Realistic Risk Assessment Research Articles

Assessing the vulnerability of empirical infrastructure networks to natural catastrophes

Human-made infrastructures are complex systems continually exposed to events that threat their function, such as cascading failures, occurring when the flow of physical quantities is redistributed within the network as a consequence of localized disruptions. Nevertheless, the role played by exogenous catastrophic events and internal failures on the robustness of critical infrastructures is usually addressed independently, under simplifying assumptions and lacking a unified picture for realistic risk assessments. Here, we fill this gap by introducing the Operational-Affected-Dismantled (OAD) model that captures both local and nonlocal failure propagation mechanisms. The model combines reaction–diffusion processes for local spreading with a global field effect for long-range interactions, allowing us to quantitatively characterize the cascade dynamics in infrastructure networks. Moreover, we include information on external stressors to assess the robustness of empirical network infrastructures and build spatial risk maps. By using data from severe storms (2009–2016) and from earthquakes (2000–2023) as stressors of the North American power grid and the worldwide airline transportation system, respectively, we offer a quantitative way to rank events by their potential to trigger systemic effects. By analyzing the response of the European power grid to simulated severe storms, we find that it can show high levels of systemic risk. Uncertainty in global climate and the accelerating frequency of extreme events all over the globe call for novel strategies to quantify, adapt to and mitigate systemic risk. Our framework provides a suitable starting point to assess the robustness of empirical systems in realistic and what-if scenarios.

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.

Estimating the impact of supply chain network contagion on financial stability

Realistic credit risk assessment, the estimation of losses due to a debtors failure, is central for maintaining financial stability. Credit risk models focus on the financial conditions of borrowers and only marginally consider other risks from the real economy, supply chains in particular. Recent pandemics, geopolitical instabilities, and natural disasters demonstrated that supply chain shocks can contribute to financial losses large enough to threaten financial stability. Based on a unique nation-wide micro-dataset, containing practically all supply chain relations of all Hungarian firms, together with their bank loans, we develop a multi-layer shock propagation framework to estimate how economic shocks to firms cascade in the supply chain network (SCN), leading to additional financial losses to firms, additional defaults of loans and, hence, losses to banks’ equity buffers. First, we estimate the financial systemic risk of individual firms, by quantifying the expected financial losses caused by a firm’s own- and all the secondary defaulting loans caused by supply chain network contagion. We find a small fraction of firms carrying substantial financial systemic risk, affecting up to 22% of the banking system’s overall equity (assuming a loss given default of 100%). These losses are predominantly caused by SCN-contagion. Second, we calculate for every bank the expected loss (EL), value at risk (VaR) and expected shortfall (ES), with and without SCN-contagion. We find that SCN-contagion amplifies EL, VaR, and ES by a factor of 5.2, 6.7 and 4.4, respectively. Third, we showcase how the new framework can be used to assess the risks of a large real economy shock for financial stability. We simulate the financial losses from a COVID-19 inspired shock calibrated from firm-level employment data in the beginning of 2020. Our simulations show that without any interventions, system-wide bank equity would suffer losses of 6%. The framework can be used to design and test targeted policy interventions, e.g., optimally providing firms with enough liquidity-support to avert their default. By supporting selected illiquid (yet solvent) firms with additional liquidity totalling 0.5% of overall bank equity, the losses can be reduced from 6% to 1% of overall bank equity. These findings indicate that for a more complete picture of financial stability and realistic credit risk assessment, SCN contagion needs to be considered. This now quantifiable contagion channel is of relevance for future systemic risk assessments of regulators.

