Comparative analysis of heavy metals toxicity in drinking water of selected industrial zones in Gujranwala, Pakistan

Abstract

Heavy metal contamination of drinking water, primarily driven by industrial activities, represents a critical challenge, with implications for human health and environmental safety. Gujranwala is an industrial and thickly populated city. The current study aimed to assess and compare heavy metal contamination levels in drinking water from five industrial areas and evaluate their potential impacts on human health. Total 100 water samples were collected and analysed for physicochemical parameters and heavy metals. Zonal variations in heavy metal concentrations revealed that Zone 2 had the highest mean levels of cadmium (0.331 mg/L), lead (0.573 mg/L), chromium (0.164 mg/L), arsenic (0.042 mg/L), and aluminium (0.484 mg/L), while Zone 4 showed elevated mean levels of iron (1.88 mg/L) and mercury (0.259 mg/L). Spearman correlation analysis among heavy metals revealed positive relationships among several heavy metals with As notably showing a strong correlation with Hg (0.701**). Kruskal–Wallis test revealed significant spatial variation (p < 0.05) in parameters like pH, EC, TDS, and heavy metals (Cd, Pb, Fe, Cu, Mn, Al), indicating spatial heterogeneity across zones. Spatial distribution maps also depicted heavy metal elevated concentrations of Cd, Pb, Fe, Cr, As, and Hg exceeding in Zone 2 (Engineering industries zone) and 4 (Iron and steel industries zone). The findings revealed a strong link between elevated heavy metal levels and health risks, including dermatological, respiratory, gastrointestinal, and neurological disorders. This study highlights the need for stricter wastewater management, regular monitoring of drinking water, and policies to address water toxicity in industrial premises and to protect public health.

Introduction

Heavy metal contamination in drinking water sources (both natural and anthropogenic) poses a serious threat to human health^1,2. According to the International Agency for Research on Cancer and the United States EPA (Environmental Protection Agency), exposure to arsenic and toxic heavy metals in drinking water is a major issue, especially owing to carcinogenic consequences. More than 30 nations have concluded that arsenic, cadmium, chromium and mercury in water are detrimental to public health^3,4.

Drinking 1 L of water with 50 μg/L As and 8.3–51 μg/L Cr per day is linked to lung, liver, bladder, and tumors of the kidneys^5,6. Arsenic concentrations present in water consumed of 0.0012 mg/kg/day can result in serious skin damage and respiratory difficulties. Long-term cadmium exposure can result in chronic renal failure, loss of smell, anaemia, heart disease, excessive blood pressure, and osteoporosis. Other consequences have been identified, including lead-induced anemia, copper-induced intestinal diseases, mercury-induced kidney along with liver damage, and elevated blood cholesterol^7–9.

Thus, determining the presence of heavy metals in various water sources is critical for accurately assessing human health hazards, especially in industrial areas, where contamination can occur due to industrial activities, and inadequate wastewater management practices¹⁰.

Heavy metals are naturally occurring substances that can have toxic effects on human health at high concentrations even at very low exposure¹¹. The most important heavy metals in drinking water are cadmium (Cd), lead (Pb), arsenic (As), mercury (Hg), and chromium (Cr). These metals can enter water sources through various means, such as industrial effluents, agricultural waste containing pesticides and chemical fertilizers, and corrosion in pipe systems^12,13.

The World Health Organization (WHO) develops guidelines and standards for drinking water quality, including maximum permitted concentrations of various heavy metals. These standards are important indicators for evaluating drinking water quality and guiding regulatory measures to protect public health^14,15. However, keeping up with these standards can be difficult in rapidly urbanizing areas, where rapid population growth and industrialization can put pressure on existing water infrastructure and management systems.

Gujranwala, a bustling city in the Pakistani province of Punjab, is widely recognized for its vibrant industrial sector, making it a significant contributor to the country’s economy. Gujranwala has grown into an industrial powerhouse, supporting a diverse range of sectors including steel, pottery, textiles, food processing, and manufacturing. Its strategic location along major trade routes enhances its importance as an industrial hub, driving economic activity^16,17.

The city’s industries are known for their high output, but this rapid industrial growth comes with significant environmental challenges, particularly in terms of wastewater management and pollution. Heavy metal discharges from industrial units are a growing concern, as they pose potential risks to water quality and public health. The wastes of various industries are indiscriminately dumped into natural or open water bodies, leading to surface and groundwater pollution. This toxic wastewater flows directly or indirectly into nearby rivers, making the water unfit for irrigation. The lack of robust regulatory mechanisms to control industrial emissions has further exacerbated these issues. Gujranwala’s industrial prominence, therefore, not only highlights its economic importance but also places it at the center of discussions on sustainable industrial practices and environmental protection^18,19.

Numerous studies have been conducted over time to investigate metal pollution in drinking water, both within Pakistan and internationally. Around the globe, high levels of hazardous heavy metals and arsenic in drinking water and groundwater have been frequently reported. In the Mexican state of Sonora, almost 43% of drinking water from reservoirs and wells included high levels of cadmium, arsenic, mercury, copper, and lead^20,21. Cadmium, lead, as well as copper concentrations in water used for consumption in ten Saudi towns, surpassed permitted levels, which were related to the Kuwaiti oil fires during the Gulf War^22,23. Mn, Cd, and Pb levels in water used for drinking in India exceed guideline limits, indicating pollution from terrestrial sources²⁴. In recent decades, heavy metal pollution of groundwater in rural India has surpassed World Health Organisation recommendations for arsenic, manganese, chromium, lead, nickel, and zinc^25,26. Pb, Cr, Ni, and Zn are found in high levels of drinking water in major cities in Iran^27,28 and cities of Thailand also exceed the drinking water standards²⁹. Studies from Bangladesh also highlighted that groundwater in industrial areas, especially around Dhaka and Chittagong, shows elevated levels of toxic metals, particularly cadmium (Cd), lead (Pb), and chromium (Cr), which are often linked to industrial effluents^30,31. In north-eastern China, research conducted in rural villages identified that iron (Fe) and manganese (Mn) exceeded safe drinking water standards in 17.9% and 19.6% of the samples, respectively³². However, comparing to standards is insufficient to determine the health hazards associated with hazardous material exposure through drinking water. In recent times, human health risk evaluation techniques have been used to establish if hazardous chemical exposure increases the chance of negative human health impacts³³.

Heavy metal contamination in drinking water across Pakistan presents a critical public health issue, with elevated concentrations of metals like arsenic (As), lead (Pb), chromium (Cr), and nickel (Ni) found in various regions. Research indicates that industrialization, agricultural runoff, and inadequate wastewater treatment have significantly contributed to the contamination of both surface and groundwater sources, especially in cities like Lahore, Karachi, Faisalabad and Gujranwala. Studies have shown that heavy metals in drinking water exceed the World Health Organization (WHO) guidelines, particularly in industrial zones and agricultural areas where industrial effluents and pesticide runoff are prevalent³⁴.

In Gujranwala, there is currently no public information documenting the direct contamination of drinking water from industrial sites, nor on the associated health risks to industrial workers and nearby residents resulting from exposure to such contamination^35,36. This study aims to quantify the concentrations of heavy metals in drinking water samples from industrial areas, and evaluate their potential impact on both industrial workers and surrounding communities.

