An integrated environmental risk assessment framework for coal-fired power plants: A fuzzy logic approach

This study quantifies the environmental risk of a coal-fired thermal power plant during operation by using environmental monitoring data, site surveys, and documented evidence. The following criteria are assessed: emissions (CO, SO₂, NO_x, PM₁₀), impact on aquatic ecosystem (fish protection at cooling water intake and cooling water discharge temperature), and waste management (fly ash and bottom ash). Fuzzy sets were defined for each criterion, taking environmental regulatory context as an expert judgment. A survey was conducted with multiple stakeholders to determine the relative importance of risk factors. The survey results showed that the most concerned risks are SO₂ and NO_x emissions. The proposed method estimates the risk of each environmental criterion separately and then accumulates them into an environmental risk index (ERI). Accordingly, we assessed the Catalagzi coal-fired power plant, which has been in operation on the Black Sea coast in northwestern Turkey. For this case study, the ERI resulted in a value of 0.78 (on a scale of 0–1), showing high environmental risk to the facility. Moreover, the applicability of the proposed framework was tested in several existing coal-fired power plants using simultaneous measurements. All studied coal-fired power plants in Turkey have unacceptable pollutants (PM₁₀, SO₂, and NO_x) concentration levels indicating high health risk potential. The application of the integrated environmental risk assessment framework showed that new environmental regulations are needed in Turkey to specify more strict emission limits and to monitor CO₂, fine particulate matter emissions, cooling water discharge, and fish protection at cooling water intake.     

Weighing the Risks of Nuclear Energy and Climate Change: Trust in Different Information Sources,Perceived Risks,and Willingness to Pay for Alternatives to Nuclear Power

We examined how individuals perceive nuclear energy in the context of climate change mitigation and how their perceptions are associated with trust in different risk information sources. We analyzed the interrelationships between trust, perceived risk of nuclear power, climate change concern, perception of nuclear energy as an acceptable way to mitigate climate change, and willingness to pay (WTP) for alternatives to nuclear power. A nationwide survey (N = 967) collected in Finland was analyzed with structural equation modeling. The associations between trust and perceived risk of nuclear power, climate change concern, and perception of nuclear power as a way to mitigate climate change varied by the type of information source. Political party support and other background variables were associated with trust in different information sources. The effect of trust in information sources on WTP was mediated by perceived risks and benefits. The results will increase our understanding of how individuals perceive nuclear energy as a way to cut CO₂ emissions and the role of trust in different information sources in shaping nuclear risk perceptions and energy choices.     

Comparison of Risk Predicted by Multiple Norovirus Dose–Response Models and Implications for Quantitative Microbial Risk Assessment

The application of quantitative microbial risk assessments (QMRAs) to understand and mitigate risks associated with norovirus is increasingly common as there is a high frequency of outbreaks worldwide. A key component of QMRA is the dose–response analysis, which is the mathematical characterization of the association between dose and outcome. For Norovirus, multiple dose–response models are available that assume either a disaggregated or an aggregated intake dose. This work reviewed the dose–response models currently used in QMRA, and compared predicted risks from waterborne exposures (recreational and drinking) using all available dose–response models. The results found that the majority of published QMRAs of norovirus use the ₁F₁ hypergeometric dose–response model with α = 0.04, β = 0.055. This dose–response model predicted relatively high risk estimates compared to other dose–response models for doses in the range of 1–1,000 genomic equivalent copies. The difference in predicted risk among dose–response models was largest for small doses, which has implications for drinking water QMRAs where the concentration of norovirus is low. Based on the review, a set of best practices was proposed to encourage the careful consideration and reporting of important assumptions in the selection and use of dose–response models in QMRA of norovirus. Finally, in the absence of one best norovirus dose–response model, multiple models should be used to provide a range of predicted outcomes for probability of infection.     

