heavy metal water pollution a case study

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A case study of heavy metal pollution in water of bone river by artisanal small-scale gold mine activities in eastern part of gorontalo, indonesia.

1. Introduction

2. materials and methods, 2.1. research locality, 2.2. sampling, 2.3. analytical method, 2.4. quality control, 4. discussion, 4.1. heavy metal risk to the aquatic system due to asgm activities, 4.2. mercury pollution, 4.3. arsenic pollution, 4.4. lead pollution, 4.5. practical implication, 5. conclusions, author contributions, acknowledgments, conflicts of interest.

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Gafur, N.A.; Sakakibara, M.; Sano, S.; Sera, K. A Case Study of Heavy Metal Pollution in Water of Bone River by Artisanal Small-Scale Gold Mine Activities in Eastern Part of Gorontalo, Indonesia. Water 2018 , 10 , 1507. https://doi.org/10.3390/w10111507

Gafur NA, Sakakibara M, Sano S, Sera K. A Case Study of Heavy Metal Pollution in Water of Bone River by Artisanal Small-Scale Gold Mine Activities in Eastern Part of Gorontalo, Indonesia. Water . 2018; 10(11):1507. https://doi.org/10.3390/w10111507

Gafur, Nurfitri Abdul, Masayuki Sakakibara, Sakae Sano, and Koichiro Sera. 2018. "A Case Study of Heavy Metal Pollution in Water of Bone River by Artisanal Small-Scale Gold Mine Activities in Eastern Part of Gorontalo, Indonesia" Water 10, no. 11: 1507. https://doi.org/10.3390/w10111507

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ORIGINAL RESEARCH article

Impact of heavy metals on aquatic life and human health: a case study of river ravi pakistan.

• 1 College of Geography and Environmental Science/Key Research Institute of Yellow River Civilization and Sustainable Development and Collaborative Innovation Center on Yellow River Civilization of Henan Province, Henan University, Kaifeng, China
• 2 Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions (Henan University), Ministry of Education/National Demonstration Center for Environment and Planning, Henan University, Kaifeng, China
• 3 Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Zhengzhou, China
• 4 Laboratory of Climate Change Mitigation and Carbon Neutrality, Henan University, Zhengzhou, China
• 5 Xinyang Academy of Ecological Research, Xinyang, China
• 6 Henan Key Laboratory of Earth System Observation and Modeling, Henan University, Kaifeng, China
• 7 College of Urban and Environmental Sciences, Northwest University, Xi’an, China
• 8 Department of Geography, University of Connecticut, Storrs, CT, United States
• 9 The Forest Science Research Institute of Xinyang, Henan, Xinyang, China
• 10 Henan Jigongshan Forest Ecosystem National Observation and Research Station, Henan, Xinyang, China

Heavy-metal contamination in river and ocean is a critical environmental issue that endangers marine ecosystems and human health. Therefore, conducting extensive research to devise effective mitigation measures is imperative. Sediment samples were taken randomly throughout the study area. Analysis was done to determine the presence of different metals, including arsenic, cadmium, chromium, nickel, copper, zinc, lead, and manganese. The assessment of different pollution levels was done by using various pollution indicators including “geo-accumulation index (Igeo), contamination factor (CF), enrichment factor (EF)” for accuracy. The Igeo measurement for Cd indicated varying pollution, ranging from moderate to significantly polluted, while Mn revealed no contamination. Elements such as Ni, Cr, Cu, and Zn showed a moderate level of contamination. The contamination factor values exhibited a range of 0.436 (Pb) to 7.637 (Cd), with average values spanning from 0.9176 (Mn) to 4.9714 (Cd), suggesting significant regional variation. EF exhibits a pattern of contamination comparable to that of Igeo. The noncarcinogenic risk associated with exposure to Cd and As exceeded the higher limit (HI > 1) for children and adults. Furthermore, the carcinogenic risk presented by pollutants such as copper (Cu), arsenic (As), nickel (Ni), cadmium (Cd), and chromium (Cr) was found to exceed the limits in children. In adults, only arsenic (As) and copper (Cu) were shown to represent a higher risk of cancer than the limit of 10 −4 . The PCA analysis revealed that two (PCs) accounted for more than 65% of the total variance in the River Ravi, as determined by eigenvalues greater than 1. This study underscores the importance of the ongoing monitoring and management of heavy-metal pollution to ensure sustainable marine ecosystem development and public health protection.

1 Introduction

Anthropogenic activities impact aquatic habitats, altering water and sediment quality and affecting the organisms. The marine ecosystem near developed land is exposed to various forms of pollution, such as agricultural, industrial, and health-related contaminants ( Chen et al., 2022 ).

The issue of pollution in aquatic environments has emerged as a prominent concern in recent decades, primarily due to its significant implications for public health and the well-being of marine organisms. Underrated sources of pollution, such as the rise in navigation and maritime transport, the growth of tourism, the release of untreated wastewater into waterways, and the use of pesticides in agriculture, which often find their way into aquatic environments through estuaries, contribute significantly to both point and non-point pollutants ( Svavarsson et al., 2021 ). This pollution disrupts the aquatic life cycle and poses a public safety risk to humans ( Mofijur et al., 2021 ).

Coastal areas worldwide face increasing pressure from anthropogenic activities, leading to various environmental concerns, including the pollution of aquatic ecosystems by heavy metals ( Zhang et al., 2015 ; El Zokm et al., 2020 ). These contaminants are of particular concern due to their toxicity, persistence, and bioaccumulation, which pose significant threats to marine life and human health through the food chain ( Tao et al., 2012 ; Briffa et al., 2020 ). Coastal sediments often serve as the final repository of contaminants originating from multiple sources (e.g., riverine input, atmospheric deposition, and direct industrial discharges) and usually act as important sinks for trace elements through adsorption and subsequent sedimentation ( Al-Mur, 2021 ). Therefore, the pollution status of marine sediments has often been used as an important criterion to evaluate the condition of coastal environments and understand the possible environmental changes caused by anthropogenic activities ( Kwok et al., 2014 ; Beltrame et al., 2009 ).

Furthermore, heavy metals present in sediments and seawater are assimilated by aquatic organisms and subsequently bioaccumulate through the food chain, posing a potential threat to marine ecosystems and human health ( Zhou et al., 2022 ). Heavy metal concentrations in fish and other marine species are indicators of environmental contamination. By investigating heavy metals in marine fauna, we are able to assess the impact of heavy metals in the ambient environment on marine biodiversity and evaluate the potential risks to ecosystems ( Singh and Turner, 2009 )

Heavy metal contamination has emerged as a significant environmental challenge in contemporary times ( Chen et al., 2023 ), drawing considerable attention due to its toxic nature, long-lasting effects, tendency to accumulate in living organisms, and resistance to degradation ( Palansooriya et al., 2022 ; Wang et al., 2022 ). Thus pollution not only poses threats to aquatic life but also endangers human health and the marine ecosystem ( Gu et al., 2021 ). Within riverine ecosystems, heavy metal pollution typically arises from both natural processes and human activities ( Dan et al., 2022 ), with anthropogenic factors being the primary drivers ( Li et al., 2022 ). Upon entering riverine environments, heavy metals often accumulate in sediments, where they interact with the overlying water ( Gu et al., 2021 ), maintaining an equilibrium. However, alterations in environmental conditions such as pH and redox potential can lead to the release of these metals back into the water column, subsequently entering the food web ( Bao et al., 2023 ). This process, characterized by bioaccumulation and biomagnification, has detrimental effects on aquatic ecosystems and poses risks to human health ( Gu et al., 2023 ).

Metals are significant environmental contaminants due to their stability in the ecosystem, capacity to accumulate in living organisms, and their movement through the food chain ( Sarmiento et al., 2016 ). These substances possess enduring presence in the environment, the ability to be taken up by living species, and the potential to accumulate and increase in concentration within the food chain ( Vicente-Martorell et al., 2009 ). As a result, they can pose a significant threat to living organisms over an extended period of time ( Agnaou et al., 2014 ). These components pose both carcinogenic and non-carcinogenic concerns, particularly affecting the brain and endocrine systems. The dangers are more pronounced in children compared to adults ( Issac and Kandasubramanian, 2021 ).

Marine organisms are highly valuable for biomonitoring research due to their biological usefulness. As a result, they are commonly used as biological indicators to assess the environmental condition. They are crucial for assessing the impact of metal contamination on the ecosystem, particularly in the initial phases. The level of metal buildup in their bodies is influenced by physiological activities, particularly feeding, as well as variations in the physico-chemical characteristics of the environment ( Vicente-Martorell et al., 2009 ).

In a similar manner, sediments accumulate significant amounts of heavy metals due to their low degradability. The concentration of metals in sediments is positively correlated with the abundance of organic matter in the sediment ( Hsu et al., 2016 ). As a result, heavy metals build up in the sediment at the bottom of streams and in suspended solids (SS), which can be harmful to the environment, plants, animals, and human health because of their chemical characteristics. Living creatures can become contaminated by these metallic elements either through direct exposure to the pollution or indirectly through the trophic chain ( Islam et al., 2018 ; Lei et al., 2011 ).

In the context of Pakistan, several studies have reported on different rivers. Still, a comprehensive study on the River Ravi regarding environmental pollution is lacking despite the continuous influx of artificial pollutants, including heavy metals, driven by increased human activity and industrial development ( Abdul et al., 2009 ; Shahid et al., 2023 ). This study aims to provide a comprehensive analysis of heavy-metal concentrations in surface sediments, uncovering the extent of contamination and its ecological impacts, with the aim of helping policymakers in the River Ravi to devise effective environmental protection strategies.

The primary objectives of this study were: 1) to investigate the levels of heavy-metal concentrations in surface sediments to evaluate the potential ecological risks posed by these contaminants; 2) to describe the spatial distribution patterns of heavy-metal concentrations in sediments and to identify and quantify the respective sources and their contributions; and 3) human health risk assessment and; 4) to explore correlations among heavy metals in the River Ravi. The ultimate goal of this study is to provide strategic guidance for the management of heavy-metal pollution in the River Ravi.

2 Materials and methods

2.1 study area.

The River Ravi is a river that flows across borders, starting in the northeast of the Kangra district in Himachal Pradesh and the southeast of the Chamba district in the Jammu Kashmir Region of India ( Chand and Sharma, 2015 ). It flows across Pakistan and serves as a notable tributary of the Indus System in the Punjab state ( Akhtar et al., 2014 ). After covering a distance of 100 km from its upper catchments in India, the Ravi River enters Pakistan. The Ravi River is the most diminutive of the five other rivers in the Indus basin system that flow through Pakistan ( Aslam et al., 2020 ).

The total length of the Ravi River is approximately 720 kilometres. The study region is situated within the latitude range of 31° 1’ 45.30” N to 31° 55’ 19.43” N and the longitude range of 73° 20’ 6.19” E to 74° 39’ 21.00” E. The study region is the Lahore division, which has four districts: Sheikhupura, Lahore, Kasur, and Nankana Sahib ( Figure 1 ). The length of the Ravi River within the research region is 219.87 kilometres.

Figure 1 Study Area Map showing Sampling Sites.

2.2 Sampling and analysis

This investigation involved the collection of sediment samples from various sites along the Ravi River. The collection of these samples were done during the May-June 2023. Twenty-two stations were sampled along the mainstream of the River. The sampling locations were chosen randomly. The sediment samples were obtained using a securely sealed grab sampler from a boat. Subsequently, the specimens were enclosed in plastic bags and preserved in an ice box maintained at 4°C. In the laboratory, samples were allowed to dry naturally at the ambient temperature. Before screening, the sediment samples were subjected to disaggregation and sorted into different particle sizes using a stainless-steel mesh ( Feng et al., 2017 ). Samples were grounded to a fine powder using an electric agate mortar for less than 20 minutes, with around ten grams of each sample. The obtained powder was subsequently employed for the analysis of heavy metal presence. To conduct heavy metal analysis, 0.5 g of the powdered samples were treated with concentrated nitric acid (HNO 3 ) and concentrated chloric acid (HClO 3 ) in a ratio of 3:1 until practically all the liquid evaporated ( Chester et al., 1994 ). Subsequently, deionized distilled water was added. After dilution, the insoluble residue was eliminated, and deionized water was added until the total volume reached 25 ml. The samples were analyzed for the existence of certain heavy metals using a flame atomic absorption spectrometer (AAS, GBC-932, Australia). To ensure the highest levels of accuracy, three replicates of each measurement were performed, with variations between them not exceeding 3% ( Das et al., 2022 ).

2.3 Heavy metals analysis

The measurement of selected metals in sediment samples was done using the methodologies from the literature with certain modifications ( Bu et al., 2020 ). After every fifth sample run, an examination was conducted to assess contamination and drift in a certified standard and a blank solution. All the chemicals and reagents utilized were of duly confirmed analytical purity. The metals were analyzed with a Buck 230ATS Atomic Absorption Spectrophotometer (AAS). The device was utilized at its utmost sensitivity and adjusted according to the manufacturer’s guidelines. The replication findings were computed following data acquisition for each metal parameter, expressed as milligrams per kilogram (mg/kg).

2.4 Pollution indicators

Sediment quality assessment involves the use of several indices to determine the extent of heavy-metal contamination. The indices employed include geo-accumulation index (Igeo), contamination factor (CF), and enrichment factor (EF). Collectively, these indices offer a robust framework for evaluating heavy-metal pollution in sediments. The degree of ecological risk associated with heavy-metal contamination can be systematically categorized as listed below.

2.4.1 Geo-accumulation index

Muller (1969) used this idea to measure the spatial arrangement of hazardous substances and their corresponding detrimental impacts within a particular ecosystem. The Igeo index is extensively used to assess pollution levels. The geo-accumulation index (Igeo) can be determined using Equation 1 .

where Cn represents the measured metal in the sediment sample, Bn is the geochemical background concentration, many researchers have employed several elements such as silicon (Si), aluminium (Al), Manganese (Mn) and Fe ( Nazeer et al., 2014 ; Yu et al., 2017 ). However, the current research uses Mn to normalize heavy metals (HMs).

2.4.2 Contamination factor

It is an important criteria for determining the source and extent of pollution caused by specific pollutants in the ecosystem. It serves as a direct sediment index, allowing for the estimation of individual pollution indexes. The CF was determined by the Equation 2 .

Wang et al. (2006) classified the CF into four distinct categories, ranging from 1 to (see Table 1 ).

Table 1 Pollution indices used in the present study and their classifications of ecological risk degrees.

2.4.3 Enrichment factor

Enrichment factors (EF) are used to evaluate the pollution level and quantify the extent of human influence. The calculation used Equation 3 ( Loska et al., 2004 ; Kara et al., 2014 ).

Ci is the metal element concentration with index i, measured in milligrams per kilogram (mg/kg). Cref represents the concentration of the reference element, also measured in mg/kg.

The data presented in Table 1 help to identify areas with elevated levels of heavy metals, prompting further investigation and remediation efforts. By proactively addressing these issues, their potential impact on both marine life and human health can be mitigated.

2.5 Health risk assessment

The non-cancerous and cancerous risk conditions due to exposure to heavy meats have been assessed by considering the overall metal content of specific heavy metals. The equations used in this investigation to calculate the human health risk assessment (HRA) associated with metal exposure are derived from existing literature and methodologies ( Zheng et al., 2010 ; Li et al., 2015 ) ( Table 2 ).

Table 2 Parameters used in equation IV-VI.

Hence, regardless of gender, children and adults were categorized. The metal exposure can occur through three main pathways: direct inhalation, dermal absorption,” and ingestion of deposited atmospheric particles as indicated in Equation 4 – Equation 6

The metal content (mg/kg) is represented by C. The estimates are based on parameters provided by the literature ( Li et al., 2015 ).

The HQ was measured per body weight and represented as milligrams per kilogram per day (mg/kg/day). To achieve this objective, the estimated ADDi values were used as the numerator, and the matching RfD values were used as the divisor to calculate HQi using Equation 7 .

