Challenges in Development and Implementation of Health‐Risk‐Based Soil Quality Guidelines: Turkey's Experience

Management of contaminated sites is a critical environmental issue around the world due to the human health risk involved for many sites and scarcity of funding. Moreover, clean‐up costs of all contaminated sites to their background levels with existing engineering technologies may be financially infeasible and demand extended periods of operation time. Given these constraints, to achieve optimal utilization of available funds and prioritization of contaminated sites that need immediate attention, health‐risk‐based soil quality guidelines should be preferred over the traditional soil quality standards. For these reasons, traditional soil quality standards are being replaced by health‐risk‐based ones in many countries and in Turkey as well. The need for health‐risk‐based guidelines is clear, but developing these guidelines and implementation of them in contaminated site management is not a straightforward process. The goal of this study is to highlight the problems that are encountered at various stages of the development process of risk‐based soil quality guidelines for Turkey and how they are dealt with. Utilization of different definitions and methodologies at different countries, existence of inconsistent risk assessment tools, difficulties in accessing relevant documents and reports, and lack of specific data required for Turkey are among these problems. We believe that Turkey's experience may help other countries that are planning to develop health‐risk‐based guidelines achieve their goals in a more efficient manner.     

Setting Risk‐Informed Environmental Standards for Bacillus Anthracis Spores

In many cases, human health risk from biological agents is associated with aerosol exposures. Because air concentrations decline rapidly after a release, it may be necessary to use concentrations found in other environmental media to infer future or past aerosol exposures. This article presents an approach for linking environmental concentrations of Bacillus. anthracis (B. anthracis) spores on walls, floors, ventilation system filters, and in human nasal passages with human health risk from exposure to B. anthracis spores. This approach is then used to calculate example values of risk‐informed concentration standards for both retrospective risk mitigation (e.g., prophylactic antibiotics) and prospective risk mitigation (e.g., environmental clean up and reoccupancy). A large number of assumptions are required to calculate these values, and the resulting values have large uncertainties associated with them. The values calculated here suggest that documenting compliance with risks in the range of 10^?4 to 10^?6 would be challenging for small diameter (respirable) spore particles. For less stringent risk targets and for releases of larger diameter particles (which are less respirable and hence less hazardous), environmental sampling would be more promising.     

Risk‐Based Probabilistic Approach to Assess the Impact of False Mussel Invasions on Farmed Hard Clams

The purpose of this article is to provide a risk‐based predictive model to assess the impact of false mussel Mytilopsis sallei invasions on hard clam Meretrix lusoria farms in the southwestern region of Taiwan. The actual spread of invasive false mussel was predicted by using analytical models based on advection‐diffusion and gravity models. The proportion of hard clam colonized and infestation by false mussel were used to characterize risk estimates. A mortality model was parameterized to assess hard clam mortality risk characterized by false mussel density and infestation intensity. The published data were reanalyzed to parameterize a predictive threshold model described by a cumulative Weibull distribution function that can be used to estimate the exceeding thresholds of proportion of hard clam colonized and infestation. Results indicated that the infestation thresholds were 2–17 ind clam^?1 for adult hard clams, whereas 4 ind clam^?1 for nursery hard clams. The average colonization thresholds were estimated to be 81–89% for cultivated and nursery hard clam farms, respectively. Our results indicated that false mussel density and infestation, which caused 50% hard clam mortality, were estimated to be 2,812 ind m^?2 and 31 ind clam^?1, respectively. This study further indicated that hard clam farms that are close to the coastal area have at least 50% probability for 43% mortality caused by infestation. This study highlighted that a probabilistic risk‐based framework characterized by probability distributions and risk curves is an effective representation of scientific assessments for farmed hard clam in response to the nonnative false mussel invasion.     

Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

The Value of a Statistical Life for Risk‐Averse and Risk‐Seeking Individuals

This article estimates the value of a statistical life (VSL) for Chile under the hedonic wage method while accounting for individual risk preferences. Two alternative measures of risk aversion are used. First, risk aversion is directly measured using survey measures of preferences over hypothetical gambles, and second, over observed individual behaviors that may proxy for risk preferences, such as smoking status, are used. I reconcile the results with a theoretical model of economic behavior that predicts how the wage‐risk tradeoff changes as risk aversion differs across individuals. The VSL estimates range between 0.61 and 8.68 million dollars. The results using smoking behavior as a proxy for risk attitudes are consistent with previous findings. However, directly measuring risk aversion corrects the wage‐risk tradeoff estimation bias in the opposite direction. The results are robust to other observed measures of risk aversion such as drinking behavior and stock investments. Results suggest that, consistent with the literature that connects smoking behavior with labor market outcomes, smoking status could be capturing poor health productivity effect in addition to purely risk preferences.     

Consumer Responses to Integrated Risk‐Benefit Information Associated with the Consumption of Food

The risk analysis of the health impact of foods is increasingly focused on integrated risk‐benefit assessment, which will also need to be communicated to consumers. It therefore becomes important to understand how consumers respond to integrated risk‐benefit information. Quality‐adjusted‐life‐years (QALYs) is one measure that can be used to assess the balance between risks and benefits associated with a particular food. The effectiveness of QALYs for communicating both positive and negative health effects associated with food consumption to consumers was examined, using a 3 × 2 experiment varying information about health changes in terms of QALYs associated with the consumption of fish (n = 325). The effect of this information on consumer perceptions of the usefulness of QALYs for describing health effects, on risk and benefit perceptions, attitudes, and intentions to consume fish was examined. Results demonstrated that consumers perceived QALYs as useful for communicating health effects associated with food consumption. QALYs communicated as a net effect were preferred for food products associated with negative net effects on health, while separate communication of both risks and benefits may be preferred for food products associated with positive or zero net health effects. Information about health changes in terms of QALYs facilitated informed decision making by consumers, as indicated by the impact on risk and benefit perceptions as intended by the information. The impact of this information on actual food consumption choices merits further investigation.     

Advancing Risk‐Informed Decision Making in Managing Defense Nuclear Waste in the United States: Opportunities and Challenges for Risk Analysis

An omnibus spending bill in 2014 directed the Department of Energy to analyze how effectively Department of Energy (DOE) identifies, programs, and executes its plans to address public health and safety risks that remain as part of DOE's remaining environmental cleanup liabilities. A committee identified two dozen issues and associated recommendations for the DOE, other federal agencies, and the U.S. Congress to consider, as well as other stakeholders such as states and tribal nations. In regard to risk assessment, the committee described a risk review process that uses available data, expert experience, identifies major data gaps, permits input from key stakeholders, and creates an ordered set of risks based on what is known. Probabilistic risk assessments could be a follow‐up from these risk reviews. In regard to risk management, the states, in particular, have become major drivers of how resources are driven. States use different laws, different priorities, and challenge DOE's policies in different ways. Land use decisions vary, technology choices are different, and other notable variations are apparent. The cost differences associated with these differences are marked. The net result is that resources do not necessarily go to the most prominent human health and safety risks, as seen from the national level.     

Risk‐Based Sampling: I Don't Want to Weight in Vain

Recently, there has been considerable interest in developing risk‐based sampling for food safety and animal and plant health for efficient allocation of inspection and surveillance resources. The problem of risk‐based sampling allocation presents a challenge similar to financial portfolio analysis. Markowitz (1952) laid the foundation for modern portfolio theory based on mean‐variance optimization. However, a persistent challenge in implementing portfolio optimization is the problem of estimation error, leading to false “optimal” portfolios and unstable asset weights. In some cases, portfolio diversification based on simple heuristics (e.g., equal allocation) has better out‐of‐sample performance than complex portfolio optimization methods due to estimation uncertainty. Even for portfolios with a modest number of assets, the estimation window required for true optimization may imply an implausibly long stationary period. The implications for risk‐based sampling are illustrated by a simple simulation model of lot inspection for a small, heterogeneous group of producers.     

