Comprehensive Risk Assessment Research Articles

An integrated Fine-Kinney risk assessment model utilizing Fermatean fuzzy AHP-WASPAS for occupational hazards in the aquaculture sector

The multidisciplinary field of occupational health and safety (OHS) aims to identify, assess, and mitigating hazards that arise in the workplace or due to the execution of the work. The Fine-Kinney method is a quantitative risk assessment widely applied in many industries to identify, evaluate and prevent potential hazards. Despite the widespread usage of the classic Fine-Kinney approach in several research, its effectiveness in addressing the issue of objective risk appraisal among decision-makers is limited. Therefore, this study proposes a comprehensive occupational risk assessment framework that integrates the Fine-Kinney method with the analytical hierarchy process (AHP) and weighted aggregated sum product assessment (WASPAS) under the Fermatean fuzzy environment. The Fermatean fuzzy set (FFS) offers a more flexible solution in determining the uncertainty and vagueness of information, as it covers membership and non-membership degrees in a wider area compared to intuitionistic and Pythagorean fuzzy sets. The AHP approach is used to calculate the weightage of each each Fine-Kinney risk parameter (probability (P), exposure (E), and consequence (C)), and WASPAS method is used to find the ranking of the hazards. In the proposed model, Fermatean fuzzy weighted geometric (FFWG) operator is used for aggregation of expert opinions. Finally, sensitivity and comparative analyses are also performed to further highlight the adaptability and efficiency of the proposed occupational health and safety risk assessment (OHSRA) model, and a case study analyzing occupational hazards for aquaculture operations is presented to demonstrate the model's rationality and feasibility.

From drought hazard to risk: A spring wheat vulnerability assessment in the Canadian Prairies

The Canadian drought monitor (CDM) provides monthly assessments of drought severity but does not provide guidance on the potential agricultural impacts. This study extends the CDM to a comprehensive risk assessment framework by assessing the vulnerability of spring wheat to drought timing and severity in the Canadian Prairies. To achieve this, a bayesian neural network (BNN) model was developed that predicts high-resolution wheat yields and uncertainties using the CDM and normalized difference vegetation index (NDVI) data from 2010 to 2021. Model performance is comparable to previous studies, with errors ranging from 13% to 18% of mean yields and R2 values from 0.43 to 0.70 that peak in early August. The model was then applied under various drought conditions to evaluate how different drought timings and severities affect yield vulnerability. The results demonstrate that drought exposure from June to August reduces wheat yields in proportion to drought severity while abnormally dry conditions may have a positive impact of yield, particularly in August. Yield uncertainty decreases substantially from June to July while yield prediction accuracy increases, suggesting that mitigation efforts are most effective prior to July. The final component of the research involved creating operational risk maps that integrate the yield vulnerability assessment with the CDM. These maps were applied in a case study to demonstrate their utility in providing actionable, real-time information for agricultural stakeholders. Overall, this study contributes to the body of knowledge regarding crop sensitivities to varying drought conditions and represents a methodological advancement by enabling high-resolution, operational agricultural risk assessments at scale. By integrating detailed vulnerability data with drought hazard monitoring from the CDM, the information provided by these risk maps can be leveraged by agricultural stakeholders in near real-time to make well-informed decisions that mitigate the impacts of drought on wheat production, enhancing the resilience of agricultural systems to climatic variability.

Advancing rapid urban flood prediction: a spatiotemporal deep learning approach with uneven rainfall and attention mechanism

