Application of Uncertainty Analysis to Ecological Risks of Pesticides  book cover
1st Edition

Application of Uncertainty Analysis to Ecological Risks of Pesticides

ISBN 9781138114814
Published May 31, 2017 by CRC Press
228 Pages 46 B/W Illustrations

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Book Description

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.

Table of Contents

Introduction and Objectives, A. Hart, D. Farrar, D. Urban, D. Fischer, T. La Point,
K. Romijn, and S. Ferson
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

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
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

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

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
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

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
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

Bounding Uncertainty Analyses, S. Ferson, D. R. J. Moore, P. Van den Brink, T. L. Estes, K. Gallagher, R. O’Connor, and F. Verdonck
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

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
Variability and Uncertainty
Simple 2nd-Order Monte Carlo Analysis Case Study
Bayesian Hierarchical Modeling

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
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

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
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, 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
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?

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