Hazard Assessment Research Articles

Quantitative Assessments of the Liquefaction Hazard of Soils considering Possible Strong Earthquakes in Seismically Active Regions of Russia

Introduction The seismogenic liquefaction of the soil poses a great hazard to society and the environment. Therefore, it is actively studied in many countries. In Russian engineering and seismological practice, this area is not sufficiently developed. The deterministic approach still prevails in Russian research on this topic. More modern probabilistic estimates are very rare. Methods This paper describes examples of both deterministic and probabilistic assessments of the seismogenic liquefaction hazard performed in certain areas of the Russian territory with different seismogeological conditions. Deterministic estimates were made using the Iwasaki-Seed-Finn methods and their modifications. Probability distribution functions of a random variable, the “seismic potential of liquefaction” (SPL), were developed for probabilistic estimates. These functions are regional in nature and take into account two types of uncertainties. The first is the uncertainty in achieving “critical” values by the SPL value in the event of potentially dangerous earthquake sources for a given location. The second is the uncertainty in the very occurrence of these sources in a given place for a given period of time. The “critical” SPL values are determined by the strength properties of the site soils. All estimates are based on multivariate calculations using various models of strong ground motions and seismicity. In all cases, the probability of liquefaction of water-saturated sandy and sandy-loam deposits was estimated which were found near the seabed and at depths of up to 80 m in the waters of Pogibi cape (the coast of Sakhalin island), in the districts of Sochi and Novorossiysk, as well as in land conditions (Stavropol, Krasnodar). Results The results of the research made it possible to correctly (at the quantitative level) take into account this component of the seismic hazard of the studied territories. Conclusion The variants of practical use of the obtained data are offered. An assessment of the possibilities and limitations of the developed methodology is made, and ways to improve it are outlined.

Risk Assessment of Overturning of Freestanding Non-Structural Building Contents in Buckling-Restrained Braced Frames

The increasing demand in structural engineering now extends beyond collapse prevention to encompass business continuity planning (BCP). In response, energy dissipation devices have garnered significant attention for building response control. Among these, buckling-restrained braces (BRBs) are particularly favored due to their stable hysteretic behavior and well-established design provisions. However, BCP also necessitates the prevention of furniture overturning—an area that remains quantitatively underexplored in the context of buckling-restrained braced frames (BRBFs). Addressing this gap, this research designs BRBFs using various design criteria and performs incremental dynamic analysis (IDA) with artificially generated seismic waves. The results are compared with previously developed fragility curves for furniture overturning under different BRB design conditions. The findings demonstrate that the fragility of furniture overturning can be mitigated by a natural frequency shift, which alters the threshold of critical peak floor acceleration. These results, combined with hazard curves obtained from various locations across Japan, quantify the mean annual frequency of furniture overturning. The study reveals that increased floor acceleration in stiffer BRBFs can lead to a 3.8-fold higher risk of furniture overturning compared to frames without BRBs. This heightened risk also arises from the greater hazards at shorter natural periods due to stricter response reduction demands. The probabilistic risk analysis, which integrates fragility and hazard assessments, provides deeper insights into the evaluation of BCP.

Assessment of natural radioactivity and radiological hazards in rocks from Thiruvananthapuram, Kerala

Assessment of natural radioactivity and radiological hazards in rocks from Thiruvananthapuram, Kerala

Machine learning-assisted risk evaluation of heavy metals in the Hainan gold mining region, China.

This study employed machine learning (ML) to thoroughly investigate the impact of informal mining activities on the distribution and pollution status of heavy metals in soils near private gold mines in Hainan Province, southern China, a region known for its ecological sensitivity and economic importance. By systematically collecting surface soil samples and samples at depths of 0.5-1m from 175 drilling sites, a comprehensive quantitative analysis was conducted on major heavy metal elements, including lead (Pb), copper (Cu), cadmium (Cd), nickel (Ni), mercury (Hg), chromium (Cr), arsenic (As), and zinc (Zn). Combined with evaluation methods such as the Pollution Load Index (PLI), Normalized Pollution Index (NIPI), and Ecological Risk Index (ERI), the study revealed a high level of soil pollution at informal mining sites. The findings indicated that the average concentrations of Pb, Cd, Hg, As, and Zn in surface soils significantly exceeded the background values for soils in China, with a pronounced positive correlation observed between these heavy metal elements in both surface and deep soil profiles (r > 0.5). Furthermore, leveraging the heavy metal content in surface soils and the constructed environmental indicators, the predictive accuracy for metal content in deep soils was found to range from R2 = 0.27 to 0.68, suggesting that informal mining activities have led to substantial variations in metal content across different soil profiles. Through the application of a random forest model for predictive analysis of the PLI, NIPI, and ERI, high prediction accuracy was achieved (R2 = 0.78, 0.86, and 0.60, respectively). The study demonstrates that informal mining activities not only elevate the risk of soil pollution but also alter the distribution patterns of heavy metals. Also, this study provides a crucial foundation for the scientific assessment of soil quality and potential environmental hazards, while also affirming the efficacy of ML techniques in forecasting soil quality parameters.

