Mapping Socioeconomic Exposure for Flood Risk Assessment in Italy

Detailed spatial representation of socioeconomic exposure and the related vulnerability to natural hazards has the potential to improve the quality and reliability of risk assessment outputs. We apply a spatially weighted dasymetric approach based on multiple ancillary data to downscale important socioeconomic variables and produce a grid data set for Italy that contains multilayered information about physical exposure, population, gross domestic product, and social vulnerability. We test the performances of our dasymetric approach compared to other spatial interpolation methods. Next, we combine the grid data set with flood hazard estimates to exemplify an application for the purpose of risk assessment.     

Probabilistic Models for Risk Assessment

Recent concern with the potential for stray carbon fibers to damage electronic equipment and cause economic losses has led to the development of advanced risk-assessment methods. Risk assessment often requires the synthesis of risk profiles which represent the probability distribution of total annual losses due to a certain set of events or activities. A number of alternative probabilistic models are presented which the authors have used to develop such profiles. Examples are given of applications of these methods to assessment of risk due to conductive fibers released from aircraft or automobile fires. These assessments usually involve a two-stage approach: estimation of losses for several subclassifications of the overall process, and synthesis of the results into an aggregate risk profile. The methodology presented is capable of treating a wide variety of situations involving sequences of random physical events.     

Integration of Probabilistic Exposure Assessment and Probabilistic Hazard Characterization

A method is proposed for integrated probabilistic risk assessment where exposure assessment and hazard characterization are both included in a probabilistic way. The aim is to specify the probability that a random individual from a defined (sub)population will have an exposure high enough to cause a particular health effect of a predefined magnitude, the critical effect size ( CES ). The exposure level that results in exactly that CES in a particular person is that person's individual critical effect dose ( ICED ). Individuals in a population typically show variation, both in their individual exposure ( IEXP ) and in their ICED . Both the variation in IEXP and the variation in ICED are quantified in the form of probability distributions. Assuming independence between both distributions, they are combined (by Monte Carlo) into a distribution of the individual margin of exposure ( IMoE ). The proportion of the IMoE distribution below unity is the probability of critical exposure ( PoCE ) in the particular (sub)population. Uncertainties involved in the overall risk assessment (i.e., both regarding exposure and effect assessment) are quantified using Monte Carlo and bootstrap methods. This results in an uncertainty distribution for any statistic of interest, such as the probability of critical exposure ( PoCE ). The method is illustrated based on data for the case of dietary exposure to the organophosphate acephate. We present plots that concisely summarize the probabilistic results, retaining the distinction between variability and uncertainty. We show how the relative contributions from the various sources of uncertainty involved may be quantified.     

Hazard Experience,Geophysical Vulnerability,and Flood Risk Perceptions in a Postdisaster City,the Case of New Orleans

This article investigates the determinants of flood risk perceptions in New Orleans, Louisiana (United States), a deltaic coastal city highly vulnerable to seasonal nuisance flooding and hurricane‐induced deluges and storm surges. Few studies have investigated the influence of hazard experience, geophysical vulnerability (hazard proximity), and risk perceptions in cities undergoing postdisaster recovery and rebuilding. We use ordinal logistic regression techniques to analyze experiential, geophysical, and sociodemographic variables derived from a survey of 384 residents in seven neighborhoods. We find that residents living in neighborhoods that flooded during Hurricane Katrina exhibit higher levels of perceived risk than those residents living in neighborhoods that did not flood. In addition, findings suggest that flood risk perception is positively associated with female gender, lower income, and direct flood experiences. In conclusion, we discuss the implications of these findings for theoretical and empirical research on environmental risk, flood risk communication strategies, and flood hazards planning.     

The Distinction Between Risk and Hazard: Understanding and Use in Stakeholder Communication

A major issue in all risk communication efforts is the distinction between the terms “risk” and “hazard.” The potential to harm a target such as human health or the environment is normally defined as a hazard, whereas risk also encompasses the probability of exposure and the extent of damage. What can be observed again and again in risk communication processes are misunderstandings and communication gaps related to these crucial terms. We asked a sample of 53 experts from public authorities, business and industry, and environmental and consumer organizations in Germany to outline their understanding and use of these terms using both the methods of expert interviews and focus groups. The empirical study made clear that the terms risk and hazard are perceived and used very differently in risk communication depending on the perspective of the stakeholders. Several factors can be identified, such as responsibility for hazard avoidance, economic interest, or a watchdog role. Thus, communication gaps can be reduced to a four‐fold problem matrix comprising a semantic, conceptual, strategic, and control problem. The empirical study made clear that risks and hazards are perceived very differently depending on the stakeholders’ perspective. Their own worldviews played a major role in their specific use of the two terms hazards and risks in communication.     

