Risk Assessment Method Research Articles

Evaluation of physical risk factors by fuzzy failure mode and effects analysis: an apparel mill example

This study investigates the evaluation of risks faced by employees in a selected large-scale apparel mill using a risk assessment method with a fuzzy logic approach. The study found that risk assessment in the apparel industry is more accurate and reliable when using a fuzzy logic approach. Measurements were taken in three different time periods for noise, vibration, thermal comfort and lighting, which are physical risk factors. To evaluate the identified failure modes, the Failure Mode and Effects Analysis (FMEA) method was first applied. Then, for the same failure modes, the fuzzy FMEA method was applied using Fuzzy Logic Designer, an add-on to MATLAB. The study concludes that the fuzzy FMEA method can be a more useful method for risk assessment in the apparel industry.

Quantification of uncertainty in short-term tropospheric column density risks for a wide range of carbon monoxide

The short-term risks associated with atmospheric trace gases, particularly carbon monoxide (CO), are critical for ecological security and human health. Traditional statistical methods, which still dominate the assessment of these risks, limit the potential for enhanced accuracy and reliability. This study evaluates the performance of traditional models (ARIMA), machine learning models (LightGBM, ConvLSTM2D), and optimized machine learning solutions (Bayes residual optimization ConvLSTM2D LightGBM, Bayes_CL) in predicting Sentinel 5P columnar CO levels. This study findings demonstrate that machine learning models and their optimized versions significantly outperform traditional ARIMA models in cross-validation (CV), visualization, and overall prediction performance. Notably, machine learning model based on Bayes and residual optimization (Bayes_CL) achieved the highest CV score (Bayes_CL R2 = 0.8, LightGBM R2 = 0.79, ConvLSTM2D R2 = 0.75, ARIMA R2 = 0.61), along with superior visualization and other metrics. Using Bayes_CL, we effectively quantified a 2.4% increase in columnar CO levels in mainland China in the second half of 2023, following the complete lifting of COVID-19 lockdowns. This study confirms that machine learning models can effectively replace traditional methods for short-term risk assessment of Sentinel 5P columnar CO. This transition holds significant implications for policy formulation, greenhouse effect assessment, and population health risk evaluation, especially in uncertain situations where human activities are severely disrupted, thereby affecting environmental safety.

Exploring Machine Learning Approaches for Predicting Lung Cancer: A Comparative Study

Lung cancer is becoming more common, which emphasizes the significance of precise and user-friendly prediction methods for risk assessment and early identification. This research makes use of a dataset (284 cases and 16 attributes) that was acquired from the Online Lung Cancer Prediction System. The main goal is to find out how well machine learning techniques can predict the likelihood of lung cancer and give people useful information for making decisions. The collection contains a wide range of characteristics, such as lifestyle factors, health markers, and demographic data. To thoroughly assess predictive models, the study uses a variety of machine learning methods, including Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), Decision Tree, Random Forest, XGBOOST, CATBOOST, LightGBM and Gradient Boosting. The study also investigates the effects of hyperparameter tuning, visualization strategies, and data preprocessing techniques on model performance. Keywords: Cancer prediction· Machine learning· Ensemble models

The age-adjusted Charlson comorbidity index in acetabular fracture in geriatric acetabular fractures