The study examines the maximum allowable risk of exposure to RfD, considering both children and adults, throughout a lifetime. If HQ< 1 ( Zheng et al., 2010 ). Noncarcinogenic substances have a threshold limit below which no adverse response occurs. The RfD values employed in this study are cited by other studies ( Li et al., 2015 ). The hazard index (HI), which is calculated by summing the hazard quotients (HQs) for the three main pathways of exposure, is represented by Equation 8 ( De Miguel, 2007 ; Zheng et al., 2010 ).

A hazard index (HI) value of less than one is considered safe, while an HI value of more than 1 indicates the potential for non-cancerous events ( Zheng et al., 2010 ; Hussain et al., 2015 ).

2.6 Statistical analysis

The heavy metals concentration was analyzed using Microsoft Office Excel to determine the minimum, maximum, and average concentrations and Pearson correlation analysis. ArcGIS software (version 10.8) was utilized for the spatial interpolations of heavy metal concentration in the River, employing spatial distribution techniques ( Shetaia et al., 2023 ). The R programming language ( Shetaia et al., 2023 ) was used to identify the correlation (corrplot) between heavy metals.

3.1 Spatial distribution of the heavy metal

Figure 2 presents the statistical data and regional patterns of heavy metal concentrations in the sediment of River Ravi. The presence of heavy metals (HMs) in the soil is influenced by factors such as pH soil chemistry (including moisture, soil organic matter, clay, silt, and sand). The mean concentrations of metals were as follows: manganese (Mn) had the highest average value at 779.996 mg/kg, followed by nickel (Ni) at 131.163 mg/kg, chromium (Cr) at 129.594 mg/kg, zinc (Zn) at 128.112 mg/kg, copper (Cu) at 62.514 mg/kg, lead (Pb) at 50.504 mg/kg, arsenic (As) at 33.742 mg/kg, and cadmium (Cd) at 1.491 mg/kg. Regarding spatial distribution, most metal concentrations increase near densely inhabited and industrialized regions, particularly in the river section adjacent to Lahore. Various anthropogenic effluents are discharged into this area, such as home sewage, industrial wastes from the city, and agricultural wastes from neighbouring cultivated fields. Additionally, the presence of domestic and industrial waste may also contribute to the increased metal content. Zinc is readily absorbed by soil organic matter (SOM), although it is subsequently liberated in an acidic environment ( Metsalu and Vilo, 2015 ; Nassar and Fahmy, 2016 ).

Figure 2 Spatial Distribution of Heavy Metals in Sediment of River Ravi.

Water pollution significantly threatens aquatic life, including fish, shrimp, and crabs, thereby escalating health risks at various trophic levels ( Ariyaee et al., 2015 ). This is particularly concerning for commercially valuable fish species because elevated levels of heavy metals could have deleterious health effects on humans, especially in vulnerable populations such as children, women, and individuals with pre-existing health conditions. Consequently, the vigilant monitoring of heavy-metal concentrations in organisms is imperative for the effective regulation and management of aquatic ecosystems, ensuring the safety of seafood for human consumption ( Caeiro et al., 2005 ).

Table 3 summarizes the concentrations of heavy metals in the River Ravi with other rivers in Pakistan and globally. The River Ravi has moderate heavy metal concentration levels, with all measured elements falling within the range observed in other rivers, except for metals As, Cr, Ni, and Cu, which show higher levels at some sampling sites. The heightened levels of the elements in the River indicate that, in addition to the River’s natural flow, pollutants from other sources also infiltrate this unique ecosystem and contribute to the increase in pollution. Examples of causes contributing to water pollution can be categorized as anthropogenic, such as urban and industrial wastewater, and natural, connected to sediment texture and geological features ( Hashemi, 2018 ). Upon comparing the average concentration of Arsenic (As) in various elements, it was discovered that the River Ravi exhibited a lower concentration (33.74 mg/kg) in comparison to the Zhejiang River in China (6.90–74.34 mg/kg) and the Yellow River in China (14-48 mg/kg) ( Liu et al., 2009 ; Zhang et al., 2018 ). The Sediment of the River Ravi exhibited a significantly higher concentration of heavy metals compared to the Tanjan River in Iran (12.8 mg/kg) ( Liu et al., 2016 ), the Yangtze River in China (29.9 mg/kg) ( Yang et al., 2009 ), the Jialu River in China (2.39–14.57 mg/kg) ( Fu et al., 2011 ), the Kabul River in Pakistan (1.26 mg/kg), the Chenab River in Pakistan (.65 mg/kg), and the Indus River in Pakistan (7.65 mg/kg) ( Nawab et al., 2018 ). This phenomenon can be explained by the substantial influx of this element from rivers. Arsenic can be found naturally in rocks along the River, but it can also come from fertilizers and fungicides in rice crops along the River ( Szolnoki et al., 2013 ; Wang et al., 2019 ).

Table 3 Comparison of the heavy metal contents in the River Ravi surface sediment with other rivers of the world (unit mg/kg).

3.2 Pollutions levels

The heavy-metal concentrations in sediment samples can be evaluated using various pollution assessment indicators ( Hahladakis et al., 2013 ; Liu et al., 2018 ; Zhou et al., 2022 ). These include the geo-accumulation index (Igeo), which measures the degree of contamination compared with pre-industrial levels; the contamination factor (CF), which quantifies the relative enrichment of metals; the enrichment factor (EF), which assesses the potential harm to living organisms. The levels of these indicators are listed below

The highest level of contamination, as indicated by the Igeo values, was attributed to Cd (-0.519 – 2.348), in agreement with the pollution degree defined by Igeo ( Muller, 1969 ). The sediments in the research area are categorized as uncontaminated by copper (Cu), nickel (Ni), lead (Pb), manganese (Mn), iron (Fe), and zinc (Zn). The Igeo analysis indicates that the sediments of River Ravi displayed a range of pollution levels, ranging from moderate to extremely high, as depicted in Figure 3 . The mean Igeo values ranged from -0.792 (Mn) to 1.583 (Cd), with the minimum and maximum Igeo values being -1.783 (Pb) and 2.348 (Cd), respectively. According to the criteria, it is essential to highlight that As, Cd, Cr, Ni, Cu, Zn, and Pb displayed distinct contamination patterns. Cadmium showed moderate pollution, whereas lead contamination varied from unpolluted to moderate. The soil samples primarily exhibited low pollution levels for chromium (Cr), nickel (Ni), copper (Cu), and zinc (Zn), ranging from unpolluted to moderately polluted. The soil analysis revealed varied degrees of contamination, ranging from non-polluted to moderately polluted, as indicated by the presence of As and Pb. The soil samples were sorted in descending order based on the average sequence of Igeo values as follows: Cd (1.583), As (0.654), Pb (0.467), Ni (0.328), Cr (-0.130), Zn (-0.213), Cu (-0.234), Mn (-0.792).

Figure 3 (A) Geo-accumulation Index, (B) Contamination factor.

The contamination factor values exhibited a range of 0.436 (Pb) to 7.637 (Cd), with average values spanning from 0.9176 (Mn) to 4.9714 (Cd), suggesting significant regional variation. Analysis of the average contamination factor values from all test locations revealed no evidence of soil contamination by Mn. Nevertheless, As, Ni, Cr, Cu, and Zn in soils suggest a moderate pollution level. In contrast, Cd indicates a high level of contamination based on the criteria. The soils in different sampling sites displayed varying contamination patterns. Specifically, 9.0% of the sites had low contamination, 50% had moderate contamination, and 43.0% had considerable contamination. Regarding Cd (cadmium) levels in the soils, 9% had low contamination, 22% had moderate contamination, 36% had considerable contamination, and 33% exhibited very high contamination. Remarkably, the soil sample recovered from S-14 displayed the greatest Igeo and CF values for Cd, although elements like Cr, Cu, and Ni did not demonstrate a comparable trend. These data indicate that soil pollution in the study area is likely caused by processes other than only metal contamination.

The research region faces significant heavy metal pollution, as indicated by the Contamination Factor (CF) evaluation results. The Enrichment Factor (EF) was also utilized to assess the quantity of pollution caused by a particular metal ( Figure 4 ). The EF analysis reveals a consistent pattern of pollution in heavy metals, which is also observed in the Igeo analysis. This indicates a certain amount of consistency in the assessed repercussions of human activities on pollution. Based on the EF categorization ( Hakanson, 1980 ), approximately 55% of the sampling locations exhibited a moderate to high pollution level regarding Cd contamination. The average concentration of arsenic (As), lead (Pb), and nickel (Ni) indicate a low level of pollution, ranging from deficiency to minor contamination. The soil samples exhibited the following sequence of EF values: Cd (5.4581) > As (2.8254) > Pb (2.6236) > Ni (2.2159) > Cr (1.6189) > Zn (1.5159) > Cu (1.4985). The mean EF values for magnesium were below 1, suggesting a natural source. The Ni, Cr, Cu, and Zn levels ranged from 1 to 2, indicating that the sources are most likely of anthropogenic origin ( Zhang et al., 2018 ). The concentrations of Cd, As, and Pb exhibited a moderate level of enrichment, suggesting the presence of human-induced impacts and a moderate impact from human activities ( Figure 4 ).

Figure 4 Figure showing variation in Enrichment Factor (EF).

3.3 Human health risk assessment

The study aimed to assess the potential human health risks from exposure to some aspects of the Ravi River. Specifically, the non-cancerous effects of arsenic (As), cadmium (Cd), chromium (Cr), nickel (Ni), copper (Cu), zinc (Zn), lead (Pb), and manganese (Mn), as well as the carcinogenic risks of As, Cd, Cr, Ni, Cu, and Pb, were investigated. The study focused on three main pathways of exposure: inhalation, ingestion, and dermal interaction. Figure 5 demonstrated that the eating route had the highest danger, followed by cutaneous interaction and inhalation. Other scholars have also documented this phenomenon ( Xu et al., 2017 ; Ur Rehman et al., 2018 ). Among the metals tested, the sequence of causing health impairment in children through diverse exposure routes was as follows: Cd > Mn > Cr > Ni > Pb > As > Cu > Zn. A similar pattern was seen in adulthood except for Copper (Cu), which came before Nickel (Ni). The sequencing of the remainder of the metals in the HI sequence remained consistent. According to the health risk assessment model employed by USEPA and IRIS, it was determined that children had a higher level of exposure to metals compared to adults. This conclusion was based on many criteria and approaches. Children are more exposed to dust and soil particles than adults because of their active behaviour, extended playtime, and lack of caution while eating and drinking. Precautionary steps should be implemented to reduce children’s exposure to potentially harmful health conditions. Unlike several other studies ( Xu et al., 2017 ; Ur Rehman et al., 2018 ). In our investigation, Cd exhibited the most considerable noncarcinogenic risk in adults, followed by As, Mn, Cr, Ni, Cu, Pb, and Zn. Hence, Cd, As, Mn, and Cr are the elements that should be given the highest consideration regarding their propensity to cause health concerns. Multiple paths contribute to the intake of harmful substances during the day and night. In addition to these pathways, various food products (both wet and dry) from the same area are significant sources of health hazards in toxicology and hygiene. The hazard quotient (HQ) values for children were more important than those for adults, although they were still within the safe limit (HQ < 1). However, due to exposure to Cd, the HQ (Hazard Quotient) for children exceeded the established limit of 1.08E+00. Furthermore, HQderm was discovered to exceed the acceptable threshold (HQ > 1) in both children and adults due to exposure ( Aslam et al., 2020 ). The study demonstrates that Cd and As were the sole metals shown to have detrimental health impacts in our research. These effects are expected to intensify with an increase in the HQ value. Similarly, the computed Hazard Index (HI) values for children were higher than those for adults in all of the metals investigated. Nevertheless, the analysis revealed that all these elements’ exposure remained below the established safety threshold (HI < 1).

Figure 5 (A) Average Daily Dose (ADD measured in the unit of mg/kg/day) in three modes: (B) Health risk from heavy metals in the study area.

Figure 5B unambiguously demonstrated that the cancer risks associated with several metals studied much exceeded the permissible threshold of RI (1 × 10−4). The values for children were ranked in the following order: Cu (6.65E-03) > As (3.65E-03) > Ni (3.88E-04) > Cd (3.65E-04) > Cr (3.62E-04) and Pb (7.15E-07). Only As (5.22E-04) and Cu (4.22E-04) exhibited RI values above the acceptable limit for adults, indicating a high risk of human carcinogenicity. Nevertheless, Cd, Ni, Cr, and Pb concentrations were determined to be under the acceptable threshold of 1 × 10 −4 .

Correlation analyses can reflect whether different heavy metals share a common source or undergo similar contamination processes ( Hu et al., 2013 ). To further elucidate the origins of the heavy metals in the sediments and quantify their respective source contributions, Positive Matrix Factorization (PMF) was employed for source apportionment and contribution elucidation.

The metal concentration in aquatic habitats is affected by anthropogenic activities and natural phenomena ( Li et al., 2020 ). Human-induced pollution includes agricultural effluents caused by the overuse of pesticides, herbicides and fertilizers, together with traffic pollution, industrial discharges, and home wastewater ( Magnusson et al., 2018 ; Shetaia et al., 2022 ). Furthermore, natural characteristics refer to an ecosystem’s chemical and physical properties, its environmental context, and the biological factors that affect it ( Zhang et al., 2019 ; Ytreberg et al., 2022 ). The interrelationships among the analyzed metals can aid in determining the origin(s) of pollution ( Zhao et al., 2023 ). Moreover, the HCA approach offers a fitting explanation for variables in the sample region that exhibit similar geochemical behaviour or have been impacted by human activities. The reference is attributed to Thiombane et al. (2018) . Figure 6 displayed the correlation of the heavy metals samples.

Figure 6 Correlation (Corrplot) for the relationships between different metals in River Ravi.

The examination of heavy metals indicated a substantial correlation between the existence of arsenic (As) and manganese (Mn), as well as between As and chromium (Cr). Furthermore, a moderate positive association was detected between arsenic (As) and copper (Cu) elements with a correlation coefficient of 0.48. Corelation analysis of heavy metals showed that surface sediments of the River Ravi originate from natural sources and three anthropogenic sources. Anthropogenic source 1 originated primarily from industrial effluent, which is characterized by a high load of metals. Anthropogenic source 2 originated primarily from metal processing and vehicular activities. Anthropogenic source 3 originated primarily from coastal industrial activities and fossil fuel combustion.

Heavy metal pollution in rivers can have detrimental effects on aquatic ecosystems. Metals can accumulate in sediments, water, and biota, leading to toxicological effects on aquatic organisms. Rivers serve as conduits for transporting heavy metal pollution from inland areas to coastal regions and ultimately the ocean. As rivers discharge into the sea, they carry their pollutant loads, including dissolved metals and sediment-bound contaminants. Coastal ecosystems may act as sinks for heavy metals, where deposition and accumulation occur, affecting marine organisms and habitats ( Zhang et al., 2019 ; Ytreberg et al., 2022 ; Zhao et al., 2023 ).

4 Conclusions

This study focused on the levels of heavy metals in seawater, surface sediments, and organisms in the River Ravi. A total of twenty-two stations along the main course of the River, including Sheikhupura (5), Lahore (9), Kasur (3), and Nankana Sahib (5), were sampled. The average Igeo values varied from -0.792 (Mn) to 1.583 (Cd), while the minimum and maximum Igeo values were -1.783 (Pb) and 2.348 (Cd), respectively. Cadmium exhibited a moderate pollution level, whereas lead showed contamination ranging from unpolluted to moderate. The contamination factor values varied between 0.436 (Pb) and 7.637 (Cd), with average values ranging from 0.9176 (Mn) to 4.9714 (Cd), indicating substantial regional diversity.The enrichment factor analysis indicates a consistent pattern of pollution for heavy metals, which is also observed in the Igeo analysis. Cadmium (Cd) exhibited significant pollution, ranging from moderate to high, at approximately 55% of the sampled locations, as determined by the EF categorization.

