The present research study explores the drinking water quality of Rawalpindi and Islamabad to identify the potent dissolved contaminants and carry out a health risk assessment as the study area houses more than 3 million people. A total of 95 drinking water samples were collected from the union councils of the selected study area and analyzed for 12 physicochemical water quality indicators. The collected datasets were interpreted using general statistics, principal component analysis and spatial analysis for knowing the variations among the collected samples. The results revealed that overall 51.57% of the drinking water samples were unsatisfactory for human consumption. The rate of physicochemical contamination was 87.27% in the rural and unauthorized housing societies. Arsenic (As) and lead (Pb) were the potent contaminants in the drinking water samples. The health risk assessment uncovered that 31.57 and 10.45% of samples had a hazard quotient (HQ) >1 for arsenic and lead, respectively. Collectively, 41 drinking water sources were identified as potential health risk sources for the residents.

  • Planned sampling from 95 locations was carried out from in-use drinking water sources from the capital city Islamabad and Rawalpindi, Pakistan.

  • Assessment of health risks for arsenic and lead was carried out using the standard guidelines of USEPA.

  • The combination of spatial analysis with water quality indicators provides disseminated research findings to the general public along with scientific researchers.

Water is the basic requirement of all living organisms. An adult human body usually contains about 65% of water and this varies with age, gender and various lifestyle aspects. The water concentration in the human body is preserved by drinking water directly and by foods we eat (Acharjee & Ahmed 2016). The quality of available drinking water can define the human health condition. Geogenic and anthropogenic activities can alter hydro-geochemistry, leading to the leaching and solubility of physicochemical contaminants, such as heavy metals, pesticides and organic chemicals to groundwater channels (Ayari et al. 2021). Exposure to groundwater contaminants through drinking water can lead to a variety of acute and chronic health effects, ranging from gastrointestinal illness and skin irritation to cancer, organ damage and neurological disorders (Hisam et al. 2014). The severity of the health impacts depends on the type and concentration of the contaminant and the duration and frequency of exposure. Vulnerable populations, such as pregnant women, infants and the elderly, may be at risk. Therefore, it is essential to monitor and regulate the presence of physicochemical contaminants in drinking water to ensure the protection of public health (Sharma et al. 2014).

In under developing countries, the guidelines for the quality of drinking water issued by the regulators were often violated and ignored in the race of urbanization, population and water scarcity (Sohail et al. 2020). Several research studies, which aimed to identify the drinking water quality in Pakistan, Bangladesh, India, China, Vietnam, Cambodia and other SouthEast Asian countries, showed that the collected samples significantly surpassed the permissible concentrations of WHO for heavy metals, e.g. As, Pb, Cr, Ni, Cu, Mn and Co (Arshad & Imran 2017). In Pakistan, the Pakistan Council of Research in Water Resources (PCRWRs) carried out various drinking water quality monitoring studies and marked that the Lahore, Vehari, Multan, Shikhupura, Gujranwala and Kasur have elevated concentrations of heavy metals in the drinking water samples (Hussain et al. 2019).

Multiple statistic tools were developed and used by the scientists to observe the variation among the collected datasets of water quality including the use of analysis of variance, principal component analysis (PCA) and spatiotemporal analysis. Geo-spatiotemporal analysis was considered to be the most powerful tool, especially for understanding the distribution and how the patterns of quality change with the locations and sample sites (Vasistha & Ganguly 2022). It is supported by geo-mapping and analysing the spatial data, it is possible to identify the sources of contamination, areas with high- and low-quality water and areas with potential risks to public health (Li et al. 2019).

