved from http://www.cdc.gov/niosh/docs/2009-125/  144  APPENDIX A  Appendices Appendix A Supporting Information for Chapter 2 Table A.1 Applicability triggers, thresholds and exemptions, responsibilities and requirements under US Federal EHS regulations  145  APPENDIX A  146  APPENDIX A  147  APPENDIX B  Appendix B Supporting Information for Chapter 3 Table B.1 One-Way Analysis of Variance (ANOVA) measuring significance of differences in mean Risk Perceptions by expert group for 14 nanotechnology scenarios (scale: ‘1- almost no risk’, ‘2 –slight risk’, ‘3 – moderate risk’, ‘4 – high risk’)  Nanotechnology Scenario  Drug delivery via nano-capsules Nanotechnology vitamin and mineral supplements Cosmetics with nanoparticle additives Nano-particle based cleaning products Nano-particles in environmental remediation (contaminated site cleanup) applications Nano-based food ingredients Nano-particles released to the environment (air, water, soil) from consumer products Nano-particle coating on children’s toys Nano-particles as fuel additives Nanomaterials in air or water emissions  GROUP  N  Mean  S.D.  Levene Test for Homogeneity of Variances  ANOVA  Levene Statistic  pvalue  F-value  pvalue  NSE NEHS NREG  138 98 73  2.24 2.46 2.47  0.74 0.802 0.709  1.826  0.163  3.344  0.037  NSE  124  2.36  0.79  2.255  0.107  1.955  0.144  NEHS  92  2.45  0.918  NREG  67  2.61  0.778 1.257  0.286  5.346  0.005  1.786  0.169  5.796  0.003  2.295  0.103  1.453  0.236  0.957  0.385  1.868  0.156  1.932  0.147  2.191  0.113  0.732  0.482  3.473  0.032  1.159  0.315  4.13  0.017  0.952  0.387  4.743  0.009  NSE  137  2.44  0.746  NEHS  95  2.61  0.842  NREG  79  2.81  0.878  NSE  135  2.41  0.776  NEHS  92  2.68  0.889  NREG  74  2.78  0.798  NSE  134  2.29  0.724  NEHS  95  2.44  0.808  NREG  76  2.45  0.839  NSE  133  2.61  0.851  NEHS  87  2.69  0.88  NREG  74  2.85  0.871  NSE  141  2.72  0.848  NEHS  96  2.85  0.781  NREG  79  2.95  0.815  NSE  130  2.77  0.885  NEHS  95  2.96  0.874  NREG  75  3.09  0.857  NSE  140  2.31  0.856  NEHS  91  2.53  0.861  NREG  72  2.65  0.937  NSE NEHS  140 97  2.84 2.99  0.825 0.784  148  APPENDIX B  from production facilities Nano-materials in occupational settings Nanomaterials in industrial waste products  Nanotechnology Scenario  Nanotechnology based computer chips and devices Clothing with antibacterial nanoparticle treatments  NREG  79  3.19  0.786  NSE  135  2.6  0.812  NEHS  97  3  0.829  NREG  80  3.43  0.689  NSE  140  2.73  0.847  NEHS  95  2.87  0.854  NREG  75  3.05  0.751  GROUP  N  Mean  S.D.  1.703  0.184  27.985  0  1.468  0.232  3.815  0.023  Levene Test for Homogeneity of Variances  Welch Test for equality of means  Levene Statistic  Sig.  Statistica  Sig.  NSE NEHS NREG  150 99 80  1.24 1.67 1.63  0.514 0.728 0.682  23.677  0  18.18  0  NSE  139  2.27  0.788  3.173  0.043  2.587  0.078  NEHS  93  2.35  0.905  NREG  79  2.54  0.874  Table B.2 Games-Howell post hoc analysis indicating significant differences in means between NSE-NEHS, NSE-NREG, and NEHS-NREG group pairings  Dependent Variable  Nanotechnology based computer chips and devices Drug delivery via nanocapsules Nanotechnology vitamin and mineral supplements Clothing with antibacterial nano-particle treatments Cosmetics with nano-particle additives  (I) GROUP  (J) GROUP  Mean Difference (I-J)  Std. Error  pvalue  NEHS  -.427*  0.084  NREG  -.385*  NREG NEHS  95% Confidence Interval Lower Bound  Upper Bound  0  -0.63  -0.23  0.087  0  -0.59  -0.18  0.042  0.106  0.918  -0.21  0.29  -0.22  0.103  0.084  -0.46  0.02  NREG  -0.227  0.104  0.079  -0.47  0.02  NREG  -0.007  0.116  0.998  -0.28  0.27  NEHS  -0.083  0.119  0.767  -0.36  0.2  NREG  -0.249  0.119  0.093  -0.53  0.03  NREG  -0.166  0.135  0.436  -0.49  0.15  NEHS  -0.081  0.115  0.759  -0.35  0.19  NREG  -0.271  0.119  0.062  -0.55  0.01  NREG  -0.189  0.136  0.346  -0.51  0.13  NEHS  -0.173  0.107  0.245  -0.43  0.08  NREG  -.372*  0.118  0.005  -0.65  -0.09  NEHS  NREG  -0.2  0.131  0.284  -0.51  0.11  NSE  NEHS  -0.27*  0.114  0.05  -0.54  0  NSE NEHS NSE NEHS NSE NEHS NSE NEHS NSE  149  APPENDIX B  Nano-particle based cleaning products Nano-particles in environmental remediation (contaminated