The noncarcinogenic risk associated with chromium exposure was higher than that of other selected elements for both children and adults. Furthermore, the carcinogenic risk associated with exposure to heavy metals such as Cd, Cr, and Zn in youngsters exceeded the established limits. Among adults, only Cadmium (Cd) presented a greater risk of cancer when exposed to it in comparison to other elements. The HMs showed a higher likelihood of noncarcinogenic hazards (HQ > 1) in children, ranked in decreasing order as follows: Cd > Mn > Cr > Ni > Pb > As > Cu > Zn. The analysis of heavy metals revealed a strong association between Arsenic (As) and Manganese (Mn) with a correlation coefficient (r) of 0.87. Similarly, a considerable correlation was seen between As and Chromium (Cr) with a r value 0.71.

In conclusion, As coastal-region development accelerates and the global population increasingly relies on marine resources, comprehending and mitigating the repercussions of human activities on coastal ecosystems has become imperative. The insights gained from this research are instrumental in crafting strategies that advocate sustainable development in coastal areas. These strategies strive to harmonize economic growth with the imperatives of environmental protection and public health.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Author contributions

MIA: Conceptualization, Writing – original draft. ZY: Software, Writing – review & editing. LR: Investigation, Writing – review & editing. CZ: Data curation, Writing – review & editing. TL: Methodology, Writing – review & editing. HL: Supervision, Writing – review & editing. MSM: Formal Analysis, Writing – review & editing. AR: Validation, Writing – review & editing. MA: Visualization, Writing – review & editing. SL: Software, Writing – review & editing. WF: Writing – review & editing.

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This study is under the auspices of NSFC 42071267, the Scientific and Technological Research Projects in Henan Province (242102321158, 232102320047) and Xinyang Academy of Ecological Research Open Foundation (2023XYMS02) and the Postdoctoral startup research fund of Henan University number (FJ3050A0670814).

Acknowledgments

We thank the editor and reviewer for their valuable comments and suggestions. We are also thankful to our lab fellow and all those who assisted during this study.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: heavy metals, risk assessment, geoaccumulation index, carcinogenic risk, River Ravi

Citation: Ahamad MI, Yao Z, Ren L, Zhang C, Li T, Lu H, Mehmood MS, Rehman A, Adil M, Lu S and Feng W (2024) Impact of heavy metals on aquatic life and human health: a case study of River Ravi Pakistan. Front. Mar. Sci. 11:1374835. doi: 10.3389/fmars.2024.1374835

Received: 22 January 2024; Accepted: 20 February 2024; Published: 01 May 2024.

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Copyright © 2024 Ahamad, Yao, Ren, Zhang, Li, Lu, Mehmood, Rehman, Adil, Lu and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Heli Lu, [email protected] ; Siqi Lu, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

• Open access
• Published: 08 January 2024

Human health and ecology at risk: a case study of metal pollution in Lahore, Pakistan

• Hafiza Hira Iqbal 1 ,
• Ayesha Siddique 2 ,
• Abdul Qadir 1 ,
• Sajid Rashid Ahmad 1 ,
• Matthias Liess 2 &
• Naeem Shahid 2 , 3  

Environmental Sciences Europe volume  36 , Article number:  9 ( 2024 ) Cite this article

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With rapid industrial development, heavy metal contamination has become a major public health and ecological concern worldwide. Although knowledge about metal pollution in European water resources is increasing, monitoring data and assessments in developing countries are rare. In order to protect human health and aquatic ecosystems, it is necessary to investigate heavy metal content and its consequences to human health and ecology. Accordingly, we collected 200 water samples from different water resources including groundwater, canals, river and drains, and investigated metal contamination and its implications for human and ecological health. This is the first comprehensive study in the region that considered all the water resources for metal contamination and associated human health and ecological risks together.

Here we show that the water resources of Lahore (Pakistan) are highly contaminated with metals, posing human and ecological health risks. Approximately 26% of the groundwater samples are unsuitable for drinking and carry the risk of cancer. Regarding dermal health risks, groundwater, canal, river, and drain water respectively showed 40%, 74%, 80%, and 90% of samples exceeding the threshold limit of the health risk index (HRI > 1). Regarding ecological risks, almost all the water samples exceeded the chronic and acute threshold limits for algae, fish, and crustaceans. Only 42% of groundwater samples were below the acute threshold limits. In the case of pollution index, 72%, 56%, and 100% of samples collected from canals, river Ravi, and drains were highly contaminated.

Conclusions

In conclusion, this comprehensive study shows high metal pollution in water resources and elucidates that human health and aquatic ecosystems are at high risk. Therefore, urgent and comprehensive measures are imperative to mitigate the escalating risks to human health and ecosystems.

Heavy metal contamination has become a global ecological and public health concern [ 1 , 2 , 3 ], particularly due to persistence and higher toxicity [ 4 , 5 , 6 ]. Pakistan is one of the developing countries facing water scarcity and metal contamination, and therefore, struggling with both quantity and quality issues [ 7 , 8 , 9 ].

Rapid population growth, urbanization, industrialization and agricultural activities put great pressure on both the quantity and quality of water resources. Misuse of water resources, non-compliance with pollution standards and disposal of untreated effluents into freshwater resources are common practices [ 10 ], which may increase water scarcity, and health and ecological consequences. The situation is even worse in urban areas where sewerage water directly enters into canals and rivers. Besides direct disposal, atmospheric, anthropogenic and geogenic chemical pollutants trickle down into the groundwater basin in the process of recharging the aquifer through precipitation [ 11 ]. In addition, saltwater intrusion, and leakage of septic tanks and landfills also lead to groundwater contamination [ 12 ]. As a result, surface water quality is deteriorating and not suitable for drinking and agricultural usage [ 13 , 14 , 15 ]. Various studies and surveys have reported increased water pollution especially in big cities of Pakistan [ 15 , 16 ]. Some studies have also reported the accumulation of heavy metals in soil irrigated with contaminated canal water [ 17 , 18 ]. As a consequence of widespread metal contamination, water-borne diseases have become very common in Pakistan, constituting about 80% of total diseases and about 30% of deaths [ 19 ]. Due to the continuous use of contaminated water, people are at a high risk of cancer, birth defects, post-neonatal mortality, and other chronic diseases [ 20 , 21 ]. Therefore, it is necessary to regularly monitor the water quality of all major water bodies to design the appropriate mitigation strategies.

Although, several studies have investigated heavy metal contamination in groundwater [ 21 , 22 ], canals [ 23 , 24 ], rivers [ 25 , 26 ], and drains [ 27 , 28 ] separately, none of the studies considered all these water resources together, and focus on human health and ecology. We hypothesized that the metal contamination of water resources in Lahore might pose both human health and ecological risks. Here we report the first comprehensive study in the region that considered all the water resources for metal contamination and associated human health and ecological risks.

In the present study, we aimed at monitoring the heavy metals (Cu, Cr, Ni and Pb) contamination in groundwater, canals, river Ravi, and drains of Lahore, Pakistan. Although there are several toxic metals in the environment, we focused on these four metals due to their well documented health [ 29 , 30 ] and ecological impacts [ 31 ], and association with local urban and industrial activities [ 29 , 32 ]. Focusing on these heavy metals ensures compliance with regulations and efficient resource allocation to address potential risks to public health and the environment. We further aimed at analyzing the human health and ecological risks associated with metal contamination in terms of (i) health risk index (HRI) for children, females and males, (ii) toxic pressure (TU) and risk quotient (RQ) for aquatic organisms and (iii) pollution index (PI).

Description of the study sites and sampling

The current study was conducted in Lahore, the second-largest metropolitan city in Pakistan. It is ranked as the 18th most populous city in the world with an 11.13 million total population and 6300 persons/km 2 population density according to the Census of 2017 [ 33 ]. The sampling sites were identified with a global positioning system (GARMIN eTrex 30) and a field survey was carried out. A total of 200 water samples were collected from various sources, including 50 each from groundwater, canals, river, and drains (Fig.  1 , Additional file 1 : Table S1). The rainfall events are suggested to have the potential to alter the metal contamination of water [ 34 ]. To rule out the impact of rainfall events, samples were collected from March to April 2019 using a grab sampling technique. Thus, we mainly focused on metal contamination of dry season. Briefly, groundwater samples were collected from 50 tube wells located across the city. For canal water, samples were collected from the Lahore canal and BRB canal (Bambawali-Ravi-Bedian). For the river, all the samples were collected from river Ravi, from Syphon to Sagian pull in the downstream direction. For wastewater, major polluted drains were selected such as Hudiara drain (20 sites), Cantt drain (20 sites), and Sattukatla drain (10 sites). A minimum distance of 1 km was maintained between every two sampling sites. A detailed description of sampling sites is provided in supporting information (Additional file 1 : Table S1).

Location of the sampling sites from different water resources of Lahore. Circles represent the sampling sites and are colored according to the type of samples (groundwater: yellow, canal water: green, river water: pink, and drain water: red)

Samples were collected during the daytime between 8 a.m. to 4 p.m. with the help of pre-washed buckets and transferred to 1 L glass containers. To avoid any contamination, each container was placed into a zip-lock polythene bag and transported to the lab for preservation and analysis. Samples were stored at –4 0 C until analyzed. Physico-chemical parameters such as pH, temperature, EC, and TDS were recorded with multi-meter (EUTECH instruments PC510) at each site (Additional file 1 : Table S2).

Sample analysis

Samples were analyzed following the “Standard Methods for the Examination of Water and Wastewater” by the American Public Health Standard Association, 21st edition [ 35 ]. Briefly, the samples were subjected to filtration using a 0.45 µm filter. Additionally, we added 10 mL of nitric acid (HNO 3 ) to the samples to prevent any heavy metal precipitation. The analysis of Cu, Cr, Ni, and Pb was carried out at the Irrigation Department, Lahore, Punjab through atomic absorption spectroscopy using an equipment Varian FS 240AA (Varian Medical Systems, Palo Alto, CA, USA). For quality assurance, standard reference materials from the National Institute of Standards and Technology (NIST) were used. The relative standard deviation of analytical procedures ranged from 5 to 10%. The analysis was conducted thrice and the average value was used for statistical evaluation.

Human health risk assessment

Human health risk assessment for heavy metals was calculated by considering oral and dermal exposures. The potential hazard for each metal was calculated by Chronic Daily Intake (CDI) and Hazard Quotient (HQ). For CDI, we used the following equations suggested by the Agency for Toxic Substances and Disease Registry [ 36 ].

where C is the concentration of metal, IR is the intake rate of water, ET is exposure time, EF is the exposure factor, CF is the conversion factor, P is the permeability coefficient, SA is the total surface area of skin, and BW is body weight. The average values of EF, ET, IR, SA, P, CF, and BW are provided in the supporting information (Additional file 1 : Table S2). The body weight (BW) was calculated for adults aged between 15–67 years for females, 15–66 years for males, and 0–15 years for children [ 30 , 37 ]. Similarly, the average value of skin surface area for adults is 18,450 cm 2 and for children is 16,450 cm 2 . Furthermore, the non-carcinogenic effects of metals were calculated by using HQ (Eq.  3 ).

Reference dose (RfD) values for oral and dermal exposure pathways are provided in supporting information (Additional file 1 : Table S3). The HQ < 1 shows the concentration of metal does not produce carcinogenic effects.

Health risk index (HRI) was calculated by adding all HQ (Eq.  4 ). Oral and dermal health risk index was calculated for each site as well as for different population groups e.g. children, males, and females.

Ecological risk assessment

We analyzed the ecological risk of metal contamination based on the toxic unit (TU) for three trophic levels i.e., algae, fish and crustaceans [ 38 ] (Eq.  5 ). The toxic unit is defined as the ratio of measured concentration (for metals) and effect concentration (lethal and sub-lethal) for three organisms (algae, fish and crustaceans). Reference values for EC 50 or LC 50 were obtained from previous studies [ 39 , 40 , 41 , 42 , 43 , 44 ] and are provided in supporting information (Additional file 1 : Table S4).

where TU sum is the sum of the effect of “n” metals detected at each site, Ci is the concentration (μg/L) of the respective metal “i”, and LC 50i is the median lethal concentration (μg/L) of that metal for the reference organisms.

Further, the risk quotient (RQ) was calculated to assess the ecological risk for aquatic organisms. RQ is the ratio of the measured environmental concentration (MEC) of metals and predicted no-effect concentration (PNEC). The PNEC values were obtained from a previous study [ 45 ] and the NORMAN Ecotoxicology Database [ 46 ]. RQ sum was calculated using the following equation.

where RQ sum is the sum of the risk of n metals detected at each site, MECi is the measured environmental concentration of respective metal “i”, and PNECi is the predicted no-effect concentration of respective metal “i” at each site.

Water pollution index

To compare metal concentration in different matrices, we calculated the pollution index (PI) by dividing metal concentration by its permissible limits, and then taking the average of all metals (Eq.  7 ).

where PI > 1 indicates that metal concentrations are above the permissible limit and can cause hazards. PI was classified as low (PI ≤ 1), moderate (1 < PI ≤ 3) and highly polluted (PI > 3) [ 47 ].

Data analysis

For statistical analyses and figures, we used RStudio version 2022.2.3.492 for Windows [ 48 ] and the basic R version 4.2.1 for Windows [ 49 ]. A spatial map was produced in ARC Map, ArcGIS V. 10.1 (ESRI 2012).

Heavy metal contamination

Overall, water samples collected from all resources showed heavy metal contamination. Approximately 61% of the water samples exhibited contamination with all four metals, 31% with three metals, 8% with two metals, and less than 1% with one metal. More specifically, 42% of the groundwater samples exceeded the permissible limits for drinking water set by the World Health Organization (WHO) (Fig.  2 ; Additional file 1 : Table S5). Among different metals, Pb frequently surpassed the WHO permissible standards followed by Cr and Ni. In general, the trend of metal contamination (µg/L) in groundwater samples was as follows: Cr > Pb > Cu > Ni. Furthermore, none of the water samples collected from canals, river, and drains were deemed suitable for drinking. Among surface water samples, approximately 98% (147 of 150) exceeded the water quality standards set by the World Health Organization (WHO). The metal concentrations detected in river and drains followed a consistent trend: Cu > Cr > Ni > Pb. However, in canals, Ni concentrations were higher than Cr, slightly altering the trend to Cu > Ni > Cr > Pb. Notably, the average concentration of Cu was consistently high in all surface water bodies, with drains showing two to threefold higher concentrations than river and canals (Additional file 1 : Table S5). In most of the cases, Cr and Ni were exceeding the permissible limits.

Spatial heat map showing metal concentrations exceeding the threshold limits. The exceedance is calculated as the ratio between the detected concentration and the permissible limits set by the World Health Organization (WHO). Values are presented for water samples collected from groundwater (GW), canals (CW), river (RW) and drains (DW)

To identify insightful patterns and relationships within the data, we applied Principal Component Analysis (PCA). The first two components of PCA explained 82.4% of the total variance (Additional file 1 : Fig. S1). PC1 explained 70.3% of the total variance and showed maximum loadings on Cr and Cu. PC2 explained 12.1% of the variance, with maximum loading on Ni.

Health risk assessment

To evaluate drinking water quality, we calculated the Health Risk Index (HRI) for two major exposure pathways such as ingestion (oral) and absorption through skin (dermal). For the potential health risks associated with the ingestion of metals, we considered only groundwater samples. Overall, 26% of groundwater samples exceeded the threshold limit for oral intake (Fig.  3 , Additional file 1 : Table S6), with HRI oral ranging from 0 to 2.5, mainly due to higher concentrations of Cr. Results of the dermal health risk index (HRI dermal ) revealed that 71% (142 out of 200) of the water samples were deemed unfit, with Cr as the main cause of dermal risk (Fig.  4 ). Specifically, 40% of groundwater samples, 74% of canal water samples, 80% of river water samples, and 90% of drain water samples had the potential to cause dermal health risks with HRI dermal values up to 4.3, 36.4, 43.3, and 87, respectively.

