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Most popular vegetables and fruits and their corresponding soil from the sub-urban industrial area of Bangladesh were collected and the concentration of carcinogenic (Pb, As, and Cd) and non-carcinogenic (Fe, Co, V, Cu, Cr, Zn, Mn, and Ni) heavy metals was determined. Health risk was evaluated by estimating daily heavy metal intake and computing cancer and non-cancer risks (ILCR and THQ) using probabilistic risk assessment model of US-EPA. Heavy metals in vegetables varied with vegetable species as well as metal types. Higher daily intake of As, Fe, Mn, and Pb was observed from the consumption of root and leafy vegetables. Moreover, the probability of an adult for developing cancer from the consumption of studied vegetables was greater than US-EPA threshold risk limit (>10−4) for As and Cd. In addition, cumulative cancer risk (∑ILCR) of all the studied vegetables and fruits exceeded the limit for fruit, root, leafy vegetables, and fruits (22, 15, 59, and 4%) with As, Cd, and Pb as 17, 81, and 2%, respectively. Non-cancer risk index also presented Pb, As, Mn, and Fe as the dominant contaminants of root and leafy vegetables that contributed 80–90% of HI. It suggests that the study area is unsuitable for growing leafy and root vegetables due to the risk of higher intakes of heavy metals which affect food safety. Mn, Pb, Fe, and As are the most predominant heavy metals posing non-cancer risk while Cd caused the highest cancer risk.

A probabilistic risk assessment (PRA) is commonly accepted as a tool for performance based design in fire safety engineering, but the position of PRA in the design process, the relationship between different acceptance concepts (absolute, comparative, ALARP), and the responsibilities of the designer remain unclear. Aiming to clarify these aspects, the safety foundation of fire safety solutions is investigated, indicating that PRA is necessary for demonstrating adequate safety when no appeal can be made to the collective experience of the profession. It is suggested that PRA is not a methodology for ‘future fire safety engineering’, but rather a necessary methodology to provide an objective safety foundation for uncommon fire safety designs. Acknowledging that what constitutes ‘acceptable safety’ is subjective and may change over time, an objective proxy of ‘adequate safety’ is defined and proposed as a benchmark against which to assess the adequacy of fire safety designs. In order to clarify the PRA process, a hierarchy of different acceptance concepts is presented. Finally, it is shown how, depending on the applied acceptance concepts, the designer takes responsibility for different implicit assumptions regarding the safety performance of the final design.

In probabilistic risk assessment (PRA), two main methods exist for quantifying fault trees: theoretical and empirical (sampling). The efficiency of PRA quantification varies depending on the sampling method used. This study evaluated the feasibility of using quasi-Monte Carlo simulation (Quasi-MCS) and progressive Latin hypercube sampling (P-LHS) for PRA quantification. Eight risk outcomes were derived through PRA for internal and external events in four cases. The PRA convergence, variability, and error rates of each sampling method were compared and analyzed. The comparison analysis revealed that all sampling methods had an error rate of approximately 2% with 9,000 total samples. P-LHS exhibited the best convergence and variability among the methods, followed by Quasi-MCS and LHS. Although Quasi-MCS showed more significant variability than LHS as the number of events increased, its error rate remained within 2% with 9,000 samples. Therefore, both P-LHS and Quasi-MCS are feasible for PRA quantification.

While traditional risk importance measures in probabilistic risk assessment are effective for ranking safety-significant components, they often overlook critical aspects such as the timing of accident progression and consequences. Dynamic probabilistic risk assessment offers a framework to quantify such risk information, but standardized approaches for estimating risk importance measures remain underdeveloped. This study addresses this gap by: (1) reviewing traditional risk importance measures and their regulatory applications, highlighting their limitations, and introducing newly proposed risk-triplet-based risk importance measures, consisting of timing-based worth, frequency-based worth, and consequence-based worth; (2) conducting a case study of Level 2 dynamic probabilistic risk assessment using the Japan Atomic Energy Agency’s RAPID tool coupled with the severe accident code of MELCOR 2.2 to simulate a station blackout scenario in a boiling water reactor, generating probabilistically sampled sequences with quantified timing, frequency, and consequence of source term release; (3) demonstrating that the new risk importance measures provide differentiated insights into risk significance, enabling multidimensional prioritization of systems and mitigation strategies; for example, the timing-based worth quantifies the delay effect of mitigation systems, and the consequence-based worth evaluates consequence-mitigating potential. This study underscores the potential of dynamic probabilistic risk assessment and risk-triplet-based risk importance measures to support risk-informed and performance-based regulatory decision-making, particularly in contexts where the timing and severity of accident consequences are critical.

This paper presents a modular framework designed for dynamic probabilistic risk assessment of electric grid systems facing cybersecurity threats. It details various modules, such as the protection systems module, the operator module, and the attacker module, developed to simulate the responses of different stakeholders during cybersecurity incidents. The paper outlines the requirements necessary for conducting dynamic probabilistic risk assessment under such threats, describes the design and implementation of these modules, and elaborates on the simulation algorithms used. The integration of these modules with mature power grid system simulators enables the framework to effectively replicate the spread and impact of diverse cyberattacks targeting electric grid systems. Additionally, the flexibility of the framework allows for easy reconfiguration and adaptation of module connections to examine different system topologies and configurations. The functionality and efficacy of the framework have been demonstrated using an IEEE 14‐bus system in a case study. This paper presents a modular framework designed for dynamic probabilistic risk assessment of electric grid systems facing cybersecurity threats. The functionality and efficacy of the framework have been demonstrated using an IEEE 14‐bus system in a case study.