Exploring the inclusion of soil management practices in erosion models towards the improvement of post-fire predictions

Wildfires are recognized for having a strong impact on forest soils, a situation aggravated by inadequate pre-fire land management practices. Land management operations, such as plowing, are routinely carried out for cultural reasons and can impact soils for decades after their implementation. Therefore, it is crucial to take into account the pre-fire land management history when predicting post-fire sediment losses in burnt areas. This consideration is critical for a realistic assessment of soil erosion risk and, consequently, for effectively implementing emergency stabilization and/or rehabilitation measures.The aim of the study was to integrate pre-fire land management practices into erosion models, to enhance post-fire sediment losses predictions at slope scale. To accomplish this goal, both Multiple Linear Regression (MLR) and the revised-Morgan-Morgan-Finney model (revised-MMF) were applied in the Colmeal burnt area (Central Portugal). These models were adapted to account the impacts of different management options, specifically no plowing, downslope-plowing and contour-plowing, on the erosive response following a wildfire.The results revealed fluctuations in the performance of both models across different soil management, and over time since the wildfire. Despite the observed variability, it is important to highlight the positive outcomes achieved with the revised-MMF model over the three monitoring years where contour-plowing was applied. These results demonstrate that the best model performances are achieved when soil management is individualized and analyzed independently. Similarly, the MLR model exhibited improved performance when incorporating management practices into its predictions. This study confirms that disturbances on topsoil, whether caused by wildfires or soil management operations, play key roles in driving change in soil erosion. Hence, integrating these factors into models is essential for providing relevant information for the development of mitigation and/or restoration strategies in areas at high risk of erosion.

No impact of cyantraniliprole on the hibernation success of bumble bees (Bombus terrestris audax) in a soil-mediated laboratory exposure study.

Increasing evidence shows that wild bees, including bumble bees, are in decline due to a range of stressors, including pesticides. Our knowledge of pesticide impacts has consequently grown to enable the design of increasingly realistic risk assessment methods. However, one area where knowledge gaps may still hinder our ability to assess the full range of bee-pesticide interactions is the field of exposure. Exposure has historically been linked to either direct contact with pesticides or the ingestion of contaminated pollen and nectar by bees. However, bumble bees, and other wild bees, may also be exposed to pesticides while using contaminated soil as an overwintering substrate. Yet knowledge of how soil-mediated exposure affects bumble bee health is lacking. Here we take one of the first steps towards addressing this knowledge gap by designing a method for testing the effects of soil-mediated pesticide exposure on bumble bee queen hibernation success. We measured hibernation survival, body weight change and abdominal fat content and found that none of these responses were affected by a field realistic soil exposure to the novel insecticide cyantraniliprole. Our study may help in developing a standardised method to test the effects of the soil-mediated pesticide exposure route in bumble bee queens.

Measuring and Mitigating the Risk of Advanced Cyberattackers

Sophisticated cyberattackers (commonly known as advanced persistent threats (APTs)) pose enormous risks to organizations such as financial institutions, industrial and commercial firms, government institutions, and power grids. This study presents a method and an index to measure the vulnerability of organizations to APT risk and shows why a one-size-fits-all solution to mitigate APT risk does not exist. Our vulnerability index is based on a model that describes the optimal behavior of a cyberattacker (APT) with research and development capabilities aspiring to attack a network that manages the organization and a network operator that deploys blocking and detection measures to protect its organization from the attack. We demonstrate how our vulnerability index, which accounts for the network’s structure and the APTs’ resources and strategy, can be used in realistic risk assessments and optimal resource allocation procedures and serve as a benchmark for organizations’ preparedness against APTs’ cyberattacks. We also propose that regulatory agencies of financial (and other) institutions provide the parameters that define an APT’s profile and request, as part of their periodic assessments of the organizations that they regulate, that our (or similar) vulnerability index will be reported to them by the regulated institutions. Finally, the viability of our index in modeling modern cybersecurity defense procedures shows that not only there is no silver bullet defense against all types of APTs, it is also imperative to account for APTs’ heterogeneity because detection and blocking measures can be complements, substitutes, or even degrade each other. For example, when the attacker’s (defender’s) budget is extremely large (small), the defender should deploy only detection measures, strongly advocating Zero Trust practices. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0072 .