Ontology-Based Landslide Knowledge Representation and Hazard Assessment Research

The occurrence of landslides is characterized by suddenness and unpredictability, with many complex relationships among various influencing factors. This study, by introducing the ontology of knowledge engineering, summarizes the characteristics of landslide knowledge and constructs a multidisciplinary, multi-level, and multi-perspective ontology of the landslide domain. Through in-depth analysis of existing landslide research systems and methods, the study defines the basic concepts, attributes, and their complex relationships and rules related to landslides. This research expresses landslide knowledge using the Web Ontology Language (OWL), enabling effective storage and retrieval of landslide knowledge, thus promoting the systematization and standardization of knowledge in the landslide domain. Furthermore, leveraging the high knowledge density feature of the constructed landslide domain ontology, this study extends traditional landslide evaluation and prediction methods by introducing more evaluation indicators based on the constructed landslide ontology. The comprehensive evaluation of landslide risk is conducted using the contribution-weighting method, providing new insights and tools for landslide risk management and decision-making.

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

Uncertainty-Aware Seismic Signal Discrimination using Bayesian Convolutional Neural Networks

Seismic signal classification plays a crucial role in mitigating the impact of seismic events on human lives and infrastructure. Traditional methods in seismic hazard assessment often overlook the inherent uncertainties associated with the prediction of this complex geological phenomenon. This work introduces a probabilistic framework that leverages Bayesian principles to model and quantify uncertainty in seismic signal classification by applying a Bayesian Convolutional Neural Network (BCNN). The BCNN was trained on a dataset that comprises waveforms detected in the Southern California region and achieved an accuracy of 99.1%. Monte Carlo Sampling subsequently creates a 95% prediction interval for probabilities that considers epistemic and aleatoric uncertainties. The ability to visualize both aleatoric and epistemic uncertainties provides decision-makers with information to determine the reliability of seismic signal classifications. Further, the use of Bayesian CNN for seismic signal classification provides a more robust foundation for decision-making and risk assessment in earthquake-prone regions.

Onboard multivariate hazard assessment for UIKKU chemical tanker by Gaidai reliability method

The current study outlines a novel multimodal structural reliability methodology, suitable for naval dynamic systems, that are either numerically simulated, or directly physically measured. Cross-correlations between the system's principal/key dimensions/components, along with the high dimensionality of complex naval dynamic systems, are not easily addressed by existing reliability methods, that are mostly univariate or bivariate. The objective of this study had been to apply a novel reliability/hazard assessment methodology to UIKKU chemical tanker onboard dynamic measurements to demonstrate the efficiency of the proposed multimodal structural reliability methodology. Current investigation utilized onboard measured ice loadings, exerted on the UIKKU tanker hull and propulsion devices during its Arctic voyage. Onboard dynamic data recordings then were interpreted to gain knowledge about dynamic load levels. The hull of UIKKU had been instrumented so that ice-induced loads/stresses on vessel shell plating had been monitored at various key locations, both in longitudinal as well as in vertical directions, along the vessel’s hull. Longitudinal bending stresses at several locations on the vessel hull and vessel vertical accelerations had been measured to analyze global vessel behaviour when colliding e.g., moderately heavy ice ridges. In addition to UIKKU onboard measurements onboard, Russian partners had instrumented icebreaker vessel Capitan Dranitsyn to real-time measure strains/stresses within ice belt grillage, located at the vessel’s bow. Icebreaker vessel Capitan Dranitsyn acted as escort icebreaker during the whole Arctic voyage. Obtained results may be utilized to harmonize IACS Polar ship rules. In the current study, the onboard measuring system along with measured results, during the whole voyage have been briefly represented.

