Groundwater contamination is a major environmental concern in many regions of India, including several districts of Punjab. In this study, a comparison is carried out between the deterministic and probabilistic approaches for calculating health hazard parameters due to arsenic contamination in groundwater in India. The probabilistic calculations are carried out through Monte-Carlo simulations to quantify the hazard index (HI) and carcinogenic risk. Sensitivity analysis is carried out to determine the contribution of input variables to the HI. From the deterministic and probabilistic approach, an HI greater than one is obtained in adults for all districts except Moga, whereas, for children, the HI is greater than 1 in all districts. This suggests a higher probability of risks, such as developmental problems and cardiovascular disorders. Excess lifetime carcinogenic risk, a measure of carcinogenic risk, is found above the US Environmental Protection Agency's recommended range of 1 × 10−6 to 1 × 10−4 in all districts. These values clearly indicate the long-term carcinogenic danger associated with arsenic exposure since they are far above the tolerable risk threshold of 1 × 10–4. Thus, necessary mitigation measures should be taken, and routine groundwater monitoring should be performed.

  • Health impact.

  • Risk assessment.

  • Water contamination.

  • Population health risk.

  • Monte-Carlo simulations.

Arsenic (As), characterized by its atomic number 33, is a widespread element that exists ubiquitously in the environment, seamlessly integrating into air, water, soil, and rocks (Ali et al. 2019). In developing countries, most of the general population consumes arsenic-contaminated drinking water several times higher than the World Health Organization's (WHO) acceptable limit of 10 μg L−1 (World Health Organization 1996; Hassan et al. 2009). According to numerous reports, over 100 million population globally have been affected by arsenic concentrations of greater than 10 μg L−1 (Rahman et al. 2005; Nriagu et al. 2007). The acceptable limit of arsenic in groundwater is set at 50 μg L−1 in many developing countries (Nickson et al. 2005), and 42 million people are exposed to more than 50 μg L−1 arsenic concentration in potable water. Due to arsenic's high toxicity, it can have adverse health effects even at low exposure levels, including dryness of the throat and mouth, dysphasia, profuse diarrhea, vomiting, melanosis, gastrointestinal distress, immune system suppression, superficial as well as internal cancers, and cardiovascular and neurological effects (Murphy et al. 1981; Wu et al. 1989; Chiou et al. 1995; Mandal et al. 1996; Knobeloch & Zierold 2006). It is a common metalloid prevalent in igneous and sedimentary rocks on the surface of the Earth's crust (Singh et al. 2010). In addition, human activities can discharge high contents of arsenic into the environment (Saha et al. 2017; Senila et al. 2017). There are four oxidation states of arsenic found in the environment: elemental arsenic (0), arsenate (+5), arsenite (+3), and arsenic (−3) (Ammann 2011). Arsenic (+5) exists in four different forms in aqueous solution, depending on the pH: H3AsO4, H2AsO4, HAsO42−, and AsO43−. Arsenic (+3) exists in five different forms in an aqueous solution: H4AsO3+, H3AsO3, H2AsO3, HAsO3, and AsO33−. The solubility of arsenic salts depends on pH, but elemental arsenic is insoluble in water (Edwards et al. 1998). Inorganic arsenic compounds contain trivalent and pentavalent arsenic; trivalent arsenic is more toxic and is four to ten times more soluble in water than pentavalent arsenic (Tuzen et al. 2009; Brahman et al. 2013). Analysis of the valency and speciation of soluble arsenic can significantly help in improving the preparation of an arsenic removal strategy. The most prevalent species of arsenic is arsenite, whereas arsenate is stable in oxygenated aquatic environments. Arsenic, in its elemental form, is redox-sensitive. Numerous geochemical parameters, including pH levels, reduction–oxidation processes, aquatic chemistry, distribution of other ionic species, and microbial activity, combine to determine its presence, mobility, distribution, and forms. Groundwater is more susceptible to arsenic contamination than surface water because anoxic conditions in subsurface environments increase arsenic's mobility (Shih 2005).

Groundwater in Argentina, Hungary, Mexico, and various regions of the United States is heavily polluted by arsenic (Thakur & Semil 2013). According to several studies (Mandal & Suzuki 2002; Smedley & Kinniburgh 2002; Agusa et al. 2004; Stanger et al. 2005; Sengupta et al. 2006; Yadav et al. 2014), there is a concerning danger of arsenic pollution in Southeast Asian nations, including India, Bangladesh, China, Nepal, and Vietnam. According to Nriagu et al. (2002), thermal springs and mining operations are important sources of arsenic release into groundwater under oxidizing circumstances. The highest concentration of arsenic in Canada has been found to be roughly 100 mg L−1 in groundwater (Nriagu et al. 2002). Australia has underground water significantly contaminated with concentrations of arsenic in groundwater that have been reported as high as 300 mg L−1 (Nriagu et al. 2002). Mining operations, oxidation of minerals containing sulfides, and volcanic eruptions have all been identified as major causes of arsenic release into groundwater (Boyle et al. 1998). In developing nations including India, groundwater contamination with arsenic is one of the most significant and hazardous environmental problems (Environmental Protection Agency 2001; Prakash & Verma 2021). Kumar et al. (2016) and Shaikh et al. (2016) reported 84.28 μg L−1 arsenic concentration in the groundwater of Bihar state. In Jharkhand, Alam et al. (Singh 2004; Alam et al. 2016) reported the arsenic concentration higher than 10 μg L−1. Sharma et al. (2013) reported that arsenic contamination is greater than 10 μg L−1 in groundwater in the eastern part of India. In the Indian state of Punjab, groundwater is an essential source of drinking water and is used in all aspects of human life, including industries, agriculture, and residences (Gupta 2009). About 95% of the people in Punjab get their drinking water from groundwater (Hundal et al. 2009).

