Abstract
Risk assessment analysis related to groundwater contamination by heavy metals was performed in the Shiraz city (Iran). We compared the traditional deterministic methodologies with a probabilistic approach based on the concentration of different heavy metals determined from many sampling points. The relationships between the variables by the multivariate statistical analysis were assessed, and the target hazard quotient (THQ) was calculated in children, women, and men groups. Results showed that analyzed water samples were suitable for drinking, although alkaline. Concentrations of the heavy metals were: Zn > Ni > Cu > Se > Co > Sb. The THQ values for non-carcinogenic elements showed no significant risk for population of studied age groups, although a higher THQ value was observed for the water from the northwest and some central areas of city. Mean values of cancer risk for Ni were 1.77 × 10–5, 4.36 × 10–5, and 3.32 × 10–5 in children, women, and men, respectively. The multivariate approach indicated that the carcinogenic risk certainty level was 97.6, 91.2, and 94.3% for children, women, and men, respectively, and the model sensitivity analysis showed that the most effective parameter for carcinogenicity was Ni concentration. The probabilistic analysis also showed the relative influence of geogenic and anthropogenic processes on the quality of the water of Shiraz city. We concluded that risk assessment using a probabilistic approach could be better predictive of chronic exposure to hazardous elements in drinking water, which possibility the implementation of better protective measures than the current deterministic approaches.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
Introduction
Freshwater represents less than 3% of all water on our planet, and groundwater availability and quality are vital natural resources for human beings. Actuality, more than 30% of the world's population relies on groundwater for drinking water, especially in arid and semi-arid regions (Jamshidi et al. 2021). Continuous urbanization, industrialization, poor management of waste and industrial effluents, and agricultural activities represent major threats to groundwater quality due to the release of persistent pollutants such as potentially toxic elements (PTEs) (Soleimani et al. 2020b).
Some PTEs, such as Zn, Ni, Cu, Se, Co, are essential in low concentrations for healthy development of human well-being, and living organisms' function (Organization 1996). Some others, e.g., As, Cd, Pb, Sb, are not required for biological functions and are toxic even at low concentrations (Sharafi et al. 2019; Soleimani et al. 2020a). Kidney damage, degenerative neurological conditions, respiratory and cardiovascular disease, and cancer have been reported through groundwater contamination to PTEs (Badeenezhad et al. 2021). Due to their immutable nature, PTEs are persistent, and their accumulation in groundwater represents a primary route of exposure for humans (Kiani et al. 2022). For these reasons, the concentration of several PTEs is regularly monitored by public Authorities to prevent potential hazards to public health.
Risk assessment is a systematic process aiming at determining human exposure to significant risks and implementing control actions to decrease exposure to acceptable levels (Raza et al. 2017). Risk assessment related to groundwater contamination by PTEs is generally conducted by deterministic approaches considering point estimators (Moghtaderi et al. 2020; Abolfazli et al. 2021). While consensual, this approach to risk assessment, does not take into account natural variability, data uncertainty, measurement uncertainty, and eventual lack of environmental data. The point estimation approach also does not account for the dispersion of the analytical data around mean values, being based on fixed equal weights to all of the available data. whereas Differently, the probabilistic risk assessment approach is a more comprehensive methodology for evaluating risks related to environmental pollution (Gebeyehu and Bayissa 2020; Silvestri et al. 2021), because its statistic representation can encompass all or selected subsets of the physical and chemical environmental features. For example, it considers the actual concentration range of each PTEs in groundwater of a given area in time and space, not only mean values. Hence, the multivariate approach to risk assessment can lead to more proper risk assessment and sustainable risk prevention measures. This approach is based on using all data in a distribution function, which can significantly improve the risk assessment by accounting for the spatial variability and other main factors of the studied environment (Rivera-Velasquez et al. 2013; Shahsavani et al. 2023), especially if coupled with sensitivity analysis.