The capital city of Pakistan (Islamabad) and its adjoining city Rawalpindi were collectively known as twin cities. A burst of urbanization and population was observed in the twin cities and the area was also going through serious water scarcity since last decade. The reviews identified that very limited individual studies with few quality parameters were found that explained the inferior quality of drinking water in the twin cities and their results also contradicted the water quality monitoring results found by PCRWR (Sohail et al. 2020; Nazir et al. 2022). Abeer et al. (2020) conducted a health risk assessment and investigated the provenance of arsenic and heavy metals in drinking water in a selected area of Islamabad, Pakistan. Their results indicated that the hazard quotient (HQ) values for heavy metals were below 1. However, arsenic HQ values exceeded 1 in 3.34% of the samples for children's drinking water. The study suggests the need for arsenic removal through treatment processes and served as an inspiration for the planning of the present research work. The present research was planned with the following objectives: (a) Sample selection and investigation, the well-populated sample sites were selected (administratively declared union councils on the basis of population) and investigated for the latest level of 12 selected physicochemical quality indicators, i.e. total dissolved solids (TDS), total hardness (TH), electrical conductivity (EC), pH, turbidity, Cu, Mg, As, Pb, Ni, Mn and Na in the drinking water samples. (b) Health risk assessments for arsenic and lead were carried out using the guidelines of the United States Environmental Protection Agency. (c) Disseminating information among scientific researchers and the general public, the results of the study were investigated for variation among the drinking water samples using multiple statistic tools such as analysis of variance, use of PCA for the major variables TDS, TH, EC, pH and turbidity and spatial analysis and google maps were used to develop the variation pattern maps for each quality indicator and identify potential health risks areas for As and Pb.

Sample collection

The study was conducted at the Institute of Food and Nutritional Sciences, PMAS-Arid Agriculture University Rawalpindi. Drinking water samples were collected in triplicates from the administratively defined 95 union councils of the Metropolitan Corporation of Islamabad and the Municipal Corporation of Rawalpindi. The collected samples were of domestic bore water (55 samples) and water supply plants/wells (40 samples) installed by the Water and Sanitation Authority (WASA) and Capital Development Authority (CDA).

Physicochemical analysis

The analysis for TDS, TH, EC, pH and turbidity was carried out using the reference methods of Rice & Bridgewater (2012). The multi-element analysis for the detection of different selected elements, i.e. Cu, Mg, As, Pb, Ni, Mn and Na was carried out by the inductively coupled plasma optical emission spectroscopy (ICP-OES) using the already reported method of Cheng et al. (2004).

Health risk assessment

The human health risk assessment was carried out using the model derived by USEPA (2010) to evaluate the toxic effects of potent contaminants, arsenic and lead, in drinking water on the health of people as the procedure and method used by Shakoor et al. (2015). For this purpose, the average daily dose (ADD) of contaminant due to the intake of contaminated drinking water was calculated by Equation (1):
formula
(1)
The HQ was determined using Equation (2) with the already reported method (USEPA 2010).
formula
(2)
where RfD represents the oral reference dose in the present study.

RfD is used for arsenic (As) = 0.0003 mg/kg/day;

RfD is used for lead (Pb) = 0.004 mg/kg/day; and

the HQ was considered to be present if HQ >1.00.

Statistical analysis

To compare different quality parameters of the selected samples, the analysis of variance was identified using the statistics 8 (Version 8.1). The PCA analysis of the major selected variables, TDS, TH, EC, pH and turbidity, was performed using the R-studio.

Spatial analysis

The 12 selected drinking water quality indicators and levels of HQ were analysed for spatiotemporal variation and development of pattern maps using the ARC-GIS 10.6 software (Li et al. 2019).

In the present study, 57% area relies on domestic bore water to fulfil its drinking water needs, whereas 43% area has the availability of water supply by WASA and CDA (Figure 1 and Table 1). The results of the analysis for TDS, TH, EC, pH and turbidity showed that 30, 15.7, 19, 17 and 1.05% of samples surpassed the permissible limits defined by the regulators and results were statistically highly significant (Table 2). The values of TDS showed the sum of all the inorganic materials found in the samples. The presence of inorganic materials in the earth's crust varies widely from region to region and water as a universal solvent is capable of dissolving these inorganic and organic matters that come in contact with the surface of water (Islam et al. 2016). The TDS value in the selected research area varied from 456.57 to 1628.36 mg/L. The total hardness in the drinking water samples indicates the sum of calcium and magnesium ion concentrations present in the drinking water samples. The hardness concentration varied from 74.57 to 391.48 mg/L and it depends upon the concentration of calcium and magnesium deposits that occur naturally in the region. Geologically, the study region contains both mountainous to semi-mountainous areas and the presence of natural marble deposits in the region was identified as the major cause of variation in the total hardness of the samples. The value for total hardness increases as we move towards Rawat and Taxila from the central region of Faizabad (Figure 2).
Table 2

