Risk Assessment Scenarios Research Articles

Towards realism in hazard assessment of plastic and rubber leachates – Methodological considerations

While plastic chemicals are key drivers of observed effects to aquatic species, there remains a lack of standardized and fit-for-purpose approaches for experimentally deconvoluting the effects of plastic chemicals from particle effects. This study investigated differences in chemical composition determined using two different organic solvents for extractions (dichloromethane-ethyl acetate, methanol) and by thermal desorption applied to 51 thermoplastic and elastomer products. The composition of natural water leachates of four select elastomers was also investigated. The number of chemical features in each material varied according to the extraction method, with solvent extracts exhibiting the most chemicals, and only 19 compounds commonly identified by all three methods. The number of chemical features in leachates was generally similar to the corresponding chemical extracts, but strong differences in relative composition were detected. While turbulence had minimal impact on leachate composition, particle loading strongly influenced leachate composition, temperature and salinity influenced the leachate concentration for some chemicals, and leaching time depended upon chemical mobility. Leachate composition cannot be readily predicted from particle characterization and multiple parameters are drivers of compositional variance in aquatic leachates. Recommendations for performing leaching studies that are relevant for hazard characterization in a realistic aquatic environment risk assessment scenario are suggested, with a particular focus on particle loading.

Relationship between Cognitive Demands and Biomechanical Indicators Associated with Anterior Cruciate Ligament Injury: A Systematic Review.

Anterior cruciate ligament (ACL) injury during contact sports has a high incidence that has not been reduced despite the immense resources devoted to understanding its aetiology. A neurocognitive approach could increase knowledge of the mechanisms contributing to ACL injury enabling practitioners to address and minimise future risk. To systematically review the influence of manipulating cognitive demands during motor tasks (i.e. degree of uncertainty) on biomechanical variables associated with ACL injury risk. A systematic review was performed according to the Cochrane Handbook for Systematic Reviews of Interventions by searching the major sporting electronic databases. The search strategy included four groups of terms and was conducted by two authors independently. All studies were screened using unique inclusion criteria, with the included studies assessed for risk of bias. Twenty-five studies were identified from 2031 records and included into the review process. During the experimental conditions where cognitive demands were higher, most biomechanical indicators associated with a greater risk of ACL injury during landing and cutting tasks were significantly enhanced compared with conditions with low or no cognitive demands. An increase in task complexity through cognitive load significantly leads to changes in mechanisms associated with ACL injury during single-leg landings and cutting movements. Consequently, coaches and exercise professionals should consider inclusion of dual-task paradigms or uncertainty during injury risk assessment scenarios and injury prevention programs to help identify athletes at risk of ACL injury and reduce ACL injury frequency. This protocol was registered in the PROSPERO database ( https://www.crd.york.ac.uk/PROSPERO ) in May 2022, with the registration number CRD42022315795.

Chronic oral toxicity protocol for adult solitary bees (Osmia bicornis L.): Reduced survival under long-term exposure to a “bee-safe” insecticide

Pollinators are essential for crop productivity. Yet, in agricultural areas, they may be threatened by pesticide exposure. Current pesticide risk assessments predominantly focus on honey bees, with a lack of standardized protocols for solitary bees. This study addresses this gap by developing a long-term oral exposure protocol tailored for O. bicornis. We conducted initial trials to determine optimal container sizes and feeding methods, ensuring high survival rates and accurate syrup consumption measurements. A validation test involving five laboratories was then conducted with the insecticide Flupyradifurone (FPF). Control mortality thresholds were set at ≤ 15% at 10 days. Three laboratories achieved ≤10%, demonstrating the protocol's effectiveness in maintaining healthy test populations. The seasonal timing of experiments influenced control mortality, underscoring the importance of aligning tests with the natural flight period of the population used. Our findings revealed dose-dependent effects of FPF on syrup consumption, showing stimulatory effects at lower concentrations and inhibitory effects at higher ones. The 10-day median lethal daily dose (LDD50) of FPF for O. bicornis (531.92 ng/bee/day) was 3.4-fold lower than that reported for Apis mellifera (1830 ng/bee/day), indicating Osmia's higher susceptibility. Unlike other insecticides, FPF did not exhibit time-reinforced toxicity. This study introduces a robust protocol for chronic pesticide exposure in solitary bees, addressing a critical gap in current risk assessment. Based on its low risk to honey bees and bumblebees, FPF is approved for application during flowering. However, our results suggest that it may threaten Osmia populations under realistic field conditions. Our findings underscore the need for comparative toxicity studies to ensure comprehensive protection of all pollinators and the importance of accounting for long term exposure scenarios in risk assessment. By enhancing our understanding of chronic pesticide effects in solitary bees, our study should contribute to the development of more effective conservation strategies and sustainable agricultural practices.