The primary source of arsenic in groundwater is the dissolution of minerals from weathered rocks and soils. In 1967, Punjab had just about 50,000 tube wells, but in 2008, this number surpassed one million (Hundal et al. 2009). In the previous three decades, groundwater extraction has increased by 200 times in this state.

In this paper, a comparative study of both approaches for the risk assessment due to contamination in groundwater of Moga, Faridkot, Fazilka, Patiala, Ferozepur, Rupnagar, and Amritsar districts of Punjab is performed. These regions are chosen based on their geographic locations and the increasing rate of cancer-related mortality due to the intake of water of inferior quality. The dependance of health risk parameters on the input variables is determined using the sensitivity analysis. The concentration of arsenic in these districts is reported in the literature (Central Ground Water Board 2013; Kumar et al. 2017; Sidhu et al. 2018; Virk 2019). In these studies, health risk assessment due to arsenic contamination is not carried out.

In the previous studies, the deterministic approach was used for the human health risk assessment due to arsenic contamination in groundwater. This method considers the site-specific data to estimate the risk of non-carcinogenic adverse health effects in adults and children, but it does not address the inherent uncertainty of input parameters. There are various variables in calculations of health risk for which assuming a point distribution might not incorporate the inherent variability in the input parameters used for health risk assessment. In order to avoid the uncertainty associated with the deterministic methodology, Monte-Carlo simulations were carried out for the non-carcinogenic as well as carcinogenic risk assessment in adults and children due to the presence of arsenic in groundwater.

Study area

Punjab is a state in India from 29.30° N to 32.32°N and 73.55° E to 76.50° E. The occupied area of this state is 50,476 km2. Unconsolidated alluvium covers almost an entire Punjab area. The climate of Punjab affects its hydrogeology. Groundwater availability and recharge are affected by the state's varying rainfall patterns. The rivers Beas, Ravi, and Sultej, and the seasonal Ghaggar flow through the state, and the average annual precipitation ranges from 400 to 1,300 mm. A comprehensive network of canals supports the state's irrigation system. Geographically, Punjab state is segregated into three different regions: Majha, which lies between the rivers Ravi and Beas; Doaba, which lies between the rivers Sutlej and Beas; and Malwa, which is located south of the Sutlej. The mean value of arsenic concentration in groundwater in the chosen districts of Punjab is is listed in Table 1. Moga, Faridkot, Fazilka, Patiala, and Ferozepur districts are in the Malwa region, Rupnagar is located in the Doaba region and Amritsar is in the Majha region of Punjab. The locations of the studied districts are shown in Figure 1.
Table 1

Mean value of arsenic concentration in groundwater in the studied regions (Central Ground Water Board 2013; Kumar et al. 2017; Sidhu et al. 2018; Virk 2019)

DistrictsMean value of arsenic concentration in groundwater (mg L−1)
Moga 0.0043 
Faridkot 0.017 
Fazilka 0.037 
Patiala 0.021 
Ferozepur 0.027 
Rupnagar 0.035 
Amritsar 0.083 
DistrictsMean value of arsenic concentration in groundwater (mg L−1)
Moga 0.0043 
Faridkot 0.017 
Fazilka 0.037 
Patiala 0.021 
Ferozepur 0.027 
Rupnagar 0.035 
Amritsar 0.083 
Figure 1

Location of districts of Punjab for which health risk assessment is carried out.

Figure 1

Location of districts of Punjab for which health risk assessment is carried out.

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Sample collection and technique

In order to evaluate the arsenic level in groundwater, Mandeep Sindhu et al. collected water samples using hand pumps from several locations in the Moga and Faridkot districts of Punjab (Sidhu et al. 2018). The author collected a total of 40 groundwater samples from each district using a random sampling approach. For a comprehensive coverage of the region, these samples were obtained from several hand pumps. After collection, 1 milliliter (mL) of hydrochloric acid was added to each sample for preservation after measuring the pH of the sample. Each water sample was tested by pouring 100 mL into a glass beaker, and 2 mL of concentrated nitric acid (HNO3) was added. The combination was brought to a temperature of 90–95 °C in a water bath, covered with a watch glass, and left there until the liquid evaporated to the extent of 20 mL. After the solution cooled, any insoluble minerals or silicates in the sample were removed by filtering it using the Whatman filter paper (grade I). The samples were prepared for analysis after adding reagent water to get the volume back up to 100 mL. After that, the arsenic concentration of the water was determined using the inductive coupled argon plasma–atomic emission spectroscopy (ICAP–AES). To analyze the level of arsenic contamination in groundwater samples from the Fazilka, the digested filtrates were produced using a diethylenetriamine penta acetic acid extraction procedure (Virk 2019). After being produced, the quantity of arsenic in these filtrates was determined by using an inductively coupled plasma–AES (Thermoelectric ICAP-6300 model). Groundwater samples were taken using a random sampling technique in Patiala, Ferozepur, Rupnagar, and Amritsar. To avoid contamination, HNO3 was used to clean the polyethylene plastic bottles before the sample collection. The bottles were then given a final washing with distilled water prior to the collection of 1,000 mL of flowing groundwater. Three milliliters of pure nitric acid of analytical grade were added to each sample. The arsenic content of the groundwater was measured using a graphite furnace–atomic absorption spectrometer, more precisely the 240 FS AA model from Agilent Technologies, Santa Clara, California, USA. Before analyzing the samples, the spectrometer was carefully calibrated, and a blank run was performed to ensure accuracy in the readings (Central Ground Water Board). The measured concentration of arsenic in groundwater in all districts is above the permissible limit of 0.01 mg/L.

Non-carcinogenic risk assessment

Human health risks are quantified using the United States Environmental Protection Agency's (USEPA) approved health risk model. A quantitative evaluation of the impact of environmental pollution on human health is known as a health risk assessment. In the present study, the non-cancer risk due to arsenic contamination in groundwater is calculated by evaluating the hazard quotient (HQ) considering ingestion and dermal routes.