Iran, like other arid and semi-arid areas, relies 60% on groundwater for freshwater supply, with an increasing trend of use (Karamia et al. 2019), but risk assessment using probabilistic approaches to the water quality in Iran is still scarce. Shiraz, the most important city in Southern Iran, is faced with urban sprawl and vegetation decline, increasing drilling of deeper wells to reach lower groundwater levels, and a significant increase in the pressure on natural resources. Among the probabilistic approaches for risk assessment, the Monte Carlo method is one of the most widely used, especially for large monitoring surveys (Kavcar et al. 2006). In this study, the concentration of some toxic elements in the drinking water of Shiraz city was determined and their spatial distribution was drawn using ArcGIS software. To identify the sources of pollution and the relationships between different parameters, multivariate statistical analysis techniques were used. Finally, the health risk assessment and distribution patterns of different PTEs in groundwater were conducted on the drinking water of the city of Shiraz, South Iran, using both a deterministic and a probabilistic approach based a Monte Carlo simulation. This study illustrates how a probabilistic approach can provide more robust results for land planning and implementing appropriate groundwater preservation measures and actions for better protecting human health in complex urban areas where groundwater presents broad ranges of concentration of different PTEs.
Materials and methods
Study area
The present study was conducted in the city of Shiraz in SW Iran (52°29′E, 52°36 29° 33 ′, 29°36′ N), the Fars province (Fig. 1). Shiraz city has a population of more than 1.5 million people and covers an area of 240 km2, and drinking water wells are scattered in the urban area boundary line (Fig. 1). The geology of the Shiraz area includes Asmari, Razak, and Razzaq formations in the mountainous region of Zagros, forming closed basins of central Iran, characterized by alkaline and sodic soils developed from chalky marl parent rocks. The climate is temperate, with an average annual temperature of 18.6° C, precipitation amounting to 325.6 mm that represents the primary source of water supply in the area, and wind speed of 2.35 m/s (Keshavarzi et al. 2015).
Water sampling and chemical analysis
Fifty-nine samples were taken from Shiraz urban drinking water plumbing and transmission system based on the population distribution in the summer 2021 selected with a spatial distribution developed using Arc GIS 10.3 software (ESRI, Redlands, CA, USA), (Aleem et al. 2018). A global positioning system (GPS) was applied to locate the sampling points, the World Geodetic System (WGS-1984) was used to fix the selected points, and the inverse distance weight (IDW) method was applied to draw the spatial distribution of heavy metal concentration in the study area (Mosaferi et al. 2014).
Polyethylene bottles previously washed with distilled water, and 20% HNO3 were used to collect water samples. Water samples were filtered using Whatman filters with pores of 0.45 μm to prevent adsorption and crystallization of trace elements. Then, 3 ml of 69% HNO3 was added to each sample to prevent turbidity due growth of microbial colonies prior to elemental analysis and shipped to the analytical laboratory in refrigerated at 4 °C boxes. Concentrations of cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), selenium (Se), and antimony (Sb) were measured by Inductively Coupled Plasma followed by Mass Spectrometry (ICP-MS Agilent 7800, USA). Total dissolved solids (TDS), electrical conductivity (EC), and pH values of all samples were measured by a conductivity meter (WTW Cond 720) and pH meter (Metrom, Model 827), respectively. All determinations were performed in triplicates.
Health risk assessment
Deterministic approach
Because in most studies, the amount of carcinogenic risk in the adult group and the non-carcinogenic risk in the children group is higher than the other age groups, for this reason the city population was divided into three age groups: children (< 6 years), adult women (20–70 y), and adult men (20–70 y), age groups generally accepted in environmental risk assessment (Mohammadi et al. 2017). The Chronic Daily Intake, i.e., the average daily dose (CDI) for the analyzed elements, was calculated according to (Joodavi et al. 2021; Shafiuddin Ahmed et al. 2021) using the following equation:
where Ci is the average contamination concentration in water (mg l−1), IR is the ingestion rate of water (l d−1, EF is the exposure frequency (d year−1), ED is the exposure duration for cancer risk assessment (years), BW is the average body weight (kg), AT is the averaging time (day). According to the USEPA, the threshold values for the carcinogenic risks posed by the analyzed elements were in the range of 10–6–10–4, unacceptable for CDI > 10–4 (Ogamba et al. 2021). The following equation calculated the carcinogenic risk for Ni:
where CDI is the Chronic Daily Intake and S.F. is the cancer slope factor, which differs for each element (Farokhneshat et al. 2016).The following equation calculated the target hazard quotient (THQ):
where the RfD is the Reference Dose value of each element based on the (US EPA 1989) screening level values (EPA 1989).