Feasibility Study on the Use of Probabilistic Migration Modeling in Support of Exposure Assessment from Food Contact Materials

The use of probabilistic approaches in exposure assessments of contaminants migrating from food packages is of increasing interest but the lack of concentration or migration data is often referred as a limitation. Data accounting for the variability and uncertainty that can be expected in migration, for example, due to heterogeneity in the packaging system, variation of the temperature along the distribution chain, and different time of consumption of each individual package, are required for probabilistic analysis. The objective of this work was to characterize quantitatively the uncertainty and variability in estimates of migration. A Monte Carlo simulation was applied to a typical solution of the Fick's law with given variability in the input parameters. The analysis was performed based on experimental data of a model system (migration of Irgafos 168 from polyethylene into isooctane) and illustrates how important sources of variability and uncertainty can be identified in order to refine analyses. For long migration times and controlled conditions of temperature the affinity of the migrant to the food can be the major factor determining the variability in the migration values (more than 70% of variance). In situations where both the time of consumption and temperature can vary, these factors can be responsible, respectively, for more than 60% and 20% of the variance in the migration estimates. The approach presented can be used with databases from consumption surveys to yield a true probabilistic estimate of exposure.     

Assessing the Exacerbations Risk of Influenza‐Associated Chronic Occupational Asthma

The purpose of this article was to conduct a risk‐based study based on a linkage of experimental human influenza infections and fluctuation analysis of airway function to assess whether influenza viral infection was risk factor for exacerbations of chronic occupational asthma. Here we provided a comprehensive probabilistic analysis aimed at quantifying influenza‐associated exacerbations risk for occupational asthmatics, based on a combination of published distributions of viral shedding and symptoms scores and lung respiratory system properties characterized by long‐range peak expiratory flow (PEF) dynamics. Using a coupled detrended fluctuation analysis‐experimental human influenza approach, we estimated the conditional probability of moderate or severe lung airway obstruction and hence the exacerbations risk of influenza‐associated occupational asthma in individuals. The long‐range correlation exponent (α) was used as a predictor of future exacerbations risk of influenza‐associated asthma. For our illustrative distribution of PEF fluctuations and influenza‐induced asthma exacerbations risk relations, we found that the probability of exacerbations risk can be limited to below 50% by keeping α to below 0.53. This study also found that limiting wheeze scores to 0.56 yields a 75% probability of influenza‐associated asthma exacerbations risk and a limit of 0.34 yields a 50% probability that may give a representative estimate of the distribution of chronic respiratory system properties. This study implicates that influenza viral infection is an important risk factor for exacerbations of chronic occupational asthma.     

A Tiered Approach for Risk‐Benefit Assessment of Foods

Risk‐benefit analyses are introduced as a new paradigm for old problems. However, in many cases it is not always necessary to perform a full comprehensive and expensive quantitative risk‐benefit assessment to solve the problem, nor is it always possible, given the lack of required date. The choice to continue from a more qualitative to a full quantitative risk‐benefit assessment can be made using a tiered approach. In this article, this tiered approach for risk‐benefit assessment will be addressed using a decision tree. The tiered approach described uses the same four steps as the risk assessment paradigm: hazard and benefit identification, hazard and benefit characterization, exposure assessment, and risk‐benefit characterization, albeit in a different order. For the purpose of this approach, the exposure assessment has been moved upward and the dose‐response modeling (part of hazard and benefit characterization) is moved to a later stage. The decision tree includes several stop moments, depending on the situation where the gathered information is sufficient to answer the initial risk‐benefit question. The approach has been tested for two food ingredients. The decision tree presented in this article is useful to assist on a case‐by‐case basis a risk‐benefit assessor and policymaker in making informed choices when to stop or continue with a risk‐benefit assessment.     

