Probabilistic Risk Assessment Model Research Articles

A Probabilistic Statistical Risk Assessment Method for Soil Erosion Using Remote Sensing Data: A Case Study of the Dali River Basin

Soil erosion risk assessment enables the identification of areas requiring priority treatment and avoids wasting human and material resources. The factor scoring method used in existing studies has high subjectivity, and the method of expressing erosion risk according to the soil erosion intensity ignores the random nature of the occurrence of erosion; therefore, neither method accurately reflects the risk of soil erosion. In order to address this issue, this study proposes a soil erosion risk assessment method that integrates the outcome and the probability of occurrence of soil erosion by means of a probabilistic statistical model. Subsequently, experimental research is conducted in the Dali River Basin. On the basis of long time-series data, using mathematical statistics as a tool and drawing on the empirical frequency formula, the probabilistic statistical risk assessment model is combined with the Modified Universal Soil Loss Equation (RUSLE) model to account for the probability of regional soil erosion at different intensity levels in the long time-series, which is combined with the intensity of erosion to carry out soil erosion risk assessment. The results of our study show the following: (1) The central and southwestern regions of the Dali River Basin (DRB) present medium and high levels of soil erosion risk, with the proportion of low-risk areas increasing annually, accounting for 78.97% of the DRB in 2020, while extremely high-risk areas account for only 0.40% of the DRB. (2) The major components impacting soil erosion risk in the DRB, as revealed by the geodetector, are the normalized difference vegetation index (NDVI) and slope, where the interaction between the two dominated the spatial variation in soil erosion risk. (3) Comparing the soil erosion risk and its status in the coming years, the proposed assessment method based on the occurrence probability can reveal the future soil erosion risk better than the traditional assessment method.

Development of an integrated framework for dissecting source-oriented ecological and health risks of heavy metals in soils

Heavy metals (HMs) in soils pose significant risks on ecosystem and human health. To design targeted regulatory measures for mitigating and controlling the risk, it is necessary to accurately identify the pollution sources and environmental risks of soil HMs, as well as to reveal the linkages between them. To date, yet systematic investigation aimed at deciphering the links between source apportionment of soil HMs and their associated environmental risks is still lacking. To fill the gap, an integrated framework has been developed in this study and applied for dissecting the source-sink relationship and source-oriented ecological and health risks of soil HMs in Shanxi, a province with rich coal resource, in which long-term coal mining activities in history has resulted in soil HMs pollution and unavoidably posed environmental risks. Two advanced receptor models, multivariate curve resolution alternating least squares based on maximum likelihood principal component analysis (MCR-ALS/MLPCA) and multilinear engine 2 (ME2), have been employed for apportioning the potential sources, and their apportionment results are jointly incorporated into a modified ecological risk index and a probabilistic health risk assessment model for identifying the source-oriented ecological and health risks posed by soil metals. The results show that the soils in study area have been polluted by HMs (i.e., Cd, Cr, Hg and As) to varying degrees. Industrial activities (35%–35.8%), agricultural activities (11.1%–20.5%), atmospheric deposition (10.5%–13%) and mix source (31.5%–42.6%) are apportioned as the main contributors of soil HMs in the area. The source-oriented ecological risk assessment suggests Hg has presented significant ecological risk and largely contributed by the sources from atmospheric deposition and industrial activities. The source-oriented health risk assessment shows the non-carcinogenic hazard level and carcinogenic risk posed by soil HMs in the study area are acceptable. Relatively, industrial activities and mix source have contributed more on the health risks.

Quantitative risk assessment of coal mine gas explosion based on a Bayesian network and computational fluid dynamics

A gas explosion is one of the most serious coal mine accidents. The complexity and dynamics of gas explosion accidents, resulting from both multiple risk factors and time-dependent physical parameters, necessitates the use of risk assessment techniques to analyze the accident. A quantitative risk assessment method is proposed for coal mine gas explosions based on a Bayesian Network (BN) and Computational Fluid Dynamics (CFD). The results show that, in addition to the problems of “low vigilance” and “substandard safety education”, the “irrational design of mine gas extraction” has become another major risk factor at the new stage of development of mine excavation technology. Three typical gas explosion areas are selected to analyze the possible consequences of an accident. Based on the probabilistic risk assessment model, time-dependent physical parameters such as accident conditions, location and time are analyzed. The probability of occurrence of time-dependent risk factors is quantified to calculate the risk values of the consequences of explosion accidents corresponding to different times. The proposed method helps to improve the calculation accuracy of updating the risk probability value over time and provides theoretical support for the risk analysis of coal mine gas explosion accidents.