This study also aimed to conduct a comprehensive comparative analysis of contamination levels(heavy metal pollution) in the drinking water of different industrial areas of Gujranwala. This study has used systematic random sampling and robust standard analytical methods to provide valuable information on the current status of drinking water quality in Gujranwala. By the quantification of physico-chemical parameters (pH, Electrical conductivity, Total dissolved solids) and certain heavy metals, such as Cd, Pb, Fe, Cu, Mn, Zn, As, Hg, Cr, Ni and Al this study aimed to evaluate the level of heavy metal pollution and their potential impact on the health of industrial workers and residents. By comparing measured heavy metal concentrations to World Health Organization standards, this study has identified potential areas of concern and informs targeted interventions to improve water quality and protect public health. In addition, this study contributes to the literature about heavy metal contamination in drinking water, especially in industrial areas in developing countries. By explaining the sources, distribution and health risks of heavy metals in drinking water, this study aims to support evidence-based decision-making and policy development to ensure that all residents of Gujranwala have access to safe and clean drinking water.

Materials and methods

Sampling site and zones

Gujranwala is located at 32.16° north, 74.18° east and is 226 m (744 ft) above sea level. It is in between Lahore, Gujrat and Wazirabad. It is situated on GT road so have an easy connection to Islamabad, Lahore³⁷. It is an industrial city in the northeast of the Punjab Province of Pakistan. The total surface area of Gujranwala District is 8,809 km² whereas the city comprises 87 km². The estimated population of the urban area of Gujranwala is 500,000. Gujranwala is known as the industrial heart of Punjab³⁸.

Gujranwala is an industrial and thickly populated city, pollution in its various forms is increasing tremendously day by day so the present study is conducted to assess heavy metal pollution in drinking water in industrial areas and to assess the potential public health risks associated with it. The main water source is groundwater which is available at a depth of 55 ft and depths of Tube Wells vary from 450 to 650 ft. Water and Sanitation Agency (WASA) is responsible for the provision of water and sanitation services to the city^39,40.

Groundwater contamination in Gujranwala is primarily driven by industrial effluents, agricultural runoff, improper waste disposal, and sewage infiltration. The city’s rapid industrialization has led to the discharge of untreated wastewater, which introduces heavy metals and chemicals into the water table. Agricultural activities, particularly the use of chemical fertilizers and pesticides, contribute to the leaching of harmful compounds into groundwater. Inadequate waste management, with open dumping sites and landfills, further exacerbates contamination risks, while untreated sewage infiltration introduces pathogens and organic pollutants. Additionally, over-extraction of groundwater lowers the water table, increasing the vulnerability to contamination. The area’s alluvial geology, characterized by permeable sand, silt, and clay layers, facilitates the infiltration of pollutants, making groundwater in Gujranwala particularly susceptible to contamination⁴¹.

The geology of Gujranwala primarily consists of Quaternary alluvial deposits formed by the deposition of sediments from the Indus River system. The aquifers in this region are typically composed of fine to medium sands interbedded with silt and clay layers, making them highly porous and permeable, which facilitates the movement of groundwater. The water table is shallow, and the region’s flat topography promotes infiltration, allowing contaminants from surface activities to percolate through the soil into groundwater. The shallow nature of the aquifers, coupled with anthropogenic activities, exacerbates the risk of groundwater contamination in the area^42,43.

The study area was meticulously divided into five distinct zones based on a combination of geographical, administrative, and industrial criteria (Fig. 1). Each zone was carefully delineated to ensure adequate representation of various environmental and industrial characteristics within the study area. These zones are selected to obtain a range of pollution levels; from highly polluted to low pollution sites.

• Zone 1: Main City.

• Zone 2: GT Road towards Rawalpindi.

• Zone 3: GT Road towards Lahore.

• Zone 4: Sialkot road.

• Zone 5: Sheikhupura Road.

Fig. 1.

Sampling Zones of Gujranwala (Study area). Software: ArcGIS 10.2 (Idea adopted from^44,45).

A systematic random sampling approach was adopted. Drinking water samples were collected from industries located within the defined 10 km radius from the main city for each zone. Water samples were collected from groundwater sources of drinking water and drinking water from other sources (Supply water, filtered water, bottled water). Sampling locations were strategically chosen to encompass a diverse range of water sources, including wells, boreholes, motor pumps and hand pumps.100 water samples (20 samples from each zone) were meticulously collected from designated sampling points across the study area, ensuring adequate representation of different water sources and their respective contamination levels. 1 sample of Groundwater and 1 sample of other sources of water (along with their respective replicates) were collected from every industry. Sampling was carried out in March 2021.

Sampling strategy, preparation and analysis

Water samples were collected in 500 ml polyethylene (PET) bottles. Clean, non-contaminated bottles were used for sample collection. Every bottle was rinsed 3 times with the sample water before the collection of sample. Rinsing helps to ensure that the collected sample is as representative of the water source as possible, with no interference from external materials. The samples were labelled with the specified code. After collection, the samples were refrigerated at 4 °C to prevent changes in chemical composition. The samples were transported to the laboratory. During transport, samples were kept in insulated containers, particularly to prevent contamination or evaporation, and were analyzed within 24 h of collection.

Upon arrival at the laboratory physico-chemical parameters (pH, EC and TDS) were measured immediately by using CRISON multimeter⁴⁶. The CRISON multimeter has detection ranges of − 2.00 to 19.99 for pH, 0.01 µS/cm to 500 mS/cm for electrical conductivity (EC), and 0 mg/L to 500 g/L for total dissolved solids (TDS). pH calibration was done using three buffers: 4.01, 7.00, 9.21 at 25 °C. EC calibration was employed using standards of 147 µS/cm, 1413 µS/cm, 12.88 mS/cm, and 111.8 mS/cm at 25 °C. CRISON multimeter ensures reproducibility of ± 0.01 pH and ± 0.1% for conductivity. The pH electrode was stored in KCl solution to maintain functionality.

Took 100 ml water in a beaker and the electrode of the multimeter was dipped in it.3 readings were taken for these 3 parameters of every sample of water. After that, the water samples were subjected to a series of standardized procedures outlined in the American Public Health Association Protocols⁴⁷. For heavy metals, initially, the samples underwent filtration to remove any particulate matter or impurities that could interfere with subsequent analyses. Following filtration, acidification and digestion of the samples were carried out to ensure the complete dissolution of heavy metal compounds present in the water samples.

Subsequently, Flame Atomic Absorption Spectrophotometer (SHIMADZU-AA-7000), was employed to quantify the concentrations of selected heavy metals (Cr, Cd, Ni, Fe, Co, Mn, Pb, Zn, As, Hg and Al) in the samples. FAAS enabled the precise measurement of metal concentrations by detecting the absorption of specific wavelengths of light emitted by metal atoms in the flame^48–50.

Initially, the calibration of FAAS was done. Firstly, metal-specific standard solutions were prepared using certified reference materials. Deionized water was used as a blank to account for background absorption. Multi-point calibration curves are plotted to ensure linearity Calibration curves were created by measuring absorbance for multiple concentrations (3–5 points). The optimum analytical conditions maintained on FAAS for the analysis of selected heavy metals are given in Table 1.

Table 1.

Instrumental conditions on FAAS for selected heavy metals.