Overview of statistical analysis and results summary of various quality indicators

S. N0Quality indicatorsUnitsPermissible limits PSQCA/WHO/EPA for drinking waterANOVA
Minimum concentrationsMaximum concentrationsPercentage (%) samples surpassed the permissible limit
Mean squares for samplesp-value
1. TDS mg/L 1,000 255411 0.001 456.57 1628.36 30 
2. TH mg/L 250 14769 0.001 74.57 391.48 15.7 
3. EC mS/cm 0.899 0.001 0.32 2.96 19 
4. Turbidity NTU <5 3.641 0.001 0.24 5.3 1.05 
5. pH  6.5–8.5 1.801 0.001 6.1 9.03 17 
6. Cu mg/L 1.753 0.001 3.48 44 
7. Mg mg/L 50 3644.25 0.001 7.25 117.94 36 
8. As mg/L 0.01 0.001 0.001 0.08 31.57 
9. Pb mg/L 0.01 0.039 0.001 0.64 47 
10. Ni mg/L 0.5 0.017 0.001 0.59 
11. Mn mg/L 0.5 0.203 0.001 0.97 12.5 
12. Na mg/L 200 387.382 0.001 0.81 63.92 
13. HQ (As)  <1 21.564 0.001 9.47 31.57 
14. HQ (Pb)  <1 3.673 0.001 0.02 6.13 10.45 
S. N0Quality indicatorsUnitsPermissible limits PSQCA/WHO/EPA for drinking waterANOVA
Minimum concentrationsMaximum concentrationsPercentage (%) samples surpassed the permissible limit
Mean squares for samplesp-value
1. TDS mg/L 1,000 255411 0.001 456.57 1628.36 30 
2. TH mg/L 250 14769 0.001 74.57 391.48 15.7 
3. EC mS/cm 0.899 0.001 0.32 2.96 19 
4. Turbidity NTU <5 3.641 0.001 0.24 5.3 1.05 
5. pH  6.5–8.5 1.801 0.001 6.1 9.03 17 
6. Cu mg/L 1.753 0.001 3.48 44 
7. Mg mg/L 50 3644.25 0.001 7.25 117.94 36 
8. As mg/L 0.01 0.001 0.001 0.08 31.57 
9. Pb mg/L 0.01 0.039 0.001 0.64 47 
10. Ni mg/L 0.5 0.017 0.001 0.59 
11. Mn mg/L 0.5 0.203 0.001 0.97 12.5 
12. Na mg/L 200 387.382 0.001 0.81 63.92 
13. HQ (As)  <1 21.564 0.001 9.47 31.57 
14. HQ (Pb)  <1 3.673 0.001 0.02 6.13 10.45 
Table 1

Percentage number of samples found unsatisfactory in the domestic bore water region and water supply areas

Origin of samplesNumbersNo. of samples found unsatisfactoryPercentage (%) samples found unsatisfactory
Domestic bore water 55 45 87.27 
Supply water WASA and CDA 40 7.5 
Total 95 49 51.57 
Origin of samplesNumbersNo. of samples found unsatisfactoryPercentage (%) samples found unsatisfactory
Domestic bore water 55 45 87.27 
Supply water WASA and CDA 40 7.5 
Total 95 49 51.57 
Figure 1

Drinking water source-wise sample distribution in the region.

Figure 1

Drinking water source-wise sample distribution in the region.

Close modal
Figure 2

Spatiotemporal analysis for TDS, TH, EC, turbidity, pH, Cu, Mg, As, Pb, Ni, Mn and Na.

Figure 2

Spatiotemporal analysis for TDS, TH, EC, turbidity, pH, Cu, Mg, As, Pb, Ni, Mn and Na.