ABSTRACT Urban floods pose a significant threat to human communities, making its prediction essential for comprehensive flood risk assessment and the formulation of effective resource allocation strategies. Data-driven deep learning approaches have gained traction in urban emergency flood prediction, addressing the efficiency constraints of physical models. However, the spatial structure of rainfall, which has a profound influence on urban flooding, is often overlooked in many deep learning investigations. In this study, we introduce a novel deep learning model known as CRU-Net equipped with an attention mechanism to predict inundation depths in urban terrains based on spatiotemporal rainfall patterns. This method utilizes eight topographic parameters related to the height of urban waterlogging, combined with spatial rainfall data as inputs to the model. Comparative evaluations between the developed CRU-Net and two other deep learning models, U-Net and ResU-Net, reveal that CRU-Net adeptly interprets the spatiotemporal traits of rainfall and accurately estimates flood depths, emphasizing deep inundation and flood-vulnerable regions. The model demonstrates exceptional accuracy, evidenced by a root mean square error of 0.054 m and a Nash–Sutcliffe efficiency of 0.975. CRU-Net also accurately predicts over 80% of inundation locations with depths exceeding 0.3 m. Remarkably, CRU-Net delivers predictions for 3 million grids in 2.9 s, showcasing its efficiency.

Risk assessment of organ transplant operation: A fuzzy hybrid MCDM approach based on fuzzy FMEA.

Nowadays, most fatal diseases are attributed to the malfunction of bodily. Sometimes organ transplantation is the only possible therapy, for instance for patients with end-stage liver diseases, and the preferred treatment, for instance for patients with end-stage renal diseases. However, this surgical procedure comes with inherent risks and effectively managing these risks to minimize the likelihood of complications arising from organ transplantation (maximizing life years from transplant and quality-adjusted life years) is crucial. To facilitate this process, risk ranking is used to identify and promptly address potential risks. Over recent years, considerable efforts have been made, and various approaches have been proposed to enhance Failure Modes and Effects Analysis (FMEA). In this study, taking into account the uncertainty in linguistic variables (F-FMEA), we introduce an approach based on Fuzzy Multi Criteria Decision Making (F-MCDM) for effectively evaluating scenarios and initial failure hazards. Nevertheless, the results of ranking failure modes generated by different MCDM methods may vary. This study is a retrospective study that suggests a comprehensive unified risk assessment model, integrating multiple techniques to produce a more inclusive ranking of failure modes. Exploring a broad spectrum of risks associated with organ transplant operations, we identified 20 principal hazards with the assistance of literature and experts. We developed a questionnaire to examine the impact of various critical factors on the survival of transplanted organs, such as irregularities in immunosuppressive drug consumption, inappropriate dietary habits, psychological disorders, engaging in strenuous activities post-transplant, neglecting quarantine regulations, and other design-related factors. Subsequently, we analyzed the severity of their effects on the durability of transplanted organs. Utilizing the Mamdani algorithm as a fuzzy inference engine and the Center of Gravity algorithm for tooling, we expressed the probability and severity of each risk. Finally, the failure mode ranking obtained from the F-FMEA method, three fuzzy MCDM methods, and the proposed combined method were identified. Additionally, the results obtained from various methods were evaluated by an expert team, demonstrating that the highest consistency and effectiveness among different methods are attributed to the proposed method, as it achieved a 91.67% agreement with expert opinions.

Responsible Integration of Large Language Models (LLMs) in Navy Operational Plan Generation

This paper outlines an approach for assessing and quantifying the risks associated with integrating Large Language Models (LLMs) in generating naval operational plans. It aims to explore the potential benefits and challenges of LLMs in this context and to suggest a methodology for a comprehensive risk assessment framework.

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability

The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination on people’s immunity vary across several factors. After the pandemic, real-life post-vaccination anomalies significantly impact women’s health, access to medical treatments and medications, mental well-being, and daily physical activities. However, there has been scant investigation into the physical, psychological, social, and economic ramifications of vaccine effects on women in the post-pandemic era. Therefore, conducting a comprehensive risk assessment is crucial to safeguard women from the post-vaccination effects.To address this issue, the research encompasses complex bipolar spherical fuzzy ℵ-soft set, which has two-sided periodic ambiguous data due to its parametric properties as an adaptable ℵ-soft set and distinguishing criteria as a complex bipolar spherical fuzzy set. In addition, some fundamental operations and properties are presented in a complex bipolar spherical fuzzy ℵ-soft environment. Furthermore, the robust assessment of a real-world application demonstrate the efficacy of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach to optimise the decision result. Finally, the provided decision-making approach is compared with existing techniques to illustrate their remarkable credibility and integrity.

