Step In Risk Assessment Research Articles

In vitro bioaccessibility of cyclodi-BADGE present in canned seafood: A new approach for the estimation of dietary exposure of the Spanish population

Human dietary exposure to chemical compounds is a priority issue for public health authorities since it constitutes a key step in risk assessment, and food packaging could be an important source of contamination. In this study, the bioaccessibility of cyclodi-BADGE was evaluated in canned seafood samples using a standardized protocol of in vitro gastrointestinal digestion and an analytical method based on liquid chromatography coupled to tandem mass spectrometry. The impact of enzymes, different gastric pHs, and food-covering liquids on the bioaccessibility of cyclodi-BADGE was studied. The results highlighted that cyclodi-BADGE was available to be absorbed at the intestinal level (90.9–112.3%), and its bioaccessibility increased substantially in fat food samples. Finally, the estimated dietary exposure to cyclodi-BADGE in the Spanish adult population reached values of 14.26 μg/kg bw/day for tuna in tomato, exceeding the tolerable daily intake (1.5 μg/kg bw/day) recommended for chemicals with high toxicological risk.

Interlaboratory comparison exercises for organically-bound tritium in the development of reference materials of environmental samples

The complex behavior of tritium and the probability of increasing tritium concentrations released in the environment were the promotors for the research and development of laboratory methods that enable to accurately determine the various forms of tritium including organically-bound tritium (OBT) for public and regulatory assurance. The measurement of tritium is a key step for dose and risk assessment. The Cernavoda Nuclear Power Plant (NPP) in Romania improved preparation methods and tested environmental matrices for OBT analysis through intercomparison exercises. This paper describes the international Organically-Bound Tritium (OBT) intercomparison exercise, organized by the Cernavoda Nuclear Power Plant (NPP) in 2019–2020, using fruit sample (quince) from Cernavoda town. Evaluation of the results from the participating laboratories was performed using both robust analysis (Algorithm A) method described in the ISO 13528:2015 standard and ANOVA method. The results obtained are encouraging as an increased number of participating laboratories did not change the observed dispersion of the results for activity concentration level around 50 Bq/L of combustion water. The stability of the remaining sample will be checked in time to investigate its use as a reference material for OBT analysis at the environmental levels.

Advancing Toxicity Predictions: A Review on in Vitro to in Vivo Extrapolation in Next-Generation Risk Assessment.

As a key step in next-generation risk assessment (NGRA), in vitro to in vivo extrapolation (IVIVE) aims to mobilize a mechanism-based understanding of toxicology to translate bioactive chemical concentrations obtained from in vitro assays to corresponding exposures likely to induce bioactivity in vivo. This conversion can be achieved via physiologically-based toxicokinetic (PBTK) models and machine learning (ML) algorithms. The last 5 years have witnessed a period of rapid development in IVIVE, with the number of IVIVE-related publications increasing annually. This Review aims to (1) provide a comprehensive overview of the origin of IVIVE and initiatives undertaken by multiple national agencies to promote its development; (2) compile and sort out IVIVE-related publications and perform a clustering analysis of their high-frequency keywords to capture key research hotspots; (3) comprehensively review PBTK and ML model-based IVIVE studies published in the last 5 years to understand the research directions and methodology developments; and (4) propose future perspectives for IVIVE from two aspects: expanding the scope of application and integrating new technologies. The former includes focusing on metabolite toxicity, conducting IVIVE studies on susceptible populations, advancing ML-based quantitative IVIVE models, and extending research to ecological effects. The latter includes combining systems biology, multiomics, and adverse outcome networks with IVIVE, aiming at a more microscopic, mechanistic, and comprehensive toxicity prediction. This Review highlights the important value of IVIVE in NGRA, with the goal of providing confidence for its routine use in chemical prioritization, hazard assessment, and regulatory decision making.

