Skip to main content

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



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


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


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


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

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

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

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


Description of the study sites and sampling

The current study was conducted in Lahore, the second-largest metropolitan city in Pakistan. It is ranked as the 18th most populous city in the world with an 11.13 million total population and 6300 persons/km2 population density according to the Census of 2017 [33]. The sampling sites were identified with a global positioning system (GARMIN eTrex 30) and a field survey was carried out. A total of 200 water samples were collected from various sources, including 50 each from groundwater, canals, river, and drains (Fig. 1, Additional file 1: Table S1). The rainfall events are suggested to have the potential to alter the metal contamination of water [34]. To rule out the impact of rainfall events, samples were collected from March to April 2019 using a grab sampling technique. Thus, we mainly focused on metal contamination of dry season. Briefly, groundwater samples were collected from 50 tube wells located across the city. For canal water, samples were collected from the Lahore canal and BRB canal (Bambawali-Ravi-Bedian). For the river, all the samples were collected from river Ravi, from Syphon to Sagian pull in the downstream direction. For wastewater, major polluted drains were selected such as Hudiara drain (20 sites), Cantt drain (20 sites), and Sattukatla drain (10 sites). A minimum distance of 1 km was maintained between every two sampling sites. A detailed description of sampling sites is provided in supporting information (Additional file 1: Table S1).

Fig. 1
figure 1

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

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

Sample analysis

Samples were analyzed following the “Standard Methods for the Examination of Water and Wastewater” by the American Public Health Standard Association, 21st edition [35]. Briefly, the samples were subjected to filtration using a 0.45 µm filter. Additionally, we added 10 mL of nitric acid (HNO3) to the samples to prevent any heavy metal precipitation. The analysis of Cu, Cr, Ni, and Pb was carried out at the Irrigation Department, Lahore, Punjab through atomic absorption spectroscopy using an equipment Varian FS 240AA (Varian Medical Systems, Palo Alto, CA, USA). For quality assurance, standard reference materials from the National Institute of Standards and Technology (NIST) were used. The relative standard deviation of analytical procedures ranged from 5 to 10%. The analysis was conducted thrice and the average value was used for statistical evaluation.

Human health risk assessment

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

$${{\text{CDI}}}_{{\text{Oral}}}=\frac{\left(\mathrm{C }\times \mathrm{ IR }\times \mathrm{ EF}\right)}{{\text{BW}}},$$
$${{\text{CDI}}}_{{\text{Dermal}}}=\frac{\left(\mathrm{C }\times \mathrm{ SA }\times \mathrm{ ET }\times \mathrm{ P }\times \mathrm{ CF}\right)}{{\text{BW}}},$$

where C is the concentration of metal, IR is the intake rate of water, ET is exposure time, EF is the exposure factor, CF is the conversion factor, P is the permeability coefficient, SA is the total surface area of skin, and BW is body weight. The average values of EF, ET, IR, SA, P, CF, and BW are provided in the supporting information (Additional file 1: Table S2). The body weight (BW) was calculated for adults aged between 15–67 years for females, 15–66 years for males, and 0–15 years for children [30, 37]. Similarly, the average value of skin surface area for adults is 18,450 cm2 and for children is 16,450 cm2. Furthermore, the non-carcinogenic effects of metals were calculated by using HQ (Eq. 3).


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

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

$${\text{HRI}}= \sum {\text{RQ}}.$$

Ecological risk assessment

We analyzed the ecological risk of metal contamination based on the toxic unit (TU) for three trophic levels i.e., algae, fish and crustaceans [38] (Eq. 5). The toxic unit is defined as the ratio of measured concentration (for metals) and effect concentration (lethal and sub-lethal) for three organisms (algae, fish and crustaceans). Reference values for EC50 or LC50 were obtained from previous studies [39,40,41,42,43,44] and are provided in supporting information (Additional file 1: Table S4).

$${{\text{TU}}}_{{\text{sum}}}= {\text{log}}\left[\sum_{i = 1}^{n}\left(\frac{{\text{Ci}}}{{{\text{LC}}}_{50{\text{i}}}}\right)\right],$$

where TUsum is the sum of the effect of “n” metals detected at each site, Ci is the concentration (μg/L) of the respective metal “i”, and LC50i is the median lethal concentration (μg/L) of that metal for the reference organisms.