Coordination of Supply Chains with Risk‐Averse Agents

The extant supply chain management literature has not addressed the issue of coordination in supply chains involving risk‐averse agents. We take up this issue and begin with defining a coordinating contract as one that results in a Pareto‐optimal solution acceptable to each agent. Our definition generalizes the standard one in the risk‐neutral case. We then develop coordinating contracts in three specific cases: (i) the supplier is risk neutral and the retailer maximizes his expected profit subject to a downside risk constraint; (ii) the supplier and the retailer each maximizes his own mean‐variance trade‐off; and (iii) the supplier and the retailer each maximizes his own expected utility. Moreover, in case (iii), we show that our contract yields the Nash Bargaining solution. In each case, we show how we can find the set of Pareto‐optimal solutions, and then design a contract to achieve the solutions. We also exhibit a case in which we obtain Pareto‐optimal sharing rules explicitly, and outline a procedure to obtain Pareto‐optimal solutions.     

A Spatio‐Temporal Exposure‐Hazard Model for Assessing Biological Risk and Impact

We developed a simulation model for quantifying the spatio‐temporal distribution of contaminants (e.g., xenobiotics) and assessing the risk of exposed populations at the landscape level. The model is a spatio‐temporal exposure‐hazard model based on (i) tools of stochastic geometry (marked polygon and point processes) for structuring the landscape and describing the exposed individuals, (ii) a dispersal kernel describing the dissemination of contaminants from polygon sources, and (iii) an (eco)toxicological equation describing the toxicokinetics and dynamics of contaminants in affected individuals. The model was implemented in the briskaR package (b iological risk a ssessment with R ) of the R software. This article presents the model background, the use of the package in an illustrative example, namely, the effect of genetically modified maize pollen on nontarget Lepidoptera, and typical comparisons of landscape configurations that can be carried out with our model (different configurations lead to different mortality rates in the treated example). In real case studies, parameters and parametric functions encountered in the model will have to be precisely specified to obtain realistic measures of risk and impact and accurate comparisons of landscape configurations. Our modeling framework could be applied to study other risks related to agriculture, for instance, pathogen spread in crops or livestock, and could be adapted to cope with other hazards such as toxic emissions from industrial areas having health effects on surrounding populations. Moreover, the R package has the potential to help risk managers in running quantitative risk assessments and testing management strategies.     

Risk‐Cost‐Benefit Analysis for Transportation Corridors with Interval Uncertainties of Heterogeneous Data

Access management, which systematically limits opportunities for egress and ingress of vehicles to highway lanes, is critical to protect trillions of dollars of current investment in transportation. This article addresses allocating resources for access management with incomplete and partially relevant data on crash rates, travel speeds, and other factors. While access management can be effective to avoid crashes, reduce travel times, and increase route capacities, the literature suggests a need for performance metrics to guide investments in resource allocation across large corridor networks and several time horizons. In this article, we describe a quantitative decision model to support an access management program via risk‐cost‐benefit analysis under data uncertainties from diverse sources of data and expertise. The approach quantifies potential benefits, including safety improvement and travel time savings, and costs of access management through functional relationships of input parameters including crash rates, corridor access point densities, and traffic volumes. Parameter uncertainties, which vary across locales and experts, are addressed via numerical interval analyses. This approach is demonstrated at several geographic scales across 7,000 kilometers of highways in a geographic region and several subregions. The demonstration prioritizes route segments that would benefit from risk management, including (i) additional data or elicitation, (ii) right‐of‐way purchases, (iii) restriction or closing of access points, (iv) new alignments, (v) developer proffers, and (vi) etc. The approach ought to be of wide interest to analysts, planners, policymakers, and stakeholders who rely on heterogeneous data and expertise for risk management.     