Close modal

EC of the drinking water is the measurement of current that is conducted by the samples. Naturally, water is a nonconductor but the presence of ion concentration makes it a conductor for the transmission of signal and current. The increased EC values show a higher concentration of ions that can be easily correlated with the values of TDS and TH in the drinking water samples. The EC value in the study zone varied from 0.32 to 2.96 mS/cm as shown in Table 2. The pH means ‘to measure the negative log of H+ ion concentration of an aqueous solution’. The recommended pH of the drinking water ranges from 6.5 to 8.5 (PCRWR 2016). Significant variation in the pH measurement varied from 6.1 to 9.03 among the collected samples (Table 2). The turbidity value of the drinking water samples depicts the cloudiness of the samples that is mostly due to the presence of organic matter, soil particles and colouring pigments that entered the drinking water sources due to their existence in the earth's crust and the plumbing/water supply systems. The study results showed a variation of 0.24 to 5.3 NTU in the collected drinking water samples and most of the samples showed passing results for the turbidity value in the study region. This good sign is due to the long bore depths as most of the drinking water reservoirs found in the region fall below 100 feet. Our findings for TDS, TH, EC, pH and turbidity are in line with the previous findings of Arshad & Imran (2017) and Daud et al. (2017).

The results of the quality indicators for TDS, TH, EC, pH and turbidity were statistically analysed using the PCA in order to infer the variation among a large number of samples. The PCA analysis (Figure 3) showed that based on eigenvalues the first two dimensions explain about 85% of variability in the collected data. The variation in the behaviour of bore water and water supply samples is shown in the individual PCA graph. The domestic bore water samples showed huge variation in their physicochemical nature and the variation is equally distributed in dimensions 1 and 2. This variation in the domestic bore water is obvious due to the contribution of various aspects that include anthropogenic activities, bore depth, geological elevations and depressions in the semi-mountainous and arid regions. The samples of water supply areas also showed variation among the quality indicators, but their variation is much lesser than the domestic bore water samples as they were supervised by the WASA-Rawalpindi and CDA-Islamabad, majority of the supply water contributed to dimension 2. Four outliers were also identified among the samples having a physicochemical nature, not matched with the other samples.
Figure 3

Graphical representation of scree plot, individuals PCA, variable graphs and bi-plot.

Figure 3

Graphical representation of scree plot, individuals PCA, variable graphs and bi-plot.

Close modal
The major contributors to variation in the physicochemical properties of the drinking water were TDS, EC, TH, pH and turbidity. The PCA graph for variables shows that the four quality indicators, TDS, EC, TH and pH, showed identical behaviour among the samples, whereas the turbidity had a dissimilar distribution than the rest of the selected quality indicators. The correlation graph, Figure 4, shows highly significant results for the collected drinking water samples. The TDS concentration showed a positive and direct relationship with the TH, EC and pH. The turbidity concentration showed a weak correlation with other selected parameters. The scree plot of PCA (Figure 4) shows that the first principal component (Dim-1) explains 71.2% of the variance, while the second principal component (Dim-2) explains 14.6% of the variance. Together, these two components account for 85.8% of the total variance, indicating that they capture the majority of the information in the dataset. The contribution of each selected variable to dimensions 1 and 2 is shown in Figure 5. Turbidity contributes approximately 80% to Dim-1, pH has a smaller contribution, less than 10% and TH, TDS and EC each contribute minimal amounts to Dim-1, with values close to zero. This indicates that turbidity is the dominant variable influencing the first principal component which is Dim-1. TDS and EC contribute equally, about 22–23% each, to Dim-2, TH also contributes significantly, around 20%, pH contributes around 18% and turbidity contributes the least, around 12% to the second principal component which is Dim-2.
Figure 4

Correlation graph for the selected major variables (TDS, TH, EC, pH and turbidity).

Figure 4

Correlation graph for the selected major variables (TDS, TH, EC, pH and turbidity).

Close modal
Figure 5

Contribution of the selected major variables (TDS, TH, EC, pH and turbidity) for dimensions 1 and 2.

Figure 5

Contribution of the selected major variables (TDS, TH, EC, pH and turbidity) for dimensions 1 and 2.

Close modal

The presence of higher concentrations of TDS, TH, EC, pH and turbidity, especially where surpassing the permissible limits is associated with taste change, bad odour and mineral deposit stains on the surface of utensils, in severe cases leads to health complications in humans and increased chances for the existence of other toxic multi-elements (Islam et al. 2016). The analysis of variance for the selected multi-elements, Cu, Mg, As, Pb, Ni, Mn and Na, showed highly significant results and the percentage samples surpassed the permissible limits, as shown in Table 2.