Situational NMAC risk assessment using conditional random field for UAV tiered aerial passage operations

Complex urban unmanned aerial vehicle (UAV) operations necessitate near mid-air collision (NMAC) risk assessment to ensure safety. In this work, a novel situational NMAC risk assessment approach is developed by integrating the conditional random field (CRF) algorithm with safety metrics and conflict resolution performance to facilitate UAV tiered aerial passage operations. Firstly, a concept of tiered aerial passages is designed to provide a practical urban airspace planning scheme that addresses UAV heterogeneity. For UAV tiered aerial passage operations, a situational NMAC risk assessment framework is formulated, enabling the host UAV to evaluate and respond to real-time risks, ensuring avoidance of potential collisions. Specifically, a probabilistic risk assessment model is proposed based on the CRF algorithm, considering UAV safety metrics and resolution performance. Then the NMAC risks in overtaking, approaching head-on, and converging UAV situational encounters are quantified considering advisory, caution, and warning risk alert levels. To resolve risks assessed, acceptable, tolerable, and unacceptable scenarios are proposed to enhance the flexibility and personalization of the resolution decision-making. The proposed method is validated by testing encounter situations and comparative study. The results show that the proposed method outperforms UAV NMAC risk assessment, effectively tailoring different risk requirements for diverse UAV operations.

Assessment of potentially toxic and mineral elements in paddy soils and their uptake by rice (Oryza sativa L.) with associated health hazards in district Malakand, Pakistan

Rice, a primary food source in many countries of the world accumulate potentially harmful elements which pose a significant health hazard to consumers. The current study aimed to evaluate potentially toxic and mineral elements in both paddy soils and rice grains associated with allied health risks in Malakand, Pakistan. Rice plants with intact root soil were randomly collected from paddy fields and analyzed for mineral and potentially toxic elements (PTEs) through inductively coupled plasma optical emission spectrometry (ICP‒OES). Through deterministic and probabilistic risk assessment models, the daily intake of PTEs with allied health risks from consumption of rice were estimated for children and adults. The results of soil pH (< 8.5) and electrical conductivity (EC > 400 μs/cm), indicated slightly saline nature. The mean phosphorus concentration of 291.50 (mg/kg) in soil samples exceeded FAO/WHO permissible limits. The normalized variation matrix of soil pH with respect to Ni (0.05), Ca (0.05), EC (0.08), and Mg (0.09), indicated significant influence of pH on PTEs mobility. In rice grains, the mean concentrations (mg/kg) of Mg (463.81), Al (70.40), As (1.23), Cr (12.53), Cu (36.07), Fe (144.32), Mn (13.89), and Ni (1.60) exceeded FAO/WHO safety limits. The transfer factor >1 for K, Cu, P and Zn indicated bioavailability and transfer of these elements from soil to rice grains. Monte Carlo simulations of hazard index >1 for Cr, Zn, As, and Cu with certainties of 89.93% and 90.17%, indicated significant noncarcinogenic risks for children and adults from rice consumption. The total carcinogenic risk (TCR) for adults and children exceeded the USEPA acceptable limits of 1×10−6 to 1×10−4, respectively. The sensitivity analysis showed that the ingestion rate was a key risk factor. Arsenic (As) primarily influenced total cancer risk (TCR) in children, while chromium (Cr) significantly impacted adults. Deterministic cancer risk values slightly exceeded probabilistic values due to inherent uncertainties in deterministic analysis. Rice consumption poses health risks, mainly from exposure to Cr, Ni and As in the investigated area.