The average daily dose for ingestion and dermal is calculated for adults and children by using the following equations, respectively.
(1)
(2)
where ADDing is the average daily dose for ingestion (mg kg−1 day−1), ADDdermal is the average daily dose due to dermal contact with (mg kg−1 day−1), C is the concentration of arsenic in the water sample (mg L−1), IR is the daily ingestion rate of water (L day−1), EF is the exposure frequency (day year−1), ED is the exposure duration (years), BW is the body weight (kg), AT is the average time (days), SA is the exposed skin surface area (m2), CF is the conversion factor (L-m/cm-m3). For the deterministic and probabilistic calculations, the values of these parameters are listed in Table 2, respectively. The HQ for ingestion and dermal is calculated from the following equations, respectively.
(3)
(4)
where RfDing and RfDdermal are the reference doses (mg kg−1 day−1) of arsenic for ingestion and dermal, respectively. Reference doses are limited up to which intake of chemical doses does not pose a significant chemical risk. The non-cancer risk associated with arsenic ingestion and dermal contact with arsenic is determined through the hazard index (HI). The HI is calculated by the sum of the HQ of ingestion and dermal exposure, expressed as follows:
(5)
Table 2

The model parameters assumed in the deterministic approach for the calculation of HI in adults and children (USEPA 1989, 1996, 2004, 2007; Sharma et al. 2013)

Deterministic approach
Probabilistic approach
Input parametersValues (units)Distribution typeValues (units)
Concentration of arsenic (CMean (mg/L) Lognormal Metal specific (mg L−1
IR adult 2 (L day−1Lognormal 1.26 ± 0.66 (L day−1
IR children 1.8 (L day−1Lognormal 1.26 ± 0.66 (L day−1
EF 365 (day year−1Triangular 345 (180–365) (day year−1
ED adult 70 (years) Point 70 (years) 
ED children 6 (years) Point 6 (years) 
Exposure time (ET) 0.58 (h day−1Triangular 0.20 (0.13–0.33) (h day−1
BW adult 70 (kg) Lognormal 77.1 ± 31.5 (kg) 
BW children 15 (kg) Triangular 26.1 (6.5–15) (kg) 
AT adult 25,550 (days) Point 25,550 (days) 
AT children 2,190 (days) Point 2,190 (days) 
Permeability constant (Kp0.001 (cm h−1Point 0.001 (cm h−1
Exposed skin SA adult 1.8 (m2Lognormal 1.42 ± 0.31 (m2
Exposed skin SA children 0.6 (m2Triangular 0.6800 ± 0.600 (m2
CF 10 Point 10 (L-m) (m3-cm) 
Reference dose (RfDing) ingestion 0.0003 (mg kg−1 day−1Point 0.0003 (mg kg−1 day−1
Reference dose (RfDdermal) dermal 0.000285 (mg kg−1 day−1Point 0.000285 (mg kg−1 day−1
Deterministic approach
Probabilistic approach
Input parametersValues (units)Distribution typeValues (units)
Concentration of arsenic (CMean (mg/L) Lognormal Metal specific (mg L−1
IR adult 2 (L day−1Lognormal 1.26 ± 0.66 (L day−1
IR children 1.8 (L day−1Lognormal 1.26 ± 0.66 (L day−1
EF 365 (day year−1Triangular 345 (180–365) (day year−1
ED adult 70 (years) Point 70 (years) 
ED children 6 (years) Point 6 (years) 
Exposure time (ET) 0.58 (h day−1Triangular 0.20 (0.13–0.33) (h day−1
BW adult 70 (kg) Lognormal 77.1 ± 31.5 (kg) 
BW children 15 (kg) Triangular 26.1 (6.5–15) (kg) 
AT adult 25,550 (days) Point 25,550 (days) 
AT children 2,190 (days) Point 2,190 (days) 
Permeability constant (Kp0.001 (cm h−1Point 0.001 (cm h−1
Exposed skin SA adult 1.8 (m2Lognormal 1.42 ± 0.31 (m2
Exposed skin SA children 0.6 (m2Triangular 0.6800 ± 0.600 (m2
CF 10 Point 10 (L-m) (m3-cm) 
Reference dose (RfDing) ingestion 0.0003 (mg kg−1 day−1Point 0.0003 (mg kg−1 day−1
Reference dose (RfDdermal) dermal 0.000285 (mg kg−1 day−1Point 0.000285 (mg kg−1 day−1

An HI of more than one indicates that there may be adverse effects on human health, emphasizing the need for careful consideration and possible mitigating actions.

Excess lifetime carcinogenic risk

The excess lifetime carcinogenic risk (ELCR) has been determined by using the average daily dose and cancer slope factor (CSF). The cancer-causing threat posed by oral ingestion of carcinogens is calculated from the following equation.
(6)

CSF is a heavy metal-dependent factor based on actual studies demonstrating the adverse health effects of specific carcinogenic pollutants. According to the USEPA recommendation, for arsenic, the CSF is 1.5 mg kg−1 day−1 for the ingestion pathway.

The permissible range of carcinogenic risk is between 10−6 and 10−4, whereas the maximum acceptable level recommended by the WHO is 10−4.

Deterministic approach

A deterministic approach is used when limited data are available or when the system being studied is well understood. In the deterministic method, the health risk is assessed from the known parameters, and it focuses on assessing the impact of single-value risk. This method considers the site-specific data to estimate the risk of carcinogenic and non-carcinogenic elements on human health in adults and children, but there is inherent uncertainty and variability in the parameters used in the calculations. To calculate a single-point estimate of risk, the various parameters used in calculations are listed in Table 2.

