Risk Assessment Methodology Research Articles

Low-impact, near real-time risk assessment for legacy IT infrastructures

In an era where cybersecurity threats are evolving at an unprecedented pace, this paper introduces a methodology for near real-time risk assessment of high-profile, high security infrastructures, where data security and operational continuity inherently limits observability. Our approach addresses the challenges of this limited observability and minimized disruption, offering a new perspective on processing and evaluating cybersecurity knowledge. We present an innovative method that leverages attack graphs and attacker behavior analysis to assess risks and vulnerabilities. Our research includes the development of an automated risk assessment mechanism, graphical security modeling, and a Markov chain-based model for attacker behavior. Our methodology utilizes a blend of direct and indirect event sources, incorporating an attacker behavioral model based on a random walk method akin to Google’s PageRank. The proof-of-concept solution calculates potential risk according to the actual threat landscape, providing a more accurate and timely assessment.

Environmental risk assessment methodology for urban tropical lagoons based on feasible lines of evidence under limited resources conditions: Jacarepaguá Lagoon/Brazil.

Determining environmental risk levels posed to different urban lagoon can provide an important overview regarding the relative severity of the environmental degradation of these ecosystems, increasing the risks visibility, which can be used as an important decision-making tool to prioritize investments. Jacarepaguá Lagoon (JPAL) is part of a coastal lagoon system comprising four interconnected lagoons in Rio de Janeiro city, Southeastern Brazil. Real estate speculation and insufficient sanitation infrastructure resulted in untreated sewage discharge into this ecologically sensitive lagoon system. An Environmental Risk Assessment for Jacarepaguá lagoon was carried out integrating three Lines of Evidence (LoE): (i) Water Quality LoE; (ii) Ecotoxicological LoE; (iii) Ecological LoE to estimate an Environmental Risk Index (EnvRI) for this area. Surface water samples were collected during four bimonthly campaigns at five JPAL sampling points as well as an upstream reference area. The Water Quality LoE based on physicochemical parameters was used to estimate the Water Quality Risk Index (WQRI); the Ecotoxicological LoE based on two chronic ecotoxicity bioassays was used to estimate the Ecotoxicological Risk Index (EcotoxRI); and the Ecological LoE based on the richness and the abundance of phytoplankton taxa was used to estimate the Ecological Risk Index (EcoRI). The final EnvRI was then estimated by integrating these three Risk Indices. The WQRI (0.79 ± 0.07), as well as the EcotoxRI (0.80 ± 0.21), and the EcoRI (0.78 ± 0.13), were all in the range classified as very high. The EnvRI for JPAL was consequently also very high (0.81 ± 0.12), which indicates urgent intervention. EnvRI combined with high concentrations of organic compounds directly affected the diversity of phytoplankton species; however, it was observed that the high content of phytoplankton biomass also represents a part of the organic matter in question. The final goal was achieved: a feasible risk assessment tool available for comparison of different aquatic ecosystems, to facilitate decision making establishing priorities of investments under a limited resources scenario.

Integrated Risk Framework (IRF)—Interconnection of the Ishikawa Diagram with the Enhanced HACCP System in Risk Assessment for the Sustainable Food Industry

This paper presents a new risk assessment methodology called the Integrated Risk Framework (IRF) through the application of Ishikawa diagrams combined with the enhanced Hazard Analysis and Critical Control Point (HACCP) system. This risk investigation technique aims to ensure a significantly higher level of quality, safety, and sustainability in food products by using improved classical methods with strong intercorrelation capabilities. The methodology proposes expanding the typology of basic physical, chemical, and biological risks outlined by the ISO 22000 Food Safety Management System standard, adding other auxiliary risks such as allergens, fraud/sabotage, Kosher/Halal compliance, Rapid Alert System for Food and Feed notification, or additional specific risks such as irradiation, radioactivity, genetically modified organisms, polycyclic aromatic hydrocarbons, African swine fever, peste of small ruminants, etc. depending on the specific technological process or ingredients. Simultaneously, it identifies causes for each operation in the technological flow based on the 5M diagram: Man, Method, Material, Machine, and Environment. For each identified risk and cause, its impact was determined according to its severity and likelihood of occurrence. The final effect is defined as the risk class, calculated as the arithmetic mean of the impact derived at each process stage based on the identified risks and causes. Within the study, the methodology was applied to the spring water bottling process. This provided a new perspective on analyzing the risk factors during the bottling operations by concurrently using Ishikawa diagrams and HACCP principles throughout the product’s technological flow. The results of the study can form new methodologies aimed at enhancing sustainable food safety management strategy. In risk assessment using these two tools, the possibility of cumulative or synergistic effects is considered, resulting in better control of all factors that may affect the manufacturing process. This new perspective on studying the dynamics of risk factor analysis through the simultaneous use of the fishbone diagram and the classical HACCP system can be extrapolated and applied to any manufacturing process in the food industry and beyond.