site cleanup) applications Nano-based food ingredients  NEHS NSE NEHS NSE NEHS  Nano-particles released to the environment (air, water, soil) from consumer products Nano-particle coating on children’s toys Nano-particles as fuel additives Nanomaterials in air or water emissions from production facilities Nano-materials in occupational settings  Nanomaterials in industrial waste products  NSE NEHS NSE NEHS NSE NEHS NSE NEHS NSE NEHS NSE NEHS  NREG  -.369*  0.114  0.004  -0.64  -0.1  NREG  -0.099  0.131  0.731  -0.41  0.21  NEHS  -0.151  0.104  0.315  -0.4  0.09  NREG  -0.156  0.115  0.364  -0.43  0.12  NREG  -0.005  0.127  0.999  -0.31  0.3  NEHS  -0.081  0.12  0.779  -0.36  0.2  NREG  -0.242  0.125  0.133  -0.54  0.05  NREG  -0.162  0.138  0.474  -0.49  0.17  NEHS  -0.138  0.107  0.403  -0.39  0.11  NREG  -0.233  0.116  0.114  -0.51  0.04  NREG  -0.095  0.121  0.714  -0.38  0.19  NEHS  -0.189  0.119  0.252  -0.47  0.09  NREG  -.324*  0.126  0.029  -0.62  -0.03  NREG  -0.135  0.134  0.569  -0.45  0.18  NEHS  -0.22  0.116  0.14  -0.49  0.05  NREG  -.346*  0.132  0.026  -0.66  -0.03  NREG  -0.125  0.143  0.655  -0.46  0.21  NEHS  -0.147  0.106  0.349  -0.4  0.1  NREG  -.347*  0.113  0.007  -0.61  -0.08  NREG  -0.2  0.119  0.215  -0.48  0.08  NEHS  -.400*  0.109  0.001  -0.66  -0.14  NREG  -.825*  0.104  0  -1.07  -0.58  NREG  -.425*  0.114  0.001  -0.69  -0.16  NEHS  -0.145  0.113  0.406  -0.41  0.12  NREG  -.325*  0.112  0.012  -0.59  -0.06  NREG  -0.18  0.123  0.314  -0.47  0.11  *. The mean difference is significant at the 0.05 level.  Survey Instrument NOTE: All text in square brackets indicate alternative question wording for version A and B of the survey, chosen at random for each respondent. [SURVEY START] This survey examines perceptions and attitudes toward nanotechnology, by which we mean the manipulation or creation of materials at the nanoscale (1 to 100 nanometers) where unique physical, chemical, and biological properties enable novel applications. [We recognize that nanotechnology is a broad class and so not necessarily accepted as useful by scientists and engineers. That said, we use it here in order to uphold comparability with other survey results.] A. ATTITUDES TOWARDS SCIENCE AND NANOTECHNOLOGY  150  APPENDIX B  1) From the following list, please indicate whether you strongly disagree, disagree, agree, or strongly agree with the following statements.  Strongly disagree  Disagree Agree  Strongly agree  Don’t Know / Not Sure  Nanoscience/nanotechnology is a new and distinctive research domain I generally consider myself to be a nanoscientist or nanotechnologist. In my work, I am strongly motivated by basic (or fundamental) research and the promise of new scientific discovery The term nanotechnology is a buzzword that means little to me. In my work, I am strongly motivated by applied science and the development of new technologies Nanoscience/nanotechnology requires more inter-disciplinary collaboration than other areas of science. In my work, I am strongly motivated to assess the impacts of technologies on society and the environment Purpose- or problem-driven science (as opposed to basic science) is increasingly common High-quality scientific knowledge is generated in a wider variety of sites than ever before (ie. not just universities and industry, but also in other sorts of research centers, consultancies, and think-tanks) B. RISK AND BENEFIT JUDGMENTS 2) Overall, do you think that: [VERSION A] the risks of nanotechnology will greatly outweigh its benefits the risks of nanotechnology will somewhat outweigh its benefits the risks of nanotechnology will equal its benefits the benefits of nanotechnology will somewhat outweigh its risks the benefits of nanotechnology will greatly outweigh its risks  151  APPENDIX B  don’t know / not sure [VERSION B] the benefits of nanotechnology will greatly outweigh its risks the benefits of nanotechnology will somewhat outweigh its risks the benefits of nanotechnology will equal its risks the risks of nanotechnology will somewhat outweigh its benefits the risks of nanotechnology will greatly outweigh its benefits don’t know / not sure  2a) Please list or comment on what you think will be