Flood Hazard and Flood Risk Assessment Using a Time Series of Satellite Images: A Case Study in Namibia

In this article, the use of time series of satellite imagery to flood hazard mapping and flood risk assessment is presented. Flooded areas are extracted from satellite images for the flood‐prone territory, and a maximum flood extent image for each flood event is produced. These maps are further fused to determine relative frequency of inundation (RFI). The study shows that RFI values and relative water depth exhibit the same probabilistic distribution, which is confirmed by Kolmogorov‐Smirnov test. The produced RFI map can be used as a flood hazard map, especially in cases when flood modeling is complicated by lack of available data and high uncertainties. The derived RFI map is further used for flood risk assessment. Efficiency of the presented approach is demonstrated for the Katima Mulilo region (Namibia). A time series of Landsat‐5/7 satellite images acquired from 1989 to 2012 is processed to derive RFI map using the presented approach. The following direct damage categories are considered in the study for flood risk assessment: dwelling units, roads, health facilities, and schools. The produced flood risk map shows that the risk is distributed uniformly all over the region. The cities and villages with the highest risk are identified. The proposed approach has minimum data requirements, and RFI maps can be generated rapidly to assist rescuers and decisionmakers in case of emergencies. On the other hand, limitations include: strong dependence on the available data sets, and limitations in simulations with extrapolated water depth values.     

Probabilistic Integrated Human Mixture Risk Assessment of Multiple Metals Through Seafood Consumption

Inorganic arsenic (iAs), cadmium (Cd), lead (Pb), and methylmercury (MeHg) are toxic metals that cause substantial health concern and are present in various seafood items. This study linked probabilistic risk assessment to the interactive hazard index (HI_INT) approach to assess the human mixture risk posed by the dietary intake of iAs, Cd, Pb, and MeHg from seafood for different age populations, and joint toxic actions and toxic interactions among metals were also considered in the assessment. We found that, in combination, an iAs–Cd–Pb–MeHg mixture synergistically causes neurological toxicity. Furthermore, an iAs–Cd–Pb mixture antagonistically causes renal and hematological effects and additively causes cardiovascular effect. Our results demonstrated that if toxic interactions are not considered, the health risk may be overestimated or underestimated. The 50th percentile HI_INT estimates in all age populations for neurological, renal, cardiovascular, and hematological effects were lower than 1; however, the 97.5th percentile HI_INT estimates might exceed 1. In particular, toddlers and preschoolers had the highest neurological risk, with 0.16 and 0.19 probabilities, respectively, of neurological HI_INT exceeding 1. Saltwater fish consumption was the principal contributor to the health risk. We suggest that regular monitoring of metal levels in seafood, more precise dietary surveys, further toxicological data, and risk–benefit analysis of seafood consumption are warranted to improve the accuracy of human mixture risk assessment and determine optimal consumption.     

Multicriteria Decision Framework for Cybersecurity Risk Assessment and Management

Risk assessors and managers face many difficult challenges related to novel cyber systems. Among these challenges are the constantly changing nature of cyber systems caused by technical advances, their distribution across the physical, information, and sociocognitive domains, and the complex network structures often including thousands of nodes. Here, we review probabilistic and risk-based decision-making techniques applied to cyber systems and conclude that existing approaches typically do not address all components of the risk assessment triplet (threat, vulnerability, consequence) and lack the ability to integrate across multiple domains of cyber systems to provide guidance for enhancing cybersecurity. We present a decision-analysis-based approach that quantifies threat, vulnerability, and consequences through a set of criteria designed to assess the overall utility of cybersecurity management alternatives. The proposed framework bridges the gap between risk assessment and risk management, allowing an analyst to ensure a structured and transparent process of selecting risk management alternatives. The use of this technique is illustrated for a hypothetical, but realistic, case study exemplifying the process of evaluating and ranking five cybersecurity enhancement strategies. The approach presented does not necessarily eliminate biases and subjectivity necessary for selecting countermeasures, but provides justifiable methods for selecting risk management actions consistent with stakeholder and decisionmaker values and technical data.     