Functional safety-oriented hazard analysis and risk assessment for vehicular fuel cell systems.

Functional safety is an emerging trend for safety design in the automobile industry. This study is an innovative exploration of a hazard analysis and quantitative risk assessment (QRA) methodology based on the functional safety of vehicular fuel cell systems. In this study, the potential hazards that fuel cell vehicles may encounter during operation were identified using the automobile generic hazard list. The failure modes and influences of the main components of the fuel cell system were determined via hazard and operability studies. Furthermore, the risks and consequences of the fuel cell system were explored using the QRA method. The failure frequency and unavailability of the fuel-cell system were calculated, and the architecture was optimized using a redundant design with a focus on hydrogen safety. The results showed that the optimized system architecture could effectively reduce the hazard of hydrogen leakage, and the unavailability and failure frequency of hydrogen leakage risk were reduced by 94.4% and 36.8%, respectively. Thus, this study provides powerful insights for future research and improvement of vehicular fuel cell systems.

Seasonal variability and risk evaluation of emerging organic contaminants in European river: Linking in silico and in vitro approaches to prioritize hazardous EOCs.

Emerging organic contaminants (EOCs) are a growing concern for aquatic ecosystems, underscoring the need for advanced risk assessment methodologies. This study employed an integrated approach to evaluate the risks associated with 563 EOCs across 13 monitoring sites along the Sava River in Croatia. Sampling was conducted during the winter and spring months, spanning February to May. The detected substances, totaling 551 compounds, included pharmaceuticals, illicit drugs, pesticides, industrial chemicals, hormones, artificial sweetener, and sunscreen chemical. We hypothesized that combining high-resolution chemical monitoring with bio-effect assessment tools could enhance the prioritization of high-risk EOCs and their mixtures. Persistent, bioaccumulative, and toxic (PBT) profiling and risk quotient (RQ) evaluations identified pesticides and industrial chemicals as key contributors to biological impacts, peaking in April due to agricultural activities. In vitro prioritization using exposure-activity ratios (EARs) derived from the ToxCast database revealed exacerbated biological effects during colder months (February and March), driven by elevated levels of pharmaceuticals, particularly synthetic glucocorticoids used for treatments of respiratory infections. By integrating PBT, RQ, and EAR assessments, eight EOCs, including methylprednisolone (a synthetic glucocorticoid), bisphenol A (an industrial chemical), nicotine (a stimulant), and five pesticides (temephos, disulfoton, pyridaben, bifenthrin, and chlorpyrifos-methyl), were prioritized for monitoring and regulatory attention. This multi-method framework advances next-generation risk assessment, offering critical insights for managing EOC pollution and safeguarding aquatic ecosystems and public health.

Key recommendations and research priorities of the 2021 AMAP human health assessment

ABSTRACT Over the last three decades, the Arctic Monitoring and Assessment Programme has published five human health assessments. These assessments have summarised the current state of the science regarding environmental contaminants and human health in the Arctic. The 2021 Human Health Assessment Report had a particular focus on dietary transitions, in addition to human biomonitoring levels and trends, health effects, risk assessment methodologies, risk communication and multi-disciplinary approaches to contaminants research. The recommendations and research priorities identified in the latest assessment are summarised here to assist decision- and policy-makers in understanding and addressing the impacts of contaminants on human populations in the Arctic.

