Application Of Probabilistic Risk Assessment Research Articles

Probabilistic Approach for Assessing the Occupational Risk of Olfactometric Examiners: Methodology Description and Application to Real Exposure Scenario

Human examiners, known as panelists, are exposed to an unknown occupational exposure risk while determining odor concentration (Cod) using dynamic olfactometry. In the literature, a few papers, based on a deterministic approach, have been proposed to establish this occupational risk. As a result, the purpose of this study is to develop and apply a probabilistic approach, based on the randomization of exposure parameters, for assessing and evaluating the occupational exposure risk among olfactometric examiners. In this methodology, the risk is assessed by computing the hazard index (HI) and inhalation risk (IR) to determine the non-carcinogenic and carcinogenic risks. To randomize the exposure parameters, a Monte Carlo simulation was described and then applied in real exposure scenario to establish the exposure risk in terms of probability. Therefore, a one-year survey of the working activity of olfactometric examiners of Laboratorio Olfattometrico of Politecnico di Milano university was conducted. Based on this data collection (exposure parameters and chemical data, divided according to sample categories), a randomized exposure scenario was constructed to estimate the probability and cumulative distribution function of risk parameters. Different distributions were obtained for different industrial samples categories and were compared with respect to acceptability criteria (the value of HI and IR at 95th percentile of distribution). The elaboration provided evidence that negligible non-carcinogenic and carcinogenic risks are associated with the panelists’ activity, according to an entire annual dataset. The application of probabilistic risk assessment provides a more comprehensive and effective characterization of the general exposure scenario for olfactometric examiners, surpassing the limitations of a deterministic approach. This method can be extended to future exposure scenarios and enables the selection of the most effective risk management strategies to protect the health of olfactometric examiners.

Probabilistic Validation: Computational Platform and Application to Fire Probabilistic Risk Assessment of Nuclear Power Plants

Abstract Recently, there has been an increasing use of advanced modeling and simulation in the nuclear domain across academia, industry, and regulatory agencies to improve the realism in capturing complex and highly spatiotemporal phenomena within the probabilistic risk assessment (PRA) of existing nuclear power plants (NPPs). Advanced modeling and simulation have also been used to accelerate the risk-informed design, licensing, and operationalization of advanced nuclear reactors. Validation of simulation models traditionally relies on empirical validation approaches which require enough validation data. Such validation data are, however, usually costly to obtain in the contexts of the nuclear industry. To overcome this challenge and to effectively support the use of simulation models in PRA and risk-informed decision-making applications, a systematic and scientifically justifiable validation methodology, namely, the probabilistic validation (PV) methodology, has been developed. This methodology leverages uncertainty analysis to support the validity assessment of the simulation prediction. The theoretical foundation and methodological platform of the PV methodology have been reported in the first paper of this two-part series. The purpose of this second paper is to computationalize the PV methodology, embedded in an integrated PRA framework, and apply it for a hierarchical fire simulation model used in NPP Fire PRA.

A Limited-Scope Probabilistic Risk Assessment Study to Risk-Inform the Design of a Fuel Storage System for Spent Pebble-Filled Dry Casks

This limited-scope study demonstrates the application of probabilistic risk assessment (PRA) methodologies to a spent fuel storage system for spent pebble-filled dry cask with a focus only on the necessary PRA technical elements sufficient to risk-inform the spent fuel storage system design. A dropping canister scenario in a silo of the spent fuel storage system is analyzed through an initiating event (IE) identification from the Master Logic Diagram (MLD); event sequence analysis (ES) by establishing the event tree; data analysis (DA) for event sequence quantification (ESQ) with uncertainty quantification; mechanistic source term (MST) analysis by using ORIGEN; radiological consequence analysis (RC) by deploying MicroShield, and risk integration (RI) by showing the Frequency-Consequence (F-C) target curve in the emergency area boundary (EAB). Additionally, a sensitivity study is conducted using the ordinary least square (OLS) regression method to assess the impact of variables such as failed pebble numbers, their location in the canister, and building wall thickness. Furthermore, the release categories grouped from the end states in the event tree are verified as safety cases through the F-C curve. This study highlights the implementation of PRA elements in a logical and structured manner, using appropriate methodologies and computational tools, thereby showing how to risk-inform the design of a dry cask system for storing spent pebble-filled fuel.