Probabilistic approach
The PTEs analytical data were modeled with a probabilistic approach to improve the risk assessment by the Monte Carlo method coupled with a sensitivity analysis (Soleimani et al. 2020a). Input values for Monte Carlo simulation and sensitivity analysis are reported in Table 1.
The Crystal Ball®software, an 'add-in for Microsoft Excel, was used to perform analysis, produce the input distribution values, collect the output graphically, and calculate summary statistics, and the distribution factor of heavy metals concentration can be obtained in the 'Definition assumption' tool of the Crystal Ball® software (Shalyari et al. 2019). Because no distribution parameters were available from previous studies conducted in the area, the other probability distribution functions employed in the sensitivity analysis (SA) and Monte Carlo simulation were those suggested by the U.S. Environmental Protection Agency (Fitzpatrick et al. 2017).
Principle component analysis and cluster analysis method
The use of principal component analysis (PCA) is one of the most common environmetric techniques for determining the contributions of human and natural resources (Nasir et al. 2011). The application of PCA is for comparing the compositional and spatial patterns of water samples and finding possible sources of trace metals in them (Barakat et al. 2016).
Because heavy metals enrichment in groundwater is influenced by site-specific factors such as rock–mineral weathering, drainage density, geological and hydro-geological settings, and anthropogenic activities, we used the Hierarchical Cluster Analysis (HCA) method to classify water samples in quality classes and assess the dissimilarities between different classes. Ward's linkage method, including squared Euclidean and z-score standardization, was used for analysis.
Data analysis
Statistical analysis was performed using IBM SPSS software (version 16; SPSS Inc., Chicago, IL, USA). Nonparametric tests were used to analyze the results since the data were not normally distributed, which was determined by the Kolmogorov–Smirnov test (P < 0.05). Then, the uncertainty analysis was calculated using Oracle Crystal Ball software (version 11.1.2.3). Spatial analysis of geochemical data was performed by inverse distance weighting (IDW) using ArcGIS software (10.8). PCA and the cluster analysis method used software by IBM SPSS software.
Results and discussion
PTEs Concentration in drinking water and spatial variation
Mean values and ranges of PTEs concentrations in drinking water samples from Shiraz city are reported in Table 2.
All groundwater samples had a sub-alkaline alkaline pH value (Table 2), with an average pH value of 7.91 due to the presence of soluble carbonates, mainly bicarbonate (HCO3−) ions (Adams et al. 2001).; This result was expected due to the geological features of the Shiraz area. The average value of water EC was 643 μS cm−1, with all values below the recommended maximum threshold value for drinking water (Table 2). The range of TDS concentrations had an average value of 257 mg l−1, all samples had values below the maximum threshold recommended by the US EPA (Table 2), although some samples presented values (e.g., 481 mg l−1) to the maximum admissible value. The TDS value of water is an important parameter in determining the water quality for human consumption because high levels of TDS in waters are generally due to Na, K, and chlorides which may affect human health upon prolonged exposure. Waters with high TDS values are also considered unsuitable for irrigation. According to (Rusydi 2018), based on the TDS values the water can be classified into four categories: freshwater (TDS < 1 g l−1), brackish waters (1 < TDS < 10 g l−1), saline waters (10 < TDS < 100 g l−1), and brine waters (TDS > 100 g l−1). According to this classification, all the analyzed water samples could be considered freshwater.
Mean concentrations and concentration ranges of the measured PTEs are reported in Table 2, and ranked as it follows:6 Zn (9.62) > Ni (1.36) > Cu (0.56) > Se (0.23) > Co (0.21) > Sb (0.09). Means concentrations and concentration ranges for the studied PTEs in the groundwater of Shiraz city were comparable to those reported by Tavanpour et al. (2016), who attributed the relatively high Zn concentrations in water samples to their natural occurrence or the corrosion of galvanized pipes; however, though all mean values of PTEs concentrations were below the recommended threshold limits, the broad concentration ranges observed for several PTEs clarify the need for a probabilistic approach for a more reliable risk assessment.
