Introduction

Groundwater is the utmost valuable source for drinking which is extensively used in various parts of the world (Khanoranga and Khalid 2019). A worldwide challenge of water contamination has developed into a serious subject particularly with augmented urbanization (Titilawo et al. 2018; Ghorade et al. 2014; Li et al. 2014). It significantly impacts on water resource management by many ways including overexploitation, land-use change and adulteration (Grimm et al. 2008; Jia et al. 2013; Nawab et al. 2018; Vesali Naseh et al. 2018). The unrestrained urban and industrial wastewater release has substantial influence on the soil, plants and river and stream water quality (Qadir et al. 2007; Yousaf et al. 2016; Abbas et al. 2017; Mukatea et al. 2018), especially trace elements’ impact on human health and ecology (Xiao et al. 2019; Dong et al. 2017; Chowdhury et al. 2016; Zeng et al. 2015). High concentrations of metals including chromium, cadmium, lead and manganese are lethal for aquatic and human life especially causing breath shortening and different types of cancers (Muhammad et al. 2011; Khan et al. 2013b).

Existing water resources lead to insufficiency and deteriorating quality of freshwater which lead to serious water scarcity (Khair et al. 2012; Khan et al. 2013a; Hussain et al. 2017a, b; Khan et al. 2017; Ishaque and Shaikh 2017). Irrigation practices consume almost 70% of this water extraction (FAO 2013). Pakistan is ranked as the seventh highest region of the globe exposed to water scarcity. Farmers’ response to water scarcity insight also showed that it directly impacts on the economic sector. Farmers always practice numerous methods for adaptation of farming to climate change susceptibility in which water scarcity is a major concern (Tang et al. 2013; Fahad and Wang 2018).

Most of the developing countries including Pakistan, India, Africa and Bangladesh are using water with worsened quality due to man-made activities day by day (Chabukdhara et al. 2017; Li et al. 2017; Yousaf et al. 2016b). Pakistan is situated in southern Asia with arid to semi-arid climatic conditions in different areas. The large population is facing a lot of water-related problems due to urbanization (Azizullah et al. 2011). In Pakistan, water availability is continuously decreasing; it will further drop to 877 m3/annum which will shockingly decrease to 660 to 575 ft3 by year 2025 and 2050 respectively. In KPK <half of the six million population in different districts have no access to clean water due to high contamination of metals from adjacent sources (Khan et al. 2013a). In Baluchistan and Sindh, the groundwater table is reducing by 3.5 m, and soon, it will be entirely exhausted (Khanoranga and Khalid 2019; Van Steenbergen et al. 2015; Khair et al. 2015). Numerous researchers (Shakoor et al. 2018; Khalid et al. 2018; Daud et al. 2017) studied the water quality of different districts of Punjab including Sheikhupura (73%), Lahore (100%), Gujranwala (64%), Multan (94%), Kasur (100%) and Bahawalpur (88%) and found that the water quality of these districts exceeded the limit of arsenic in groundwater; diarrhoea was also observed due to the deteriorated water quality. Currently, arsenic is becoming the major pollutant in various surface and groundwater (Baig et al. 2010a; Arain et al. 2009; Fatmi et al. 2009; Farooqi et al. 2007). Faisalabad is recognised as a contaminated industrial (industries including textile, ice, pharmaceutical, flour, cotton, sugar and food) city because of insufficient treatment facilities and < 90% of samples were above the WHO limits with respect to K, Na, Cl, total dissolved solids (TDS) and SO4 (Daud et al. 2017).

Therefore, it is necessary to assess the concentration of different pollutants and their health risks in water. Globally, the use of different statistical analysis, indices and health risk assessment in different water bodies have been reported earlier (Avino et al. 2011; Wen et al. 2011; Escudero et al. 2010). Water quality index (WQI) proved an easy approach to measure quality of water in a quantitative way. It changes the concentration of pollutants to sub-indexes and then convert these into one numerical score, based on their quality (Fox 2014). It makes it easy to assess water quality for policymakers to comprehend the condition of a freshwater body or aquatic environment (Feng et al. 2015). Mostly, different multivariate and univariate statistical analyses which include principal component analysis and inter-metal correlation are used for analysing complex and huge data matrices. These methods have been used previously in numerous studies to investigate the contamination and health impacts (Wunderlin et al. 2001; Muhammad et al. 2011; Khan et al. 2013b).

