Risk Assessment Purposes Research Articles

A Systematic Review on Decision-Making Models for Evaluating Supply Risks

Objective: The objective of the present study is to carry out a systematic review of the literature regarding quantitative models for assessing supply risks. Theoretical Framework: Organizations and their supply chains are increasingly exposed to risks. Given the uncertainties of the business environment, the occurrence of adverse events may result in the interruption of supply in these chains, causing disruption and losses. The development of decision-making models to support risk assessment helps prevent supply discontinuity and can contribute to increasing the robustness of supply chains. Method: Aiming to map quantitative models to support supply risk assessment, the systematic literature review was carried out based on the PRISMA method. Seven factors were considered to classify the selected studies, namely: year of publication, purpose of risk assessment, technique(s) used, criteria, types of input data, support for group decisions and application sector. Results and Discussion: It was found that most of the techniques used are aimed at environments of uncertainty and group decision-making processes. There was also a predominance of applications in companies in the automotive industry and the focus on risk assessment for supplier selection. The most frequent decision technique was AHP. Research Implications: In addition to mapping the state of the art on this topic, some opportunities for future studies were identified in order to assist managers and researchers interested in developing new decision-making models for assessing supply risks. Originality/Value: Based on the bibliographical research carried out, it can be stated that this is the first study focused on mapping decision-making models for assessing supply risks. This study complements previous systematic reviews on risk management in supply chains and can be valuable for the development of tools aimed at mitigating supply risks.

Soil erodibility properties experimental quantification: a meta-analysis.

The focus in the present study is on the quantification soil erodibility properties (representing an erosion threshold (such as the critical shear stress) and a resistance property (e.g., the soil erosion coefficient)). These are necessary for an adequate assessment of soil erosion mechanisms affecting earth-made hydraulic structures (e.g., dams, dykes and levees). The paper gives a quantitative statistical analysis of the aforementioned soil erodibility parameters. To do so, a wide range of experimental tests, used for the study of internal erosion (hole erosion test (HET) and slot erosion test (SET)) and surface erosion (jet erosion test (JET), flume test (FT), erosion function apparatus (EFA), and rotating cylinder test (RCT)), were examined. A dataset of previously published experimental data was collected, harmonized, structured, treated, and used in a multicriteria analysis. The outcomes of the study provide a better understanding of the limitations and ambiguity in the assessment soil erodibility properties, highlight the differences among tests and between processes, and assess their inter-useability. Correlations between the erodibility properties themselves and between each of them and an in-study-defined midrange soil texture diameters were evaluated at specific level of erosion process, erosion test, and/or soil texture. Furthermore, a set of new empirical formulas has been proposed linking soil erodibility properties to themselves or each erodibility properties to the midrange soil texture diameter. A set of reference values and ranges and trends for the studied erodibility properties, useful for design or risk assessment purposes or for evaluating the quality of experimental data, are derived.

Inhibition of Neural Crest Cell Migration by Strobilurin Fungicides and Other Mitochondrial Toxicants.

Cell-based test methods with a phenotypic readout are frequently used for toxicity screening. However, guidance on how to validate the hits and how to integrate this information with other data for purposes of risk assessment is missing. We present here such a procedure and exemplify it with a case study on neural crest cell (NCC)-based developmental toxicity of picoxystrobin. A library of potential environmental toxicants was screened in the UKN2 assay, which simultaneously measures migration and cytotoxicity in NCC. Several strobilurin fungicides, known as inhibitors of the mitochondrial respiratory chain complex III, emerged as specific hits. From these, picoxystrobin was chosen to exemplify a roadmap leading from cell-based testing towards toxicological predictions. Following a stringent confirmatory testing, an adverse outcome pathway was developed to provide a testable toxicity hypothesis. Mechanistic studies showed that the oxygen consumption rate was inhibited at sub-µM picoxystrobin concentrations after a 24 h pre-exposure. Migration was inhibited in the 100 nM range, under assay conditions forcing cells to rely on mitochondria. Biokinetic modeling was used to predict intracellular concentrations. Assuming an oral intake of picoxystrobin, consistent with the acceptable daily intake level, physiologically based kinetic modeling suggested that brain concentrations of 0.1-1 µM may be reached. Using this broad array of hazard and toxicokinetics data, we calculated a margin of exposure ≥ 80 between the lowest in vitro point of departure and the highest predicted tissue concentration. Thus, our study exemplifies a hit follow-up strategy and contributes to paving the way to next-generation risk assessment.