Spatial distribution maps of the studied trace elements observed using the IDW method in GIS software showed that high Sb, Ni, and Co concentrations were mainly located in the northwest of the city area, whereas waters of the central part of the city were characterized by higher concentrations of Cu, Zn, and Co (Fig. 2). Enrichment with Ni in the waters of the northwest of the city could be attributed to the advanced corrosion of metal pipes and pipe fittings in contact with drinking water (Adhikari et al. 2021). In contrast, galvanic reactions at the boundary between copper pipes and brass fittings in the household plumbing system could be responsible for higher Cu concentrations of water in the city central area (Harvey et al. 2016). Concerning Ni contamination in suburban areas, groundwater contamination in agricultural soils, could be attributed to the use of wastewater for irrigation.
Multivariate analysis of elements in drinking water
Cluster analysis of water quality parameters showed four distinct clusters, one formed by TDS and EC values and Cu and Zn concentrations, and a second cluster formed by Ni, Sb, and Co concentrations, whereas Se concentration and pH value clustered separately (Fig. 3). While sub-clustering of TDS and EC values could be expected because suspended soils generally exhibit sorption sites for alkaline metals on their surface, clustering of EC and TDS with Cu and Zn concentrations could indicate a potential association of these PTEs with dissolved solids, which in turn could have adverse effects on human health. Possible sources of Pb and Zn can be leachates and/or leakage from hazardous waste dumpsites or uncontrolled release into industrial effluents. Cluster of Ni, Sb, and Co elements may have originated from geogenic sources or anthropogenic activities such as improper waste management, use of phosphate fertilizers and fossil fuels in agriculture, release of wastewater from chemical industry (Moghtaderi et al. 2020).
The PCA Factor loading for the first three principal components with maximum variance, accounting for 71.7% of the total variance, is shown in (Table 3). The PC1 accounted for 28.2% of the total variance and included the TDS and EC (positive loading) and pH (negative loading), the PC2 accounted for 23.6% of the total variance and included Sb, Ni and Co concentrations, whereas the third PC3 accounted for 19.9% of the total variance and included Cu and Zn concentrations (Table 3). The PC1 showed that the interaction between rock substrate and water increases the concentration of dissolved ions in groundwater, and the negative relationship with pH is related to the carbonate origin. Human activities, especially industry, agriculture, untreated sewage, and landfill leachate, could be possible sources of PTEs loaded in PC2, whereas Copper and Zn loaded in PC3 could be attributed to the use of fertilizers, and accumulation of pesticides or fungicides in agricultural soils, acting as secondary source toward groundwater (Qishlaqi and Moore 2007).
The positive coefficients for all of the measured parameters loaded on PC1 and PC2, except for the pH value, indicated that the measured parameters were related among them, and that all of them are influenced by the pH value of the studied waters (Fig. 4).
Health risk assessment
The minimum, mean and maximum values of the CDI and THQ for different population age groups in the study area are reported in Tables 4 and 5.
The THQ index value obtained for each age group was generally < 1; however, higher THQ values were observed for the population living in the northwest of the urban areas and small city center areas (Table 5).
Mean CR values calculated on the base of the Ni concentration were 2.19 × 10–5 for children, 6.06 × 10–5 for women, and 4.73 × 10–5 for men, indicating moderate carcinogenic risk for all three population age groups, whereas the CR values ranged between 10–6 and 10–4 for all of the population groups (Table 6).
The spatial variation map of THQ and CR in children and women showed that carcinogenic risks due to Ni intake through the consumption of drinking water in higher in the northwest of the city area (Fig. 5a, b). Appropriate monitoring and protective measures should be taken to reduce the risk in that area of Shiraz city.
