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This book outlines how to conduct a complete environmental risk assessment. The first part documents the psychology and philosophy of risk perception and assessment, introducing a taxonomy of uncertainty and the importance of context. It provides a critical examination of the use and abuse of expert judgement and goes on to outline approaches to hazard identification and subjective ranking that account for uncertainty and context. The second part of the book describes technical tools that can assist risk assessments to be transparent and internally consistent. These include interval arithmetic, ecotoxicological methods, logic trees and Monte Carlo simulation. These methods have an established place in risk assessments in many disciplines and their strengths and weaknesses are explored. The last part of the book outlines some new approaches, including p-bounds and information-gap theory, and describes how quantitative and subjective assessments can be used to make transparent decisions.

Tabla de contenidos provista por Syndetics

• Preface(p. ix)
• Acknowledgements(p. xi)
• 1 Values, history and perception(p. 1)
• 1.1 Uncertainty and denial(p. 1)
• 1.2 Chance and belief(p. 6)
• 1.3 The origin of ideas about risk(p. 10)
• 1.4 Perception(p. 13)
• 1.5 The pathology of risk perception(p. 19)
• 1.6 Discussion(p. 24)
• 2 Kinds of uncertainty(p. 26)
• 2.1 Epistemic uncertainty(p. 26)
• 2.2 Linguistic uncertainty(p. 33)
• 2.3 Discussion(p. 39)
• 3 Conventions and the risk management cycle(p. 42)
• 3.1 Risk assessments in different disciplines(p. 44)
• 3.2 A common context for environmental risk assessment(p. 50)
• 3.3 The risk management cycle(p. 54)
• 3.4 Discussion(p. 60)
• 4 Experts, stakeholders and elicitation(p. 62)
• 4.1 Who's an expert?(p. 65)
• 4.2 Who should be selected?(p. 70)
• 4.3 Eliciting conceptual models(p. 73)
• 4.4 Eliciting uncertain parameters(p. 75)
• 4.5 Expert frailties(p. 82)
• 4.6 Are expert judgements any good?(p. 94)
• 4.7 When experts disagree(p. 100)
• 4.8 Behavioural aggregation(p. 103)
• 4.9 Numerical aggregation(p. 107)
• 4.10 Combined techniques(p. 113)
• 4.11 Using expert opinion(p. 120)
• 4.12 Who's a stakeholder?(p. 121)
• 4.13 Discussion(p. 124)
• 5 Conceptual models and hazard assessment(p. 127)
• 5.1 Conceptual models(p. 127)
• 5.2 Hazard identification and assessment(p. 130)
• 5.3 Discussion(p. 142)
• 6 Risk ranking(p. 145)
• 6.1 Origins of risk ranking methods(p. 145)
• 6.2 Current applications(p. 147)
• 6.3 Conducting a risk ranking analysis(p. 149)
• 6.4 Pitfalls(p. 151)
• 6.5 Performance(p. 155)
• 6.6 Examples(p. 158)
• 6.7 Discussion(p. 165)
• 7 Ecotoxicology(p. 169)
• 7.1 Dose-response relationships(p. 170)
• 7.2 Extrapolation(p. 177)
• 7.3 Deciding a safe dose(p. 188)
• 7.4 Transport, fate and exposure(p. 192)
• 7.5 Examples(p. 199)
• 7.6 Discussion(p. 205)
• 8 Logic trees and decisions(p. 207)
• 8.1 Event trees(p. 207)
• 8.2 Fault trees(p. 223)
• 8.3 Logic trees and decisions(p. 229)
• 8.4 Discussion(p. 240)
• 9 Interval arithmetic(p. 242)
• 9.1 Worst case analysis(p. 242)
• 9.2 Defining and eliciting intervals(p. 247)
• 9.3 Interval arithmetic(p. 254)
• 9.4 Discussion(p. 262)
• 10 Monte Carlo(p. 264)
• 10.1 The modelling process(p. 265)
• 10.2 Kinds of distributions(p. 268)
• 10.3 Choosing the right distributions(p. 272)
• 10.4 Generating answers(p. 280)
• 10.5 Dependencies(p. 283)
• 10.6 Extensions of Monte Carlo(p. 287)
• 10.7 Sensitivity analyses(p. 290)
• 10.8 Some examples(p. 291)
• 10.9 How good are Monte Carlo predictions?(p. 306)
• 10.10 p-bounds(p. 310)
• 10.11 Discussion(p. 313)
• 11 Inference, decisions, monitoring and updating(p. 318)
• 11.1 Monitoring and power(p. 318)
• 11.2 Calculating power(p. 322)
• 11.3 Flawed inference and the precautionary principle(p. 334)
• 11.4 Overcoming cognitive fallacies: confidence intervals and detectable effect sizes(p. 338)
• 11.5 Control charts and statistical process control(p. 342)
• 11.6 Receiver operating characteristic (ROC) curves(p. 357)
• 11.7 Discussion(p. 368)
• 12 Decisions and risk management(p. 370)
• 12.1 Policy and risk(p. 370)
• 12.2 Strategic decisions(p. 377)
• 12.3 Stochastic analyses and decisions(p. 383)
• 12.4 Info-gaps(p. 391)
• 12.5 Evaluating attitudes to decisions(p. 399)
• 12.6 Risk communication(p. 410)
• 12.7 Adaptive management, precaution and stakeholder involvement(p. 416)
• 12.8 Conclusions(p. 421)
• Glossary(p. 423)
• References(p. 457)
• Index(p. 485)

Reseñas proporcionadas por Syndetics

CHOICE Review

Environmental risk assessment is widely used for the management of biodiversity and biological conservation, but the practice is not standardized. This work outlines what Burgman (Univ. of Melbourne) hopes is the framework for a professional standard for risk assessments, one that incorporates the necessary assumptions and uncertainties throughout the exercise in a transparent, thorough way. This standard does not mean new methods, but the explicit recognition of uncertainties in expert selection, expert judgments, model selection, ecotoxicology studies, environmental monitoring, and risk ranking. Also, the personal and subjective elements of risk assessment are necessary and must be seen as such. The book includes separate chapters on technical applications such as logic trees, the Monte Carlo method, and interval arithmetic. The discussion of statistical inference is particularly valuable. Although aimed at environmental (ecological) risk, this work can be used profitably by those conducting risk assessments of chronic and catastrophic hazards to human health. The numerous figures and tables are clear and well chosen. This volume is part of a series on the theory and practice of conserving biodiversity. Extensive references and a glossary. ^BSumming Up: Recommended. Upper-level undergraduates and above. B. C. Wyman McNeese State University

Notas de autor provistas por Syndetics

Mark Burgman is Professor of Environmental Science in the School of Botany at the University of Melbourne, Australia.