Probabilistic approach (Monte-Carlo simulations)

Evaluation of potential risks to human health is inherently ambiguous due to individual human characteristics and environmental variables. A more realistic approach (Monte-Carlo simulations) to risk calculation can be achieved by addressing issues of variability and uncertainty. It is used to assess the risks of carcinogenic and non-carcinogenic risks in adults and children due to direct exposure to contaminated water via ingestion and dermal pathways. To accomplish this, Monte-Carlo simulations are used to estimate the non-carcinogenic and carcinogenic risks based on their probability distributions of various parameters. In this method, input parameters are defined as probability distributions resulting in the average daily dose and the HI. The value of each parameter is randomly chosen from the probability distribution function as given in Table 2. To test the reliability of the results, various independent runs were conducted with 10,000 iterations to estimate the non-cancer risk. The results of non-carcinogenic and carcinogenic risks are given in Table 4. The non-carcinogenic risk falls within an acceptable range if their HI value is less than 1.

Non-carcinogenic risk assessment

The analysis of groundwater pollution by arsenic in Punjab's districts shows significant regional variation. Amritsar has the highest arsenic content, whereas Moga has the lowest. All districts except Moga have arsenic levels over the WHO-recommended 0.01 mg/L threshold. A previous study on groundwater contamination due to arsenic exposure in the area affirms these results (Table 3). The deterministic approach is used to calculate the HI for adults and children in the seven districts (Table 4). The findings demonstrate that children are more vulnerable than adults. In Moga, Faridkot, Fazilka, Patiala, Ferozepur, Rupnagar, and Amritsar, the corresponding HI values for adults are 0.41, 1.62, 3.54, 2.01, 2.58, 3.35, and 7.94. The equivalent values for children are much higher: 1.72, 6.83, 14.87, 8.44, 10.85, 14.07, and 33.26. According to the deterministic analysis, children are more at risk for non-carcinogenic diseases due to arsenic exposure; in all districts, including Moga, the HI value is more significant than 1 for children. On the other hand, adults in Moga have the HI values of less than 1, indicating no risk. All other districts, however, surpass the threshold, with Amritsar's adults being at the highest risk. The elevated HI values in children highlight their heightened vulnerability, especially in areas like Fazilka, Ferozepur, and Amritsar, where the HI levels surpass 10, indicating severe health issues such as cardiovascular disease, skin lesions, and cancer.

Table 3

Arsenic concentration in groundwater of the study area to India and world

Study areaConcentration (mg L−1)Reference
Moga 0.0043 Present study 
Faridkot 0.017 
Fazilka 0.037 
Patiala 0.021 
Ferozepur 0.027 
Rupnagar 0.035 
Amritsar 0.083 
Ropar 0.011 Mandal et al. (1996)  
West Bengal 0.025 Yadav et al. (2020)  
Fatehabad 0.050 to 4.531 Vige et al. (2023)  
Jharkhand 0.082 Giri & Singh (2015)  
Iran 0.012 Barzegar et al. (2019)  
China 0.002–0.188 Zhaoyong et al. (2018)  
Vietnam 0.211–0.348 Nguyen et al. (2009)  
Pakistan 0.107 Mushtaq et al. (2020)  
Srilanka 0.34 Bandara et al. (2018)  
Taiwan 0.975 Maity et al. (2019)  
Qatar 0.15 Da'ana et al. (2021)  
Mexico 0.073 Ruj et al. (2021)  
USA 0.150 Tsuji et al. (2019)  
Study areaConcentration (mg L−1)Reference
Moga 0.0043 Present study 
Faridkot 0.017 
Fazilka 0.037 
Patiala 0.021 
Ferozepur 0.027 
Rupnagar 0.035 
Amritsar 0.083 
Ropar 0.011 Mandal et al. (1996)  
West Bengal 0.025 Yadav et al. (2020)  
Fatehabad 0.050 to 4.531 Vige et al. (2023)  
Jharkhand 0.082 Giri & Singh (2015)  
Iran 0.012 Barzegar et al. (2019)  
China 0.002–0.188 Zhaoyong et al. (2018)  
Vietnam 0.211–0.348 Nguyen et al. (2009)  
Pakistan 0.107 Mushtaq et al. (2020)  
Srilanka 0.34 Bandara et al. (2018)  
Taiwan 0.975 Maity et al. (2019)  
Qatar 0.15 Da'ana et al. (2021)  
Mexico 0.073 Ruj et al. (2021)  
USA 0.150 Tsuji et al. (2019)  
Table 4

From the deterministic approach, the HI and carcinogenic risk values for adults and children

DistrictsHI (adults)HI (children)Carcinogenic risk (adults)Carcinogenic risk (children)
Moga 0.41 1.72 1.84 × 10−4 7.74 × 10−4 
Faridkot 1.62 6.83 7.29 × 10−4 3.06 × 10−3 
Fazilka 3.54 14.87 1.59 × 10−3 6.66 × 10−3 
Patiala 2.01 8.44 9.01 × 10−4 3.78 × 10−3 
Ferozepur 2.58 10.85 1.16 × 10−3 4.80 × 10−3 
Rupnagar 3.35 14.07 1.50 × 10−3 6.30 × 10−3 
Amritsar 7.94 33.26 3.50 × 10−3 1.40 × 10−2 
DistrictsHI (adults)HI (children)Carcinogenic risk (adults)Carcinogenic risk (children)
Moga 0.41 1.72 1.84 × 10−4 7.74 × 10−4 
Faridkot 1.62 6.83 7.29 × 10−4 3.06 × 10−3 
Fazilka 3.54 14.87 1.59 × 10−3 6.66 × 10−3 
Patiala 2.01 8.44 9.01 × 10−4 3.78 × 10−3 
Ferozepur 2.58 10.85 1.16 × 10−3 4.80 × 10−3 
Rupnagar 3.35 14.07 1.50 × 10−3 6.30 × 10−3 
Amritsar 7.94 33.26 3.50 × 10−3 1.40 × 10−2 