A Bayesian Deep Learning-Based Probabilistic Risk Assessment and Early-Warning Model for Power Systems Considering Meteorological Conditions

The ongoing process of decarbonizing the power system and the frequent occurrence of extreme weather have led to a significant increase in operating uncertainty and greatly reduced the system controllability. An effective risk assessment and early-warning tool will significantly assist system operators in monitoring and controlling power systems. However, existing methods mainly rely on simplified analytical conditional probability models to describe the component failure probability under different operating conditions and are not suitable for low-probability risk assessment. Inspired by the idea of Bayesian statistics, a Bayesian deep learning-based probabilistic risk assessment and early-warning (BDL-PRAEW) model considering meteorological conditions is proposed in this paper. A new Bayesian neural network (BNN) is proposed which efficiently utilizes expert experience and domain knowledge as prior to model the contingency probability. And a hybrid neural network is developed to rationally utilize the multi-source heterogeneous data and comprehensively analyze the historical and forecast information. Finally, a novel risk assessment and early-warning model for high-impact, low-probability (HILP) extreme events is proposed, which can predict the risk for the next n time-steps. The proposed method is tested on a modified IEEE 118-bus system and a real-world provincial grid, and the results verify its effectiveness.

Exhaustive Test Case Generation for Nuclear Safety Software Based on the Software Logic Model

An issue regarding the incorporation of software reliability within the nuclear power plant (NPP) probabilistic risk assessment model has emerged in the licensing processes of digitalized NPPs. Since software failure induces common-cause failure of the processor modules, the reliability of the software used in the NPP safety-critical instrumentation and control systems must be quantified and verified with proper test cases and environments. In this study, a software testing method based on the minimal cut set (MCS)–based exhaustive test case generation scheme is proposed where the software logic model is developed from available information on the software development and the MCSs that represent the necessary and sufficient conditions for the software variables’ states to produce safety software outputs are generated. The MCSs are then converted into the test cases, which can be used as inputs to the test bed to verify that the test cases produce correct outputs after software execution. The effectiveness of the proposed method is demonstrated with the safety-critical trip logic software of the APR-1400 reactor protection system. The method provides a systematic way to conduct exhaustive software testing and prove the functionality of the nuclear safety software based on the test result without uncertainties.

Identifying and quantifying a complete set of full-power initiating events during early design stages of high-temperature gas-cooled reactors

Identifying initiating events is the first step in building a probabilistic risk assessment model. The history of commercial nuclear power plant is primarily light-water reactors, hence, a comprehensive set of possible initiating events that covers most available light-water reactors is identified allowing for a frequentist approach to estimating their associated frequencies. However, other technologies of nuclear power plants do not share the same operating experience, hence, there are no comprehensive lists of initiating events for non-light-water reactors. This paper presents an approach that utilizes top-down deductive methodologies, master logic diagrams, and heat balance fault trees; the bottom-up inductive methodology of failure modes and effects analysis; and legacy and contemporary information to identify a complete list of initiating events for the Xe-100 high-temperature gas-cooled reactor (HTGR). Moreover, the paper presents the approach on how to estimate the frequencies using legacy sources along with deterministic analysis and fault trees. Furthermore, the paper presents an approach to estimate uncertainty parameters associated with each of these initiating events using constrained non-informative distributions to account for lack of operating experience. Finally, the paper presents the list of identified initiating events for the Xe-100 along with their frequencies and uncertainty parameters which serves, along with other contemporary sources, as another building block in having a comprehensive set of initiating events for HTGRs.

Quantitative microbial risk assessment of haemolytic uremic syndrome associated with Argentinean kosher beef consumption in Israel.

