Distribution, health and ecological risk assessments of trace elements in Nigerian oil sands

The Nigerian oil sands represent the largest oil sand deposit in Africa, yet there is little published information on the distribution and potential health and ecological risks of trace elements in the oil resource. In the present study, we investigated the distribution pattern of 18trace elements(including biophile and chalcophile elements) as well as the estimated risks associated with exposure to these elements. The results of the study indicated that Fe was the most abundant element, with a mean concentration of 22,131 mg/kg while Br had the lowest mean concentration of 48 mg/kg. The high occurrence of Fe and Ti suggested a possible occurrence of ilmenite(Fe TiO_(3)) in the oil sands. Source apportionment using positive matrix factorization showed that the possible sources of detected elements in the oil sands were geogenic, metal production, and crustal. The contamination factor, geo-accumulation index, modified degree of contamination, pollution load index, and Nemerow pollution index indicated that the oil sands are heavily polluted by the elements. Health risk assessment showed that children were relatively more susceptible to the potentially toxic elements in the oil sands principally via ingestion exposure route(HQ > 1E-04). Cancer risks from inhalation are unlikely due to CR < 1E-06 but ingestion and dermal contact pose severe risks(CR > 1E-04). The high concentrations of the elements pose serious threats due to the potential for atmospheric transport, bioaccessibility, and bioavailability.

Identifying the Hydrochemical Characteristics,Genetic Mechanisms and Potential Human Health Risks of Fluoride and Nitrate Enriched Groundwater in the Tongzhou District,Beijing,North China

Fluoride and nitrate enriched groundwater are potential threats to the safety of the groundwater supply that may cause significant effects on human health and public safety,especially in aggregated population areas and economic hubs.This study focuses on the high F^(−)and NO_(3)^(−)concentration groundwater in Tongzhou District,Beijing,North China.A total of 36 groundwater samples were collected to analyze the hydrochemical characteristics,elucidate genetic mechanisms and evaluate the potential human health risks.The results of the analysis indicate:Firstly,most of the groundwater samples are characterized by Mg-HCO_(3) and Na-HCO_(3) with the pH ranging from 7.19 to 8.28 and TDS with a large variation across the range 471-2337 mg/L.The NO_(3)^(−)concentration in 38.89%groundwater samples and the F^(−)concentration in 66.67%groundwater samples exceed the permissible limited value.Secondly,F^(−)in groundwater originates predominantly from water-rock interactions and the fluorite dissolution,which is also regulated by cation exchange,competitive adsorption of HCO_(3)−and an alkaline environment.Thirdly,the effect of sewage disposal and agricultural activities have a significant effect on high NO3-concentration,while the high F^(−)concentration is less influenced by anthropogenic activity.The alkaline environment favors nitrification,thus being conducive to the production of NO_(3)^(−).Finally,the health risk assessment is evaluated for different population groups.The results indicate that high NO_(3)^(−)and F^(−)concentration in groundwater would have the largest threat to children’s health.The findings of this study could contribute to the provision of a scientific basis for groundwater supply policy formulation relating to public health in Tongzhou District.

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

With the development of the integration of aviation safety and artificial intelligence,research on the combination of risk assessment and artificial intelligence is particularly important in the field of risk management,but searching for an efficient and accurate risk assessment algorithm has become a challenge for the civil aviation industry.Therefore,an improved risk assessment algorithm(PS-AE-LSTM)based on long short-term memory network(LSTM)with autoencoder(AE)is proposed for the various supervised deep learning algorithms in flight safety that cannot adequately address the problem of the quality on risk level labels.Firstly,based on the normal distribution characteristics of flight data,a probability severity(PS)model is established to enhance the quality of risk assessment labels.Secondly,autoencoder is introduced to reconstruct the flight parameter data to improve the data quality.Finally,utilizing the time-series nature of flight data,a long and short-termmemory network is used to classify the risk level and improve the accuracy of risk assessment.Thus,a risk assessment experimentwas conducted to analyze a fleet landing phase dataset using the PS-AE-LSTMalgorithm to assess the risk level associated with aircraft hard landing events.The results show that the proposed algorithm achieves an accuracy of 86.45%compared with seven baseline models and has excellent risk assessment capability.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Machine Learning-Based Decision-Making Mechanism for Risk Assessment of Cardiovascular Disease