Metals	Wavelength (nm)	Slit width (nm)	Flame type	Flow rate (L/min)
Cadmium (Cd)	228.8	0.7	Air/Acetylene	1.8
Lead (Pb)	217	0.7	Air/Acetylene	2
Iron (Fe)	248.3	0.2	Air/Acetylene	2.2
Nickel (Ni)	232	0.2	Air/Acetylene	1.6
Copper (Cu)	324.8	0.7	Air/Acetylene	1.8
Manganese (Mn)	279.5	0.2	Air/Acetylene	2
Chromium (Cr)	357.9	0.7	Air/Acetylene	2.8
Zinc (Zn)	213.9	0.7	Air/Acetylene	2
Arsenic (As)	193.7	0.7	Air/Acetylene	2
Mercury (Hg)	253.7	0.7	Cold vapor system used	-
Aluminum (Al)	309.3	0.7	Nitrous oxide/Acetylene	6.6

The measured values for metals in every sample, obtained from the FAAS analysis were then entered in Excel and were compared against established regulatory standards, such as WHO drinking water guidelines and PSQCA Water Quality Standards to assess compliance with permissible limits for heavy metal concentrations in drinking water.

To ensure quality control during sample collection and handling, all necessary precautions were taken to minimize contamination. Equipment was regularly calibrated, and the accuracy of analytical results was verified by analyzing blank samples and replicate samples under identical conditions. All chemicals and reagents used were of analytical reagent grade quality. Glass and plastic ware were thoroughly cleaned by soaking in 14% HNO₃ overnight, followed by multiple rinses with deionized water.

In addition to collecting drinking water samples from industrial sites, face-to-face interviews were conducted to administer the questionnaire with industrial workers to gather personal and medical histories, as well as other relevant information related to heavy metal exposure and associated risk factors. To ensure consistency across interviews, interviewers were trained on study objectives, questionnaire content and ethical considerations. Pilot testing was done. Interviews were conducted in a smaller sample to refine methods and resolve ambiguities.

The questionnaire comprised a series of self-designed inquiries designed to elicit information on a range of factors that could influence heavy metal levels in the study population. Socio-demographic variables such as age and gender were included to provide insights into demographic characteristics that may impact heavy metal exposure and susceptibility.

The questionnaire included inquiries into lifestyle factors known to influence heavy metal exposure, such as drinking water consumption, smoking, alcohol consumption, and regulated eating habits. These factors were considered critical as they can significantly contribute to the accumulation of heavy metals in the body, affecting overall health outcomes. By addressing these variables, the study aimed to capture a comprehensive view of potential exposure sources.

Questions related to medical histories were included in the questionnaire to assess potential health issues linked to heavy metal exposure. This aimed to gather relevant information on any pre-existing or current health conditions, providing valuable insights into the potential health impacts of water contamination in the study population. In addition to these variables, the questionnaire also addressed occupational factors that could contribute to heavy metal exposure. Questions related to food sources (local village or market), occupational mode, protection mode, and duration of exposure time were included to capture information on potential sources and routes of heavy metal exposure in the study participants’ daily lives.

Interpretation of data

The results of drinking water analysis were interpreted using statistical analysis, spatial distribution maps, and indexes. Spearman correlation, Mann–Whitney U Test and the Kruskal–Wallis Test were conducted to calculate the relationship among water quality parameters and to compare the distributions of these parameters between different zones. These tests were conducted by using SPSS. Spatial distribution maps (IDW plots) were formed by using ArcGIS. Indexes such as water quality and health risk were conducted to ascertain water suitability for human consumption and health risks. The descriptions are given below:

Statistical analysis

Statistical analysis plays a critical role in interpreting complex datasets, particularly in studies that evaluate environmental parameters. Statistical analysis was conducted using SPSS software. The Spearman Correlation is a non-parametric measure that evaluates the strength and direction of the association between two variables. Unlike Pearson correlation, which measures linear relationships and assumes data normality, Spearman correlation is ideal for assessing monotonic relationships where the association between variables is consistent in direction but not necessarily linear. Spearman correlation converts the data into ranks, making it robust to outliers and suitable for ordinal, interval, and ratio-level data. It is particularly useful for understanding how one variable consistently increases or decreases with another, even if the relationship is not strictly linear. These methods are widely used in environmental, biological, and social sciences to explore relationships where data may not meet parametric assumptions⁵¹.

When data deviates from normality or consists of ordinal variables, non-parametric tests like the Kruskal–Wallis Test and the Mann–Whitney U Test become invaluable tools for analysing differences among groups or zones. These tests are used to compare distributions of data between two groups or across multiple groups respectively⁵². The Mann–Whitney U Test, compares two groups directly, assessing whether the distributions of the variable are significantly different. It reports U and Z statistics and provides a p-value for the significance of the difference. The Kruskal–Wallis Test is an extension of the Mann–Whitney U Test and is applied when comparing more than two groups. It evaluates whether the median values of a variable differ significantly across multiple groups, producing a Chi-Square statistic and a p-value. If the p-value is less than 0.05, it indicates that at least one group differs significantly from the others.

Spatial distribution maps

Spatial distribution maps were created by using IDW (Inverse Distance Weighting) in ArcGIS software. IDW is a spatial interpolation technique used to estimate values at unsampled locations based on the principle that points closer to a known value have a greater influence than points farther away. The method calculates values using a weighted average of nearby sampled points, with the weights inversely proportional to the distance. This technique is widely used in environmental sciences to analyze spatial distributions of pollutants or natural resources^53,54.

During sampling, the geographic coordinates (latitude and longitude) of each location were recorded using a GPS device. After laboratory analysis, the concentrations of heavy metals in the drinking water from each sampling point were compiled. This georeferenced data was imported into ArcGIS as a point shapefile. Using the Inverse Distance Weighting (IDW) interpolation method, a raster layer was generated to visualize the spatial distribution of heavy metal concentrations across the study area, providing a clear representation of contamination patterns.

Water quality index

The WQI was computed to determine water quality in the research region since it is a helpful tool for evaluating the total drinking water quality. Various physico-chemical parameters (TDS, Cd, Pb, Fe, Ni, Cu, Mn, Cr, Co, Zn, As, Hg and Al) of water were chosen, and WHO drinking water guidelines were considered for these parameters. Then, these parameters were given weights (wi) ranging from one to five, with five reflecting the maximum weight based on their estimated influence on human health^55,56. The toxic metals i.e. cadmium, lead, chromium, arsenic, and mercury were assigned the maximum weight of 5, because of their deleterious effect on the drinking water and due to this reason, their importance in determining the water quality. Other parameters were given weights ranging from 1 to 5 based on their significance in determining water quality.

The subsequent stage was calculating the relative weight as described by Eq. 1.

1

wi is each parameter’s weight, Wi is the relative weight, and n is the total number of parameters (Table 1).

Subsequently, a qi scale representing the quality rating scale of each parameter was calculated using Eq. 2.

2

where qi is the quality rating, Ci is the measured concentration of each parameter in the sample of water (mg/L), and Si is the corresponding WHO standard (mg/L) (Table 2).

Table 2.

Weight (wi), Relative weight (Wi) and WHO standards (Si) of studied parameters.