Assessing the attractiveness of chemical and process facilities to terrorism using a situational crime prevention approach

As intentional attacks to industry have been on the rise in the last years, researchers and institutions put effort in addressing risks of industrial assets exposed to acts of interference. One of the key steps of security risk assessment is the evaluation of the attractiveness, i.e. determining which installation or part of it could be of interest to a potential attacker. As security concerns are intrinsically tied to human intention, attractiveness does not only depend on technical characteristics of the facility, but also on the type of threat and its geographic, economic, and socio-political context. Therefore, interdisciplinarity is among the key features of a team tasked with assessing attractiveness. This work is the result of the joint effort of Physical Sciences and Engineering experts and Social Sciences and Humanities experts, and it presents a methodology for the assessment of attractiveness of process facilities to physical terroristic attacks developed using a “terrorist thinking” approach. Technical factors such as plant layout and physical protection systems were combined with modern criminology principles (namely Situational Crime Prevention, i.e. how actors take advantage of situated opportunities to commit a crime) to obtain a comprehensive picture of the possible motives behind a physical terroristic attack to a chemical and process facility (meant as facilities where chemical substances are present and where chemical and/or physical processes occurs). The methodology was applied to a case study featuring facilities located in a critical context; a ranking among facilities was obtained, and the robustness of the results was tested through a Monte Carlo-based uncertainty analysis. The results revealed that it is not possible to obtain a clear ranking of critical facilities accounting only for technical or non-technical factors, but both are necessary. The method developed was demonstrated easy to apply and could be used not only by plant security managers, but also by Institutions and Governments to prioritize critical assets in threatening contexts.

Predicting capacity models and seismic fragility estimation for precast parking structures based on machine learning techniques

AbstractThe development of fragility curves is an important step in seismic risk assessment within the scope of performance‐based earthquake engineering. The goal of this work is to use machine learning methods (regression‐based tools) to forecast the large‐span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large‐span precast parking structures, including: neural networks, genetic algorithm‐based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large‐span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization‐based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention.

A comprehensive technology strategy for microbial identification and contamination investigation in the sterile drug manufacturing facility-a case study.

A comprehensive strategy for microbial identification and contamination investigation during sterile drug manufacturing was innovatively established in this study, mainly based on MALDI-TOF MS for the identification and complemented by sequencing technology on strain typing. It was implemented to monitor the bacterial contamination of a sterile drug manufacturing facility, including its bacterial distribution features and patterns. In three months, two hundred ninety-two samples were collected covering multiple critical components of raw materials, personnel, environment, and production water. Based on our strategy, the bacterial profile across the production process was determined: 241/292 bacterial identities were obtained, and Staphylococcus spp. (40.25%), Micrococcus spp.(11.20%), Bacillus spp. (8.30%), Actinobacteria (5.81%), and Paenibacillus spp. (4.56%) are shown to be the most dominant microbial contaminants. With 75.8% species-level and 95.4% genus-level identification capability, MALDI-TOF MS was promising to be a first-line tool for environmental monitoring routine. Furthermore, to determine the source of the most frequently occurring Staphylococcus cohnii, which evidenced a widespread presence in the entire process, a more discriminating S. cohnii whole-genome SNP typing method was developed to track the transmission routes. Phylogenetic analysis based on SNP results indicated critical environment contamination is highly relevant to personnel flow in this case. The strain typing results provide robust and accurate information for the following risk assessment step and support effective preventive and corrective measures. In general, the strategy presented in this research will facilitate the development of improved production and environmental control processes for the pharmaceutical industry, and give insights about how to provide more sound and reliable evidence for the optimization of its control program.

Management of links of interest in European Union expertise authorities dealing with plant protection products: comparative analysis and recommendations

BackgroundTo ensure a high level of public health and environmental protection, authorities that deliver scientific expertise to inform decision-makers and the public at large need to be independent from external stakeholders and free of conflicts of interest. This independence requires effective rules for managing links of interest (LoIs) and a high level of transparency, including publication of declarations of interest (DoIs) where appropriate. In the particular case of plant protection products (PPPs) within the European Union, these requirements should apply to all Competent Authorities contributing to the marketing authorization processes.MethodsA comparative analysis of LoIs management procedures was performed on a selection of ten National Competent Authorities from different member states (NCAs). This analysis was based on (i) the identification of 17 criteria aiming at characterizing good practices for LoIs management; (ii) a survey of ten NCAs, based on an analysis of their institutional websites and their responses to official mail requests.ResultsThe comparative analysis showed: (i) a frequent lack of transparency of NCAs regarding their procedures for managing LoIs; (ii) a significant heterogeneity between the NCAs’ LoIs management rules, even though they are in charge of comparable missions regarding the marketing of PPPs; (iii) substantial gaps between the LoIs management procedures adopted by several NCAs and the good practices that are promoted by EFSA.Current limits on their practices regarding LoIs management might open ways for undue external influences on scientific expertise, and ultimately impact negatively the risk management options adopted by national or European authorities. Limitations of this study and its extension for a more thorough overview of the current LoIs management practices are also discussed.ConclusionsLoIs management and transparency rules need to be improved across NCAs, given their contribution as (co-)rapporteurs or peer reviews participants to the health and environmental risk assessment steps of the EFSA processes. To this end, a common minimum set of rules should be defined by EFSA; recommendations are proposed, based on the best practices implemented by the investigated NCAs. Such progress would contribute promoting high-quality unbiased scientific expertise and enhance EU citizens’ trust.