Agent-Based Modeling for Household Decision-Making in Adoption of Private Flood Mitigation Measures: The Upper Kan Catchment Case Study

Residential areas in developing arid and semi-arid countries are highly vulnerable to flooding, and water shortages have forced their inhabitants to live close to rivers. While climate change is expected to cause more extreme weather conditions in the future, adopting private loss-reduction measures can diminish flood risk. Although the number of flood models has grown significantly for developing arid and semi-arid lands, these models suffer from being incapable of performing micro-scale flood risk analysis and including household behaviors. This research work presents a novel socio-economic simulation model in the framework of flood risk management (FRM) to couple household adaptive responses with flood risk analysis. Agent-based modeling (ABM) embeds human behaviors in a flood-simulating environment. The focus of this research is the upper Kan catchment in Iran with a long history of severe flash flooding. Our results show the ability of the developed framework to address household participation in FRM activities through private precautionary measures. Moreover, the results indicate the importance of presenting such micro-level behaviors in flood modeling for a more realistic flood risk assessment. It is also demonstrated that household adaptation in a continuous way can lead to less flood risks in the region, even under climate change and the future economy. Finally, the results reveal that the remaining and diminished regional flood risks are influenced by the behavioral framework through which the individuals make decisions in adopting flood-loss-reduction measures. A similar pattern is, however, observed in household contributions over time among the discussed behavioral approaches.

Risk Assessment of Large Language Models Beyond Apocalyptic Visions

The remarkable development of Large Language Models (LLMs) continues to revolutionize various human activities in different societal domains like education, communications, and healthcare. While facilitating the generation of coherent and contextually relevant text across a diverse plethora of topics, LLMs became a set of instruments available in different toolboxes of decision makers. In this way, LLMs moved from a hype to an actual underlying mechanism for capturing valuable insights, revealing different perspectives on topics, and providing real-time decision-making support. As LLMs continue to increase in sophistication and accessibility, both societal and academic effort from AI and cyber security is projected in this direction, and a general societal unrest is seen due to their unknown consequences. Nevertheless, an apocalyptic vision towards their risks and impact does not represent a constructive and realistic approach. Contrarily, this could be an impediment to building LLMs that are safe, responsible, trustworthy, and have a real contribution to the overall societal well-being. Hence, understanding and addressing the risks of LLMs is imperative for building them in an ethical, social, and legal manner while making sure to consider control mechanisms for avoiding, mitigating, accepting, and transferring their risks and harmful consequences. Taking into consideration that these technological developments find themselves in an incipient phase, this research calls for a multi-angled perspective and proposes a realistic theoretical risk assessment method for LLMs.

Modeling the Regional Distribution of International Travelers in Spain to Estimate Imported Cases of Dengue and Malaria: Statistical Inference and Validation Study.

Understanding the patterns of disease importation through international travel is paramount for effective public health interventions and global disease surveillance. While global airline network data have been used to assist in outbreak prevention and effective preparedness, accurately estimating how these imported cases disseminate locally in receiving countries remains a challenge. This study aimed to describe and understand the regional distribution of imported cases of dengue and malaria upon arrival in Spain via air travel. We have proposed a method to describe the regional distribution of imported cases of dengue and malaria based on the computation of the "travelers' index" from readily available socioeconomic data. We combined indicators representing the main drivers for international travel, including tourism, economy, and visits to friends and relatives, to measure the relative appeal of each region in the importing country for travelers. We validated the resulting estimates by comparing them with the reported cases of malaria and dengue in Spain from 2015 to 2019. We also assessed which motivation provided more accurate estimates for imported cases of both diseases. The estimates provided by the best fitted model showed high correlation with notified cases of malaria (0.94) and dengue (0.87), with economic motivation being the most relevant for imported cases of malaria and visits to friends and relatives being the most relevant for imported cases of dengue. Factual descriptions of the local movement of international travelers may substantially enhance the design of cost-effective prevention policies and control strategies, and essentially contribute to decision-support systems. Our approach contributes in this direction by providing a reliable estimate of the number of imported cases of nonendemic diseases, which could be generalized to other applications. Realistic risk assessments will be obtained by combining this regional predictor with the observed local distribution of vectors.