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

$${{\text{RQ}}}_{{\text{sum}}}= \text{ } \sum_{{\text{i}}=1}^{{\text{n}}}\left[\frac{{{\text{MEC}}}_{{\text{i}}}}{{{\text{PNEC}}}_{{\text{i}}}}\right],$$

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

Water pollution index

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

$${\text{PI}}=\frac{\frac{{\text{Cu}}}{2000} + \frac{{\text{Cr}}}{50} + \frac{{\text{Ni}}}{20} + \frac{{\text{Pb}}}{10}}{4},$$

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

Data analysis

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


Heavy metal contamination

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

Fig. 2
figure 2

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

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

Health risk assessment

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

Fig. 3
figure 3

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

Fig. 4
figure 4

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

Ecological risk

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

Fig. 5
figure 5

Characterization of metal contamination. The toxic units (TUsum) are presented for different water resources including groundwater (GW: blue), canal water (CW: cyan), river water (RW: green) and drain water (DW: red). For the calculation of Toxic Units, we used LC50 or EC50 of algae, crustaceans and fish. The boundaries of the central box are the 25th and 75th percentiles; the horizontal line is the median; and the whiskers of the boxplot represent the minimum and maximum values. The black dashed line indicates the threshold limit for acute risks for algae, crustaceans and fish, whereas, the red dashed lines represent the threshold limit for chronic risks

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

Fig. 6
figure 6

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

Pollution index

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

Fig. 7
figure 7

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


Metal contamination and health risks

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

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

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

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

Ecological risks

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

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

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


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

Availability of data and materials

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





Chronic daily intake






Electric conductivity




Canal water


River water


Drain water


Hazard Quotient


Health risk index


Intake rate


Exposure time


Exposure factor


Conversion factor


Body weight


Measured environmental concentration




National Institute of Standards and Technology




Pollution index


Predicted no-effect concentration


Risk quotient


Total dissolved solids


Toxic unit


World Health Organization


  1. Wang T, Pan J, Liu X (2017) Characterization of heavy metal contamination in the soil and sediment of the Three Gorges Reservoir, China. J Environ Sci Health. 52(3):201–209

    Article  CAS  Google Scholar 

  2. Ali MM et al. (2021) Environmental pollution with heavy metals: a public health concern. Heavy Metals Their Environ Impacts Mitigat 771–783

  3. Han R et al (2020) Bibliometric overview of research trends on heavy metal health risks and impacts in 1989–2018. J Clean Prod 276:123249

    Article  CAS  Google Scholar 

  4. Ustaoğlu F, Islam M (2020) Potential toxic elements in sediment of some rivers at Giresun Northeast Turkey: a preliminary assessment for ecotoxicological status and health risk. Ecol Indicat 113:106237

    Article  Google Scholar 

  5. Liu B et al (2021) Ecological risk assessment and heavy metal contamination in the surface sediments of Haizhou Bay, China. Mar Pollut Bull 163:111954

    Article  CAS  Google Scholar 

  6. Wu X et al (2016) A review of toxicity and mechanisms of individual and mixtures of heavy metals in the environment. Environ Sci Pollut Res 23:8244–8259

    Article  CAS  Google Scholar 

  7. Salam M et al (2021) Assessing the drinking water quality of educational institutions at selected locations of district Swat, Pakistan. Environ Earth Sci 80:1–11

    Article  Google Scholar 

  8. Rehman H et al (2022) Heavy metals, pesticide, plasticizers contamination and risk analysis of drinking water quality in the newly developed housing societies of Gujranwala, Pakistan. Water 14(22):3787

    Article  CAS  Google Scholar 

  9. Farid S, Baloch MK, Ahmad SA (2012) Water pollution: major issue in urban areas. Int J water Resour Environ 4(3):55–65

    CAS  Google Scholar 

  10. Haider H (2010) Water quality management model for Ravi River. Univ. of Engineering & Technology, Lahore

    Google Scholar 

  11. Remoundaki E et al (2016) Groundwater deterioration: the simultaneous effects of intense agricultural activity and heavy metals in soil. Proc Eng 162:545–552

    Article  CAS  Google Scholar 

  12. Malana MA, Khosa MA (2011) Groundwater pollution with special focus on arsenic, Dera Ghazi Khan-Pakistan. J Saudi Chem Soc 15(1):39–47

    Article  CAS  Google Scholar 

  13. Iqbal HH et al (2017) Hydrological and ichthyological impact assessment of Rasul Barrage, River Jhelum, Pakistan. Pol J Environ Stud 26(1):107–114

