Should we risk it? : exploring environmental, health, and technological problem solving / Daniel M. Kammen and David M. Hassenzahl.

Format:

Book

Author/Creator:

Kammen, Daniel M., 1962-

Contributor:

Hassenzahl, David M.

Language:

English

Subjects (All):

Environmental sciences--Decision making.

Environmental sciences.

Environmental policy--Government policy.

Environmental policy.

Risk assessment.

Health risk assessment.

Technology--Risk assessment.

Technology.

Science and state.

Physical Description:

xx, 404 pages : illustrations ; 25 cm

Place of Publication:

Princeton, N.J. : Princeton University Press, [1999]

Summary:

How dangerous is smoking? What are the risks of nuclear power or of climate change? What are the chances of dying on an airplane? More importantly, how do we use this information once we have it? The demand for risk analysts who are able to answer such questions has grown exponentially in recent years. Yet programs to train these analysts have not kept pace. In this book Daniel Kammen and David Hassenzahl address that problem.They draw together, organize, and seek to unify previously disparate theories and methodologies connected with risk analysis for health, environmental, and technological problems. They also provide a rich variety of case studies and worked problems, meeting the growing need for an up-to-date book suitable for teaching and individual learning.

The specific problems addressed in the book include order-of-magnitude estimation, doseresponse calculations, exposure assessment, extrapolations and forecasts based on experimental or natural data, modeling and the problems of complexity in models, fault-tree analysis, managing and estimating uncertainty, and social theories of risk and risk communication. The authors cover basic and intermediate statistics, as well as Monte Carlo methods, Bayesian analysis, and various techniques of uncertainty and forecast evaluation. The volume's unique approach will appeal to a wide range of people in environmental science and studies, health care, and engineering, as well as to policy makers confronted by the increasing number of decisions requiring risk and cost/benefit analysis. Should We Risk it? will become a standard text in courses involving risk and decision analysis and in courses of applied statistics with a focus onenvironmental and technological issues.

Contents:

Defining Risk 3

Risk Analysis and Public Policy 8

Problem 1-2. Data Needs 21

Problem 1-3. Using Data 23

Problem 1-A. Additional Cases 24

Problem 1-B. Additional Curves 24

Problem 1-C. Does the Dose Make the Poison? 25

Problem 1-D. One in a Million Risks 25

Problem 1-E. Surfing and Smoking 26

Problem 1-F. Risks of Nuclear Power 26

2 Basic Models and Risk Problems 31

Basic Modeling 32

Problem 2-1. Volatile Organic Emissions from Household Materials: Wallpaper Glue 35