Risk assessment of mold growth across the US due to weather variations

Mold growth in residential and commercial buildings poses a serious problem to various communities. There are several facets of the problem, the most prominent of which are chronic public health risks, business losses, and monetary and labor resources needed for mold containment and mitigation. In addition, there may be severe renovation work needed if the problem is left unattended. This paper describes a probabilistic framework for mold growth risk assessment on various construction materials and shows its application across a variety of climate conditions. Mold growth is contingent upon three ingredients: temperature, moisture, and nutrients. A specific construction material of interest represents the latter, while the other two are modeled through Monte-Carlo simulations. Mold growth is characterized by the mold growth index, which is tracked over ten years, with hourly time resolution. Analysis of the temperature and relative humidity empirical data revealed that these quantities are statistically dependent. Accounting for these interdependencies is crucial for realistic simulations of temperature and humidity and were numerically reproduced in the developed method. The developed framework allowed for realistic assessment of mold growth risk. It was applied across 60 US metropolitan areas in different climatological zones to quantify mold growth risk. The proposed framework allows for quantitative risk assessment of mold growth in different climatological conditions. Such a capability is needed for decision makers for timely mold mitigation, preventing health hazards, and avoiding large financial losses for emergency mold remediation and building repair.

Safety risk assessment model of a subway tunnel collapse system based on improved DOW-FFTA method

Abstract Green construction considers factors such as quality, safety, efficiency, environmental protection and ecology. Under the premise of balancing basic construction capacity and green construction, prioritizing construction safety requirements and exploring the risk assessment system for the green construction of subway projects is crucial. Traditional risk assessment methods in the study of local system risks have been mostly applied to simple systems; thus, risk identification and assessment methods lack universality. The questions of how to change the existing structure of the system safety risk assessment model and establish a realistic modeling approach, as well as implement dynamic risk supervision, have become urgent problems. Here, we investigated the event risk point and proposed an integrated evaluation method of inherent, initial and real risks based on system attributes. The improved DOW chemical method was used to solve the static inherent risk severity index, and fuzzy fault tree analysis (FFTA) was used to judge the probability of out-of-control local state management. Considering the abnormal situation and the emergence of new risk information, a dynamic correction model was proposed. Finally, a static risk assessment model was established in line with the actual state of the system with local management and realistic risk assessment models that could modify the initial risk over time.

A novel bioaccessibility-based probabilistic risks assessment of potentially toxic elements (PTEs) in earthworm.

Introduction: Early risk assessment studies usually based on total heavy metal (loid) contents, inevitably leading to an overestimation of the health risks. In addition, inputs are represented as single-point estimates in deterministic models, leading to underestimation or overestimation of the health risks. Methods: To overcome these barriers, a novel probabilistic risk assessment strategy based on the combinational use of bioaccessibility and Monte Carlo simulation was developed to assess heavy metal (loid) associated health risks of earthworms in this study. To obtain a realistic and robust probabilistic risk assessment, heavy metal (loid) exposure duration and frequency were determined using our questionnaire data. Results: As a result, the mean gastrointestinal bioaccessibility was in the order: Cd > As > Cu > Hg. The mean hazard index (HI) values for investigated metal (loid)s were 0.65 and 0.59 for male and female, respectively, demonstrating an acceptable health risk in an average community. However, the 90th percentile of HI values was 1.87 and 1.65 for male and female, respectively. And the total non-cancer risks of heavy metal (loid) exposure exceeded the acceptable threshold for 19.9% and 17.8% of male and female, respectively. In addition, the total cancer risk (TCR) value through co-exposure to As and Cd suggested that the carcinogenic risks may be of concern for average exposure population. Sensitivity analyses revealed that the exposure frequency and bioaccessible As concentration were the dominant contributors to the total risk variance, which provided meaningful implications for environmental management. Conclusion: Altogether, the refined strategy based on bioaccessibility and Monte Carlo simulation is the first of its kind, such effort attempts to scientifically guide the rational clinic use of TCM and the improvement of population-health.