The aim of this study was to perform a quantitative microbial risk assessment (QMRA) of Shiga toxin-producing Escherichia coli hemolytic uremic syndrome (STEC-HUS) linked to the consumption of Kosher beef produced in Argentina and consumed in Israel in children under 14 years. A probabilistic risk assessment model was developed to characterize STEC prevalence and contamination levels in the beef supply chain (cattle primary production, cattle transport, processing and storage in the abattoir, for export and at retail, and home preparation and consumption). The model was implemented in Microsoft Excel 2016 with the @Risk add-on package. Results of 302 surveys with data collected in Israel were as follows: 92.3% of people consumed beef, mostly at home, and 98.2% preferred levels of cooking that ensured STEC removal from the surface of beef cuts. The preferred degree of ground beef doneness was "well-done" (48.2%). Cooking preference ranged from red to "medium-well done" (51.8%). Median HUS probability from Argentinean beef cut and ground beef consumption in children under 14 years old was <10-15 and 8.57x10-10, respectively. The expected average annual number of HUS cases and deaths due to beef cut and ground beef consumption was zero. Risk of infection and HUS probability correlated with salting effect on E. coli count, processing raw beef before vegetables, ways of storage and refrigeration temperature at home, joint consumption of salad and beef cuts, degree of beef doneness and cutting board washing with detergent after each use with beef and vegetables. The STEC-HUS risk in Israel from consumption of bovine beef produced in Argentina was negligible. The current QMRA results were similar to those of previous beef cut consumption QMRA in Argentina and lower than any of the QMRA performed worldwide in other STEC-HUS linked to ground beef consumption. This study confirms the importance of QMRA to estimate and manage the risk of STEC-HUS from beef consumption. The impact variables identified in the sensitivity analysis allowed us to optimize resources and time management, to focus on accurate actions and to avoid taking measures that would not have an impact on the risk of STEC-HUS.

Importance measure evaluation based on sensitivity coefficient for probabilistic risk assessment

The failure probability of the target event might not be linear to that of the component in the probabilistic risk assessment model if the component is correlated with another one or it has redundancies. If the failure probability of the component is uncertain in the non-linear model, the failure probability of the target event cannot be accurately obtained only from the expectation value of the component failure probability. Therefore, generally, a lot of Monte Carlo simulations are required for obtaining the importance measure such as FV, RRW, RAW, and BI. Since the calculation cost of the simulation is large in the analysis of complicated systems, the present paper proposes the estimation technique based on the sensitivity coefficient for importance measures. In the proposed method, the sensitivity coefficient is estimated from one Monte Carlo simulation result, thus the calculation cost is much less than the direct Monte Carlo simulations. As the non-linear model, the fault tree model with correlated components and that with the β factor method were analyzed. The results show that the proposed method based on the sensitivity coefficient can accurately estimate the importance measure for these models.

Chronicles of security risk assessment in process industries: Past, present and future perspectives

Security incidents around the world causes enormous loss in terms of life, economy, and environment. The 9/11 ′triggered the serious onset of research initiatives in the field of security with a more specific focus on the protection of high sensitive and hazardous facilities. It is well known that technology is advancing in a very rapid phase, proportionally increasing the security risk of the facilities. Conventional deterministic risk techniques widely found their applications in the earlier stage but owing to the increasing dynamic nature of the security risk, probabilistic dynamic risk assessment models were developed over time. In this article, the authors attempted to present the evolution of security risk concepts, their present status, and their future scope in a precise and consolidated form. The last 20 years of development in the security risk assessment concept involving deterministic and dynamic risk assessment models and applications in security risk assessment relevant to physical security are discussed in this article. The objective of the article is to outline the past, current and future directions of security risk highlighting their strength, weakness and limitations.

Investigating the applicability of human reliability analysis methods during early design stages of non-light-water nuclear power plants

Human interventions are an integral part of any system, whether through design, operation, maintenance, or upgrading. Moreover, although the reliance on human intervention in safety-related actions in advanced reactors (e.g., Generation IV) is expected to be reduced or completely replaced by automated actions, nuclear power plants (NPP) require human actions throughout their lifecycle from design, construction, operation, and decommissioning. Hence, the impacts of all operator actions are required to be captured and incorporated in the probabilistic risk assessment (PRA) of the modeled plant. Furthermore, a risk-informed and performance-based design and licensing approach expects that a PRA model, including human reliability analysis (HRA), is developed starting from the early design stages and used to inform all design iterations. However, due to the lack of details during the early design stages, HRA is often postponed until the design is mature enough. Conducting HRA in the final design stages, though adequate in capturing pre-, at-, and post-initiators, comes short of informing the design itself in the iterative design lifecycle. Moreover, due to most HRA methodologies being developed utilizing mostly the operating experience of existing light water reactors, limited guidance is available to the applicability of different HRA methodologies during the early stages of the design. Limited guidance is available, as well, on how to utilize the results of HRA in informing the design of later iterations. Hence, this study presents an investigation of the applicability of different HRA methodologies during the early stages of the design. The structure of a representative set of nine HRA methodologies is assessed against the available operating and emergency procedures within different design stages. Furthermore, these different HRA methodologies are assessed based on the availability of guidance on how to use their results to risk-inform the design in an iterative design process. Finally, the applicability of Open Preliminary Human Importance (OpenPHI) methodology, introduced specifically to implement HRA during early design stages, is assessed during the early stages of the design and compared against other methodologies.