The probabilistic approach used the 95th percentile values to consider variability and avoid overestimation, and the HI values thus calculated are as listed in Table 5. The findings demonstrate that non-carcinogenic hazards are significantly high. In Moga, Faridkot, Fazilka, Patiala, Ferozepur, Rupnagar, and Amritsar, the HI values at the 95th percentile for adults are 0.78, 2.70, 4.78, 2.76, 3.45, 6.97, and 10.32, respectively. The corresponding values for children are 3.53, 11.42, 20.93, 11.80, 14.47, 30.44, and 44.73, respectively. This supports the idea that exposure to arsenic presents serious health hazards, particularly for children. Additionally, district-specific risk exposure patterns are shown using the probabilistic estimation. 94% of adults and 98% of children in Amritsar have an HI of more than 1, and more than 87% of people have an HI of 2, which indicates possible severe health problems. There are also alarming trends in other areas, such as Rupnagar, Fazilka, and Ferozepur, especially among children, where more than 70% of the child population has HI values of more than 1. The detailed results showing various percentiles for all districts from Monte-Carlo simulations are shown from Figures 2 to 8. These results demonstrate the concerning health effects of groundwater consumption-related arsenic exposure, particularly for children, who are disproportionately more vulnerable in all districts except Moga. A large population may have negative health impacts in areas with the greatest arsenic contamination such as Amritsar. This highlights how urgently targeted actions are needed to reduce arsenic exposure, especially in areas where most children have the HI values much higher than 2.
Table 5

From the probabilistic approach, HI, and carcinogenic risk values for adults and children

Districts95th percentile of the HI (adults)95th percentile of cancer risk (adult)95th percentile of the HI (children)95th percentile of cancer risk (children)
Moga 0.78 3.51 × 10−4 3.53 1.58 × 10−3 
Faridkot 2.70 1.21 × 10−3 11.42 5.14 × 10−3 
Fazilka 4.78 2.15 × 10−3 20.93 9.41 × 10−3 
Patiala 2.76 1.24 × 10−3 11.80 5.30 × 10−3 
Ferozepur 3.45 1.46 × 10−3 14.47 6.51 × 10−3 
Rupnagar 6.97 3.10 × 10−3 30.44 1.36 × 10−2 
Amritsar 10.32 4.60 × 10−3 44.73 2.01 × 10−2 
Districts95th percentile of the HI (adults)95th percentile of cancer risk (adult)95th percentile of the HI (children)95th percentile of cancer risk (children)
Moga 0.78 3.51 × 10−4 3.53 1.58 × 10−3 
Faridkot 2.70 1.21 × 10−3 11.42 5.14 × 10−3 
Fazilka 4.78 2.15 × 10−3 20.93 9.41 × 10−3 
Patiala 2.76 1.24 × 10−3 11.80 5.30 × 10−3 
Ferozepur 3.45 1.46 × 10−3 14.47 6.51 × 10−3 
Rupnagar 6.97 3.10 × 10−3 30.44 1.36 × 10−2 
Amritsar 10.32 4.60 × 10−3 44.73 2.01 × 10−2 
Figure 2

Probabilistic estimates of the HI for (a) adults and (b) children in Moga district.

Figure 2

Probabilistic estimates of the HI for (a) adults and (b) children in Moga district.

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Figure 3

Probabilistic estimates of the HI for (a) adults and (b) children in Faridkot district.

Figure 3

Probabilistic estimates of the HI for (a) adults and (b) children in Faridkot district.

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Figure 4

Probabilistic estimates of the HI for (a) adults and (b) children in Fazilka district.

Figure 4

Probabilistic estimates of the HI for (a) adults and (b) children in Fazilka district.

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Figure 5

Probabilistic estimates of the HI for (a) adults and (b) children in Patiala district.

Figure 5

Probabilistic estimates of the HI for (a) adults and (b) children in Patiala district.

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Figure 6

Probabilistic estimates of the HI for (a) adults and (b) children in Ferozepur district.

Figure 6

Probabilistic estimates of the HI for (a) adults and (b) children in Ferozepur district.

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Figure 7

Probabilistic estimates of the HI for (a) adults and (b) children in Rupnagar district.

Figure 7

Probabilistic estimates of the HI for (a) adults and (b) children in Rupnagar district.

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Figure 8

Probabilistic estimates of the HI for (a) adults and (b) children in Amritsar district.

Figure 8

Probabilistic estimates of the HI for (a) adults and (b) children in Amritsar district.

Close modal

Carcinogenic risk assessment

Long-term exposure to contaminated groundwater raises the risk of acquiring cancer since arsenic is a proven carcinogen. The ELCR was assessed using probabilistic and deterministic approaches in each of the studied districts. According to the deterministic analysis, the cancer risk for adults in the Moga, Faridkot, Fazilka, Patiala, Ferozepur, Rupnagar, and Amritsar are 1.84 × 10⁻4, 7.29 × 10⁻4, 1.59 × 10⁻3, 9.01 × 10⁻4, 1.16 × 10⁻3, 1.50 × 10⁻3, and 3.50 × 10⁻3, respectively. The ELCR for children are significantly higher: 7.74 × 10⁻4, 3.06 × 10⁻3, 6.66 × 10⁻3, 3.78 × 10⁻3, 4.80 × 10⁻3, 6.30 × 10⁻3, and 1.40 × 10⁻2, in the same order. Particularly in Fazilka, Rupnagar, and Amritsar, the values in several districts above the WHO's threshold limit of tolerable cancer risk (1 × 10⁻4). The cancer risk for adults in Moga, Faridkot, Fazilka, Patiala, Ferozepur, Rupnagar, and Amritsar is estimated using the 95th percentile probabilistic approach to be 3.51 × 10⁻4, 1.21 × 10⁻3, 2.15 × 10⁻3, 1.24 × 10⁻3, 1.46 × 10⁻3, 3.10 × 10⁻3, and 4.60 × 10⁻3, respectively. 1.58 × 10⁻3, 5.14 × 10⁻3, 9.41 × 10⁻3, 5.30 × 10⁻3, 6.51 × 10⁻3, 1.36 × 10⁻2, and 2.01 × 10⁻2 are the similar values for children. More than 10 children out of every 1,000 might get cancer from arsenic exposure in Rupnagar and Amritsar, where the cancer risk for children is more than 1.36 × 10⁻2 and 2.01 × 10⁻2, respectively. These findings highlight the seriousness of the carcinogenic hazards associated with arsenic exposure, especially for children. Long-term exposure to tainted groundwater may raise the incidence of cancer in these areas, which has serious public health ramifications. Focused remediation techniques and public health initiatives are essential to address this issue effectively.