Cardiovascular disease(CVD)has gradually become one of the main causes of harm to the life and health of residents.Exploring the influencing factors and risk assessment methods of CVD has become a general trend.In this paper,a machine learning-based decision-making mechanism for risk assessment of CVD is designed.In this mechanism,the logistics regression analysismethod and factor analysismodel are used to select age,obesity degree,blood pressure,blood fat,blood sugar,smoking status,drinking status,and exercise status as the main pathogenic factors of CVD,and an index systemof risk assessment for CVD is established.Then,a two-stage model combining K-means cluster analysis and random forest(RF)is proposed to evaluate and predict the risk of CVD,and the predicted results are compared with the methods of Bayesian discrimination,K-means cluster analysis and RF.The results show that thepredictioneffect of theproposedtwo-stagemodel is better than that of the comparedmethods.Moreover,several suggestions for the government,the medical industry and the public are provided based on the research results.

Risk Assessment of Deep-Water Horizontal X-Tree Installation

Due to the high potential risk and many influencing factors of subsea horizontal X-tree installation,to guarantee the successful completion of sea trials of domestic subsea horizontal X-trees,this paper established a modular risk evaluation model based on a fuzzy fault tree.First,through the analysis of the main process oftree down and combining the Offshore&Onshore Reliability Data(OREDA)failure statistics and the operation procedure and the data provided by the job,the fault tree model of risk analysis of the tree down installation was established.Then,by introducing the natural language of expert comprehensive evaluation and combining fuzzy principles,quantitative analysis was carried out,and the fuzzy number was used to calculate the failure probability of a basic event and the occurrence probability of a top event.Finally,through a sensitivity analysis of basic events,the basic events of top events significantly affected were determined,and risk control and prevention measures for the corresponding high-risk factors were proposed for subsea horizontal X-tree down installation.

Health risk assessment of trace metal(loid)s in agricultural soils based on Monte Carlo simulation coupled with positive matrix factorization model in Chongqing, southwest China

This study aimed to investigate the pollution characteristics, source apportionment, and health risks associated with trace metal(loid)s(TMs) in the major agricultural producing areas in Chongqing, China. We analyzed the source apportionment and assessed the health risk of TMs in agricultural soils by using positive matrix factorization(PMF) model and health risk assessment(HRA) model based on Monte Carlo simulation. Meanwhile, we combined PMF and HRA models to explore the health risks of TMs in agricultural soils by different pollution sources to determine the priority control factors. Results showed that the average contents of cadmium(Cd), arsenic (As), lead(Pb), chromium(Cr), copper(Cu), nickel(Ni), and zinc(Zn) in the soil were found to be 0.26, 5.93, 27.14, 61.32, 23.81, 32.45, and 78.65 mg/kg, respectively. Spatial analysis and source apportionment analysis revealed that urban and industrial sources, agricultural sources, and natural sources accounted for 33.0%, 27.7%, and 39.3% of TM accumulation in the soil, respectively. In the HRA model based on Monte Carlo simulation, noncarcinogenic risks were deemed negligible(hazard index <1), the carcinogenic risks were at acceptable level(10^(-6)<total carcinogenic risk ≤ 10^(-4)), with higher risks observed for children compared to adults. The relationship between TMs, their sources, and health risks indicated that urban and industrial sources were primarily associated with As, contributing to 75.1% of carcinogenic risks and 55.7% of non-carcinogenic risks, making them the primary control factors. Meanwhile, agricultural sources were primarily linked to Cd and Pb, contributing to 13.1% of carcinogenic risks and 21.8% of non-carcinogenic risks, designating them as secondary control factors.