Байыту фабрикасындағы апаттар қаупін бағалауды зерттеу

The application of the accident risk assessment methodology, which is the most effective way to predict accidents, is considered. The accident risk assessment takes into account not only the accidents that have occurred in the industry, but also the dangers, the probability of which is very high. Analysis of modern accidents at a hazardous production facility has shown that when designing an object, all adverse events that can be identified during its operation are not taken into account, for example, climate conditions, the location of the object, the use of outdated technologies, qualification and psychophysiological requirements for personnel are not taken into account. As the analysis of the causes of accidents at hazardous production facilities has shown, it is necessary not only to find «weak links» in technological chains, but also to predict how events caused by an accident at a hazardous production facility will develop, indicate how to reduce their consequences. The article presents the results of the application of the methodology for determining the criteria for assessing the degree of risk and checklists in the field of industrial safety at the facility.

Quantitative risk assessment: methodology and application in an automaker body-in-white production line project

Purpose: this research aims to introduce a novel methodology, termed Quantitative Risk Assessment for body-in-white projects, to forecast the expected project completion time and total project cost. This methodology is demonstrated through application to a real project within an automotive manufacturer in Brazil. Theoretical framework: the study employs established risk analysis methods (e.g., program evaluation and review technique, preliminary hazard analysis, Gaussian curve, Monte Carlo simulation) alongside project management tools for problem characterization (e.g., project requirements, assumptions, work breakdown structure). Methodology/Approach: the methodology presented in this paper follows a sequence of methods that: (i) identify the hazards; (ii) evaluate the probability of risks associated with each project task; (iii) assess their consequences and impact on project costs; and, finally, (iv) quantify the risks to predict a probability spectrum for the project’s total cost. This allows estimating an emergency reserve and categorizing the risk level to inform stakeholders. Findings: The paper reveals that, among the 66 identified hazards, there is a 37% probability of the project duration exceeding 200 days, and the project has been categorized as high risk (i.e., more than a 20% probability that additional costs will exceed 20% of the estimated project budget). Research, practical & social implications: this study enables organizations to forecast the potential impacts of risks on project schedules and costs through comprehensive risk assessments, providing valuable input for project risk management. Originality/ Value: the study’s value lies in the originality of its risk assessment methodology applied to automakers’ body-in-white projects. Keywords: Automaker. Body-in-White. Monte Carlo Simulation. Probabilistic modeling. Quantitative risk assessment.

Precision risk assessment in wheat allergy: Leveraging advanced quantitative models for safer food consumption.

Food allergy is a significant public health concern and food safety issue. Deriving from classical toxicology principle, the food allergen risk assessment has been considered a science-based strategy to identify, quantify, and manage the food allergy risks as such risk represent a significant food safety. Moreover, the implication of the precautionary allergen labeling in most jurisdictions is voluntary, resulting potential risk to allergic consumers. In this study, a quantitative risk assessment technique was employed to evaluate the risk of wheat allergy in prepackaged foods. The assessment utilized probabilistic models, including the lognormal, Weibull, Gamma distributions, and Bayesian model averaging. The predicted allergic reactions were determined to be 682, 854, 677, and 721 incidents per 10,000 eating occasions within wheat allergic population, respectively. The findings of this study revealed that the consumption of prepackaged foods containing gluten without wheat/gluten summary (i.e., ingredient) labeling would potentially pose the risk of allergic reactions to wheat allergic individuals. The utilization of quantitative risk assessment methodology at different points of the food system facilitates alleviating unnecessary concerns to stakeholders while maintaining a reasonable knowledge of allergy risk and providing valuable guidance in formulating effective management strategies to mitigate the risk of food allergies, thereby contributing to the overall safety of the sustainable food system.

Research on Deflection Risk Assessment for Steel Box Girder Bridges Based on GA-BP Neural Network

Steel box girder bridges constitute a pivotal structural component in modern bridge engineering, confronting intricate mechanical environments and dynamic conditions during construction, with a particularly notable risk of deflection. Risk assessments predominantly rely on traditional mechanical analyses and empirical judgments, which need help to fully capture the dynamic construction changes and latent risks. This study introduces an innovative risk assessment methodology grounded in finite element analysis (FEA) and optimized by a genetic algorithm-enhanced back propagation neural network (GA-BP) to address these limitations. This approach entails constructing an FEA model to precisely simulate and predict the mechanical behavior during the construction phase, with field data validation ensuring the model’s accuracy. The GA-BP assessment model is established by further incorporating the genetic algorithm to optimize the BP neural network, enabling comprehensive, systematic, and efficient risk assessment. Through practical application case studies, this methodology demonstrates the ability to accurately identify the critical risk factors influencing deflection during the construction phase of steel box girder bridges, providing a scientific basis for construction control. This research holds significant theoretical value and practical significance, and it offers a scientific foundation for risk management, construction optimization, and safety assurance in future bridge engineering projects, thereby enhancing the overall quality and safety of bridges.

