Health risk assessment of heavy metals in groundwater sources: carcinogenic and non-carcinogenic evaluation

Water quality issues in India are escalating rapidly owing to contamination from various sources (Nath et al. 2021; Kushwaha et al. 2024). Contaminants in groundwater, when present above permissible levels, pose significant health risks (Chinchmalatpure et al. 2019). Numerous studies globally have evaluated the physico-chemical characteristics of groundwater and assessed its potential health risks (Tirkey et al. 2017; Adimalla et al. 2020; Shukla et al. 2021). Developing effective strategies to prevent pollution and protect human health is critical (Sahoo & Goswami 2024). Hence, assessing contamination levels and associated risks with various pollutants is essential for determining the suitability of groundwater for drinking purposes.

Excessive use of phosphate fertilizers has been identified as a major source of arsenic contamination of groundwater, with levels often exceeding safe limits (Kaur et al. 2017). In Saudi Arabia's Hail region, lead levels in water samples have been found to surpass drinking water guidelines (Abdel-Satar et al. 2017; El-Ezaby 2019). Carcinogenic pollutants such as uranium, arsenic, lead, cadmium, radon, and pesticides are prevalent in Indian waters due to untreated industrial effluents, excessive pest control measures, and chemical usage (Malyan et al. 2019). Studies in Limpopo National Park indicated trace metal concentrations in drinking water exceeding permissible values (Ricolfi et al. 2020). Overpopulation and industrial development have exacerbated groundwater pollution. Discharge of heavy metals from processing units, including iron, copper, lead, and chromium, poses serious health risks (Khan et al. 2022). Research has shown that increased municipal, industrial waste, and agricultural applications significantly affect groundwater aquifers (Ahmed et al. 2022). Toxic metals such as nickel, cadmium, and chromium in drinking water exhibited high cancer risk indices, with lead showing the lowest values (Shehu et al. 2022). In Punjab, toxic trace metals including arsenic, aluminium, iron, cadmium, copper, and lead were reported in groundwater, often exceeding permissible limits (Krishan et al. 2021).

One-way analysis of variance (ANOVA) at 0.05 indicated no significant difference in values of calcium, hardness, fluoride, iron, nitrate, and sulphate (Gawle et al. 2021). Significant differences in arsenic concentrations were observed between shallow and deep groundwater in North Macedonia's Strumica region (Kovacevik et al. 2021). In Al Wasit Nature Reserve, UAE, a non-parametric ANOVA test detected sources of differences among clusters for each parameter (Mohammed et al. 2022). ANOVA was also used to analyse deep wells, semi-deep wells, aquifers, and springs in Kermanshah, Iran (Dargahi et al. 2022).

Multivariate statistical analysis, including principal component analysis (PCA), was applied to water quality data in Argentina to evaluate spatial variation. This analysis suggested the greatest contributors to water quality variation and potential pollution sources (Bonansea et al. 2015). PCA is a tool used to reduce data dimensions, based on either covariance or correlation matrices (Jolliffe & Cadima 2016; Kumar et al. 2019; Greenacre et al. 2022; Jat et al. 2022). PCA using Varimax rotation was employed to reduce dimensionality, with eigenvectors >1 considered for data interpretation (Singh et al. 2017). Varimax and Kaiser normalization methods were used in PCA to reduce data (Solangi et al. 2020). Pearson correlation coefficient matrix showed high positive correlations between Ca²⁺, Mg²⁺, Na⁺, Cl⁻, and SO₄²⁻, indicating contamination from fertilizers and industrial activities (Hyarat et al. 2022). Correlation matrices were computed to support PCA results, showing correlations among Mg²⁺, Na⁺, Ca²⁺, and Cl⁻, confirming their origin from the same source (Rehman et al. 2018).

Factor analysis (FA) with varimax rotation was applied to the study area data. Geostatistical techniques, including FA, cluster analysis, and kriging, were used to evaluate groundwater pollution and identify contamination sources (Venkatramanan et al. 2016). Contamination sources in Rupnagar were identified using exploratory FA and ordinary kriging (Chaudhry et al. 2019). The extracted factors showed a variance of 78.5% (Hyarat et al. 2022). The samples subjected to FA revealed significant reductions in dimensionality (Thomas 2023).

Geographic information system (GIS) is a reliable tool for predicting and visualizing complex data (Kumar et al. 2022; Sarkar et al. 2022; Dutta et al. 2023; Gururani et al. 2023; Qazi et al. 2023; Abdo et al. 2024). Inverse distance weighted (IDW) interpolation was used for spatial distribution mapping with ArcGIS software (Tiwari et al. 2016). Spatial distribution was developed using ArcGIS by interpolating water quality index values for sampling locations (Prasad et al. 2021). IDW interpolation was used to create spatial distribution maps for different parameters in Bokaro district (Dandge & Patil 2022; Ghosh et al. 2023).