A proof-of-concept multi-tiered Bayesian approach for the integration of physiochemical properties and toxicokinetic time-course data for Daphnia magna

The use of in silico and in vitro methods, commonly referred to as New Approach Methodologies (NAMs), has been proposed to support environmental (and human) chemical safety decisions, ensuring enhanced environmental protection. Toxicokinetic models developed for environmentally relevant species are fundamental to the deployment of a NAMs-based safety strategy, enabling the conversion between external and internal chemical concentrations, although they require historical toxicokinetic data and robust physical models to be considered a viable solution. Daphnia magna is a key model organism in ecotoxicology albeit with limited and scattered quantitative toxicokinetic data, as for most invertebrates, resulting in a lack of robust toxicokinetic models. Moreover, current D. magna models are chemical specific, which restricts their applicability domain. One aim of this study was to address the current data availability limitations by collecting toxicokinetic time-course data for D. magna covering a broad chemical space and assessing the dataset's uniqueness. The collated toxicokinetic dataset included 48 time-courses for 30 chemicals from 17 studies, which was developed into an R package named AquaTK, with 11 studies unique to our work when compared to existing databases. Subsequently, a proof-of-concept Bayesian analysis was developed to estimate the steady-state concentration ratio (internal concentration / external concentration) from the data at three different levels of precision given three different data availability scenarios for environmental risk assessment. Specifically, an atrazine case study illustrates the multi-level modelling approach providing improvements (uncertainty reductions) in predictions of ratios for increasing amounts of data availability. Our work provides a consistent and self-contained Bayesian framework that irrespective of the hierarchy or resolution of individual experiments, can utilise the available information to generate optimal predictions of steady-state concentration ratios in D. magna. This approach is paramount to supporting the implementation of a NAMs based environmental safety paradigm shift in environmental risk assessment.

Estimation of Prediction Intervals for Performance Assessment of Building Using Machine Learning.

This study utilizes artificial neural networks (ANN) to estimate prediction intervals (PI) for seismic performance assessment of buildings subjected to long-term ground motion. To address the uncertainty quantification in structural health monitoring (SHM), the quality-driven lower upper bound estimation (QD-LUBE) has been opted for global probabilistic assessment of damage at local and global levels, unlike traditional methods. A distribution-free machine learning model has been adopted for enhanced reliability in quantifying uncertainty and ensuring robustness in post-earthquake probabilistic assessments and early warning systems. The distribution-free machine learning model is capable of quantifying uncertainty with high accuracy as compared to previous methods such as the bootstrap method, etc. This research demonstrates the efficacy of the QD-LUBE method in complex seismic risk assessment scenarios, thereby contributing significant enhancement in building resilience and disaster management strategies. This study also validates the findings through fragility curve analysis, offering comprehensive insights into structural damage assessment and mitigation strategies.

Statistically based approach to select worst-case groundwater scenarios for environmental risk assessment of pesticides in Brazil

Exposure scenarios in combination with simulation models have been used in the procedure of Environmental Risk Assessment (ERA) of pesticides to estimate exposure concentrations in environmental compartments.The appropriate definition of exposure scenarios is primordial to guarantee ERA purpose to evaluate if a pesticide use in an intended area can be considered safe. This work had the aim to present and test a geo- and statistically based approach to define worst-case groundwater exposure scenarios for Brazil. To do so, soil-climate data of Mato Grosso do Sul state consisting of 42 locations (3 soil classes and 14 meteorological stations) were used to generate populations of Predicted Environmental Concentrations (PEC) at 3 and 5-m depth for a simulation period of 24 years. A total of 3,780 graphs were generated of PEC populations for 15 “dummy” pesticides. After setting equal risks in both dimensions (spatial and temporal) equal to 20%, the overall risks exceedance of tested worst-case groundwater exposure scenarios were about 10% for both depths. Thus, this approach can be considered suitable to guarantee a pre-defined overall risk exceedance once defined by the regulatory authorities in Brazil.