Innovative Approach to Conceptual Design of Enterprise Risk Management Software

The paper shows the integration of the theoretical and practical aspects of designing an ERM software tool. The basic idea of the designed ERM is conceived in the form of an algorithm using the integration of ISO 3100, the COSO framework, the risk matrix designed according to the risk appetite of the observed company, quantitative and qualitative models for risk assessment, and the generation of consequences and mitigating measures for each identified risk. Methodologies for risk assessment in the presented ERM include the following: For business risks, questionnaires were generated for different business areas (from knowledge bases) and the assessment was conducted according to risk matrices. Workplace risk is assessed according to the Kinney method, while technical risks are assessed according to the API 580, 581 standard. Software is created to outbalance problems of all types of risk that may arise regardless of the complexity of the business and the risk itself. The algorithm, model and software were developed and successfully tested in two Serbian companies.

How to describe the time-dependent dissolution of engineered nanomaterials?

Numerous processes such as solubility, agglomeration/aggregation, or protein corona formation may change over time and significantly affect engineered nanomaterial (ENM) structure, property, and availability, resulting in their reduced or increased toxicological activity. Therefore, understanding the dynamics of these processes is essential for assessing and managing the risks of ENMs during their lifecycle, ensuring safety by design. Of these processes, the importance of solubility (i.e., the ability to release ions from the surface) is undeniable. Thus, we propose a practical approach, the Kalapus equation (KEq), to determine ENMs' dissolution over time. As a proof-of-concept, the KEq was applied to determine the solubility of six commercially used metal and metal oxide nanoparticles over time. The KEq exhibited a higher coefficient of determination (R2=0.995-0.999) than the logarithmic equation (R2=0.835-0.986), and the pseudo-first-order equation (R2=0.915-0.994) over a range of experimental data. The newly introduced Kalapus equation outperformed the logarithmic and pseudo-first-order equations when extrapolating beyond the time range in which solubility was experimentally determined. The mean absolute error in solubility prediction for the KEq was 3.29% and 4.28% for the first and second data points, respectively, significantly lower than the 13.46% and 18.05% observed for the pseudo-first-order/first-order equation. The proposed equation can be used as a part of New Generation Risk Assessment (NGRA) methodology, especially new Integrated Approaches to Testing and Assessments (IATAs).

New approach methodologies for risk assessment using deep learning.

The advancement of technologies and the development of more efficient artificial intelligence (AI) enable the processing of large amounts of data in a very short time. Concurrently, the increase in information within biological databases, such as 3D molecular structures or networks of functional macromolecule associations, will facilitate the creation of new methods for risk assessment that can serve as alternatives to animal testing. Specifically, the predictive capabilities of AI as new approach methodologies (NAMs) are poised to revolutionise risk assessment approaches. Our previous studies on molecular docking predictions, using the software Autodock Vina, indicated high-affinity binding of certain toxic chemicals to the 3D structures of human proteins associated with nervous and reproductive functions. Similar approaches revealed potential sublethal interactions of neonicotinoids with proteins linked to the bees' immune system. Building on these findings, we plan to develop an AI-based decision tool that exploits the data available on the toxicity of the most know chemical, such as LD50, and the data obtainable by their interaction with the human proteins to support risk assessment studies for multiple stressors still not characterised. Our focus will be on utilising these new bioinformatics methodologies to develop specific experimental designs that allow for confident and predictable study of the toxic and sublethal effects of pesticides on humans. We will also validate the developed NAMs by integrating existing invivo information from scientific literature and technical reports. These approaches will significantly impact toxicity studies, guiding researchers' experiments and greatly reducing the need for animal testing.