Health risk of metal contamination through ingestion. Oral Health Risk Index values are presented for groundwater samples. The boundaries of the central box are the 25th and 75th percentiles; the horizontal line is the median; and the whiskers of the boxplot represent the minimum and maximum values. The Red dashed line represents the threshold limit for oral health risk

Health risk of metal contamination through dermal contact. Dermal Health Risk Index values are presented for different water sources including groundwater samples, canals, and river and drain water samples. The boundaries of the central box are the 25th and 75th percentiles; the horizontal line is the median; and the whiskers of the boxplot represent the minimum and maximum values. The Red dashed line represents the threshold limit for dermal health risk

Ecological risk

To characterize heavy metal contamination, we calculated toxic units assuming concentration addition (logTU sum , see methods). All the water resources were highly contaminated with heavy metals. Overall, 88% of samples were exceeding the acute threshold limits. The least contamination was detected in groundwater samples (Fig.  5 , Additional file 1 : Table S7). The toxic unit (logTU sum ) ranged from − 2.04 to − 0.04 for algae, − 1.91 to 0.174 for crustaceans, and − 3.81 to 0.1 for fish. For canal water, the TU sum ranged from 0.15 to 1.07 for algae, 0.57 to 1.22 for crustaceans, and 0.36 to 0.96 for fish. River water was slightly less contaminated as compared to canal and drain. The TU sum ranged from − 2.9 to 1.03 for algae, − 0.5 to 1.41 for crustaceans and 0.64 to 0.8 for fish. Drain water was highly contaminated with heavy metals, and toxic units (logTU sum ) ranging from 0 to 1.3 for algae, − 1.1 to 1.35 for crustaceans, and − 1.2 to 2.0 for fish.

Characterization of metal contamination. The toxic units (TU sum ) are presented for different water resources including groundwater (GW: blue), canal water (CW: cyan), river water (RW: green) and drain water (DW: red). For the calculation of Toxic Units, we used LC 50 or EC 50 of algae, crustaceans and fish. The boundaries of the central box are the 25th and 75th percentiles; the horizontal line is the median; and the whiskers of the boxplot represent the minimum and maximum values. The black dashed line indicates the threshold limit for acute risks for algae, crustaceans and fish, whereas, the red dashed lines represent the threshold limit for chronic risks

The metal concentrations were also transformed into risk quotients (RQ) by dividing the detected concentrations by the corresponding threshold values. Furthermore, RQ sum was calculated by summing the risks caused by individual metals at each site. Overall, all the water samples showed higher RQ sum (Fig.  6 ), indicating a higher risk for aquatic organisms. The RQ sum ranged from 0.9 to 88 for groundwater, 71 to 637 for canals, 23 to 759 for river and 348 to 1905 for drains (Additional file 1 : Table S8). In different water resources, different metals were responsible for the higher RQ sum . For example, in more than half of the groundwater samples, Cr caused a higher risk. In the case of canals, Ni and Cu showed higher risks to aquatic organisms. For river and drains, respectively, Cu and Pb were often responsible for higher RQ sum .

Characterization of ecological risk. The sums of risk quotients (RQ sum ) are presented for different water resources including groundwater (GW: blue), canal water (CW: cyan), river water (RW: green) and drain water (DW: red). The boundaries of the central box are the 25th and 75th percentiles; the horizontal line is the median; and the whiskers of the boxplot represent the minimum and maximum values. The Red dashed line represents the threshold limit for the risk

Pollution index

According to the pollution index, more than half of the water samples (114 out of 200) were classified as highly polluted, and the trend was as follows: Drains > Canals > River > Groundwater (Fig. 7 ). The pollution index for drain samples ranged from 7.3 to 46.8 (mean 29), and all the samples were categorized as highly polluted. In canals, PI values ranged from 0.2 to 15.6, with an average of 7.7. River samples showed relatively less pollution among surface water samples. The pollution index ranged from 0.33 to 14.24, with an average value of 4.83, which is twofold lower than the canal’s pollution and sixfold lower than the drains. In contrast, none of the groundwater samples were highly polluted. About 28% were categorized as moderately polluted, and 72% were classified as lowly polluted based on the pollution index values (Fig. 7 ).

Heavy metals pollution. Pollution Index values are presented for different water resources including groundwater (GW: blue), canal water (CW: cyan), river water (RW: green) and drain water (DW: red). The boundaries of the central box are the 25th and 75th percentiles; the horizontal line is the median; and the whiskers of the boxplot represent the minimum and maximum values. The Red dashed line represents the threshold limit for pollution

Metal contamination and health risks

In the present study, high concentrations of heavy metals were found in most of the water samples. Approximately, 42% of the groundwater samples exceeded the permissible limits for drinking water set by the World Health Organization (WHO). Cr was detected in high concentrations and Pb was the most frequently detected heavy metal. High concentrations of Cr could be due to its extensive use in different industrial [ 50 ] and agricultural practices [ 51 , 52 ], which end up in groundwater by leaching [ 53 , 54 ]. Furthermore, weak and corrosive plumbing of pipes is also a source of Cr in drinking water. Consequently, these high concentrations of Cr in drinking water may cause different health issues such as respiratory problems [ 55 ], tumor formation and weak immunity [ 56 , 57 ].

Among surface water samples, ~ 98% exceeded the surface water quality standards of the World Health Organization (WHO). In most of the cases, Cr and Ni were exceeding the permissible limits, and Cu concentrations were consistently high in all surface waters. The high concentrations of Cu could be attributed to its common use in the production of electronic chips, cell phones, batteries, semiconductors, the paper and pulp industry, metal processing units, and the production of insecticides and fungicides [ 58 , 59 ]. Copper may enter into water bodies due to corrosion and leaching of Cu polishing, electronic plating, wood preservatives, wire drawing and printing process [ 60 ]. Ultimately, Cu might enter into human bodies via oral and dermal exposure through polluted water and cause serious gastrointestinal problems [ 61 , 62 ]. A similar trend was observed in previous studies due to uncontrolled and unprocessed disposal of industrial effluents into surface water bodies [ 63 , 64 ].

Ni concentrations in the river and drains fluctuated between 0–720 µg/L and 30–1789 µg/L, respectively and were higher than in previous studies [ 26 , 63 ]. High concentrations of Ni might be due to industrial activities as well as erosion of mafic and ultramafic rocks [ 65 , 66 ]. Although Ni is a basic constituent of dietary intake, its higher concentration may cause lung fibrosis, skin allergies, asthma and respiratory tract cancer [ 67 ]. The Pb concentrations found in the current study were similar as reported by Hussain et al. [ 68 ]. The Pb contamination could be attributed to the excessive use of agricultural insecticides, leaching and weathering of rocks and plumbing of pipes [ 69 ]. In the human body, Pb affects the gastrointestinal and respiratory systems and then enters into the circulatory system, binds to erythrocytes and distributes into soft tissues. Ultimately, it accumulates in bones, where it can persist for several years and cause lead poisoning [ 70 , 71 ].

Although seasonal variation can significantly affect the contamination level, the present study focused metal contamination during dry season. Several authors have reported [ 34 ] significantly different metal contamination levals across various seasons. The variation in metal contamination might be attributed to rainfall events, temperature fluctuations and seasonal changes in industrial effluents [ 72 , 73 ].

Ecological risks

In water samples collected from groundwater, canals and drains, Ni was mainly responsible for the higher toxic units. However, in the case of river water, Cu and Cr mainly contributed to the higher toxicity. The high level of Ni might be due to anthropogenic pollution in water bodies near industries [ 74 ] and mining activities. Several studies showed that an excess of Ni affects the survival of aquatic organisms by disturbing their enzymatic system [ 75 , 76 ]. Several studies have reported strong negative effects of metal pollution on benthic macroinvertebrates [ 77 , 78 , 79 ]. Liess et al. [ 80 ] also reported the effects of Cu on predatory stream invertebrates. Furthermore, Cu is considered an inhibitor of photosystem II, leading to decreased chlorophyll content [ 81 ]. It has been reported that Cu is more toxic for algae than crustaceans [ 82 ].

Almost all the water samples collected from drains, canals and river exceeded the chronic and acute threshold limits for algae, fish and crustaceans, and indicated that these water bodies are not safe for aquatic organisms. Until now, there hasn't been any investigation focusing on the ecological risks of heavy metals available in the region to make a comparison. However, when compared to other studies conducted in Turkey [ 83 , 84 ], the ecological risks in the present study are quite high.

According to the risk quotient (RQ), all metals showed high ecological risk in all water resources. Briefly, Cr was mainly responsible for potential ecological risks in 78% of canal water samples, whereas, Ni and Pb highly contaminated the drain water samples in terms of ecological pollution. The risk quotient in the present study is quite high as compared to other investigations conducted in different countries, and indicates stronger ecological effects [ 84 , 85 , 86 ]. Due to the exceedance of the threshold limit (> 1), the adverse ecological effects of these metals cannot be neglected.

Monitoring and risk assessment are crucial to protect human health and aquatic ecosystems from metal contamination. The present study represents the first comprehensive assessment in the region, considering all the water resources for metal contamination and associated human health and ecological risks together. Our results show that the water resources of Lahore are highly polluted with heavy metals, and can have serious health and ecological consequences. Therefore, urgent and comprehensive measures are imperative to mitigate the escalating risks to human health and ecosystems. Industrial effluents should be properly treated before disposal into surface water bodies. Moreover, it is highly important to make better policies and implement them to reduce environmental pollution.

Availability of data and materials

All data generated or analyzed during this study are included in this article. However, any further details are available from the corresponding author on reasonable request.

Abbreviations

Bambawali-Ravi-Bedian

Chronic daily intake

Electric conductivity

Groundwater

Canal water

River water

Drain water

Hazard Quotient

Health risk index

Intake rate

Exposure time

Exposure factor

Conversion factor

Body weight

Measured environmental concentration

National Institute of Standards and Technology

Predicted no-effect concentration

Risk quotient

Total dissolved solids

World Health Organization

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Acknowledgements

The authors are grateful to Mr. Muhammad Iqbal and Mr. Muhammad Afzal from the Institute of Nuclear Medicine & Oncology (INMOL) Lahore for their support in collecting water samples. We also acknowledge the Higher Education Commission (HEC), Pakistan, for their financial support of HHI through the IRSIP fellowship (International Research Support Initiative Program).

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HHI: conceptualization, study design, investigation, statistical analysis, interpretation of results, writing—initial draft, AS: statistical analysis, interpretation of results, writing—extension of initial draft, AQ: conceptualization, study design, supervision, writing—review and editing, SRA: supervision, funding acquisition, writing—review and editing, ML: extension of formal analysis, writing—extensive review and editing, NS: conceptualization, study design, supervision, statistical analysis, interpretation of results, visualization, writing—extension of initial draft, and review and editing.

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file 1: Table S1. Detail of sampling locations with respect to water resource, site ID, sampling date and coordinates. Table S2. Physicochemical properties of the water samples collected from groundwater, canals, river Ravi, drains, and respective National Environmental Quality Standards. Table S3. Parameters used for the calculation of Chronic Daily Intake (CDI) through oral and dermal exposures are enlisted in the table. Values are presented with units and references. Table S4. Reference values of EC 50 /LC 50 (μg/L) for algae, fish and crustaceans used for the calculation of Toxic Units (TU). Table S5. Descriptive summary of the metal concentration (μg/L) in water bodies. Table S6. Hazard quotients and Health Risk Index: Oral Hazard Quotients (HQ oral ) and Oral Health Index (HRI oral ) are presented only for groundwater samples, as other water resources are not commonly used for drinking. Dermal Hazard Quotient (HQdermal) and Dermal Health Index (HRIdermal) are presented for all water samples collected from grounderwater, canals, river and drains. Data is presented in the form of minimum (Min.), maximum (Max.), average (Mean) and standard deviation. Table S7. Ecological risk of metal contamination based on the toxic unit (TU) is presented for three trophic levels: algae, fish and crustaceans. Toxic Units (TU) are given for each metal, and for the total toxicity of all metals detected at each site (TU sum ). For illustration purposes, log-transformation was performed. Table S8. Risk Quotients: Risk Quotients (RQ) and sum of the Risk Quotients (RQsum) are presented for all water samples collected from grounder water, canals, river and drains. Data is presented in the form of minimum (Min.), maximum (Max.), average (Mean) and standard deviation. Figure S1. Principal component analysis of heavy metals: Each vector in the plot represents a variable, and the direction and length of the vector indicate the contribution and correlation of each variable to the top two principal components.

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Iqbal, H.H., Siddique, A., Qadir, A. et al. Human health and ecology at risk: a case study of metal pollution in Lahore, Pakistan. Environ Sci Eur 36 , 9 (2024). https://doi.org/10.1186/s12302-023-00824-2

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Heavy metal water pollution- A case study

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Comprehensive health risk analysis of heavy metal pollution using water quality indices and Monte Carlo simulation in R software

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Rapid urbanization, population growth, agricultural practices, and industrial activities have led to widespread groundwater contamination. This study evaluated heavy metal contamination in residential drinking water in Shiraz, Iran (2021). The analysis involved 80 groundwater samples collected across wet and dry seasons. Water quality was comprehensively assessed using several indices, including the heavy metals evaluation index (HEI), heavy metal pollution index (HPI), contamination degree (CD), and metal index (MI). Carcinogenic and non-carcinogenic risk assessments were conducted using deterministic and probabilistic approaches for exposed populations. In the non-carcinogenic risk assessment, the chronic daily intake (CDI), hazard quotient (HQ), and hazard index (HI) are employed. The precision of risk assessment was bolstered through the utilization of Monte Carlo simulation, executed using the R software platform. Based on the results, in both wet and dry seasons, Zinc (Zn) consistently demonstrates the highest mean concentration, followed by Manganese (Mn) and Chromium (Cr). During the wet and dry seasons, 25% and 40% of the regions exhibited high CD, respectively. According to non-carcinogenic risk assessment, Cr presents the highest CDI and HQ in children and adults, followed by Mn, As and HI values, indicating elevated risk for children. The highest carcinogenic risk was for Cr in adults, while the lowest was for Cd in children. The sensitivity analysis found that heavy metal concentration and ingestion rate significantly impact both carcinogenic and non-carcinogenic risks. These findings provide critical insights for shaping policy and allocating resources towards effectively managing heavy metal contamination in residential drinking water.

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Introduction.

Groundwater, a life-sustaining source, is imperilled as an unwitting repository of trace elements that wield formidable health risks. Heavy metals can infiltrate our bodies through contaminated water, air, and food, posing a threat to human health. This situation emerges from rapid urbanization, population growth, intensified agriculture, and industrial activities, surpassing natural processes and human actions 1 , 2 , 3 . The gravity of the situation is noteworthy, as toxic heavy metal exposure can lead to neurological disorders, cancers, and even fatalities. This extends beyond individuals, affecting plants that unwittingly enter the food chain, escalating risks. Amidst this complexity, understanding diverse heavy metals and their health effects is crucial for deciphering contaminated groundwater hazards 4 , 5 .

For instance, arsenic exposure has been linked to skin lesions, cardiovascular diseases, and cancer 6 , 7 . Zinc, although essential, can cause gastrointestinal disturbances 8 . Lead exposure, especially in children, can lead to developmental delays, neurological disorders, and cognitive impairments 9 Cadmium is linked to kidney damage, lung diseases, and cancer risk. Chromium exposure harms respiration, increasing lung cancer risk. Copper toxicity causes gastrointestinal symptoms, liver damage, and impaired kidney function. Elevated Manganese levels associate with neurotoxicity, cognitive issues, and movement disorders 10 . Reliable water quality indices and health risk assessment methods are essential for understanding the potential health risks associated with heavy metal concentrations.

Water quality indices are pivotal in assessing contamination levels and potential risks linked to heavy metals within water sources 11 . They offer a structured framework to evaluate overall water quality and the extent of heavy metal pollution. Amid the diverse array of water quality indices, several hold notable significance in the context of heavy metal contamination assessment. Among these is the heavy metals index (MI), a parameter considering the concentrations of distinct heavy metals within water samples 12 . MI is precisely calculated to assess water resource drinkability. Heavy metal pollution index (HPI) is another way to estimate water quality based on heavy metals and their effect on human health 13 . The heavy metals evaluation index (HEI) also connects heavy metal concentrations and toxicity levels, offering insights into potential health risks 14 . The contamination degree (Cd) evaluation model determines the combined effects of several qualitative parameters, which can affect drinking water quality unfavourably 13 , 15 .