Mercury risk assessment scenarios: exposure from fish dietary behaviors of Katingan River Basin community

ABSTRACT Katingan River has been contaminated by mercury from ASGM activities since the early 2000s. This study aims to assess the risk of mercury exposure from Katingan fish consumption and analyze relationships between exposure variables. We proposed two risk assessment scenarios based on mercury analysis of 74 fish samples and fish consumption questionnaire. The risk assessment result revealed that estimated daily intakes (EDIs) of Scenario 2 were generally 3–4 times higher than Scenario 1. The statistical test results showed significant differences (p < 0.05) in EDI values between scenarios, indicating that variations in fish consumption influenced the mercury intake. Three different health reference-based values (HRVs) were applied in estimating the hazard quotient (HQ) and it influenced the HQ results (p < 0.05), for both scenarios. The relationship analysis between variables presented a strong non-linear correlation between EDI and hair mercury level for both scenarios, but a weak relationship between age and hair mercury level.

Impacts and risks of “realistic” global warming projections for the 21st century

The IPCC AR6 assessment of the impacts and risks associated with projected climate changes for the 21st century is both alarming and ambiguous. According to computer projections, global surface temperature may warm from 1.3 °C to 8.0 °C by 2100, depending on the global climate model (GCM) and the shared socioeconomic pathway (SSP) scenario used for the simulations. Actual climate-change hazards are estimated to be high and very high if the global surface temperature rises, respectively, more than 2.0 °C and 3.0 °C above pre-industrial levels. Recent studies, however, showed that a substantial number of CMIP6 GCMs run “too hot” because they appear to be too sensitive to radiative forcing, and that the high/extreme emission scenarios SSP3-7.0 and SSP5-8.5 are to be rejected because judged to be unlikely and highly unlikely, respectively. Yet, the IPCC AR6 mostly focused on such alarmistic scenarios for risk assessments. This paper examines the impacts and risks of “realistic” climate change projections for the 21st century generated by assessing the theoretical models and integrating them with the existing empirical knowledge on global warming and the various natural cycles of climate change that have been recorded by a variety of scientists and historians. This is achieved by combining the SSP2-4.5 scenario (which is the most likely SSP according to the current policies reported by the International Energy Agency) and empirically optimized climate modeling. According to recent research, the GCM macro-ensemble that best hindcast the global surface warming observed from 1980 to 1990 to 2012–2022 should be made up of models that are characterized by a low equilibrium climate sensitivity (ECS) (1.5 °C < ECS ≤ 3.0 °C), in contrast to the IPCC AR6 likely and very likely ECS ranges at 2.5–4.0 °C and 2.0–5.0 °C, respectively. I show that the low-ECS macro-GCM with the SSP2-4.5 scenario projects a global surface temperature warming of 1.68–3.09 °C by 2080–2100 instead of 1.98–3.82 °C obtained with the GCMs with ECS in the 2.5–4.0 °C range. However, if the global surface temperature records are affected by significant non-climatic warm biases — as suggested by satellite-based lower troposphere temperature records and current studies on urban heat island effects — the same climate simulations should be scaled down by about 30%, resulting in a warming of about 1.18–2.16 °C by 2080–2100. Furthermore, similar moderate warming estimates (1.15–2.52 °C) are also projected by alternative empirically derived models that aim to recreate the decadal-to-millennial natural climatic oscillations, which the GCMs do not reproduce. The proposed methodologies aim to simulate hypothetical models supposed to optimally hindcast the actual available data. The obtained climate projections show that the expected global surface warming for the 21st-century will likely be mild, that is, no more than 2.5–3.0 °C and, on average, likely below the 2.0 °C threshold. This should allow for the mitigation and management of the most dangerous climate-change related hazards through appropriate low-cost adaptation policies. In conclusion, enforcing expensive decarbonization and net-zero emission scenarios, such as SSP1-2.6, is not required because the Paris Agreement temperature target of keeping global warming < 2 °C throughout the 21st century should be compatible also with moderate and pragmatic shared socioeconomic pathways such as the SSP2-4.5.