    Article  CAS  Google Scholar 

  14. Shahid N et al (2015) Assessing drinking water quality in Punjab, Pakistan. Pol J Environ Stud 24(6):2597–2606

    Article  CAS  Google Scholar 

  15. Azizullah A, Khattak MNK, Richter P, Häder D-P (2011) Water pollution in Pakistan and its impact on public health—a review. Environ Int. 37(2):479–497

    Article  CAS  Google Scholar 

  16. Noor R et al (2023) A comprehensive review on water pollution, South Asia Region: Pakistan. Urban Clim 48:101413

    Article  Google Scholar 

  17. Rai S et al (2011) Comparative study of some physicochemical parameters of soil irrigated with sewage water and canal water of Dehradun city, India. Arch Appl Sci Res 3(2):318–325

    CAS  Google Scholar 

  18. Ismail A et al (2014) Heavy metals in vegetables and respective soils irrigated by canal, municipal waste and tube well waters. Food Addict Contaminant 7(3):213–219

    Article  CAS  Google Scholar 

  19. Daud M et al (2017) Drinking water quality status and contamination in Pakistan. BioMed Res Int 2017:1–18

    Article  Google Scholar 

  20. Ferreccio C et al (2000) Lung cancer and arsenic concentrations in drinking water in Chile. Epidemiology. 11:673–679

    Article  CAS  Google Scholar 

  21. Baig JA et al (2009) Evaluation of arsenic and other physico-chemical parameters of surface and ground water of Jamshoro, Pakistan. J Hazard Mater 166(2–3):662–669

    Article  CAS  Google Scholar 

  22. Khan S et al (2013) Drinking water quality and human health risk in Charsadda district, Pakistan. J Cleaner Prod 60:93–101

    Article  CAS  Google Scholar 

  23. Malferrari D, Brigatti MF, Laurora A, Pini S (2009) Heavy metals in sediments from canals for water supplying and drainage: mobilization and control strategies. J Hazard Mater 161(2–3):723–729

    Article  CAS  Google Scholar 

  24. Hashim KS, Al-Saati NH, Hussein AH, Al-Saati ZN (2018) An investigation into the level of heavy metals leaching from canal-dreged sediment: a case study metals leaching from dreged sediment. IOP Conf Ser Mater Sci Eng. 454:012022

    Article  Google Scholar 

  25. Gupta N, Yadav KK, Kumar V, Singh D (2013) Assessment of physicochemical properties of Yamuna River in Agra City. Int J ChemTech Res 5(1):528–531

    CAS  Google Scholar 

  26. Baqar M, Arslan M, Mahmood A (2014) Characterization and load assessment of wastewater drains outfalls points into River Ravi, Lahore, Pakistan: an application of GIS. In: The 9th national GIS symposium in Saudi Arabia, at Sheraton Hotel & Towers, Dammam–Eastern Province. Citeseer

  27. Noreen M, Shahid M, Iqbal M, Nisar JJM (2017) Measurement of cytotoxicity and heavy metal load in drains water receiving textile effluents and drinking water in vicinity of drains. Measurement 109:88–99

    Article  Google Scholar 

  28. Khanum K et al (2017) Heavy metal toxicity and human health risk surveillances of wastewater irrigated vegetables in Lahore District, Pakistan. Carpathian J Earth Environ Sci 12(2):403–412

    Google Scholar 

  29. Jehan S et al (2020) Human health risks by potentially toxic metals in drinking water along the Hattar Industrial Estate, Pakistan. Environ Sci Pollut Res 27:2677–2690

    Article  CAS  Google Scholar 

  30. Khan K et al (2013) Health risks associated with heavy metals in the drinking water of Swat, northern Pakistan. J Environ Sci 25(10):2003–2013

    Article  CAS  Google Scholar 

  31. Liu X et al (2022) Analysis and potential ecological risk assessment of heavy metals in surface sediments of the freshwater ecosystem in Zhenjiang City, China. SN Appl Sci 4(10):258

    Article  CAS  Google Scholar 

  32. Younas M et al (1998) Assessment of Cd, Ni, Cu, and Pb pollution in Lahore, Pakistan. Environ International 24(7):761–766

    Article  CAS  Google Scholar 

  33. Pakistan Bureau of Statistics. Brief Regarding Census-2017. 2017 [cited 2023 March 01]; Available from:

  34. Razali A et al (2020) The impact of seasonal change on river water quality and dissolved metals in mountainous agricultural areas and risk to human health. Environ Foren 21(2):195–211

    Article  CAS  Google Scholar 

  35. American Public Health Association. Standard Methods for the Examination of Water and Wastewater. 21st edn. New York: American Public Health Association. 2005 [cited 2023 March 01]