Rapid assessment of risks in tug operations within port areas

Summary: This article presents a rapid risk assessment methodology specifically designed for tug operations within port areas, addressing the unique challenges of these high-risk environments. Tug operations require efficient risk mitigation due to confined spaces, unpredictable environmental conditions, and potential human factors that may lead to incidents. Traditional, detailed risk assessment models are often impractical in real-time scenarios. Therefore, this study focuses on developing a streamlined, quantitative approach for assessing and managing risks in port towage. The paper introduces an enhanced Risk Factor (RF) model as a key component of the proposed methodology. Traditionally, the RF is calculated using the formula RF=P×C, where P denotes the probability of occurrence, and C represents the severity of the consequence. This study expands on the conventional formula by including two additional elements relevant to towage: E (Environmental Conditions) and H (Human Factors). Thus, the modified formula RF=P×C×E×H provides a more comprehensive risk profile for rapid assessment in dynamic port conditions. Each section of this paper systematically explores the variables of the extended RF model and applies it to common scenarios in port tug operations. The findings demonstrate that incorporating environmental and human factors into the RF calculation enhances the model’s reliability and relevance. Finally, the paper provides recommendations for implementing the proposed RF methodology, supporting port operators and tug crews in real-time risk assessment and decision-making, thereby ensuring safety outcomes, minimizing possible risks involved, and improving maritime safety overall

Unveiling mercury's hidden threat: An integrated methodology for soil mercury risk assessment in Syr Darya River Basin, Central Asia

Mercury (Hg) contamination of soil poses a significant threat to ecological and human health. Integrating risk assessment with a comprehensive analysis of the physical and chemical properties of soil enables macroscopic understanding of the potential risks associated with Hg. The integrated risk assessment framework was achieved by applying a projection pursuit clustering (PPC) model that considered ecological and human health risks, soil environmental factors derived from the SHapley Additive Explanation–eXtreme Gradient Boosting (SHAP-XGBoost) model, and exposure risk vulnerability. It was found that the concentrations of Hg in the soils of the Syr Darya River Basin ranged from 3.70 to 40.10 ng/g and Fe2O3, Al2O3, and soil organic carbon (SOC) were important factors in the variation in Hg concentrations. Regions with a high risk of soil Hg were identified using the proposed integrated risk assessment framework, with the geographical distribution concentrated near the cities of Kyzylorda and Kazalinsk. From the perspective of different land use types, shrub soil sampling sites had the largest percentage of high Hg risk values, followed by cropland, bare land, and grassland. These findings confirm that the combined risk values depend not only on Hg concentrations, but also on environmental variables and socioeconomic conditions. Integrated risk assessment of soil Hg is based on machine learning and projection pursuit clustering models, which can provide a novel perspective for potential toxic element pollution evaluation, prevention, and treatment.

Comparative fire risk assessment in the historic center of Ouro Preto: using three different methodologies

This work deals with the assessment of fire risk in Cultural Heritage, specifically in the historic center of Ouro Preto. Three different fire risk assessment methodologies were used: Chichorro Method, Global Analysis and ARICA. Each methodology addresses different parameters for analysis, resulting in a comprehensive and unprecedented assessment of fire risk in historic sites. Steps such as data collection, georeferencing, diagnosis, Geodesign, and prognosis were carried out. The Geodesign involved a collaborative workshop to create ideas and solutions for fire issues in cultural heritage, resulting in suggestions such as heritage education, physical interventions, emergency procedures, and tax incentives. The diagnosis evaluated 146 buildings, revealing that the majority lack fire safety measures according to at least one of the methodologies used. This highlighted the need for actions such as prevention projects, public awareness, adjustments to electrical and gas installations, among others. The prognosis suggested targeted interventions in buildings with higher risk and the implementation of emergency and evacuation plans. Therefore, the study pointed to the importance of preventive interventions and awareness-raising efforts aimed at increasing the resilience of Ouro Preto's historic center against fires.

An integrated industrial risk assessment methodology for accident safety, chronic health, and chemical exposure

An integrated industrial risk assessment methodology for accident safety, chronic health, and chemical exposure

Towards risk-informed design and operation of ammonia-powered ships: Critical aspects and prospective solutions

