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Application of Uncertainty Analysis to Ecological Risks of Pesticides

Edited by William J. Warren-Hicks, Andy Hart 
CRC Press  April 2010  



Hardcover  228 pp  ISBN 9781439807347      £77.00
While current methods used in ecological risk assessments for pesticides are largely deterministic, probabilistic methods that aim to quantify variability and uncertainty in exposure and effects are attracting growing interest from industries and governments. Probabilistic methods offer more realistic and meaningful estimates of risk and hence, potentially, a better basis for decision-making. Application of Uncertainty Analysis to Ecological Risks of Pesticides examines the applicability of probabilistic methods for ecological risk assessment for pesticides and explores their appropriateness for general use.

The book presents specific methods leading to probabilistic decisions concerning the registration and application of pesticides and includes case studies illustrating the application of statistical methods. The authors discuss Bayesian inference, first-order error analysis, first-order (non-hierarchical) Monte Carlo methods, second-order Bayesian and Monte Carlo methods, interval analysis, and probability bounds analysis. They then examine how these methods can be used in assessments for other environmental stressors and contaminants.

There are many methods of analyzing variability and uncertainty and many ways of presenting the results. Inappropriate use of these methods leads to misleading results, and experts differ on what is appropriate. Disagreement about which methods are appropriate will result in wasted resources, conflict over findings, and reduced credibility with decision makers and the public. There is, therefore, a need to reach a consensus on how to choose and use appropriate methods, and to present this in the form of guidance for prospective users. Written in a clear and concise style, the book examines how to use probabilistic methods within a risk-based decision paradigm.

Contents

Introduction and Objectives, A. Hart, D. Farrar, D. Urban, D. Fischer, T. La Point, K. Romijn, and S. Ferson
Introduction Variability and Uncertainty Importance of Variability and Uncertainty in Risk Assessment Current Methods for Dealing with Variability and Uncertainty Are Inadequate Variability and Uncertainty Hinder the Regulatory Process Understanding Uncertainty and Variability Is Critical When Developing a Credible Risk Assessment Quantitative Analysis of Variability and Uncertainty Can Help When Is Quantitative Analysis of Variability and Uncertainty Required? What If the Bounds Are Very Wide? Need for Consensus on Appropriate Methods Workshop Objectives and Key Issues References

Problem Formulation for Probabilistic Ecological Risk Assessments, A. Hart, S. Ferson, J. Shaw, G. W. Suter II, P. F. Chapman, P. L. de Fur, W. Heger, and P. D. Jones
Introduction Main Steps in Problem Formulation Integration of Available Information for Probabilistic Assessments Definition of Assessment Endpoints for Probabilistic Assessments Definition of Assessment Scenarios Developing Conceptual Models for Probabilistic Assessments Analysis Plans for Probabilistic Assessment References

Issues Underlying the Selection of Distributions, D. Farrar, T. Barry, P. Hendley, M. Crane, P. Mineau, M. H. Russell, and E. W. Odenkirchen
Introduction Technical Background Some Practical Aspects of the Selection of Univariate Distributions Using Scanty and Fragmentary Data References

Monte Carlo, Bayesian Monte Carlo, and First-Order Error Analysis, W. J. Warren-Hicks, S. Qian, J. Toll, D. L. Fischer, E. Fite, W. G. Landis, M. Hamer, and E. P. Smith
Introduction Practical Aspects of a Monte Carlo Analysis Mathematical and Statistical Underpinnings of Monte Carlo Methods Bayesian Monte Carlo Analysis First-Order Error Analysis A Monte Carlo Case Study: Derivation of Chronic Risk Curves for Atrazine in Tennessee Ponds Using Monte Carlo Analysis Conclusions References

The Bayesian Vantage for Dealing with Uncertainty, D. A. Evans, M. C. Newman, M. Lavine, J. S. Jaworska, J. Toll, B. Brooks, and T. C. M. Brock
Introduction Conventional (Frequentist) Inference Methods Experiments Change the State of Knowledge Rules of Probability Bayes€s Theorem Examples Relevant to Uncertainty in Risk Assessment Quantifying Plausibility of a Cause€Effect Model Conclusion References

Bounding Uncertainty Analyses, S. Ferson, D. R. J. Moore, P. Van den Brink, T. L. Estes, K. Gallagher, R. O€Connor, and F. Verdonck
Introduction Robust Bayes Probability Bounds Analysis Numerical Example How to Use Bounding Results Seven Challenges in Risk Analyses What Bounding Cannot Do Example: Insectivorous Birds€ Exposure to Pesticide Conclusion Appendix References

Uncertainty Analysis Using Classical and Bayesian Hierarchical Models, D. R. J. Moore, W. J. Warren-Hicks, S. Qian, A Fairbrother, T. Aldenberg, T. Barry, R. Luttik, and H.-T. Ratte
Introduction Variability and Uncertainty Simple 2nd-Order Monte Carlo Analysis Case Study Bayesian Hierarchical Modeling References

Interpreting and Communicating Risk and Uncertainty for Decision Making, J. L. Shaw, K. R. Tucker, K. Aden, J. M. Giddings, D. M. Keehner, and C. Kriz
Introduction Participants in Risk Communication Communicating Uncertainty to Stakeholders and Participants Process for Communication Risk Assessor and Decision Maker Roles and Responsibilities Communication of Uncertainty for Regulatory Decision Making References

How to Detect and Avoid Pitfalls, Traps, and Swindles, G. Joermann, T. W. La Point, L. A. Burns, J. P. Carbone, P. D. Delorme, S. Ferson, D. R. J. Moore, and T. P. Traas
Introduction Meaningful Problem Formulation Suitability of Input Data Parameterization of the Distribution of Input Variables Correlations and Dependencies Model Uncertainties Software Tools and Computational Issues Presentation and Interpretation of Results Conclusions References

Conclusions, A. Hart, T. Barry, D. L. Fischer, J. M. Giddings, P. Hendley, G. Joermann, R. Luttik, D. R. J. Moore, M. C. Newman, E. Odenkirchen, and J. L. Shaw
Introduction Which Methods of Uncertainty Analysis Are Appropriate under What Circumstances? What Are the Implications of Probabilistic Methods for Problem Formulation? How Can Uncertainty Analysis Methods Be Used Efficiently and Effectively in Decision Making? When and How Should We Separate Variability and Uncertainty? How Can We Take Account for Uncertainty Concerning the Structure of the Risk Model for the Assessment? How Should We Select and Parameterize Input Distributions When Data Are Limited? How Should We Deal with Dependencies, Including Nonlinear Dependencies and Dependencies about Which Only Partial Information Is Available? How Can We Take Account of Uncertainty When Combining Different Types of Information in an Assessment (e.g., Quantitative Data and Expert Judgment, Laboratory Data, and Field Data)? How Can We Detect and Avoid Misleading Results? How Can We Communicate Methods and Outputs Effectively to Decision Makers and Stakeholders? What Are the Priorities for Further Development, Implementation, and Training? References Glossary

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