A combined strategy for dynamic probabilistic risk assessment of fission battery designs using EMRALD and DEPM

The notion of nuclear reactors with battery-like capabilities, called fission batteries, puts forth system requirements and design constraints that have so far been unseen in the nuclear power production industry. Such restrictions require fission batteries to be modular, integrated, autonomous, tamper-proof (i.e., resilient, fault-tolerant, all-weather, and safe), and affordable. With design requirements specifying no human intervention for operation, and minimal connectivity to remote monitoring networks, fission batteries are unique among existing nuclear power plants and emerging advanced reactor designs. Given these attributes, traditional probabilistic risk assessment (PRA) of fission batteries is expected to require dynamic methods to model advanced aspects, such as self-diagnosis, self-adjustment, and duration-prediction capabilities, as they are key ingredients for unattended operations. In addition, availability models need to integrate autonomous control, associated error-detection algorithms, and adversarial human actions. Currently, no existing framework demonstrably assesses these advanced attributes. This paper introduces and demonstrates an integrated framework for the dynamic modeling of fission battery designs. The proposed framework comprises a combined modeling strategy that uses the dual-graph error propagation methodology (DEPM) based on the continuous-time Markov chain (CTMC) models implemented in OpenPRA Error Propagation (OpenErrorPro) and the dynamic PRA tool, Event Modeling Risk Assessment using Linked Diagrams (EMRALD), based on discrete dynamic event trees (DDET). This combination overcomes some of the limitations of the tools when used independently. It enables detailed dynamic analysis to produce time explicit results to support the development of fission battery traditional PRA models. To evaluate the utility of this novel approach, a demonstration case is shown that models the hypothesized response of a fission battery design to an external fire event. DEPM CTMCs and alternative failure approaches are coupled with EMRALD to characterize and quantify the likelihood of the event sequences. The results show that the combined framework effectively captures the dynamic aspects of fission battery design in terms of the timing and realism of modeled events. Given the complexity of the failure scenarios, we believe that EMRALD and DEPM are necessary and complementary when the need for high-resolution analysis offsets the challenges of detailed modeling.

Non-negligible health risks caused by inhalation exposure to aldehydes and ketones during food waste treatments in megacity Shanghai

Aldehydes and ketones in urban air continue to receive regulatory and scientific attention for their environmental prevalence and potential health hazard. However, current knowledge of the health risks and losses caused by these pollutants in food waste (FW) treatment processes is still limited, especially under long-term exposure. Here, we presented the first comprehensive assessment of chronic exposure to 21 aldehydes and ketones in urban FW-air environments (e.g., storage site, mechanical dewatering, and composting) by coupling substantial measured data (383 samples) with Monte Carlo-based probabilistic health risk and impact assessment models. The results showed that acetaldehyde, acetone, 2-butanone and cyclohexanone were consistently the predominant pollutants, although the significant differences in pollution profiles across treatment sites and seasons (Adonis test, P < 0.001). According to the risk assessment results, the estimated cancer risk (CR; mean range: 1.6 × 10−5-1.12 × 10−4) and non-cancer risk (NCR; mean range: 2.98–22.7) triggered by aldehydes and ketones were both unacceptable in most cases (CR: 37.8%–99.3%; NCR: 54.2%–99.8%), and even reached the limit of concern to CR (1 × 10−4) in some exposure scenarios (6.18%–16.9%). Application of DALYs (disability adjusted life years) as a metric for predicting the damage suggested that exposure of workers to aldehydes and ketones over 20 years of working in FW-air environments could result in 0.02–0.14 DALYs per person. Acetaldehyde was the most harmful constituent of all targeted pollutants, which contributed to the vast majority of health risks (>88%) and losses (>90%). This study highlights aldehydes and ketones in FW treatments may be the critical pollutants to pose inhalation risks.