A review of approaches to assessment of potential radon hazard of land plots

According to Rosstat statistics, in recent years there has been a tendency in the Russian Federation to increase the intensity of construction, while both the number of buildings put into operation and their total area are increasing. The assessment of the potential radon hazard of land plots for construction provides the possibility of timely inclusion of the necessary protective (preventive) measures against radon in the design of buildings. This is a legal requirement in cases where the density of radon flux from the ground surface within the building area exceeds the established hygienic standards (action levels). The paper presents a review of Russian and foreign approaches to assessing the potential radon hazard of land plots conducted within the framework of engineering and environmental surveys. The current regulatory requirements to radiation safety indicators of land plots for construction of residential, public and industrial buildings and facilities in the Russian Federation are analyzed. The main drawbacks of the algorithm for determining the potential radon hazard of land plots, used in the current guidelines MU 2.6.1.2398-08 approved at the federal level more than 15 years ago, are described. Critical remarks (the unsuitability of the value of density of radon flux from the ground surface for designing radon protective and mitigation (remedial) measures, lack of consideration of seasonal variations of radon flux density, etc.) accumulated over the years as a result of practical application of these guidelines are presented. Benefits and drawbacks of foreign approaches to assessing the potential radon hazard of the territory based on the results of measurements of radon concentration in soil gas, as well as the very possibility of a transition in Russian regulations from the density of radon flux from the ground surface to the radon concentration in soil gas, are analyzed. Some rational proposals of various Russian research teams on improving the algorithm for determining the potential radon hazard of land plots are considered.

EVs meet health: A complex-systems policy integration approach for electric vehicle adoption and its impact on respiratory and cardiovascular disease

The transition from gasoline-combustion to zero-emission vehicles represents one of the most ambitious public and private enterprises around the globe to mitigate air pollution and its subsequent effects on climate change. The present study uses the rapid transition in the state of California to analyze the connections between the increasing utilization of electric vehicles (EVs) and two critical health outcomes: Asthma and acute myocardial infarction (AMI) emergency department (ED) visits. We fit simultaneous quantile and interquartile-range regression models to health and sociodemographic data from the California Office of Health and Hazard Assessment and electric vehicles registration data, 2011–2017. Results show that changes in EV utilization, from 2011 to 2013 to 2015–2017, have larger and more favorable effects as asthma and AMI deteriorate across zip codes. Looking at zip codes located at the 75th and 25th percentiles of the change in the asthma- and AMI-related ED visits distribution, a 5-percentage points change in the EV-to-gasoline combustion utilization ratio is associated with a 25-percentage points (p<.01) and 12.5-percentage points (p<.01) decline in asthma- and AMI-related ED visits, respectively. Our findings also suggest that, should the U.S. meet the goals of Executive Order N-79-20 by 2035, we could observe about 37,000 fewer asthma-related ED visits (95% CI: 16,154–59,280) per year, with an overall possible annual savings of about $65.5 million dollars (95% CI: $29.1M – $106.7M), and a possible decline of about 19,292 AMI-related hospitalizations, with possible annual savings of about $425 million dollars (95% CI: $365M – $509M). Our findings are optimistic in that spillover effects from increasing EV utilization can reduce healthcare costs while improving population health. Research should point toward the development of public-private coalitional strategies to effectively bridge usually disparate sectors of the economy and policy-making like energy and health.

3D structural modelling of the Kopili fault zone in North-East India: Seismotectonic analysis utilising focal mechanism solutions of small-to-moderate earthquakes

This work presents new findings from recent seismic data which have never been exploited so far for the weak-to-moderately energetic (ML 2.3–6.4) earthquakes and to depict a 3D scheme for the Kopili Fault (KF) Zone. For the first time, we figure out different fault kinematics in the upper crust such as reverse and strike-slip mechanisms located along the KF zone. Moment Tensor (MT) solutions are successfully obtained through full-waveform inversions for 60 seismic events. The stress inversions are estimated based on the MT results as well as previous findings from 16 small to moderate earthquakes (MW 3.6–6.3). The MT solutions demonstrate that strike-slip kinematics is the most prevalent while a few thrust/normal earthquake events are observed in the area. The seismic cross-sections display that these earthquakes took place at a depth of 40 km and are mostly generated on the eastern side of the fault. According to the 3D spatial and temporal distribution of relocated events, the KF zone is not a completely vertical fault but rather one with a slight eastward dip. Our stress inversion results also indicate that this fault is equivalent to a fault plane with a dip angle of around 80˚, which is compatible with the previously estimated dip angle (75˚). We interpret that the research region is under a transpressional stress regime, which is likely to be the reason for the current activity of the KF zone. We postulate that our findings can be useful to enhance seismic hazard assessment in the densely populated region, which is also covered by vital infrastructure.