the greatest benefit(s) of nanotechnologies:  2b) Please list up to five specific nanomaterials or nano-structures that you think are the most risky, with respect to their potential impacts on society:  2c) Please list up to five specific nanomaterials or nano-structures that you think are the least risky, with respect to their potential impacts on society:  3) Considering the following list of technologies or events, please indicate whether you think they pose almost no risk, slight risk, moderate risk, or high risk to society: Almost no risk  Slight risk  Moderate risk  High risk  Don't know / Not sure  Genetically Modified Crops Cellular phone communications Nuclear power plants Food additives and preservatives Climate change Nanotechnologies Prescription drugs  152  APPENDIX B  Pesticides and herbicides Biofuels Vaccines Lead in paint or dust Non-prescription vitamins and supplements  4) From the following list of nanomaterial applications and situations, please indicate whether you think they pose almost no risk, slight risk, moderate risk, or high risk to society:  Almost no risk  Slight risk  Moderate risk  Don't High know / risk Not Sure  Nanotechnology based computer chips and devices Drug delivery via nano-capsules Nanotechnology vitamin and mineral supplements Clothing with antibacterial nanoparticle treatments Cosmetics with nano-particle additives Nano-particle based cleaning products Nano-particles in environmental remediation (contaminated site cleanup) applications Nano-based food ingredients Nano-particles released to the environment (air, water, soil) from consumer products  153  APPENDIX B  Nano-particle coating on children’s toys Nano-particles as fuel additives Nanomaterials in air or water emissions from production facilities Nano-materials in occupational settings Nanomaterials in industrial waste products C. NANOTECHNOLOGY NOVELTY: 5) For the following statements, please indicate whether you strongly disagree, disagree, agree, or strongly agree:  Strongly disagree  Don’t Know Strongly / Not Disagree Agree agree Sure  Nano-scale materials promise benefits for society that are not possible with bulk (non nano-scale) materials Nano-scale materials possess novel properties that are not expressed in their corresponding bulk forms The novel properties of nano-scale materials can be anticipated by knowing the properties of the same material in its bulk form Nano-scale materials pose risks for society that are not present with bulk (non nano-scale) materials The health and environmental risks from nano-scale materials are well known to scientists The existing methods for assessing health and environmental risks from bulk materials are suitable for determining risks from nano-scale materials There is more uncertainty about the risks from nano-scale materials than the risks from bulk forms. In my lab we exercise greater precaution in our practices when working with nano-scale materials  154  APPENDIX B  D. ATTITUDES TOWARDS REGULATION D1. ATTITUDES TOWARDS REGULATION: REGULATION IN GENERAL 6) For the following statements, please indicate whether you strongly disagree, disagree, agree, or strongly agree:  Strongly disagree  Disagree Agree  Strongly agree  Don’t Know / Not Sure  The government should err on the side of precaution to protect the public from the risks from technology Regulations unduly prevent society from reaping the benefits of technology Chemical risks are sufficiently regulated in this country Voluntary approaches for risk management are effective for protecting human health and the environment. Market-based approaches are an effective means of managing health and environmental risks from technology Environmental groups and nongovernmental organizations (NGOs) play an important role in protecting people and the environment from risks from technology Consumers should be provided with more product information to allow them to better understand a product’s risks and benefits  155  APPENDIX B  [VERSION A] Traditional government regulation too frequently determines that a product is dangerous when it is really safe.  [VERSION B] Traditional government regulation too frequently assumes that a product is safe when it is not. D2: ATTITUDES TOWARDS REGULATION: NANOTECHNOLOGY SPECIFIC 7) For the following statements, please indicate whether you strongly disagree, disagree, agree, or strongly agree:  Strongly disagree  Disagree Agree  Strongly agree  Don’t Know / Not Sure  Because current regulations do not take into account novel (sizedependent) properties of nano-scale materials, they are inadequate for protecting society from risks [VERSION A] Government should restrict commercial development of nanotechnology until studies have been done on how to control risks [VERSION B] Restricting commercial development of nanotechnology until more risk studies are done is a bad idea Companies utilizing nano-materials in their products should be required to perform more stringent toxicity testing for the products they create  156  APPENDIX B  Consumers, through their purchasing decisions, are able to avoid products containing nano-scale materials if they deem them to be too risky Government regulations, as they currently exist, will do a good job of managing risks across the entire lifecycle of nanomaterials (from initial production to end-of-life) Government should focus on developing voluntary programs rather than mandatory programs to manage risks from nanotechnology  8) Are you more familiar with US or Canadian government agencies that deal with health, safety, and/or environment? __ US government agencies __ Canadian government agencies  [VERSION A] 9) Please indicate whether you strongly disagree, disagree, agree, or strongly agree that current US government agencies are adequately prepared for controlling risks from nanotechnologies in the following categories:: [VERSION B] 9) Please indicate whether you strongly disagree, disagree, agree, or strongly agree that current Canadian government agencies are adequately prepared for controlling risks from nanotechnologies in the following categories:  Strongly disagree  Disagree Agree  Strongly agree  Not familiar with relevant agency or its regulations / can’t answer  Don’t Know / Not Sure  Cosmetics Pesticides and agricultural applications  157  APPENDIX B  Industrial workplaces Pharmaceuticals Medical devices and treatments Industrial releases to the environment (air, water, soil) Food and food packaging Environmental releases (air, water, soil) from consumer products Computers and electronic devices Vitamins and supplements Environmental remediation (contaminated site cleanup) Waste products and contaminated sites Chemicals and product additives Other consumer products  10) [QUESTION 10 has been removed]  158  APPENDIX B  D3: ATTITUDES TOWARDS REGULATION: LOCUS OF RESPONSIBILITY FOR REGULATION 11) For the following list of groups or stakeholders, please indicate whether they should: Bear none of the responsibility, some of the responsibility, most of the responsibility, or all of the responsibility, for managing risks that emerge from nanotechnologies.  Should bear none of the responsibilit y  Should bear some of the responsibilit y  Should bear most of the responsibilit y  Should bear all of the responsibilit y  Don’t Kno w/ Not Sure  Academic basic sciences and R&D laboratories (ie. Physics, chemistry, engineering) Academic environmental and health sciences laboratories (ie. toxicology, epidemiology) Private research and development laboratories Smaller companies developing nanotechnology products Larger companies developing nanotechnology products Government agencies (eg. EPA, FDA, Health Canada, Environment Canada) Environmental groups and nongovernmental organizations (NGOs)  159  APPENDIX B  Consumers, through their product purchasing decisions OTHER (please specify) ______________ _ ______________ _  E. VIEWS OF THE ROLES OF SCIENTISTS AND THE PUBLIC 12) For the following statements, please indicate whether you strongly disagree, disagree, agree, or strongly agree:  Strongly disagree  Disagree Agree  Strongly agree  Don’t Know / Not Sure  The public’s risk judgments are more likely to be emotionally based than rationally based Direct involvement of citizens in science policy and R&D funding decisions threatens scientific innovation The public should have greater input into risk assessment and policy