Significant copper (Cu) erosion in the collected drinking water samples was observed that varied from 0 to 3.48 mg/L among the collected water samples (Table 2). Cu erosion is directly associated with hardness and the presence of anions. Eroded copper from the taps and valves installed on the plumbing system were found to be the major sources of Cu contamination in the drinking water. A similar trail of Cu contamination was also reported in school drinking water by Barn et al. (2014). Cu toxicity can pose serious health problems including diarrhoea, vomiting and kidney and liver damage as reported by Uauy et al. (2008). Magnesium found abundantly (7.25–117.94 mg/L) in the samples can contribute to the total hardness too. The higher concentration of magnesium in the samples was due to the leaching of magnesium carbonates from the rocks present in the region and similar results was reported by Daud et al. (2017). The high magnesium concentrations were associated with scaling and taste changes in the drinking water and in severe cases it may adversely affect human health (Wodschow et al. 2021).

The concentration of arsenic (As) exceeded 29.5% of the collected drinking water samples. Exposure through drinking water remained the top priority of Pakistani researchers during the last decade due to its toxic health impacts including skin irritation, neurotoxic effects and carcinogenicity. The As concentration varied from 0 to 0.08 mg/L among the collected samples the higher As concentrations can be from anthropogenic activities and geological reasons (Amir et al. 2021). Lead (Pb) is another potent contaminant that exceeded the permissible limits in 47% of the collected samples and Pb concentration varied from 0 to 0.64 mg/L. Pb toxicity is well known for its carcinogenic effects and effects on human memory and it is involved in the dysfunction of kidneys. Pb enters the drinking water channels due to both anthropogenic and geogenic activities (Ayari et al. 2023). In Pakistan, corrosion of plumbing lines, atmospheric lead pollution from automobiles and burning the Pb-containing fuel are the main sources of Pb contamination (Ali et al. 2019). The drinking water sample sites close to the highways were identified as exceeding the permissible limits of WHO and PSQCA for lead. The results of our findings for As and Pb were in line with the findings of Arshad & Imran (2017) and Sohail et al. (2020).

In the selected study region, nickel (Ni) concentration was found within permissible limits that varied from 0 to 0.59 mg/L among all the collected samples, depending upon the level and exposure. Nickel may become immunotoxin and carcinogenic and initiate various human health complications related to the heart, kidneys and lungs (Genchi et al. 2020). Manganese concentration was surpassed in 12.5% of the collected samples and the Mn concentration varied from 0 to 0.97 mg/L in the study region. Mn is an important micro-nutrient but at higher concentrations, it becomes neurotoxic and poses a negative impact on the intellectual abilities of the children (Iyare 2019). The concentration of sodium (Na) was found well below the permissible limits and it varied from 0.81 to 63.92 mg/L that was a good sign in the selected study region as the higher concentrations of Na are responsible for many health impacts, especially the high blood pressure and kidney failure. From the results of the physicochemical evaluation, arsenic (As) and lead (Pb) were the most potent contaminants in the study region. Our multi-element analysis results for Cu, Mg, Pb, Ni, Mn and Na align with the findings of Rana et al. (2022). However, unlike their research, which found arsenic (As) levels within permissible limits, our study detected higher concentrations of arsenic.

An evaluation of the possible effects of drinking contaminated water on human health was done through the process known as health risk assessment. The chance and severity of health impacts depend on the water sources, amount and kind of contaminant, drinking frequency and length, age and metabolic response of the human body. Through the health risk assessment, we can identify the areas where the chances of hazards exist; this tool helps in public policy developments and dissemination of knowledge among native individuals. The United States Environmental Protection Agency (USEPA) established guidelines and tools for the calculation and identification of the health risk areas for heavy metals and metalloids (Gul et al. 2015; Shakoor et al. 2015; Adimalla 2020). Using the same guidelines, the health risk areas for arsenic and lead toxicity were identified in the current study region.

