Risk Assessment
of
Extreme Events
Rae Zimmerman
(New York University)
Vicki M. Bier
(University of Wisconsin-Madison)
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I. Introduction and Scope
• Risk assessment is a means to
characterize and reduce uncertainty to
support our ability to deal with
catastrophe
• Scope of this paper:
– Application of risk assessment to both the
built and natural environments under extreme
events
– Understanding and management of human
health, safety, and security
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I. Introduction and Scope (cont.)
• Modern risk assessment for engineering
began with Reactor Safety Study (1975):
– Applications to engineered systems and
infrastructure are common
• Applications to chemical risks under
dozens of federal environmental statutes:
– E.g., drinking water, ambient water quality, and
air quality standards
– Review and renewal of pesticide applications
– Levels of site cleanup under Superfund
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II. What is Risk Assessment?
• Definition of risk assessment:
“A systematic approach to organizing
and analyzing scientific knowledge and
information for potentially hazardous
activities or for substances that might
pose risks under specified
circumstances”
National Research Council (NRC), 1994
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II.A Definitions of Risk
• “Both uncertainty and some kind of loss
or damage” (Kaplan and Garrick 1981)
• “The potential for realization of unwanted,
negative consequences of an event”
(Rowe 1976)
• “The probability per unit time of the
occurrence of a unit cost burden” (Sage
and White 1980)
• “The likelihood that a vulnerability will be
exploited” (NRC 2002)
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II.A Definitions of Risk (cont.)
• Terms to characterize acceptable risk in
health and safety legislation:
–
–
–
–
–
–
–
–
Adequate
Imminent
Substantial
Reasonable (vs. unreasonable)
Posing grave danger
At a zero level
Significant (vs. de minimus)
An ample or adequate margin of safety
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II.B Relationship of Risk to Other
Concepts
• Merriam-Webster’s Collegiate Dictionary 2002:
– Hazard (“a source of danger”)
– Catastrophe (“a momentous tragic event”)
– Chronic (“long duration or frequent recurrence”)
• NRC 2002: Threat (“an adversary”)
– Vulnerability (“an error or a weakness”)
•
•
•
•
•
Extreme events (low frequency and high severity)
Counter-expected events (believed to be unlikely)
Unexpected events (not even anticipated)
Uncertainty (lack of knowledge)
Variability (differences among a population)
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II.C Paradigms for Risk
Assessment
• A form of systems analysis
• Answers three questions (Kaplan and
Garrick 1981):
– “What can go wrong?”
– “How likely is it that that will happen?”
– “If it does happen, what are the
consequences?”
• Several integrated risk assessment/risk
management frameworks have been
proposed
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II.C Paradigms for Risk
Assessment (cont.)
• “Deliberation frames analysis and
analysis informs deliberation” (Stern
and Fineberg 1996):
– The combination of these two steps is
termed the “analytic-deliberative”
process
– An iterative process
– Deliberation and analysis are viewed as
complementary
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III.A Health Risk Assessment
• Hazard identification
• Risk estimation:
– Exposure assessment
– Dose/response relationships (toxicity
assessment)
– Risk characterization or risk calculation
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III.A Health Risk Assessment
(cont.)
• Hazard identification:
– Structure activity relationships
(structural toxicology)
– Case clusters
– Epidemiological studies
– Experimental chemical tests on lower
order organisms (rapid screening)
– Animal tests
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III.A Health Risk Assessment
(cont.)
• Exposure assessment:
– Sources, pathways, and sinks (or
receptors)
– Health effects assessment
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III.A Health Risk Assessment
(cont.)
• Sources, pathways, and sinks (receptors):
– Source characterization (substances released,
rates of release, temporal variations, location)
– Fate and transport
– Routes or pathways of exposure from
environmental end points to human organisms
– Size, type, and sensitivity of population at risk
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III.A Health Risk Assessment
(cont.)
– Health effects assessment:
• Dose estimates or intake levels
• Absorption by the body
• General toxicity of the risk agent in the body
(e.g., target organs, types of effects)
• State of health of the organism
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III.A Health Risk Assessment
(cont.)