Beyond smoking: Risk assessment of nicotine in pouches

Orally consumed nicotine pouches that contain no tobacco are marketed as a less harmful alternative to tobacco products. This study aims to assess nicotine in pouches and potential risks for health damage. 31 samples of nicotine pouches were collected and analyzed. The median nicotine uptake from single pouches was calculated to be 65 µg/kg bw. Consumption of more than one pouch per day would lead to a strikingly higher nicotine dose over the day. Health effects after nicotine exposure are, among others, cardiovascular effects. An acute reference dose (ARfD) of 0.8 µg/kg bw was proposed for risk assessment purposes based on an observed increased heart rate in a human study after acute nicotine uptake. The ARfD was exceeded by at least 20-fold, even when considering the consumption of a single pouch with the lowest detected nicotine level. With higher nicotine contents in pouches or with an increasing number of pouches per day, vast ARfD exceedances are possible. Therefore, a clinically relevant elevation of heart rate is very likely to occur in consumers after acute consumption of nicotine pouches.

Risk assessment of gas plume dispersion in bunkering of alternative fuel through validated computational fluid dynamics approach

Abstract Maritime decarbonization is crucial in fighting climate change. Adopting low-carbon alternative fuels could help reduce greenhouse gas (GHG) emissions by 50% by 2050 compared to 2008. While liquified natural gas (LNG) has gained a strong foothold in the maritime sector over the past decades, the trend is shifting toward adopting lower and zero-carbon hydrogen and its carriers, such as methanol and ammonia, as alternative fuels. Quantifying risks from accidental leakages during storage and transfer of these alternative fuels using numerical models is required and commonly adopted in industries. The process leverages multiple models and tools for risk assessments with different accuracy levels and costs ranging from integral models to Lagrangian techniques and Eulerian approaches such as computational fluid dynamics (CFD). Increasingly, CFD approaches are employed for near-field dispersion analysis to assess the risk of accidental leakages. Despite their high computational cost, CFD models offer better accuracy than integral and Lagrangian models. In this work, we developed a CFD-based framework as a quantitative tool for risk assessment purposes. Our framework aims to (1) offer finer gas dispersion resolution with a detailed representation of surrounding structures and geometries, (2) minimize reliance on assumptions and empirical models to capture flow dynamics and gas dispersions accurately, and (3) mitigate conservative estimates and over-design of safety measures. The framework was validated using experimental data from various field tests, including a recent one involving releasing cryogenic liquids into the atmosphere. After successfully validating and comparing with experiments and other available tools, the proposed framework investigated gas plume dispersion from accidental leakages during fuel bunkering operations in ship-to-ship and shore-to-ship scenarios. The data obtained from CFD simulations was subsequently utilized to construct a surrogate model for fast prediction of gas plume behavior under varying environmental conditions. This facilitated the development of an effective emergency response plan.

Advancing Maternal Transfer of Organic Pollutants across Reptiles for Conservation and Risk Assessment Purposes.