In a survey on the quality of the drinking water of Shiraz city, Abolfazli et al. (2021) reported Hazard Quotient (HQ) values < 1 for different PTEs, but a carcinogenic risk for Cr 30 times higher than the permissible limit, with higher cancer and non-cancer risks for children than adults. Sener et al. (2016) reported that HQ values for Cu, Ni, and Zn children were equal to (1.21–2–9.09–3), (1.07–2–3.35–4), and (1.68−2–9.96–4) and in the adult, the group was equal to (1.29–2–9.97–3), (1.02–3–1.45–4) and (1.14–3–9.15–5), indicating low HQ values for the studies area. Values of CDI of Cu in the tap water of the Kerman region for children and adults of 3.9 × 10–5 and 1.74 × 10–5 mg kg−1 d−1, respectively, were by (Abedi Sarvestani and Aghasi 2019).
Estimation of the probability of developing adverse effects on the health of children, women, and men related to exposure time and concentration of the studied PTEs by the calculation of THQ95% values by the Monte Carlo simulation of non-carcinogenic risk resulted in values of 0.09 for the children and 0.02 for the men and women groups therefore below the threshold of 1 (Fig. 6).
Estimation of carcinogenic risk for Ni indicated higher CR values in the following ranking order: women > men > children (Fig. 7). Simulation results showed that the CR95% values in the population groups were 1.47 × 10–4 for women, 1.10 × 10–4 for men, and 6.24 × 10–5 for children, indicating carcinogenic risk in all population groups (Fig. 7). Higher carcinogenic risk in men and women could be related to their longer exposure duration (ED) than children, but also the higher water intake-to-body weight ratio ( \(\frac{{{\text{IR}}}}{{{\text{BW}}}}({\text{men}}) < \frac{{{\text{IR}}}}{{{\text{BW}}}}({\text{women}})\) which is higher in women compared to men.
The sensitivity analysis to assess the main factors influencing carcinogenic and non-carcinogenic risks in the three population groups showed that the ingestion rate (IR) and Sb concentration for women were the main variables affecting the non-carcinogenic risk (Figs. 6, 7). In contrast, Ni concentration was the most critical factor for the carcinogenic risk for all three population groups.
The presented results can complement other population exposure data, such as that of airborne PM10 associated with traffic emissions, which have been recently found to exceed the US EPA levels in the Shiraz urban area, and it cannot be excluded that deposition and leaching of airborne particulate matter could contribute to groundwater enrichment with Zn and other trace elements in the critical city center area. Such anthropogenic factors have been proven to be additional sources of PTEs of groundwater to the natural sources (Soleimani et al. 2022; Sharafi et al. 2022).
Conclusion
The present study showed that different concentrations of various trace elements in drinking water cause different exposure levels to non-carcinogenic and carcinogenic risks for different age and gender groups living in the Shiraz urban area. Risk assessment evidenced risks due to Zn, Ni, Cu, Se, Co, and Sb in groundwater, which was below the acceptability threshold of 1. The model simulation confirmed that the carcinogenic risk assessment results were below the 1 × 10–4, highlighting Sb concentration as the most impacting element in carcinogenic risk for children, men, and women. Our work showed that risk assessment of the probabilistic approach better predicted human exposure to different PTEs and in different city areas compared to the deterministic one and highlighted the factors that influence the obtained results by the sensitivity analysis. Though Ni concentration in water resulted lower than the legislation limits, monitoring the transmission lines, especially in the northwest of the city, should not be relaxed. This approach could be taken into account by the land-use and land planning Authorities, for example in the sight of the city development towards the northwest are, where groundwater quality may be lower than in other city areas, or where new industrial and commercial sites may further impact the water resources.
References
Abedi Sarvestani R, Aghasi M (2019) Health risk assessment of heavy metals exposure (lead, cadmium, and copper) through drinking water consumption in Kerman city, Iran. Environ Earth Sci 78:714
Abolfazli D, Abbasi F, Baghapour MA, Samaei MR, Tabatabaeei HR (2021) Hazardous exposure of children with heavy metal in a southwestern city of Iran. Int J Environ Anal Chem, pp 1–13.