A python system for regional landslide susceptibility assessment by integrating machine learning models and its application

Landslide susceptibility assessment is considered the first step in landslide risk assessment, but current studies mostly rely on GIS platforms or other software for data preprocessing. The modeling process is relatively complicated and multi-models cannot be integrated. With regard to this issue, this study develops a Python system for automatic assessment of regional landslide susceptibility. The Python system implements landslide susceptibility assessment through three modules: geographic data processing, machine learning modeling and result evaluation analysis. For geographic data processing, ten landslide influencing factors can be used to construct an evaluation factor dataset and reclassify the thematic maps based on the frequency ratio method. Four built-in machine learning models (logistic regression (LR), multi-layer perceptron (MLP), support vector machine (SVM) and extreme gradient boosting (XGBoost)) are integrated into the system to complete susceptibility modeling and calculation. Additionally, receiver operating characteristic (ROC) curves can be automatically generated to evaluate the accuracy. The system was then applied into Lantian County in Shaanxi Province as a demonstration example. The results show that the areas under the ROC curve (AUC) of the four models are 0.838 (LR)、0.882 (SVM)、0.809 (MLP) and 0.812 (XGBoost), respectively, indicating that the SVM model was the most suitable model for landslide susceptibility assessment in Lantian County in the Loess Plateau of China. The system has now been made open source on Github, which can effectively improve the efficiency of regional landslide susceptibility assessment, especially provide tools for data processing and modeling for non-professionals.

Boosting Government Assets as A Revenue Center: Risk and Treatment

The Ministry of Finance, through the Directorate General of State Assets Management (DGSAM), strives to increase the contribution of asset management as a source of Non-Tax State Revenue through the Asset Utilization mechanism (in the form of assets rent, etc.). However, empirical data indicates that Asset Utilization activities are not optimal. This study aims to conduct a risk assessment of the government assets rental business process. The study was conducted qualitatively following the risk assessment steps according to ISO 31000:2018 Risk Management. Based on data obtained (by document analysis and interviews) and referring to the organization's risk appetite, the risk assessment shows that eight significant risks need to be anticipated and mitigated by DGSAM related to the assets rental business process. To increase the effectiveness of the assets rental business process in generating Non-Tax State Revenue, we recommend DGSAM as Assets Manager to increase the effectiveness of control in the assets rental business process and implement risk mitigation as an embedded strategy in improving asset management.

Rapid screening and risk assessment of various elements in ultrafine granular powder of Panax quinquefolius L.

IntroductionPanax quinquefolius L. is obtained from a man-shaped root of several species of the genus Panax of the family Araliceae indigenous, which is thought to increase memory, decrease aging, and possess both digestion regulating and liver-protective activity. The widely used in daily life, making its risk assessment a matter of great importance. This research investigates the application of inductively coupled plasma mass spectrometry (ICP-MS) for rapid, non-targeted screening and risk assessment of various heavy metals and toxic agents in Panax quinquefolius L.'s ultrafine granular powder. MethodsThe Panax quinquefolius L. was ground to ultrafine granular powder and subjected to microwave digestion for pre-treatment. ICP-MS was then employed to scan all elements within the full spectrum range, allowing identification of heavy metals and toxic elements using the non-targeted detection method. Subsequently, risk assessment was conducted for Pb, Cu, Ni, Mn, Al, Hg, As, and Cd present in the powder, following the basic steps of risk assessment. ResultsThe concentrations of Pb, Cd, As, Hg, and Cu in ultrafine granular Panax quinquefolius L. powder were found to be below the limits established by the current pharmacopeia. Notably, essential trace elements for the human body, namely Ca, Fe, Mn, Zn, and Mg, were relatively abundant in the powder, with concentrations ranging from 1441.30 to 4016.20, 51.16 to 577.04, 34.78 to 121.99, 17.85 to 83.49, and 1017.69 to 3194.16 mg/kg, respectively. The risk assessment indicated that, in terms of exposure to toxic elements, the order for a person consuming ultrafine granular Panax quinquefolius L. powder would be: Al > Mn > Cu > Ni > Pb > Cd > As > Hg. Importantly, the non-cancer risks associated with Ni, Pb, Al, Mn, Cd, As, Hg, and Cu in the ultrafine granular Panax quinquefolius L. powder were all within acceptable levels. ConclusionThis study successfully establishes a multi-element non-targeted rapid screening method combined with risk assessment techniques, enabling swift determination of harmful target element concentrations and risk profiles in ultrafine granular Panax quinquefolius L. powder. This approach serves as a technical reserve for effectively responding to incidents of toxicity potentially associated with traditional Chinese medicine.