S. no	Physico-chemical parameters	Weight (wi)	Relative weight (Wi)	WHO standards (Si)(mg/L)
1	TDS	3	0.06	1000
2	Cd	5	0.09	0.003
3	Pb	5	0.09	0.01
4	Fe	4	0.07	0.3
5	Ni	4	0.07	0.02
6	Cu	4	0.07	2
7	Mn	3	0.06	0.5
8	Cr	5	0.09	0.05
9	Co	4	0.07	0.05
10	Zn	3	0.06	3
11	As	5	0.09	0.01
12	Hg	5	0.09	0.006
13	Al	4	0.07	0.2
	Sum	54	1.00

Afterwards, WQI was calculated using Eq. 3.

3

After the calculation of WQI by the above given equations the water samples were categorized into different categories based on WQI values^56,57. The WQI range and type of water quality is given in Table 3.

Table 3.

WQI values and their corresponding types of water.

WQI range	Type of water
< 50	Excellent
50–100	Good water
100–200	Poor water
200–300	Very poor water
> 300	Unfit for drinking

Health risk index (HRI)

To assess the human health risk of heavy metals, it was necessary to calculate the level of human exposure to that metal by tracing the route of exposure of pollutants to the human body. HRI depends upon the chronic daily intake of metals (CDI) and oral reference dose (RfD).

RfD = estimated per day exposure of metal to the human body that has no hazardous effect during lifetime.

The health risk index for selected heavy metals—by consumption of contaminated drinking water was calculated by following equation ^57,58

• Mc = Concentration of metal in water(mg/L),

• Lw = Daily water intake (2 L for male, 1.5 L for female and 1 L for Child)

• Wb = Average body weight (70 kg for males, 65 for females, and 30 kg for children)

The oral reference doses (RfDs) for the selected heavy metals are as: Cadmium has a RfD of 0.0005 mg/kg/day; Lead has a RfD of 0.0035 mg/kg/day; Iron is set at 0.007 mg/kg/day; Nickel at 0.02 mg/kg/day; Copper at 0.04 mg/kg/day; and Manganese at 0.14 mg/kg/day. The RfD for Chromium is 0.003 mg/kg/day, for Cobalt it is 0.0014 mg/kg/day, and for Zinc it is 0.3 mg/kg/day. Both Arsenic and Mercury have a RfD of 0.0003 mg/kg/day⁵⁹.

Ethical consideration

Ethical considerations are paramount in research, particularly when conducting studies involving human participants and sensitive data collection. In this study, several ethical principles and guidelines were adhered to, ensuring the protection of participants’ rights, confidentiality, and integrity throughout the research process.

First and foremost, informed consent was provided and obtained from all participants in the study. This process involved clearly explaining the study’s aim and procedure to analyze heavy metal contamination in drinking water in industrial areas and its potential health implications. They were informed about potential risks (e.g., health assessments) and benefits (e.g., contributing to public health knowledge). Participants were assured that personal information and health data will be kept confidential. They were provided with detailed information about study and allowed to ask questions before voluntarily agreeing to take part.

Confidentiality of participant information was strictly maintained throughout the study. Measures were implemented to ensure that all data collected, including personal details, remained anonymous, securely stored and protected from unauthorized access. Participant’s identities were anonymized in all research documentation to prevent any potential breaches of confidentiality. Furthermore, the research protocol adhered to ethical guidelines outlined by regulatory bodies and institutional review boards. Ethical approval was obtained from the Fatima Jinnah Women University research ethics committee before commencing data collection, ensuring that the study adhered to established ethical standards and guidelines.

To fulfil the ethical imperative of communicating research findings to the affected communities’ local authorities were collaborated and community meetings were organized, easy-to-understand informational materials were distributed. These efforts ensured that industrial workers and nearby residents were informed about the findings and potential health risks, empowering them to take informed actions and advocate for improved water safety practices.

Results and analysis

Quantification of physico-chemical parameters and heavy metals in drinking water

The descriptive statistics (range and mean) of studied physico-chemical parameters (pH, EC and TDS) and selected heavy metals (Cd, Pb, Fe, Ni, Cu, Mn, Cr, Co, Zn, As, Hg, and Al) in drinking water of 5 zones of Gujranwala are given in Table 3.

The term “pH” stands for "potential of hydrogen" (or "power of hydrogen"). It denotes the concentration of hydrogen ions in a solution. It is a chemical scale used to quantify the acidity or basicity of any solution. Acidic solutions (solutions with greater H + ion concentrations) have lower pH values than basic solutions⁶⁰.

Electrical conductivity (EC) is a measure of water’s ability to conduct an electrical current, primarily determined by the concentration of dissolved ions, such as salts and minerals. It’s a measure of how well a material conducts electric current. High EC levels may indicate elevated concentrations of dissolved solids, reflecting natural processes like mineral weathering or human activities like industrial discharges and agricultural runoff. Total dissolved solids (TDS) in water are represented by the weight of left residue when a water sample is evaporated to dryness. It encompasses various inorganic and organic substances dissolved in water, including minerals, salts, metals, and organic compounds⁶¹.

Table 4 shows the difference in levels of selected heavy metals in drinking water from five zones. Each zone shows different mean values for the various physico-chemical parameters i.e. pH, electrical conductivity (EC), total dissolved solids (TDS) and different heavy metals including cadmium (Cd), lead (Pb), iron (Fe), nickel (Ni), copper (Cu), manganese (Mn), chromium (Cr), zinc (Zn), arsenic (As), mercury (Hg), and aluminium (Al).

Table 4.

Descriptive statistics of studied physico-chemical parameters and heavy metals in drinking water.

	Zone 1		Zone 2		Zone 3		Zone 4		Zone 5
	Range	Mean	Range	Mean	Range	Mean	Range	Mean	Range	Mean
pH	5.2–8.4	7.01	5.3–7.35	6.51	6.51–7.8	7.253	5.7–7.77	7.093	5.66 –7.52	6.642
EC(μS/cm)	400–1604	871.2	404–1680	1135	305–1770	867.6	296–1206	701.55	432–1670	964
TDS(mg/L)	256–1026	557.7	259–1075	726	195–1133	555.316	189–772	448.99	277–1069	617
Cd (mg/L)	0–0.4	0.05	0–2.44	0.331	0–0.361	0.056	0–0.057	0.011	0.00–1.14	0.174
Pb (mg/L)	0–0.8	0.07	0–3.18	0.573	0–0.892	0.172	0–0.532	0.064	0.02–2.24	0.279
Fe (mg/L)	0–7.3	0.99	0–5.74	0.786	0–0.771	0.215	0.04–9.4	1.88	0.01–7.65	1.284
Ni (mg/L)	0–0.7	0.14	0–0.541	0.136	0–0.641	0.193	0.01–0.574	0.111	0.01–0.64	0.129
Cu (mg/L)	0–2.4	0.5	0–0.581	0.211	0–0.612	0.241	0.02–0.982	0.365	0.05–2.93	0.743
Mn (mg/L)	0–1.5	0.17	0–0.068	0.02	0–0.061	0.012	0–0.145	0.019	0.01–0.80	0.238
Cr (mg/L)	0–1.4	0.16	0–0.822	0.164	0–0.1	0.028	0–0.102	0.041	0.01–0.94	0.13
Zn (mg/L)	0–2.7	1.16	0–2.82	0.563	0–3.54	0.64	0–2.36	0.486	0.01–2.96	1.035
As (mg/L)	0–0.7	0.06	0–0.275	0.042	0–0.076	0.015	0–1.05	0.032	0.00–0.52	0.029
Hg (mg/L)	0–1	0.11	0–1.05	0.206	0–0.75	0.038	0–3.05	0.259	0.00–0.82	0.075
Al (mg/L)	0–0.7	0.14	0–3.13	0.484	0–0.141	0.014	0–0.83	0.072	0.00–1.65	0.446

In general, the pH in all studied zones is within acceptable limits of WHO (6-5-8.5) for drinking water. However, heavy metal concentrations vary greatly between zones. For example, Zone 2 showed higher concentrations of Cd, Pb, and Al indicating that this area has a higher level of contamination compared to other areas (Table 5). In comparison, zone 4 showed the highest concentration of Fe and Hg. These changes reflect the spatial variability in sources and levels of heavy metal contamination of drinking water in Gujranwala.