  36. ATSDR. Agency for Toxic Substances and Disease Registry (2005 [cited 2023 March 01]; Available from:

  37. USEPA. US Environmental Protection Agency, Civil Enforcement Information Resources. 2011 [cited 2023 March 01]; Available from:

  38. Sprague J (1970) Measurement of pollutant toxicity to fish. II. Utilizing and applying bioassay results. Water Res 4(1):3–32

    Article  CAS  Google Scholar 

  39. Wu B et al (2009) Preliminary risk assessment of trace metal pollution in surface water from Yangtze River in Nanjing Section, China. Bull Environ ContaminToxicol 82(4):405–409

    Article  CAS  Google Scholar 

  40. Taju G, Majeed SA, Nambi K, Hameed AS (2017) Application of fish cell lines for evaluating the chromium induced cytotoxicity, genotoxicity and oxidative stress. Chemosphere 184:1–12

    Article  CAS  Google Scholar 

  41. Karthikeyan P et al (2021) Prescribing sea water quality criteria for arsenic, cadmium and lead through species sensitivity distribution. Ecotoxicol Environ Saf 208:111612

    Article  CAS  Google Scholar 

  42. Guo R et al (2022) Toxic effect of nickel on microalgae Phaeodactylum tricornutum (Bacillariophyceae). Ecotoxicology 31(5):746–760

    Article  CAS  Google Scholar 

  43. Feng Y, Li H (2022) Study on the Biological Toxicity of Lead-zinc Mine Waste water on fresherwater fish about the winter

  44. Arambawatta-Lekamge SH, Pathiratne A, Rathnayake IVN (2021) Sensitivity of freshwater organisms to cadmium and copper at tropical temperature exposures: derivation of tropical freshwater ecotoxicity thresholds using species sensitivity distribution analysis. Ecotoxicol Environ Saf. 211:111891

    Article  CAS  Google Scholar 

  45. Tang Z et al (2023) Ecological risk assessment of aquatic organisms induced by heavy metals in the estuarine waters of the Pearl River. Sci Rep 13(1):9145

    Article  CAS  Google Scholar 

  46. NORMAN. Network of reference laboratories, research centres and related organisations for monitoring of emerging environmental substances. 2021 [cited 2023 March 01]; Available from:

  47. Nimick DA, Moore JN (1991) Prediction of water-soluble metal concentrations in fluvially deposited tailings sediments, Upper Clark Fork Valley, Montana, USA. Appl Geochem 6(6):635–646

    Article  CAS  Google Scholar 

  48. RStudio, RStudio: integrated development for R, in RStudio, Inc., Boston, MA URL 2022.

  49. R Core Team, R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (Aavailable from 2022

  50. Adibmehr M, Bagheri Sadeghi H, Dehghan Abkenar S (2014) Preconcentration and speciation of chromium using dispersive liquid–liquid microextraction; application to milk and different water samples. Anal Bioanal Chem 1(1):20–28

    CAS  Google Scholar 

  51. Vogel C et al (2020) Chromium (VI) in phosphorus fertilizers determined with the diffusive gradients in thin-films (DGT) technique. Environ Sci Pollut Res 27:24320–24328

    Article  CAS  Google Scholar 

  52. Gattullo CE et al (2020) Assessing chromium pollution and natural stabilization processes in agricultural soils by bulk and micro X-ray analyses. Environ Sci Pollut Res 27:22967–22979

    Article  CAS  Google Scholar 

  53. Rangasamy S, Purushothaman G, Alagirisamy B, Santiago M (2015) Chromium contamination in soil and groundwater due to tannery wastes disposals at Vellore district of Tamil Nadu. Int J Environ Sci 6(1):114–124

    CAS  Google Scholar 

  54. Hausladen DM et al (2018) Hexavalent chromium sources and distribution in California groundwater. Environ Sci Technol 52(15):8242–8251

    Article  CAS  Google Scholar 

  55. Işsever H et al (2007) Respiratory problems in tannery workers in Istanbul. Indoor Built Environ 16(2):177–183

    Article  Google Scholar 

  56. Zhitkovich A (2011) Chromium in drinking water: sources, metabolism, and cancer risks. Chem Res Toxicol 24(10):1617–1629

    Article  CAS  Google Scholar 

  57. Welling R et al (2015) Chromium VI and stomach cancer: a meta-analysis of the current epidemiological evidence. Occup Environ Med 72(2):151–159