Projection of Health Benefits from Ambient Ozone Reduction Related to the Use of Methyl tertiary Butyl Ether (MTBE) in the Reformulated Gasoline Program

To estimate potential public health benefits from ozone (O₃) pollution reduction attributable to the use of methyl tertiary-butyl ether (MTBE) in gasoline, O₃ dose-response estimates from the biomedical literature were combined with model estimates of O₃ reduction. Modeling employed EPA MOBILE5a and Complex models to predict emission changes, industry AQIRP techniques to predict ambient O₃ changes, and the National Exposure Model to predict human exposures. Human health effects considered were lung function decrements and respiratory irritant symptoms (using dose-response functions measured in laboratory and field studies), and increased death rates (using concentration-response functions inferred statistically from public-health data). Other reported health effects, such as lung inflammation, increases in asthma attacks, and hospitalizations, were not addressed because of inadequate dose-response information. Even for the health responses considered, quantitation of improvements due to MTBE use is problematical, because MTBE affects only a small percentage of existing O₃ pollution, and because exposure-response relationships are not well understood for population subgroups most likely to be affected. Nevertheless, it is reasonable to conclude that even small MTBE-associated reductions in peak ambient O₃ levels (1–5 ppb, according to model estimates) should yield considerable public health benefits. Tens of millions of Americans are potentially exposed to O₃ in the concentration range associated with health effects. Even if only a small percentage of them are susceptible, any incremental reduction in O₃ (as with MTBE use) must mitigate or prevent effects for a meaningful number of people. Better quantitative estimates of benefit must await a more detailed understanding of each link in the chain of causation.     

Risk ranking of food categories associated with Salmonella enterica contamination in the central region of Mexico

To prevent and control foodborne diseases, there is a fundamental need to identify the foods that are most likely to cause illness. The goal of this study was to rank 25 commonly consumed food products associated with Salmonella enterica contamination in the Central Region of Mexico. A multicriteria decision analysis (MCDA) framework was developed to obtain an S. enterica risk score for each food product based on four criteria: probability of exposure to S. enterica through domestic food consumption (Se); S. enterica growth potential during home storage (Sg); per capita consumption (Pcc); and food attribution of S. enterica outbreak (So). Risk scores were calculated by the equation Se*W₁+Sg*W₂+Pcc*W₃+So*W₄, where each criterion was assigned a normalized value (1–5) and the relative weights (W) were defined by 22 experts’ opinion. Se had the largest effect on the risk score being the criterion with the highest weight (35%; IC95% 20%–60%), followed by So (24%; 5%–50%), Sg (23%; 10%–40%), and Pcc (18%; 10%–35%). The results identified chicken (4.4 ± 0.6), pork (4.2 ± 0.6), and beef (4.2 ± 0.5) as the highest risk foods, followed by seed fruits (3.6 ± 0.5), tropical fruits (3.4 ± 0.4), and dried fruits and nuts (3.4 ± 0.5), while the food products with the lowest risk were yogurt (2.1 ± 0.3), chorizo (2.1 ± 0.4), and cream (2.0 ± 0.3). Approaches with expert-based weighting and equal weighting showed good correlation (R² = 0.96) and did not show significant differences among the ranking order in the top 20 tier. This study can help risk managers select interventions and develop targeted surveillance programs against S. enterica in high-risk food products.     

A Multicriteria Decision Analysis Model and Risk Assessment Framework for Carbon Capture and Storage

Multicriteria decision analysis (MCDA) has been applied to various energy problems to incorporate a variety of qualitative and quantitative criteria, usually spanning environmental, social, engineering, and economic fields. MCDA and associated methods such as life‐cycle assessments and cost‐benefit analysis can also include risk analysis to address uncertainties in criteria estimates. One technology now being assessed to help mitigate climate change is carbon capture and storage (CCS). CCS is a new process that captures CO₂ emissions from fossil‐fueled power plants and injects them into geological reservoirs for storage. It presents a unique challenge to decisionmakers (DMs) due to its technical complexity, range of environmental, social, and economic impacts, variety of stakeholders, and long time spans. The authors have developed a risk assessment model using a MCDA approach for CCS decisions such as selecting between CO₂ storage locations and choosing among different mitigation actions for reducing risks. The model includes uncertainty measures for several factors, utility curve representations of all variables, Monte Carlo simulation, and sensitivity analysis. This article uses a CCS scenario example to demonstrate the development and application of the model based on data derived from published articles and publicly available sources. The model allows high‐level DMs to better understand project risks and the tradeoffs inherent in modern, complex energy decisions.     