Limitations and future considerations

While this study provides critical insights into the health risks posed by arsenic contamination in groundwater, limitations are acknowledged. This study only looks at arsenic, leaving out other potentially dangerous pollutants, including nickel (Ni), lead (Pb), and iron (Fe), which are often co-contaminants in groundwater. A more thorough examination that considers a number of pollutants should be included in future research. To increase the accuracy of risk evaluations, long-term studies with more detailed exposure data should be performed. Furthermore, a more comprehensive knowledge of the health effects of arsenic exposure in Punjab could be achieved by diversifying the research to include other areas and using data from continuous monitoring.

Sensitivity analysis

Sensitivity analysis is used to determine the predictor factors' contribution to the prediction. It serves as an example of the most crucial elements that affect the assessment's outcome. The study included a sensitivity analysis of the non-carcinogenic risk simulation parameters. The findings are plotted as tornado plots that display the Spearman rank-order correlation coefficients on a decimal scale. The sensitivity analysis of variables in calculating HI for two exposed groups, adults and children, in the different given districts is shown in Figures 915. The sensitivity analysis findings indicated that the arsenic concentration parameter had the most impact on increasing sensitivity in the two exposed groups under study. Consequently, mitigation strategies should focus on decreasing arsenic concentration in drinking water, which will probably reduce health risks. The concentration of arsenic in the groundwater is the most significant variable for these two groups of the population, with the Spearman rank-order correlation coefficient being 0.831 and 0.847 in Moga, 0.786 and 0.839 in Faridkot, 0.680 and 0.733 in Fazilka, 0.647 and 0.722 in Patiala, 0.693 and 0.788 in Ferozepur, 0.871 and 0.895 in Rupnagar, and 0.739 and 0.812 in Amritsar for adult and child populations, respectively. The health risk is found to be least sensitive to ED. As expected, BW has a negative correlation with health risks.
Figure 9

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Moga district.

Figure 9

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Moga district.

Close modal
Figure 10

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Faridkot district.

Figure 10

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Faridkot district.

Close modal
Figure 11

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Fazilka district.

Figure 11

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Fazilka district.

Close modal
Figure 12

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Patiala district.

Figure 12

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Patiala district.

Close modal
Figure 13

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Ferozepur district.

Figure 13

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Ferozepur district.

Close modal
Figure 14

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Rupnagar district.

Figure 14

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Rupnagar district.

Close modal
Figure 15

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Amritsar district.

Figure 15

Sensitivity analysis of the non-carcinogenic risk of (a) adults and (b) children in the Amritsar district.

Close modal

This study highlights the significant health concerns associated with arsenic-contaminated groundwater in Punjab's seven districts, where the majority of the population, especially children, is at higher risk for both non-carcinogenic and carcinogenic diseases. For children in every district, the HI values for non-carcinogenic hazards are more than one, suggesting possible health problems such as neurological damage, cardiovascular illnesses, and developmental delays. Moreover, high ELCR indicates substantial long-term cancer risks, with certain districts such as Amritsar, Rupnagar, and Ferozepur exhibiting the greatest risk of developing cancer as a result of arsenic exposure. Monte-Carlo simulations have revealed the variability in exposure and risk, helping to provide a more nuanced risk assessment that highlights the disproportionate vulnerability of children. These results highlight how urgent arsenic mitigation measures, including better water treatment, public health campaigns, and continuous groundwater quality monitoring, are needed. In order to protect the health of the impacted communities and reduce exposure levels, particularly for pediatric populations, immediate action is essential. The study's findings provide authorities with a solid basis on which to build their priorities for reducing arsenic and enhancing public health in Punjab's impacted areas.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