The objective of the present study was to identify the quality and risk assessment from different surface and drinking water sources, through multivariate indexes and risk assessments in an industrial and populous city where there is no data available on these selected areas with quantification of pollution and health risk assessment in children and adults.

Methodology

Study area

In Punjab, Faisalabad is accounted as the second mega metropolitan with a rising population. The accounted population of the city was 3.2 million in 2015 at 3.58% growth rate. It comprised of a 157-km2 area and is located at Rechna Doab (Awais et al. 2017). The intensity of rainfall was recorded at 408 mm per year. The highest temperature was recorded at 45 °C and the documented wind speed was 94 mph (Ali et al. 2017). Figure S1 shows the sample collection sites in Faisalabad city.

Sampling and laboratory analysis

Drinking water samples (n = 48) collected from tap water and hand pumps. Surface water samples (n = 37) collected from the Chenab river (n = 9) and canals (Jhang Branch Upper Canal n = 8, Rakh Branch Canal n = 9, Gugera Branch Canal n = 11) were collected from Faisalabad to identify the water quality for drinking and irrigation purposes. Samples were collected in pre-washed plastic bottles and brought to the laboratory for analysis of selected parameters including physical, chemical, biological and trace metal analyses according to the standard method of APHA (American Public Health Association) (2012) (Table S1 supplementary information).

Water quality index

Water quality index (WQI) was calculated by following different steps. WQI was firstly proposed by Horton (1965); later, it developed into different methods (Alobaidy et al. 2010). A total of 19 parameters were selected and were assigned their weight by considering previous studies. Dividing each assigned weight with the total assigned weight of all parameters gave the value of relative weight (RW) (Table 1). Qi was calculated by the following formula:

$$ Qi=\left\lfloor \frac{C_i}{S_i}\right\rfloor \times 100 $$

where Qi is the quality rating obtained from assigned weight, Ci is the concentration of parameters and Si is the standard value given by WHO.

Table 1 Assigned, relative weight of selected parameters and WQI
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To calculate the water quality index, the sub-indices (SIi) of every parameter were calculated prior. This value was used in calculating the quality index by the given formula

$$ {\displaystyle \begin{array}{c}S{I}_i= RW\times {Q}_i\\ {} WQI={\sum}_{i=1}^nS{I}_i\end{array}} $$

The classification of WQI, described by Goher et al. (2014), is as follows: a value 0 to 25 WQI was categorized as excellent water, between 26 and 50 is good, from 51 to 75 is poor, and between 56 to 100 is considered very poor, while above 100, water becomes unsuitable for drinking.

Water quality for irrigation

Surface water quality was evaluated by calculating the salinity hazard and sodium (alkalinity) hazard (SAR) and magnesium absorption ratio (MAR). Xiao et al. (2019) divided salinity hazard into four classes: if electrical conductivity is less than 250 μS/cm, it is considered low (C1); if the value lies between 250 and 750 μS/cm, it is considered as medium (C2); it is categorized as high if EC is found between 750 and 2250 μS/cm (C3); and it is considered very high if the value of EC is found above 2250 μS/cm (C4). Sodium (alkalinity) hazard (SAR) is categorised into four classes, as mentioned by Raju et al. (2011): (S1) low if the value of SAR is less than 10; (S2) medium if between 10 and 18; (S3) high if between 18 and 26; and (S4) considered very high if SAR is more than 26. SAR was calculated by using the following formula in which cation and anion concentration was taken in milliequivalents per liter:

$$ \mathrm{SAR}=\frac{\mathrm{N}{\mathrm{a}}^{+}}{\sqrt{\frac{C{a}^{2+}+\mathrm{M}{\mathrm{g}}^{2+}}{2}}} $$

Magnesium absorption ratio was also calculated by using the following formula (Obiefuna and Sheriff 2011):

$$ \mathrm{MAR}=\frac{\mathrm{M}{\mathrm{g}}^{2+}\times 100}{C{a}^{2+}+\mathrm{M}{\mathrm{g}}^{2+}} $$

The acceptable level for MAR is 50 meq/L; above this level, the water is considered unsuitable.