Real-time hazard assessment of maize based on the chilling injury process —— Using a standard curve to establish a daily cumulative assessment method

Cold damage caused by low temperatures is known as chilling injury (CI), and it has consistently been one of the primary meteorological disasters affecting maize. With ongoing global climate change, the issue of chilling injury is becoming more prominent, exhibiting new characteristics and presenting new challenges. Consequently, understanding the disaster process and conducting a more refined real-time chilling injury identification have become significant challenges. In this study, we divided maize planting areas into seven maturity types based on the accumulated temperature, constructed a standard curve of the daily accumulated temperature from 1991 to 2020, proposed real-time identification indicators based on the CI process, and developed a real-time CI hazard assessment model. The results indicated that the model can capture independent CI events and rapidly determine the location, intensity, duration and scope of CIs, thereby providing a basis for accurately understanding the impact of chilling injury and taking timely countermeasures. The combination of accumulated temperature standard curves for seven maturity types of maize and the CI curve was used to construct the CI daily scale identification indicator, ΔEAT. Judgment thresholds for the CI identification indicators at various maturity levels were obtained by correlating them with historical disaster data. The frequency and intensity of maize CI gradually increased from the extremely late-maturing zone to the extremely early-maturing zone, with the seeding and emergence periods being the peak periods for CI. The spatiotemporal evolution characteristics of the three different degrees of CI events in 1992, 2004, and 2017 were consistent with the historical disaster records. Northeastern Inner Mongolia and most of Heilongjiang were found to be high-hazard areas for maize CIs. The constructed daily CI identification indicators can accurately and rapidly identify maize CIs, providing practical and targeted guidance for combating these injuries.

A Stochastic Model for Induced Seismicity at the Geothermal Systems: A Case of the Geysers

Abstract Induced seismicity has emerged as a source of a significant earthquake hazard associated with recent development of unconventional energy resources. Therefore, it is imperative to develop stochastic models that can accurately describe the observed seismicity rate and forecast its evolution. In this study, a mechanism suggested by linear response theory is incorporated into a stochastic earthquake model to account for changes in the seismicity rate. It is derived that the induced rate can be modeled as a convolution of the forcing, related to fluid injection operations, and a specific response kernel. The model is incorporated into a Bayesian framework to compute the probabilities for the occurrence of the largest expected events during future time intervals. The applicability of the model is illustrated by analyzing the injection and seismicity data at the Geysers geothermal field in California. The suggested approach provides further insight into the probabilistic assessment of earthquake hazard associated with fluid injection operations. It also can be used for probing the rheological properties of the subsurface by analyzing the inherent characteristic timescales associated with the subsurface response to external forcing.

Assessment of biochemical biomarkers and environmental stress indicators in some freshwater fish

The mechanism by which an organism can adapt to subtle environmental changes is predicated on modifications to biochemical processes within the cellular metabolism in response to such changes. Changes in these processes have the potential to induce alterations in cellular structures and tissue organization, as well as establish a causal link between fluctuations in these parameters and stressors exposure. This investigation's main goal and innovation is to evaluate the environmental stress indicators in the aquatic ecosystem of Lake Qarun. Pesticide residues in freshwater fish should be the primary focus of evaluation of environmental stressor concentrations, since they serve as bioindicators at different times and places on a spatiotemporal scale. A thorough analysis of suggestive biochemical biomarker reactions should also be conducted. The effects of environmental stressors, specifically pesticide contamination in Qarun Lake, have been observed and investigated in relation to two fish species: Solea aejabtiaca and Oreochronis niloticus. The results of a hazard assessment conducted at six sampling sites using spatio-temporal data revealed elevated mean values for the pesticides, persistent organic pollutants (POPs), organochlorines, organophosphates, and pyrethroids that were detected. A multi biomarker approach facilitates a more comprehensive understanding of stress responses induced by exposure to pollutants. As a result, the activities of the biochemical biomarkers CYP-450, GST, GSH, and LDH in the blood and liver of fish samples were found to be notably elevated. The suitability of the identified variables for biomonitoring of aquatic pollution was validated, and the data unveiled variations in sensitivity among species, implying that Nile tilapia could potentially function as a bioindicator with high sensitivity. The findings were correlated with the concentrations of detrimental organochlorines, organophosphorus, and pyrethroids in the muscles and gills. The data indicates that pollutants linked to agricultural wastes, runoff, and municipal effluent may be discharged into the lake ecosystem. Consequently, to safeguard the environment, it is essential to enforce and implement policies, acts, and regulations that already exist. Assessing the effects of additional environmental stressors on aquatic ecosystems is another way in which biomarker screening with an integrative approach improves our comprehension of how toxicants impact various levels of biological organization and is particularly useful in realistic environmental exposure scenarios.