Problem 2-2. Indoor Radon Exposure 37

Problem 2-A. Problem 2-2 Revisited 50

Problem 2-B. Equilibrium Concentration 50

Problem 2-3. Simple PBPK Model

Continuous Dose 51

Problem 2-C. Alternative Depictions 56

Problem 2-4. PBPK

Finite Dose of Barium 56

Problem 2-D. How Much Resolution Is Too Much? 63

Problem 2-E. How Much Information Is Needed? 64

Problem 2-F. Sensitivity Analysis 64

Cause and Effect Relationships 65

Problem 2-5. Radon and Cancer 65

Mechanistic Models and Curve Fitting 69

Problem 2-6. Conceiving "Mechanistic" Models 69

Problem 2-7. Using the Wrong Model, Getting the Model Wrong 73

Problem 2-8. Empirically Derived Dose Response 75

Problem 2-9. Earthquakes versus Traffic Risks 77

3 Review of Statistics for Risk Analysis 83

Introduction: Statistics and the Philosophy of Risk Assessment 83

Problem 3-1. Average Radon Exposure 85

Problem 3-A. Radon Exposures in Different Regions 87

Problem 3-2. Working with Data 87

Problem 3-3. Mean and Median: Why Worry? 90

Problem 3-4. Sample Data Revisited 93

Problem 3-5. Hypothesis Testing and Confidence Intervals 96

Problem 3-6. Making Decisions 102

Distributions 104

Problem 3-7. Moving Away from Ignorance 104

Problem 3-8. Fitting a Model 111

Problem 3-B. R[superscript 2] Versus x[superscript 2] 117

Problem 3-C. How Many Bins? 117

Problem 3-9. Distributional Models 117

Problem 3-D. Fitting the Lognormal Distribution 120

Problem 3-E. Dealing with Grouped Data 120

4 Uncertainty, Monte Carlo Methods, and Bayesian Analysis 122

Problem 4-1. Measuring the Speed of Light 124

Problem 4-A. Energy Forecasts 126

Problem 4-B. Forecasting the Impacts of Climate Change 128

Bayesian Statistics 128

Problem 4-2. Interpreting Test Results 132

Problem 4-C. Bayesian Experts 134

Problem 4-3. Bayesian Analysis of Radon Concentrations 134

Monte Carlo Analysis 142

Problem 4-4. Exposure to Tap Water in the Home 143

Problem 4-D. Uncertainty or Incommensurability? 150

5 Toxicology 153

Critical Assumptions for Modeling Disease 154

Assumptions Specific to Toxicology 159

Assumptions Specific to Epidemiology 163

Problem 5-1. Test Data and Carcinogenesis: The Kil-EZ Example 166

Problem 5-A. Calculating LED10 174

Problem 5-B. Maximum Tolerated Dose 174

Problem 5-C. 1,3-Butadiene 174

Problem 5-2. Fitting Data to Mechanistic Models: One-Hit, Two-Hit, Two-Stage Problem 175

Problem 5-D. The Cost of Better Data 189

Problem 5-E. Model-Free Extrapolation 190

Problem 5-F. Variation in Cancer Susceptibility 190

Problem 5-G. Variation in Sensitivity and Exposure 191

Problem 5-H. Additional Data Set 191

Problem 5-I. Exact Two-Stage Formulation 191

Problem 5-3. Noncarcinogenic Effects: The EPA Approach 194

Problem 5-J. Additional Noncancer End Points 196

Problem 5-K. Formaldehyde 196

6 Epidemiology 199

Problem 6-1. Cigarette Smoking and Cancer 199

Problem 6-A. The Heavy Smoker 206

Problem 6-B. All Deaths in the United States 206

Problem 6-2. Risk in a Time of Cholera 207

Problem 6-C. Pooled Data 210

Problem 6-D. What Might Be Missing? 211

Problem 6-E. Measurement Error 211

Problem 6-3. Benzene Revisited: The Pliofilm Cohort Study 211

Problem 6-F. Additional Data 214

Problem 6-G. One-Hit Model and Epidemiological Data 214

Problem 6-H. Additional Data 216

Problem 6-4. Catching Cold: Exponential Spread of Disease 216

Problem 6-I. Graphical Presentation 218

Problem 6-J. Small Groups 218

Problem 6-5. The Spread of AIDS: An Empirical Analysis, or, Does the Model Fit the Data? 218