Adams S, Titus R, Pietersen K, Tredoux G, Harris C (2001) Hydrochemical characteristics of aquifers near Sutherland in the Western Karoo, South Africa. J Hydrol 241:91–103
Adhikari S, Yanuar E, Ng D-Q (2021) Widespread nickel contamination in drinking water supplies of elementary schools in Taichung, Taiwan. Environ Sci Pollut Res, pp 1–9.
Aleem M, Shun C, Li C, Aslam A, Yang W, Nawaz M, Ahmed W, Buttar N (2018) Evaluation of groundwater quality in the vicinity of Khurrianwala industrial zone. Pakistan Water 10:1321
Badeenezhad A, Radfard M, Abbasi F, Jurado A, Bozorginia M, Jalili M, Soleimani H (2021) Effect of land use changes on non-carcinogenic health risks due to nitrate exposure to drinking groundwater. Environ Sci Pollut Res 28:41937–41947
Barakat A, El Baghdadi M, Rais J, Aghezzaf B, Slassi M (2016) Assessment of spatial and seasonal water quality variation of Oum Er Rbia River (Morocco) using multivariate statistical techniques. Int Soil Water Conserv Res 4:284–292
Bazeli J, Ghalehaskar S, Morovati, M, Soleimani H, Masoumi S, Rahmani Sani A, Saghi MH, Rastegar A (2020) Health risk assessment techniques to evaluate non-carcinogenic human health risk due to fluoride, nitrite and nitrate using Monte Carlo simulation and sensitivity analysis in Groundwater of Khaf County, Iran. Int J Environ Anal Chem, pp 1–21.
Bortey-Sam N, Nakayama SMM, Ikenaka Y, Akoto O, Baidoo E, Mizukawa H, Ishizuka M (2015) Health risk assessment of heavy metals and metalloid in drinking water from communities near gold mines in Tarkwa. Ghana Environ Monit Assess 187:397
Epa A (1989) Risk assessment guidance for superfund. Volume I: human health evaluation manual (part a). EPA/540/1–89/002.
Fallahzadeh RA, Ghaneian MT, Miri M, Dashti MM (2017) Spatial analysis and health risk assessment of heavy metals concentration in drinking water resources. Environ Sci Pollut Res 24:24790–24802
Fallahzadeh RA, Miri M, Taghavi M, Gholizadeh A, Anbarani R, Hosseini-Bandegharaei A, Ferrante M, Oliveri Conti G (2018) Spatial variation and probabilistic risk assessment of exposure to fluoride in drinking water. Food Chem Toxicol 113:314–321
Farokhneshat F, Mahvi A, Jamali Y (2016) Carcinogenic and non-carcinogenic risk assessment of chromium in drinking water sources: Birjand, Iran. Res J Environ Toxicol 10:166–171
Fitzpatrick J, Schoeny R, Gallagher K, Deener K, Dockins C, Firestone M, Jordan W, Mcdonough M, Murphy D, Olsen M (2017) US Environmental Protection Agency’s framework for human health risk assessment to inform decision making. Int J Risk Assess Manage 20:3–20
Gebeyehu HR, Bayissa LD (2020) Levels of heavy metals in soil and vegetables and associated health risks in Mojo area. Ethiopia Plos One 15:e0227883
Harvey P, Handley H, Taylor M (2016) Widespread copper and lead contamination of household drinking water, New South Wales, Australia. Environ Res 151:275–285
Jamshidi A, Morovati M, Golbini Mofrad MM, Panahandeh M, Soleimani H, Abdolahpour Alamdari H (2021) Water quality evaluation and non-cariogenic risk assessment of exposure to nitrate in groundwater resources of Kamyaran, Iran: spatial distribution, Monte-Carlo simulation, and sensitivity analysis. J Environ Health Sci Eng 19:1117–1131
Joodavi A, Aghlmand R, Podgorski J, Dehbandi R, Abbasi A (2021) Characterization, geostatistical modeling and health risk assessment of potentially toxic elements in groundwater resources of northeastern Iran. J Hydrol Regional Stud 37:100885
Karamia L, Alimohammadia M, Soleimania H, Askaria M (2019) Assessment of water quality changes during climate change using the GIS software in a plain in the southwest of Tehran province, Iran. Desalination Water Treatment 148:119–127
Karunanidhi D, Aravinthasamy P, Subramani T, Muthusankar G (2021) Revealing drinking water quality issues and possible health risks based on water quality index (WQI) method in the Shanmuganadhi River basin of South India. Environ Geochem Health 43:931–948.