Agusa
T.
,
Kunito
T.
,
Fujihara
J.
,
Kubota
R.
,
Minh
T. B.
,
Trang
K.
,
Subramanian
A.
,
Iwata
H.
,
Viet
P. H.
&
Tanabe
S.
(
2004
)
Contamination by trace elements in groundwater of Vietnam
,
Biomedical Research on Trace Elements
,
15
,
339
341
.
Alam
M. O.
,
Shaikh
W. A.
,
Chakraborty
S.
,
Avishek
K.
&
Bhattacharya
T.
(
2016
)
Groundwater arsenic contamination and potential health risk assessment of Gangetic Plains of Jharkhand, Indi
,
Exposure and Health
,
8
,
125
-
142
.
Bandara
U. G. C.
,
Diyabalanage
S.
,
Hanke
C.
,
van Geldern
R.
,
Barth
J. A.
&
Chandrajith
R.
(
2018
)
Arsenic-rich shallow groundwater in sandy aquifer systems buffered by rising carbonate waters: A geo-chemical case study from Mannar Island, Sri Lanka
,
Science of The Total Environment
,
633
,
1352
1359
.
Barzegar
R.
,
Asghari Moghaddam
A.
,
Soltani
S.
,
Fijani
E.
,
Tziritis
E.
&
Kazemian
N.
(
2019
)
Heavy metal (loid) s in the groundwater of Shabestar area (NW Iran): Source identification and health risk assessment
,
Exposure and Health
,
11
,
251
265
.
Boyle
D. R.
,
Turner
R. J. W.
&
Hall
G. E. M.
(
1998
)
Anomalous arsenic concentrations in groundwaters of an island community, Bowen Island, British Columbia
,
Environmental Geo Chemistry and Health
,
20
,
199
212
.
Central Ground Water Board
(
2013
)
Ministry of Water Resources, Government of India, North Western Region, Chandigarh
.
Faridabad: Central Ground Water Board. Available at: https://cgwb.gov.in/index.php/hi.
Chiou
H. Y.
,
Hsueh
Y. M.
,
Liaw
K. F.
,
Horng
S. F.
,
Chiang
M. H.
,
Pu
Y. S.
&
Chen
C. J.
(
1995
)
Incidence of internal cancers and ingested inorganic arsenic: A seven-year follow-up study in Taiwan
,
Cancer Research
,
55
(
6
),
1296
1300
.
Da'ana
D. A.
,
Zouari
N.
,
Ashfaq
M. Y.
,
Abu-Dieyeh
M.
,
Khraisheh
M.
,
Hijji
Y. M.
&
Al-Ghouti
M. A.
(
2021
)
Removal of toxic elements and microbial contaminants from groundwater using low-cost treatment options
,
Current Pollution Reports
,
7
,
300
324
.
Edwards
M.
,
Patel
S.
,
McNeill
L.
,
Chen
H. W.
,
Frey
M.
,
Eaton
A. D.
&
Taylor
H. E.
(
1998
)
Considerations in As analysis and speciation
,
Journal of The American Water Works Association
,
90
(
3
),
103
113
.
Environmental Protection Agency
(
2001
)
National primary drinking water regulations; arsenic and clarifications to compliance and new source contaminants monitoring: Final rule
,
Federal Register
,
66
,
6976
7066
.
Gupta
S.
(
2009
). '
Groundwater management in alluvial areas
’,
Technical Paper in Special Session on Groundwater in the Fifth Asian Regional Conference on Indian National Committee on Irrigation and Drainage (INCID)
.
New Delhi
.
Hassan
K. M.
,
Fukuhara
T.
,
Hai
F. I.
,
Bari
Q. H.
&
Islam
K. M. S.
(
2009
)
Development of a bio-physicochemical technique for arsenic removal from groundwater
,
Desalination
,
249
(
1
),
224
229
.
Hundal
H. S.
,
Singh
K.
&
Singh
D.
(
2009
)
Arsenic content in ground and canal waters of Punjab, North-West India
,
Environmental Monitoring and Assessment
,
154
,
393
400
.
Knobeloch
L. M.
&
Zierold
K. M.
(
2006
)
Association of arsenic-contaminated drinking-water with prevalence of skin cancer in Wisconsin's Fox River Valley
,
Journal of Health, Population and Nutrition
,
2006
,
206
213
.
Kumar
A.
,
Rahman
M. S.
,
Iqubal
M. A.
,
Ali
M.
,
Niraj
P. K.
,
Anand
G.
&
Ghosh
A.
(
2016
)
Ground water arsenic contamination: A local survey in India
,
International Journal of Preventive Medicine
,
7
(
1
),
100
.
doi:10.4103/2008-7802.188085
.
Mandal
B. K.
&
Suzuki
K. T.
(
2002
)
Arsenic round the world: A review
,
Talanta
,
58
,
201
235
.
Mandal
B. K.
,
Chowdhury
T. R.
,
Samanta
G.
,
Basu
G. K.
,
Chowdhury
P. P.
,
Chanda
C. R.
&
Chakraborti
D.
(
1996
)
Arsenic in groundwater in seven districts of West Bengal, India–the biggest arsenic calamity in the world
,
Current Science
,
1996
,
976
986
.
Murphy
M. J.
,
Lyon
L. W.
&
Taylor
J. W.
(
1981
)
Subacute arsenic neuropathy: Clinical and electrophysiological observations
,
Journal of Neurology, Neurosurgery & Psychiatry
,
44
(
10
),
896
900
.
Nguyen
V. A.
,
Bang
S.
,
Viet
P. H.
&
Kim
K. W.
(
2009
)
Contamination of groundwater and risk assessment for arsenic exposure in Ha Nam province, Vietnam
,
Environment International
,
35
(
3
),
466
472
.
Nickson
R. T.
,
McArthur
J. M.
,
Shrestha
B.
,
Kyaw-Nyint
T. O.
&
Lowry
D.
(
2005
)
Arsenic and other drinking water quality issues, Muzaffargarh District, Pakistan
,
Applied Geochemistry
,
20
(
1
),
55
68
.
Nriagu
J.
,
Kim
M. J.
,
Nriagu
J.
&
Haack
S.
(
2002
)
Arsenic species and chemistry in groundwater of southeast Michigan
,
Environmental Pollution
,
120
,
379
390
.
Nriagu
J. O.
,
Bhattacharya
P.
,
Mukherjee
A. B.
,
Bundschuh
J.
,
Zevenhoven
R.
&
Loeppert
R. H.
(
2007
)
Arsenic in soil and groundwater: An overview
,
Trace Metals and Other Contaminants in the Environment
,
9
,
3
60
.
Prakash
S.
&
Verma
A. K.
(
2021
)
Arsenic: It's toxicity and impact on human health
,
International Journal of Biological Innovations, IJBI
,
3
(
1
),
38
47
.
Rahman
M. M.
,
Sengupta
M. K.
,
Ahamed
S.
,
Chowdhury
U. K.
,
Hossain
M. A.
,
Das
B.
&
Chakraborti
D.
(
2005
)