Health risk assessment

Non-carcinogenic risk assessment

There are three exposure routes of heavy metals in the human body including oral ingestion, dermal interaction and inhalation (Agency for Toxic Substances, Disease Registry (ASTDR) 2007). Average daily dose (ADD) was computed by using following formula

$$ \mathrm{ADD}=\frac{C\times \mathrm{IR}\times \mathrm{EF}\times \mathrm{CF}\times \mathrm{ED}\ }{\mathrm{BW}\times \mathrm{AT}} $$

where C was the concentration of metals (mg/L), the water consumption rate (IR) was 2 L day−1 for adults and 0.63 L day−1 for children, the values 72 kg for adults and 15 kg for children were considered average body weight (BW) (Rehman et al. 2018), the frequency of exposure (EF) which was 350 days year−1, ED was the duration of exposure (70 years for adults and 6 years for children) (USEPA 2004) and the average time (AT) for adults 25,550 days and for children 2190 days were taken for non-carcinogenic risk assessment, while for carcinogenic risk, it was 25,550 days for both adult and children (USEPA 2014).

To evaluate non-carcinogenic risk value, the hazard quotient of average daily dose was calculated, which was assumed to be the threshold value. It was calculated by dividing the ADD with (RfD) oral reference dose (Table 2) (United State Environmental Protection Agency (USEPA) 1992).

$$ \mathrm{HQ}=\mathrm{ADD}/\mathrm{RfD} $$
Table 2 Reference dose and cancer slope factor values
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Carcinogenic risk assessment

The probability of producing cancer risk is identified due to the presence of any metal. It was calculated by the multiplication of cancer slope factor (CSF) (Table 2) with lifetime average daily dose (LADD). Here, LADD was calculated with an AT value of 25,550 both in children and adults (USEPA 2014). Cancer risk is high with an at-risk value reaching 10−3 (Wei et al. 2015). Carcinogenic risk was evaluated only for As, Cr and Ni, due to accessibility of cancer slope factor values.

$$ \mathrm{Cancer}\ \mathrm{Risk}\ \mathrm{Assessment}=\mathrm{CSF}\times \mathrm{LADD} $$

Principal component analysis (PCA) combined with cluster analysis (CA) were completed by Origin 2017 software, while R software was used for correlation matrix.

Results and Discussion

Overall physiognomies of water parameters

Drinking and surface water samples (n = 48, n = 37 respectively) from different locations were analysed for physicochemical and biological parameters and metal values. Average concentrations of all drinking and surface water parameters are shown in Table 3. Management of groundwater is significantly affected by these physicochemical parameters (Kattan 2018). Average concentrations of some physical parameters in drinking water include pH (6.93 ± 0.16), chloride (431.8 ± 343.8), TDS (1275.5 ± 773) and phosphate (0.22 ± 0.13), and in groundwater, turbidity (32.21 ± 18.95), electrical conductivity (297.08 ± 108.34) and F (3.49 ± 0.74) were higher than their permissible standard values. Increased value of pH in water may cause a decrease in the metal toxicity (Aktar et al. 2010). A high concentration of TDS in water is the root cause of sewage and industrial effluent discharge. (Phiri et al. 2005; Rim-Rukeh et al. 2006). More TDS increases the level of COD and BOD in water which ultimately impact on dissolve oxygen (DO) which contribute in gastro-intestinal irritation, alter taste and corrosion etc. (Patil et al. 2012; Mahananda et al. 2010). Increased concentration of chloride applies osmotic pressure in marine living organisms and change the taste of water, and also cause increased blood pressure and hypertension between individuals (Kattan 2018). Correlation matrix was measured in drinking and surface water parameters (Figure S2 and S3). In drinking water, calcium strongly correlates with sulphate, magnesium and hardness, sulphate with total dissolved solids while electrical conductivity with chloride and chloride with sodium and potassium has a strong correlation. This can be also supported by principal component analysis and cluster analysis in Figure 1a and 2a. In surface water samples COD has strong correlation with temperature, TDS, EC and NO2, Cl has significant correlation with DO and hardness while Fe was correlated with DO also shown their variance in principal component analysis and cluster analysis (Figs. 1b and 2b). Biological parameters including total coliform (114.1 ± 16.4) and E. coli (106.8 ± 12.7 and 479 ± 161) in drinking and surface water were many folds higher than WHO and PEQ permissible limits (0/100 mL). Drinking water is highly polluted with numerous anthropogenic activities in largely populated cities of Pakistan like Lahore, Rawalpindi, Karachi, Peshawar, Kasur, Faisalabad, Gujrat and Sialkot and cannot be suggested for human intake (Bhutta et al. 2002; Mumtaz et al. 2010; Azizullah et al. 2011). A was study conducted by PCRWR (2005) in metropolises of Pakistan and stated that 35% and 65% of groundwater samples were polluted with E. coli and total coliform, respectively, while surface water samples had 100% contamination of total coliform and E. coli. Another research was conducted by Khan et al. (2018) on bacterial contamination of the Swat river, Pakistan, and found high faecal contamination in surface water that could be credited to increasing urbanization at downstream, direct discharge of municipal effluents, excreta from human beings and agricultural runoffs which may spread possible health risks in the native communal. As (0.032 ± 0.03) and Ni (0.04 ± 0.03) were found higher in drinking water, while in surface water, As (0.013 ± 0.02) and Cr (0.24 ± 0.08) were higher than their permissible limits. The major cause of metal pollution is anthropogenic activity which decreases the water quality (Noreen et al. 2017). Ingestion of Ni-sulphate and Ni-chloride can be a reason of lethal cardiac capture and other major health complications (Muhammad et al. 2011). Karavoltsos et al. (2008) determined the value of nickel in surface water which exceeded 2.1% than Greece standards. In many national and international health and environmental organisations, it is admitted that arsenic is one of the highest cancer-causing and toxic pollutants which poses an extreme risk to the environmental and health (Rafiq et al. 2018). Arsenic exposure (acute and chronic) results in As-associated illness called arsenicosis and their adverse influences on health strongly depend on the dietary status of the exposed living being (Shahid et al. 2018). Arsenic in groundwater with a higher level was reported in many studies, which implicated geohydrological, biogeochemical and geothermal factors, telling arsenic is mobilised in aquifers by numerous arsenic-bearing oxides (Abbas et al. 2018; Mehmood et al. 2017; Shakoor et al. 2016; Brahman et al. 2013; Baig et al. 2010b; Singh 2006). This type of non-point sources of pollution could be accountable too for the high concentration of heavy metals in water, like deposition through the atmosphere becomes a reason for seepage of some heavy metals in different underground resources of water (Ali et al. 2017).