Keshavarzi B, Tazarvi Z, Rajabzadeh MA, Najmeddin A (2015) Chemical speciation, human health risk assessment and pollution level of selected heavy metals in urban street dust of Shiraz. Iran Atmos Environ 119:1–10
Kiani A, Sharafi K, Omer AK, Matin BK, Davoodi R, Mansouri B, Sharafi H, Soleimani H, Massahi T, Ahmadi E (2022) Accumulation and human health risk assessment of nitrate in vegetables irrigated with different irrigation water sources-transfer evaluation of nitrate from soil to vegetables. Environ Res 205:112527
Moghtaderi T, Alamdar R, Rodríguez-Seijo A, Naghibi SJ, Kumar V (2020) Ecological risk assessment and source apportionment of heavy metal contamination in urban soils in Shiraz, Southwest Iran. Arab J Geosci 13:1–12
Mohammadi AA, Yousefi M, Yaseri M, Jalilzadeh M, Mahvi AH (2017) Skeletal fluorosis in relation to drinking water in rural areas of West Azerbaijan, Iran. Sci Reports 7:1–7
Mosaferi M, Pourakbar M, Shakerkhatibi M, Fatehifar E, Belvasi M (2014) Quality modeling of drinking groundwater using GIS in rural communities, northwest of Iran. J Environ Health Sci Eng 12:99
Nasir MFM, Samsudin MS, Mohamad I, Awaluddin MRA, Mansor MA, Juahir H, Ramli N (2011) River water quality modeling using combined principle component analysis (PCA) and multiple linear regressions (MLR): a case study at Klang River, Malaysia. World Appl Sci J 14:73–82
Ogamba EN, Charles EE, Izah SC (2021) Distributions, pollution evaluation and health risk of selected heavy metal in surface water of Taylor creek, Bayelsa State, Nigeria. Toxicol Environ Heal Sci 13:109–121
Organization WH (1996) Trace elements in human nutrition and health, World Health Organization.
Qishlaqi A, Moore F (2007) Statistical analysis of accumulation and sources of heavy metals occurrence in agricultural soils of Khoshk River Banks, Shiraz, Iran. Am Eurasian J Agric Environ Sci 2(5):565–573
Raza M, Hussain F, Lee J-Y, Shakoor MB, Kwon KD (2017) Groundwater status in Pakistan: a review of contamination, health risks, and potential needs. Crit Rev Environ Sci Technol 47:1713–1762
Rivera-Velasquez MF, Fallico C, Guerra I, Straface S (2013) A Comparison of deterministic and probabilistic approaches for assessing risks from contaminated aquifers: an Italian case study. Waste Manage Res 31:1245–1254
Shafiuddin Ahmed AS, Hossain MB, Babu SMOF, Rahman MM, Sarker MSI (2021) Human health risk assessment of heavy metals in water from the subtropical river, Gomti, Bangladesh. Environ Nanotechnol Monit Manage 15:100416
Shahsavani S, Mohammadpour A, Shooshtarian MR, Soleimani H, Ghalhari MR, Badeenezhad A, Baboli Z, Morovati R, Javanmardi P (2023) An ontology-based study on water quality: probabilistic risk assessment of exposure to fluoride and nitrate in Shiraz drinking water, Iran using fuzzy multi-criteria group decision-making models. Environ Monit Assess 195:1–14
Shalyari N, Alinejad A, Hashemi AHG, Radfard M, Dehghani M (2019) Health risk assessment of nitrate in groundwater resources of Iranshahr using Monte Carlo simulation and geographic information system (GIS). MethodsX 6:1812–1821
Sharafi K, Mansouri B, Omer AK, Bashardoust P, Ebrahimzadeh G, Sharifi S, Massahi T, Soleimani H (2022) Investigation of health risk assessment and the effect of various irrigation water on the accumulation of toxic metals in the most widely consumed vegetables in Iran. Sci Rep 12:1–12
Sharafi K, Yunesian M, Nodehi RN, Mahvi AH, Pirsaheb M, Nazmara S (2019) The reduction of toxic metals of various rice types by different preparation and cooking processes–Human health risk assessment in Tehran households. Iran Food Chem 280:294–302
Silvestri L, Forcina A, Di Bona G, Silvestri C (2021) Circular economy strategy of reusing olive mill wastewater in the ceramic industry: How the plant location can benefit environmental and economic performance. J Clean Prod 326:129388
Soleimani H, Azhdarpoor A, Hashemi H, Radfard M, Nasri O, Ghoochani M, Azizi H, Ebrahimzadeh G, Mahvi AH (2020a) Probabilistic and deterministic approaches to estimation of non-carcinogenic human health risk due to heavy metals in groundwater resources of torbat heydariyeh, southeastern of Iran. Int J Environ Anal Chem, 1–15.