Simulation modeling is crucial in support of probabilistic risk assessment (PRA) for nuclear power plants (NPPs). There is a challenge, however, associated with simulation modeling that relates to the time and resources required for collecting data to determine the values of the input parameters. To alleviate this challenge, this article develops a formalized methodology to generate surrogate values of input parameters grounded on the decomposition of phenomenological nondimensional parameters (PNPs) while avoiding detailed data collection. While the fundamental principles of the proposed methodology can be applicable to various hazards, the developments in this article focus on fire PRA as an example application area for which resource intensiveness is recognized as a practical challenge. This article also develops a computational platform to automate the PNP decomposition and seamlessly integrates it with state-of-practice fire scenario analysis. The applicability of the computational platform is demonstrated through a multi-compartment fire case study at an NPP. The computational platform, with its embedded PNP decomposition methodology, can substantially reduce the effort required for input data collection and extraction, thereby facilitating the efficient use of simulation modeling in PRA and enhancing the fire scenario screening analysis.

Human Health Risk Assessment (HHRA) is a widely applied method to make decisions about the environmental status of sites affected by toxic substances. Its conclusions are affected by the variability and uncertainty of the input variables in the HHRA model. The aim of this work is to apply an algorithm based on 2D Monte Carlo simulations to integrate the variability and uncertainty of exposure factors, concentration, and bioaccessibility, reported by various information sources, to assess and compare their influence on the risk outcome. The method is applied to a specific case study of exposure of children to arsenic from accidental soil ingestion in a residential setting in the city of Madrid (Spain) by combining information from 12 studies. The consideration of the variability and uncertainty of the exposure parameters in the Baseline Risk Assessment (BRA, deterministic) resulted in a greater reduction in the numerical value of risk estimations than that produced by considering only the bioaccessibility factor. The results of the Probabilistic Risk Assessment (PRA) showed that the risk distribution was more sensitive to the variabilities of the accidental soil intake rate and the total arsenic concentration than to other variables such as bioaccessibility. In this case study, the uncertainty introduced by using the \"default\" reasonable maximum exposure factors in the HHRA model and the variability of the concentration term produce overestimates of risk that are at least in the range of those produced by omitting the bioaccessibility term. Thus, the inclusion of bioaccessibility is, alone, insufficient to improve the HHRA since the selection of the exposure factors can significantly affect the estimates of risk for the soil ingestion pathway. In other sites or for other contaminants, however, the role of the uncertainties associated with the bioaccesible fraction could be more pronounced. The method applied in this work may be useful in updating exposure factors to reduce uncertainties in HHRAs.

Numerical integration methods have the characteristics of high efficiency and precision, making them attractive for aero-engine probabilistic risk assessment and design optimization of an inspection plan. One factor that makes the numerical integration method a suitable approach to in-service inspection uncertainties is the explicit derivation of the integration formula and integration domains. This explicit derivation ensures accurate characterization of a multivariable system’s failure risk evolution mechanism. This study develops an efficient numerical integration algorithm for probabilistic risk assessment considering in-service inspection uncertainties. The principle of probability conservation is applied to the transformation of the integration domain from the current flight cycle to the initial (N = 0) computational space. Consequently, the integration formula of failure probability is deduced, and a detailed mathematical demonstration of the proposed method is provided. An actual compressor disk is evaluated using the efficient numerical integration algorithm and the Monte Carlo simulation to validate the accuracy and efficiency of the proposed method. Results show that the time cost of the proposed algorithm is dozens of times lower than that of the Monte Carlo simulation, with a maximum relative error of 5%. Thus, the efficient numerical integration algorithm can be applied to failure analysis in the airworthiness design of commercial aero-engine components.

A probabilistic risk assessment (PRA) using two-dimensional Monte Carlo analysis was followed to inspect the health risk associated with consumption of groundwater contaminated with nitrate in 26 wells located in rural areas of Malayer, Iran. In this technique, probability distributions were assigned to the concentration of nitrate in groundwater, daily intake rate of water, frequency of exposure together with total duration of exposure and the risk levels were worked out for adults and children, accordingly. In addition, four scenarios were investigated with an emphasis on the effect of correlation between exposure frequency and ingestion rate of water on estimated risk values. It was indicated that inclusion of correlation between parameters would influence the upper median quartile values of estimated risk however the total impact on the results of health risk assessment is not significant. Moreover, considering the 3rd quartile value of risk level for the total size of the study area, the risk level related to children was higher than that of the adults with respective values of 0.621 and 0.989. The spatial variation of nitrate was considered using ordinary kriging and a novel combined method of random forest and spatial proximity as covariate. It was concluded that the predictions made by ordinary kriging were more accurate than the random forest technique which can be attributed to the small data sets used in this research.

This paper conducts a focused probabilistic risk assessment (PRA) on the reliability of commercial off-the-shelf (COTS) drones deployed for surveillance in areas with diverse radiation levels following a nuclear accident. The study employs the event tree/fault tree digraph approach, integrated with the dual-graph error propagation method (DEPM), to model sequences that could lead to loss of mission (LOM) scenarios due to combined hardware–software failures in the drone’s navigation system. The impact of radiation is simulated by a comparison of the total ionizing dose (TID) with the acceptable limit for each component. Errors are then propagated within the electronic hardware and software blocks to determine the navigation system’s reliability in different radiation zones. If the system is deemed unreliable, a strategy is suggested to identify the minimum radiation-hardening requirement for its subcomponents by reverse-engineering from the desired mission success criteria. The findings of this study can aid in the integration of COTS components into radiation-hardened (RAD-HARD) designs, optimizing the balance between cost, performance, and reliability in drone systems for nuclear-contaminated search and rescue missions.