Table 3 Mean concentrations in drinking and surface water samples and comparison with standard values
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Fig. 1
figure 1

Principal component analysis for a drinking water and b surface water

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Fig. 2
figure 2

Cluster analysis for a drinking water and b surface water samples

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Table 4 presents the comparison of metal concentration in water with other national and international studies. Arsenic was found higher in the present study in comparison with others except Ghaderpoori et al.’s (2018) study in Iran and Kumari et al. (2017) study in India. Cadmium was found higher in Kohistan northern Pakistan (Muhammad et al. 2011), Peninsular Malaysia (Azrina et al. 2011) and Bangladesh (Rahman et al. 2016), while lower in all other studies especially in Swat and Quetta in Pakistan (Khan et al. 2013b; Khanoranga and Khalid 2019). Chromium was lower in South Africa as studied by Nuthunya et al. (2017) and higher in all other studies. Copper was higher in western Nigeria and Khorramabad, Iran (Ayandiran et al. 2018; Ghaderpoori et al. 2018), and lower in all other studies. Lead was found lower in the present study except in Peninsular Malaysia (Azrina et al. 2011). Nickel values were higher than those in Charsadda (Khan et al. 2013a), northern Pakistan (Muhammad et al. 2011), Chinese Loess Plateau (Xiao et al. 2019) and Malaysia (Azrina et al. 2011; Kato et al. 2010). Zinc values were found lower than those in northern Pakistan (Muhammad et al. 2011), western Nigeria (Ayandiran et al. 2018) and Iran (Ghaderpoori et al. 2018), and higher from those in India (Kumari et al. 2017). In the present study, all the samples were taken from residential areas, but due to massive industrialization and overpopulation, the underground water characteristics deteriorated with the passage of time (Noreen et al. 2017; Ali et al. 2017; Yamin et al. 2015). Overall concentration was found lower than those in other studies in Pakistan but higher than those in other different countries. Increased concentration of these heavy metals might be credited to anthropogenic activities mainly improper agriculture runoff (Kumari et al. 2017).