The magnitude of arsenic contamination in groundwater and its health effects to the inhabitants of the Jalangi one of the 85 arsenic affected blocks in West Bengal, India
,
Science of The Total Environment
,
338
(
3
),
189
200
.
Ruj
B.
,
Chakrabortty
S.
,
Nayak
J.
&
Chatterjee
R.
(
2021
)
Treatment of arsenic sludge generated from groundwater treatment plant: A review towards a sustainable solution
,
South African Journal of Chemical Engineering
,
37
,
214
226
.
Saha
N.
,
Rahman
M. S.
,
Ahmed
M. B.
,
Zhou
J. L.
,
Ngo
H. H.
&
Guo
W.
(
2017
)
Industrial metal pollution in water and probabilistic assessment of human health risk
,
Journal of Environmental Management
,
185
,
70
78
.
Sengupta
A.
,
Mukherjee
A.
,
Sengupta
M. K.
,
Hossain
M. A.
,
Ahamed
S.
,
Das
B.
,
Nayak
B.
&
Chakraborti
D.
(
2006
)
Arsenic contamination in groundwater: A global perspective with emphasis on the Asian scenario
,
Journal of Health, Population and Nutrition
,
24
,
142
163
.
Senila
M.
,
Levei
E.
,
Cadar
O.
,
Senila
L. R.
,
Roman
M.
,
Puskas
F.
&
Sima
M.
(
2017
)
Assessment of availability and human health risk posed by arsenic contaminated well waters from Timis-Bega area, Romania
,
Journal of Analytical Methods in Chemistry
,
2017
(
1
),
3037651
.
Shaikh
M. O.
,
Alam
M. O.
,
Shaikh
W. A.
,
Chakraborty
S.
,
Avishek
K.
&
Bhattacharya
T.
(
2016
)
Groundwater arsenic contamination and potential health risk assessment of Gangetic Plains of Jharkhand, India
,
Exposure and Health
,
8
,
125
142
.
Sharma
C.
,
Mahajan
A.
&
Garg
U. K.
(
2013
)
Assessment of arsenic in drinking water samples in south-western districts of Punjab India
,
Desalination and Water Treatment
,
51
(
28–30
),
5701
5709
.
Sidhu
M.
,
Sama
P.
&
Bhatt
S. M.
(
2018
)
Arsenic detection in hand pump water samples of districts of Punjab (Bhatinda, Faridkot and Moga)
,
International Journal of Research and Analytical Reviews (IJRAR)
,
5
(
2
),
378
383
.
Singh
A. K.
(
2004
). '
Arsenic contamination in groundwater of North Eastern India
',
Proceedings of 11th National Symposium on Hydrology with Focal Theme on Water Quality
.
National Institute of Hydrology
,
Roorkee
.
Singh
N.
,
Kumar
D.
,
Lal
K.
,
Raisuddin
S.
&
Sahu
A. P.
(
2010
)
Adverse health effects due to arsenic exposure: Modification by dietary supplementation of jaggery in mice
,
Toxicology and Applied Pharmacology
,
242
(
3
),
247
255
.
Smedley
P. L.
&
Kinniburgh
D. G.
(
2002
)
A review of the source, behavior and distribution of arsenic in natural waters
,
Applied Geochemistry
,
17
,
517
568
.
Stanger
G.
,
Truong
T. V.
,
Ngoc
K. L. T. M.
,
Luyen
T. V.
&
Thanh
T. T.
(
2005
)
Arsenic in groundwaters of the Lower Mekong
,
Environmental Geochemistry and Health
,
27
,
341
357
.
Thakur
L. S.
&
Semil
P.
(
2013
)
Removal of arsenic in aqueous solution by low cost adsorbent: A short review
,
International Journal of ChemTech Research
,
5
(
3
),
1299
1308
.
Tsuji
J. S.
,
Chang
E. T.
,
Gentry
P. R.
,
Clewell
H. J.
,
Boffetta
P.
&
Cohen
S. M.
(
2019
)
Dose-response for assessing the cancer risk of inorganic arsenic in drinking water: The scientific basis for use of a threshold approach
,
Critical Reviews in Toxicology
,
49
,
36
84
.
USEPA
(
1989
)
Risk Assessment Guidance for Superfund. Volume I: Human Health Evaluation Manual (Part A)
.
Washington, DC
:
US Environmental Protection Agency
.
USEPA
(
1996
)
Quantitative Uncertainty Analysis of Superfund Residential Risk Pathway Models for Soil and Groundwater: White Paper
.
USA
:
US Environmental Protection Agency
.
USEPA
(
2004
)
Risk Assessment Guidance for Superfund Volume I: Human Health Evaluation Manual (Part E, Supplemental Guidance foy6r Dermal Risk Assessment)
.
Washington, DC
:
US Environment Protection Agency
.
USEPA
(
2007
)
Drinking water standards and health advisories table
. In:
Edition of the Drinking Water Standards and Health Advisories
.
Vige
N.
,
Ravindra
K.
&
Mor
S.
(
2023
)
Heavy metal pollution assessment of groundwater and associated health risks around coal thermal power plant, Punjab, India
,
International Journal of Environmental Science and Technology
,
20
(
6
),
6259
6274
.
Virk
H. S.
(
2019
)
Groundwater contamination of Amritsar District of Punjab due to heavy metals iron and arsenic and its mitigation
,
Research & Reviews: A Journal of Toxicology
,
9
(
2
),
18
27p
.
World Health Organization
(
1996
)
Guidelines for Drinking-Water Quality
, 2nd edn.
Geneva
:
World Health Organization
.
Wu
M. M.
,
Kuo
T. L.
,
Hwang
Y. H.
&
Chen
C. J.
(
1989
)
Dose-response relation between arsenic concentration in well water and mortality from cancers and vascular diseases
,
American Journal of Epidemiology
,
130
(
6
),
1123
1132
.
Yadav
P.
,
Garg
V. K.
,
Singh
B.
&
Mor
S.
(
2020
)
Assessment of arsenic in groundwater of southwestern Haryana, India and chemical body burden caused by its ingestion
,
Journal of the Geological Society of India
,
96
,
521
525
.
Zhaoyong
Z.
,
Xiaodong
Y.
&
Shengtian
Y.
(
2018
)
Heavy metal pollution assessment, source identification, and health risk evaluation in Aibi Lake of northwest China
,
Environmental Monitoring and Assessment
,
190
,
1
13
.
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