In the first step of health risk assessment exposure assessment for arsenic (As) and lead (Pb) in individuals through the ingestion of contaminated water was conducted using the ADD calculation based on the USEPA Equation (2). The findings, illustrated in Figure 6, indicate that the ADD for arsenic varies from 0 to 0.0035 mg/kg/day. The majority of samples exhibit an ADD for arsenic ranging from 0 to 0.0015 mg/kg/day, with a few outliers showing values from 0.0015 to 0.0035 mg/kg/day. As for lead, the ADD ranges from 0 to 0.025 mg/kg/day, with the majority of samples showing an ADD for Pb in the range of 0 to 0.005 mg/kg/day. However, a few outliers were observed with ADD for Pb values between 0.005 and 0.025 mg/kg/day.
Figure 6

Average daily dose identified for As and Pb among the collected drinking water samples.

Figure 6

Average daily dose identified for As and Pb among the collected drinking water samples.

Close modal
In the second step of health risk assessment, HQ was identified that is a crucial metric in environmental risk assessment and provides insight into potential health risks associated with exposure to specific contaminants. HQ was measured in specific individuals or groups by comparing the ADD to the daily reference dose. The ratio of HQ > 1 depicts that the health hazards exist due to exposure to any specific contaminant. The statistical study results showed that the mean square values for HQ (As) and HQ (Pb) such as 21.5642 and 3.67376, respectively, and p-value < 0.05 suggests that the data are highly significant, as shown in Table 2. The spatial variation for HQ (As) ranges from 0 to 9.47 among the collected samples (Figure 7), the results revealed 30 locations where the HQ (As) > 1 and these available drinking water sources may initiate serious health complications among the individuals. The names of the locations were Malpur, Kot Hathiyal North, Kot Hathiyal South, Phul Garan, Pind Biggu, Charah, Kirpa, Mughal, Rawat, Humak, Sihala, Village Lohi Bhar, Darwala, Koral, Khana Dak, Tarlai, Ali Pur, Sohan Village, Chak Shahazad, Rawal Town, Golara Sharif, Badhan Kalan, Tarnol, Dhoke Ratta, Pirwadhai, Khyabane E Sir Syed South, Hazara Colony, Karnal Yousaf Colony, Chah Sultan and Imam Bara.
Figure 7

Spatiotemporal variation of (a) arsenic (As), (b) lead (Pb), (c) HQ (As) and (d) HQ (Pb) in the study region.

Figure 7

Spatiotemporal variation of (a) arsenic (As), (b) lead (Pb), (c) HQ (As) and (d) HQ (Pb) in the study region.

Close modal

The spatial variation for HQ (Pb) ranges from 0.02 to 6.31 (Figure 7) and the results for HQ (Pb) identified 11 drinking water sources had HQ(Pb) > 1 and these drinking water sources may cause serious health effects due to lead toxicity among the users and the locations with lead contamination were Malpur, Pind Biggu, Tumair, Rawat, Mughal, Sector I-8, Sector I 9/4, Dhoke Hassu North, Pirwadhi, Dhoke Alia Kbar and Mohan Pora.

The data from the health risk assessment Equations (1) and (2), for arsenic (As) and lead (Pb), were investigated through vast level sampling and interpreted by spatial analysis and Google map identified that the areas of twin cities that are near to the Tarnol, Islamabad expressway, Sirinagar High Way, Barakhu, IJP-Road, Sihala and Taramari were identified as health hazardous areas (Figure 8), due to the presence of HQ > 1 for As and Pb at 41 drinking water sources. The presence of a higher concentration of As and Pb at the identified location can be interconnected with the heavy anthropogenic and geogenic activities that occurred in the twin cities during the last decade the study results were in line with the findings of Abeer et al. (2020). The huge population of more than 3 million in the twin cities also forced the people to live in illegal housing societies near the highways and industrial estates without any water interventions and these residents are at high risk of As and Pb toxicity.
Figure 8

Potential health risk areas for arsenic (As) and lead (Pb) in the study region.

Figure 8

Potential health risk areas for arsenic (As) and lead (Pb) in the study region.

Close modal
  • 1. Overall water quality showed that 51.57% of drinking water samples were unsatisfactory for drinking.

  • 2. Rural areas of Islamabad have higher concentrations of TDS, arsenic (As) and lead (Pb) which were not supervised by the CDA, and it adds up to other correlated contaminants.