Table 4 Comparison of metal concentrations (mg/L) in water with other studies
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Assessment of Water pollution

Water quality index was determined for a comprehensive understanding about drinking water quality. WQI in all samples were ranged from 28 to119 with a mean value of 61.8 indicating poor water quality (Table 1). Out of 48 sites, three sites (6%) were identified as unsuitable water quality (>100 WQI) while 4 sites (8%) have very poor water quality ranged from 76 to 100 WQI and 27 sites (56%) have poor water quality with WQI < 50–75. All unsuitable water sites were located near the sewage drains which deteriorate the drinking water quality through seepage. A similar study was conducted by Xiao et al. (2019) and 3% of samples were found poor and 3% unsuitable for drinking purposes. Khanoranga and Khalid (2019) determined the ground water quality index of Baluchistan district and found that all selected sites were poor in quality for drinking purposes due to variations in some physicochemical parameters and the occurrence of several heavy metals from anthropogenic sources. Al-Mutairi et al. (2014) found that only 3.1% surface water samples had excellent WQI in 2010 which deteriorated after years of anthropogenic activity. Groundwater of Faisalabad is being contaminated by nearby industrial effluent and sewerage wastewater drains and showed a noteworthy increase by WHO drinking water standards (Yamin et al. 2015). Alobaidy et al. (2010) investigated WQI in Iraq and found that it was 75 in 2008 and increased to < 100 during 2009 which indicates that the preventive movements conducted by the establishments were not adequate to improve water quality.

In agricultural countries, water needs for irrigation are higher than those for domestic and drinking purposes. Numerous indices were developed for evaluating irrigation water quality from surface and groundwater sources, which were substantial methods and commonly used worldwide (Singh et al. 2018; Rana et al. 2018; Shooshtarian et al. 2018). Electrical conductivity and sodium absorption ratio were generally used reciprocally to determine the irrigation water quality (Awais et al. 2017). SAR was applied to river and canal water samples in China and found that the value of sodium (alkalinity) hazard ranged from 2.12 to 45.88 with average value 14.8 (Xiao et al. 2019). Out of 11, only 1 sample of the Gugera Branch Canal was found with S2 sodium (alkalinity) hazard with low salinity hazard and identified in zone C1–S2; two other sites also have medium salinity. Other sites including Chenab, Jhang and Rukh showed low SAR with low alkalinity hazard and were categorized in zones S1–C1 and S1–C2 (Fig. 3a). Magnesium absorption ratio (MAR) in all the sites were above 50 meq/L except for two sites of the Chenab river (Fig. 3b). Additionally, according to RSC values, 61.89% of samples were unfit for irrigation purposes in the study area. Crop yields were affected adversely with high magnesium contents as the soils become more saline (Obiefuna and Sheriff 2011). Rasool et al. (2016) evaluated the water quality for irrigation purposes and results of his study described that water quality was slightly appropriate at some points for irrigation purposes. Khanoranga and Khalid (2019) found that soil water availability was reduced by the high value of SAR which affected crop growth and lowered the major nutrient contents, i.e. calcium and magnesium. Water quality varied with average discharge also. The Jhang Branch Upper Canal discharge was 89 m3/s and 52 m3/s at the upstream end and downstream end, respectively. The Rakh Branch Canal discharge was 38 m3/s and 11 m3/s at the upstream end and downstream end, respectively. The Lower Gugera Branch Canal discharge was about 64 m3/s and 15 m3/s at the upstream end and downstream end, respectively. At the Chenab river, the minimum and maximum monthly average discharges were 51 m3/s in September and 1940 m3/s in July, respectively. Groundwater quality was investigated by Sarkar and Hassan (2006) for irrigation and detected that average water quality indices like pH, EC, SAR, MAR and TDS were in the permissible level for production of crops. A pictorial presentation of groundwater quality was presented by Raihan and Alam (2008) in the Sunamganj district and they found suitable water quality for irrigation. Obiefuna and Orazulike (2010) experimented on a similar study in the Yola area of Northeast Nigeria which also showed the ground water of that zone was appropriate for irrigation.

Fig. 3
figure 3

Water quality for irrigation purposes. a Sodium (alkalinity) and salinity hazard. b Magnesium absorption ratio