Risk Evaluation and Attack Detection in Heterogeneous IoMT Devices Using Hybrid Fuzzy Logic Analytical Approach.

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.

РИЗИКИ ЯКОСТІ АУДИТОРСЬКИХ ПОСЛУГ

Introduction. Today, more than a million subjects of economic activity in the world are certified according to the ISO 9001 standard - quality management system. The quality management system (QMS) is an integral part of management that follows both market norms and business rules. The practice of implementing QMS, along with the optimistic results of quality improvements, reveals the debatable aspects of such processes. Problem Statement. The International Quality Management Standards application (which is one of the main audit reform directions in Ukraine) is today complicated both by the active pace of the mentioned processes (insufficient time for the unification of quality policies and procedures) and the difficulty of international regulatory practical application policies and procedures regarding audit quality. Subjects quite often postpone their orders for conducting a mandatory audit during the period of martial law, which leads to the emergence of quality control risks. Purpose – coverage of modern trends in the audit quality risks identification and assessment, justification for an audit firm of an alternative internal quality management system as an integral operational management component, that will enable the systematic approach formation to conduct a competitive audit. Methods. General scientific and special methods are used: analysis, synthesis, grouping, description, comparison, theoretical generalization and abstract-logical. Results. The complex nature of quality risks in modern audit conditions, their impact on the validity, reliability and persuasiveness of the auditor's opinion is revealed. The quality risk is investigated based on a comprehensive risk assessment in terms of the audit company's “quality management system” components. Common errors of auditing companies in the quality management system organization, the significance of their impact on compliance with norms and goals of audit quality are given. Based on the conceptual rules and principles of the International Quality Management Standards, alternative components of the “Quality Management System” for the audit company, their structural and logical connections with the identification of the functional role are proposed. Based on the international practice of risk-oriented management, methodical approaches to the identification and risk assessment are proposed, alternatives for internal regulation and specified processes documentation are revealed. Conclusions. The quality management system does not have a simple cycle, its effectiveness lies in systemic regulatory and internal management development. The internal audit of the QMS cannot fulfill its function by declarative decisions only. Due to the established communication with the auditors, interest in the demand for his role, the person responsible for the company's QMS should raise his competence' level, work with quality risks “in advance”. Issues of audit quality risk management highlighted in the article are quite new and require the methodological and practical principles formation of regulation and conceptually unified mechanisms for the implementation of such processes by audit companies.

RISK EVALUATION OF BLOCKCHAIN POWERED SUPPLY CHAIN FINANCING

This paper seeks to conduct a comprehensive risk assessment of blockchain-enabled supply chain financing serving as the basis for supply chain operations of Tomorrowland. The purpose is to identify main risks associated with blockchain implementation in supply chain finance and ways to mitigate them to enable the most secure and reliable financial processes possible. This research paper delves into the critical examination of risk factors associated with blockchain-enabled supply chain financing. Through a thorough literature review and empirical analysis, this study identifies and evaluates the various risks inherent in utilizing blockchain technology for supply chain financing. The findings provide valuable insights for stakeholders to mitigate risks and optimize the efficiency and security of supply chain finance processes. KEYWORDS: Research Paper, Blockchain, Management, Risk Evaluation, Empirical Analysis

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Functional trait-based phytoplankton biomass and assemblage analyses in the pre-growing season for comprehensive algal bloom risk assessment

Algal bloom (AB) risk assessment is critical for maintaining ecosystem health and human sustainability. Previous AB risk assessments have focused on the potential occurrence of ABs and related factors in the growing season, whereas their hazards, especially in the pre-growing season, have attracted less attention. Here, we performed a comprehensive AB risk assessment, including water trophic levels, phytoplankton biomass, functional trait-based assemblages, and related environmental factors, in the pre-growing season in Dongting Lake, China. Although mesotrophic water and low phytoplankton biomass suggested low AB potential, toxic taxa, which constituted 13.28% of the phytoplankton biomass, indicated non-negligible AB hazards. NH4+ and water temperature were key factors affecting phytoplankton motility and toxicity. Our study establishes a new paradigm for quantitative AB risk assessment, including both potential AB occurrence and hazards. We emphasize the importance of phytoplankton functional traits for early AB warning and NH4+ reduction for AB control in the pre-growing season.