  • 3. Urban areas of Islamabad and Rawalpindi have water supply systems by the Water and Sanitation Agency (WASA) Rawalpindi and CDA showed satisfactory results for physicochemical parameters.

  • 4. A significant population resides in illegal housing societies and small settlements (Dhokes) without a centralized water supply, relying on domestic bore wells with no water quality supervision.

  • 5. Health risk assessment revealed that 30 drinking water sources had a HQ > 1 for arsenic and 11 sources had a HQ > 1 for lead, indicating significant potential health risks.

  • 6. The study recommends that implementing planned drinking water supply systems in the rural areas of Islamabad and Rawalpindi, consistent monitoring of installed water supply units, a strict ban on illegal settlements and infrastructure and financial support by the government is essential to ensure the safety and health of residents, particularly in rural and underserved regions.

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

The authors declare there is no conflict.

Abeer
N.
,
Khan
S. A.
,
Muhammad
S.
,
Rasool
A.
&
Ahmad
I.
2020
Health risk assessment and provenance of arsenic and heavy metal in drinking water in Islamabad, Pakistan
.
Environmental Technology and Innovation
20
,
101171
.
Acharjee
S.
&
Ahmed
S. I.
2016
Access to water supply among the tea pickers in Sylhet, Bangladesh
.
Space and Culture, India
4
(
1
),
100
108
.
Ali
U.
,
Batool
A.
,
Ghufran
M. A.
,
Asad-Ghufran
M.
,
Sabahat-Kazmi
S.
&
Hina-Fatimah
S.
2019
Assessment of heavy metal contamination in the drinking water of Muzaffarabad, Azad Jammu and Kashmir, Pakistan
.
International Journal of Hydrology
3
(
5
),
331
337
.
Amir
M.
,
Asghar
S.
,
Ahsin
M.
,
Hussain
S.
,
Ismail
A.
,
Riaz
M.
&
Naz
S.
2021
Arsenic exposure through drinking groundwater and consuming wastewater-irrigated vegetables in Multan, Pakistan
.
Environmental Geochemistry and Health
43
(
12
),
5025
5035
.
Ayari
J.
,
Barbieri
M.
,
Agnan
Y.
,
Sellami
A.
,
Braham
A.
,
Dhaha
F.
&
Charef
A.
2021
Trace element contamination in the mine-affected stream sediments of Oued Rarai in north-western Tunisia: A river basin scale assessment
.
Environmental Geochemistry and Health
43
(
10
),
4027
4042
.
Ayari
J.
,
Barbieri
M.
,
Barhoumi
A.
,
Boschetti
T.
,
Braham
A.
,
Dhaha
F.
&
Charef
A.
2023
Trace metal element pollution in media from the abandoned Pb and Zn mine of Lakhouat, Northern Tunisia
.
Journal of Geochemical Exploration
247
,
107180
.
Barn
P.
,
Nicol
A. M.
,
Struck
S.
,
Dosanjh
S.
,
Li
R.
&
Kosatsky
T.
2014
Investigating elevated copper and lead levels in school drinking water
.
Environmental Health Review
56
(
04
),
96
102
.
Cheng
Z.
,
Zheng
Y.
,
Mortlock
R.
&
Van Geen
A.
2004
Rapid multi-element analysis of groundwater by high-resolution inductively coupled plasma mass spectrometry
.
Analytical and Bioanalytical Chemistry
379
(
3
),
512
518
.
Daud
M. K.
,
Nafees
M.
,
Ali
S.
,
Rizwan
M.
,
Bajwa
R. A.
,
Shakoor
M. B.
,
Arshad
M. U.
,
Chatha
S. A. S.
,
Deeba
F.
,
Murad
W.
&
Malook
I.
2017
Drinking water quality status and contamination in Pakistan
.
BioMed Research International
17
(
1
),
1
18
.
Genchi
G.
,
Carocci
A.
,
Lauria
G.
,
Sinicropi
M. S.
&
Catalano
A.
2020
Nickel: Human health and environmental toxicology
.
International Journal of Environmental Research and Public Health