A novel multi-hazard risk assessment framework for coastal cities under climate change

Coastal cities, as centres of human habitation, economic activity and biodiversity, are confronting the ever-escalating challenges posed by climate change. In this work, a novel Multi-Hazard Risk Assessment framework is presented with the focus on Coastal City Living Labs. The methodology provides a comprehensive assessment of climate-related hazards, including sea-level rise, coastal flooding, coastal erosion, land flooding, heavy precipitation, extreme temperatures, heatwaves, cold spells, landslides and strong winds. Its application is illustrated through a case study: the Coastal City Living Lab of Benidorm, Spain. The methodology incorporates remote sensing data from various satellite sources, such as ERA5, Urban Atlas and MERIT DEM, to evaluate multiple hazards through a systematic and standardized indicator-based approach, offering a holistic risk profile that allows for comparison with other European coastal cities. The integration of remote sensing data enhances the accuracy and resolution of hazard indicators, providing detailed insights into the spatiotemporal dynamics of climate risks. The incorporation of local expertise through the Coastal City Living Lab concept enriches data collection and ensures context-specific adequacy. The integration of local studies and historical extreme climate events enhances the validity and context of the risk indicators. The findings align with regional trends and reveal specific vulnerabilities, particularly related to heatwaves, heavy rainfall, and coastal flooding. Despite its strengths, the MHRA methodology faces limitations, including reliance on outdated datasets and the complexity of integrating multiple hazards. Continuous updates and adaptive management strategies are essential to maintain the accuracy and relevance of risk assessments. The broader implications of the methodology for global coastal cities highlight its potential as a model for developing targeted adaptation strategies.

Smart hotspot detection using geospatial artificial intelligence: A machine learning approach to reduce flood risk

This study employs Geospatial Artificial Intelligence (GeoAI) and the Random Forest Machine Learning (ML) algorithm to enhance flood hazard assessments in Portugal. It utilizes NASA's LP DAAC (2023) Digital Elevation Model (DEM) and slope data from EPIC WEBGIS PORTUGAL DATA, offering detailed topographical insights for environmental planning. Additionally, it incorporates data on proximity to water bodies from the Portuguese Environment Agency and the European Environment Agency, and soil characteristics from EPIC WEBGIS PORTUGAL DATA, facilitating a thorough examination of flood risks. This approach prioritizes long-term land features over short-term weather patterns, providing a comprehensive understanding of flood vulnerability. The study processes data at a 1 km x 1 km resolution, adapting TIFF maps for compatibility with the Random Forest model. The produced flood hazard maps identify potential flood hotspots at both national and city levels, crucial for urban planning. These maps aid in assessing the vulnerability of key infrastructure and assets, such as transport networks and buildings. The research highlights the importance of integrating additional data on assets and socioeconomic factors to enhance urban resilience. It sets the stage for future research aimed at improving predictive accuracy and underscores the necessity of extensive geospatial analytics in managing infrastructure risks.

Seismic Vulnerability Indices of Facades of Colonial Houses in the Historic Center of Morelia, México

Evaluating the seismic vulnerability of facades of historic masonry buildings is essential not only for their significant historical and heritage value, but also to evaluate the safety of this type of construction. This work applies a simplified methodology to assess the seismic vulnerability of the facade of masonry buildings in the historic center of Morelia, Michoacán, México. The historic center of Morelia was declared a World Cultural Heritage Site by UNESCO in 1991. On the facades, there is ornamentation with sculptural and vegetal decorative elements. The methodology involved conducting visual inspections to identify the location, type of structure, construction materials, doors, windows, balconies, cornices, ironwork, pediments, niches, and sculptures, among other characteristic elements of colonial architecture. The seismic demands were determined specifically for the city’s historic center based on a recent seismic hazard assessment of Morelia. Based on the methodology and the compiled database, characterized vulnerability indices were defined for the different damage scenarios that buildings may present. Results indicate that earthquakes with intensities greater than VIII on the Modified Mercalli scale risk collapsing heritage masonry buildings’ facades.