Decision analysis and risk models for land development affecting infrastructure systems

Coordination and layering of models to identify risks in complex systems such as large-scale infrastructure of energy, water, and transportation is of current interest across application domains. Such infrastructures are increasingly vulnerable to adjacent commercial and residential land development. Land development can compromise the performance of essential infrastructure systems and increase the costs of maintaining or increasing performance. A risk-informed approach to this topic would be useful to avoid surprise, regret, and the need for costly remedies. This article develops a layering and coordination of models for risk management of land development affecting infrastructure systems. The layers are: system identification, expert elicitation, predictive modeling, comparison of investment alternatives, and implications of current decisions for future options. The modeling layers share a focus on observable factors that most contribute to volatility of land development and land use. The relevant data and expert evidence include current and forecasted growth in population and employment, conservation and preservation rules, land topography and geometries, real estate assessments, market and economic conditions, and other factors. The approach integrates to a decision framework of strategic considerations based on assessing risk, cost, and opportunity in order to prioritize needs and potential remedies that mitigate impacts of land development to the infrastructure systems. The approach is demonstrated for a 5,700-mile multimodal transportation system adjacent to 60,000 tracts of potential land development.     

A Robust Approach for Mitigating Risks in Cyber Supply Chains

In recent years, there have been growing concerns regarding risks in federal information technology (IT) supply chains in the United States that protect cyber infrastructure. A critical need faced by decisionmakers is to prioritize investment in security mitigations to maximally reduce risks in IT supply chains. We extend existing stochastic expected budgeted maximum multiple coverage models that identify “good” solutions on average that may be unacceptable in certain circumstances. We propose three alternative models that consider different robustness methods that hedge against worst‐case risks, including models that maximize the worst‐case coverage, minimize the worst‐case regret, and maximize the average coverage in the $(1?\alpha )$ worst cases (conditional value at risk). We illustrate the solutions to the robust methods with a case study and discuss the insights their solutions provide into mitigation selection compared to an expected‐value maximizer. Our study provides valuable tools and insights for decisionmakers with different risk attitudes to manage cybersecurity risks under uncertainty.     

A Cognitive‐Affective Scale for Hurricane Risk Perception

The aim of this study was to develop a reliable and valid measure of hurricane risk perception. The utility of such a measure lies in the need to understand how people make decisions when facing an evacuation order. This study included participants located within a 15‐mile buffer of the Gulf and southeast Atlantic U.S. coasts. The study was executed as a three‐wave panel with mail surveys in 2010–2012 (T₀ baseline N = 629, 56%; T₁ retention N = 427, 75%; T₂ retention N = 350, 89%). An inventory based on the psychometric model was developed to discriminate cognitive and affective perceptions of hurricane risk, and included open‐ended responses to solicit additional concepts in the T₀ survey. Analysis of the T₀ data modified the inventory and this revised item set was fielded at T₁ and then replicated at T₂. The resulting scales were assessed for validity against existing measures for perception of hurricane risk, dispositional optimism, and locus of control. A measure of evacuation expectation was also examined as a dependent variable, which was significantly predicted by the new measures. The resulting scale was found to be reliable, stable, and largely valid against the comparison measures. Despite limitations involving sample size, bias, and the strength of some reliabilities, it was concluded that the measure has potential to inform approaches to hurricane preparedness efforts and advance planning for evacuation messages, and that the measure has good promise to generalize to other contexts in natural hazards as well as other domains of risk.     

Subsea Release of Oil from a Riser: An Ecological Risk Assessment

This study illustrates a newly developed methodology, as a part of the U.S. EPA ecological risk assessment (ERA) framework, to predict exposure concentrations in a marine environment due to underwater release of oil and gas. It combines the hydrodynamics of underwater blowout, weathering algorithms, and multimedia fate and transport to measure the exposure concentration. Naphthalene and methane are used as surrogate compounds for oil and gas, respectively. Uncertainties are accounted for in multimedia input parameters in the analysis. The 95th percentile of the exposure concentration (EC_95%) is taken as the representative exposure concentration for the risk estimation. A bootstrapping method is utilized to characterize EC_95% and associated uncertainty. The toxicity data of 19 species available in the literature are used to calculate the 5th percentile of the predicted no observed effect concentration (PNEC_5%) by employing the bootstrapping method. The risk is characterized by transforming the risk quotient (RQ), which is the ratio of EC_95% to PNEC_5%, into a cumulative risk distribution. This article describes a probabilistic basis for the ERA, which is essential from risk management and decision‐making viewpoints. Two case studies of underwater oil and gas mixture release, and oil release with no gaseous mixture are used to show the systematic implementation of the methodology, elements of ERA, and the probabilistic method in assessing and characterizing the risk.     

DEFINING THE ROLE AND ESTIMATION OF THE DISCOUNT FACTOR IN A SECURITY VALUATION MODEL

In this paper we show that a striking improvement in the explanatory power of a “dividend type” of security valuation model can be obtained by classifying companies into equivalent risk categories, estimating the discount factor for a category, and then constructing a cross-sectional model for it. The increased homogenity of the data base improves the model's sensitivity to systematic forces, but does not sacrifice the heterogeneity of the independent variables. Assuming that the difference between the intrinsic value of a security and its market value should be zero, the authors demonstrate a method for estimating k_jt, the market discount rate for the j^th risk category in the t^th period. The results of the estimation procedure appear to be reasonable and when used in our security valuation model they produce higher coefficients of determination (R²) than those previously published for similar models.     

Managing Demand Risk in Tactical Supply Chain Planning for a Global Consumer Electronics Company

We consider the problem of managing demand risk in tactical supply chain planning for a particular global consumer electronics company. The company follows a deterministic replenishment‐and‐planning process despite considerable demand uncertainty. As a possible way to formally address uncertainty, we provide two risk measures, “demand‐at‐risk” (DaR) and “inventory‐at‐risk” (IaR) and two linear programming models to help manage demand uncertainty. The first model is deterministic and can be used to allocate the replenishment schedule from the plants among the customers as per the existing process. The other model is stochastic and can be used to determine the “ideal” replenishment request from the plants under demand uncertainty. The gap between the output of the two models as regards requested replenishment and the values of the risk measures can be used by the company to reallocate capacity among different products and to thus manage demand/inventory risk.     

Probabilistic Risk Analysis and Terrorism Risk

Since the terrorist attacks of September 11, 2001, and the subsequent establishment of the U.S. Department of Homeland Security (DHS), considerable efforts have been made to estimate the risks of terrorism and the cost effectiveness of security policies to reduce these risks. DHS, industry, and the academic risk analysis communities have all invested heavily in the development of tools and approaches that can assist decisionmakers in effectively allocating limited resources across the vast array of potential investments that could mitigate risks from terrorism and other threats to the homeland. Decisionmakers demand models, analyses, and decision support that are useful for this task and based on the state of the art. Since terrorism risk analysis is new, no single method is likely to meet this challenge. In this article we explore a number of existing and potential approaches for terrorism risk analysis, focusing particularly on recent discussions regarding the applicability of probabilistic and decision analytic approaches to bioterrorism risks and the Bioterrorism Risk Assessment methodology used by the DHS and criticized by the National Academies and others.     

Maximizing Health Benefits and Minimizing Inequality: Incorporating Local‐Scale Data in the Design and Evaluation of Air Quality Policies

The U.S. Environmental Protection Agency undertook a case study in the Detroit metropolitan area to test the viability of a new multipollutant risk‐based (MP/RB) approach to air quality management, informed by spatially resolved air quality, population, and baseline health data. The case study demonstrated that the MP/RB approach approximately doubled the human health benefits achieved by the traditional approach while increasing cost less than 20%—moving closer to the objective of Executive Order 12866 to maximize net benefits. Less well understood is how the distribution of health benefits from the MP/RB and traditional strategies affect the existing inequalities in air‐pollution‐related risks in Detroit. In this article, we identify Detroit populations that may be both most susceptible to air pollution health impacts (based on local‐scale baseline health data) and most vulnerable to air pollution (based on fine‐scale PM_2.5 air quality modeling and socioeconomic characteristics). Using these susceptible/vulnerable subpopulation profiles, we assess the relative impacts of each control strategy on risk inequality, applying the Atkinson Index (AI) to quantify health risk inequality at baseline and with either risk management approach. We find that the MP/RB approach delivers greater air quality improvements among these subpopulations while also generating substantial benefits among lower‐risk populations. Applying the AI, we confirm that the MP/RB strategy yields less PM_2.5 mortality and asthma hospitalization risk inequality than the traditional approach. We demonstrate the value of this approach to policymakers as they develop cost‐effective air quality management plans that maximize risk reduction while minimizing health inequality.     

Improving the Predictive Accuracy of Hurricane Power Outage Forecasts Using Generalized Additive Models

Electric power is a critical infrastructure service after hurricanes, and rapid restoration of electric power is important in order to minimize losses in the impacted areas. However, rapid restoration of electric power after a hurricane depends on obtaining the necessary resources, primarily repair crews and materials, before the hurricane makes landfall and then appropriately deploying these resources as soon as possible after the hurricane. This, in turn, depends on having sound estimates of both the overall severity of the storm and the relative risk of power outages in different areas. Past studies have developed statistical, regression-based approaches for estimating the number of power outages in advance of an approaching hurricane. However, these approaches have either not been applicable for future events or have had lower predictive accuracy than desired. This article shows that a different type of regression model, a generalized additive model (GAM), can outperform the types of models used previously. This is done by developing and validating a GAM based on power outage data during past hurricanes in the Gulf Coast region and comparing the results from this model to the previously used generalized linear models.     

Risk of Fetal Mortality After Exposure to Listeria monocytogenes Based on Dose-Response Data from Pregnant Guinea Pigs and Primates

One‐third of the annual cases of listeriosis in the United States occur during pregnancy and can lead to miscarriage or stillbirth, premature delivery, or infection of the newborn. Previous risk assessments completed by the Food and Drug Administration/the Food Safety Inspection Service of the U.S. Department of Agriculture/the Centers for Disease Control and Prevention (FDA/USDA/CDC)^{( 1 )} and Food and Agricultural Organization/the World Health Organization (FAO/WHO)^{( 2 )} were based on dose‐response data from mice. Recent animal studies using nonhuman primates^{( 3 , 4 )} and guinea pigs^{( 5 )} have both estimated LD₅₀s of approximately 10⁷ Listeria monocytogenes colony forming units (cfu). The FAO/WHO^{( 2 )} estimated a human LD₅₀ of 1.9 × 10⁶ cfu based on data from a pregnant woman consuming contaminated soft cheese. We reevaluated risk based on dose‐response curves from pregnant rhesus monkeys and guinea pigs. Using standard risk assessment methodology including hazard identification, exposure assessment, hazard characterization, and risk characterization, risk was calculated based on the new dose‐response information. To compare models, we looked at mortality rate per serving at predicted doses ranging from 10^?4 to 10¹² L. monocytogenes cfu. Based on a serving of 10⁶ L. monocytogenes cfu, the primate model predicts a death rate of 5.9 × 10^?1 compared to the FDA/USDA/CDC (fig. IV‐12)^{( 1 )} predicted rate of 1.3 × 10^?7. Based on the guinea pig and primate models, the mortality rate calculated by the FDA/USDA/CDC^{( 1 )} is underestimated for this susceptible population.     

Stochastic Counterfactual Risk Analysis for the Vulnerability Assessment of Cyber‐Physical Attacks on Electricity Distribution Infrastructure Networks

In December 2015, a cyber‐physical attack took place on the Ukrainian electricity distribution network. This is regarded as one of the first cyber‐physical attacks on electricity infrastructure to have led to a substantial power outage and is illustrative of the increasing vulnerability of Critical National Infrastructure to this type of malicious activity. Few data points, coupled with the rapid emergence of cyber phenomena, has held back the development of resilience analytics of cyber‐physical attacks, relative to many other threats. We propose to overcome data limitations by applying stochastic counterfactual risk analysis as part of a new vulnerability assessment framework. The method is developed in the context of the direct and indirect socioeconomic impacts of a Ukrainian‐style cyber‐physical attack taking place on the electricity distribution network serving London and its surrounding regions. A key finding is that if decision‐makers wish to mitigate major population disruptions, then they must invest resources more‐or‐less equally across all substations, to prevent the scaling of a cyber‐physical attack. However, there are some substations associated with higher economic value due to their support of other Critical National Infrastructures assets, which justifies the allocation of additional cyber security investment to reduce the chance of cascading failure. Further cyber‐physical vulnerability research must address the tradeoffs inherent in a system made up of multiple institutions with different strategic risk mitigation objectives and metrics of value, such as governments, infrastructure operators, and commercial consumers of infrastructure services.     

Visualizing the factory through meta- manufacturing processes

Milestone or deadline driven production management is predominant in most manufacturing companies. Problems and other disturbing occurrences tend to see their first daylight during phases where plans are implemented into reality, for example, when production commences, prototypes enter manufacturing and deliveries are expected. Conventional control routines fail to depict the true progress and procedures of the company on an on-line basis, which means that instead of being proactive they serve as means to react to already existing problems. This paper studies the application of advanced visualization techniques to the already existing information embedded in a comany's information infrastructure, and how it can help management to anticipate probable near future pitfalls. By studying those daily operations of a company which share a document relationship with the true manufacturing process, that is, the meta-manufacturing processes, a completely new perspective on the company's value-adding activities is drawn. By mining the existing data reservoirs of a company traditionally difficult management processes, such as product development, vendor integration, production planning, can be analysed and the problems identified in a novel way to react in advance. The paper displays several empirical examples from bulk to one-of-a-kind production where the method has been successfully implemented.     

Comparison of cancer slope factors using different statistical approaches.

The U.S. Environmental Protection Agency's cancer guidelines ( USEPA, 2005 ) present the default approach for the cancer slope factor (denoted here as s*) as the slope of the linear extrapolation to the origin, generally drawn from the 95% lower confidence limit on dose at the lowest prescribed risk level supported by the data. In the past, the cancer slope factor has been calculated as the upper 95% confidence limit on the coefficient (q*₁) of the linear term of the multistage model for the extra cancer risk over background. To what extent do the two approaches differ in practice? We addressed this issue by calculating s* and q*₁ for 102 data sets for 60 carcinogens using the constrained multistage model to fit the dose‐response data. We also examined how frequently the fitted dose‐response curves departed appreciably from linearity at low dose by comparing q₁, the coefficient of the linear term in the multistage polynomial, with a slope factor, s_c, derived from a point of departure based on the maximum liklihood estimate of the dose‐response. Another question we addressed is the extent to which s* exceeded s_c for various levels of extra risk. For the vast majority of chemicals, the prescribed default EPA methodology for the cancer slope factor provides values very similar to that obtained with the traditionally estimated q*₁. At 10% extra risk, q*₁/s* is greater than 0.3 for all except one data set; for 82% of the data sets, q*₁ is within 0.9 to 1.1 of s*. At the 10% response level, the interquartile range of the ratio, s*/s_c, is 1.4 to 2.0.     

Risks of Allergic Contact Dermatitis Elicited by Nickel,Chromium, and Organic Sensitizers: Quantitative Models Based on Clinical Patch Test Data

Risks of allergic contact dermatitis (ACD) from consumer products intended for extended (nonpiercing) dermal contact are regulated by E.U. Directive EN 1811 that limits released Ni to a weekly equivalent dermal load of ≤0.5 μg/cm². Similar approaches for thousands of known organic sensitizers are hampered by inability to quantify respective ACD‐elicitation risk levels. To help address this gap, normalized values of cumulative risk for eliciting a positive (“≥+”) clinical patch test response reported in 12 studies for a total of n = 625 Ni‐sensitized patients were modeled in relation to observed ACD‐eliciting Ni loads, yielding an approximate lognormal (LN) distribution with a geometric mean and standard deviation of GM_Ni = 15 μg/cm² and GSD_Ni = 8.0, respectively. Such data for five sensitizers (including formaldehyde and 2‐hydroxyethyl methacrylate) were also ∼LN distributed, but with a common GSD value equal to GSD_Ni and with heterogeneous sensitizer‐specific GM values each defining a respective ACD‐eliciting potency GM_Ni/GM relative to Ni. Such potencies were also estimated for nine (meth)acrylates by applying this general LN ACD‐elicitation risk model to respective sets of fewer data. ACD‐elicitation risk patterns observed for Cr(VI) (n = 417) and Cr(III) (n = 78) were fit to mixed‐LN models in which ∼30% and ∼40% of the most sensitive responders, respectively, were estimated to exhibit a LN response also governed by GSD_Ni. The observed common LN‐response shape parameter GSD_Ni may reflect a common underlying ACD mechanism and suggests a common interim approach to quantitative ACD‐elicitation risk assessment based on available clinical data.