Soleimani H, Azhdarpoor A, Hashemi H, Radfard M, Nasri O, Ghoochani M, Azizi H, Ebrahimzadeh G, Mahvi AH (2022) Probabilistic and deterministic approaches to estimation of non-carcinogenic human health risk due to heavy metals in groundwater resources of torbat heydariyeh, southeastern of Iran. Int J Environ Anal Chem 102:2536–2550
Soleimani H, Nasri O, Ghoochani M, Azhdarpoor A, Dehghani M, Radfard M, Darvishmotevalli M, Oskoei V, Heydari M (2020b) Groundwater quality evaluation and risk assessment of nitrate using monte carlo simulation and sensitivity analysis in rural areas of Divandarreh County, Kurdistan province, Iran. Int J Environ Anal Chem, pp 1–19.
Tomlinson MS, Bommarito P, George A, Yelton S, Cable P, Coyte R, Karr J, Vengosh A, Gray KM, Fry RC (2019) Assessment of inorganic contamination of private wells and demonstration of effective filter-based reduction: a pilot-study in Stokes County. North Carolina Environ Res 177:108618
Wang Y, Zhu G, Engel B, Wu Y (2020) Probabilistic human health risk assessment of arsenic under uncertainty in drinking water sources in Jiangsu Province, China. Environ Geochem Health 42:2023–2037
Wu B, Zhao DY, Jia HY, Zhang Y, Zhang XX, Cheng SP (2009) Preliminary risk assessment of trace metal pollution in surface water from Yangtze River in Nanjing Section, China. Bull Environ Contam Toxicol 82:405–409
Acknowledgements
The authors would like to thank Dr. Paolo Carletti for improving the use of English in the manuscript. This research was supported by the Shiraz University of Medical Sciences (23048), and the authors would like to thank the Vice-Chancellor for Research of Shiraz University of Medical Sciences for their financial support.
Funding
This research was funded by Shiraz University of Medical Sciences (Grant Number 23048).
Author information
Authors and Affiliations
Contributions
AM contributed to data collection, funding acquisition, conceptualization, investigation, writing, reviewing. MM-J contributed to conceptualization, investigation, writing, writing the original draft. HS contributed to validation, visualization methodology, reviewing, and editing. RD contributed to validation, visualization, software, writing the original draft. ZED contributed to visualization, writing, reviewing, and editing. MRS contributed to investigation, data collection, methodology, conceptualization, supervision validation. ZD contributed to conceptualization, investigation, writing, reviewing. GR contributed to investigation, validation, visualization, software. AHM contributed to investigation, visualization, project administration, conceptualization, supervision validation, visualization, resources.
Corresponding authors
Ethics declarations
Conflict of interest
All the authors declare that they have no conflict of interest.
Ethics approval and consent to participate
Not applicable.
Additional information
Editorial responsibility: Samareh Mirkia.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mohammadpour, A., Motamed-Jahromi, M., Soleimani, H. et al. Trace elements human health risk assessment by Monte Carlo probabilistic method in drinking water of Shiraz, Iran. Int. J. Environ. Sci. Technol. 20, 3775–3788 (2023). https://doi.org/10.1007/s13762-023-04815-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13762-023-04815-x