Embryonic exposure through maternally transferred pollutants can affect embryo vitality, survival, and health. Reptiles face global declines and are sensitive to embryonic pollutant exposure. Yet, they are often neglected in pollution risk assessment and conservation. We analyzed maternal transfer of organic pollutants in reptiles through a systematic extraction, homogenization, and integration of published data on organic pollutants measured in mother-egg pairs into a comprehensive database (DOI:10.5281/zenodo.10900226), complemented with molecular physical-chemical properties of the pollutants. Over four decades, 17 publications provided 19,955 data points shifting from legacy to emerging contaminants although research on newer contaminants lags regulatory and societal demands. Challenges including taxonomic bias, heterogeneity in sampled tissues, and 73% of censored data complicate comparative analyses. However, significant opportunities were identified including the use of the turtle Malachlemys terrapin and snake Enhydris chinensis as flagship species where a large amount of data is available across tissues (allowing investigation into physiological relations) and compounds (allowing insights into maternal transfer across the chemical universe). Data on other freshwater and marine turtles provide the possibility of exploring taxonomic patterns in this subgroup. The analysis, integrated database, and discussion present opportunities for research in an era where science needs to achieve more with limited wildlife data.

Energy Content, Moisture Content, and Energy Assimilation Efficiency by Birds and Mammals of Oil-Containing Seeds and Implications for Seed Treatment Risk Assessments for Birds and Mammals.

Energy content, moisture content, and energy assimilation efficiency are essential parameters in the food intake rate (FIR) and exposure calculations for bird and mammal risk assessments. The updated European Food Safety Authority guidance document on risk assessment for birds and mammals summarizes these parameters for different food items. For seed treatments, values for cereal seeds are proposed as surrogates for other crops. Oil-containing seeds are expected to have a higher energy content than cereal seeds. This would result in lower FIR and, thus, exposure from consuming such seeds. To be able to calculate reliable exposure values for risk-assessment purposes, we conducted a systematic literature review to collect information on these three parameters for oil-containing seeds (sunflower, oilseed rape, soybean, peanut, sesame, safflower, linseed [flax], white mustard, and castor bean). The search yielded 401 papers, of which 151 contained values for at least one of the parameters of the crops in focus. The overall average energy content value of oil-containing seeds was 24.25 kJ/g (n = 124, SD = 3.00), whereas that for moisture content was 6.57% (n = 296, SD = 1.15). Energy assimilation values were only available for peanut, oilseed rape, soy, linseed, and sunflower for a limited number of bird and mammal species. Mean energy assimilation efficiency for mammals was 82.69% (n = 4, SD = 1.55), whereas values for birds were 57.54% (n = 2, SD = 6.77) for Galliformes and 79.25% (n = 2, SD = 1.82) for Passeriformes. The values presented can be used to calculate appropriate FIR values for future bird and mammal risk assessments. Environ Toxicol Chem 2024;43:2080-2085. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Regional ecological risk assessment through the application of production-living-ecology land use function indicators: a case study of the eco-economic zone of Yellow River Delta.

To date, land use structure information has been employed extensively for ecological risk assessment (ERA) purpose in regional/landscape scales; in contrast, land use function (LUF) information-based ERA research is still scarce. Therefore, it is necessary to carry out more ERA case studies in macroscale with the help of pertinent LUF indicators. As an important way to construct production-living-ecology LUF indexes, this study employs the weighted stacking method and related economic statistical data for regional ecological risk assessment (RERA) purpose within Yellow River Delta High-efficiency Eco-economic Zone (YRDHEZ), China. This YRDHEZ-RERA research pointed out that (1) it was rational to use a series of economic statistical data to more comprehensively and precisely characterize regional production and living function grades in YRDHEZ. (2) The Yellow River Delta had lower agriculture and non-agriculture production functions, whereas the rest of the zone had higher production functions. (3) Most people lived in the south part, whereas north coastal zone had very low population density; the east part had higher per capita disposable income of urban/rural households than that of west. (4) The south part of the zone had higher production/living functions and integrated ecological risk source intensity than those of north coastal zone, whereas the coastal zone had higher ecology function, eco-environmental vulnerability, and final integrated ecological risk than those of inland region. As for regional ecological risk management, establishing nature reserve with strict spatial governance for coastal/estuarine wetlands and coordinating production/ecology functions of coastal salterns/breeding ponds are relevant feasible measures.