Comprehensive risk assessment of metals in surface sediments of the Southern Black Sea coastal and transition waters

This study was carried out to determine metal levels accumulated in the sediments collected from the transitional and coastal waters of the southern Black Sea. Samples were taken from 32 stations in May and September 2018. The concentrations of elements were measured by an Inductively Coupled Plasma Mass Spectrophotometer (ICP-MS). Enrichment Factor (EF), Geo-accumulation Index (Igeo), Pollution Load Index (PLI), Contamination Factor (Cf), and Modified Degree of Contamination (mCd) were applied to the data for assessing the sediment quality. Associations between the variables were also revealed by factor analysis. Igeo of metals are obtained in coastal sediments only when Hg in May and Cr in September are classified as moderately polluted. PLIs exceeded one at stations in the industrial zone, indicating getting worse. It is necessary to prevent pollution from industrial, domestic, and agricultural sources and to control agricultural lands.

Nano-revolution in heavy metal removal: engineered nanomaterials for cleaner water

Nano-revolution in heavy metal removal: engineered nanomaterials for cleaner water

Redefining Benggang Management: A Novel Integration of Soil Erosion and Disaster Risk Assessments

In the granite regions of southern China, benggang poses a substantial threat to the ecological environment due to significant soil erosion. This phenomenon also imposes constraints on economic development, necessitating substantial investments in restoration efforts in recent decades. Despite these efforts, there remains a notable gap in comprehensive risk assessment that integrates both the erosion risk and disaster risk associated with benggang. This study focuses on a representative benggang area in Wuhua County, Guangdong province, employing transformer methods and high-resolution imagery to map the spatial pattern of the benggang. The integrated risk of benggang was assessed by combining soil-erosion risk and disaster risk, and cultivated land, residential land, and water bodies were identified as key disaster-affected entities. The machine-learning Segformer model demonstrated high precision, achieving an Intersection over Union (IoU) of 93.17% and an accuracy (Acc) of 96.73%. While the number of large benggang is relatively small, it constitutes the largest area proportion (65.10%); the number of small benggang is more significant (62.40%) despite a smaller area proportion. Prioritization for benggang management is categorized into high, medium, and low priority, accounting for 17.98%, 48.34%, and 33.69%, respectively. These priorities cover areas of 30.27%, 42.40%, and 27.33%, respectively. The findings of this study, which offer benggang management priorities, align with the nature-based solutions approach. Emphasizing the importance of considering costs and benefits comprehensively when formulating treatment plans, this approach contributes to sustainable solutions for addressing the challenges posed by benggang.

Some evidence supporting the use of optically pure R-(−)-diniconazole: Toxicokinetics and configuration conversion on chiral diniconazole

Diniconazole is a chiral pesticide that exists in two enantiomers, R-(−)-diniconazole and S-(+)-diniconazole, with the R-enantiomer being much more active than the S-enantiomer. Previous enantioselective toxicology studies of diniconazole focused mostly on simple environmental model organisms. In this study, we evaluated the toxicokinetics of the two diniconazole enantiomers in rats and mice to provide a more comprehensive risk assessment. The two enantiomers displayed clear differences in their stereoselective contents in vivo. The t1/2 of R-(−)-diniconazole was 7.06 ± 3.35 h, whereas that of S-(+)-diniconazole was 9.14 ± 4.60 h, indicating that R-(−)-diniconazole was eliminated faster in vivo. The excretion rates of R-(−)-diniconazole and S-(+)-diniconazole were 4.08 ± 0.50 % and 2.68 ± 0.58 %, respectively, indicating more excretion of R-(−)-diniconazole. S-(+)-diniconazole had a higher bioavailability than R-(−)-diniconazole (52.19 % vs. 42.44 %). S-(+)-Diniconazole was also found in relatively high abundance in tissues such as the stomach, large intestine, small intestine, cecum, liver, kidney, brain, and testes, with the abundance being 1.71–2.48-fold that of R-(−)-diniconazole. The selective degradation of both enantiomers in the tissues and their mutual conversion in vivo were not observed, and this could indicate that configuration conversion did not contribute to the differences in the content of enantiomers in the tissues. Instead, such differences were mainly caused by the differences in affinity of each enantiomer for the tissues. Furthermore, investigation of the interconversion between optically pure R-(−)-diniconazole and S-(+)-diniconazole monomers in soil revealed no interconversion. All of the above results indicated no interconversion between R-(−)-diniconazole and S-(+)-diniconazole in vivo and in the soil, and that S-(+)-diniconazole tends to have a greater potential to accumulate in vivo. Thus, if only R-(−)-diniconazole is used as a pesticide, the negative impact on mammals and the environment will be reduced, suggesting that in agriculture, the application of optically pure R-(−)-diniconazole may be a better strategy.