Risk Analysis of a Gas-Processing Complex in India

ONGC's Hazira Gas-Processing Complex (HGPC) consists of facilities for receiving natural gas along with associated condensate from an off-shore field at a rate of 20 MMN M3 per day. After separating the condensate, which is processed in condensate fractionation units, the gas is processed through various steps to recover LPG and to reduce its dew point to less than 5 degrees C in order to make it suitable for transportation over long distances. The acid gas recovered during the gas-sweetening step is processed to obtain sulphur. The major products manufactured at HGPC therefore are lean sweet gas, LPG, NGL, and sulphur. The Oil and Natural Gas Commission awarded the assignment on Hazard Study and Risk Analysis of their Hazira Gas-Processing Complex (HGPC) to the Council of Scientific and Industrial Research (CSIR) in association with the Netherlands Organisation for Applied Scientific Research (TNO). The scope of this assignment covered a number of closely related and fully defined activities normally encountered in this type of work. Identification of hazards through the most appropriate methods, assigning frequency of occurrence of major unwanted incidents, quantification and assessment of probable damage to plant equipment, environment, human and animal life due to an unexpected event, and evaluation of various methods for reducing risk, together constituted the methodology for this assignment. Detailed recommendations aimed at reducing risk and enhancing reliability of plant and machinery were made. This article gives an overview of the assignment.     

A Probabilistic Analysis of Surface Water Flood Risk in London

Flooding in urban areas during heavy rainfall, often characterized by short duration and high‐intensity events, is known as “surface water flooding.” Analyzing surface water flood risk is complex as it requires understanding of biophysical and human factors, such as the localized scale and nature of heavy precipitation events, characteristics of the urban area affected (including detailed topography and drainage networks), and the spatial distribution of economic and social vulnerability. Climate change is recognized as having the potential to enhance the intensity and frequency of heavy rainfall events. This study develops a methodology to link high spatial resolution probabilistic projections of hourly precipitation with detailed surface water flood depth maps and characterization of urban vulnerability to estimate surface water flood risk. It incorporates probabilistic information on the range of uncertainties in future precipitation in a changing climate. The method is applied to a case study of Greater London and highlights that both the frequency and spatial extent of surface water flood events are set to increase under future climate change. The expected annual damage from surface water flooding is estimated to be to be £171 million, £343 million, and £390 million/year under the baseline, 2030 high, and 2050 high climate change scenarios, respectively.     

Recent Case Studies and Advancements in Probabilistic Risk Assessment

During the period from 1977 to 1984, Pickard, Lowe and Garrick, Inc., had the lead in preparing several full scope probabilistic risk assessments for electric utilities. Five of those studies are discussed from the point of view of advancements and lessons learned. The objective and trend of these studies is toward utilization of the risk models by the plant owners as risk management tools. Advancements that have been made are in presentation and documentation of the PRAs, generation of more understandable plant level information, and improvements in methodology to facilitate technology transfer. Specific areas of advancement are in the treatment of such issues as dependent failures, human interaction, and the uncertainty in the source term. Lessons learned cover a wide spectrum and include the importance of plant specific models for meaningful risk management, the role of external events in risk, the sensitivity of contributors to choice of risk index, and the very important finding that the public risk is extremely small. The future direction of PRA is to establish less dependence on experts for in-plant application. Computerizing the PRAs such that they can be accessed on line and interactively is the key.     