CropLife Europe Crop Development Database: An open-source, pan-European, harmonized crop development database for use in regulatory pesticide exposure modeling and risk assessment.

There is a regulatory need for crop development dates to assess current default values used within chemical exposure assessments as well as to justify refinements within risk assessments. However, a readily available pan-European crop phenology database covering key FOrum for the Co-ordination of pesticide fate models and their USe (FOCUS) crops and scenarios to meet this need is not currently available. Therefore, we describe the development of a harmonized, pan-European, CropLife Europe Crop Development Database (C2D2), that is fully aligned with this regulatory requirement utilizing efficacy trials data generated for regulatory submissions when registering plant protection products under Regulation (EU)1107/2009. Evaluation of C2D2 against an independent data set showed good agreement for equivalent time periods, crop growth stages, and geographical regions. We illustrate how this database can be used to evaluate existing default crop development dates mandated by regulatory agencies for use within exposure assessments. Despite the large data set compiled and the geographical coverage of C2D2, not all FOCUSsw/gw scenarios have sufficient data to facilitate comparison, with less significant scenarios, like FOCUSgw Porto, being underrepresented. For those scenarios with sufficient data, clear differences between C2D2 and crop development dates assumed in the FOCUS modeling framework (using the AppDate tool) are often indicated over many growth stages, suggesting that amendment of the existing representation of crop development within the risk assessment process may be required. C2D2 is freely available under a Creative Commons license to facilitate innovation in exposure science to allow for more accurate and realistic risk assessment leading to enhanced crop and environmental protection. Integr Environ Assess Manag 2024;20:1060-1074. © 2023 CropLife Europe (Corteva Agriscience) and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Behavioral Biases in Investment Decision-Making

This study delves into the profound impact of behavioral biases on investment decisions and provides valuable insights into methods for counteracting or surmounting these biases. Specifically, it focuses on four prevalent behavioral biases: loss aversion, endowment bias, framing bias, and overconfidence bias, examining how these biases can lead to potential investment mistakes or risks. To mitigate the influence of behavioral biases, the study proposes several strategies. Firstly, it emphasizes the importance of sourcing information from multiple perspectives and cross-referencing data to avoid reliance on biased or limited sources. Objective evaluation of one's skills and knowledge is also highlighted as a crucial step in reducing biases. Leveraging professional advice or feedback can provide an external perspective and help investors make more rational decisions. Furthermore, the study suggests that regular portfolio review and adjustments are essential to address biases and adapt to changing market conditions. Additionally, proactive visualization of potential outcomes can aid in mitigating biases by promoting a more realistic assessment of risk and reward. The study concludes that behavioral biases play a pivotal role in investment decisions. To bolster investment performance and satisfaction, investors are encouraged to comprehend and rectify these biases.

Innovative accumulative risk assessment of co-exposure to Cd, As, and Pb in contaminated rice based on their in vivo bioavailability and in vitro bioaccessibility

The consumption of cadmium (Cd), arsenic (As), and lead (Pb) co-contaminated rice exposes humans to multiple heavy metals simultaneously, with relative bioavailability (RBA) and bioaccessibility (BAc) being important determinants of potential health risks. This study evaluated the relationship between in vivo RBA and in vitro BAc of Cd, As, and Pb in rice and their cumulative risk to humans. A total of 110 rice samples were collected in Zhejiang Province, China, and 10 subsamples with varying concentration gradients were randomly selected to measure RBA using a mouse model (liver, kidney, femur, blood, and urine as endpoints) and BAc using four in vitro assays (PBET, UBM, SBRC, and IVG). Our results indicated that Cd-RBA varied from 21.2 % to 67.5 %, As-RBA varied from 23.2 % to 69.3 %, and Pb-RBA varied from 22.2 % to 68.9 % based on mouse liver plus kidneys. The BAc values for Cd, As, and Pb in rice varied according to the assay. Compared to Cd and As, Pb exhibited a lower BAc in the gastric (GP) and intestinal (IP) phases. According to the relationship between the BAc and RBA values, IVG-GP (R2 = 0.92), SBRC-IP (R2 = 0.73), and UBM-GP (R2 = 0.80) could be used as predictors of Cd-, As-, and Pb-RBA in rice, respectively. The health risks associated with co-exposure to Cd, As, and Pb in contaminated rice for both adults and children exceeded the acceptable threshold, with Cd and As being the primary risk factors. The noncarcinogenic and carcinogenic risks were markedly reduced when the RBA and BAc values were incorporated into the risk assessment. Due to the risk overestimation inherent in estimating the risk level based on total metal concentration, our study provides a realistic assessment of the cumulative health risks associated with co-exposure to Cd, As, and Pb in contaminated rice using in vivo RBA and in vitro BAc bioassays.