Determination of toxicological relevant arsenic species in urine by hydride generation microwave-induced plasma optical emission spectrometry

An analytical method for the determination of toxicological relevant species of arsenic in urine was developed and validated using hydride generation microwave-induced emission spectrometry (HG-MP-AES). This strategy can be used as an alternative to HG-HPLC-ICP-MS considered as a reference technique for arsenic speciation. This procedure is notably less expensive than other techniques and sample preparation and requires only a few steps.•Hydride generation with MP-AES detection has proven to be an effective technique for measuring arsenic metabolites in urine, which is relevant for occupational monitoring and health risk assessment purposes.•This method offers simplicity and cost-effectiveness, serving as an alternative to classical analytical procedures typically used for arsenic analysis in urine.•The methodology has been successfully applied for the purpose of workers' health surveillance.

Update of the scientific opinion on tetrabromobisphenol A (TBBPA) and its derivatives in food.

The European Commission asked EFSA to update its 2011 risk assessment on tetrabromobisphenol A (TBBPA) and five derivatives in food. Neurotoxicity and carcinogenicity were considered as the critical effects of TBBPA in rodent studies. The available evidence indicates that the carcinogenicity of TBBPA occurs via non-genotoxic mechanisms. Taking into account the new data, the CONTAM Panel considered it appropriate to set a tolerable daily intake (TDI). Based on decreased interest in social interaction in male mice, a lowest observed adverse effect level (LOAEL) of 0.2 mg/kg body weight (bw) per day was identified and selected as the reference point for the risk characterisation. Applying the default uncertainty factor of 100 for inter- and intraspecies variability, and a factor of 3 to extrapolate from the LOAEL to NOAEL, a TDI for TBBPA of 0.7 μg/kg bw per day was established. Around 2100 analytical results for TBBPA in food were used to estimate dietary exposure for the European population. The most important contributors to the chronic dietary LB exposure to TBBPA were fish and seafood, meat and meat products and milk and dairy products. The exposure estimates to TBBPA were all below the TDI, including those estimated for breastfed and formula-fed infants. Accounting for the uncertainties affecting the assessment, the CONTAM Panel concluded with 90%-95% certainty that the current dietary exposure to TBBPA does not raise a health concern for any of the population groups considered. There were insufficient data on the toxicity of any of the TBBPA derivatives to derive reference points, or to allow a comparison with TBBPA that would support assignment to an assessment group for the purposes of combined risk assessment.

Urban environmental monitoring and health risk assessment introducing a fuzzy intelligent computing model.

To enhance the precision of evaluating the impact of urban environments on resident health, this study introduces a novel fuzzy intelligent computing model designed to address health risk concerns using multi-media environmental monitoring data. Three cities were selected for the study: Beijing (B City), Kunming (K City), and Wuxi (W City), representing high, low, and moderate pollution levels, respectively. The study employs a Fuzzy Inference System (FIS) as the chosen fuzzy intelligent computing model, synthesizing multi-media environmental monitoring data for the purpose of urban health risk assessment. (1) The model reliably estimates health risks across diverse cities and environmental conditions. (2) There is a positive correlation between PM2.5 concentrations and health risks, though the impact of noise levels varies by city. In cities B, K, and W, the respective correlation coefficients are 0.65, 0.55, and 0.7. (3) The Root Mean Square Error (RMSE) values for cities B, K, and W, are 0.0132, 0.0125, and 0.0118, respectively, indicating that the model has high accuracy. The R2 values for the three cities are 0.8963, 0.9127, and 0.9254, respectively, demonstrating the model's high explanatory power. The residual values for the three cities are 0.0087, 0.0075, and 0.0069, respectively, indicating small residuals and demonstrating robustness and adaptability. (4) The model's p-values for the Indoor Air Quality Index (IAQI), Thermal Comfort Index (TCI), and Noise Pollution Index (NPI) all satisfy p < 0.05 for the three cities, affirming the model's credibility in estimating health risks under varied urban environments. These results showcase the model's ability to adapt to diverse geographical conditions and aid in the accurate assessment of existing risks in urban settings. This study significantly advances environmental health risk assessment by integrating multidimensional data, enhancing the formulation of comprehensive environmental protection and health management strategies, and providing scientific support for sustainable urban planning.