17
(
3
),
679
.
Hisam
A.
,
Rahman
M. U.
,
Kadir
E.
,
Tariq
N. A.
&
Masood
S.
2014
Microbiological contamination in water filtration plants in Islamabad
.
Journal of the College of Physicians and Surgeons Pakistan
24
(
5
),
345
350
.
Hussain
S.
,
Habib-Ur-Rehman
M.
,
Khanam
T.
,
Sheer
A.
,
Kebin
Z.
&
Jianjun
Y.
2019
Health risk assessment of different heavy metals dissolved in drinking water
.
International Journal of Environmental Research and Public Health
16
(
10
),
1737
.
Islam
M. R.
,
Sarkar
M. K. I.
,
Afrin
T.
,
Rahman
S. S.
,
Talukder
R. I.
,
Howlader
B. K.
&
Khaleque
M. A.
2016
A study on total dissolved solids and hardness level of drinking mineral water in Bangladesh
.
American Journal of Applied Chemistry
4
(
5
),
164
169
.
Li
H.
,
Smith
C. D.
,
Wang
L.
,
Li
Z.
,
Xiong
C.
&
Zhang
R.
2019
Combining spatial analysis and a drinking water quality index to evaluate monitoring data
.
International Journal of Environmental Research and Public Health
16
(
3
),
357
.
Nazir
A.
,
Akber
S.
&
Aslam
Z.
2022
Drinking water quality assessment of metro bus stations of Islamabad and Rawalpindi
.
International Journal of Economic and Environmental Geology
13
(
1
),
30
32
.
PCRWR
2016
Annual Report 2014–15. Pakistan Council of Research in Water Resources. Khayaban-e-Johar, H-8/1, Islamabad-Pakistan
, pp.
1
93
.
Available from: https://pcrwr.gov.pk/annual-reports/ (accessed 3 April 2023)
.
Rana
S. A.
,
Ali
S. M.
,
Ashraf
M.
,
Shah
A. A.
,
Iqbal
K. M. J.
,
Ullah
W.
&
Ulain
Q.
2022
GIS-based assessment of selective heavy metals and stable carbon isotopes in groundwater of Islamabad and Rawalpindi, Pakistan
.
Frontiers in Environmental Science
10
,
1027323
.
Rice
E. W.
&
Bridgewater
L.
2012
Standard Methods for the Examination of Water and Wastewater
, Vol.
10
.
American Public Health Association
,
Washington, DC
, USA.
Shakoor
M. B.
,
Niazi
N. K.
,
Bibi
I.
,
Rahman
M. M.
,
Naidu
R.
,
Dong
Z.
,
Shahid
M.
&
Arshad
M.
2015
Unraveling health risk and speciation of arsenic from groundwater in rural areas of Punjab, Pakistan
.
International Journal of Environmental Research and Public Health
12
(
10
),
12371
12390
.
Sharma
A. K.
,
Sharmab
R.
&
Sharmac
N.
2014
Ground water quality in some rural area of Khathumar Tehsil at Alwar District Rajasthan, India
.
Scientific Journal of Environmental Sciences
3
(
1
),
1
4
.
Sohail
M. T.
,
Mahfooz
Y.
,
Aftab
R.
,
Yen
Y.
,
Talib
M. A.
&
Rasool
A.
2020
Water quality and health risk of public drinking water sources: A study of filtration plants installed in Rawalpindi and Islamabad, Pakistan
.
Desalination Water Treatment
181
,
239
250
.
Uauy
R.
,
Maass
A.
&
Araya
M.
2008
Estimating risk from copper excess in human populations
.
The American Journal of Clinical Nutrition
88
(
3
),
867
871
.
USEPA – United States Environmental Protection Agency 2010 Toxicological review of inorganic arsenic in support of summary information on the Integrated Risk Information System (IRIS), Final Draft. Federal Register EPA/635/R-10/001.
Wodschow
K.
,
Villanueva
C. M.
,
Larsen
M. L.
,
Gislason
G.
,
Schullehner
J.
,
Hansen
B.
&
Ersboll
A. K.
2021
Association between magnesium in drinking water and atrial fibrillation incidence: A nationwide population-based cohort study, 2002–2015
.
Environmental Health
20
(
1
),
1
13
.
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