Initiatives to implement strict policies on industries to establish effluent treatment plants are crucial to prevent city groundwater contamination (Shankar et al. 2008). In Kampala, groundwater contamination with heavy metals posed high cancer risks to residents (Bamuwamye et al. 2017). Cadmium, lead, and chromium sources have been identified as anthropogenic inputs from mining and mineral processing activities. The health index (HI) analysis showed values greater than 1, indicating threats to human health (Singh et al. 2020). Groundwater contamination with nitrate and chromium posed potential health risks to Wuqi County's inhabitants, including children (He et al. 2019). In Giang Province, arsenic-contaminated groundwater posed high cancer risks to both children and adults (Kim Anh & Thanh Giao 2018). Nitrate levels in Malwa exceeded permissible values, presenting carcinogenic and non-carcinogenic risks to residents (Ahada & Suthar 2018). Trace metal concentrations in water samples from Nigeria were due to human activities, with the hazard quotient (HQ) values less than 1 (Adewoyin et al. 2019). Elevated arsenic levels in West Bengal's Nadia District indicated significant health risks; although no fluoride-related risks were found, nitrate posed non-carcinogenic health hazards (Das et al. 2020). In China's Luan River catchment, risk assessment showed higher values for carcinogenic risk and HQ than standard values (Liu & Ma 2020). Heavy metals in Punjab groundwater were mostly within limits, except for arsenic in some districts (Krishan et al. 2021). Groundwater near leather tanning industries in South India exceeded permissible values, with Sobol indices assessing different risk categories (Karunanidhi et al. 2021). Chromium in groundwater near industrial areas showed concentrations above acceptable limits, indicating significant carcinogenic risk (Vijayakumar et al. 2022). High arsenic concentrations posed potential carcinogenic and non-carcinogenic risks to residents (Huang et al. 2022; Chen et al. 2023). In Nigeria, elevated heavy metals from industrial activities indicated cancer risks for adults and children (Emmanuel et al. 2022). Groundwater quality in Saveh Aquifer, Iran, was assessed using Health Risk Assessment and Irrigation Indices, with most samples showing HI values >1 (Shahmirnoori et al. 2023).

This study evaluates the health risks associated with heavy metals in the groundwater of Rupnagar, Punjab. Metrics such as HQ, chronic daily intake (CDI), and Incremental Lifetime Cancer Risk (ILCR) were used to determine and assess groundwater pollutant exposure levels. HQ and CDI evaluate potential carcinogenic health risks, while ILCR estimates carcinogenic health risks. A multi-faceted approach is essential to address groundwater quality challenges in Punjab, ensuring safe drinking water. One initiative, the AMRIT programme, aims to remove arsenic and iron from drinking water through community purification plants in collaboration with IIT Madras.

Despite extensive research on groundwater contamination and health risk assessments worldwide, significant gaps remain. There is a need for region-specific studies that consider local environmental and anthropogenic factors affecting groundwater quality. Existing research in Punjab has identified the presence of various heavy metals in groundwater (Sekhon & Singh 2013; Sharma & Dutta 2017; Virk 2019a, 2019b; Tiwari et al. 2020; Bangotra et al. 2023; Romana et al. 2023; Ali et al. 2024; Sonkar et al. 2024), but comprehensive risk assessments and spatial distribution analyses using advanced multivariate techniques are limited. Moreover, the long-term effectiveness of implemented policies and treatment technologies in reducing contamination levels and mitigating health risks has not been thoroughly evaluated. This study addresses these gaps by employing detailed statistical and geostatistical analyses to assess contamination sources, spatial distribution, and associated health risks in Rupnagar, Punjab. This comprehensive approach will provide valuable insights for developing targeted interventions and policies to ensure sustainable groundwater management and public health protection.

Groundwater is a crucial resource for drinking and irrigation in Rupnagar. The local community relies on groundwater extracted from wells with depths ranging from 20 to 70 m for both agricultural practices and domestic use. The sampling points for the study area are depicted in Figure 1, indicating the strategic locations chosen for assessing groundwater quality and contamination levels. The study area has been specified as both industrial and agricultural, with agriculture being the dominant feature. The heavy metals present in the groundwater are likely sourced from both industrial activities and agricultural practices, such as the use of fertilizers, pesticides, and industrial effluents.