Table 5.

Overall mean values of heavy metals of 5 zones in comparison with WHO and PSQCA Standards^62–65.

Heavy metal	Measured mean concentration (mg/L)	WHO regulatory standard (mg/L)	PSQCA regulatory standard(mg/L)
Cadmium (Cd)	0.125	0.003	0.01
Lead (Pb)	0.231	0.01	0.05
Iron (Fe)	1.032	0.3	0.3
Nickel (Ni)	0.141	0.07	0.02
Copper (Cu)	0.412	2	2
Manganese (Mn)	0.091	0.4	0.5
Chromium (Cr)	0.105	0.05	0.05
Zinc (Zn)	0.776	3	5
Arsenic (As)	0.036	0.01	0.05
Mercury (Hg)	0.137	0.001	0.001
Aluminium (Al)	0.231	0.2	0.2

*WHO world health organization, PSQCA Pakistan standards and quality control authority.

Table 5 provides a comparative analysis of the mean concentrations of various heavy metals in drinking water samples with control standards of the World Health Organization (WHO) and Pakistan Standards and Quality Control Authority (PSQCA). The table includes the estimated concentration in mg/L for each heavy metal, and the corresponding control standard established by the WHO and PSQCA.

Among the heavy metals analysed, cadmium (Cd), lead (Pb), Iron (Fe), Nickel (Ni), chromium (Cr), and mercury (Hg) were found to exceed the regulatory standards of the WHO and PSQCA, indicating non-compliance with safety limits. Arsenic was found below the regulatory standard of PSQCA but higher than the standard of WHO. In particular, the concentration of heavy metals exceeding the standards of WHO and PSQCA indicates the potential health risks from exposure to these metals in drinking water. In comparison, the concentrations of copper (Cu), manganese (Mn), zinc (Zn) and Al were within WHO regulatory limits, indicating compliance with safety standards.

Statistical analysis of the physico-chemical parameters and heavy metals in drinking water

Spearman correlation table (Table 6) identifies relationships between physico-chemical parameters (pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS)) and heavy metals (Cd, Pb, Fe, Ni, Cu, Mn, Cr, Co, Zn, As, Hg and Al) in drinking water. This table offers insights into contamination patterns. Each cell in the table represents a correlation coefficient (r) that indicates the strength and direction of the relationship between two variables. Asterisks (*) next to the correlation coefficients indicate the significance levels:

Table 6.

Spearman correlation of physico-chemical parameters and heavy metals of drinking water.

	pH	EC	TDS	Cd	Pb	Fe	Ni	Cu	Mn	Cr	Co	Zn	As	Hg	Al
pH	1.000
EC	-.447**	1.000
TDS	-.447**	1.000**	1.000
Cd	-.390**	.463**	.463**	1.000
Pb	-.394**	.393**	.393**	.674**	1.000
Fe	-.350**	-.066	-.066	.316**	.293**	1.000
Ni	-.196*	.198*	.198*	.524**	.406**	.418**	1.000
Mn	-.314**	.147	.147	.565**	.435**	.449**	.366**	.456**	1.000
Cr	-.468**	.272**	.272**	.671**	.625**	.530**	.513**	.579**	.525**	1.000
Co	-.286**	.117	.117	.456**	.424**	.381**	.506**	.605**	.496**	.617**	1.000
Zn	-.278**	.113	.113	.521**	.361**	.450**	.325**	.509**	.652**	.696**	.571**	1.000
As	-.323**	.253**	.253**	.474**	.394**	.339**	.450**	.382**	.334**	.552**	.559**	.535**	1.000
Hg	-.369**	.292**	.292**	.440**	.338**	.520**	.355**	.284**	.298**	.514**	.398**	.422**	.701**	1.000
Al	-.330**	.120	.120	.495**	.314**	.558**	.303**	.318**	.434**	.470**	.283**	.442**	.369**	.448**	1.000

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

pH is significantly negatively correlated with EC (-0.447**), TDS (-0.447**), Cd (-0.390**), Pb (-0.394**), and Cr (-0.468**). This indicates that lower pH (more acidic water) corresponds to higher concentrations of these metals, potentially due to the solubility effects of metals in acidic conditions. EC shows strong positive correlations with TDS (1.000**), Cd (0.463**), and Pb (0.393**) (Table 6). TDS shows positive correlations with metals such as Cd (0.463**) and Pb (0.393**), similar to EC. This reinforces that higher dissolved solids are linked to increased metal concentrations.

Among the studied metals, Cd is significantly positively correlated with Pb (0.674**), Ni, (0.524**), Mn(0.565**), Cr(0.671**), and Zn (0.521**). Pb shows strong positive correlations with Cr (0.625**). Fe correlates significantly with Cr (0.530**), and Hg (0.520**), and Al (0.558**). Cr correlates highly with Co (0.617**), Zn (0.696**), As (0.552**), and Hg (0.514**). Notably, As (Arsenic) is highly correlated with Hg (0.701**), implying that elevated levels of As are strongly associated with higher Hg levels. These strong relationships indicate that the metals originate from similar industrial sources and highlight the potential for combined contamination risks.

The statistical results of both the Kruskal–Wallis Test and pairwise Mann–Whitney U Tests for physico-chemical parameters (pH, Electrical Conductivity (EC), Total Dissolved Solids (TDS)) and heavy metal concentrations in drinking water across five zones are presented in Table 7.

Table 7.

Statistical analysis of the studied physico-chemical parameters and heavy metals in drinking water.