Wildland fire fuels database for Corsican - Mediterranean Forest stand types

Wildfires represent a major concern for the forests in the Mediterranean basin regions since global climate change enhances the frequency and the severity of this natural ecological disturbance agent. Wildfire models (fire spread or fire behavior models) can be of valuable help for fire risk assessment as well as for planning mitigation actions which mainly concern forest management. These models need generalized vegetation (fuel) data sets at the scales which are significant for fire/forest management. However, despite of the efforts to develop methods for generating global fuel data sets, there is a lack of fine resolution regional scale fuel data sets for the Mediterranean basin. The aim of this work is to build a fuel data set based on existing data sources for fire/forest management, risk assessment and decision-support purposes. Thus, this paper presents a database (building methods and data) of wildland fuels for Mediterranean basin vegetation stands types and in particular for those in Corsica, which gathers together most common input parameters needed by wildfire’s models at several vegetation scales (i.e., stand, elements, particles). Moreover, an example is provided where simulations are run with a specific wildfire model by using the fuel input data extracted from the database. Fuel attributes have been defined to be meaningful at regional/landscape scales and are representative of stand-level characteristics. National (French) Forest Inventory (NFI) data have been mainly used for determining the fuel attributes for forest stand types. The harmonization of European NFIs generates a reliable data source available to extend and generalize the methodology presented herein to other regions or countries.

Evaluation of dissipation kinetic models to describe trifluralin degradation in soil and assessing the uncertainty of the half-life estimate

In Australia, trifluralin is one of the commonly used herbicides to manage annual grasses and some broadleaf weeds. However, it may have some ecosystem impacts such as high toxicity to terrestrial and aquatic life, so it is vital to monitor the degradation of trifluralin for a considerable period for environmental safety. For risk assessment purposes, it is necessary to estimate the half-life of trifluralin, which is often evaluated using derived mathematical dissipation models. In the literature, bi-exponential (BEXP) and gamma models were suggested for modelling the dissipation of trifluralin in soil. Both models provide the half-life estimate without discussing the uncertainty of the estimate, which is a shortcoming in the literature. In this paper, we used simulation to illustrate the importance of estimate's uncertainty (standard error) and demonstrated a method to compute the standard error for the half-life estimate mathematically for kinetic dissipation models. Later, we evaluated the performance of the two suggested models using statistical indices. The computation of the half-life and the standard error of the half-life estimate were discussed. This allows us to describe the inference of the half-life parameter and determine whether the half-life estimates are significantly different against the co-variate (moisture) levels. We demonstrated the method to calculate the standard error of the half-life of trifluralin, which allows us to determine the statistical difference between the estimates. In this study, we found that the half-life of trifluralin in soil tends to increase with increasing moisture levels, and the half-life of trifluralin in soil with 100% moisture level is significantly greater than 40% and 70% moisture levels. Our findings suggest that soil moisture levels should be carefully considered before trifluralin application to minimize the non-target environmental damage.