A critical examination of the protection level for primary producers in the first tier of the aquatic risk assessment for plant protection products

BackgroundThe aim of environmental risk assessment (ERA) for pesticides is to protect ecosystems by ensuring that specific protection goals (SPGs) are met. The ERA follows a prospective tiered approach, starting with the most conservative and simple step in risk assessment (RA) (so-called tier 1) using the lowest available appropriate endpoint derived from ecotoxicological tests. In 2015, for the tier 1 RA of aquatic primary producers, the recommendation was changed from using the lowest of the 50% inhibition (EC50) values based on biomass (area under the curve—EbC50), increase in biomass (yield- EyC50) or growth rate (ErC50) to only using the growth rate inhibition endpoint (ErC50) because it is independent of the test design and thus more robust. This study examines the implications of this such on the level of conservatism provided by the tier 1 RA and evaluates whether it ensures a suitable minimum protection level.ResultsOur analysis shows that replacing the lowest endpoint with the growth rate inhibition endpoint while maintaining the assessment factor (AF) of 10 significantly reduces the conservatism in the tier 1 RA. Comparing protection levels achieved with different endpoints reveals that the current assessment is less protective. To maintain the previous level of protection, and since the protection goals have not changed, we recommend to multiply the default AF of 10 by an extra factor of minimum 2.4 in the tier 1 RA based on ErC50. Independently of the endpoint selected in tier 1 RA, several issues in the general RA of pesticides contribute to uncertainties when assessing the protection levels, e.g., lack of appropriate comparison of the higher tier experimental studies (i.e., best achievable approximation of field situation, so-called surrogate reference tier) with field conditions or the regulatory framework's failure to consider realistic conditions in agricultural landscapes with multiple stressors and pesticide mixtures.ConclusionsWe advise to consider adjusting the risk assessment in order to reach at least the previous protection level for aquatic primary producers. Indeed continuing using an endpoint with a higher value and without adjustment of the assessment factor is likely to jeopardize the need of halting biodiversity loss in surface waters.

Global landslide susceptibility prediction based on the automated machine learning (AutoML) framework

Landslide susceptibility prediction (LSP) is an important step for landslide hazard and risk assessment. Automated machine learning (AutoML) has the advantages of automatically features, models, and parameters selection. In this study, we proposed an AutoML-based global LSP framework at two spatial resolutions of 90 m and 1000 m, and achieved an area under the receiver operating characteristic above 0.96. The global prediction results were then validated using additional regional landslide inventories, including three countries, three provinces, and two prefecture-level datasets. Moreover, the global prediction results of 90 m are used to improve the performance of regional LSP. Specifically, the low-and very low-prone areas in the global prediction results were used as non-landslide samples for susceptibility modeling. Results demonstrated that the model achieved a better performance than original global prediction results. We believe that this study will be able to reliably promote the application of intelligent learning methods in global LSP.

Classification of collaborative applications and key variability factors to support the first step of risk assessment when integrating cobots

Risk assessment in collaborative robotics is a challenge. This is one reason why safety is a barrier to the adoption of cobotics. To overcome this challenge, this paper proposes five classes of collaborative applications and variability factors to launch the first step of risk assessment: determination of the use, space and time limits of the application, as per ISO 10218-2:2011. The five classes are Direct alternating collaboration, Direct-assistance collaboration, Indirect sequential collaboration, Indirect parallel collaboration, and Occasional workspace sharing without collaboration. The variability factors to consider when designing a collaborative application relate to the interaction between the cobot(s), human(s) and equipment involved and their characteristics within a space throughout the application’s life cycle. The factors are concrete examples that help fulfill the standard’s requirements regarding those limits. Since robots are partly completed machinery, one must consider the collaborative application as a whole, and not only the human-robot interaction.