Following the discussion on water quality indices, the subsequent focus shifts to the methodology for assessing risks associated with heavy metals. Deterministic health risk assessment utilizes fixed values and assumptions to estimate risks, considering exposure pathways, toxicological data, and population characteristics. On the other hand, probabilistic approaches consider uncertainties and variations in exposure and toxicity data, providing a more comprehensive evaluation of potential risks 16 . The probabilistic approach employed in Monte Carlo simulation (MCS) enhances the accuracy and reliability of health risk assessments by accounting for the variability and uncertainty in input parameters. Monte Carlo simulation (MCS) analysis is a powerful and widely used method for assessing health risks associated with heavy metal contamination in water. Unlike traditional deterministic approaches, MCS considers the variability and uncertainty in input parameters, providing a more comprehensive and realistic estimation of health risks 17 .

This study introduces a novel approach by integrating water quality indices (HEI, HPI, Cd, and MI), deterministic and probabilistic (Monte Carlo simulation) methods for carcinogenic and non-carcinogenic health risk assessment in Shiraz drinking water, Iran. Additionally, the study leverages the power of R software for conducting Monte Carlo simulations. R software plays a pivotal role as it enables researchers to execute complex simulations with high precision and efficiency. Its extensive libraries and statistical capabilities are crucial in handling the variability and uncertainty in input parameters, providing a more comprehensive and realistic estimation of health risks associated with heavy metal contamination 18 . This integration represents a significant advancement in evaluating health risks associated with heavy metal contamination, offering a more holistic perspective than previous studies. By applying these advanced techniques, the research provides updated and in-depth insights into heavy metal pollution in the area under investigation.

Materials and methods

The study area was Shiraz City, located in the Fars province of southwestern Iran. The city covers an area of 1268 square kilometers and has a rectangular shape with a length of approximately 40 km and a width ranging from 15 to 30 km. Shiraz is the fifth-largest metropolis in Iran, with a population exceeding 1,565,572, according to the 2016 census report. It is the capital of Fars province and is situated in the picturesque Zagros mountain range. Shiraz is located at 29° 36ʹ 37ʹʹ N and 52° 31ʹ 52ʹʹ E. This city is 1486 m above sea level and experiences an average annual rainfall of 337 mm. In the warmest month of the year, July, Shiraz experiences an average temperature of 30 °C, while in the coldest month, January, the average temperature drops to 5 °C. During April, the temperature reaches an average of 17 °C, and in October, it settles at around 20 °C. Overall, the city maintains an average annual temperature of 18 °C. The sampling area’s location is depicted in Fig.  1 .

Location of the sampling site: Shiraz City, Iran.

Water sampling and analysis

In this cross-sectional study conducted in 2021, we collected 80 water samples during wet and dry seasons from 40 designated stations. These stations were selected based on careful consideration of geographical distribution, proximity to potential contamination sources, and representation of diverse environmental conditions. This deliberate station selection ensures the samples’ representativeness and enhances our findings’ reliability. The collection of samples followed the guidelines outlined in the Standard Methods for Water and Wastewater Examination 19 . Before sampling, the Polypropylene sampling containers were thoroughly washed and cleaned using a diluted nitric acid solution and deionized water. Stagnant water within the pipeline was removed by briefly activating the tap. The sampling points’ precise geographical coordinates were meticulously recorded using a portable GPS device (Model No. GARMIN MONTANA 650) 20 . The quantification of contaminant concentrations in the water samples was performed utilizing graphite furnace atomic absorption spectrometry (Perkin Elmer AA-Analyst 200), a well-established and dependable analytical technique renowned for its accuracy and precision in determining the levels of various contaminants in aqueous samples. This method enables precise and reliable measurements of the concentration levels of contaminants, ensuring robust and accurate data acquisition for the subsequent assessment of water quality 21 , 22 . Subsequently, the collected water samples were meticulously labelled and stored in a cool box containing ice packs, ensuring a constant temperature of 4 °C, following standard conditions. The samples were then transported to the laboratory for further analysis. Upon arrival at the laboratory, a specific standard solution was prepared to facilitate the evaluation of the concentrations of various heavy metals, including Cd, Pb, Hg, As, Cu, Cr, Zn, Fe, and Mn. The concentrations of these metals were measured and recorded in micrograms per liter (μg/l) using voltammetry techniques (Metrohm 797 V), a reliable analytical technique 23 .

Quality control

Before use, all sample bottles underwent a thorough cleaning process involving the washing of the bottles with diluted nitric acid (HNO 3 ) followed by rinsing with deionized water. Blank samples were examined after every set of five samples, and this process was iterated three times to ascertain the accuracy and precision of the analytical method utilized. Furthermore, standard reference materials were utilized for each element as a benchmark to assess the accuracy and precision of the concentration analysis of the targeted heavy metals.

Non-carcinogenic risk assessment

Risk management entails assessing the likelihood and health impacts of incidents caused by environmental risk factors on humans and animals 24 . The study includes an important non-carcinogenic risk assessment to determine the potential health hazards of metals in drinking water. In order to assess the non-carcinogenic risk associated with heavy metals, it is crucial to determine the Chronic Daily Intake (CDI) for each exposure pathway. The CDI values, expressed in milligrams per kilogram per day (mg/kg/day), are calculated for the selected heavy metals considering the ingestion route. Table 1 gives the input parameters in the CDI formula, and Eq. ( 1 ) provided below are employed to calculate the CDI values 25 :

where CDI chronic daily intake (mg/kg/day), Ci the individual metal concentrations (μg/l), IR the ingestion rate (l/day), EF exposure frequency (days/year), ED The exposure duration (year), BW the average body weight (kg/person), AT the average time (in days).

The estimation of the hazard quotient (HQ) or non-carcinogenic risk value for an individual element involves the utilization of the following mathematical Eq. ( 2 ) 26 :

where HQ hazard quotient, RFD reference dose.

The RfDing or chronic oral reference dose is a parameter utilized to estimate the daily oral exposure level for the human population, including sensitive groups, that is expected to pose minimal risks of harmful effects over a lifetime. The RfDing values for specific elements were determined as follows: For Arsenic (As), the RfDing value was established at 0.0003; for Cadmium (Cd), it was set at 0.0005; for lead (Pb), it is 0.0035, and for Chromium (Cr), it was determined as 0.003 mg/kg/day 27 .

The potential risk to humans from exposure to multiple heavy metals can be assessed using the chronic hazard index (HI). The HI is calculated as the sum of individual hazard quotients (HQs) for each heavy metal. The HQ or HI values indicate the magnitude of non-carcinogenic risks associated with the exposure. The HQ or HI value below one indicates no significant non-carcinogenic risks to human health. However, if the HQ or HI value equals or exceeds one, it signifies significant non-carcinogenic risks, which increase as the HQ or HI value increases. The HI value can be determined using the Eq. ( 3 ) 25 :

where HI hazard index.

The HI provides a comprehensive assessment of the cumulative risk of multiple heavy metals, considering the combined effects of their HQs. By comparing the calculated HI value with the threshold of one, researchers can determine the level of non-carcinogenic risks associated with exposure to the evaluated heavy metals 26 .

Carcinogenic risk assessment

Toxic metals exposure, even in low concentrations, can cause disorders in the human body (neurological disorders, different types of cancers, and death in acute cases). Long-term consumption of contaminated water with heavy metals increases the danger of cancer in humans. This study conducted carcinogenic risk assessments for a range of heavy metals. The metals that were assessed for their carcinogenic risk include Arsenic (As), Cadmium (Cd), and Chromium (Cr). The categorization of these elements into carcinogens and non-carcinogenic risks is determined according to the guidelines outlined by authoritative organizations such as the United States Environmental Protection Agency (EPA) and the International Agency for Research on Cancer (IARC) 28 . In evaluating the carcinogenic risk, crucial parameters, including the oral reference dose (RfD) and oral slope factor (CSF), are considered for chromium, cadmium, and arsenic, as detailed in Table 2 . The carcinogenicity of these elements is assessed by quantifying the Excess Lifetime Cancer Risk (ELCR), which is determined using Eq. ( 4 ) 29 , 30 :

where ELCR excess lifetime cancer risk, CDI chronic daily intakes (mg/kg/day), SF cancer slope factor (mg/kg/day).

The calculated ELCR will be compared to the acceptable maximum risk recommended by the United States Environmental Protection Agency (USEPA), which is ≤ 1 × 10 –6 . If the calculated ELCR surpasses this threshold, it indicates a potential health risk to the individuals exposed. Furthermore, the non-carcinogenic risk assessment encompasses lead (Pb), which is not classified as a carcinogen, specifically through the ingestion pathway of drinking water 31 .

Water contamination indices

Metal index (mi).

In evaluating water quality, various indices are employed to assess the level of contamination and the potential risks associated with heavy metals. One of the commonly used indices is the metal index (MI). The MI is a water quality indicator that evaluates the overall contamination level based on the concentrations of various metals compared to their respective maximum allowable concentration (MAC) values. A higher metal concentration concerning its MAC value indicates poorer water quality. If the MI value exceeds 1, it serves as a warning threshold. The MI is calculated using (Eq.  5 ) 32 :

where Ci the concentration of each metal, MAC the maximum allowable concentration.

When MI is less than 1, it signifies that the water is suitable for drinking, indicating compliance with safety standards. On the other hand, when MI exceeds 1, it indicates that the water is unsuitable for drinking due to elevated metal concentrations, suggesting potential health risks. The threshold limit of MI equal to 1 serves as a critical danger threshold, highlighting the point at which water quality transitions from drinkable to non-drinkable. This threshold is a significant determinant in assessing the safety and suitability of water for human consumption 33 .

Heavy metal pollution index (HPI)

The HPI is an important tool for evaluating heavy metal pollution in water sources. It comprehensively evaluates multiple heavy metals in the water and their collective impact on water quality. The calculation of the HPI involves assigning a weightage factor to each heavy metal based on its toxicity and potential health risks. The weightage factors are determined through extensive scientific research and regulatory guidelines. These factors reflect the relative importance of each metal in contributing to overall pollution and its potentially detrimental effects on human health and the environment. The HPI is typically calculated using the Eq. ( 6 ) 34 , 35 :

In the given Equation, Wi denotes the unit weightage assigned to the ith parameter (As–Cr, Mn: 0.02, Cd: 0.3, Cu: 0.001, Ni: 0.05, Pb: 0.7, and Zn: 0.0002). Qi represents the sub-index value of the ith parameter, and n represents the total number of parameters considered. The sub-index (Qi) for each parameter is determined using Eq. ( 7 ) 36 :

where Mi the measured metal concentration for the ith sample (μg/l), Ii the ideal concentration for the ith parameter (I i is 10, 3000, 10, 3, 50, and 2000 μg/l for As, Zn, Pb, Cd, Cr, and Cu, respectively), Si The standard value (highest permissible value for drinking water) for the ith parameter based on World Health Organization (WHO) guidelines (equal to 50, 5000, and 100 for As, Zn, and Pb, respectively).

A value of HPI below 100 indicates non-contaminated water, while a value above 100 suggests contamination by heavy metals. Furthermore, when the HPI reaches 100, it represents the threshold for dangerous contamination. The symbol (–) denotes the numerical difference between these two values, disregarding the algebraic sign 34 , 36 . While both the metal index (MI) and the heavy metal pollution index (HPI) serve to evaluate contamination levels, they do so through different approaches. MI directly measures contamination severity by assessing metal concentrations concerning their maximum allowable concentrations (MAC) values. This method is particularly adept at identifying metals that significantly exceed regulatory thresholds. On the other hand, HPI offers a more comprehensive assessment by factoring in metal toxicity through predetermined weighting factors derived from extensive scientific research and regulatory guidelines. This approach allows for a nuanced evaluation of pollution, considering each metal’s relative toxicity. In summary, MI excels at pinpointing metals that greatly surpass regulatory limits, while HPI provides a more holistic evaluation, making it effective in discerning metals with varying levels of toxicity and their contribution to overall pollution 33 .

Heavy metals evaluation index (HEI)

The HEI is a quantitative measure used to evaluate the levels of heavy metals in water samples. The HEI is calculated by summing the ratios of the measured concentration (Hc) to the maximum allowable concentration (Hmax) for each parameter. The measured concentration (Hc) is expressed in micrograms per litre (μg/l), while the maximum allowable concentration (H max ) represents the threshold value set for each specific heavy metal. The HEI is calculated using the Eq. ( 8 ) 37 :

where Hc the measured concentration for the ith parameter (μg/l), Hmax the maximum allowable concentration for the ith parameter (μg/l).

An HEI value below 40 indicates a low level of heavy metal pollution, while an HEI value between 40 and 80 suggests a medium level of contamination. HEI values exceeding 80 indicate a high level of heavy metal pollution, which poses a significant risk to water quality and potentially to human health 37 . In comparing the HEI and the HPI, HEI offers a direct assessment by comparing measured concentrations to permissible limits. In contrast, HPI provides a more intricate evaluation, incorporating the relative toxicity of each metal through predefined weighting factors. HEI simplifies the evaluation of regulatory compliance, whereas HPI furnishes a nuanced appraisal of pollution, accounting for variations in metal toxicity. On the other hand, MI offers a direct measure of contamination severity, being particularly effective at identifying metals with high health risks. Nonetheless, it may not flag metals with lower concentrations that are still of concern. The choice between HEI and MI depends on the specific objectives of the assessment and the regulatory context 32 .

Contamination degree index (Cd)

The contamination degree index (Cd) is another index used for evaluating the degree of contamination caused by heavy metals in water samples. The Cd quantitatively measures the overall contamination level based on the concentrations of different heavy metals. The Cd is calculated using the equations (Eqs. 9 , 10 ):

where Cfi contamination fit index for the ith parameter, CAi measured concentration for the ith parameter (μg/l), CNi the maximum allowable concentration for the ith parameter (As: 1, Cd: 3, Cr: 50, Cu: 2000, Fe: 300, Mn: 400, Ni: 20, Pb: 10, and Zn:5000 μg/l 38 .

The interpretation of Cd values depends on specific threshold levels or classifications established for different regions or regulatory bodies. Generally, higher Cd values indicate a higher degree of contamination, while lower Cd values suggest a lower level of contamination. Based on the obtained Cd values, water samples can be categorized into three levels: low contamination (Cd < 1), medium contamination (Cd = 1–3), and high contamination (Cd > 3) 24 , 39 . These categories provide further insight into the extent of heavy metal contamination in the water samples, helping to assess the potential risks associated with the measured concentrations.

In summary, Cd provides a comprehensive contamination assessment, considering multiple heavy metals. MI offers a straightforward evaluation, HEI directly assesses regulatory compliance, and HPI offers a comprehensive evaluation accounting for varying metal toxicity. The choice between these indices depends on the specific objectives of the assessment and the regulatory context.

Local distribution and geo-statistical modelling

In this study, an assessment of the spatial distribution of water quality parameters was conducted in Shiraz’s drinking water distribution network. This analysis aimed to characterize the variations in water quality across the study area and generate zoning maps for the specified parameters. To determine the geographic coordinates of the sampling locations, a portable GPS device (Model No. GARMIN MONTANA 650, USA) was utilized, providing latitude, longitude, and Universal Transverse Mercator (UTM) coordinates 40 .

The collected sampling location coordinates were then imported into ArcGIS 10.4.1 software, a widely used geographic information system (GIS), to prepare zoning maps. Interpolation models were employed to effectively estimate and visualize the spatial distribution of water quality within the study area. Specifically, the inverse distance weighting (IDW) interpolation method was applied to create the zoning maps, allowing for a comprehensive understanding of the spatial variations in drinking water quality 41 , 42 .

The IDW method estimates values at unsampled locations based on known values from sampled locations. This process assigns higher influence to points closer to the unsampled location, emphasizing the principle that nearby measurements carry more weight in the interpolation. Moreover, the interpolation process entailed specific steps, including considering neighbourhood size and other pertinent parameters. These choices were made judiciously to ensure the accuracy and reliability of the spatial estimations. However, it is essential to acknowledge that, like any interpolation method, IDW has inherent limitations. These considerations include assumptions related to spatial autocorrelation and the potential sensitivity of results to parameter selections. Recognizing these aspects provides a well-rounded understanding of the methodology employed in evaluating the spatial distribution of water quality parameters in this study 43 .

