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Full 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.
Contents
Introduction, Objectives and Key issues. Problem Formulation for Probabilistic Assessments. Bayesian Methods. Selecting Statistical Distributions. Propagating Uncertainty in Non-Hierarchical Models. Propagating Uncertainty in Hierarchical Models. Bounding Techniques. How to Detect and Avoid Misleading Results. Interpreting and Using PRA Results in Environmental Decision-Making. Workshop Conclusions and Findings. Glossary.