Error propagation for quantile estimation via combining Polynomial Chaos expansions and metalog distributions

Introducing parametric uncertainties to models is a quintessential step of simulation-based risk assessment and rare event simulations. A novel quantile estimation method is proposed based on generalized Polynomial Chaos expansions and metalog distribution estimation. We propose to derive the metalog distribution based on the statistical information of an expansion of the random model. The advantage of latter lies in a reduced number of evaluations of the full model, while deriving the metalog distribution avoids sampling-related challenges of quantile estimation. Error estimates and an algorithm to choose the relevant number of statistical moments are developed and analyzed, giving a framework for assessing the method applicability and the accuracy of the quantile estimation. The proposed method and the analysis are demonstrated on numerical examples. The method is applied to a complex fluid flow problem in transpiration cooling.

Workshop report: Challenges faced in developing inhalation thresholds of Toxicological Concern (TTC) - State of the science and next steps

A challenging step in human risk assessment of chemicals is the derivation of safe thresholds. The Threshold of Toxicological Concern (TTC) concept is one option which can be used for the safety evaluation of substances with a limited toxicity dataset, but for which exposure is sufficiently low. The application of the TTC is generally accepted for orally or dermally exposed cosmetic ingredients; however, these values cannot directly be applied to the inhalation route because of differences in exposure route versus oral and dermal. Various approaches of an inhalation TTC concept have been developed over recent years to address this. A virtual workshop organized by Cosmetics Europe, held in November 2020, shared the current state of the science regarding the applicability of existing inhalation TTC approaches to cosmetic ingredients. Key discussion points included the need for an inhalation TTC for local respiratory tract effects in addition to a systemic inhalation TTC, dose metrics, database building and quality of studies, definition of the chemical space and applicability domain, and classification of chemicals with different potencies. The progress made to date in deriving inhalation TTCs was highlighted, as well as the next steps envisaged to develop them further for regulatory acceptance and use.

Structural annotation, semi-quantification and toxicity prediction of pyrrolizidine alkaloids from functional food: In silico and molecular networking strategy.

Many traditional Chinese herbs contain pyrrolizidine alkaloids (PAs), which have been reported to be toxic to livestock and humans. However, the lack of PAs standards makes it difficult to effectively conduct a risk assessment in the varied components of traditional Chinese medicine. It is necessary to propose a suitable strategy to obtain the representative occurrence data of PAs in complex systems. A comprehensive approach for annotating the structures, concentration, and mutagenicity of PAs in three Chinese herbs has been proposed in this article. First, feature-based molecular networking (FBMN) combined with network annotation propagation (NAP) on the Global Natural Products Social Molecular Networking web platform speeds up the process of annotating PAs found in Chinese herbs. Second, a semi-quantitative prediction model based on the quantitative structure and ionization intensity relationship (QSIIR) is used to forecast the amounts of PAs in complex substrates. Finally, the T.E.S.T. was used to provide predictions regarding the mutagenicity of annotated PAs. The goal of this study was to develop a strategy for combining the results of several computer models for PA screening to conduct a comprehensive analysis of PAs, which is a crucial step in risk assessment of unknown PAs in traditional Chinese herbal preparations.

Left-censored data and where to find them: Current implications in mycotoxin-related risk assessment, legislative and economic impacts

BackgroundOne of the most challenging steps in chemical risk assessment is the dietary exposure due to the high percentage of left-censored data (LCD). Mycotoxins are the group of chemical contaminants known to show the highest percentage of LCD among chemical contaminants. These data are extremely important especially if they are used for the development of new legislations with permitted maximum limits. Scope and approachIn this study, we address the importance of analytical techniques and current methods of dealing with LCD, with the goal of highlighting the effect of LCD on risk assessment, which could have a potential impact on future regulations of mycotoxins in foods and the economic implications of such regulations. Key findings and conclusionsThirteen European Food Safety Authority (EFSA) Scientific Opinions and Reports on the risk of mycotoxins to humans were evaluated. The most used analytical method was based on Liquid Chromatography-Mass Spectrometry (LC-MS). Most of the occurrence data for mycotoxins in EFSA risk assessments over the last decade were condensed between 80 and 100% LCD. The typical statistical methods to handle LCD, the substitution method and cut-off values approach, frequently lead to the overestimation of the human risk which can ultimately lead to stricter law regulations with economic implication on the agricultural trades. Understanding the obstacles that analysts face and the type of data that risk assessors require will help to close the uncertainties and gaps that currently exist in exposure and risk assessment. Establishing limits of detections (LODs) and limits of quantification (LOQs) while having a clear data purpose can help reduce uncertainties in risk assessment and provide more meaningful exposure evaluations.