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Health Risk Assessment

Hand pump and tap water are the major source of drinking water in Faisalabad. For drinking and surface water, average daily dose (ADD) and HQ (hazard quotient) indices were calculated for assessment of health risk of drinking and surface water in adults and children and were also calculated by other researchers like Kamunda et al. (2016) and Titilawo et al. (2018). Table 5 presents the mean value of ADD and HQ for non-carcinogenic and CAR for carcinogenic risk assessment in drinking and surface water respectively. The HQ values of arsenic (2.84E+00, 4.30E+00) in drinking water, while in surface water arsenic (1.15E+00, 1.75E+00) and chromium (2.13E+00, 3.22E+00) were very near to threshold limit (HQ > 1) both in adult and children respectively. The order of HQ in drinking water samples were As > Cu, Cd> Cr, Ni, Pb, Zn > Fe in adult and As> Cu, Cd, Zn> Ni, Cd, Pb> Fe in children. Whereas, in surface water were As, Cr> Al, C> Ba, Ni, Pb, Fe> Cu, Zn in adults and As, Cr> Al, Ba, Cd> Ni, Pb, Fe> Cu, Zn in children. Rehman et al. (2018) determined that, in children, the HQ values of cobalt, copper, cadmium and lead were higher than those of permissible limits in groundwater of Chitral, Pakistan. The result of the present study was similar to that of Kavcar et al. (2009). Muhammad et al. (2011) stated that the hazard quotient indices for metals indicated no risk to local inhabitants compared with those of earlier studies but the hazard quotient indices of Zn, Pb, Ni, Cu and Cd were inclined to be more than what Kavcar et al. (2009) reported in drinking water and what Lim et al. (2008) reported in surface water. Xiao et al. (2019) reported that the HQ > 1 in arsenic especially in children were higher than that in adults, indicating that in a similar environment, children are more susceptible than adults. The presence of arsenic in drinking water in the long run can cause possibly cancer-causing effects and skin lesions, hypertension, diabetes, neuropathy, etc. Coal mining/usage and the arid weather can also lead to arsenic pollution (He and Charlet 2013; Xiao et al. 2016). Titilawo et al. (2018) estimated the hazard index of river water which indicated that no non-carcinogenic consequence in the inhabitant would rise by metal exposure in his study area.

Table 5 Non-carcinogenic and carcinogenic health risk metals through drinking and surface waters
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Hu et al. (2012) stated that the hazard is supposed to begin in humans if the value of cancer risk is greater than 1 × 10−4. In the present study, the values for cancer risk in drinking water was higher in As (1.28E−03) in adults, while in surface water samples, Cr was found higher (3.20E+00) than cancer the threshold limit (Table 5). Arsenic and chromium posed a high risk of carcinogenicity in adults while Nickel was also found very close to the tolerable value of carcinogenicity. Rehman et al. (2018) reported that CR above the range of carcinogenicity for these metals in the order cobalt > cadmium > chromium > nickel happened in adults and children. Cancer risk was applied in the northern side of Pakistan and resulted that 1 person in every 225,836 children and 314,206 adults might be at risk (Muhammad et al. 2011). Exposure of metal in drinking water may cause carcinogenic effects which can be lethal for the local community.

According to the results of the present study, drinking water quality was considered poor. This is correlated with health risk assessment. Poor drinking water quality leads to serious health risks in adults and children which can lead to carcinogenicity, especially in the presence of metals. The major source of contamination in water is heavy industries with no treatment facility and regular monitoring. A study conducted by Rehman et al. (2018) indicated that by consuming water for drinking and irrigation purposes, the inhabitants are more prone to different diseases including hypertension, stomach cancer, lung cancer, gastroenteritis, anaemia, cardiac arrest and intellectual disabilities in the area of industrial, agricultural and mining activities. As opposed to Aelion et al.’s (2008) report that city areas are extremely polluted, the suburban area might also pose metals of human-driven origin (using pesticide and industrial activities).

Conclusion

The present study thoroughly describes the water pollution and associated health risks in the metropolis industrial city of Pakistan. Most of the parameters including physico-chemical and biological and metals (chloride, total dissolved solids, bacteriological contamination, arsenic and nickel and chromium) exceeded the World Health Organization and Punjab environment quality standard permissible limits. WQI showed poor water quality in most of the sites where major industries occur and deteriorate water to the level where it can cause severe health hazards. The order for poor surface water quality in barrages and rivers was as follows: GBC >RBC >JBC > Chenab river, mainly depending on the average flow rate. Drinking water quality was found poor in more than 56% of samples due to major industrial pollution in the area. Irrigation water quality was medium in the case of sodium alkalinity hazard and high in the case of magnesium absorption. Non-carcinogenic and carcinogenic risk was found in children and adults for arsenic, both in drinking water and surface water. The values of hazard quotient (HQ) and CRA were more than the probable hazard which occurred in children and adults.

The industrial and domestic wastewater badly impacted on ground water quality and created an alarming situation for the local inhabitants’ health and environment. Preventive measures and monitoring are supposed to be compulsory to eliminate the health hazard in the local population. The study will be of benefit to the provision and implementation of proper monitoring and public policies in order to approve integrated and sustainable water development and to minimise the health hazards in the study area. The study is further recommended to evaluate the organic pollutants in this industrial area especially pesticides which can cause major health problems.