A total of 34 samples were randomly collected in high-density polypropylene bottles from public and private wells. Before taking samples from locations, the handpumps and tubewells were purged at least 15 min to ensure the quality of water. The water samples were collected from handpumps and tubewells at depths ranging from 20 to 70 m. The sampling took place during the pre-monsoon season in the year 2022. The groundwater samples were stored at 4°C in an ice box prior to analysis; the analysis was performed within a week's time after sample collection. Whatman Grade 40 filter paper was used for filtration of samples before instrumental analysis and no acidification was conducted before the analysis. The geographical positioning system was used to determine geographical locations for each source. The sampling points are presented blockwise (Table 1).

This research analysed the samples for seven heavy metals, namely, aluminuim (Al), arsenic (As), cadmium (Cd), chromium (Cr), iron (Fe), lead (Pb), and selenium (Se), using multiple methods to ensure accuracy and reliability of results. Initially, colourimetric methods were employed as preliminary trials.

Following these preliminary analyses, inductively coupled plasma mass spectrometry (ICP-MS) was employed using the Agilent 7,800 series for the definitive quantification of all the targeted heavy metals (Al, As, Cd, Cr, Fe, Pb, and Se). The ICP-MS was used in accordance with IS 3025: Part-65, providing high sensitivity and accuracy across the entire range of elements analysed. The use of ICP-MS at this stage allowed us to confirm and refine the results obtained from the initial methods. The operating conditions were strictly maintained per the standard operating procedures of the instruments to ensure precise and reliable results.

The sole purpose of a health risk assessment is to evaluate the potential adverse health effects on the inhabitants due to intake of contaminated groundwater. The whole process revolves around the fact of how individuals may be exposed to the pollutants and are susceptible to the health effects. Therefore, it is essential to identify vulnerable populations and take appropriate measures to reduce their exposure to heavy metals. The values for HQ, HI, and ILCR were calculated to estimate the potential non-carcinogenic and carcinogenic health risks caused by the ingestion of contaminated groundwater. The present study of health risk assessment is carried out for adults irrespective of gender.

The comparison of computed HI with standard values will indicate the non-carcinogenic impacts on inhabitants if HI > 1 while, if HI < 1, no significant harmful impact is expected from the study data.

Minimum, maximum, average values, mode, standard deviation, and confidence interval were calculated for all heavy metal parameters by using MS Excel 2018. Multivariate statistical techniques including PCA were applied using the MINITAB 19 software package (Nosrati & Van Den Eeckhaut 2012; Jat et al. 2022).

The concentration of heavy metals was compared with IS: 10500 (BIS 2012) to assess the suitability of groundwater for consumption (Table 3). Excess arsenic may cause substantial damage to human health, thereby leading to heart failure, gastrointestinal effects, anaemia, neural injury, and so on. Most importantly, it is a cumulative poison and increases the risk of lung and skin cancer. Al is not classified as a carcinogen by the International Agency for Research on Cancer (IARC). However, certain aluminium compounds may have potential health effects that are not related to cancer. Exposure to Se through food or water may lead to nausea, headache, tooth decay, staining of teeth, and nails with brittleness in humans. Excess of iron stored in the spleen, liver, and bone marrow causes haemochromatosis. Chromium is toxic and causes ulcers and dermatitis. Exposure to Cr is linked to lung cancer. Compounds containing cadmium are also carcinogenic. Ingestion of certain amounts of Cd is associated with an increased risk of lung cancer and may also be linked to prostate and kidney cancer. Pb is a highly toxic element and classified as a probable carcinogen that may damage the kidneys and the nervous and reproductive systems (Collin et al. 2022; Goutam Mukherjee et al. 2022).

The correlation matrix was developed to establish relations among eight heavy metal parameters. Table 4 presents the Pearson correlation matrix of groundwater concentration of seven heavy metals. Iron has a strong positive correlation with cadmium, selenium, arsenic, and chromium whereas lead is moderately correlated with Se, As, and Cr. Strong correlation between Fe, Cd, Se, As, and Cr depicts a similar source or geochemical behaviour during various processes.

One-way ANOVA was performed using MINITAB-19 to check the significant differences of the metal concentrations in the groundwater samples. Equal variances were assumed for the analysis. Tukey pairwise comparison indicated that in case of aluminium, Block 5 has a significantly higher mean than Block 2, whereas in the case of iron, Block 2 showed a higher mean in comparison to Block 5. Also, the mean value of arsenic was highest in Block 1 followed by Blocks 5, 3, and 2, respectively.

This analysis was used to reduce the number of variables. Therefore, FA and PCA were applied on the available data set. It was observed that eigenvalues were >1 for the first four factors. Cumulatively, the four components portrayed 75.8% of total variance of the data set. The rotated factor loadings and communalities for heavy metals of various variables are shown in Table 5.