Integrating bioavailability measurements in persistence testing of partially biodegradable organic chemicals in soil

Overestimation of risk is one of the main problems in environmental risk assessments if only total concentration of organic pollutants is considered. In this study, we integrated bioavailability measurements into persistence testing of pollutants in soil to show that it is the key to have a more realistic environmental risk assessment (ERA). To this integration, two standardized methods were used: OECD 307, as persistence test, and ISO 16751: 2020, to bioavailability measurements based on 20 h extractions with a strong adsorbent (Tenax), using pyrene and carbamazepine as model test substances. Because the ISO method was initially designed for nonpolar compounds with log Kow > 3, a slight adaptation was necessary for carbamazepine (log Kow = 2.7), assuming this also as an extension of the applicability domain of the method. During the biodegradation of these compounds, the mineralization extents did not exceed 4 %, giving rise to transformation products. Therefore, the bioavailability measurements covered both the parent compound and the metabolites produced. In the case of pyrene, the partial transformation carried out by a specialized microbial inoculum accounted for, respectively, 32 % or 40 % of the initial concentration (4 mg kg−1) in unamended or compost-amended soil. Only 1 % was present as hydrophilic transformation products that were not trapped by Tenax, but partitioned into the water. The nonbioavailable residue increased in both soils after biodegradation. The distribution of chemicals in the different phases of the system was the key to assess more realistically the shifts in bioavailability during persistence testing. The same procedure was carried out for carbamazepine where an additional desorption study showed a slower desorption rate of the parent compound after incubation. In this case, 25 % of transformation products was mobilized to the aqueous phase. Our results show that bioavailability measurements provide valuable information when integrated in persistence testing, therefore contributing to realism in prospective ERA scenarios.

Ultra-low esfenvalerate exposure may disrupt interspecific competition

Field and mesocosm studies repeatedly show that higher tier processes reduce the predictive accuracy of toxicity evaluation and thus their value for pesticide risk assessment. Therefore, understanding the influence of ecological complexity on toxicant effects is crucial to improve realism of aquatic risk assessment. Here we investigate the influence of repeated exposure to ecologically realistic concentrations of esfenvalerate on the two similarly sensitive species Daphnia magna and Culex pipiens in a food limited and highly competitive environment. We show that significant perturbations in population development are only present at 100 ng/L (close to the EC50). In contrast, interspecific competition between species is already reduced at 0.1 ng/L (≤ 3 orders of magnitude below the acute lethal EC50). We conclude that extremely low, environmentally relevant concentrations can disrupt species interactions. This toxicant mediated alteration of competitive balances in ecological communities may be the underlying mechanism for shifts in species distribution at ultra-low pesticide concentrations. A realistic risk assessment should therefore consider these processes in order to predict potential pesticide effects on the structure of communities.