IoT Smart Devices Risk Assessment Model Using Fuzzy Logic and PSO

Increasing Internet of Things(IoT)device connectivity makes botnet attacks more dangerous,carrying catastrophic hazards.As IoT botnets evolve,their dynamic and multifaceted nature hampers conventional detection methods.This paper proposes a risk assessment framework based on fuzzy logic and Particle Swarm Optimization(PSO)to address the risks associated with IoT botnets.Fuzzy logic addresses IoT threat uncertainties and ambiguities methodically.Fuzzy component settings are optimized using PSO to improve accuracy.The methodology allows for more complex thinking by transitioning from binary to continuous assessment.Instead of expert inputs,PSO data-driven tunes rules and membership functions.This study presents a complete IoT botnet risk assessment system.The methodology helps security teams allocate resources by categorizing threats as high,medium,or low severity.This study shows how CICIoT2023 can assess cyber risks.Our research has implications beyond detection,as it provides a proactive approach to risk management and promotes the development of more secure IoT environments.

Construction and validation of a risk-prediction model for anastomotic leakage after radical gastrectomy: A cohort study in China

Objectives:Anastomotic leakage(AL)stands out as a prevalent and severe complication following gastric cancer surgery.It frequently precipitates additional serious complications,significantly influencing the overall survival time of patients.This study aims to enhance the risk-assessment strategy for AL following gastrectomy for gastric cancer.Methods:This study included a derivation cohort and validation cohort.The derivation cohort included patients who underwent radical gastrectomy at Sir Run Run Shaw Hospital,Zhejiang University School of Medicine,from January 1,2015 to December 31,2020.An evidence-based predictor questionnaire was crafted through extensive literature review and panel discussions.Based on the questionnaire,inpatient data were collected to form a model-derivation cohort.This cohort underwent both univariate and multivariate analyses to identify factors associated with AL events,and a logistic regression model with stepwise regression was developed.A 5-fold cross-validation ensured model reliability.The validation cohort included patients from August 1,2021 to December 31,2021 at the same hospital.Using the same imputation method,we organized the validation-queue data.We then employed the risk-prediction model constructed in the earlier phase of the study to predict the risk of AL in the subjects included in the validation queue.We compared the predictions with the actual occurrence,and evaluated the external validation performance of the model using model-evaluation indicators such as the area under the receiver operating characteristic curve(AUROC),Brier score,and calibration curve.Results:The derivation cohort included 1377 patients,and the validation cohort included 131 patients.The independent predictors of AL after radical gastrectomy included age65 y,preoperative albumin<35 g/L,resection extent,operative time240 min,and intraoperative blood loss90 mL.The predictive model exhibited a solid AUROC of 0.750(95%CI:0.694e0.806;p<0.001)with a Brier score of 0.049.The 5-fold cross-validation confirmed these findings with a calibrated C-index of 0.749 and an average Brier score of 0.052.External validation showed an AUROC of 0.723(95%CI:0.564e0.882;p?0.006)and a Brier score of 0.055,confirming reliability in different clinical settings.Conclusions:We successfully developed a risk-prediction model for AL following radical gastrectomy.This tool will aid healthcare professionals in anticipating AL,potentially reducing unnecessary interventions.

Trends and hotspots in gastrointestinal neoplasms risk assessment: A bibliometric analysis from 1984 to 2022

BACKGROUND Gastrointestinal neoplasm(GN)significantly impact the global cancer burden and mortality,necessitating early detection and treatment.Understanding the evolution and current state of research in this field is vital.AIM To conducts a comprehensive bibliometric analysis of publications from 1984 to 2022 to elucidate the trends and hotspots in the GN risk assessment research,focusing on key contributors,institutions,and thematic evolution.METHODS This study conducted a bibliometric analysis of data from the Web of Science Core Collection database using the"bibliometrix"R package,VOSviewer,and CiteSpace.The analysis focused on the distribution of publications,contributions by institutions and countries,and trends in keywords.The methods included data synthesis,network analysis,and visualization of international collaboration networks.RESULTS This analysis of 1371 articles on GN risk assessment revealed a notable evolution in terms of research focus and collaboration.It highlights the United States'critical role in advancing this field,with significant contributions from institutions such as Brigham and Women's Hospital and the National Cancer Institute.The last five years,substantial advancements have been made,representing nearly 45%of the examined literature.Publication rates have dramatically increased,from 20 articles in 2002 to 112 in 2022,reflecting intensified research efforts.This study underscores a growing trend toward interdisciplinary and international collaboration,with the Journal of Clinical Oncology standing out as a key publication outlet.This shift toward more comprehensive and collaborative research methods marks a significant step in addressing GN risks.CONCLUSION This study underscores advancements in GN risk assessment through genetic analyses and machine learning and reveals significant geographical disparities in research emphasis.This calls for enhanced global collaboration and integration of artificial intelligence to improve cancer prevention and treatment accuracy,ultimately enhancing worldwide patient care.

Understanding Osteoporosis: Pathophysiology, Risk Factors, Diagnosis, and Management

Osteoporosis is a systemic skeletal disease characterized by low bone mineral density (BMD) and deterioration of bone architecture, resulting in reduced bone strength and, consequently, increased susceptibility to fractures which poses a significant public health concern worldwide, particularly in aging populations [1]. The health-economic impact of vertebral and hip fractures has been extensively explored and it is well known that these fractures are associated with morbidity/disability and increased mortality;they also account for a substantial portion of the direct fracture costs. This review aims to provide a comprehensive overview of osteoporosis, including its pathophysiology, risk factors, diagnostic approaches, and management strategies. By elucidating the multifaceted nature of this condition, healthcare providers can better identify individuals at risk, implement preventive measures, and optimize treatment to reduce the burden of osteoporotic fractures.

GIS Application in Urban Flood Risk Analysis: Midar as a Case Study

The significance of this study lies in its exploration of the advanced applications of Geographic Information Systems (GIS) in assessing urban flood risks, with a specific focus on Midar, Morocco. This research is pivotal as it showcases that GIS technology is not just a tool for mapping, but a critical component in urban planning and emergency management strategies. By meticulously identifying and mapping flood-prone areas in Midar, the study provides invaluable insights into the potential vulnerabilities of urban landscapes to flooding. Moreover, this research demonstrates the practical utility of GIS in mitigating material losses, a significant concern in flood-prone urban areas. The proactive approach proposed in this study, centered around the use of GIS, aims to safeguard Midar’s population and infrastructure from the devastating impacts of floods. This approach serves as a model for other urban areas facing similar challenges, highlighting the indispensable role of GIS in disaster preparedness and response. Overall, the study underscores the transformative potential of GIS in enhancing urban resilience, making it a crucial tool in the fight against natural disasters like floods.

Integrated Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comprehensive Comparative Study

Cardiovascular Diseases (CVDs) pose a significant global health challenge, necessitating accurate risk prediction for effective preventive measures. This comprehensive comparative study explores the performance of traditional Machine Learning (ML) and Deep Learning (DL) models in predicting CVD risk, utilizing a meticulously curated dataset derived from health records. Rigorous preprocessing, including normalization and outlier removal, enhances model robustness. Diverse ML models (Logistic Regression, Random Forest, Support Vector Machine, K-Nearest Neighbor, Decision Tree, and Gradient Boosting) are compared with a Long Short-Term Memory (LSTM) neural network for DL. Evaluation metrics include accuracy, ROC AUC, computation time, and memory usage. Results identify the Gradient Boosting Classifier and LSTM as top performers, demonstrating high accuracy and ROC AUC scores. Comparative analyses highlight model strengths and limitations, contributing valuable insights for optimizing predictive strategies. This study advances predictive analytics for cardiovascular health, with implications for personalized medicine. The findings underscore the versatility of intelligent systems in addressing health challenges, emphasizing the broader applications of ML and DL in disease identification beyond cardiovascular health.

Safety Risk Assessment Analysis of Bridge Construction Using Backpropagation Neural Network

The evaluation of construction safety risks has become a crucial task with the increasing development of bridge construction.This paper aims to provide an overview of the application of backpropagation neural networks in assessing safety risks during bridge construction.It introduces the situation,principles,methods,and advantages,as well as the current status and future development directions of backpropagation-related research.

Risk Assessment and Control in Medical Investment,Merger,and Acquisition

The risk assessment and control of medical investment,merger,and acquisition are crucial topics within the medical industry,encompassing various aspects of investment,merger,and acquisition within this sector.The process primarily targets the unique nature and associated risks of the medical industry,focusing on effective risk management and control strategies to facilitate the smooth progression of investment,merger,and acquisition activities.

Health risk assessment of heavy metals in soils and crops in a mining area(Au-Ag-Cutrona-oil et al.) of the Nanyang Basin, Henan Province, China

Heavy metal distribution in mining areas has always been a hot research topic due to the special environment of these areas. This study aims to explore the impact of heavy metal pollution on soils and crops in the study area, ensure the safety of local crops and the health of local residents, and provide a basis for the subsequent environmental restoration and the prevention and control of environmental pollution. Based on the analysis of the heavy metal concentrations in local soils and crops, the study investigated the spatial distribution, pollution degrees, and potential ecological risks of heavy metals in the farmland of a mining area in the southeastern Nanyang Basin, Henan province, China explored the sources of heavy metals and assessed the health risks caused by crop intake. The results of this study are as follows. The root soils of crops in the study area suffered heavy metal pollution to varying degrees. The degree of heavy metal pollution in maize fields is higher than that in wheat fields, and both types of fields suffer the most severe Cd pollution. Moreover, the root soils of different crops suffer compound pollution.The root soils in the maize fields suffer severe compound pollution at some sampling positions, whose distribution is similar to that of the mining area. Cd poses the highest potential ecological risks among all heavy metals, and the study area mainly suffers low and moderate comprehensive potential ecological risks. The principal component analysis(PCA) shows that the distribution of Zn, Cd, Pb, and As in soils of the study area is mainly affected by anthropogenic factors such as local mining activities;the distribution of Cr and Ni is primarily controlled by the local geological background;the distribution of Hg is mainly affected by local vehicle exhaust emissions, and the distribution of Cu is influenced by both human activities and the geological background. Different cereal crops in the study area are polluted with heavy metals dominated by Cd and Ni to varying degrees, especially wheat. As indicated by the health risk assessment results, the intake of maize in the study area does not pose significant human health risks;however, Cu has high risks to human health, and the compound heavy metal pollution caused by the intake of wheat in the study area poses risks to the health of both adults and children. Overall, the soils and crops in the study area suffer a high degree of heavy metal pollution, for which mining activities may be the main reason.

Oil Spill Risk Assessment of Offshore Pipeline in the Bohai Sea Under the Perspective of Ecological Protection

In recent decades,the exploration and development of marine oil and gas resources have increased significantly to meet the increasing energy demand of mankind.The Bohai Sea is a semi-closed continental sea that has a weak water exchange capacity and high ecological fragility.However,at present,more than 200 oil platforms have been built in the Bohai Sea,with more than 270 offshore oil pipelines having a length exceeding 1600 km.The oil spill pollution of offshore platforms has a great impact on the marine environment and ecosystems.Therefore,a comprehensive assessment of its risks is of great practical significance.This paper systematically constructs a comprehensive oil spill risk assessment model that combines the oil spill risk probability model and the ocean hydrodynamic model.This paper uses the Bohai Sea offshore pipeline as an example to assess its oil spill risk.The high-risk-value areas of the Bohai Sea offshore pipeline are mainly distributed at the bottom of Liaodong Bay,the bottom of Bohai Bay,near the Caofeidian area,and the northern part of the Yellow River Estuary.

Distribution and risk assessment of heavy metals in surface sediments of coastal mudflats on Leizhou Peninsula,China

Mudflats play a vital role in maintaining the dynamic balance between sea and land.To understand the characteristics,sources,and pollution risks of six heavy metals(As,Cd,Cr,Cu,Hg,and Pb)in the coastal mudflats on the Leizhou Peninsula,257 surface sediment samples were studied using mathematical statistics,correlation analysis,and factor analysis.The results show that the overall concentrations of these heavy metals are low although there are several high abnormal points in the local areas.The strong correlation between these heavy metals indicates that the sources of some of the metals are similar,yet their elemental combinations in different cities(counties)varied.According to the calculated enrichment factor(EF),anthropogenic activity-induced heavy metals were determined in order of decreasing influence:As,Cd,Pb,Cr,Cu,and Hg.The low EF values of Hg indicate that it does not present as a contaminant in the study area,while low values of Cr and Cu from the Lianjiang City suggest that these two metals were also attributed to natural sources.The presence of As,Cd,Cr,Cu,and Pb from the remaining cities(counties)should be influenced by anthropogenic activities.The overall potential ecological risk index indicates that the ecological risks posed by the six analyzed heavy metals to the Leizhou Peninsula mudflats,in order of decreasing risk,are Cd,As,Hg,Pb,Cu,and Cr.It is noteworthy that only Cd in Lianjiang City demonstrated substantial ecological risk.Other examined heavy metals in other cities of the study area showed slight ecological risk.

Detection and Analysis of Pesticide Residues in Chinese Chives and Risk Assessment of Dietary Intake

[Objectives]This study was conducted to understand the status of pesticide residues and dietary intake risk of Chinese chives in Tangshan area. [Methods] Sixty eight kinds of pesticide residues in 415 Chinese chive samples collected from Tangshan area were qualitatively and quantitatively determined by high-performance liquid chromatography-tandem mass spectrometry(HPLC-MS/MS) and gas chromatography(GC) in 2020. [Results] The results showed that 41 kinds of pesticide residues were detected in the 415 Chinese chive samples, and the detection rate was 69.4%(288/415), and there was a combination of pesticides in many samples. According to the National Food Safety Standard―Maximum Residue Limits of Pesticides in Food(GB 2763-2019), the residues of 12 pesticides exceeded the maximum residue limits(MRLs), and the unqualified rate was 38.07%(158/415). The highest detection rate of clothianidin was 41.20%(171/415), but there was no MRL in GB 2763-2019. The next was procymidone, the detection rate of which was 35.42%(147/415), and the over-standard rate was 30.12%(125/415). Forbidden and restricted pesticides were detected in some samples. According to the dietary exposure risk assessment, the NEDI/ADI values were all less than 1 and the intake risk was within acceptable range. In Tangshan area, the types of pesticides used in Chinese chive production are complex, and there are risks of multi-residue pollution and use of banned and restricted pesticides and unregistered pesticides. It is suggested that routine monitoring of pesticide residues and management of pesticide use should be strengthened to ensure the quality and safety of agricultural products. [Conclusions] This study provides a theoretical basis for the safe production of Chinese chive and the standardized and rational use of pesticides.

Pollution and health risk assessment of heavy metals in waters around mine sites of Elazig(Eastern Turkey)

Turkey’s Eastern Anatolia Region is the oldest known mineral mining area of Maden and Alacakaya.Chromite production in the Alacakaya field constitutes 50% of the country’s exports,and copper mines in the Maden region account for approximately 12% of the country’s copper production.There is a risk of water pollution due to significant mine waste which affects the Inci and Maden rivers.The water needs of many settlements are met from these streams,which run through these two mine sites.This study investigated the water pollution in the rivers.25 water samples were collected during the dry and rainy periods,and the Al,Cr,Cu,Fe,Li,Mn,Ni,Pb,Sr and Zn contents of these samples were examined in terms of health.Evaluation of element concentrations and creation of spatial distribution maps were performed using ArcGIS software.Spatial distribution maps,correlation and cluster analysis indicate that the source of heavy metals observed in waters is mine fields.The heavy metal content of the samples is higher in the dry period,the high concentration values are obtained from the mine sites,the decrease in the concentrations throughout the flow during the rainy period,are indicators of the effect of the mines on the water pollution.As a result of the comparison from the analysis results of water samples with World Health Organization(WHO),Environmental Protection Agency(EPA)and European Commission(EC)standards,the element values of Al,Cr,Fe,Mn,Ni and Pb exceeded the permissible values for health.The concentrations of these elements for dry period samples are:0-6.411 mg L^(-1),0.006-0.235 mg L^(-1),0-13.433 mg L^(-1),0-0.316 mg L^(-1),0-0.495 mg L^(-1),0-0.065mg L^(-1),and for rainy period samples are 0-1.698mg/L,0-0.2 mg L^(-1),0-9.033 mg L^(-1),0-0.173 mg L^(-1),0-0.373 mg L^(-1),0-0.034 mg L^(-1),respectively.Although the waters in the region are polluted by heavy metals,it has been determined that there is no noncarcinogenic hazard as a result of the calculation of the hazard index(HI<1)by ingestion and dermal contact within the scope of human health risk assessment.This study will be beneficial as it draws attention to the prevention of the negative effects of water pollution,which may cause serious health problems in the future as a result of mining activities in the region.