HQs are enumerated blockwise (Tables 6) for non-carcinogenic risk to adults. The HI among all the blocks is found to be in the order of Nurpur Bedi > Morinda > Chamkaur Sahib > Anandpur Sahib > Ropar. It clearly indicates that the non-caricinogenic risk to the inhabitants of the district is most in Block 5 due to contaminated groundwater induced with Al, Fe, and Se. However, HI >1 is estimated in all blocks; consequently, inhabitants of the study area are prone to non-carcinogenic risk.

ILCR values depicting carcinogenic risk are recorded (Table 7). All the blocks are expected to pose cancer risks for adults as the calculated values of ILCR are in the order of 10⁻³. However, Ropar Block showed significant cancer risk for adults. According to the USEPA, an acceptable limit of ILCR ranges from 1.00 × 10⁻⁶ to 1.00 × 10⁻⁴ (IRIS from USEPA 2009). The values showed that Cr was the major contributor to cancer risk from the different sources of water supply, whereas there was a significant difference between Cr and As. Thus, the cancer risks emerging from As, Cr, and Pb in water sources from the five blocks require further investigation for improvement of groundwater of the region.

The results clearly indicate that residents of the study area are exposed to non-carcinogenic risk. The ILCR was estimated for arsenic, chromium, and lead. The findings revealed that chromium is the most significant contributor to cancer risk for the people of Rupnagar, followed by lead and arsenic. Consequently, it can be concluded that the current groundwater condition is unsafe for consumption. Certain populations are particularly vulnerable to the health risks associated with heavy metals in groundwater. These include children, pregnant women, the elderly, and individuals with pre-existing health conditions such as compromised immune systems. The study emphasizes the need for targeted interventions in these groups to mitigate potential health risks.

The study proposes several recommendations to mitigate non-carcinogenic health risks, including the implementation of water treatment facilities, regular monitoring of groundwater quality, public awareness campaigns, and the promotion of safer agricultural practices to reduce the use of harmful chemicals. The feasibility of these recommendations has been carefully considered in the local context. For instance, the proposed water treatment solutions are cost effective and scalable, making them suitable for implementation even in resource-constrained settings. Collaboration with local governments and non-governmental organizations can further enhance the feasibility of these interventions.

The recommendations provided are both practical and actionable for policymakers and local authorities. They are designed to be integrated into existing public health and environmental protection frameworks. The study also suggests specific policy measures, such as enforcing stricter regulations on industrial discharge and providing subsidies for safer agricultural inputs, to support the adoption of these recommendations.

The present research focuses on the groundwater quality and the assessment of human health risk due to presence of heavy metals in Rupnagar district. The results with the concentrations of the heavy metals are found in the order: Al > Se > Fe > Pb > Cd > As > Cr. The results from FA indicated that the first principal component has large positive loading associations with Cr and Pb, which were primarily from the similar sources such as the textile and steel industries situated near the study area. However, Factor 2 is found to dominate with As and Fe in the groundwater of the study area, which is surrounded by the national fertilizer limited, other industry manufacturers, and automobile part manufacturing units. Evaluated HI due to Al, Fe, and Se is found to be >1 in all the five blocks. This clearly indicates that the residents of the study area are prone to non-carcinogenic risk. The ILCR is attributed to As, Cr, and Pb. The results showed that chromium is the prominent contributor to cancer risk for the people of Rupnagar followed by lead and arsenic, respectively. Hence, it can be concluded that the condition of the present groundwater is unsafe for consumption. The concentrations of these heavy metals particularly in the areas of Rupnagar are required to be reduced by adopting sustainable programmes of installing community water purification plants. Special awareness sessions on water sources and their quality among the public will help save groundwater quality. In addition, regular monitoring is suggested for checking the quality of groundwater and to prevent the further degradation of the environment by hazardous toxic metals.

K.K. and S.K. contributed to conceptualization and methodology; K.K. contributed to software development, formal analysis and resources preparation; S.K. validated and investigated the work; K.K. and D.K.V. participated in data curation and original draft preparation and also visualized the published work; D.K.V., A.J.O., and K.K.Y. wrote the original draft and reviewed and edited the manuscript; S.K. and D.K.V. supervised the work; S.K. involved in project administration. All authors have read and agreed to the published version of the paper.

The authors extend their appreciation to the Researchers Supporting Project number (RSPD2024R620), King Saud University, Riyadh, Saudi Arabia. The authors also gratefully acknowledge the Punjab Engineering College Chandigarh for Higher Education.

Data cannot be made publicly available; readers should contact the corresponding author for details.

The authors declare there is no conflict.