Groundwater nitrate pollution risk assessment based on the potential impact of land use, nitrogen balance, and vulnerability

The predicting groundwater nitrate pollution risk, especially in terms of changes in fertilizing, has not been fully investigated so far. In particular, there is no comprehensive method to assess this risk in areas of different land use type, and not only in agricultural areas. The aim of this study was to develop a novel multicriteria methodology for groundwater nitrate pollution risk assessment, which meets these issues. A further aim was to determine how much this risk would change if the amount of organic and synthetic fertilization was reduced. An assumption was that groundwater pollution risk is a combination of the potential adverse impacts of land use, fertilization, and intrinsic groundwater vulnerability to pollution. The impact of fertilization was holistically evaluated by balancing nitrogen from spatially differentiated the size of the breeding, species of livestock, manure and synthetic fertilizers input, and spatially differentiated topsoil, with nitrogen uptake by different crops. The nitrate concentration in the leachate was used as a measure of the impact of fertilization. This concentration was compared to the natural baseline nitrate concentration in groundwater. Three fertilization scenarios for groundwater pollution risk assessment in two study areas were discussed. Under typical agricultural, climatic, soil, and geological conditions in Europe for the current total fertilization level of 95-120 kg N ha−1 groundwater nitrate pollution risk is low and moderate, but for fertilization of 150-180 kg N ha−1, a reduction in the total fertilization (synthetic and manure) by 40 to 50% may be required to achieve low risk of degradation of natural groundwater quality. Predictive simulations of groundwater nitrate pollution risk confirmed that reducing synthetic and organic fertilization has an effect, especially in areas with intensive fertilization. This method may allow for a holistic and scenario-based assessment of groundwater pollution risk and may help decision-makers introduce solutions to manage this risk under conditions of climate change, preservation of groundwater quality, and food security.

Relative humidity and solar radiation exacerbate snow drought risk in the headstreams of the Tarim River

Snow drought is commonly defined as the phenomenon of abnormally low snow snowpack caused by below-normal precipitation and/or above-normal temperatures for a period of time. This poses a critical knowledge gap, requiring a deeper understanding of its multivariate risk under various scenarios, dynamics, and driving mechanisms in a changing environment. In this study, we constructed a density kernel estimation for the Nonparametric Standardized SWE Index (NSWEI) to gain new insights into the implications of snow drought. We further demonstrated its ability, specifically the ability Equitable Threat Score (ETS), to anticipate warm-season hydrological droughts using a Random Forest (RF) model. In addition, we applied the concept of multivariable return periods under different scenarios for snow drought risk assessment in combination with Copula theory and its dynamics under a changing environment. This exploration was followed by a preliminary attribution analysis. Our results indicate that: (1) the NSWEI is more reasonable in characterizing snow drought than previous snowpack metrics; (2) the most probable droughts (occurring with less than a 50-year return period) belong to the moderate drought scenario; (3) the interaction between vapor pressure deficit and solar radiation significantly influences static risk, while the Digital Elevation Model (DEM) also plays undeniable roles in affecting snow drought risk; (4) a contrasting pattern is found in spatial distribution of snow drought risk dynamics, with an exacerbating snow drought risk observed in the southern part; and (5) the increasing snow drought risks in the south are generally triggered by reducing relative humidity and increasing solar radiation. Overall, this study provides new insights into snow drought risk and its dynamics, which are important for developing robust and effective management practices.