Ecological risk assessment of tire and road wear particles: A preliminary screening for freshwater sources in Canada

Abrasion of tires on road surfaces leads to the formation of tire and road wear particles (TRWPs). Approximately 5.9 million tonnes/year of TRWPs are emitted globally, and 12–20% of emissions generated on roads are transmitted into surface waters, where they can release (i.e., leach) chemical compounds that adversely affect aquatic species. To better understand the ecological risk of TRWPs, an acute, probabilistic ecological risk assessment model was developed and applied. This was a screening-level, conceptual ecological risk assessment (ERA) based on secondary data from published scientific studies. The model was demonstrated using British Columbia (BC) Highway 97 (TRWP source) and Kalamalka Lake (receiving water) in Canada, considering two spatial scenarios with varied highway (HWY) lengths and lake volumes. TRWP-derived chemical leachates considered for ERA were aniline, anthracene (ANT), benzo(a)pyrene (B(a)P), fluoranthene (Fl), mercaptobenzothiazole (MBT), and zinc (Zn). An assumed ‘total TRWP-derived leachate set’ was also assessed, representing all compounds present in tire-derived leachate test solutions. The results indicated the risk to aquatic species in two spatial scenarios. In scenario 1, ecotoxicity risk was high from exposure to TRWP-derived zinc and the total TRWP-derived leachate set. Scenario 2 results indicated acute risk was high from all TRWP-derived chemicals examined, except MBT. This preliminary ecological risk screening provides an early signal that freshwater lakes adjacent to busy highways may be at risk from TRWP contamination, indicating a need for further research. This research is the first ERA of TRWPs in Canada, and the results and methodology provide a foundation for future research and solutions development.

Probabilistic risk assessment of dietary exposure to pentachlorophenol in Guangzhou, China

Pentachlorophenol (PCP) is a ubiquitous environmental contaminant commonly existing as its sodium salt (NaPCP), which enters the human body primarily through long term but low-level dietary exposure. PCP contributes to chemical carcinogenesis and teratogenesis. In this study, the probabilistic risk of dietary exposure to PCP in Guangzhou citizens was investigated. In total, 923 food samples in the categories of pork, livestock (beef and lamb), poultry, offal, eggs, and freshwater fish (considered to be relatively susceptible to PCP contamination) were collected from various markets in Guangzhou and tested for PCP. Probabilistic risk assessment model calculations for PCP dietary exposure and margin of exposure (MOE) values were performed using @RISK software, based on a Monte Carlo simulation with 10,000 iterations. The overall detection rate of PCP (above 1 μg kg−1, the detection limit) was 19.9% (184/923), with an average of 7.9 μg kg−1. The highest rate of PCP detection, 28.2%, was in livestock (beef and lamb). The MOE value for dietary PCP exposure in general Guangzhou residents averaged 400, which was far below 5,000 (the borderline for judging a health risk). The lowest MOE value, 190, was observed in the 3- to-6-year old population and indicates a significant risk. In conclusion, this study suggests that PCP exposure in Guangzhou residents is of considerable health risk, especially for the pre-school young children.

Probabilistic risk assessment model for released tritium into the environment for CFETR

The China Fusion Engineering Test Reactor (CFETR) is the next facility in the Chinese roadmap to realize sustainable-clean fusion energy. Because of the large tritium inventory of the CFETR, safety analyses are necessary. During the current design phase of the CFETR, performing the risk analysis of tritium released into the environment could reveal the design weaknesses and indicate improvements aimed at trtium risk (TR) reduction but also form a part of the licensing process for future CFETR construction and operation. Several CFETR safety analyses using deterministic approaches have been conducted, but relatively few studies based on probabilistic approaches are currently available. In this paper, a probabilistic risk assessment (PRA) model for tritium release into the environment for CFETR was proposed, in which the frequency and the radioactivity of the released tritium in accident sequences are considered. The event tree/fault tree (ET/FT) method was applied to investigate primary factors contributing to the tritium release into the environment and to estimate the frequency of accident sequences. We presented a model for preliminary and rapid assessment of the radioactivity of released tritium in accident sequences and developed a calculation code. The application process and validity of the model were demonstrated through the assessment of TR of the CFETR for selected postulated initiating events.