Measures of Compounding Conservatism in Probabilistic Risk Assessment

Concern about the degree of uncertainty and potential conservatism in deterministic point estimates of risk has prompted researchers to turn increasingly to probabilistic methods for risk assessment. With Monte Carlo simulation techniques, distributions of risk reflecting uncertainty and/or variability are generated as an alternative. In this paper the compounding of conservatism⁽¹⁾ between the level associated with point estimate inputs selected from probability distributions and the level associated with the deterministic value of risk calculated using these inputs is explored. Two measures of compounded conservatism are compared and contrasted. The first measure considered, F , is defined as the ratio of the risk value, R _d, calculated deterministically as a function of n inputs each at the j th percentile of its probability distribution, and the risk value, R _j that falls at the j th percentile of the simulated risk distribution (i.e., F=R_d/R_j). The percentile of the simulated risk distribution which corresponds to the deterministic value, R_d , serves as a second measure of compounded conservatism. Analytical results for simple products of lognormal distributions are presented. In addition, a numerical treatment of several complex cases is presented using five simulation analyses from the literature to illustrate. Overall, there are cases in which conservatism compounds dramatically for deterministic point estimates of risk constructed from upper percentiles of input parameters, as well as those for which the effect is less notable. The analytical and numerical techniques discussed are intended to help analysts explore the factors that influence the magnitude of compounding conservatism in specific cases.     

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness,Planning, and Response for Severe Nuclear Power Plant Accidents

In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.     

Taxonomic Analysis of Perceived Risk: Modeling Individual and Group Perceptions Within Homogeneous Hazard Domains

Previous studies of risk perception have typically focused on the mean judgments of a group of people regarding the riskiness (or safety) of a diverse set of hazardous activities, substances, and technologies. This paper reports the results of two studies that take a different path. Study 1 investigated whether models within a single technological domain were similar to previous models based on group means and diverse hazards. Study 2 created a group taxonomy of perceived risk for only one technological domain, railroads, and examined whether the structure of that taxonomy corresponded with taxonomies derived from prior studies of diverse hazards. Results from Study 1 indicated that the importance of various risk characteristics in determining perceived risk differed across individuals and across hazards, but not so much as to invalidate the results of earlier studies based on group means and diverse hazards. In Study 2, the detailed analysis of railroad hazards produced a structure that had both important similarities to, and dissimilarities from, the structure obtained in prior research with diverse hazard domains. The data also indicated that railroad hazards are really quite diverse, with some approaching nuclear reactors in their perceived seriousness. These results suggest that information about the diversity of perceptions within a single domain of hazards could provide valuable input to risk-management decisions.     

Risk Assessment for Aflatoxin: III. Modeling the Relative Risk of Hepatocellular Carcinoma

Estimates have been made of the cancer potency of aflatoxin exposure among the U.S. population. Risk modeling is used to assess the dose-response relationship between aflatoxin exposure and primary liver cancer, controlling for hepatitis B virus (HBV), based on data provided by the Yeh et al. study in China. A relative risk model is proposed as a more appropriate alternative to the additive ("absolute" risk) model for transportation of risk coefficients between populations with different baseline rates. Several general relative risk models were examined; the exponential model provided the best fit. The Poisson regression method was used to fit the relative risk model to the grouped data. The effects of exposure to aflatoxin (AFB1) and hepatitis B infection were both found to be statistically significant. The risk of death from liver cancer for those exposed to AFB1 relative to the unexposed population, increases by 0.05% per ng/kg/day exposure of AFB1 (p less than 0.001). The results also indicated a 25-fold increase in the risk of death from liver cancer among those infected with hepatitis B virus, relative to noncarriers (p less than 0.0001). With a hepatitis prevalence rate of 1%, the aflatoxin intake level associated with liver cancer lifetime excess risk of 1 x 10(-5) for the U.S. population was estimated as 253 ng/day, based on a liver cancer baseline rate of 3.4/100,000/yr.     

Low‐Probability Flood Risk Modeling for New York City

The devastating impact by Hurricane Sandy (2012) again showed New York City (NYC) is one of the most vulnerable cities to coastal flooding around the globe. The low‐lying areas in NYC can be flooded by nor'easter storms and North Atlantic hurricanes. The few studies that have estimated potential flood damage for NYC base their damage estimates on only a single, or a few, possible flood events. The objective of this study is to assess the full distribution of hurricane flood risk in NYC. This is done by calculating potential flood damage with a flood damage model that uses many possible storms and surge heights as input. These storms are representative for the low‐probability/high‐impact flood hazard faced by the city. Exceedance probability‐loss curves are constructed under different assumptions about the severity of flood damage. The estimated flood damage to buildings for NYC is between US$59 and 129 millions/year. The damage caused by a 1/100‐year storm surge is within a range of US$2 bn–5 bn, while this is between US$5 bn and 11 bn for a 1/500‐year storm surge. An analysis of flood risk in each of the five boroughs of NYC finds that Brooklyn and Queens are the most vulnerable to flooding. This study examines several uncertainties in the various steps of the risk analysis, which resulted in variations in flood damage estimations. These uncertainties include: the interpolation of flood depths; the use of different flood damage curves; and the influence of the spectra of characteristics of the simulated hurricanes.     

Probabilistic Risk Assessment of Wastes Buried in the Ground

The differences between probabilistic risk assessment (PRA) and safety analysis (SA) are discussed, and it is shown that PRA is more suitable than SA for determining the acceptability of a technology. Since a PRA by the fault tree-event tree analysis method used for reactor safety studies does not seem to be practical for buried waste, an alternative approach is suggested using geochemical analogs. This method is illustrated for the cases of high-level and low-level radioactive waste and for chemical carcinogens released in coal burning.     

Flood Risk Assessment in the Netherlands: A Case Study for Dike Ring South Holland

Large parts of the Netherlands are below sea level. Therefore, it is important to have insight into the possible consequences and risks of flooding. In this article, an analysis of the risks due to flooding of the dike ring area South Holland in the Netherlands is presented. For different flood scenarios the potential number of fatalities is estimated. Results indicate that a flood event in this area can expose large and densely populated areas and result in hundreds to thousands of fatalities. Evacuation of South Holland before a coastal flood will be difficult due to the large amount of time required for evacuation and the limited time available. By combination with available information regarding the probability of occurrence of different flood scenarios, the flood risks have been quantified. The probability of death for a person in South Holland due to flooding, the so‐called individual risk, is small. The probability of a flood disaster with many fatalities, the so‐called societal risk, is relatively large in comparison with the societal risks in other sectors in the Netherlands, such as the chemical sector and aviation. The societal risk of flooding appears to be unacceptable according to some of the existing risk limits that have been proposed in literature. These results indicate the necessity of a further societal discussion on the acceptable level of flood risk in the Netherlands and the need for additional risk reducing measures.     

A Discussion of Findings and Their Possible Implications from a Workshop on Bioterrorism Threat Assessment and Risk Management

In November 2001, the Monterey Institute of International Studies convened a workshop on bioterrorism threat assessment and risk management. Risk assessment practitioners from various disciplines, but without specialized knowledge of terrorism, were brought together with security and intelligence threat analysts to stimulate an exchange that could be useful to both communities. This article, prepared by a subset of the participants, comments on the workshop's findings and their implications and makes three recommendations, two short term (use of threat assessment methodologies and vulnerability analysis) and one long term (application of quantitative risk assessment and modeling), regarding the practical application of risk assessment methods to bioterrorism issues.     

Quantitative Risk Assessment Modeling for Nonhomogeneous Urban Road Tunnels

Urban road tunnels provide an increasingly cost‐effective engineering solution, especially in compact cities like Singapore. For some urban road tunnels, tunnel characteristics such as tunnel configurations, geometries, provisions of tunnel electrical and mechanical systems, traffic volumes, etc. may vary from one section to another. These urban road tunnels that have characterized nonuniform parameters are referred to as nonhomogeneous urban road tunnels. In this study, a novel quantitative risk assessment (QRA) model is proposed for nonhomogeneous urban road tunnels because the existing QRA models for road tunnels are inapplicable to assess the risks in these road tunnels. This model uses a tunnel segmentation principle whereby a nonhomogeneous urban road tunnel is divided into various homogenous sections. Individual risk for road tunnel sections as well as the integrated risk indices for the entire road tunnel is defined. The article then proceeds to develop a new QRA model for each of the homogeneous sections. Compared to the existing QRA models for road tunnels, this section‐based model incorporates one additional top event—toxic gases due to traffic congestion—and employs the Poisson regression method to estimate the vehicle accident frequencies of tunnel sections. This article further illustrates an aggregated QRA model for nonhomogeneous urban tunnels by integrating the section‐based QRA models. Finally, a case study in Singapore is carried out.