Uncertainty analysis by Monte Carlo simulation (MCS)

Human health is often subject to uncertainties, which, if not properly addressed, can result in the loss of valuable information. Therefore, it leads to ineffective decisions, far from reality, or inaccurate about protecting human health. Uncertainty analysis is crucial in assessing modelling results’ reliability and robustness. This study employed Monte Carlo simulation (MCS) as a powerful technique for uncertainty analysis in water quality assessment 18 .

Monte Carlo simulation is a statistical method that uses random sampling to explore the uncertainty associated with input parameters and their impact on the model’s output. By generating many random samples within specified parameter ranges, MCS allows for assessing the variability and distribution of model outputs, providing insights into the range of possible outcomes and associated uncertainties. Measured heavy metal concentrations, ingestion rate, body weight, and duration of exposure were used to determine the distribution of potential uncertainty. The calculations were repeated 10,000 times, and finally, the results are indicated with a confidence level in the 1–99% range. Through the iterative process of generating multiple simulations, each with different input parameter values, MCS estimates probability distributions for model outputs. This information can then be used to assess the likelihood of specific water quality scenarios, identify sources of uncertainty, and inform decision-making processes related to water management and risk assessment 18 .

In this study, employing the R software environment, we conducted rigorous Monte Carlo simulations and sensitivity analyses to comprehensively assess non-carcinogenic and carcinogenic risks. The initial step involved determining distribution functions for each elemental parameter using established R packages, including fitdistrplus, logspline, EnviroPRA, and survival 44 . Specifically, we modelled the parameters for Arsenic (As), Chromium (Cr), Cadmium (Cd), Lead (Pb), Zinc (Zn), Copper (Cu), and Manganese (Mn) utilizing the following distributions shown in Table 3 .

Results and discussion

Heavy metals concentration.

Heavy metals’ concentration and distribution in groundwater resources depend on mineral composition, soil compound and underground stones and their geological properties, hydro-chemical features, and anthropogenic activities on the ground surface 48 . Table 4 presents the summary statistics of pollutant concentrations in the collected samples and the potable water standard specifications provided by the WHO and EPA.

As shown in Table 4 , the maximum mean concentration of heavy metals in groundwater was as follows: Zn > Mn > Cr > Cu > pb > As > Cd. The mean concentrations of As, Cd, Pb, Cr, Cu, Zn, and Mn during the study period were lower than the standard limitations determined by WHO 20 , 49 .

Groundwater contamination and the distribution of heavy metals concentrations

The zoning maps of all heavy metals concentration in two wet and dry seasons are given in Fig.  2 . Also, the results from Table 4 indicate the summary statistics of heavy metal concentrations in the collected groundwater samples and compare them with potable water standards recommended by the World Health Organization (WHO) and the Environmental Protection Agency (EPA). For As, the mean concentrations in wet and dry seasons were 1.6 ± 1.13 and 2.29 ± 2.02 μg/l, respectively, and a meaningful statistical difference was observed between the measured concentrations in the two seasons (p < 0.05). The maximum permissible concentration of arsenic is ten μg/l in drinking water (Iran, E.P.A., and WHO standards) 10 , 50 . Therefore, the mean As concentration for 95% of samples was within the permissible limit provided by the mentioned organizations 42 , 51 . As Fig.  2 shows, the maximum concentration was observed in the northern area, wells 25, 28, and 40, in the dry season, and the lowest concentration was related to wells number 33 and 38 with values of 8.28 and 0.35 μg/l, respectively.

Geographical distribution of studied metals concentration in wet and dry seasons.

Moreover, the highest concentrations were in the wet season measured in wells placed in south and southeast regions, and the lowest value was related to sampling well 15 in the north with the value of 0.53 μg/l. During the study, an arsenic concentration increase was observed from the north and northwestern to central regions, predominantly south and southeastern parts of the research area. So, the water quality decreased during this time. It can be due to the hydraulic slope of the Shiraz aquifer (from north to south). Also, the arsenic concentration is influenced by human-made contamination, ion leaching, direct wastewater discharge, and natural processes such as dissolution and penetration in the studied area 52 , 53 .

Cadmium (Cd) concentrations remained within acceptable limits during both seasons. The mean concentration of cadmium in both low-rainfall and high-rainfall seasons were 0.29 + 1.39 μg/l and 0.31 ± 1.3 μg/l, respectively, and there is no statistical difference between the two seasons’ concentrations. Only 2.4% of samples had higher values than the maximum recommended value of Iran, WHO, and EPA recommendations 7 . According to Fig.  2 , the trend of Cd concentration was changed from the southern area in the dry season to the south and southwestern regions of the study area in the wet season (max = 8.86 µg/l in the wet season and 8.17 µg/l in the dry season), with a broader range of the study area experiencing water quality deterioration over time. The high levels in central, southern, and southwestern areas may be due to oxidation and acidification through groundwater pumping, excessive nitrate entry through agricultural fertilizers, and even industrial zones west of the study area 40 .

Lead (Pb) concentrations showed no exceedances of standards during the dry and wet seasons . The mean concentrations of lead in cold and warm seasons were 2.66 and 4.22 μg/l, respectively, and there is no statistical difference in both seasons. All sample concentrations were over the allowable range (WHO and EPA standards of 10 μg/l). The maximum concentration was observed in southwestern and western regions, wells number 2 and 38, in the dry season with the values of 9.88 and 9.5 μg/l, respectively. In the wet season, the concentration trend was changed to the southeastern region, and well number 7 had the highest concentration. The intensive presence of lead in these regions can be related to the mixed effect of human and natural resources, such as anthropogenic activities like the application of fertilizers and pesticides containing Pb (e.g., Aldrin, Dieldrin, and endosulfan) in agricultural lands, which is resulted in the high concentration of lead in groundwater 54 . In fertilizers, these heavy metals can be leached from the soil and penetrate groundwater 55 .

Copper (Cu) concentrations showed no exceedances of the EPA standard of 1300 µg/l, but during the wet season, some samples had concentrations as high as 560.33 µg/l, indicating potential localized contamination. Cu can be found in human tissues and is essential in making Red Blood Cells (RBC) and protecting neurons and the immune system 20 . The mean Cu concentrations in low- and high-rainfall seasons were 4.28 ± 3.39 and 4.43 ± 5.3 μg/l, respectively, without any statistical difference. All samples meet Iranian drinking water quality guidelines (number 1053) and EPA and WHO water quality standards, which had values below 2000 μg/l. The maximum Cu concentration was related to well numbers 12 and 23 in the dry and wet seasons. Therefore, changes in Cu concentration were detected from the southeastern south and southwestern regions. Cu concentration in groundwater resources is primarily influenced by the long-term interactions between water and rocks and the redox environment of the groundwater system 56 .

Zn is necessary for good performance, immune system health, metabolic activities, proper DNA synthesis, healthy growth, and wound healing. In contrast, its deficiency leads to delayed growth and makes the person susceptible to disease 57 . There was no statistical difference between valued concentrations wet (ranged from 5.8 to 730 μg/l, mean = 460.62 μg/l) and dry (varied from 4.31 to 368.83 μg/l, mean = 69.7 μg/l). According to Fig.  2 , the maximum levels were moved from east and southeast to south and southwest of the study area in low-and high-rainfall seasons, respectively. The presence of Zinc in phosphate and urea fertilizers indicates that agricultural activities can be considered the primary human sources of groundwater resources. Zn may be washed and leached from soil to groundwater resources 55 . Also, the concentration is primarily affected by the long-term interaction of water, rocks, and the redox environment of the groundwater system 58 .

The Mn measured values in groundwater samples varied from 4 to 163 μg/l (mean = 44.14 μg/l) in the wet season and ranged from 13 to 92 μg/l (mean = 97.29 μg/l) in the dry season with no statistical difference. Based on the geographical distribution map, the maximum concentration was quantified in northern, central, and western regions in both seasons. The content of Mn in the tailings is very high. The tailings are oxidized during long-term stacking to produce a large amount of acid, which promotes the dissolution of Mn-containing minerals and increases the Mn content 59 .

Evaluation of pollution indices and the toxic parameters

The studied metals concentration must be compared with their maximum permissible limit in standard mode to calculate the metal index and determine the water resources pollution degree to heavy metals. Figure  3 shows the values of studied indexes for all samples. The results depict that Cd has been a cumulative index evaluated as the sum of the pollution factor index for studied metals in both seasons. This index compares the measured metal concentrations with each metal’s highest permissible concentration limit 38 . Based on the results, Cd ranged from − 2.96 to 16.37 (mean = 2) and − 1.64 to 36.5 (mean = 7.3) in the wet and dry seasons, respectively. During the wet season, Cd of groundwater in 25%, 7.5%, and 67.5% of the regions shows high, medium, and low contamination. Similarly, during the dry season (Fig.  3 ), Cd of groundwater in 40% of the samples indicates a heavy contamination degree at over 3; Cd of groundwater in 2% of the were in the medium degree contamination range; and Cd in the remaining 55% of all the samples were classified in the low degree.

The classification values map of (Cd), (HEI), (HPI), and (MI) in dry and wet seasons.

Also, HEI varied from 3.30 to 12.74 (mean = 5.03) in the dry season and varied from 3.91 to 31.23 (mean = 6.97) in the wet season. The HEI of groundwater in 2.5% and 97.5% of the studied areas shows medium and low contamination, respectively, in both seasons. Values evaluation results of the HPI model show that during the wet and dry seasons (Fig.  3 ), the HPI evaluation value of the groundwater in all sampling areas is within the safe limit at less than 50. The heavy metals index (MI) is used to determine the effect of heavy metals on human health. Evaluation results indicated that the MI values in 15% and 30% of the studied are non-drinkable in dry and wet seasons, respectively. Moreover, MI in the remaining regions’ samples is within the drinkable and threshold classification. The point is that in this index, if the value of only one of the metals exceeds the maximum permissible limit, the index value becomes more than one and is placed in the non-drinkable class 32 .

Many types of research are conducted to evaluate water quality using various indexes worldwide. Jahromi et al. assessed the groundwater resource’s drinkability quality in Varamin’s aquifer. Severe changes in the metal concentration were observed, and the aquifer pollution was not dangerous regarding heavy metals. Jafari and Hassan Zadeh 60 investigated the water quality of Anzali Wetland for heavy metals using the HPI. The findings of the HPI model showed moderate heavy metal contamination and severe pollution in the eastern part of Anzali Wetland. The results of Nasr Abadi’s research 13 showed that the mean values of Cd and HPI were significantly lower than the danger threshold.

Risk assessment

Non-carcinogenic.

This study assessed the non-carcinogenic risk of heavy metals in a residential area’s drinking groundwater using deterministic and probabilistic methods. Table 5 and Fig.  4 provide a summary of the non-carcinogenic risk distribution for selected heavy metals, including Arsenic (As), Chromium (Cr), Cadmium (Cd), Lead (Pb), Zinc (Zn), Copper (Cu), and Manganese (Mn), for two different exposure groups: children and adults. The risk assessment was based on each metal’s chronic daily intake (CDI) values and hazard quotient (HQ) 61 . The calculations were based on the average of two seasons. Overall, the mean non-carcinogenic risk of As, Pb, Cr, and Mn in children is higher than in adults.

Histograms and sensitivity analysis of hazard index (HI) in heavy metals for children and adult groups.

Table 5 and Fig.  4 show that the non-carcinogenic risk levels vary among the studied heavy metals and the different age groups. Among the studied metals, Arsenic (As) and Chromium (Cr) have relatively higher non-carcinogenic risk levels than other metals. The CDI values for As in children and adults are 0.1278 and 0.0548 mg/kg/day, respectively, with corresponding HQ values of 0.426 and 0.183. Similarly, the CDI values for Cr are 1.223 and 0.524 mg/kg/day for children and adults, respectively, with HQ values of 0.408 and 0.175. However, even for these metals, the HQ values remain below 1, indicating that the health risks associated with their exposure are still within safe limits for both age groups. However, it is important to note that long-term exposure to heavy metals, even at low levels, can still have cumulative effects on health over time. Hence, continuous monitoring and assessment of water quality are essential to ensure public health safety 62 .

The hazard index (HI) values presented in the table signify the non-carcinogenic health risks associated with the examined heavy metals in the drinking groundwater, applicable to both children and adults. The HI values measure the cumulative health risk from exposure to multiple heavy metals. For the children’s group, the HI values range from a minimum of 0.461 to a maximum of 2.850, with a mean value of 1.260. These HI values suggest that, on average, children may be exposed to a health risk above the safety threshold of one, indicating a potential concern for adverse health effects from heavy metal exposure 5 .

In contrast, for the adults’ group, the HI values range from a minimum of 0.089 to a maximum of 1.071, with a mean value of 0.402. The mean HI value below one indicates that adults’ overall non-carcinogenic health risk is within acceptable limits, suggesting a relatively lower potential health risk than for children. The variations in HI values between children and adults can be attributed to differences in sensitivity to heavy metal exposure and water consumption patterns between the two age groups 63 .

In addition, the MCS technique, conducted through coding in R software version 4.2.2, considered the variability and uncertainty in input parameters such as contaminant concentration, ingestion rate, and body weight. This approach allowed for a comprehensive evaluation of potential risks associated with heavy metal exposure in different age groups. The histograms depicting the probabilistic approaches for heavy metal concentrations in the exposed groups were presented in Fig.  4 for children and adults. Sensitivity analyses were conducted to assess the influential factors on risk assessment, considering the sensitivity of various parameters. By systematically varying the input parameters in the Monte Carlo simulation, the sensitivity analysis allowed us to identify the key contributors to the variability in health risk estimates associated with heavy metal exposure. These findings provide valuable insights into which parameters significantly impact the overall risk assessment, aiding in prioritizing control measures and mitigation strategies to safeguard public health 63 . The simulation results showed that HI Values for the 95th percentile in the children and adult age groups were 5.28 and 0.89, respectively, indicating a non-carcinogenic risk for children groups. High-risk levels in infants can be due to their low body weight compared to other age groups. Also, the difference in HI values between the children and adult groups can be attributed to the probabilistic nature of the MCS method. Unlike the deterministic method used to calculate Hazard Quotient (HQ), which relies on fixed values and assumptions, the MCS accounts for uncertainties and variations in exposure and toxicity data. In the case of children, the variability and uncertainty in factors such as contaminant concentrations, ingestion rates, and body weights may lead to a more comprehensive range of possible outcomes in the MCS simulation. Consequently, this broader distribution of HI values for children includes higher values, indicating the possibility of increased non-carcinogenic health risks 20 .

On the other hand, the deterministic method for calculating HQ might have provided a single value that falls below the threshold of concern (HQ < 1), potentially underestimating the true range of potential risks. Therefore, the MCS approach offers a more comprehensive and realistic assessment of non-carcinogenic risks, capturing the uncertainty and variability inherent in the data and providing a more accurate representation of the health risk profile for heavy metal exposure, especially in vulnerable populations like children 6 .

Table 6 presents the results of the carcinogenic risk assessment associated with three specific heavy metals (Arsenic, Chromium, and Cadmium) in the drinking groundwater, categorized by different age groups (Children and Adults). The evaluation was conducted based on cancer risk parameters to understand the potential health implications of exposure to these metals.

The values in Table 6 represent the estimated excess lifetime cancer risk (ELCR) associated with each heavy metal for the different age groups. ELCR values are expressed in terms of risk per million individuals and provide insights into the likelihood of cancer development due to long-term exposure. Analyzing the results, we observe that the ELCR values for all heavy metals and age groups are generally quite low, indicating a relatively low potential for cancer risk through exposure to these metals in the drinking groundwater. The calculated ELCR values range from as low as 0 (no risk) to a maximum of around 4.56E−04, corresponding to a very low fraction of the population potentially developing cancer due to heavy metal exposure 64 .

