Application of Uncertainty Analysis to Ecological Risks of Pesticides: 1st Edition (Paperback) book cover

Application of Uncertainty Analysis to Ecological Risks of Pesticides

1st Edition

Edited by William J. Warren-Hicks, Andy Hart

CRC Press

228 pages | 46 B/W Illus.

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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?



Subject Categories

BISAC Subject Codes/Headings:
SCIENCE / Environmental Science
TECHNOLOGY & ENGINEERING / Agriculture / General
TECHNOLOGY & ENGINEERING / Environmental / General