    Article  Google Scholar 

  58. Romero-Cano LA et al (2017) Functionalized adsorbents prepared from fruit peels: Equilibrium, kinetic and thermodynamic studies for copper adsorption in aqueous solution. J Cleaner Prod 162:195–204

    Article  CAS  Google Scholar 

  59. Batool S, Idrees M, Hussain Q, Kong J (2017) Adsorption of copper (II) by using derived-farmyard and poultry manure biochars: efficiency and mechanism. Chem Phys Lett. 689:190–198

    Article  CAS  Google Scholar 

  60. Izah SC, Chakrabarty N, Srivastav ALJE (2016) A review on heavy metal concentration in potable water sources in Nigeria: human health effects and mitigating measures. Expo Health 8:285–304

    Article  CAS  Google Scholar 

  61. Srivastava A et al (2005) An epidemiological study of poisoning cases reported to the national poisons information centre, All India Institute of Medical Sciences, New Delhi. Human Exp Toxicol 24(6):279–285

    Article  CAS  Google Scholar 

  62. Davanzo F et al (2004) Agricultural pesticide-related poisonings in Italy: cases reported to the Poison Control Centre of Milan in 2000–2001. Environ Monit Asses 28(6):330–337

    Google Scholar 

  63. Majeed S et al (2018) Spatial patterns of pollutants in water of metropolitan drain in Lahore, Pakistan, using multivariate statistical techniques. Environ Monit Assess 190(3):128

    Article  Google Scholar 

  64. Mahmood A, Malik RN (2014) Human health risk assessment of heavy metals via consumption of contaminated vegetables collected from different irrigation sources in Lahore, Pakistan. Arab J Chem 7(1):91–99

    Article  CAS  Google Scholar 

  65. Kavcar P, Sofuoglu A, Sofuoglu SC (2009) A health risk assessment for exposure to trace metals via drinking water ingestion pathway. Int J hyp Environ Health 212(2):216–227

    Article  CAS  Google Scholar 

  66. Arif M, Henry D, Moon CJ (2011) Host rock characteristics and source of chromium and beryllium for emerald mineralization in the ophiolitic rocks of the Indus Suture Zone in Swat, NW Pakistan. Ore Geol Rev 39(1–2):1–20

    Article  Google Scholar 

  67. Pavela M, Uitti J, Pukkala E (2017) Cancer incidence among copper smelting and nickel refining workers in Finland. Am J Indus Med. 60(1):87–95

    Article  CAS  Google Scholar 

  68. Hussain S et al (2019) Health risk assessment of different heavy metals dissolved in drinking water. Int J Environ ResPublic Health 16(10):1737

    Article  CAS  Google Scholar 

  69. Obeng-Gyasi E (2019) Sources of lead exposure in various countries. Rev Environ Health 34(1):25–34

    Article  CAS  Google Scholar 

  70. Mikulski MA et al (2017) Toxic metals in ayurvedic preparations from a public health lead poisoning cluster investigation. Int J Occup Environ Health 23(3):187–192

    Article  CAS  Google Scholar 

  71. Hu H, Shih R, Rothenberg S, Schwartz BS (2007) The epidemiology of lead toxicity in adults: measuring dose and consideration of other methodologic issues. Environ Health Perspect 115(3):455–462

    Article  CAS  Google Scholar 

  72. Westerlund C, Viklander M (2006) Particles and associated metals in road runoff during snowmelt and rainfall. Sci Total Environ 362(1–3):143–156

    Article  CAS  Google Scholar 

  73. Wu Q et al (2014) Effects of seasonal climatic variability on several toxic contaminants in urban lakes: implications for the impacts of climate change. J Environ Sci 26(12):2369–2378

    Article  Google Scholar 

  74. Krachler M et al (2003) Atmospheric deposition of V, Cr, and Ni since the Late Glacial: effects of climatic cycles, human impacts, and comparison with crustal abundances. Environ Sci Technol 37(12):2658–2667

    Article  CAS  Google Scholar 

  75. Peters A et al (2014) Assessment of the effects of nickel on benthic macroinvertebrates in the field. Environ Sci Pollut Res 21(1):193–204

    Article  CAS  Google Scholar 

  76. Niyogi S, Brix KV, Grosell M (2014) Effects of chronic waterborne nickel exposure on growth, ion homeostasis, acid-base balance, and nickel uptake in the freshwater pulmonate snail Lymnaea stagnalis. Aquat Toxicol 150:36–44