Comparison of Different Radiological Risk Assessment Scenarios at a Coal Ash and Slag Disposal Site

Coal fly ash and slag waste residuals from coal combustion are an issue of importance as one of the possible sources of environmental contamination and exposure to NORM. This study compares the results of different radiological risk assessment scenarios targeting terrestrial biota at a legacy site in Croatia that contains large quantities of coal ash with an enhanced content of radionuclides originating from previous industrial activities. The ERICA assessment tool was used for a risk assessment, which included data from borehole samples with a maximum depth of 6 m and trees as the primary reference organisms. The results of the risk assessments from various depth ranges found the radiological risk to the reference organisms to be negligible, regardless of the depth range, since the screening dose rate of 10 µGyh−1 was not exceeded in any of the assessments. The risk assessment results from all depth ranges show higher total dose rate predictions when the tool’s default CR values are used, compared to the site-specific ones, which is in agreement with previous studies on the application of the ERICA tool. A comparison of results from different spatial radiological risk assessments showed that sample depth does not affect the estimated total dose rate to biota.

Integrated HBIM-GIS Models for Multi-Scale Seismic Vulnerability Assessment of Historical Buildings

The complexity of historical urban centres progressively needs a strategic improvement in methods and the scale of knowledge concerning the vulnerability aspect of seismic risk. A geographical multi-scale point of view is increasingly preferred in the scientific literature and in Italian regulation policies, that considers systemic behaviors of damage and vulnerability assessment from an urban perspective according to the scale of the data, rather than single building damage analysis. In this sense, a geospatial data sciences approach can contribute towards generating, integrating, and making virtuous relations between urban databases and emergency-related data, in order to constitute a multi-scale 3D database supporting strategies for conservation and risk assessment scenarios. The proposed approach developed a vulnerability-oriented GIS/HBIM integration in an urban 3D geodatabase, based on multi-scale data derived from urban cartography and emergency mapping 3D data. Integrated geometric and semantic information related to historical masonry buildings (specifically the churches) and structural data about architectural elements and damage were integrated in the approach. This contribution aimed to answer the research question supporting levels of knowledge required by directives and vulnerability assessment studies, both about the generative workflow phase, the role of HBIM models in GIS environments and toward user-oriented webGIS solutions for sharing and public use fruition, exploiting the database for expert operators involved in heritage preservation.

Scenario-based collision detection using machine learning for highly automated driving systems

Highly Automated Driving (HAD) systems implement new features to improve the performance, safety and comfort of partially or fully automated vehicles. The identification of safety parameters by means of complex systems and the driving environment is a fundamental aspect that require great attention. Therefore, much research has been conducted in the field of collision detection in the development of automated vehicles. However, the development of HAD systems faces the challenge of ensuring zero accidents. For this reason, collision detection in the safety-related concept phase as hazard identification is one of the key research points in HAD system. In this paper, a systematic approach to detect potential collisions for scenario-based hazard analysis of HAD systems is presented by using Multilayer Perceptron (MLP) as a Machine Learning (ML) technique. Moreover, the proposed approach assists in reducing the number of observed scenarios for hazard analysis and risk assessment. Additionally, two simulation-based scenario datasets are examined in the ML model to identify potential hazard scenarios. The results of this study show that MLP can support to detect the collision at safety-related concept phase. Furthermore, this paper contributes to providing arguments and evidence for ML techniques in HAD systems safety by selecting relevant use cases.

A BIA-Based Quantitative Framework for Built Physical Asset Criticality Analysis under Sustainability and Resilience

Asset-intensive industries, such as the construction industry, have experienced major catastrophes that have led to significant operational disruptions. Physical asset failure has been the primary cause of these disruptions. Therefore, implementing proper asset management plans, including continuity plans, is crucial for the business continuity of companies active in these industries. However, companies often face severe resource limitations when implementing these plans for all of their physical assets. Therefore, those critical physical assets that are vital for providing their key products should be identified. Moreover, sustainability and resilience are inseparable parts of organizations’ strategies, including strategic asset management plans. Therefore, any proposed ranking methodology for physical asset prioritization should encompass sustainability and resilience measures to ensure its practicality. This paper proposes a novel framework for physical asset criticality analysis based on the so-called business impact analysis to ensure the continuity of providing products/services through the continuity of physical assets. A hybrid fuzzy BWM-TOPSIS method is first applied to identify the key products. Then, a hybrid fuzzy DEMATEL-Bayesian network is applied based on proper sustainability and resilience factors to determine the critical physical assets, while interdependencies among these factors are well captured. The normalized expected asset criticality index is defined to guide managers in taking appropriate directions while developing asset management plans. A case study of a gas company is provided to show the applicability of the proposed decision model. The data needed for each step of the framework is gathered through experts’ judgments, historical data available on the sites, or quantitative risk assessment scenarios.