ObjectiveThis study aimed to analyze factors of comorbidity in older patients with acetabular fracture to know the role of disease in the treatment of this population. MethodsDesign: retrospective cohort study. SettingSingle Level 2 Trauma Center. Patient selection criteriaA prospective database of acetabular fractures in patients over 55 years between January 2011 and January 2021 was studied. The exclusion criteria were age, pathological fracture, associated pelvic or hip fracture, follow-up of less than one year, and not having a computed tomography. Outcome measures and comparisonsPatients were categorised as low (0–2), intermediate (3–4) or high (≥5) risk according to the age-adjusted Charlson comorbidity index (ACCI). The primary outcomes were risk of complications, number of complications and mortality at one year according to the ACCI scale. The difference between the pre-fracture Clinical Frailty Scale (CFS) and the one-year follow-up was also analysed. Descriptive and multivariable regression analyses were performed and ROC curves were constructed. ResultsA total of 119 patients with acetabular fracture were collected. Ninety-eight out of those met inclusion criteria for our study. In regard to ACCI groups, 22% were low-risk, 26% were intermediate-risk, and 52% were high-risk. Statistically significant differences were found according to age, sex, mechanism of injury, treatment type, surgical treatment type, Clinical Frailty Scale (CFS) score, readmissions, complications, and mortality. ROC curves showed an association between ACCI and CFS in terms of complications and mortality, with the exception of unadjusted Charlson comorbidity index. On the multivariable regression analysis, associations were found between ACCI and presence of complications (OR 1.37, 95% CI 1.06–1.77, p=0.015) and mortality (OR 1.32, 95% CI 1.04–1.67, p=0.025) and between CFS and complications (OR 2.01, 95% CI 1.30–3.11, p=0.001) and mortality (OR 1.59, 95% CI 1.08–2.35, p=0.019). No statistical correlation was established between ASA and complications or mortality. ConclusionPresurgical ACCI and CFS scales may serve as a risk assessment method in treatment decision-making for patients with acetabular fracture. An association is observed between patients with a pre-fracture ACCI score ≥5 (high-risk) and a pre-fracture CFS score >4 (mild frailty) and mortality and complications at one year.

Risk assessment of continued airworthiness event of transport aircraft rudder pedal strut damage

Abstract To ensure the safe operation of aircraft after delivery, a set of continued airworthiness risk assessment processes and methods for transport aircraft is studied and proposed, including unsafe condition identification, consequence severity determination, occurrence possibility assessment, risk level determination, and reaction time calculation. The continued airworthiness risk matrix is established. Based on the established process and method, the risk assessment is carried out for the continued airworthiness event of transport aircraft rudder pedal strut damage. The relationship between the risk level of the fleet aircraft and the flight duration is determined, and the reaction time of the unsafe condition of the aircraft in each flight duration is calculated. The results show that the process and method are feasible.

AI-Based Risk Assessments in Forensic Auditing: Benefits, Challenges and Future Implications

Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications. AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

An index-based multi-hazard risk assessment method for prioritisation of existing bridge portfolios

In recent years, several catastrophic collapses of existing bridges have highlighted the need for rapid risk analysis methods aimed at supporting infrastructure managers in the prioritisation of detailed assessments and, if any, risk mitigation actions. A large percentage of existing road bridges were built between the 1960s and 1980s, having thus already reached or even exceeded their design lifetime. Several studies have also shown that bridges often collapse due to either natural or human-related events, such as floods, collisions or overloading that, in addition to earthquakes, should be duly considered in risk assessment. This calls for multi-hazard approaches that provide an integrated perspective of the risk of bridge portfolios, to identify critical structures to support decision-makers. This study proposes a multi-hazard risk-based prioritisation methodology for application to a large number of bridges under limited level of knowledge. Specifically, the risk level is quantified through indices, accounting for uncertainties, that are used for comparative purposes among bridges. The methodology is applied to a highway bridge portfolio located in northern Italy, producing a risk-based ranking that is critically discussed. Analysis results are then compared with the outcome of the current Italian guidelines for safety assessment and maintenance of existing bridges.