Managing Wildfire Events: Risk‐Based Decision Making Among a Group of Federal Fire Managers

Managing wildfire events to achieve multiple management objectives involves a high degree of decision complexity and uncertainty, increasing the likelihood that decisions will be informed by experience‐based heuristics triggered by available cues at the time of the decision. The research reported here tests the prevalence of three risk‐based biases among 206 individuals in the USDA Forest Service with authority to choose how to manage a wildfire event (i.e., line officers and incident command personnel). The results indicate that the subjects exhibited loss aversion, choosing the safe option more often when the consequences of the choice were framed as potential gains, but this tendency was less pronounced among those with risk seeking attitudes. The subjects also exhibited discounting, choosing to minimize short‐term over long‐term risk due to a belief that future risk could be controlled, but this tendency was less pronounced among those with more experience. Finally, the subjects, in particular those with more experience, demonstrated a status quo bias, choosing suppression more often when their reported status quo was suppression. The results of this study point to a need to carefully construct the decision process to ensure that the uncertainty and conflicting objectives inherent in wildfire management do not result in the overuse of common heuristics. Individual attitudes toward risk or an agency culture of risk aversion may counterbalance such heuristics, whereas increased experience may lead to overconfident intuitive judgments and a failure to incorporate new and relevant information into the decision.     

Determinants of Seeking and Avoiding Risk‐Related Information in Times of Crisis

This research is designed to provide insight into the psychological (e.g., threat appraisal or coping appraisal) and other determinants (e.g., information quality judgments or demographics) of risk information seeking or avoidance in times of an acute risk, as part of the process of increasing public resilience through adherence to risk mitigating advice. Data were collected via telephone interviews. A specialized agency interviewed 1,000 Dutch citizens, randomly confronted with one of eight fictitious, but realistic, acute risk and emergency situations. Results indicate that information seeking in an acute situation is anticipated by a less elaborate set of predictors (age and risk perception) than information seeking in a nonacute situation (age and risk perception, as well as educational level and social norm). Although risk perception is a predictor for risk information seeking, its predictive value for acute‐risk‐related behavior, as one might have assumed based on theories such as protection motivation theory (PMT) or the extended parallel process model (EPPM), appears to be limited. Implications for risk communication are discussed.     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.     

The Unintended Effects of Risk‐Refuting Information on Anxiety

Researchers in the field of risk perception have been asking why people are more worried about risk today than in years past. This article explores one possible answer to this question, associative anxiety. The affect heuristic and the mental network models suggest that anxiety triggered by information regarding a particular risk can spread to other risks of the same category. Research to date, however, has not examined how information refuting the particular risk can also be generalized across other risks. The article presents two experimental studies addressing this issue. Study 1 showed that when participants were presented with information based on a real train collision, they experienced increased anxiety not only about train collisions but also about public transportation in general. In contrast, those who were informed about the train collision case as well as the preventative measures implemented after the accident experienced decreased anxiety about train collisions but not about public transportation more generally. Study 2 measured the changes in participant anxiety about a genetically modified organism (GMO) and compared the influence of information about either the existence or nonexistence of its risk. Similar to Study 1, associative anxiety rippled through the risk category. The results also suggest that the follow‐up information refuting the GMO risk reduced the anxiety toward the hazard drastically, but did not fully alleviate the anxiety toward other hazards in the category. The implications and the limitations of these studies are also discussed.     

Risk‐Based Decision Support Tools: Protecting Rail‐Centered Transit Corridors from Cascading Effects

We consider the value of decision support tools for passenger rail system managers. First, we call for models that follow events along main rail lines and then into the surrounding environment where they can cascade onto connected light rail, bus, auto, truck, and other transport modes. Second, we suggest that both probabilistic risk assessment (PRA‐based) and agent‐based models have a role to play at different scales of analysis and for different kinds of risks. Third, we argue that economic impact tools need more systematic evaluation. Fourth, we note that developers of decision support tools face a challenge of balancing their desire for theoretical elegance and the tendency to focus only on high consequence events against decisionmakers’ mistrust of complex tools that they and their staff cannot manage and incorporate into their routine operations, as well as the high costs of developing, updating, and applying decision support tools to transport systems undergoing budget cuts and worker and service reductions.