    Article  CAS  Google Scholar 

  77. Qu X et al (2010) Effects of heavy metals on benthic macroinvertebrate communities in high mountain streams. Ann Limnol Int J Limnol 46(4):291–302

    Article  Google Scholar 

  78. Bere T, Dalu T, Mwedzi T (2016) Detecting the impact of heavy metal contaminated sediment on benthic macroinvertebrate communities in tropical streams. Sci Total Environ 572:147–156

    Article  CAS  Google Scholar 

  79. Armitage PD, Bowes MJ, Vincent HM (2007) Long-term changes in macroinvertebrate communities of a heavy metal polluted stream: the river Nent (Cumbria, UK) after 28 years. River Res Appl 23(9):997–1015

    Article  Google Scholar 

  80. Liess M, Gerner NV, Kefford BJ (2017) Metal toxicity affects predatory stream invertebrates less than other functional feeding groups. Environ Pollut 227:505–512

    Article  CAS  Google Scholar 

  81. Mallick N, Mohn FH (2003) Use of chlorophyll fluorescence in metal-stress research: a case study with the green microalga Scenedesmus. Ecotoxicol Environ Saf. 55(1):64–69

    Article  CAS  Google Scholar 

  82. Ardestani MM, van Straalen NM, van Gestel CAM (2014) The relationship between metal toxicity and biotic ligand binding affinities in aquatic and soil organisms: a review. Environ Pollut. 195:133–147

    Article  CAS  Google Scholar 

  83. Tokatli C et al (2021) Ecological risk assessment of toxic metal contamination in a significant mining basin in Turkey. Environ Earth Sci 80(1):1–9

    Article  Google Scholar 

  84. Varol M, Ustaoğlu F, Tokatlı C (2022) Ecological risk assessment of metals in sediments from three stagnant water bodies in Northern Turkey. Curr Pollut Rep 8(4):409–421

    Article  CAS  Google Scholar 

  85. He N, Liu L, Wei R, Sun K (2021) Heavy metal pollution and potential ecological risk assessment in a typical mariculture area in Western Guangdong. Int J Environ Res Public Health 18(21):11245

    Article  CAS  Google Scholar 

  86. Shetaia SA et al (2023) Assessment of heavy metals contamination of sediments and surface waters of Bitter lake, Suez Canal, Egypt: ecological risks and human health. Mar Pollut Bull 192:115096

    Article  CAS  Google Scholar 

Download references


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


Open Access funding enabled and organized by Projekt DEAL.

Author information

Authors and Affiliations



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

Corresponding author

Correspondence to Naeem Shahid.

Ethics declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information


file 1: Table S1. Detail of sampling locations with respect to water resource, site ID, sampling date and coordinates. Table S2. Physicochemical properties of the water samples collected from groundwater, canals, river Ravi, drains, and respective National Environmental Quality Standards. Table S3. Parameters used for the calculation of Chronic Daily Intake (CDI) through oral and dermal exposures are enlisted in the table. Values are presented with units and references. Table S4. Reference values of EC50/LC50 (μg/L) for algae, fish and crustaceans used for the calculation of Toxic Units (TU). Table S5. Descriptive summary of the metal concentration (μg/L) in water bodies. Table S6. Hazard quotients and Health Risk Index: Oral Hazard Quotients (HQoral) and Oral Health Index (HRIoral) are presented only for groundwater samples, as other water resources are not commonly used for drinking. Dermal Hazard Quotient (HQdermal) and Dermal Health Index (HRIdermal) are presented for all water samples collected from grounderwater, canals, river and drains. Data is presented in the form of minimum (Min.), maximum (Max.), average (Mean) and standard deviation. Table S7. Ecological risk of metal contamination based on the toxic unit (TU) is presented for three trophic levels: algae, fish and crustaceans. Toxic Units (TU) are given for each metal, and for the total toxicity of all metals detected at each site (TUsum). For illustration purposes, log-transformation was performed. Table S8. Risk Quotients: Risk Quotients (RQ) and sum of the Risk Quotients (RQsum) are presented for all water samples collected from grounder water, canals, river and drains. Data is presented in the form of minimum (Min.), maximum (Max.), average (Mean) and standard deviation. Figure S1. Principal component analysis of heavy metals: Each vector in the plot represents a variable, and the direction and length of the vector indicate the contribution and correlation of each variable to the top two principal components.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Iqbal, H.H., Siddique, A., Qadir, A. et al. Human health and ecology at risk: a case study of metal pollution in Lahore, Pakistan. Environ Sci Eur 36, 9 (2024).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: