Risk Analysis, Vol. 34, No. 1, 2014

DOI: 10.1111/risa.12170

From the Editors

The editors thank authors, reviewers, and the Editorial Board for a year of extraordinary growth in submissions and for the many high-quality articles published in Risk Analysis: An International Journal in 2013. This issue begins 2014 with a strong set of valuable articles advancing both risk theory (including a proposed new quantitative measure for risk) and practical methods for improving and applying risk analysis in diverse areas. These application areas include pest risk analysis; exposure to metals and chemicals in urban garden soils and in artificial turf; food safety; flood risk management with uncertain future scenarios; benchmark dose estimation for chemical carcinogens; mining activities that are viewed very differently by different participants (the mining company, government officials, and members of the community); economic consequences of natural disasters and of terrorist attacks; and choice among different transportation modes (truck or train) for chlorine and ammonia gases. These developments are briefly summarized below. We hope you will enjoy this rich mix of methods and applications, so characteristic of the field and of the journal, and we look forward to another year of exciting advances in risk analysis theory and applications.

criteria. In domains such as pest risk analysis, simply selecting the most appropriate of these prespecified options may reflect the rough logic and level of resolution currently being used in expert judgment-based risk assessments. EXPOSURE ESTIMATION Two papers in this issue examine potential exposures to lead in urban gardening soil and to metals and semivolatile organic compounds from artificial turf fields, respectively. Concentrations of lead in urban garden soil can vary significantly from place to place on a spatial scale as small as a few meters, raising the question of what sampling approaches best reveal average soil concentrations and hot spots of high concentration. Bugdalski et al. use Monte Carlo simulation of estimated spatial distributions of garden soil lead concentrations (interpolated by kriging from measurements at an urban garden plot in Detroit) to evaluate alternative sampling strategies. They conclude that the practice of compositing and averaging samples increases the risk of overlooking hot spots, even though it reduces the risk of overestimating average soil concentrations, and urge reconsidering U.S. EPA sampling guidelines for soils in reclaimed city lots. Pavilonis et al. use artificial biofluids, representing lung, sweat, and digestive fluids, to estimate the doses of trace metals, semivolatile organics, and polycyclic aromatic hydrocarbons (PAHs) that might be received from playing sports on synthetic infill and artificial turf. They conclude that, with the possible exception of lead for some fields, exposures are expected to be very small.

SIMPLIFYING RISK MATRICES Prescriptive theories of rational decision-making typically require separate elicitation of utilities and probabilities—tasks that may be difficult, and that may yield results of uncertain reliability and validity. Is there a way to make such elicitation simpler and more robust for practical use in risk management decisions with limited-resolution data? In this issue, Holt et al. reexamine the use of tables or risk matrices for combining distributions of inputs to obtain ratings of outputs. They propose having assessors select among a limited set of matrices and three alternative distributions to represent uncertainties in

CONFLICT AND DIVERGENT PERCEPTIONS OF ROLES IN ENVIRONMENTAL RISK MANAGEMENT To what extent can risk analysis help to resolve conflicts in environmental debates? 1

C 2013 Society for Risk Analysis 0272-4332/14/0100-0001$22.00/1 

2 ´ ´ Catalan-V azquez et al. interviewed public officials, residents, and a mining company representative about perceptions of their own and each other’s roles in manganese mining in Molango, Mexico. Residents tended to view mining activities as contaminating their environment and increasing their health risks (negative affect). Public officials and the mining company emphasized the absence of evidence of harm and the role of mining activities in promoting regional development (positive affect). They interpreted residents’ expressed concerns about environmental and health harms as stances intended to win economic benefits from the mining company. Such different social representations of mining activities make it difficult for participants from the community, mining company, and public officials to work together effectively to agree on a risk management plan. However, as briefly noted by the authors, the state government and the mining company began working on action plans to decrease manganese emissions after Mexico’s National Institute of Public Health shared results with the different actors. This case study might inspire others to investigate how communicating credible risk analysis information might best enable coalitions of actors to resolve to take action, even if consensus and cooperation among all participants cannot be achieved. USING SIMULATION AND SAMPLING TO INCREASE FOOD SAFETY Two papers in this issue apply quantitative modeling to food safety risks. Tenehaus-Aziza et al. simulate the quantitative microbial risk of Listeria monocytogenes from consumption of soft cheese made from pasteurized milk. Their simulation model considers the entire production process from pasteurization to consumption, and uses scenario analysis and sensitivity analysis to identify how best to prevent and correct microbial hazards and how to improve and validate the model by replacing assumptions with measurements. Mark Powell considers how to optimize the trade-off in food safety sampling between the number of lots tested and the number of samples tested per lot when allocating a limited budget to identify nonconforming food lots. He shows that optimal decision rules have different forms, from simple random sampling of one sample per lot to multiple samples per lot, depending on the ratio of the cost per lot to the cost per unit sampled and on the prevalence of nonconforming condition.