decisions that may impact their health or their environment The public is not equipped with the scientific understanding of nanotechnology required to make rational risk judgments Better science education about the real risks of new technologies will bring public risk perceptions in line  160  APPENDIX B  with those of scientists and nanotechnology experts Generally speaking, those who gain from progress in science and technology are not the same populations who are exposed to the risks of science and technology  Scientists are more aware of the social implications of their work than they were a decade or more ago The public has become increasingly aware of the ways in which science and technology affect their interests and values Today’s scientists are more aware of the possible effect of their values on the knowledge they produce than previous generations of scientists  F. RISK WORLDVIEW QUESTIONS 13) For the following statements, please indicate whether you strongly disagree, disagree, agree, or strongly agree:  Strongly disagree  Disagree  Don’t Know Strongly / Not Agree agree Sure  Nanomaterials are a major force behind technological advancement in today’s world. People are unnecessarily frightened about very small amounts of pesticides found in groundwater and on fresh food  161  APPENDIX B  Consumers are correct in their assumption that if a product is available on the market, it must be safe The land, air, and water around us are, in general, more contaminated now than ever before Our society has perceived only the tip of the iceberg with regard to the risks associated with nanomaterials Natural nanomaterials, as a rule, are not as harmful as man-made nanomaterials People worry unnecessarily about what nanomaterials can do to their health  Q.13b. Acknowledging that toxicologists and risk assessors will consider many sources of data when estimating the toxicity of materials, please indicate whether you strongly disagree, disagree, agree, or strongly agree with the following items: The way that an animal reacts to a chemical is a reliable predictor of how a human would react to it In-vitro cell testing is a reliable method for estimating the toxicity of chemicals Computer modeling is a reliable method for estimating the toxicity of chemicals If a scientific study produces evidence that a nanomaterial causes cancer in animals, then we can be reasonably sure that the nanomaterial will cause cancer in humans  G. TRUST AND CAPACITY 14) Please indicate how trustworthy you feel the following government agencies are for effectively managing nano-specific environmental health and safety risks from: very untrustworthy, somewhat untrustworthy, somewhat trustworthy, very trustworthy.  very untrustworthy  Somewhat untrustworthy  Somewhat trustworthy  Very trustworthy  162  Don’t Know / Not  APPENDIX B  familiar with the agency Environmental Protection Agency (EPA) Food and Drug Administration (FDA) Occupational Safety and Health Administration (OSHA) Consumer Product Safety Commission (CPSC)  Environment Canada  Health Canada  H. DEMOGRAPHIC INFORMATION In this final section of the questionnaire, we would like to ask some questions about your professional biography and current employment position 15) What is the highest degree level that you have completed? Associates degree Bachelors degree Masters degree PhD degree Other (please specify) ___________________________ 16) In what year did you complete your highest degree? ______ 17) In which country did you complete your highest degree? United States Canada Other: ___________________________________ 18) Which of the following best describes your current employment position? (Please check all that apply)  163  APPENDIX B  Graduate student Post-Doctoral position Non tenure-track academic position Tenure-track academic position: Tenured Tenure-track academic position: Not Tenured Private sector / Industry Non-governmental or non-profit institution State / Provincial Government Federal Government Other (please specify) ___________________________  19) How would you categorize the primary field of your current work? (Please select no more than 2 choices) Aerospace Biochemistry Bio Engineering Biology Business