Microplastics increase the toxicity of mercury, chlorpyrifos and fluoranthene to mussel and sea urchin embryos

The objective of this study was to determine whether and to what extent microplastics (MPs) enhance the toxicity of pollutants as well as whether pollutant-loaded MPs act as relevant vectors of chemical pollutants. With this aim, the toxicity for mussel and sea urchin embryos of: 1) three dissolved pollutants (Pol): chlorpyrifos (CPF), fluoranthene (FLT) and mercury (Hg); 2) their mixture with Microplastics (MP + Pol); and 3) pollutant-loaded MPs (MPPol), was assessed. Analyses of CPF, FLT and Hg were also performed to evaluate the transfer among dissolved and particulate phases. In general, the 'MP + Pol' treatments were more toxic as 48-h EC50 (μg/L) than the 'Pol' treatments for sea urchin or mussel. The 48-h and 120-h EC50s (μg/L) for sea urchin showed little variation for CPF and MP + CPF, and no clear pattern was found for FLT and MP + FLT. The performed chemical analysis in the MPPol tests indicated that desorption was the main route to explain the observed toxicity of Hg and a relevant route for CPF and FLT. This study contributes to improve the knowledge about the interactions between MPs and chemical pollutants, which is fundamental for a more realistic ecological risk assessment in aquatic ecosystems.

Preoperative evaluation of the elderly patient.

Nowadays, the pre-operative evaluation of older patients is a critical step in the decision-making process. Clinical assessment and care planning should be considered a whole process rather than separate issues. Clinicians should use validated tools for pre-operative risk assessment of older patients to minimize surgery-related morbidity and mortality and enhance care quality. Traditional pre-operative consultation often fails to capture the pathophysiological and functional profiles of older patients. The elderly's pre-operative evaluation should be focused on determining the patient's functional reserve and reducing any possible peri-operative risk. Therefore, older adults may benefit from the Comprehensive Geriatric Assessment (CGA) that allows clinicians to evaluate several aspects of elderly life, such as depression and cognitive disorders, social status, multi-morbidity, frailty, geriatric syndromes, nutritional status, and polypharmacy. Despite the recognized challenges in applying the CGA, it may provide a realistic risk assessment for post-operative complications and suggest a tailored peri-operative treatment plan for older adults, including pre-operative optimization strategies. The older adults' pre-operative examination should not be considered a mere stand-alone, that is, an independent stage of the surgical pathway, but rather a vital step toward a personalized therapeutic approach that may involve professionals from different clinical fields. The aim of this review is to revise the evidence from the literature and highlight the most important items to be implemented in the pre-operative evaluation process in order to identify better all elderly patients' needs.

A spatiotemporally explicit modeling approach for more realistic exposure and risk assessment of off-field soil organisms.

Natural and seminatural habitats of soil living organisms in cultivated landscapes can be subject to unintended exposure by active substances of plant protection products (PPPs) used in adjacent fields. Spray-drift deposition and runoff are considered major exposure routes into such off-field areas. In this work, we develop a model (xOffFieldSoil) and associated scenarios to estimate exposure of off-field soil habitats. The modular model approach consists of components, each addressing a specific aspect of exposure processes, for example, PPP use, drift deposition, runoff generation and filtering, estimation of soil concentrations. The approach is spatiotemporally explicit and operates at scales ranging from local edge-of-field to large landscapes. The outcome can be aggregated and presented to the risk assessor in a way that addresses the dimensions and scales defined in specific protection goals (SPGs). The approach can be used to assess the effect of mitigation options, for example, field margins, in-field buffers, or drift-reducing technology. The presented provisional scenarios start with a schematic edge-of-field situation and extend to real-world landscapes of up to 5 km × 5 km. A case study was conducted for two active substances of different environmental fate characteristics. Results are presented as a collection of percentiles over time and space, as contour plots, and as maps. The results show that exposure patterns of off-field soil organisms are of a complex nature due to spatial and temporal variabilities combined with landscape structure and event-based processes. Our concepts and analysis demonstrate that more realistic exposure data can be meaningfully consolidated to serve in standard-tier risk assessments. The real-world landscape-scale scenarios indicate risk hot-spots that support the identification of efficient risk mitigation. As a next step, the spatiotemporally explicit exposure data can be directly coupled to ecological effect models (e.g., for earthworms or collembola) to conduct risk assessments at biological entity levels as required by SPGs. Integr Environ Assess Manag 2024;20:263-278. © 2023 Applied Analysis Solutions LLC and WSC Scientific GmbH and Bayer AG and The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).