Environmental protection in natural gas industry comparison of different spatio-temporal radiological risk assessment scenarios

The paper analyses results of spatio-temporal radiological risk assessment scenarios based on existing in-situ long-term monitoring data from a natural gas processing plant to analyse the effect of different input data on the assessment outcome. The ERICA Assessment Tool was used to estimate the dose rates to biota and potential impacts due to the exposure to ionising radiation. The input data for radiological risk assessment scenarios included annual data on activity concentration of radionuclides in soil from measurements performed from 1994 to 2016 and laboratory gamma-spectrometric data related to the period from 2014 to 2019. Predicted total dose rate to biota was generally below the ERICA Tool's screening dose rate of 10 ?Gyh-1 or slightly above, with the highest total dose rate estimated for lichen and bryophytes. Total dose rates to lichen and bryophytes in the studied period show certain temporal variation, but a specific trend was not detected. Estimated total dose rates to biota from different assessment scenarios were below internationally proposed reference levels for which no detrimental effects are expected. The overall potential radiological risk to terrestrial biota from the operation of the natural gas processing plant was found to be negligible.

Applying a next generation risk assessment framework for skin sensitisation to inconsistent new approach methodology information.

Cosmetic products must be safe for their intended use. Regulatory bans on animal testing for new ingredients has resulted in a shift towards the use of new approach methodologies (NAM), such as in silico predictions and in chemico/in vitro data. Defined Approaches (DA) have been developed to interpret combinations of NAM to provide information on skin sensitisation hazard and potency, three having been adopted within OECD Guideline 497. However, the challenge remains as to how DA can be used to derive a quantitative point of departure for use in next generation risk assessments (NGRA). Here we provide an update to our previously published NGRA framework and present two hypothetical consumer risk assessment scenarios (rinse-off and leave-on) on one case study ingredient. Diethanolamine (DEA) was selected as the case study ingredient based upon the existing NAM information demonstrating differences with respect to the outcomes from in silico predictions and in chemico / in vitro data. Seven DA were applied, and these differences resulted in divergent DA outcomes and reduced confidence with respect to the hazard potential and potency predictions. Risk assessment conclusion for the rinse-off exposure led to an overall decision of safe for all DA applied. Risk assessment conclusion for the higher leave-on exposure was safe when based upon some DA but unsafe for others. The reasons for this were evaluated, as well as the inherent uncertainty from the use of each NAM and DA in the risk assessment, enabling further refinement of our NGRA framework.

Experimental investigation of the effects of a sidewall and cable arrangement on a horizontal cable tray fire in an open atmosphere

AbstractThe work deals with the influence of a sidewall and cable arrangement on the behavior of a fire involving horizontal cable trays in the framework of fire safety assessments in nuclear installations. The analysis is based on large‐scale fire tests performed in the open atmosphere in the frame of the OECD Nuclear Energy Agency (NEA) PRISME 3 project and on the corresponding simulations applying the FLASHCAT model for predicting the fire heat release rate. The fire configuration consists of five horizontal trays filled with PVC‐insulated power cables. The parameters investigated are the presence or absence of a sidewall and the cable arrangement (loose or tight bundles). The results show that the presence of a sidewall increases the fire HRR in comparison to a scenario without a sidewall. This effect is due to the increase of the flame spread velocity on the lower trays. Regarding cable arrangement, a tight configuration in bundles reduces the fire HRR in comparison to a scenario with a loose arrangement. This result is due to the reduction of the fire heat release rate per unit of area (HRRPUA) as well as the flame spread velocity. The performance of the FLASHCAT model in predicting the effects of the sidewall and the cable arrangement was also assessed on the basis of the fire tests and satisfactory agreements are reported. The presented analysis demonstrates that the fire scenario with horizontal cable trays against a sidewall and with a loose cable arrangement represents a conservative scenario for fire risk assessment. In addition, on the basis of these experiments, the effect of cable arrangement is more substantial than the sidewall effect.