Comprehensive risk assessment and interactions of fluoride and boron ions in the water-sediments interface: The Red Sea, Egypt

This study presents a baseline evaluation of the distribution, human and ecotoxicological risk, and the potential interactions of fluoride and boron in the water-sediment interface in 25 locations from incredible Red Sea tourist destinations. Results showed comparable levels of B and F in the water and sediments with previous literature. Significant positive correlation was found between B and F (r=0.57; P<0.01). Based on the sediment/liquid partition coefficient (Kd), F is more likely to be released from the sediment into seawater (logKd< 3) than B (3<logKd< 4). pH and alkalinity may affect water-sediment interactions of B and F, respectively, while SO42- and Cl- ions had no significant effect on adsorption ability of F and B. The majority of minerals had average saturation Index (SI) >1 referring to the over saturation of seawater with these minerals and their inability to dissolve. The formation of CF, FAP, and CFAP may be related to the high correlation between Fw (r= 0.928, P< 0.01; r= 0.527, P< 0.01; r= 0.608, P< 0.01) and Bw (r= 0.38, P< 0.05; r= 0.38, P< 0.05; r= 0.397, P< 0.05). Total hazard quotient (THQ) for children and adults were <1, revealing no health risks from exposure to B and F through ingestion and skin contact while swimming. The risk characterization ratio; RCRmix(MEC/PNEC) showed high short-term risks to aquatic organisms. Further investigations might emphasis on emerging mitigation strategies to address these concerns.

Ecological risk assessment of commonly used antibiotics in aquatic ecosystems along the coast of China

The consistent input of antibiotics into aquatic environments may pose risks to various creatures and ecosystems. However, risk assessment of pharmaceuticals and personal care products (PPCPs) in aquatic environments is frequently limited by the lack of toxicity data. To investigate the risk of commonly used antibiotics to various aquatic creatures, we focused on the distribution patterns and temporal dynamics of antibiotics in the coastal estuary area of China and performed a comprehensive ecological risk assessment for four antibiotics: erythromycin (ERY), tetracycline (TCN), norfloxacin (NOR) and sulfamethoxazole (SMX). An interspecies correlation estimation (ICE)-species sensitivity distribution (SSD) combined model was applied to predict the toxicity data of untested aquatic species, and an accurate ecological risk assessment procedure was developed to evaluate the risk level of PPCPs. The results of risk quotient assessments and probabilistic risk assessments (PRAs) suggested that four objective antibiotics in the Chinese coastal estuary area were at a low risk level. These antibiotics posed a high risk in antibiotic-related global hot spots, with probabilistic risk values for ERY, NOR, SMX, and TCN of 81.33 %, 27.08 %, 21.13 %, and 15.44 %, respectively. We applied an extrapolation method to overcome the lack of toxicity data in ecological risk assessment, enhanced the ecological reality of water quality criteria derivation and reduced the uncertainty of risk assessment for antibiotics.