From the Editors ADAPTIVE, FLEXIBLE MANAGEMENT OF UNCERTAIN FLOOD RISKS How can planners and designers best protect against future flood risks when climate change or other uncertainties make the probabilistic characteristics of future floods highly uncertain? Woodward et al. survey several approaches to risk management decision-making with highly uncertain future risks, including robust decision-making (RDM), Info-gap, robust optimization, and real options, and then develop a proposed new approach that combines a state-of-the-art flood risk model with a multiobjective optimization engine (based on genetic algorithms) that seeks optimal adaptive strategies. Plans and designs that enable flexible adaptation of future actions in light of future information are crucial for managing future uncertainties. The multiobjective optimization approach developed by the authors, which they illustrate with an application to managing uncertain flood risks on a reach of the Thames estuary in London, provides practical decision support for risk managers trying to decide where, when, and how to take costly actions to reduce uncertain future flood risks.

DEALING WITH UNCERTAINTIES IN BENCHMARK DOSE ESTIMATION Two articles advance the state of the art in benchmark dose (BMD) estimation. Shao and Gift show how to use Bayesian model averaging (BMA) to take into account model uncertainty in BMD estimation, thus improving average predictive performance above what could be expected by selecting any single model. They show using real and simulated data sets that BMA can reduce biases in BMD estimates, improve their reliability, and tighten uncertainty intervals. Thus, BMA appears very promising for improving BMD estimation and uncertainty characterization, although fascinating technical challenges remain (e.g., how to select individual model forms to include in the average, and what prior weights to assign to different models). Piegorsch et al. take a different approach to the challenge of parametric model uncertainty in BMD estimation, developing a nonparametric estimation approach (based on isotonic regression, assuming a continuous, nondecreasing, underlying dose-response relation) and deriving lower confidence limits (BMDLs) from nonparametric bootstrapping.

From the Editors A NEW QUANTITATIVE MEASURE OF FINANCIAL RISK Risk Analysis: An International Journal focuses primarily on advances in health, safety, and environmental (HS&E) risk analysis. Submissions on purely financial, statistical, medical, or operations research risk topics are usually returned to contributors with the suggestion that they submit instead to appropriate journals in these areas. However, the Editorial Board welcomes exceptionally strong papers presenting innovations that cut across multiple areas and that are important, clear, and interesting to most of our readers. The paper by Belles-Sampera et al. extends recent progress in quantitative measures of financial risk, introducing a new class of risk measures (GlueVaR risk measures) that are intermediate between the much-used value-at-risk (VaR) measure (which is not a coherent risk measure for all distributions, and which can underestimate the capital requirements needed to manage risk of insolvency in the presence of potential catastrophic or “heavytailed” losses) and the less widely used but often much more conservative tail VaR (TVaR) measure, which overcomes these limitations of VaR. The dramatic advances in financial risk theory and practice growing out of the characterization of coherent risk measures have applications to many other areas of single-attribute utility theory and robust optimization, so advances in this area of theory, while most directly applicable to financial risks, have many other potential applications in risk analysis. PREDICTING ECONOMIC CONSEQUENCES OF DISASTERS In the past few years, several papers in this journal have used input–output models to explore the regional economic impacts over time of various types of natural disasters. In this issue, Hallegatte advances the state of the art of such modeling by explicitly

3 modeling the roles of inventories and distinguishing among essential supplies that cannot easily be stockpiled and whose disruption immediately affects other economic activity (e.g., electricity); essential supplies that can be stockpiled (e.g., chemical inventories), thus providing a buffer against short-term scarcity; and supplies that are not essential and that can readily be replaced with imports in the long run if needed. Using Hurricane Katrina as a case study, the paper identifies production bottlenecks during the first year after the hurricane as causing large losses in output. Use of an adaptive regional input–output economic model also gives insights into resilience and the time to recovery for different regional economic sectors following a disaster. Dormady et al. also assess the potential ripple effects of an initial disruption—in their case, an anthrax attack rather than a hurricane—on the local economy in Seattle. They estimate that residential property values could decline by about one-third, or 50 billion dollars, and that this could cause about 70,000 foreclosures. CHOOSING BETWEEN RAIL AND ROAD TRANSPORTATION OF TOXIC GASES The remaining paper in this issue, by Bagheri et al., addresses whether railroad or highway transportation of toxic gases, such as chlorine and ammonia, poses smaller risks. They conclude that, compared to truck transport, rail transport reduces risk for both chlorine and ammonia transport, for all of six North American transportation corridors considered. This simple conclusion holds for several different measures of risk (emphasizing probability of a release or “incident,” number of people exposed to the hazard, or the product of the two), suggesting that at least in the cases considered by the authors, there is a simple answer to the question of which mode poses less risk to the public. Tony Cox and Karen Lowrie

From the Editors.

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