Zones statistics	Description	pH	EC	TDS	Cd	Pb	Fe	Ni	Cu	Mn	Cr	Co	Zn	As	Hg	Al
Kruskal Wallis Test (Multiple Zones)	Chi-Square	28.091	18.431	18.431	22.922	16.455	15.903	.633	17.525	42.707	11.313	9.098	19.415	10.435	5.934	18.092
	Df	4	4	4	4	4	4	4	4	4	4	4	4	4	4	4
	Asymp. Sig	.000	.001	.001	.000	.002	.003	.959	.002	.000	.023	.059	.001	.034	.204	.001
Mann–Whitney U (Zone 1 & Zone 2)	Mann–Whitney U	182.000	185.500	185.500	301.500	187.000	288.000	305.500	188.500	169.500	274.500	229.000	155.500	246.500	305.500	312.000
	Wilcoxon W	482.000	536.500	536.500	652.500	538.000	639.000	605.500	488.500	469.500	625.500	529.000	455.500	546.500	656.500	612.000
	Z	-2.525	-2.457	-2.457	-.204	-2.427	-.466	-.126	-2.399	-2.768	-.728	-1.612	-3.039	-1.372	-.135	.000
	Asymp. Sig. (2-tailed)	.012	.014	.014	.838	.015	.641	.900	.016	.006	.466	.107	.002	.170	.893	1.000
Mann–Whitney U (Zone 1 & Zone 3)	Mann–Whitney U	233.500	278.500	278.500	234.000	256.000	188.500	274.500	209.000	101.000	190.500	219.000	147.000	246.000	200.000	179.000
	Wilcoxon W	584.500	531.500	531.500	487.000	607.000	441.500	625.500	462.000	354.000	443.500	472.000	400.000	499.000	453.000	432.000
	Z	-1.087	-.155	-.155	-1.077	-.621	-2.018	-.238	-1.593	-3.831	-1.976	-1.386	-2.877	-.871	-2.047	-2.287
	Asymp. Sig. (2-tailed)	.277	.877	.877	.282	.535	.044	.812	.111	.000	.048	.166	.004	.384	.041	.022
Mann–Whitney U (Zone 1 & Zone 4)	Mann–Whitney U	431.500	345.000	345.000	267.500	424.500	326.000	389.500	400.500	201.500	413.500	261.000	186.500	264.500	365.500	344.000
	Wilcoxon W	782.500	940.000	940.000	862.500	1.020E3	677.000	740.500	995.500	796.500	1.008E3	856.000	781.500	859.500	960.500	939.000
	Z	-.157	-1.447	-1.447	-2.604	-.261	-1.730	-.783	-.619	-3.589	-.425	-2.700	-3.812	-2.991	-1.304	-1.490
	Asymp. Sig. (2-tailed)	.876	.148	.148	.009	.794	.084	.434	.536	.000	.671	.007	.000	.003	.192	.136
Mann–Whitney U (Zone 1 & Zone 5)	Mann–Whitney U	205.000	261.500	261.500	187.500	170.500	279.000	295.000	228.500	215.000	250.500	287.000	272.500	218.500	269.500	208.000
	Wilcoxon W	505.000	612.500	612.500	538.500	521.500	630.000	646.000	579.500	566.000	601.500	587.000	572.500	518.500	569.500	559.000
	Z	-2.078	-.981	-.981	-2.418	-2.748	-.641	-.330	-1.622	-1.884	-1.194	-.485	-.767	-2.000	-.920	-2.038
	Asymp. Sig. (2-tailed)	.038	.327	.327	.016	.006	.522	.741	.105	.060	.232	.627	.443	.045	.358	.042
Mann–Whitney U (Zone 2 & Zone 3)	Mann–Whitney U	70.500	163.500	163.500	228.500	196.500	143.500	250.500	222.500	212.500	157.000	239.500	249.500	240.500	187.500	162.500
	Wilcoxon W	370.500	416.500	416.500	481.500	449.500	396.500	550.500	522.500	465.500	410.000	539.500	502.500	540.500	440.500	415.500
	Z	-4.256	-2.211	-2.211	-.781	-1.485	-2.650	-.297	-.913	-1.133	-2.353	-.539	-.319	-.571	-1.945	-2.281
	Asymp. Sig. (2-tailed)	.000	.027	.027	.435	.138	.008	.767	.361	.257	.019	.590	.750	.568	.052	.023
Mann–Whitney U (Zone 2 & Zone 4)	Mann–Whitney U	173.500	137.000	137.000	256.500	224.500	310.500	367.000	280.500	360.000	320.000	376.000	398.500	341.500	330.000	301.000
	Wilcoxon W	473.500	732.000	732.000	851.500	819.500	610.500	667.000	580.500	955.000	915.000	971.000	993.500	936.500	925.000	896.000
	Z	-3.703	-4.279	-4.279	-2.393	-2.897	-1.539	-.647	-2.013	-.758	-1.389	-.505	-.150	-1.281	-1.412	-1.711
	Asymp. Sig. (2-tailed)	.000	.000	.000	.017	.004	.124	.517	.044	.448	.165	.613	.881	.200	.158	.087
Mann–Whitney U (Zone 2 & Zone 5)	Mann–Whitney U	261.500	209.000	209.000	230.000	268.500	285.000	270.000	117.500	83.000	266.500	225.500	191.000	267.500	241.000	227.500
	Wilcoxon W	561.500	509.000	509.000	530.000	568.500	585.000	570.000	417.500	383.000	566.500	525.500	491.000	567.500	541.000	527.500
	Z	-.547	-1.629	-1.629	-1.196	-.402	-.062	-.371	-3.516	-4.228	-.443	-1.289	-2.000	-.504	-1.083	-1.253
	Asymp. Sig. (2-tailed)	.585	.103	.103	.232	.688	.951	.710	.000	.000	.657	.197	.045	.614	.279	.210
Mann–Whitney U (Zone 3 & Zone 4)	Mann–Whitney U	286.500	293.000	293.000	285.000	308.500	152.500	365.000	305.500	356.500	290.000	318.000	371.500	262.500	343.000	313.000
	Wilcoxon W	881.500	888.000	888.000	880.000	903.500	405.500	618.000	558.500	609.500	543.000	913.000	624.500	857.500	596.000	566.000
	Z	-1.469	-1.359	-1.359	-1.494	-1.099	-3.716	-.151	-1.149	-.294	-1.409	-.940	-.042	-2.183	-.667	-1.061
	Asymp. Sig. (2-tailed)	.142	.174	.174	.135	.272	.000	.880	.250	.769	.159	.347	.967	.029	.505	.289
Mann–Whitney U (Zone 3 & Zone 5)	Mann–Whitney U	88.000	227.000	227.000	131.500	186.000	146.000	260.000	127.000	45.000	127.500	219.000	176.000	216.000	228.000	103.500
	Wilcoxon W	388.000	480.000	480.000	384.500	439.000	399.000	560.000	380.000	298.000	380.500	472.000	429.000	516.000	481.000	356.500
	Z	-3.872	-.814	-.814	-2.916	-1.716	-2.595	-.088	-3.013	-4.817	-3.002	-.990	-1.935	-1.199	-.997	-3.606
	Asymp. Sig. (2-tailed)	.000	.416	.416	.004	.086	.009	.930	.003	.000	.003	.322	.053	.230	.319	.000
Mann–Whitney U (Zone 4 & Zone 5)	Mann–Whitney U	204.500	252.000	252.000	110.000	200.000	323.000	395.000	255.000	92.000	273.500	263.000	246.000	377.500	400.000	205.000
	Wilcoxon W	504.500	847.000	847.000	705.000	795.000	623.000	695.000	850.000	687.000	868.500	858.000	841.000	972.500	995.000	800.000
	Z	-3.214	-2.463	-2.463	-4.706	-3.284	-1.342	-.205	-2.416	-4.990	-2.124	-2.289	-2.558	-.611	-.154	-3.246
	Asymp. Sig. (2-tailed)	.001	.014	.014	.000	.001	.180	.837	.016	.000	.034	.022	.011	.541	.878	.001

Kruskal–Wallis test results

This test evaluates differences between multiple zones for each parameter. The key components include:

• Chi-Square values indicate the level of variation across zones. Larger values signify greater differences among the groups.