Scientific Support of Occupational Risk Management Decisions in Industrial Sectors in Case of Uncertainty

Introduction: One of the most important steps in risk assessment is the selection of assessment methods. Traditionally, when developing measures to reduce the level of occupational morbidity and industrial injuries, the results of an analysis of the causes, types of events and other factors that led to accidents are used. But such an approach does not meet modern requirements. For an adequate assessment of occupational risks, it is necessary to have objective data from different time periods: the onset of traumatic events in the past, the current state of threats to life and health of people, and the future state of threats in industrial sectors.
 Methods: Mathematical modeling remains the main means of scientific support for occupational risk management. The Elmeri system was chosen for occupational risk assessment in this study, which can be easily and quickly used in any industry and in enterprises of all sizes. A critical analysis of various approaches to managing occupational risks in enterprises was applied to identify the strengths and weaknesses of these approaches. The method of generalizing the most effective approaches to occupational risk management was applied to develop the algorithm of occupational risk management decisions in industrial sectors in case of uncertainty.
 Results: An occupational risk management algorithm has been developed to substantiate management decisions on planning measures to reduce risk, the implementation of which ensures the effectiveness of measures aimed at reducing risk. Research has shown that if the decision-making situation is characterized by conditions of uncertainty (it is impossible to obtain mathematical models of acceptable accuracy), the assessment of the predictive values of occupational risk is carried out exclusively by an expert. Thus, the occupational risk indicator used at the final stage of planning activities determines the degree of achievement of the result of solving the task.
 Conclusion: It has been established that in conditions of uncertainty (lack of necessary data or available data are incomplete or unreliable), experts involved in solving the problem of risk management use their own knowledge and experience in solving similar problems. As a promising direction for further research, it should be noted the development of a methodology for a comprehensive assessment of the effectiveness of operational management decisions for planning and implementing measures to reduce risks

Efforts to Improve the Safety Culture of the Elderly in Nursing Homes: A Qualitative Study.

The attention to building a safety culture in nursing homes is relatively less when compared to hospitals. Good patient safety will improve the quality of health services and minimize incidents related to patient safety. This study aims to look at efforts that can be made to improve safety culture in nursing homes. The research design uses phenomenological qualitative with in-depth interviews. Purposive sampling was used with interpretive phenomenological analysis. Participants were 29 staff from four government and private institutions in East Java, Indonesia. The sub-themes resulting from the research efforts to improve the safety culture of the elderly in nursing homes are the provision of new staff orientation, training, improvement of infrastructure, and procurement of security staff. The analysis shows that efforts to improve safety culture can be carried out with various strategies by paying attention to risk assessment steps, patient risk identification, and management, incident reporting, and analysis, the ability to learn from incidents and their follow-up, as well as implementing solutions to minimize risks and prevent them from occurring injury. Safety culture plays an essential role in improving the quality of care.

Economic Loss and Financial Risk Assessment of Ecological Environment Caused by Environmental Pollution under Big Data

With the proposal of the sustainable scientific development concept, the ecological environment problem has been paid more and more attention, and the economic loss and financial risk assessment work caused by environmental pollution is even more urgent. On the one hand, financial growth is an important reason for the decline of environmental quality; on the other hand, the change of environmental quality and the increase in pollution discharge also have counterforces to financial growth. Through big data technology, this paper established the basic steps of economic loss cost and risk assessment and provided quantifiable index information for the realization of ecological environment big data early warning. An improved analytic hierarchy process (AHP) is proposed in this paper. By checking the consistency of the judgment matrix in combination with the actual situation, it is possible to accurately analyze the degree of environmental protection for sustained economic growth, and to objectively understand the degree of financial risk. The experimental results of this paper showed that the evaluation accuracy of the improved AHP was higher. Before the improved method was used, most of the high-risk factor weights and rankings were still in the high-risk range. The results of the consistency verification of risk criteria and risk factors showed that the consistency test of each level was less than 0.1. The risk factors are classified into the high-risk factor group (50%). The impact of the air pollution category had increased from fifth place in the fuzzy AHP to the first place in the AHP risk ranking, and the risk of water pollution category dropped from the 19th medium risk in the fuzzy AHP to the 12th medium risk in the AHP. It can be seen that the weights and rankings evaluated by the fuzzy analytic hierarchy process were more in line with the actual market conditions.