Additionally, the values follow a consistent pattern, with Children generally having slightly lower ELCR values than Adults. This can be attributed to the fact that Children, being more susceptible due to their developing physiology, tend to have slightly higher exposure levels. Despite this trend, all values remain well below the acceptable cancer risk threshold, typically set at 1E−06 (or 1 in a million) 6 .

The absence of significant carcinogenic risk despite the presence of non-carcinogenic risk, as indicated by the results in Tables 5 and 6 , could be attributed to the different assessment approaches for these two types of risks. The differing outcomes between non-carcinogenic and carcinogenic risk assessments can be attributed to the differences in the toxicological properties of these heavy metals, the specific exposure pathways, and the calculated parameters used for each assessment method 65 . The absence of significant cancer risk despite non-carcinogenic risk could indicate that while exposure to these heavy metals might pose some non-carcinogenic health risks, the probability of developing cancer due to this exposure is minimal 5 .

In conjunction with the quantitative data, we have utilized histograms and diagrams for sensitivity analysis to visually expound upon the dimensions of uncertainty and sensitivity within the context of carcinogenic risk assessment (Figs. 5 , 6 ). The histograms offer a graphical dissection of risk level distribution across discrete intervals, while the sensitivity analysis diagrams shed light on the influences of discrete parameters on the calculated risk values.

Histograms of the uncertainty analysis and sensitivity analysis of children’s group.

Histograms of the uncertainty analysis and sensitivity analysis of the adult group.

These Figs.  5 and 6 reveal percentile values representing different risk levels for Arsenic (As), Cadmium (Cd), and Chromium (Cr). Specifically, for arsenic in the children group, the percentiles (5th, 50th, and 95th) of 2.1E−06, 1.0E−05, and 5.1E−05, respectively, denote the varying potential risk levels to which individuals in the children group could be exposed. However, it is essential to note that these values alone do not directly convey health impacts. The significance of these values is best understood when compared to established health standards or guidelines 57 .

On the other hand, the sensitivity analysis provides insights into the influence of specific parameters on the overall carcinogenic risk assessment. In this case, As concentration, Ingestion rate (IR), and body weight are identified as contributing factors. Arsenic concentration and Ingestion rate contribute by 51.79% and 51.75%, respectively, indicating their strong influence. Notably, Body weight, with a contribution of − 3.54%, appears to have a minor inverse impact. Similar trends for Cd and Cr are observed, with varying percentiles and corresponding sensitivity analysis outcomes. It is important to remember that interpreting these values regarding health impacts necessitates referencing relevant health guidelines. These indicators guide further assessment and informed decision-making regarding potential health risks associated with heavy metal exposure 66 .

The insights drawn from the adult group’s analysis (Fig.  6 ) echo the patterns observed in the children group, albeit with distinct percentile values. The percentiles for As, ranging from 9.1E−07 to 1.9E−05, denote potential risk levels. The sensitivity analysis for As, Cd, and Cr mirrors the findings in the children group, reaffirming the significant impact of parameters like concentration and ingestion rate. The contributions of As concentration and Ingestion rate, which are 67.75% and 33.22%, respectively, highlight their prominent influence on the overall carcinogenic risk assessment. Conversely, Body weight, contributing by − 0.97%, exhibits a relatively minor inverse effect, consistent with the trends identified in the children group 6 .

This alignment in trends underscores the robustness of the results across different age groups and provides a comprehensive understanding of the potential health risks associated with heavy metal exposure. The percentiles and sensitivity analysis offer valuable insights that guide further evaluation and decision-making processes related to health risk management and prevention strategies.

This cross-sectional study analysed 80 water samples from 40 designated stations in Shiraz, Iran, for heavy metal contamination across wet and dry seasons. We comprehensively assessed contamination risks by employing advanced Monte Carlo simulations driven by R software. The heavy metals index (MI) and heavy metals evaluation index (HEI) were pivotal in quantifying contamination levels and associated health risks. Our approach heightened precision in non-carcinogenic and carcinogenic risk assessments, providing critical insights into the complex interplay of heavy metal pollution, groundwater quality, and human health. This research significantly informs risk management strategies. Future studies may explore the long-term effects of heavy metal exposure on diverse demographics and consider the cumulative impact of multiple heavy metals on health.

Data availability

The data generated and analyzed during this study are available within the study.

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Acknowledgements

The authors thank the Research Vice-Chancellor of Behbahan University of Medical Sciences for financially supporting the research with Grant Number (99067).

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Samaneh Shahsavani

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Omid Azadbakht

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A.B. and H.S.: Data collection, Funding acquisition, conceptualization, investigation, original draft writing, and reviewing. S.S.: Conceptualization, Methodology, Validation, Investigation, Writing. I.P. and A.M.: Validation, visualization, investigation, writing. O.A.: Visualization, writing, reviewing, and editing. P.J.: Data collection, methodology. H.F.: Conceptualization, investigation, reviewing, supervision validation, visualization. K.B.N.: Software, Investigation, Project administration, conceptualization, supervision validation, visualization, resources.

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Badeenezhad, A., Soleimani, H., Shahsavani, S. et al. Comprehensive health risk analysis of heavy metal pollution using water quality indices and Monte Carlo simulation in R software. Sci Rep 13 , 15817 (2023). https://doi.org/10.1038/s41598-023-43161-3

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Heavy metal pollution and potential ecological risks in rivers: a case study from southern Italy

Affiliation.

• 1 Environmental Chemistry, Department of Public Health and Infectious Diseases, Sapienza University, P.le Aldo Moro 5, 00185, Rome, Italy, [email protected].
• PMID: 24217626
• DOI: 10.1007/s00128-013-1150-0

We monitored heavy metal (As, Cd, Hg, and Pb) concentrations in surface water, sediments, and oligochaetes in four major rivers in Calabria (southern Italy) over the course of 1 year. As, Cd, and Pb showed accumulation factors of 10(3)-10(5) for water to sediment and 1-10 for sediment to oligochaetes. Hg showed a water to sediment accumulation factor of 10-100. Finally, Hg concentrations exceeded the Italian quality standard for freshwater in all of the rivers, and As concentrations in sediments exceeded the respective Canadian standard. However, the application of an ecological risk assessment method indicated low risks for all monitored rivers.

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Heavy Metal Pollution in the Environment and Its Impact on Health: Exploring Green Technology for Remediation

Sumanta das.

1 Department of Biotechnology, The University of Burdwan, Burdwan, West Bengal, India

Kaniz Wahida Sultana

Ashwell r ndhlala.

2 Department of Plant Production, Soil Science and Agricultural Engineering, Green Biotechnologies Research Centre of Excellence, University of Limpopo, Sovenga, South Africa

Moupriya Mondal

Indrani chandra.

Along with expanding urbanization and industrialization, environmental pollution which negatively affects the surroundings, has been rising quickly. As a result, induces heavy metal contamination which poses a serious threat to living organisms of aquatic and soil ecosystems. Therefore, they are a need to ameliorate the effects cost by cost pollution on the environment. In this review, we explore methods employed to mitigate the effects caused by heavy metals on the environment. Many techniques employed to manage environmental pollution are tedious and very costly, necessitating the use of alternative management strategies to resolve this challenge. In this concept, bioremediation is viewed as a future technique, due to its environmental friendliness and cost-effective measures aligned with sustainable or climate-smart agriculture to manage contaminants in the environment. The technique involves the use of living entities such as bacteria, fungi, and plants to deteriorate toxic substances from the rhizosphere. Currently, bioremediation is thought to be the most practical, dependable, environmentally benign, and long-lasting solution. Although bioremediation involves different techniques, they are still a need to find the most efficient method for removing toxic substances from the environment. This review focuses on the origins of heavy metal pollution, delves into cost-effective and green technological approaches for eliminating heavy metal pollutants from the environment, and discusses the impact of these pollutants on human health.

Introduction

Globally, the Industrial Revolution played a significant impact on the development of the economies of many different countries because it changed an economy that was predominately based on agriculture and handicrafts into one that was dominated by industry and machine manufacturing. 1 , 2

In India, the Industrial Revolution had a pivotal role in the economic rise of developing countries. In the case of India, the Industrial Revolution commenced post-1850 and notably bolstered the rural economy. 3 However, this period of scientific and technological development had concomitantly brought pros and cons in the long run. As a result, it led to unprecedented outcomes due to human activities which were first ignored until the publication of Silent Spring by Rachel Carson on September 27, 1962. 4 The Silent Spring unveiled the mystery behind the use of synthetic chemical inputs and its negative impact on the environment. 5 Their effects were environmental pollution that was formally categorized as anthropogenic activities, 6 - 9 resulting from the dumping of industrial, home trash, and synthetic agricultural inputs. Therefore, to sustain the environment, alternative methods need to be employed to mitigate the effects induced by synthetic chemicals on the environment.

The anthropogenic activities, primarily emanating from the agricultural, industrial, and urbanization side, are currently releasing contamination to the rhizosphere or atmosphere which includes accumulation of heavy metals and other toxic fumigant chemicals that pose an environmental threat. 10 Heavy metal contamination is considered as one of the most critical environmental issues that reduce crop productivity and directly or indirectly jeopardizes the survival of almost all types of living entities on the planet. 11 The toxic metals absorbed by plants result in chemical residues on marketable produce causing mutagenic reactions which result in cancer in human beings. 12 Because wildlife depends on plants, they are also affected by heavy metal pollution, which disturbs the balance of mother nature and reduces biodiversity. 13 On the other side, pesticides used in plant protection also kill or affect the reproductive potential of untargeted organisms like beneficial nematodes, insects, birds and earthworms. 14 The prevention of heavy metal infiltration into terrestrial, atmospheric, and aquatic habitats as well as the remediation of damaged land are therefore imperative.

Heavy metals are a distinct group of metals that possess comparatively high densities, atomic numbers, and atomic weights within the periodic table. 15 , 16 Typically, heavy metals are non-biodegradable and persist in the environment for several decades. 17 Heavy metals such as mercury (Hg), cadmium (Cd), lead (Pb), chromium (Cr), and arsenic (As) are considered to pose a significant threat to untargeted living entities due to their toxicity character, even at low concentrations. 18 As a result, bioremediation is viewed as a future technique to ameliorate the effects caused by pollution on the environment due to anthropogenic activities. This technique is suitable for remediating contaminants and it is eco-friendly. 19 Bioremediation involves the use of living entities such as bacteria: Acinetobacter sp., 20 Alcaligenes odorans , 21 Bacillus subtilis , 22 Corynebacterium propinquum , 23 Microbacterium sp., 24 Pseudomonas sp., P. putida , P. aeruginosa , 25 and Ralstonia sp. 26 to deteriorate toxic substances from the rhizosphere as well as the atmosphere. 27 It also employs the use of plants, technically known as green biotechnology where Brassica juncea , 28 Helianthus annuus , 29 Pteris vittate , 30 Salix viminalis , 31 and Solanum lycopersicum 32 plants were employed and shown the ability to extract or reduce heavy metals from the soil. This review summarizes a variety of bioremediation techniques, with a focus on their efficacy in thoroughly eradicating heavy metal pollution from the environment. It does so by doing a thorough analysis of the current literature.

The Principal Sources of Pollution

Heavy metals are released into the environment from various sources including mining, urbanization, chemical industry, sewage plants, pesticide plants, biomedical and unsafe agricultural practices ( Figure 1 ) The United Nations Environment Program (UNEP/GPA) and the Global Plan of Action (GPA) recognize electronic waste (e-waste) which includes devices like mobile phones, tablets, computers, and smartwatches as a major threat to the environment and human well-being. This is primarily due to the presence of heavy metals like Hg, Cd, and Pb in electronic devices, which can pose serious risks to both the environment and human health if not properly disposed of UNEP/GPA 33 and Tchounwou et al. 34 The pollution levels of these heavy metals are influenced by industrial activities, geographic locations, regulatory oversight, and diverse sources. 35 For instance, Hg primarily emanates from coal combustion, electric/light bulb, wood preservatives, leather tanning, ointments, thermometers, adhesives and paints. 36 Cd often originates from industries like battery manufacturing, paint pigments, pesticides, galvanized pipes, plastics, polyvinyl and copper refineries. 37 Pb, an extremely toxic metal, commonly originates from substances like Pb-based paints, gasoline and mobile batteries. 38 Cr is emitted from a variety of industrial activities, including petroleum refining, electroplating, leather tanning, textile manufacturing, and pulp processing. 39 As, a naturally occurring element in the Earth’s crust, is released into the environment through a variety of human activities, including mining, agricultural practices, automobile exhaust and industrial dust, wood preservatives, and dyes. 40

A schematic diagram illustrating the origins of heavy metal pollution.

Soil plays a vital role in supporting terrestrial ecosystems and their biodiversity. Heavy metals are prevalent pollutants within the soil environment, and their presence can adversely affect microorganisms, plants and animals. The European Environment Agency (EEA) has set limit values for soil pollutant levels of various heavy metals, including Hg (0.20 ppm), Cd (0.44 ppm), Pb (0.48 ppm), Cr (0.20 ppm), and As (0.11 ppm). 41 , 42 According to World Health Organization (WHO) guidelines, the acceptable levels of heavy metal pollutants in drinking water are as stated: Hg—0.001 ppm, Cd—0.005 ppm, Pb—0.05 ppm, Cr—0.05 ppm, and As—0.05 ppm. 43 The Food and Agriculture Organization (FAO) of the United Nations (UN) and the WHO set maximum limits for the consumption of heavy metals, as higher levels can cause health problems. The permissible limits for heavy metals consumption through vegetables are as follows: Hg—0.05 mg/kg for all vegetables; Cd—0.2 mg/kg for leafy vegetables, 0.3 mg/kg for root vegetables, and 0.1 mg/kg for other vegetables; Pb—0.15 mg/kg for all vegetables; Cr—0.1 mg/kg for all vegetables, and As—0.1 mg/kg for all vegetables. 44 - 46

Managing Pollution

Several techniques are employed to decontaminate the environment from these pollutants and avert the entry of toxic metals into the environment. Nevertheless, these methods tend to be costly and exhibit suboptimal efficacy. 47 , 48 The increasing concerns surrounding environmental contamination have initiated the development of suitable technologies to assess the presence and mobility of metals in soil, water, and wastewater ( Figure 2 ). Private and government institutions face a technical challenge to removing contaminants from the environment. Phytoremediation has emerged as a popular and economical plant-based technology for effectively addressing environmental issues. The process entails utilizing plants to extract and remove elemental pollutants or lower their bioavailability in soil or water. 49 In modern science, this technology is widely accepted due to its eco-friendliness, affordability, and high effectiveness. 50 Phytoremediation takes advantage of the unique and selective uptake capabilities of plant root systems, coupled with the translocation, bioaccumulation, and contaminant degradation abilities of the entire plant body. 51 Both aquatic and terrestrial plant species have been harnessed to eliminate pollutants from the environment. 52

An illustrative diagram elucidating bioremediation, highlighting the crucial roles of plants, bacteria, and fungi (Created with BioRender.com ).

More than 400 species have been identified as metal accumulators of Hg, Cd, Pb, Cr, As, and various radionuclides from contaminated soils ( Tables 1 ​ 1 ​ – 4 ). Arabidopsis sp. is well known for its metal tolerance and hyperaccumulation of Zn. 53 Aquatic plant species such as Azolla pinnata, Ceratophyllum demersum, Eichhornia crassipes , Lemna minor, Myriophyllum spicatum, Nasturtium officinale, Pistia stratiotes, Potamogeton pectinatus, Phragmites, Salvinia herzogii, Salvinia minima, Spirodela intermedia, Scirpus spp., and Typha latifolia , are of particular importance due to their high contaminant removal capacity. 54 - 58

Heavy metal accumulation in plants.

Heavy metal accumulation in aquatic plants.

Heavy metal accumulation in genetically modified plants.

Heavy metal accumulation in ornamental plants.