Ammonia is a promising fuel for marine propulsion and generation, yet its acute toxicity and associated safety challenges necessitate careful consideration. Current regulations recognize the hazards of ammonia, introducing numerous technical safety measures in response. However, the effectiveness of these measures in ensuring acceptable risk levels for future vessels remains to be fully assessed. This study estimates the risk level onboard ammonia-powered ships, identifying the aspects with the largest and controllable influence on it. Three hypothetical concepts of ammonia fuel supply were developed in this study on an example tanker vessel and analyzed using the quantitative risk assessment (QRA) methodology. The obtained risk profiles were evaluated against the risks by an equivalent liquefied natural gas-fueled system, serving as a benchmark. The results demonstrate that ammonia-fueled ships exhibit individual risk levels for engineering crew with periodic duties in fuel preparation rooms (FPR), or similar compartments, which are 1–1.5 orders of magnitude higher than those observed for a conventional gas-fueled alternative. An analogous increase has been noted for the public potentially present onboard or in proximity. The study underscores the importance of managing human-machine interactions, enhancing the reliability of supply systems, and managing the systems’ complexity to mitigate risks in FPRs. Regarding public safety, the analysis highlights new risk mechanisms introduced by ammonia and examines how storage conditions affect exposure levels. By offering a detailed QRA framework, this research contributes to the development of effective risk management strategies for ammonia-powered ships.

Assessment of trace organic chemicals in anaerobically digested sludge and their partitioning behaviour: Simultaneous Soxhlet chemical extraction and quantification via LC-MS/MS analysis

The increasing number of trace organic contaminants (TrOCs) detected in anaerobically digested sludge (ADS) is triggering increasing concern on its circular-economy reuse practices. A large scientific effort has been performed to define their concentration limits, partition behaviour, and innovative technologies for their removal, which require the definition of versatile and economically sustainable analytical methodologies. In this study, a Soxhlet extraction method coupled with LC-MS/MS analysis was developed to simultaneously determine 32 TrOCs in ADS, 11 of them being quantified in this matrix for the first time. The targeted TrOCs were selected based on the European Urban Wastewater Treatment Directive, and on their frequency of detection in municipal wastewater and/or sludge and chemical diversity. The use of methanol as solvent allowed good recovery efficiencies from ADS solid phase, with an extraction time of 3.5 h and without the need for subsequent clean-up procedures. The targeted LC-MS/MS method enabled high-sensitivity quantification of TrOCs in the liquid phase. At least 25 out of the 32 target compounds were detected in ADS samples from two wastewater treatment plants in Germany, providing their concentration data and highlighting the influence of TrOCs characteristics and sludge properties on contaminant partition coefficients (KD). The experimental outcomes highlight the versatility of the Soxhlet method, which is effective in extracting compounds characterized by diverse properties and structures, and opens new perspectives for the analysis of various substrates. This could support the European Sewage Sludge Directive, expanding its application to soils and cultivated foods and offering insights into TrOCs transfer among different substrates and their influence when used as fertilizer, aiding in the efficient definition of risk assessment methodologies and regulatory concentration limits.

State-of-the-art nonstationary hypersurface damage assessment approach for energy harvesters

Since EH (Energy Harvesters) constitute nowadays a vital part of renewable energy engineering, experimental research is required in addition to numerical modeling, serving reliable structural design and ensuring prolonged device service time. The performance of GPEH (GalloPing EH) has been examined in this case study, utilizing comprehensive laboratory wind tunnel tests, carried out under realistic windspeed conditions. Novel structural multivariate risks assessment methodology, presented here, being feasible for nonstationary nonlinear GPEH dynamic systems, that had been either physically measured over a representative period, providing jointly quasi-ergodic time-series, or directly numerically MCS (Monte Carlo Simulated). Based on laboratory-measured GPEH dynamics, the presented analysis demonstrates that the proposed multivariate hypersurface methodology offers robust predictions of the structural failure/damage risks. Furthermore, when dealing with raw measured timeseries, representing the high-dimensional dynamic system, existing risk assessment techniques struggle to handle nonlinear inter-correlations between GPEH critical components. This case study's main objective has been to validate and benchmark the novel multimodal risk assessment methodology, which utilizes multivariate nonstationary lab-recorded time histories to extract relevant design information from the underlying GPEH dynamics.The proposed state-of-the-art nonstationary hypersurface reliability approach being of a generic nature, offering additional capacity for damage/failure risks prognostics for a wide range of nonlinear multidimensional nonstationary systems. Forecasted damage and failure risks have been supplied with confidence bands, demonstrating the experimental setup's robustness, as well as the useful design features of the presented nonstationary hypersurface risks assessment methodology. It should be noted that the presented reliability methodology being mathematically exact, and it does not rely on simplifying assumptions.