• Asymp. Sig. (p-values) determines statistical significance. If p < 0.05 the parameter varies significantly across the zones.

The Kruskal–Wallis test shows that parameters such as pH (p = 0.000), EC (p = 0.001), TDS (p = 0. 001), and heavy metals like Cd, Pb, Fe,Cu, Mn, Al and Fe (p < 0.05) vary significantly across multiple zones, suggesting spatial heterogeneity in water quality and contamination levels.

Mann–Whitney U Test results

This test evaluates pairwise differences between two zones for each parameter. The key components include:

• U-values: The test statistic reflects the ranks of the groups being compared. Lower U-values often correspond to greater differences.

• Z-scores: Standardized scores indicating the magnitude and direction of the difference.

• Asymp. Sig. (2-tailed): p-values to assess statistical significance. p < 0.05 indicates a significant difference between the two zones.

The Mann–Whitney U Test analysis revealed significant differences in water quality parameters between various zones. The findings are summarized as follows:

• Zone 1 vs. Zone 2: Significant differences were observed for pH, EC, TDS, and metals such as Pb, Cu, Mn, and Zn.

• Zone 1 vs. Zone 3: Differences were significant for Fe, Mn, Zn, Hg, and Al.

• Zone 1 vs. Zone 4: Cd, Mn, Co, Zn, and As exhibited significant differences.

• Zone 1 vs. Zone 5: pH and metals such as Cd, Pb, As, and Al showed notable differences.

• Zone 2 vs. Zone 3: Significant variations were found for pH, EC, TDS, Fe, Cr, and Al.

• Zone 2 vs. Zone 4: pH, EC, TDS, Cd, Pb, and Cu displayed significant differences.

• Zone 2 vs. Zone 5: Cu and Mn exhibited differences.

• Zone 3 vs. Zone 4: Fe and As showed significant differences.

• Zone 3 vs. Zone 5: Significant differences were observed for pH,Cd, Fe, Cu, Mn, Cr, and Al.

• Zone 4 vs. Zone 5: Differences were significant for pH, EC, TDS, Cd, Pb, Mn, Cr, Co, Zn, and Al.

These differences indicate uneven distribution and potential localized contamination influenced by the presence of various industries in the region.

Spatial distribution and variability of physico-chemical parameters and heavy metals in 5 zones

The spatial distribution of physico-chemical parameters and heavy metals in drinking water in selected areas of Gujranwala (Fig. 2) revealed significant variability and potential health hazards. The pH value ranged from 5.77 to 7.76. The mean pH of all 5 zones was within permissible limits of WHO and PSQCA(6.5–8.5) (Fig. 2a).

Fig. 2.

(a, b, c, d, e, f, g, h, I, j, k, l, m, n): Spatial distribution of various physico-chemical parameters and heavy metals of drinking water in 5 zones of Gujranwala.

The EC ranged from 328.17 to 1725 µS/cm. Zone 2 showed EC value beyond the permissible limit range of EC while the EC values at zones 1, 3, 4 and 5 showed the range within the maximum permissible limit of WHO which is 1500 µS/cm at 25 °C (Fig. 2b).

TDS of drinking water ranged between 210 and 1104 mg/L. The TDS values of all the zones were below the permissible limit of WHO except at zone 2 which exceeded the allowable limit while the other 4 zones were within the allowable limits of WHO (1000 mg/L) (Fig. 2c).

Cadmium (Cd) concentrations ranged from 0.000864 to 0.955 mg/L, with most samples exceeding the WHO limit of 0.003 mg/L (Fig. 2d). Lead (Pb) concentrations varied from 0.01048 to 1.7223 mg/l, most exceeding the WHO acceptable limit of 0.010 mg/L (Fig. 2e). Iron (Fe) levels ranged from 0.0199 to 6.979 mg/L, with 80% of samples exceeding the World Health Organization limit of 0.3 mg/L (Fig. 2f). Nickel (Ni) concentrations ranged from 0.0067 to 0.706 mg/l, with most samples exceeding the WHO limit of 0.02 mg/l (Fig. 2g). Copper (Cu) concentrations ranged from 0.0137 to 1.9594 mg/L, which is still below the WHO maximum acceptable level of 2 mg/L (Fig. 2h). Manganese (Mn) concentrations range from 0.00036 to 0.747 mg/L, mostly below the WHO limit of 0.5 mg/L (except in some areas of Zone 5) (Fig. 2i). Chromium (Cr) concentration from 0.00341 to 0.7749 mg/L, levels in Zone 1, 2 and 5. It exceeds the World Health Organization limit of 0.050 mg/L (Fig. 2j). Zinc range varied from 0.00915 to 2.592. Zone 1 showed the highest Zn concentration while zone 2 showed the lowest Zn concentration. The permissible limit of Zn is 5 mg/L by WHO (Fig. 2k). The WHO permissible limit of As and Hg is 0.010 mg/L 0.001 mg/L. As and Hg are traced in industrial areas of zone 2 and zone 4 (Fig. 2l,m). Zone 2 showed the highest Al concentration while zone 3 and 4 showed the lowest Al concentration and were within the permissible limit of 0.2 mg/L by WHO (Fig. 2n). These findings highlight the widespread presence of heavy metals in drinking water, indicating potential sources of contamination and the need for urgent remedial measures to protect public health and ensure the availability of drinking water.

Water quality index analysis

The Water Quality Index serves as a pivotal metric for gauging the extent of water pollution, with values exceeding 300 indicating substantial contamination. Figure 3 shows the percentage of water quality of all the samples obtained from selected 5 zones of Gujranwala. Out of 100 water samples, 11% water samples showed excellent water quality 26% of water samples showed good water quality. 12% of samples showed poor water quality. 11% of water samples showed very poor water quality. 40% of samples showed unsuitable water quality.

Fig. 3.

Percentage of water samples with respect to their water quality index value.

The water quality index values of groundwater were found higher than the other sources of water. Unsuitable water, (unfit for drinking) was found in groundwater sources of the majority of industries.

Spatial distribution of water quality index values in selected 5 zones of Gujranwala

Figure 4 represents the spatial distribution of water quality index among the selected 5 zones of Gujranwala. Major portion is representing the water quality index value between 100 and 200. This depicts that drinking water of major portion of the area is having poor quality while zone 2 and zone 4 shows the highest range that is far above than the range of unsuitable water. This situation is signaling potentially elevated pollution risks in these zones.

Fig. 4.

Spatial distribution of water quality of selected 5 zones of Gujranwala.

Health risk assessment of drinking water of Gujranwala

Health risk index calculation showed that 86% of the water samples were having the HRI value > 1 while 14% of the samples showed HRI value < 1 (Fig. 5). It means that only 14% of the obtained samples were safe for human use while the rest of the 86% showed they were unsafe. The sum of hazard quotients for Cd, Cr, Pb, As and Hg were > 1 for these 86% samples of water. They were unsafe for human use, especially children.

Fig. 5.

Percentage of samples with respective to their HRI Value.

Correlation of heavy metals in drinking water with health issues

Residents are facing severe health risks due to heavy metal pollution in drinking water. According to the response of the studied community they are facing health issues like respiratory problems, dermatological issues, gastrointestinal issues, and neurological disorders.