Problem 6-6. Double-Blind Study 225

Problem 6-K. Side Effects: Test for Safety 228

Problem 6-L. Low-Probability Effects 229

Problem 6-M. Death by Cheese? 229

Problem 6-N. Cancer Clusters

Real or Not? 229

7 Exposure Assessment 231

Problem 7-1. Assessment of Exposures and Risks: The ChemLawn Claim 231

Problem 7-A. Can Adults and Children Be Treated the Same? 237

Problem 7-2. Contaminated Milk 237

Problem 7-3. Biomass Fuels and Childhood Disease 239

Problem 7-4. Bioaccumulation of Heptachlor in Beef 241

Problem 7-B. Sensitive Receptors: Exposure to Children 247

Problem 7-C. Exposure via Breast Milk 247

Problem 7-5. Tricholoroethylene Exposure at Woburn, Massachusetts 247

Problem 7-D. TCE at Woburn: The Big Picture 256

Problem 7-E. Probability Distribution for Radon Exposures (or Risks) 257

Problem 7-F. PBPK Models and Gender Differences in the Uptake of Benzene 258

Problem 7-G. Acme Landfill 260

8 Technological Risk 266

Problem 8-1. Lethality of Plutonium 267

Problem 8-A. Cassini Spacecraft Reentry Risk 270

Event Trees and Fault Trees 271

Problem 8-2. Simple Pressure Relief System 272

Problem 8-B. Additional Fault Tree 278

Problem 8-C. Calculations from Fault Trees 278

Problem 8-3. Missing Components, Common-Mode Failures, and the Human Element 278

Problem 8-D. Additional Fault Trees 280

Problem 8-E. Identifying Problems 281

Problem 8-F. Anticipating the Unknown 281

Problem 8-G. Oleum and the "Clever-Proofing" Problem 281

Problem 8-4. Coal-Burning Power Plant Emissions 282

Problem 8-5. Commercial Nuclear Power Safety: An Empirical Analysis 285

Problem 8-H. The Risk from Nuclear Accidents 287

Problem 8-6. Long-Term Risks from High-Level Nuclear Waste: A Case of Extreme Uncertainty 290

Problem 8-7. Military Fighter Aircraft 295

Problem 8-I. Who Decides What's Important? 299

Problem 8-8. Risk of Domestic Airplane Flight 299

Problem 8-J. Verifast Airlines 301

Problem 8-K. Risk of International Airplane Flight 301

9 Decision Making 304

Problem 9-1. Comparing Risk Reduction Measures By Dollar Values 304

Problem 9-A. Cost Per Life-Year versus One in a Million 308

Problem 9-B. Too Much or Too Little Spending? 308

Problem 9-C. Mandatory Helmet Laws 309

Problem 9-E. Is It Worth It? 309

Problem 9-F. High-Level Nuclear Waste 309

Organizing Processes 309

Problem 9-2. Event Trees and Decision Analysis 310

Problem 9-G. Who Lives There (and Does It Matter)? 317

Problem 9-H. Additional Calculations 318

Problem 9-I. Additional Uncertainty 318

Problem 9-3. Analysis or Abuse? Transmission of Hoof Blister 318

Problem 9-J. Dollars and Decisions 326

Problem 9-4. Health and Environmental Technology Policy: Superfund Remediation 326

Problem 9-K. Discounting or Dodging? 335

Problem 9-L. Taking a Viewpoint 336

Problem 9-M. Event-Decision Tree 336

Problem 9-N. Regulatory Impact Analysis: Where the Rubber Meets the Road 336

10 Risk Perception and Communication 353

Problem 10-1. Same Numbers, Different Stories 354

Problem 10-A. Opening Dialogue 357

Problem 10-B. Explaining Numbers 357

Problem 10-2. Framing a Question: Loss or Gain? 358

Problem 10-C. A Project to "Restore" or "Improve" a Wetland 360

Problem 10-D. A Little Bit or a Lot? Violent Agreement on the Numbers 360

Problem 10-3. Risk Level, Risk Perceptions, and Psychometric Models 361

Problem 10-E. Cognitive Maps 363

Problem 10-F. Radiation By Any Other Name 364

Problem 10-G. Surfers and the Sun Revisited 365

Problem 10-4. Ranking the Risks: Are the Experts Right? 366

Problem 10-H. Implications of Differing Perspectives 369

Problem 10-I. Risk, Trust, and Rationality 369

Problem 10-J. Who Is More Concerned? 370

Problem

10-5. The Availability Bias 370

Problem 10-K. Aggregation 372

Problem 10-6. How Intuitive Are Statistics? The Case of Electromagnetic Fields and Cancer 373

Problem 10-L. Alternative Interpretations 377

Problem 10-7. Use of Point Estimates versus Distributions 377

Problem 10-M. Alternative Options 381

Problem 10-N. When Is the New Leaf Turned? 381

Problem 10-8. What Will They Think It Means? 382

Problem 10-O. What To Tell Them? 383

Problem 10-P. Alternative Labeling 384

Problem 10-Q. Situational Differences 384

Problem 10-9. Saccharin and Alar: Why the Difference? 384

Problem 10-R. When To Spin? 387

Problem 10-10. Can or Should "Zero Risk" Be a Goal? 387.

Notes:

Includes bibliographical references and index.

ISBN:

0691004269

OCLC:

40249801