Establishment of a human cell-based in vitro battery to assess developmental neurotoxicity hazard of chemicals

Developmental neurotoxicity (DNT) is a major safety concern for all chemicals of the human exposome. However, DNT data from animal studies are available for only a small percentage of manufactured compounds. Test methods with a higher throughput than current regulatory guideline methods, and with improved human relevance are urgently needed. We therefore explored the feasibility of DNT hazard assessment based on new approach methods (NAMs). An in vitro battery (IVB) was assembled from ten individual NAMs that had been developed during the past years to probe effects of chemicals on various fundamental neurodevelopmental processes. All assays used human neural cells at different developmental stages. This allowed us to assess disturbances of: (i) proliferation of neural progenitor cells (NPC); (ii) migration of neural crest cells, radial glia cells, neurons and oligodendrocytes; (iii) differentiation of NPC into neurons and oligodendrocytes; and (iv) neurite outgrowth of peripheral and central neurons. In parallel, cytotoxicity measures were obtained. The feasibility of concentration-dependent screening and of a reliable biostatistical processing of the complex multi-dimensional data was explored with a set of 120 test compounds, containing subsets of pre-defined positive and negative DNT compounds. The battery provided alerts (hit or borderline) for 24 of 28 known toxicants (82% sensitivity), and for none of the 17 negative controls. Based on the results from this screen project, strategies were developed on how IVB data may be used in the context of risk assessment scenarios employing integrated approaches for testing and assessment (IATA).

Pedestrian Single and Multi-Risk Assessment to SLODs in Urban Built Environment: A Mesoscale Approach

Pedestrians are increasingly exposed to slow-onset disasters (SLODs), such as air pollution and increasing temperatures in urban built environments (BEs). Pedestrians also face risks that arise from the combination of the BE features, the effects of SLODs on the microclimate, their own characteristics (e.g., health and ability), and the way they move and behave in indoor and outdoor BE areas. Thus, the effectiveness of sustainable risk-mitigation solutions for the health of the exposed pedestrians should be defined by considering the overlapping of such factors in critical operational scenarios in which such emergency conditions can appear. This work provides an innovative method to define a BE-oriented pedestrian risk index through a dynamic meso-scale approach that considers the daily variation of risk conditions. The method is ensured by a quick-to-apply approach, which also takes advantage of open-source repositories and tools to collect and manage input data, without the need for time-consuming in situ surveys. The resulting risk conditions are represented through meso-scale maps, which highlight the risk differences between BEs by focusing on their open spaces as fundamental parts of the urban road network. The method is applied to a significant case study (in Milan, Italy). The results demonstrate the ability of the approach to identify key input scenarios for risk assessment and mapping. The proposed methodology can: (1) provide insights for simulation activities in critical BE conditions, thanks to the identification of critical daily conditions for each of the factors and for single and multiple risks and (2) support the development of design and regeneration strategies in SLOD-prone urban BEs, as well as the identification of priority areas in the urban BE.

Case study on the impact of the source of metabolism parameters in next generation physiologically based pharmacokinetic models: Implications for occupational exposures to trimethylbenzenes

Physiologically based pharmacokinetic (PBPK) models are a means of making important linkages between exposure assessment and in vitro toxicity. A key constraint on rapid application of PBPK models in risk assessment is traditional reliance on substance-specific in vivo toxicokinetic data to evaluate model quality. Bounding conditions, in silico, in vitro, and chemical read-across approaches have been proposed as alternative sources for metabolic clearance estimates. A case study to test consistency of predictive ability across these approaches was conducted using trimethylbenzenes (TMB) as prototype chemicals. Substantial concordance was found among TMB isomers with respect to accuracy (or inaccuracy) of approaches to estimating metabolism; for example, the bounding conditions never reproduced the human in vivo toxicokinetic data within two-fold. Using only approaches that gave acceptable prediction of in vivo toxicokinetics for the source compound (1,2,4-TMB) substantially narrowed the range of plausible internal doses for a given external dose for occupational, emergency response, and environmental/community health risk assessment scenarios for TMB isomers. Thus, risk assessments developed using the target compound models with a constrained subset of metabolism estimates (determined for source chemical models) can be used with greater confidence that internal dosimetry will be estimated with accuracy sufficient for the purpose at hand.