Chemical Engineering Chemistry Computer Science Economics Electrical Engineering Environmental Science Environmental Engineering Epidemiology Health Science History Law Management Materials Science Mathematics Mechanical Engineering Medicine Physics Policy Analysis Psychology Public Health Public Policy (including Risk Perception) Regulatory science Social Sciences (Anthropology, sociology, or political science) Risk Assessment Toxicology Other (please specify) ___________________________ 20) What proportion (expressed as a percentage) of your work would you consider to involve nanoscience or nanotechnology? (0-100 percent) _____  164  APPENDIX B  21) What proportion (expressed as a percentage) of your work do you think other scientists would consider nanoscience or nanotechnology based on the definition below? “Nanoscience and nanotechnology involve the understanding and control of matter at dimensions of roughly 1 to 100 nanometers, where unique [size dependent] physical, chemical, and biological properties enable novel applications.” (0-100 percent) _____  22) How would you best describe the main thrust of your current work? Creating, developing, or studying new nano-materials or nanotechnologies Testing or modeling health, safety, or environmental effects or risks of nanomaterials Policy, governance, and/or regulation with respect to nano-materials Other (Please specify) __________________  23) If your time is split between the duties listed above, what proportion of your time is given to each of these areas of work? ___% Creating, developing, or studying new nano-materials or nanotechnologies ___% Testing or modeling health, safety, or environmental effects or risks of nanomaterials ___% Policy, governance, and/or regulation with respect to nano-materials ___% Other (Please specify) __________________ [IF THE FIRST OPTION WAS SELECTED IN Q22), THEN CONTINUE TO Q24), OTHERWISE IF THE SECOND OR THIRD OPTION IN Q22) WAS CHOSEN, THEN SKIP TO Q25)] 24) If you can identify any of your work as nanoscience, how would you best categorize that work? Nanostructure Chemistry and Materials (e.g., nanotubes, quantum dots, etc.) Nanomedicine and Nano-biotechnology (e.g., biosensors, drug delivery, etc.) Nanodevices and Nanoelectronics (e.g., nanosemiconductors, nanolithography, etc.) Metrology and Nanoprocesses (e.g., self assembly, self tunneling microscopy, etc.) Other (please indicate) ___________________________________ 25) Is conducting research a significant component of your work? Yes No [IF NO, SKIP TO QUESTION 29), IF YES - CONTINUE] 26) Does your research primarily take place in a laboratory: Yes No  165  APPENDIX B  27) How would you generally categorize the nature of the research that you do? Basic science Applied science and/or Engineering About equally basic and applied Regulatory Science (eg. Science used to help set health & safety guidelines) Policy analysis Other (please specify) ___________________________ 28) In one sentence, how would you describe the primary research you are conducting? _____________________________________________________________ The terms “liberal” and “conservative” may mean different things to people, depending on the kind of issue one is considering. 29) In terms of economic issues, would you say you are: Very liberal  Somewhat liberal  Somewhat conservative  Very conservative  1  2  3  4  Don’t know / not sure 5  30) In terms of social issues, would you say you are: Very liberal  Somewhat liberal  Somewhat conservative  Very conservative  1  2  3  4  Don’t know / not sure 5  31) In which country do you currently reside and/or work? United States Canada Other: ___________________________________ 32) In which country do you hold your primary citizenship? United States Canada Other: ___________________________________ [If the answer for Q32) is ‘United States’ or ‘other’ then use VERSION A of Q33) below, otherwise if the answer is ‘Canada’, then use VERSION B of Q34)]  166  APPENDIX B  [VERSION A] 33) Which of the following best describes how you (would) usually vote? Mostly Republican Mostly Democrat Other / Independent I do not vote along party lines [VERSION B] 34) Which of the following best describes