ESASeedPARAM: A seed treatment model for threatened and endangered bird species in the United States.

The USEPA, National Marine Fisheries Service, and Fish and Wildlife Service are required to assess the risks of pesticides undergoing registration or reregistration to threatened and endangered (i.e., listed) species. Currently, the USEPA lacks a refined model to assess the risks of seed treatments to listed bird species. We developed the Endangered Species Assessment Seed Treatment Probabilistic Avian Risk Assessment Model (ESASeedPARAM) to incorporate species-specific diets, body weights, and food ingestion rates for potentially exposed listed bird species. The model also incorporates information on dissipation of seed residues after planting, and metabolism and elimination by birds during exposure. The ESASeedPARAM estimates hourly intake from ingestion of treated seeds for up to 50 days after planting. For each simulated bird, maximum retained dose (= body burden) and maximum rolling average total daily intake are estimated for acute and chronic exposure, respectively. The model is probabilistic and estimates exposure and risk for 20 birds on each of 1000 fields. The model accounts for interfield variation in the amount of waste grain on the soil surface in tilled, reduced till, and untilled fields. To estimate the fate of each bird from acute exposure, a random value is selected from the appropriate dose-response relationship and compared with the maximum retained dose. If acute exposure exceeds the randomly chosen effects value, mortality is assumed. For chronic risk, the most sensitive No Observed Adverse Effects Level (NOAEL) and Lowest Observed Adverse Effects Level (LOAEL) for an apical endpoint (survival, growth, reproduction) are compared with maximum rolling average total daily intake. In this article, we describe a case study conducted with the ESASeedPARAM for imidacloprid used as a seed treatment on wheat and soybean. Integr Environ Assess Manag 2023;19:527-546. © 2022 SETAC.

Automatic Generation of Event Trees and Fault Trees: A Model-Based Approach

In the past few decades, the increasing complexity of modern engineering systems has been driven by the integration of a large number of components whose operations may involve many disciplines (e.g., thermal hydraulics, plant operations, cybersecurity). Most computational tools used by industry and regulators for system safety and reliability assessments are still based on the traditional fault tree (FT) and event tree (ET) approach, which may not be able to capture complex interactions among system constituents. The use of simulation tools has widely increased in the past few decades to improve the fidelity of the reliability and safety analyses. However, the direct use of simulation tools as part of dynamic probabilistic risk assessment (DPRA) methods is not getting traction since (1) modeling the whole system under consideration with DPRA methods may be computationally expensive and unnecessary, and (2) the manual integration of DPRA models into existing state-of-practice probabilistic risk assessment models (i.e., based on FTs and ETs) can be time consuming and prone to errors. In this paper we propose a procedure to overcome this limitation by presenting several algorithms designed to automatically construct subsystem ETs and FTs from DPRA methods for integration into an existing ET/FT system model.

Editorial: Natural-hazard risk assessment in developing countries

Developing countries are disproportionately affected by natural hazards and lack of coping capacities. 15 This combination sets back progress on poverty alleviation and slows long-term development, 16 contributing the aggravation of the many obstacles already faced by these countries and their 17 communities. Probabilistic risk assessment models are increasingly popular tools for estimating 18 potential human and economic loss due to natural hazards. Within such a context, risk modeling for 19 developing countries presents specific challenges in terms of quantity and quality of the available input 20 data at all components of risk: hazard, exposure, and vulnerability. As such, widening the types of 21 hazards and ensuring that models are contextualized to the local needs of these countries is also crucial 22 and may require innovative or, some times, simplified, yet sound, approaches that tackle the well-23 known deficiencies in this context. Some of the above-mentioned challenges include: (i)