Probabilistic Validation: Theoretical Foundation and Methodological Platform

Abstract Addressing safety concerns in commercial nuclear power plants (NPPs) often requires the use of advanced modeling and simulation (M&S) in association with the probabilistic risk assessment (PRA). Advanced M&S are also needed to accelerate the analysis, design, licensing, and operationalization of advanced nuclear reactors. However, before a simulation model can be used for PRA, its validity must be adequately established. The objective of this research is to develop a systematic and scientifically justifiable validation methodology, namely, probabilistic validation (PV), to facilitate the validity evaluation (especially when validation data are not sufficiently available) of advanced simulation models that are used for PRA in support of risk-informed decision-making and regulation. This paper is the first in a series of two papers related to PV that provides the theoretical foundation and methodological platform. The second paper applies the PV methodological platform for a case study of fire PRA of NPPs. Although the PV methodology is explained in the context of PRA of the nuclear industry, it is grounded on a cross-disciplinary review of literature and so applicable to validation of simulation models, in general, not necessarily associated with PRA or nuclear applications.

Reducing the risk of intentional domino effects in process plants: A risk‐based minimax strategy

AbstractCompared with safety assessment, security risk assessment in chemical and process plants is more challenging. On top of uncertain environmental and operational parameters and interdependent failures, which are common in the safety risk assessment of complex systems and infrastructures, there are other uncertain parameters such as the likelihood of attack scenarios and attackers' expected outcomes. As such, the application of probabilistic risk assessment (PRA) techniques, which have long been applied to safety risk assessment and management, to security risk management may result in nonoptimal or suboptimal decisions. In the present study, we will demonstrate how a combination of PRA and game theory may outperform PRA and lead to a more cost‐effective allocation of security measures. For this purpose, the outcome of a dynamic Bayesian network—as a PRA technique—is used as input to the minimax strategy—as a game theoretic strategy—for security risk management of a tank terminal under attacks with a homemade bomb. The proposed risk‐based minimax strategy alleviates the need for estimation of attack likelihoods or attacker payoffs, which would have otherwise been too challenging to estimate if the analyst solely depended on a PRA technique.

Application of probabilistic risk assessment: Evaluating remedial alternatives at the Portland Harbor Superfund Site, Portland, Oregon, USA.

A probabilistic risk assessment (PRA) was performed to evaluate the range of potential baseline and postremedy health risks to fish consumers at the Portland Harbor Superfund Site (the "Site"). The analysis focused on risks of consuming fish resident to the Site containing polychlorinated biphenyls (PCBs), given that this exposure scenario and contaminant are the primary basis for US Environmental Protection Agency's (USEPA's) selected remedy per the January 2017 Record of Decision (ROD). The PRA used probability distributions fit to the same data sets used in the deterministic baseline human health risk assessment (BHHRA) as well as recent sediment and fish tissue data to evaluate the range and likelihood of current baseline cancer risks and noncancer hazards for anglers. Areas of elevated PCBs in sediment were identified on the basis of a geospatial evaluation of the surface sediment data, and the ranges of risks and hazards associated with pre- and postremedy conditions were calculated. The analysis showed that less active remediation (targeted to areas with the highest concentrations) compared to the remedial alternative selected by USEPA in the ROD can achieve USEPA's interim risk management benchmarks (cancer risk of 10-4 and noncancer hazard index [HI] of 10) immediately postremediation for the vast majority of subsistence anglers that consume smallmouth bass (SMB) fillet tissue. In addition, the same targeted remedy achieves USEPA's long-term benchmarks (10-5 and HI of 1) for the majority of recreational anglers. Additional sediment remediation would result in negligible additional risk reduction due to the influence of background. The PRA approach applied here provides a simple but adaptive framework for analysis of risks and remedial options focused on variability in exposures. It can be updated and refined with new data to evaluate and reduce uncertainty, improve understanding of the Site and target populations, and foster informed remedial decision making. Integr Environ Assess Manag 2018;14:63-78. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