Basin scale variability of Ostreopsis spp. blooms provides evidence of effectiveness of an integrated sampling approach

Ostreopsis spp. blooms have been occurring in the last two decades in the Mediterranean Sea in association with a variety of biotic and abiotic substrata (macroalgae, seagrasses, benthic invertebrates, sand, pebbles and rocks). Cells proliferate attached to the surfaces through mucilaginous trichocysts, which lump together microalgal cells, and can also be found in the plankton and on floating aggregates: such tychoplanktonic behavior makes the quantitative assessment of blooms more difficult than planktonic or benthic ones. Different techniques have been so far applied for quantifying cell abundances of benthic microalgae for research, monitoring and risk assessment purposes. In this context, the Benthic Dinoflagellates Integrator (BEDI), a non-destructive quantification method for benthic dinoflagellate abundances, was developed and tested within the EU ENPI-CBCMED project M3-HABs. This device allows mechanical detachment of cells without collecting the benthic substrate, providing an integrated assessment of both epiphytic and planktonic cells, i.e. of the number of cells potentially made available in the water volume from “resuspension” which could have harmful effects on other organisms (including humans).The present study confirms the effectiveness of the BEDI sampling device across different environments across the Mediterranean Sea and constitutes the first large-scale study of Ostreopsis spp. blooms magnitude in function of different macro- and meso‑habitat features across the basin.

Gaussian process regression for three-dimensional soil mapping over multiple spatial supports

This study investigates the complexity of spatial soil modelling, particularly focusing on the challenge of variable vertical support in traditional soil data collection. Traditional soil sampling, described in terms of horizons, often fails to accurately pinpoint the specific depths for specific soil properties. This gap is significant, as depth-specific data is crucial for a thorough understanding of soil formation processes and for assessing potential environmental impacts. In digital soil mapping (DSM), the prevalent reliance on standardised depth intervals and mass-preserving spline functions for data resampling results in a modelling approach that tends to disregard depth-related details, thereby introducing potential uncertainties.To address these limitations, this work explores how effectively Gaussian process regression (GPR) can model soil in 3D. This technique comprises two key components: a mean function and a semivariogram-like kernel. Unlike conventional methods that make a single prediction, GPR provides detailed probability distributions. This capability allows for the quantification of prediction uncertainty at various points, offering insights for decision-making and risk assessment purposes. Moreover, GPR has the capability to make volume or block estimates and assess associated uncertainties. Enabling volume-based predictions enriches the range of strategies available for land management.In this research, we employ GPR as a novel 3D soil modelling technique and compare its performance with traditional spline-based methods. The comparison is conducted through a case study on a farm in northern New South Wales, Australia, focusing on the 3D mapping of soil pH and electrical conductivity (EC). Our results demonstrate the ability of GPR to estimate soil properties across various volumes, utilising data from multiple vertical supports, thereby offering a more versatile approach for soil modelling in diverse spatial contexts.

Exposure Assessment of Pesticides in Surface Waters of Ontario, Canada Reveals Low Probability of Exceeding Acute Regulatory Thresholds.

The objectives of this study were to: (1) characterize the exposure of aquatic ecosystems in Southern Ontario, Canada to pesticides between 2002 and 2016 by constructing environmental exposure distributions (EEDs), including censored data; and (2) predict the probability of exceeding acute regulatory guidelines. Surface water samples were collected over a 15-year period by Environment and Climate Change Canada. The dataset contained 167 compounds, sampled across 114 sites, with a total of 2,213 samples. There were 67,920 total observations of which 55,058 were non-detects (81%), and 12,862 detects (19%). The most commonly detected compound was atrazine, with a maximum concentration of 18,600 ngL- 1 and ~ 4% chance of exceeding an acute guideline (1,000 ngL- 1) in rivers and streams. Using Southern Ontario as a case study, this study provides insight into the risk that pesticides pose to aquatic ecosystems and the utility of EEDs that include censored data for the purpose of risk assessment.