Quality Assessment of Multiple UAV-SfM DEMs Derived for Impact Assessment of a Co-Seismic Avalanche in the Himalayas

Combined with the structure from motion (SfM) technique, unmanned aerial vehicles (UAVs) are powerful tools for generating high-resolution digital elevation models (DEMs) for application in hazard assessments. During our field observations in October 2015 at Langtang Village, which was destroyed by the Gorkha earthquake in April 2015, three different UAVs with mounted cameras were operated to evaluate the volume of the avalanche deposit covering the village. This study evaluated the performance of DEMs created from the different cameras on board those UAVs. Multiple DEMs for the different cameras, including Sony-α7R (PA7), Ricoh-GR (XGR), and Canon-IXUS (EIX), were created using SfM software. All DEMs were compared with a base DEM created from differential global positioning system survey data, which was obtained simultaneously with the UAV campaigns. The results show that the elevation difference of PA7-, XGR-, and EIX-DEMs are within ±0.14 m; the standard deviations of elevation difference range from 0.33 to 0.40 m. Although there were slightly larger differences in elevation on the southwest-to-west sides of the XGR- and EIX-DEMs, which can be attributed mainly to the flight paths and ground control point network, our DEMs are still of high enough quality to be used in hazard assessments.

Elucidating enantioselective fate and sensitive biomarkers in zebrafish of chiral pesticide fenpropidin: Insights into metabolic pathways and hazard assessment

Fenpropidin (FPD), a widely utilized chiral fungicide, has been detected in aquatic environments. This study systematically evaluated the bioaccumulation, depuration, biotransformation, and sensitive biomarkers of FPD enantiomers in zebrafish to assess their environmental risks. Compared with S-FPD, R-FPD demonstrated a higher rate of enrichment and an increased level of bioaccumulation. The half-lives of R-FPD and S-FPD were 0.49 ± 0.01 and 0.91 ± 0.02 days at 0.05 mg/L and 1.65 ± 0.01 and 1.85 ± 0.03 days at 0.5 mg/L. Nontarget metabolism analysis identified nine metabolites, primarily formed through hydroxylation, oxidation, dehydration, glutathione conjugation, and glucuronidation pathways. Some metabolites exhibited high toxicity, underscoring the necessity for continuous monitoring of their toxicological effects and environmental fate in risk assessments. The toxicity of S-FPD in zebrafish was 1.21 times greater than that of R-FPD. Furthermore, this study identified sensitive markers for the enantiomers at both protein and transcriptional levels using an integrated biomarker response approach. S-FPD exhibited increased sensitivity to apoptosis and metabolic gene expression, while R-FPD showed greater sensitivity to antioxidant kinase activity. These findings facilitate timely monitoring of environmental pollution caused by FPD enantiomers. This study provides critical insights for assessing potential risks associated with pesticide exposure to human health.

Update of the risk assessment of brominated phenols and their derivatives in food.

The European Commission asked EFSA to update its 2012 risk assessment on brominated phenols and their derivatives in food, focusing on five bromophenols and one derivative: 2,4,6-tribromophenol (2,4,6-TBP), 2,4-dibromophenol (2,4-DBP), 4-bromophenol (4-BP), 2,6-dibromophenol (2,6-DBP), tetrabrominated bisphenol S (TBBPS), tetrabromobisphenol S bismethyl ether (TBBPS-BME). Based on the overall evidence, the CONTAM Panel considered invivo genotoxicity of 2,4,6-TBP to be unlikely. Effects in liver and kidney were considered as the critical effects of 2,4,6-tribromophenol (2,4,6-TBP) in studies in rats. A BMDL10 of 353 mg/kg body weight (bw) per day for kidney papillary necrosis in male rats was identified and was selected as the reference point for the risk characterisation. The derivation of a health-based guidance value was not considered appropriate due to major limitations in the toxicological database. Instead, the margin of exposure (MOE) approach was applied to assess possible health concerns. Around 78,200 analytical results for 2,4,6-TBP in food were used to estimate dietary exposure for the European population. Considering the resulting MOE values, all far above an MOE of 6000 that does not raise a health concern, and accounting for the uncertainties affecting the exposure and hazard assessments, the CONTAM Panel concluded with at least 95% probability that the current dietary exposure to 2,4,6-TBP does not raise a health concern. Due to lack of occurrence data, no risk assessment could be performed for breastfed or formula-fed infants. No risk characterisation could be performed for any of the other brominated phenols and derivatives included in the assessment, due to lack of data both on the toxicity and occurrence.