A comprehensive risk assessment of microplastics in soil, water, and atmosphere: Implications for human health and environmental safety

Microplastics (MPs) are pervasive across ecosystems, likely posing significant environmental and health risks based on more and more evidence. In this study, we searched through the Web of Science Core Collection and obtained 1039 papers for visualization and analysis. In order to discuss the chemical composition, migration, transformation and potential risk of MPs, 135 sets of relevant data in soil, water, and atmosphere were collected in China as a typical region, which is a hotspot region for investigation of MPs. The results showed that the primary polymer categories of MPs in the environment to be polypropylene, polyethylene, and polystyrene. The soil contains a significant quantity of MPs, averaging at 12,107.42 items·kgdw−1, while water contains averaging at 97,271.18 items m−3. The total pollution load indexes for all three environments are at risk level I. Based on current risk assessment methods, the potential ecological risk of MPs is low. However, based on the polymer components, migration and transformation patterns, and especially the complexes with other pollutants, it indicates an increasing indirect risk. Interactions with some other pollutants are likely amplify the ecological and health risks associated with MPs. Aggregative results showed that the present risk assessment models could not assess the risks of MPs well. Thus, we suggested develop a risk assessment methodology for MPs based on relevant research progress. Some factors such as the size and form of MPs, sources and distribution, bioaccumulation, social acceptance and economic costs could be considered adding in the present risk assessment models. Finally, promotion of development and application of green chemically synthesized bioplastics such as using synthetic biology to help degrade plastics would be an alternative and sustainable option to relieve the adverse environmental and health concerns of MPs.

Environmental health risk assessment and acute effects of sulfur dioxide (SO2) inhalation exposure on traditional sulfur miners at Ijen Crater Volcano, Indonesia

The Ijen Crater volcano is one of the geological wonders recognized by UNESCO. Inside it is a blue lake with a high acidity level, and a blue fire phenomenon has formed due to the very high concentration of sulfur. This crater is also one of Indonesia's largest sources of sulfur and is used by locals as a traditional sulfur mine. This study aims to measure SO2 concentrations and assess the health risks of SO2 exposure in traditional sulfur mine workers. The SO2 measurements were taken using impingers at six sample points along the mine workers' path. In addition, anthropometric data, work activity patterns, and health complaints during work were collected through direct interviews with 30 respondents selected based on inclusion criteria. Short-Term Health Impact Method was carried out based on a comparison of threshold level values and acute effects obtained from interviews regarding health complaints. The Hazard Question Index (HQ Index) of SO2 exposure was calculated using the health risk assessment method. The SO2 concentrations between 3.14 and 18.24 mg/m3. All sample points were above the quality standard threshold set by the EPA of 1.97 mg/m3. The most common health complaints workers experienced were eye irritation and coughing while working, followed by headache, shortness of breath, and skin irritation. The HQ index of SO2 exposure in workers was 1.02 for real-time exposure and 2.15 for long-term exposure. An HQ index ≥ 1 indicates a potential health risk for workers. Therefore, it is important to control workers' SO2 exposure.

A methodology for assessing multiple hazards applied to Sweden

Despite extensive efforts through various international initiatives to reduce global warming, it has been determined that human induced climate change is here, that the present scale of disruption of the climate is unprecedented and will continue. Increasing weather volatility can be expected, which will most likely increase exposure to weather related hazards, e.g. wildfires, flooding. The aim of this paper is to present an index-based multi-hazard risk assessment method to assess wildfires and flooding hazard for two municipalities within Sweden. The method is designed to be used by the Fire and Rescue Service (FRS) for planning purposes and can be modified to take the local FRS’s capabilities and local conditions into account, thereby improving hazard preparedness at a local level. The analysis presented indicates that, while the frequency of multi-hazard overlap from wildfires and flooding is greatest in more northern parts of Sweden, the method provides important information even when applied to areas with limited overlap. A variation of the hazard assessment using a box kernel sliding window was studied to investigate the sensitivity of the model for rapid variations of an individual hazard level. Given that resource needs will typically spread over several days for large scale natural hazards, the box kernel approach is valuable in helping to identify a span of days when resources associated with incident response might be needed. In the future, the model should be expanded to include additional single hazards, the application to additional municipalities and extension to FRS planning exercises for natural hazards.

Research on Risk Assessment Methods of Earthquake Disasters in Grotto Temple: Taking the Yungang Grottoes as an Example

Research on Risk Assessment Methods of Earthquake Disasters in Grotto Temple: Taking the Yungang Grottoes as an Example

Work Posture Analysis in The Chips Frying Section Using Workplace Ergonomic Risk Assessment Method

U.D. Keripik Mustika is a small and medium enterprise (SME) that processes yam raw materials into special snacks in great demand for consumption and souvenirs. According to observations, the worker experienced pain complaints during frying in the shoulder area, arms, back, waist, hands, and feet. The complaint experienced by this worker was due to the worker's posture that had to stand and bend slightly with the position of the hands continuously mixing for 7 hours a day and done manually repeatedly over long periods. This is if left, then the worker will suffer musculoskeletal disorders (MSDs). The research was conducted using the WERA method (Workplace Ergonomic Risk Assessment), which explains the development of ergonomic risk assessment at the workplace to detect physical risk factors associated with musculoskeletal disorders (MSDs). The spread of the Nordic Body Map questionnaire on frying workers showed pain complaints from an unergonomic working posture. The study results were obtained from Employee 1, with a physical indicator score of 39 (Medium), and Employee 2, with a physical indicator score of 37 (Medium). Therefore, there is a need for improvement of the work facilities or the worker's posture. Based on the assessment results, the researchers suggested additional facilities for workers to design standing aids that reduce pain in the leg muscles and complaints of Musculoskeletal Disorders (MSDs).

Design and implementation of an accelerating rate calorimeter based on Modelica modeling

The safety of self-reactive chemicals has garnered attention due to their immense destructive power. The use of an accelerating rate calorimeter allows for more accurate measurement and understanding of the kinetic parameters and reaction mechanisms of self-reactive chemicals, thereby reducing the occurrence of major accidents. In this study, a new accelerating rate calorimeter is designed and constructed by combining Modelica and a fuzzy Proportional-Integral-Derivative algorithm. The feasibility of the calorimeter data was verified by using two solutions with significantly different reaction rates: 20 % mass fraction di‑tert‑butyl peroxide/toluene and tert-butylperoxy-2-ethylhecanoate. Their thermal hazard characteristic parameters were compared with the literature data. In addition, the risk level of thermal runaway was determined using the Stoessel risk assessment method. These results demonstrate that the accelerating rate calorimeter based on Modelica modeling meets the accuracy of thermal hazard characteristic parameters. It is capable of performing risk assessments for runaway reactions of self-reactive chemicals.

Credit risk assessment method driven by asymmetric loss function

Credit risk assessment is significantly hindered by the problem of class imbalance, and cost-sensitive methods represent an effective strategy to address this issue. However, most algorithms tend to approach the imbalance from a class perspective, overlooking the finer details at the sample level. Moreover, such methods are susceptible to interference from noise and outliers. In response to these challenges, this paper proposes an asymmetric cost-sensitive support vector machine (QTSVM) that combines the quadratic type squared error loss function (QTSELF) with support vector machine (SVM). It not only leverages the asymmetry of the loss function by applying varying penalties based on classification errors but also employs different processing measures for samples from different classes. Additionally, it enhances model robustness by imposing a tiny penalty on noise or outliers. The adaptive moment estimation (Adam) algorithm is employed to optimize QTSVM. Extensive experiments and statistical tests profoundly demonstrate the superior performance of QTSVM.

Urban Natural Gas Pipeline Operational Vulnerability Under the Influence of a Social Spatial Distribution Structure: A Case Study of the Safety Risk Patterns in Kunming, China.

Frequent urban natural gas pipeline accidents pose a serious threat to the safety of people and property in surrounding areas. However, current research on natural gas pipeline risks primarily focuses on evaluating the pipelines themselves, with no established method for assessing the impact of pipeline disasters on surrounding areas. This paper proposes an urban natural gas pipeline risk assessment method that integrates the physical attributes of the pipelines with an analysis of social vulnerability based on urban social spatial distribution. Using urban Point of Interest (POI) data, a social spatial distribution model for potential natural gas pipeline accidents is constructed. The risk of pipeline failure is assessed based on physical vulnerability, while the consequences of failure are evaluated through social vulnerability. This method combines the analysis of physical and social vulnerabilities to achieve a comprehensive urban natural gas pipeline risk assessment. The results identified 68 out of 6148 pipelines in the study area as "double high" pipelines, characterized by high physical vulnerability (relatively high risk pipelines) and high social vulnerability (involving level IV areas). The high risk communities identified in the study area are the Cuihu West Road Community and the Daguan Commercial City Community, highlighting the characteristics of risk distribution. The findings suggest that this study contributes to improving urban resilience to natural gas pipeline incidents, reducing potential economic losses and public impacts, and enhancing urban public safety. It also provides new insights into natural gas pipeline risk assessment and urban public safety research.

Managing the risk of suicide in a psychiatry clinic: An ethnographic study on the work atmosphere of nurses during the Covid-19 pandemic

The aim of this study was to describe the approaches of nurses working in an inpatient psychiatry clinic during the pandemic period to managing suicide risk in the context of their work atmosphere. This ethnographic research design used a sample of 13 psychiatric nurses in a psychiatric clinic in Ankara, Turkey. Data were collected with in-depth interviews and 612 h of observation. Qualitative thematic analysis was used for data analysis. Two themes emerged from the data analysis, including ‘De-prioritizing the uniqueness of the patient in managing suicide risk’ and ‘A condition that remains important for uniqueness: personality disorders’. The current risk assessment methods for suicide, pandemic measures, and past inadequacies in the approach to patients with personality disorders have revealed the risk of alienating from the individual risk factors as well as the patients' history in risk management. Risk management practices that consider the psychosocial assets of patients at risk of suicide, addressing the risk level with a safe measurement scale and performing triage according to this grading system can provide an atmosphere that allows nurses to make risk assessments greater self-confidence.

An aircraft structural risk assessment method considering fatigue crack propagation based on fatigue damage diagnosis and prognosis

An aircraft structural risk assessment method based on fatigue damage diagnosis and prognosis has been developed, considering fatigue crack propagation. The process is divided into three stages: initial crack diagnosis, crack diagnosis, and prediction, utilizing Monte Carlo simulation. Using 2024 aluminum alloy specimens with central holes, the study indicates that in the initial crack diagnosis stage, an inspection standard with a Single Flight Probability of Failure (SFPOF) less than 10-7 and a threshold method enhances structural fatigue crack diagnosis. In the crack diagnosis and prediction stages, iterative updates using Gaussian Process Regression (GPR) within a Dynamic Bayesian Network (DBN) improve crack propagation prediction and risk assessment accuracy. The diagnostic interval significantly impacts SFPOF, with an optimized interval balancing accuracy and computation time. Simplified and precise K value calculation methods enhance efficiency and accuracy. The method reduces costs and improves risk assessment accuracy, providing new insights for SPHM-based aircraft structural risk assessment.

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.

Досвід прогнозування ризиків, пов’язаних з механічним пошкодженням кісткової тканини людини з використанням рекурентних нейронних мереж

Introduction. There are new, in addition to probabilistic and statistical, methods of risk assessment, which require their own methods of analysis and numerical estimation. One of these methods, recognized in a variety of application fields, is associated with the use of direct propagation neural networks. This approach makes it possible to expand the range of tasks that are solved in the field of risk analysis. There are quite a number of systems that require assessment in terms of risk formation, but which are associated with a large number of random factors related to the risk-forming events of the system and its states. Such systems are difficult to model with the help of well-known neural networks. Within the framework of the work, it is proposed to use the capabilities of deep recurrent neural networks with feedback as stabilizing factors with minimization of operational information that needs to be remembered in the process of calculating and operating such a network. Such a model for mechanical damage to human bone tissue depending on a large number of random or indeterminate input signals is proposed to be used in this work. The purpose of the paper is to develop a technique for the use of deep recurrent neural networks and to create a model for predicting event risks associated with the impact of input signals with a high degree of uncertainty or random signals on the system. To provide opportunities for predicting such risks using examples related to injuries of the human skeletal system for its various conditions and conditions. Results. A technique for using recurrent neural networks to predict the risks associated with the violation of the integrity of the human skeletal system was developed. A model of a recurrent neural network was created to predict random events associated with a violation of the integrity of the human skeletal system. Double calculation, aimed at a variety of results, is a confirmation of the performance of the proposed model. It is shown that, depending on the scope of the task set in the analysis, its result is a three-dimensional matrix in coordinates (Xij ∧ Yp;T; φ). At each subsequent step of the iteration in the matrix (φ Xij ∧ Yp;T), by cutting off that part of the potential risk-generating events that, in the opinion of the neural network, are less predicted for each age composition, in favor of other events, real risk-forming events are filtered out, which have predominant values for the system. Conclusions. On the example of random events that accompany mechanical damage to human bone tissue, the ability of models created on the basis of RNN with feedbacks to avoid the uncertainty of risks accompanying human life in four specified ranges of life time and to determine the most effective ones for each of them for a modern person is shown. The ability of activation functions of the bifurcation nature of one of the synapse layers to qualitatively filter random signals in systems of recurrent neural networks with DT-RNN (Deep Transition RNN) feedbacks is shown. The use of deep recurrent neural networks in the formalized version provides new opportunities for taking into account groups of random but real events in the analysis of event risk by clarifying the feedback, and at each subsequent step of their iteration to obtain more accurate data to predict such risk, avoiding the uncertainty of the system state. The formalization of this process provides opportunities to predict random risks for certain groups of the population as a priority, and to use them in the preventive work of medical institutions of the first group of care. Keywords: risks, random events, recurrent neural network, human skeletal system.

Large scale screening and quantification of micropollutants in fish from the coastal waters of Cochin, India: Analytical method development and health risk assessment

Urban estuarine and coastal water receive several micropollutants through industrial and agricultural influxes. The bioaccumulation of these micropollutants in fish and their entry into the coastal population's food chain raises significant food safety concerns. Hence, a comprehensive analytical method was developed for ultra-trace level quantification of 345 micropollutants in fish. The optimized sample preparation method could extract compounds suitable for both GC–MS/MS and LC-MS/MS analysis simultaneously. The target list of contaminants included 278 agricultural pesticides and also 102 endocrine disruptors covering polyaromatic hydrocarbons, polychlorinated biphenyls, organochlorines, and endocrine-disrupting pesticides. The GC–MS/MS with large volume injection (LVI) technique, and LC-MS/MS operating in MRM mode, achieved an LOQ of <2.00 ng/g for most of the analytes. The extraction strategy involved tri-phase partitioning between water, acidified acetonitrile, and hexane, followed by salting out. Dispersive solid phase cleanup (dSPE) with C18, Z-Sep+, CaCl2, and MgSO4 was able to reduce the matrix influence, and the method achieved satisfactory recovery in the range of 70.0–120.0 % for all the target analytes. The repeatability and reproducibility relative standard deviation values of the measured analytes were <20.0 %, and the Horwitz ratio values were well below 2. The method was used to accurately measure the target micropollutants in fish from the Cochin estuary, the highly urbanized portion of the Vembanad Lake, and an important Ramsar site. At least one or more of the 41 different micropollutants were identified and quantified in about 90.7 % of the 108 samples analyzed. The importance of large-scale screening and trace-level quantification methods in environmental monitoring and risk assessment is underscored by the results. The risk assessment showed a moderate risk of exposure to the nearby coastal population through the food chain.