how you (would) usually vote? Mostly Conservative Mostly Liberal Mostly NDP Other / Independent I do not vote along party lines 35) What is your racial or ethnic background? Do you consider yourself: White Hispanic Black Asian American Indian Multiracial or multi-ethnic Other: ___________________________________ 36) What is your gender? Male Female  Agencies involved in NREG sample selection CANADIAN FEDERAL AGENCIES* Environment Canada Health Canada CFIA – Canadian Food Inspection Agency CIHR – Canadian Institutes of Health Research DFAIT – Foreign Affairs and International Trade Canada IC – Industry Canada NRC – National Research Council NRCAN – Natural Resources Canada * due to the difficulty of identifying subjects at the provincial level in Canada, subject selection was limited to federal agencies US AGENCIES 167  APPENDIX B  Federal Level Regulatory Agencies: EPA – Environmental Protection Agency – National Offices, Region 1, Region 5 FDA – US Food and Drug Administration OSHA – US Occupational Safety and Health Administration USDA – US Department of Agriculture Other Federal Level (non-regulatory) Agencies, Labs, and Institutes involved in Nano risk research and Regulation: NIH – National Institutes of Health NIOSH – National Institute for Occupational Safety and Health NIST – National Institutes of Standards and Technology ANL - Argonne National Lab BNL - Brookhaven National Lab LANL – Los Alamos National Lab LBL – Lawrence Berkeley Lab LLNL – Lawrence Livermore National Lab NCI - National Cancer Institute ORNL - Oak Ridge National Lab PNNL – Pacific Northwest National Lab Air Force Ames Laboratory Army Navy NSF - National Science Foundation State Level Agencies: California EPA – DTSC – Department of Toxic Substances Control Massachusetts Department of Environmental Protection Massachusetts Department of Labor Massachusetts Department of Public Health Massachusetts Division of Occupational Safety Massachusetts Office of Business Development Massachusetts Office of Technical Assistance and Technology North Carolina NCDENR - NC Dept. of Environment and Natural Resources New York Department of Environmental Conservation New York Department of Health Washington Department of Ecology  168  APPENDIX C  Appendix C Supporting Information for Chapter 4 Table C.1 One-Way Analysis of Variance (ANOVA) measuring the significance of differences in 'agency preparedness' ratings by expert group for 14 nanotechnology scenarios. (scale: ‘1- strongly disagree’, ‘2 – disagree’, ‘3 – agree’, ‘4 – strongly agree’)  Nanotechnology Scenario  Industrial workplaces  Pharmaceuticals  Medical devices and treatments Industrial releases to the environment (air, water, soil)  Food and food packaging  Environmental releases (air, water, soil) from consumer products Computers and electronic devices  Vitamins and supplements Environmental remediation (contaminated site cleanup)  GROUP  NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total NSE NEHS NREG Total  N  78 78 53 209 83 79 44 206 82 77 42 201 82 76 52 210 81 72 43 196 80 77 53 210 83 70 47 200 72 74 42 188 77 72 46 195  Mean  2.55 2.47 1.92 2.36 2.55 2.49 2.09 2.43 2.61 2.53 2.24 2.5 2.21 2.07 1.85 2.07 2.36 2.21 1.86 2.19 2.14 1.95 1.74 1.97 2.87 2.36 2.19 2.53 2.1 1.92 1.6 1.91 2.42 2.32 2.11 2.31  S.D.  0.907 0.785 0.781 0.867 0.845 0.766 0.858 0.834 0.766 0.718 0.79 0.762 0.766 0.718 0.751 0.755 0.795 0.749 0.774 0.793 0.742 0.667 0.763 0.735 0.712 0.66 0.77 0.763 0.754 0.636 0.544 0.688 0.732 0.668 0.795 0.731  Levene Test for Homogeneity of Variances  ANOVA  Levene Statistic  pvalue  F-value  pvalue  2.553  0.08  10.067  0  0.572  0.565  4.974  0.008  0.026  0.975  3.481  0.033  1.295  0.276  3.738  0.025  0.861  0.425  5.822  0.004  2.124  0.122  4.988  0.008  1.158  0.316  16.863  0  0.762  0.468  7.547  0.001  0.556  0.574  2.598  0.077  APPENDIX B  NSE NEHS NREG Total  Waste products and contaminated sites  Nanotechnology Scenario  GROUP  NSE NEHS Cosmetics NREG Total NSE NEHS Pesticides and agricultural applications NREG Total NSE NEHS Chemicals and product additives NREG Total NSE NEHS Other consumer products NREG Total a. Asymptotically F distributed.  