Unchannelized granular flows: Effect of initial granular column geometry on fluid dynamics

In recent years, significant progress has been made in modelling granular flows using the μI-rheology model, which connects the viscosity of a granular medium to the pressure and strain rate via a dimensionless quantity called the inertial number, I. This model allows treating the granular material as a non-Newtonian liquid with a yield stress, making it possible to model the flow using the continuum approach, which is less computationally expensive than discrete element methods.In this paper, we implement the μI-rheology model in a computational fluid dynamics (CFD) code and couple it with the volume of fluid (VOF) interface tracking approach to model the three-dimensional (3D) flow of monodisperse granular materials. After validating the model using experimental data, we briefly describe a trial-and-error method for evaluating the material properties of powders via a simple collapse experiment. Then, employing the CFD model, we investigate the physics of the unchannelized collapse of granular materials and perform an energy budget analysis to demonstrate the different stages of the granular collapse.To further investigate the effect of the initial shape of the pile on the spreading dynamics, we run a campaign of 3D simulations. Our results show that the μI-rheology model accurately reproduces the dynamics of the granular material during the collapse and can be used for risk assessment purposes in natural disasters. The findings from our simulations can also aid in developing preventative measures to minimize potential harm.

Proof of concept for quantitative adverse outcome pathway modeling of chronic toxicity in repeated exposure

Adverse Outcome Pathway (AOP) is a useful tool to glean mode of action (MOE) of a chemical. However, in order to use it for the purpose of risk assessment, an AOP needs to be quantified using in vitro or in vivo data. Majority of quantitative AOPs developed so far, were for single exposure to progressively higher doses. Limited attempts were made to include time in the modeling. Here as a proof-of concept, we developed a hypothetical AOP, and quantified it using a virtual dataset for six repeated exposures using a Bayesian Network Analysis (BN) framework. The virtual data was generated using realistic assumptions. Effects of each exposure were analyzed separately using a static BN model and analyzed in combination using a dynamic BN (DBN) model. Our work shows that the DBN model can be used to calculate the probability of adverse outcome when other upstream KEs were observed earlier. These probabilities can help in identification of early indicators of AO. In addition, we also developed a data driven AOP pruning technique using a lasso-based subset selection, and show that the causal structure of AOP is itself dynamic and changes over time. This proof-of-concept study revealed the possibility for expanding the applicability of the AOP framework to incorporate biological dynamism in toxicity appearance by repeated insults.

Research gaps and future needs for allergen prediction in food safety.

The allergenicity and protein risk assessments in food safety are facing new challenges. Demands for healthier and more sustainable food systems have led to significant advances in biotechnology, the development of more complex foods, and the search for alternative protein sources. All this has increased the pressure on the safety assessment prediction approaches anchored into requirements defined in the late 90's. In 2022, the EFSA's Panel on Genetically Modified Organisms published a scientific opinion focusing on the developments needed for allergenicity and protein safety assessments of new products derived from biotechnology. Here, we further elaborate on the main elements described in this scientific opinion and prioritize those development needs requiring critical attention. The starting point of any new recommendation would require a focus on clinical relevance and the development of a fit-for-purpose database targeted for specific risk assessment goals. Furthermore, it is imperative to review and clarify the main purpose of the allergenicity risk assessment. An internationally agreed consensus on the overall purpose of allergenicity risk assessment will accelerate the development of fit-for-purpose methodologies, where the role of exposure should be better clarified. Considering the experience gained over the last 25 years and recent scientific developments in the fields of biotechnology, allergy, and risk assessment, it is time to revise and improve the allergenicity safety assessment to ensure the reliability of allergenicity assessments for food of the future.

Development of a Korean offshore wind power HSE risk assessment module based on systems engineering approach

This study provides an in-depth comparison of various risk assessment models widely used in modern industry and proposes the most suitable models for offshore wind power risk assessment, focusing on the characteristics and requirements of the offshore wind power industry. The selection of the assessment models is based on various factors, such as the purpose of risk assessment, the stakeholder requirements, and the characteristics of offshore wind power. This study emphasizes the importance of using a combination of different risk assessment models in situations of high uncertainty. Using model-based systems engineering, risk assessment module is designed and implemented with traceability from requirements to detailed functionality. The risk assessment module supports organic conversion between risk assessment models to assess the risks of offshore wind power generation. The functionality and usability of the offshore wind risk assessment module were verified through the evaluation and the field test.