Toxicological evaluation and preliminary exposure assessment on glycerol monocaprylate

As a commonly used food preservative, glycerol monocaprylate (GMC) has limited information and lacked a comprehensive risk assessment. In this study, we conducted in vitro genotoxicity tests, a 90-day subchronic toxicity study, and dietary exposure assessment in China. Rats (n=10/sex/group) were orally administered GMC at doses of 1.02, 2.04, and 4.08 g/kg BW/day along with a water and corn oil for 90 days, including satellite groups (n =5/sex/group) in the control groups and 4.08 g/kg BW dose group for observation after 90 days. Body weight, food consumption, hematology, serum biochemistry, urinalysis, endocrine hormone level and other metrics were examined. GMC did not exhibit genotoxicity based on the genotoxicity tests results, and an acceptable daily intake (ADI) of 40.8 mg/kg BW/day was established based on the 90-day subchronic toxicity study. Estimated daily intake of GMC for general population and consumer population in China were 0.99 mg/kg BW/day and 3.19 mg/kg BW/day respectively, which were significantly lower than the ADI. Our findings suggest that GMC does not pose a known health risk to Chinese consumers at the current usage level.

Proposal of a comprehensive risk assessment model for cut slope considering long-term deterioration characteristics based on the concept of disaster immunity

In Japan, the stability of cut slopes alongside expressways has deteriorated over time due to factors such as weathering, rendering them susceptible to significant collapses, especially during heavy rainfall. This study presents an evaluation model to assess the stability of cut slopes, with the aim of capturing their long-term deterioration characteristics using the logistic regression method. Additionally, to explore variations in deterioration patterns among cut slopes in diverse regions with varying geological compositions, a hierarchical Bayesian model is utilized to estimate parameters in the logistic regression model. This methodology enables the creation of a novel stability probability evaluation model for cut slopes that is adaptable to regions with different geological structures. Moreover, to comprehensively evaluate changes in the disaster resistance capabilities of extensively used cut slopes, the post-disaster recovery capabilities in the slope region are assessed through a comprehensive weighted evaluation method (EWM-TOPSIS). Building on this assessment, a novel, simplified, and more generalized resilience evaluation method is proposed. By integrating stability probability and resilience, an evaluation metric named "disaster immunity" is introduced to measure alterations in the disaster risk resistance of extensively utilized cut slopes. A case study is presented to illustrate the application of the evaluation model on selected cut slopes from various regions. This evaluation framework can assist road managers in predicting the disaster risk status of extensively used cut slopes and identifying priority slopes for implementing preventive measures to mitigate potential losses in the event of slope disasters.

Assessment of parent and alkyl -PAHs in surface sediments of Iranian mangroves on the northern coast of the Persian Gulf: Spatial accumulation distribution, influence factors, and ecotoxicological risks

Spatial patterns, potential origins, and ecotoxicological risk of alkylated (APAH) –and parent –(PPAH) polycyclic aromatic hydrocarbons (PAHs) were studied in mangrove surface sediments along the northern coasts of the Persian Gulf, Iran. The mean total concentrations (ngg−1dw) ∑32PAH, ∑PPAHs and ∑APAHs in sediments were 3482 (1689–61228), 2642 (1109–4849), and 840 (478–1273), respectively. The spatial variability was similar among these PAH groups, with the highest levels occurring in Nayband National Marine Park (NNMP). Physicochemical environmental factors, such as sediment grain size, and total organic carbon (TOC) contents, are significant factors of PAH distribution. These findings suggest that PAH pollution level is moderate-to-high, supporting the current view that mangrove ecosystems are under intensive anthropogenic impacts, such as petrochemical, oil and gas loads, port activities, and urbanization. Non-parametric multidimensional scaling (NPMDS) ordination demonstrated that NNMP mangrove is the critical site exhibiting high loading of PAH pollutants. Here, for the first time in this region, Soil quality guidelines (SQGs), Toxic equivalency quotient (TEQ), Mutagenic equivalency quotient (MEQ), and composition indices comprising Mean maximum permissible concentration quotient (m-MPC-Q), and Mean effect range median quotient (m-ERM-Q) methods were used to have a comprehensive risk assessment for PAH compounds and confirmed medium-to-high ecological risks of PAHs in the study area, particularly in the western part of the Gulf, highlighting the industrial impacts on the environment.