77 75 48 200  N  68 72 48 188 75 72 48 195 80 74 49 203 56 65 47 168  2.38 2.31 1.9 2.24  Mean  2.18 1.97 1.83 2.01 2.28 2.39 2 2.25 2.48 2.23 1.82 2.23 2.29 2 1.94 2.08  0.744 0.657 0.66 0.716  S.D.  0.828 0.671 0.724 0.753 0.894 0.761 0.772 0.827 0.795 0.653 0.755 0.776 0.756 0.637 0.734 0.718  2.471  0.087  Levene Test for Homogeneity of Variances  7.766  0.001  Welch Test for equality of means  Levene Statistic  Sig.  Statistica  Sig.  4.475  0.013  2.855  0.062  3.704  0.026  3.738  0.027  3.11  0.047  11.066  0  3.657  0.028  3.426  0.036  Table C.2 Games-Howell post hoc analysis indicating significant differences in means between NSE-NEHS, NSE-NREG, and NEHS-NREG group pairings  Dependent Variable  Cosmetics  (I) GROUP  NSE NEHS  Pesticides and agricultural applications Industrial workplaces Pharmaceuticals  NSE NEHS NSE NEHS NSE NEHS NSE  (J) GROUP  Mean Difference (I-J)  Std. Error  pvalue  NEHS NREG NREG NEHS NREG NREG NEHS NREG NREG NEHS NREG NREG NEHS  0.204 0.343 0.139 -0.109 0.28 .389* 0.077 .627* .550* 0.061 .463* .403* 0.077  0.128 0.145 0.131 0.137 0.152 0.143 0.136 0.148 0.139 0.127 0.159 0.155 0.118  0.25 0.05 0.54 0.71 0.16 0.02 0.84 0.00 0.00 0.88 0.01 0.03 0.79  95% Confidence Interval Lower Bound  Upper Bound  -0.1 0 -0.17 -0.43 -0.08 0.05 -0.24 0.27 0.22 -0.24 0.08 0.03 -0.2  0.51 0.69 0.45 0.21 0.64 0.73 0.4 0.98 0.88 0.36 0.84 0.77 0.36  170  APPENDIX B  NREG NREG NEHS NREG  .372* 0.294 0.142 .361*  0.148 0.147 0.118 0.134  0.04 0.12 0.46 0.02  0.02 -0.06 -0.14 0.04  0.73 0.65 0.42 0.68  NREG  0.22  0.133  0.23  -0.1  0.54  NEHS NREG NREG NEHS NREG  0.15 .498* 0.348 0.189 .402*  0.125 0.147 0.147 0.112 0.134  0.46 0.00 0.05 0.22 0.01  -0.15 0.15 0 -0.08 0.08  0.45 0.85 0.7 0.46 0.72  NREG  0.212  0.129  0.23  -0.1  0.52  NEHS NREG NREG NEHS NREG NREG NEHS NREG  .510* .676* 0.166 0.178 .502* .324* 0.096 0.307  0.111 0.137 0.137 0.116 0.122 0.112 0.115 0.144  0.00 0.00 0.45 0.27 0.00 0.01 0.68 0.09  0.25 0.35 -0.16 -0.1 0.21 0.06 -0.18 -0.04  0.77 1 0.49 0.45 0.79 0.59 0.37 0.65  NREG  0.211  0.141  0.30  -0.13  0.55  NEHS 0.07 NREG .481* NEHS NREG .411* NEHS 0.245 NSE Chemicals and NREG .659* product additives NEHS NREG .413* NEHS 0.286 NSE Other consumer NREG 0.35 products NEHS NREG 0.064 *. The mean difference is significant at the 0.05 level.  0.114 0.128 0.122 0.117 0.14 0.132 0.128 0.147 0.133  0.81 0.00 0.00 0.09 0.00 0.01 0.07 0.05 0.88  -0.2 0.18 0.12 -0.03 0.33 0.1 -0.02 0 -0.25  0.34 0.78 0.7 0.52 0.99 0.73 0.59 0.7 0.38  Medical devices and treatments Industrial releases to the environment (air, water, soil) Food and food packaging Environmental releases (air, water, soil) from consumer products Computers and electronic devices Vitamins and supplements Environmental remediation (contaminated site cleanup) Waste products and contaminated sites  NEHS NSE NEHS NSE NEHS NSE NEHS NSE NEHS NSE NEHS NSE NEHS NSE  Response options for ‘Disciplinary Field’: Q. How would you categorize the primary field of your current work? Aerospace  Chemistry  Biochemistry  Computer Science  Bio Engineering  Economics  Biology  Electrical Engineering  Business  Environmental Science  Chemical Engineering  Environmental Engineering  171  APPENDIX B  Epidemiology Health Science History Law Management Materials Science Mathematics Mechanical Engineering Medicine Physics Policy Analysis Psychology Public Health Public Policy (including Risk Perception) Regulatory science Social Sciences (Anthropology, sociology, or political science) Risk Assessment Toxicology Other (please specify)  172  APPENDIX B  Responses were categorized as:  Physical Sciences  Biological, Environmental,  Social Sciences,  Aerospace  Health Sciences  Policy, Management  Chemical Engineering  Biochemistry  Business  Chemistry  Bio Engineering  Economics  Electrical Engineering  Biology  History  Materials Science  Environmental Science  Law  Mechanical Engineering Environmental Engineering  Management  Physics  Epidemiology  Public Policy  Health Science  (including Risk  Public Health  Perception)  Regulatory science  Policy Analysis  Risk Assessment  Psychology  Toxicology  Social Sciences (Anthropology, sociology, or political science)  173  

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