APROBA-Plus: A probabilistic tool to evaluate and express uncertainty in hazard characterization and exposure assessment of substances

To facilitate the application of probabilistic risk assessment, the WHO released the APROBA tool. This tool applies lognormal uncertainty distributions to the different aspects of the hazard characterization, resulting in a probabilistic health-based guidance value. The current paper describes an extension, APROBA-Plus, which combines the output from the probabilistic hazard characterization with the probabilistic exposure to rapidly characterize risk and its uncertainty. The uncertainty in exposure is graphically compared with the uncertainty in the target human dose, i.e. the dose that complies with the specified protection goals. APROBA-Plus is applied to several case studies, resulting in distinct outcomes and illustrating that APROBA-Plus could serve as a standard extension of routine risk assessments. By visualizing the uncertainties, APROBA-Plus provides a more transparent and informative outcome than the more usual deterministic approaches, so that risk managers can make better informed decisions. For example, APROBA-Plus can help in deciding whether risk-reducing measures are warranted or that a refined risk assessment would first be needed. If the latter, the tool can be used to prioritize possible refinements. APROBA-Plus may also be used to rank substances into different risk categories, based on potential health risks without being compromised by different levels of conservatism that may be associated with point estimates of risk.

Application of Probabilistic Risk Assessment in Establishing Perchlorate and Goitrogen Risk Mitigation Strategies

This paper applies probabilistic risk assessment in quantifying risks from cumulative and aggregate risk pathways for selected goitrogens in water and food. Results show that the percentages of individuals with a Hazard Index (HI) value above 1 ranges between 30% and 50% both with and without serum half-life correction when a traditional regulatory assessment approach based on establishment of a No Observed Effects Level (NOEL) is used. When an exposure-response curve is instead used and a threshold of 50% inhibition is assumed, 1.1% or less of the population exceeds an HI value of 1 with no serum half-life correction, rising to as high as 11% when serum half-life correction is applied. If 0% to 5% threshold for iodide uptake inhibition is assumed for production of adverse effects, the percentage of the population with an HI above 1 is 46.2% or less with no serum half-life correction, and 47.2% or less when serum half-life correction is applied. The probabilistic analysis shows that while there are exposed groups for whom perchlorate exposures are the primary cause of individuals having HI values above 1, these constitute significantly less than 1% of the population. Instead, the potential risk from exposure to goitrogens is dominated by nitrates without serum half-life correction and thiocyanates with serum half-life correction, suggesting public health protection is better accomplished by a focus on these and other goitrogens expect in highly limited cases where waterborne perchlorate is at unusually high concentrations.

Grid Inertial Response-Based Probabilistic Determination of Energy Storage System Capacity Under High Solar Penetration

It is well known that responsive battery energy storage systems (BESSs) are effective means to improve the grid inertial response to various disturbances including the variability of the renewable generation. One of the major issues associated with its implementation is the difficulty in determining the required BESS capacity mainly due to the large amount of inherent uncertainties that cannot be accounted for deterministically. In this study, a probabilistic approach is proposed to properly size the BESS from the perspective of the system inertial response, as an application of probabilistic risk assessment (PRA). The proposed approach enables a risk-informed decision-making process regarding 1)the acceptable level of solar penetration in a given system and 2)the desired BESS capacity (and minimum cost) to achieve an acceptable grid inertial response with a certain confidence level.

Application of probabilistic risk assessment at a coking plant site contaminated by polycyclic aromatic hydrocarbons

Application of Probabilistic Risk Assessment (PRA) and Deterministic Risk Assessment (DRA) at a coking plant site was compared. By DRA, Hazard Quotient (HQ) following exposure to Naphthalene (Nap) and Incremental Life Cancer Risk (ILCR) following exposure to Benzo(a)pyrene (Bap) were 1.87 and 2.12 × 10−4. PRA revealed valuable information regarding the possible distribution of risk, and risk estimates of DRA located at the 99.59th and 99.76th percentiles in the risk outputs of PRA, which indicated that DRA overestimated the risk. Cleanup levels corresponding acceptable HQ level of 1 and ILCR level of 10−6 were also calculated for both DRA and PRA. Nap and Bap cleanup levels were 192.85 and 0.14 mg·kg−1 by DRA, which would result in only 0.25% and 0.06% of the exposed population to have a risk higher than the acceptable risk, according to the outputs of PRA. The application of PRA on cleanup levels derivation would lift the cleanup levels 1.9 times for Nap and 2.4 times for Bap than which derived by DRA. For this coking plant site, the remediation scale and cost will be reduced in a large portion once the method of PRA is used. Sensitivity analysis was done by calculating the contribution to variance for each exposure parameter and it was found that contaminant concentration in the soil (Cs), exposure duration (ED), total hours spent outdoor per day (ETout), soil ingestion rate (IRs), the air breathing rate (IRa) and bodyweight (BW) were the most important parameters for risk and cleanup levels calculations.

Parsimonious modeling of hysteretic structural response in earthquake engineering: Calibration/validation and implementation in probabilistic risk assessment

Under strong seismic excitations structural systems exhibit inelastic, hysteretic behavior. Their response can be accurately predicted through high-fidelity numerical models, an approach that typically involves a significant computational cost. An alternative simulation framework is discussed here, based on parsimonious modeling of the hysteretic behavior, established by describing globally the restoring force–drift relationship for each floor. Three different candidate models are considered in this study for the latter, ultimately corresponding to different types of hysteretic behavior, and their implementation using the versatile SIMULINK modeling environment in MATLAB is presented. The parameters of these models may be chosen to establish specific modal properties and yield displacement, ultimate strength and deterioration characteristics for each floor. The fundamental research question addressed is how one can select the model (along with its model-parameters) that will best describe the behavior of a given structural system, focusing on probabilistic risk assessment applications. For this purpose a comprehensive calibration stage for the parsimonious modeling is introduced using information obtained by the response of a high-fidelity structural model under simple excitation conditions (cyclic pushover analysis and sinusoidal loading). This stage determines the characteristics of each parsimonious model but also allows selection of the one providing overall the best fit. A case study for a three-storey concrete structure is presented, for which the accuracy and the computational savings of the parsimonious modeling is examined in two steps. The first step focuses on a detailed comparison over an ensemble of 25 ground motion records. The second step focuses on comparison of statistical measures (failure probabilities, mean response and fragility) within a probabilistic earthquake engineering setting, using a stochastic ground motion model to describe the seismic hazard. The results show that the calibration stage provides good guidance for selection of the most appropriate (in terms of accuracy) parsimonious model, whereas great computational savings are established through the proposed modeling approach, at the expense of a small only reduction in accuracy.

Comparing Deterministic and Probabilistic Risk Assessment at a Coking Plant Site in China

Application of probabilistic risk assessment (PRA) and deterministic risk assessment (DRA) at a coking plant site were compared. By DRA, Hazard Quotient (HQ) following exposure to Naphthalene (Nap) and Incremental Life Cancer Risk (ILCR) following exposure to Benzo(a)pyrene (Bap) are 1.87 and 2.12×10-4. PRA reveals valuable information regarding the possible distribution of risk, and the outputs show that Nap HQ ranges from 6.18×10-7to 6.62 and Bap ILCR ranges from 8.67×10-10to 6.89×10-4. Risk estimates of DRA locates at the 99.59th and 99.76th percentiles in the risk outputs of PRA, indicating the DRA overestimates the risk. Sensitivity analysis was done by calculating a contribution to variance for each exposure parameter and it is found that contaminant concentration in the soil (Cs), exposure duration (ED), soil ingestion rate (IRs) are the most important parameters for risk calculations

Risk‐Informed Regulation and Safety Management of Nuclear Power Plants—On the Prevention of Severe Accidents

There are four operating nuclear power plant (NPP) units in Finland. The Teollisuuden Voima (TVO) power company has two 840 MWe BWR units supplied by Asea-Atom at the Olkiluoto site. The Fortum corporation (formerly IVO) has two 500 MWe VVER 440/213 units at the Loviisa site. In addition, a 1600 MWe European Pressurized Water Reactor supplied by AREVA NP (formerly the Framatome ANP--Siemens AG Consortium) is under construction at the Olkiluoto site. Recently, the Finnish Parliament ratified the government Decision in Principle that the utilities' applications to build two new NPP units are in line with the total good of the society. The Finnish utilities, Fenno power company, and TVO company are in progress of qualifying the type of the new nuclear builds. In Finland, risk-informed applications are formally integrated in the regulatory process of NPPs that are already in the early design phase and these are to run through the construction and operation phases all through the entire plant service time. A plant-specific full-scope probabilistic risk assessment (PRA) is required for each NPP. PRAs shall cover internal events, area events (fires, floods), and external events such as harsh weather conditions and seismic events in all operating modes. Special attention is devoted to the use of various risk-informed PRA applications in the licensing of Olkiluoto 3 NPP.

How Probabilistic Risk Assessment Can Mislead Terrorism Risk Analysts

Traditional probabilistic risk assessment (PRA), of the type originally developed for engineered systems, is still proposed for terrorism risk analysis. We show that such PRA applications are unjustified in general. The capacity of terrorists to seek and use information and to actively research different attack options before deciding what to do raises unique features of terrorism risk assessment that are not adequately addressed by conventional PRA for natural and engineered systems-in part because decisions based on such PRA estimates do not adequately hedge against the different probabilities that attackers may eventually act upon. These probabilities may differ from the defender's (even if the defender's experts are thoroughly trained, well calibrated, unbiased probability assessors) because they may be conditioned on different information. We illustrate the fundamental differences between PRA and terrorism risk analysis, and suggest use of robust decision analysis for risk management when attackers may know more about some attack options than we do.

Concentrations of undeclared allergens in food products can reach levels that are relevant for public health

Food products can become contaminated with food allergens due to cross-contact. Precautionary ‘may contain’ labelling may alert to the possible presence of an allergen, but guidance for such labelling is lacking. As a result, allergy information on the packaging may not be reliable and allergic consumers might be at risk of allergic reactions after consuming unlabelled, but indeed contaminated, products. Recently, a cow's milk protein allergic patient experienced a severe allergic reaction to a dark chocolate product containing undeclared milk proteins. This case induced the authors to investigate to what extent allergen concentrations of unlabelled products reach levels that are of public health relevance. The concentrations of milk proteins in the complaint sample and a collection of products of other batches and brands purchased from different stores were analysed. Together with appropriate threshold and food consumption data, the risks of allergic reactions and the severity of these reactions within the adult milk-allergic population were determined using probabilistic risk assessment techniques. The results show that milk protein concentrations in unlabelled products reach levels that may elicit allergic reactions in up to 68% of the adult allergic consumers. Therefore, concentrations of allergens in unlabelled products could reach levels that are of public health relevance. Application of probabilistic risk assessment can be an aid in revealing the public health consequences of undeclared allergens in food, in risk management decision-making and developing guidance in terms of risk-based concentration limits for precautionary labelling.

On the nature and practical handling of the Bayesian aggregation anomaly

This paper addresses the issue, arising in probabilistic parameter estimation, which is sometimes referred to as the “Bayesian anomaly,” or also as the problem of “imperfect aggregation in Bayesian estimation.” The issue is clarified here from a generalized and theoretical point of view, but also in terms of a practical and mathematically consistent solution for probabilistic parameter estimation cases arising in actual reliability analysis and probabilistic risk assessment applications. More specifically, both theoretical and practical technical arguments are presented to show the reasons that make the purported anomaly manifest itself, and how the situations where it could in theory lead to serious estimation errors can be correctly handled. Interpretations and conclusions previously drawn with respect to the anomaly are also examined and re-evaluated in light of the new information and developments presented by the paper.

Development of a dynamical systems model of plant programmatic performance on nuclear power plant safety risk

Abstract Application of probabilistic risk assessment (PRA) techniques to model nuclear power plant accident sequences has provided a significant contribution to understanding the potential initiating events, equipment failures and operator errors that can lead to core damage accidents. Application of the lessons learned from these analyses has resulted in significant improvements in plant operation and safety. However, this approach has not been nearly as successful in addressing the impact of plant processes and management effectiveness on the risks of plant operation. The research described in this paper presents an alternative approach to addressing this issue. In this paper we propose a dynamical systems model that describes the interaction of important plant processes on nuclear safety risk. We discuss development of the mathematical model including the identification and interpretation of significant inter-process interactions. Next, we review the techniques applicable to analysis of nonlinear dynamical systems that are utilized in the characterization of the model. This is followed by a preliminary analysis of the model that demonstrates that its dynamical evolution displays features that have been observed at commercially operating plants. From this analysis, several significant insights are presented with respect to the effective control of nuclear safety risk. As an important example, analysis of the model dynamics indicates that significant benefits in effectively managing risk are obtained by integrating the plant operation and work management processes such that decisions are made utilizing a multidisciplinary and collaborative approach. We note that although the model was developed specifically to be applicable to nuclear power plants, many of the insights and conclusions obtained are likely applicable to other process industries.

Some lessons learnt from the use of PRA during the design phase

The development of the pebble bed module reactor (PBMR) as a gas cooled thermal reactor type includes the use of probabilistic risk assessment (PRA) during the design phases. This paper presents some of the basic issues and results of the application of PRA in the design process and as part of the licensing process.

Dairy Manure Management: An Application of Probabilistic Risk Assessment

AbstractContemporary agricultural enterprises are having difficulty dealing with changing public perceptions about the environmental consequences of current management practices. This study was initiated to investigate the role Probabilistic Risk Assessment (PRA) could play in managing risks and communicating risk information to regulators and the public. The utility of PRA for dairy manure and wastewater management systems was evaluated on three Texas dairies beginning in spring 1992. Information collected from dairymen, along with computerized risk analysis, was used to develop risk estimates for various environmental hazards associated with the dairies. Qualitative and quantitative analysis of individual probability estimates allowed evaluation of the risks posed by various potential nutrient loss pathways. Integration of these probability estimates with simple cost‐effectiveness evaluations provided a risk/cost‐effectiveness value used in comparing alternative waste management technologies. Results indicate PRA, wedded with cost‐effectiveness analysis, may provide a new and effective way for the agricultural community to evaluate, manage, and communicate risk information.

Probabilistic-risk-assessment applications in the nuclear-power industry

There has been a remarkable growth in the use of probabilistic risk assessment (PRA) methods in the US nuclear industry, both commercial and government. Use of PRA techniques has been expanded appreciably in the 1980s and 1990s as a result of the Individual Plant Examination (IPE) process. The IPE program calls for all existing nuclear utilities to use PRA methods to seek out plant vulnerabilities, and for all new applications for commercial nuclear power licenses to include full-scope PRA for design certification under 10CFR part 52. Effort must be spent to improve the consistency of PRA models, applications, evaluation approaches and processes among the nuclear utility industry. Standards are needed to guarantee proper application of PRA methods in risk-based regulation (RBR) in order to establish and maintain consistency in PRA applications. One standard application method that fits every aspect of RBR is difficult if not impossible. However, some of the PRA tasks or processes can be effectively standardized, e.g., level of detail, bounding limits on the failure rates of human recovery actions, and ways and means of data analysis. This paper discusses some historical background about the application of the PRA and identifies: appropriate efforts that the industry can spend to standardize the PRA applications in RBR; improvements in PRA techniques to achieve standardization; the benefits utilities would gain as a result of this harmony; and some of the limitations in areas which stubbornly resists standardization.