Mechanism of phytoremediation

Phytoremediation encompasses several processes, including phytoextraction, phytoaccumulation, phytovolatilization, phytostabilization, and phytotransformation ( Figure 3 ). 49 Phytoextraction is a technique that involves the absorption of organic and inorganic pollutants through the roots and stems. Besides, some particular plant species, like Brassica juncea, Cassia alata, Celosia argentea, Kummerowia striata, Helianthus annuus, Momordica charantia, Nicotiana tabacum, Salix mucronata, Salix viminalis, Solanum lycopersicum, Solanum melongena, Swietenia macrophylla, Pteris vittata , and Vigna unguiculata , have the potential to be used as suitable plant selections to enhance the phytoextraction process. 83 - 86 In phytostabilization, in this process, plants accumulate and immobilize heavy metals by binding with biomolecules. 87 Miscanthus giganteu s, Avena sativa , and Sinapis alba can also help to stabilize heavy metal compounds in the soil. 88 There are several processes by which plants can reduce contaminants.

A schematic diagram depicting the underlying mechanisms of phytoremediation processes (Created with BioRender.com ).

Phytoextraction

Phytoextraction, also called phytoaccumulation, involves the accumulation of heavy metals from earthland. In this method, the uptake and translocation of contaminants by plants root into the aerial portions of plants and deposited into vacuoles. The mechanism during the accumulation process is used to absorb and precipitate the toxic metals by metal-phytochelatin complex before translocating into the shoot, leaf and stem parts of the plant. The hyperaccumulator species accumulate a higher concentration of heavy metals. 89 , 90

Rhizofiltration

Rhizofiltration involves the elimination of heavy metals using plant roots. Though it is comparable to phytoextraction, in this process, plants remove contaminants from wastewater or groundwater rather than soil. In this process, plant roots assimilate or adsorb pollutants from wastewater, groundwater, or surface water. Generally, aquatic plant species are employed to eliminate pollutants through rhizofiltration. Rhizofiltration is effective for removing Cd, Pb, and Cr, which are primarily accumulated in the roots. Sunflower, tobacco, and spinach exhibit promising potential in removing Pb from water. 91

Phytostabilization

Plant roots can limit the movement of heavy metals by phytostabilization, a process that reduces toxic effects. This process involves the capture of contaminants on the root surface using transport proteins or secondary metabolites. Furthermore, the process involves the breakdown of complex organic molecules into simpler ones by coupling them with protein, amino acid, and sugar derivatives. Black nightshade, sunflower, and cowpea are among the plant species that employ phytostabilization mechanisms. 92

Phytovolatilization

This process entails the uptake of contaminants by plants from the soil and their conversion into less toxic volatile compounds that are released into the atmosphere. The volatile compounds are primarily released from aerial plant parts such as stems and leaves. This mechanism is effective when the contaminants are less toxic. 93

Phytodegradation

Phytotransformation, also known as this process, refers to the absorption of contaminants by plants, which are then metabolized or broken down into less toxic compounds and translocated to various plant organs. The organic compounds are then degraded into non-toxic forms inside the plant tissue. 94

Microbial-assisted remediation of heavy metal

Microbial remediation is the process of using living microorganisms such as bacteria, fungi, and archaea to break down and detoxify various chemical and metallic hazardous wastes from the environment. 95 Bioremediation involves the direct application of microorganisms to the polluted site in order to facilitate the degradation of contaminants. Microorganisms are used in a variety of remediation techniques, including bioaugmentation and biostimulation. In bioaugmentation, specific microorganisms are added to a contaminated site to enhance the breakdown of contaminants. In biostimulation, the environmental conditions at the site are modified to promote the growth and activity of naturally occurring microorganisms that can degrade contaminants. Physical and chemical treatments are conventional remediation methods that have drawbacks such as high cost, heavy machinery, logistical glitches, and potential environmental toxicity. 96 In contrast, bioremediation technologies have seen significant growth and development, making it a promising method for treating soil and water contamination ( Table 5 ). Among these methods, bioremediation of oil spills is the most lucrative and environment-friendly technique. 97

Bioremediation of heavy metal by microorganisms.

Fungi are used for the remediation of pollutants in mycoremediation, a type of bioremediation. Fungi play a vital role in cleaning up contaminated sites in both soil and aquatic ecosystems. 111 These microorganisms, which are widely present in nature, can thrive in a diverse range of environmental conditions. These microorganisms survive in extreme conditions and produce some extracellular ligninolytic enzymes like peroxidase and laccases. These enzymes help fungi to transform pollutants into non-toxic forms. Pollutants can be adsorbed by extracellular enzymes. 112 Diverse fungal species such as Aspergillus sp., Bjerkandera adusta, Coriolus versicolor, Cryptococcus sp. Hirschioporus laricinus, Inonotus hispidus, Mucor sp., Penicillium sp., Phanerochaete chrysosporium, Phlebia tremellosa, Phanerochaete chrysosporium, Pleurotus sp., and Trametes versicolor , have been reported for bioremediation. 113 , 114

Role of genetical engineering microbes in bioremediation

The potential of microbes for bioremediation is vast but unexploited. Genetically engineered organisms are the best way to enhance bioremediation activity. 115 , 116 Further research is required to formulate advanced bioremediation techniques in engineering that can effectively eliminate the complex mixtures of pollutants found at various sites. Several microbes use the contaminants as an energy source through their metabolic processes. Bacteria and fungi in the environment help to degrade or detoxify harmful substances. Modern science relies on biotechnology to facilitate the development of genetically modified organisms (GMOs), which can be instrumental in comparing them with their wild-type variant. GMOs possess the necessary protein machinery, which they utilize to uptake and regulate heavy metals through the implementation of gene regulatory elements such as promoters, binders, and terminators. These organisms produce a heavy metal binding protein that protects from toxicity by strongly binding to heavy metals ( Figure 4 ). Mesorhizobium huakuii strain B3, produces phytochelatin protein which accumulates Cd as reported by Sriprang et al 117 . Bae et al 118 reported that P. putida 06909 produced metal-binding peptide (MBP) EC-20 that has a high affinity for Cd. Al Hasin et al 119 found that Methylococcus capsulatus can remediate Cr (VI). Wagner-Döbler 120 demonstrated that recombinant bacteria allow detoxifying Hg 2+ to the non-toxic form of Hg0 through mercury reductase and subsequent release of Hg. The mechanism for detoxification of heavy metals is controlled by the mer operon gene that regulates transcription levels at both positive and negative. P. fluorescens HK44 was applied for large-scale field-based remediation of pollutants. 121 Patel et al 122 reported that the recombinant Caulobacter crescentus strain JS4022/p723-6H was able to eliminate Cd.

Illustrating the process of heavy metal degradation using genetically modified organisms (GMOs) (Created with BioRender.com ).

According to several researchers, certain microbes can remove heavy metals from their environment by either accumulating them or developing a tolerance toward them. There are several microorganisms, including Acinetobacter sp., Alcaligenes odorans , Aspergillus niger (fungus), Aspergillus versicolor , Bacillus subtilis , Corynebacterium propinquum , Fomitopsis pinicola , Microbacterium sp, Pseudomonas sp., P. putida , P. aeruginosa, Ralstonia sp., and Streptomyces , that play a role in removing heavy metals. 123 - 126

Effect of Heavy Metal on Human Health

Certain edible crops can accumulate heavy metals, even in very small amounts. When these heavy metals enter our food chain, they disrupt the food pyramid and pose a threat to human health by causing cancer and liver diseases. Vegetables such as brinjal, gourd, spinach, coriander, tomato and pumpkin are particularly susceptible to heavy metal uptake by their roots, which can then be transported to the edible portions of the plant. 127 , 128 As a result, consuming these vegetables that contain heavy metals can be extremely hazardous to human health. Alexander et al 129 carried out research involving vegetables cultivated in soil contaminated with heavy metals. Significant variations were observed among the vegetables in terms of the levels of metal accumulation. For Cd, lettuce exhibited a higher accumulation (8.6 mg/kg dry matter) compared to spinach (5.8 mg/kg dry matter), onion (3.6 mg/kg dry matter), carrot (2.0 mg/kg dry matter), pea (0.29 mg/kg dry matter), and French bean (0.07 mg/kg dry matter). Remarkably, lettuce recorded the highest concentration of Pb, nearly double that of onions, which held the second-highest average value. The sequence was as follows: lettuce (14.6 mg/kg dry matter) > onion (7.5 mg/kg dry matter) > carrot (5.8 mg/kg dry matter) > spinach (1.8 mg/kg dry matter) > pea (0.78 mg/kg dry matter) > French bean (0.34 mg/kg dry matter). A study conducted by Zhu et al, 63 revealed that the concentration of heavy metals in the edible parts of vegetables varied, with leafy vegetables having the highest amounts, followed by stalk vegetables, root vegetables, and solanaceous vegetables, and then legume vegetables and melon vegetables. Previous reports have also suggested that edible crops grown in industrial areas such as coal mines and petrochemical plants tend to contain higher levels of heavy metals. 130 Human exposure to heavy metals primarily occurs through the consumption of edible crops, which accounts for 90% of the exposure. The remaining 10% is attributed to the inhalation of polluted air particles as reported by Khan et al. 131

Excessive levels of heavy metals have the potential to pose harm to the body. They have the capacity to inflict damage on various organs such as the brain, muscles, nerves, liver, kidneys, and heart ( Figure 5 ). Previous studies have specified that heavy metals can impair different organs within the human body, as illustrated in Table 6 . The European Protection Agency (EPA) has reported that prolonged exposure to heavy metals can result in severe cancer. Research conducted by the WHO has shown that higher exposure to heavy metals puts 10% of women at risk of infertility. 132 , 133

Illustrating the health implications of exposure to heavy metals on human well-being (Created with BioRender.com).

Effects of heavy metal contamination on human well-being.

Hg, a highly toxic metal found in air, water, and soil, is considered to be highly carcinogenic by the EPA. Hg exposure can result in various health problems, including Alzheimer’s disease, lung damage, and skin issues such as the common ailment. 151 Acrodynia is a common skin ailment in which skin color becomes pink. 152 Similarly, Cd is a highly toxic metal that causes bone damage and acute exposure can lead to renal dysfunction, while prolonged exposure to high levels of Cd can result in lung damage. Heavy metals such as these can also induce DNA damage, cause chromosome aberrations, and alter DNA replication and transcription. 153 - 155 Exposure to Cr over a long period can result in the formation of ulcers. Human activities have resulted in the contamination of the environment with heavy metals, which can have adverse effects on human health. Excessive uptake of heavy metals poses a significant threat to human health. The entering of heavy metals into the human body can initiate cancer by the production of reactive oxygen species (ROS) which mainly disrupts DNA molecules. Heavy metals can cause damage to specific organs within the human body. In an animal model of acute toxicity, Wister rats exposed to 1 mg/kg of Hg caused alterations in their kidneys. A study reported that oral exposure to Hg in rats resulted in diarrhea. Additionally, scientists found that guinea pigs exposed to 0.1 to 0.4 M of Pb increased serum endothelial and serum total protein levels, along with lung infection. 156 Male adult rats exposed continuously to Pb (0.4%) exhibited a significant reduction in white blood cell count, as reported by Mugahi et al. 157 Furthermore, the administration of Pb (10 mg/kg) was observed to increase the levels of lactate dehydrogenase and acid phosphatase in rats. 158 In rats, Patlolla et al 159 demonstrated that the administration of 10 mg/kg of Cr increased the levels of ROS and malondialdehyde in the liver and kidney. High doses of Cr(VI) caused the immune system to reduce, resulting in the development of allergic contact dermatitis. 160 , 161 Fay et al 162 investigated Cd toxicity (0.6 mg/kg for 12 weeks) in the renal cortex of rats and found that Cd exposure significantly increased the volume of urine while decreasing the excretion of protein in urine. Cd toxicity can cause osteoporosis and bone fracture by increasing the dynamin-related protein, as demonstrated by Ma et al. 163 A close relationship between osteoporosis and high intake of Cd was also proven by Pouillot et al. 164

Recent technological advances have made bioremediation a more effective tool. This method is distinct and effective because it does not rely on chemicals or complex machinery. In the current study, bioremediation was shown to be a potential technique for resolving or reducing the negative effects of environmental contamination. Since it uses living entities to manage pollution, it cannot worsen the problem of heavy metal buildup or ozone depletion and is thought to be both environmentally friendly and economically effective, making it applicable to both emerging and developed nations globally. The results of the toxicity evaluation indicated that heavy metals constitute a substantial threat to living entities that are not specifically targeted. Therefore, funding ongoing research and innovation in bioremediation technologies is crucial for solving the 21st century’s expanding environmental issues.

Acknowledgments

Department of Biotechnology, The University of Burdwan, Burdwan, India, and the University of Limpopo’s Department of Plant Production, Soil Science and Agricultural Engineering, Green Biotechnologies Research Centre of Excellence, Private Bag X1106, Sovenga, 0727, South Africa, are gratefully acknowledged by the authors. Authors are sincerely acknowledged Tshepo S. Mashela, the University of Limpopo’s Department of Plant Production, Soil Science and Agricultural Engineering, Green Biotechnologies Research Centre of Excellence, Private Bag X1106, Sovenga, 0727, South Africa, for his invaluable assistance in manuscript preparation.

Funding: The author(s) received no financial support for the research, authorship, and/or publication of this article.

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

CRediT Authorship Contribution Statement: SD: Conceptualization, Methodology, Validation, Visualization, Writing- Original draft, KWS, MM: Visualization, ARN : Writing- review and editing, IC: Supervision.

Data Availability: The manuscript contains all the necessary data to support the findings of this study.

Ethical Approval: This article does not contain any studies with human participants or animals performed by any of the authors.

Evaluation of water pollution monitoring for heavy metal contamination: A case study of Agodi Reservoir, Oyo State, Nigeria

• Published: 16 August 2022
• Volume 194 , article number  675 , ( 2022 )

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• Ajibare A.O. 1 ,
• Ogungbile P.O. 2 &
• Ayeku P.O. 3  

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Heavy metals affect the suitability of aquatic environment for all purposes; hence, this study evaluated heavy metal contamination in Agodi Reservoir Oyo State, Nigeria. Atomic Absorption Spectrophotometry was used to determine heavy metal concentrations. Heavy Metal Pollution Index ( HPI ), Water Quality Index ( WQI ), Pollution Index ( PI ), Comprehensive Pollution Index ( CPI ), Contamination Index ( CI ), Single-Factor Pollution Index ( SFPI ), Heavy metal Evaluation Index ( HEI ), and Human Health Risk Assessments ( HRA ) were used to determine the extent of heavy metal pollution and their impact on the aquatic environment. The order of heavy metal concentrations in both wet and dry seasons was Fe > Mn > Zn > Cu > Cd > Ni > Cr > Pb > Co and Mn > Fe > Zn > Cu > Co > Ni > Cd > Cr > Pb, respectively. WQI for both wet (3182.6) and dry (3649.5) seasons classified the reservoir as “unsuitable for aquatic life.” Also, the CPI rated the reservoir to be “severely polluted” in both dry (311.2) and wet (268.7) seasons. Similarly, HEI (951.3 and 2059.7) and Cd (942.3 and 2050.7) rated the reservoir as “highly polluted” in wet and dry seasons, respectively. The Hazard Quotient (HQ ingestion) was in the order of Mn > Cu > Cd > Zn > Fe > Co > Ni > Cr > Pb in the dry season while the order was Cu > Mn > Cd > Fe > Zn > Ni > Pb > Co > Cr in the wet season. The HQ ingestion revealed that Cr (0.00), Ni (0.33; 022), and Pb (0.00; 0.06) were the only metals with HQ values lesser than 1 (HQ < 1) while the values of HQ (dermal) were less 1 (HQ < 1) indicating that there was no health risk in association with the domestic use of the water. The pollution level of the reservoir means that urgent attention is needed from different agencies for the conservation, management, and sustainable development of the reservoir.

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Ajibare A.O.

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A.O., A., P.O., O. & P.O., A. Evaluation of water pollution monitoring for heavy metal contamination: A case study of Agodi Reservoir, Oyo State, Nigeria. Environ Monit Assess 194 , 675 (2022). https://doi.org/10.1007/s10661-022-10326-y

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Received : 02 February 2022

Accepted : 25 July 2022

Published : 16 August 2022

DOI : https://doi.org/10.1007/s10661-022-10326-y

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