• Dose/response relationships (toxicity
assessment):
– Dose/response models
– Empirical relationships between levels
of exposure and effects
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III.A Health Risk Assessment
(cont.)
• Risk characterization or calculation:
– Risk estimate
– Characterization of uncertainties,
assumptions, and data quality
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IIIB Engineering Risk Assessment
• Hazard identification
• Assessment of accident occurrence
frequencies
• Consequence analysis
• Risk characterization
• Uncertainty analysis
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III.B Engineering Risk
Assessment (cont.)
• Hazard identification:
– System familiarization
– Hazard and operability studies
– Failure modes and effects analysis
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III.B Engineering Risk
Assessment (cont.)
• Assessment of accident occurrence
frequencies:
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III.B Engineering Risk
Assessment (cont.)
• Consequence analysis has two stages:
– Migration of hazardous materials from sources
to sinks
– Consequences of those materials for public
health and safety
• Relevant consequence measures include:
–
–
–
–
Structural response of a building
Costs of property damage, loss of use, repair
Amount of hazardous material released
Numbers of fatalities or other health effects
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III.B Engineering Risk
Assessment (cont.)
• Risk characterization:
– Results presented graphically
– Probability distribution,
complementary cumulative
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III.C Spatial Dimensions
• Proximity is a key factor in the
exposure portion of the risk equation
• Proximity can also affect:
– Perceived severity of particular
scenarios
– Conditional failure probabilities
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III.C Spatial Dimensions
• Despite this, risk analyses rarely use
sophisticated spatial concepts or
models:
– Methodology for doing so tends to be
ad hoc
– Takes little advantage of GIS systems
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IV. Understanding Uncertainty
• Sources of uncertainty:
–
–
–
–
–
–
–
–
Statistical variation
Systematic error
Subjective judgment
Linguistic imprecision
Variability
Inherent randomness or unpredictability
Disagreement
Approximation
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IV. Understanding Uncertainty
(cont.)
• Uncertainty and variability have different
implications for decision-making (NRC
1994):
– “Uncertainty forces decision makers to judge
how probable it is that risks will be
overestimated or underestimated”
– “Variability forces them to cope with the
certainty that different individuals will be
subjected to [different] risks”
• Large uncertainty suggests that further
research may be desirable
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V. Human Perceptions,
Behavior, and Performance
• Evacuation responses in emergencies
differ substantially from performance in
tests and simulations
• Behavioral assumptions underlying many
building codes and strategies are flawed
• Human behavior is extremely variable:
– Healthy versus elderly, ill, or disabled
– Familiarity with a particular environment
• Predicting the behavior of the public is a
difficult challenge
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V. Human Perceptions,
Behavior, Performance (cont.)
• Intentional hazards:
– Estimating the likelihood and nature of
intentional attacks “is needed for intelligent
benefit-cost analysis” (Woo 2002)
• Protection from an adversary is different
than protection against accidents:
– Adversaries can choose to attack targets that
have not been hardened
– Defensive measures may be less effective if
they are known
– Optimal strategy depends on attacker behavior
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VI. World Trade Center Disaster
• Unexpected or counter-expected
• Past experiences could have helped to
identify risk of an attack (Barnett 2001):
– “Lots of events…could be interpreted as
precursors of the calamity”
– “All the elements of the Sept. 11 catastrophe…
had historical precedent”
• This points out the need for:
– Methods of learning from past experience
– Vigilance to signs of problems
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VII. Conclusions
• Risk assessment is a vital tool for dealing
with extreme events
• Capabilities of risk assessment are
challenged when we attempt to apply it to
extreme and unanticipated events
• Need for methodological improvements to
more fully incorporate:
– Spatial dimensions
– Human values, attitudes, beliefs, and behavior
– Past experience
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Acknowledgments
• This material is based upon work
supported in part by:
– The U.S. Army Research Laboratory and the
U.S. Army Research Office under grant number
DAAD19-01-1-0502
– The National Science Foundation under
Cooperative Agreement No. CMS-9728805
• Any opinions, findings, conclusions, or
recommendations expressed in this
document are those of the authors
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