28% of respondents reported no disease. These respondents were mostly from the age group of 19–35. Gastrointestinal issues emerged as the most commonly reported health problems, with 34% of respondents indicating experiencing such symptoms. Following gastrointestinal issues, dermatological issues emerge as the second most prevalent health issue, with 17% of respondents reporting experiencing skin issues. Respiratory problems and neurological symptoms are also reported, although less frequently, with 11 and 6% of respondents, respectively, indicating experiencing such health problems. Additionally, the figure acknowledges the presence of other reported health issues, with 4% of respondents indicating experiencing miscellaneous symptoms not categorized under the predefined health issue categories (Fig. 6). While the specific nature of these health concerns may vary, their inclusion underscores the multifaceted nature of health impacts associated with environmental pollution and underscores the importance of holistic approaches to address public health challenges in Gujranwala.

Fig. 6.

Percentage responses about health issues of the studied population.

Discussion

The results of the study revealed significant differences in physico-chemical parameters and heavy metal concentrations in different areas of the city, indicating spatial differences in pollution levels. The variations in pH, electrical conductivity, and total dissolved solids levels across different zones, underscore the complex interplay of geological and anthropogenic factors in shaping water quality parameters⁶⁶. Zone 2(GT road towards Rawalpindi showed the lowest value for pH and the highest values for EC and TDS. The probable reason for this is the presence of small and heavy engineering industries on that site. These findings are consistent with prior research highlighting the influence of industrial activities on water chemistry and emphasize the importance of ongoing monitoring and management efforts to ensure the sustainability and safety of water resources⁶⁷.

Overall, the variations in heavy metal concentrations across different zones underscore the heterogeneous nature of heavy metal distribution in drinking water sources, influenced by industrial factors. Zone 2(GT road towards Rawalpindi) appears to be a hot spot with high concentrations of Cadmium (Cd), Lead (Pb) and aluminium (Al) due to the presence of engineering industries in this area, possibly related to the manufacturing of heavy-duty vehicles and equipment. Industries engaged in the manufacturing of heavy-duty vehicles and equipment have been frequently cited as notable contributors to heavy metal pollution. Within these sectors, aluminium is commonly utilized metal in the vehicular body and parts production processes. Cadmium is used in alloy production and plating. Additionally, lead finds its way into products such as paints and batteries utilized in vehicles and machinery. These findings are in agreement with the findings of Romero⁶⁸. They also quoted higher levels of cadmium, aluminium and lead in the industrial effluents of auto engineering industries.

Zone 4(Sialkot Road) showed higher concentrations of Iron (Fe) and Mercury (Hg). This may be attributed to the presence of iron and steel industries, steel melting furnaces and re-rolling mills on that site. Effluents from iron and steel manufacturing plants contain high levels of iron as well as other metals and pollutants associated with the production process. Iron and steel industries involve various chemical reactions and treatments that release iron and other contaminants into the environment. The findings of higher levels of iron and mercury were also reported in the industrial water of iron and steel industries in studies conducted by other authors^69,70. These findings highlight the persistent challenges associated with heavy metal contamination and emphasize the need for ongoing monitoring and management strategies to safeguard public health.

Correlation analysis provides insight into the underlying characteristics of heavy metal contamination in drinking water. The positive correlation observed between electrical conductivity (EC) and total dissolved solids (TDS) and most heavy metals suggests that higher values of EC and TDS are associated with increased concentrations of these metals. This means that parameters related to water quality such as EC and TDS can serve as indicators of heavy metal pollution, helping to identify dangerous areas and implement preventive measures⁷¹.

The Kruskal–Wallis and Mann–Whitney U tests revealed significant spatial heterogeneity in water quality across zones, with notable variations in pH, EC, TDS, and heavy metals (Cd, Pb, Fe, Cu, Mn, Zn, Al, Cr, Co, Hg, and As). Zone-wise comparisons highlighted significant differences in these parameters, reflecting distinct contamination patterns due to different type of industries among zones. Studies have consistently shown that industrial discharges, and urbanization contribute to spatial variations in heavy metal concentrations and physico-chemical properties of water bodies (e.g., pH, EC, TDS) across different zones. Multiple studies noted similar patterns of metal heterogeneity in industrial regions, attributing variations to uneven pollutant dispersion and localized waste management practices. Similarly significant zone-wise differences were observed in heavy metals like Cd, Pb, and Zn in riverine systems near industrial clusters, emphasizing the role of point-source pollution. The observed differences in metals such as Fe, Cu, and Mn between zones resonate with literature. Existing studies have reported these metals as key indicators of industrial contamination, particularly in regions with poor waste treatment infrastructure^72–75. The current study adds to this body of work, providing a foundation for further exploration of contamination dynamics and their implications for water quality and public health.

The water quality index values of groundwater were found higher than the other sources of drinking water. Unsuitable water, (unfit for drinking) was found in groundwater sources of the majority of industries. Leaching from the soil may be the primary cause of the elevated metal content in the groundwater of this region, as industry and urbanization have substantially increased environmental pollution. These results are in co-ordnance with the study conducted in India⁷⁶.

The spatial distribution analysis also highlighted the widespread presence of heavy metals in Gujranwala’s drinking water samples, with concentrations of Cd, Pb, Fe, Ni, Cr and Hg exceeding WHO regulatory standards (Fig. 2), indicating a widespread and potentially hazardous situation. This poses a serious health risk to residents as evident from hotspots and highlights the urgent need for remedial measures to reduce sources of pollution and ensure access to safe drinking water⁷⁷. Failure to comply with regulatory standards demonstrates the shortcomings of current water management practices and the need for stronger monitoring and enforcement measures to protect public health.

The health risk assessment serves as a valuable tool for informing regulatory decisions and prioritizing remediation efforts to reduce heavy metal exposure in drinking water. Health risk index calculation showed that 86% of the water samples had the HRI value > 1 while 14% of the samples showed HRI value < 1. Cd, Pb, Fe, Cr and Hg have hazard quotients greater than 1. It means they are significant contributors to adverse health effects and underscores the urgency of implementing regulatory measures and remediation efforts to mitigate exposure to these toxic metals and safeguard public health. Lead exposure, even at low levels, poses serious health risks. Chronic exposure to lead can result in neurological impairments, developmental delays, cognitive deficits, and behavioural disorders⁷⁸. Furthermore, lead exposure has been associated with increased blood pressure, cardiovascular diseases, and kidney dysfunction in adults⁷⁹. Similarly, iron, while an essential nutrient for human health, can pose health risks when present in elevated concentrations in drinking water. Excessive iron intake from drinking water has been linked to gastrointestinal disturbances, such as nausea, vomiting, and diarrhoea, as well as potential long-term effects on liver function and metabolic processes⁸⁰. The recognition of iron as a significant contributor to adverse health effects underscores the importance of implementing water treatment methods, such as oxidation, filtration, and ion exchange, to mitigate iron contamination and ensure the safety of drinking water supplies.

The reported health issues resonate with existing literature on the adverse health effects of environmental pollution and poor sanitation practices⁸¹. Gastrointestinal issues emerge as the most commonly reported health issue, with 34% of respondents indicating experiencing such symptoms. Gastrointestinal issues encompass a range of symptoms, including nausea, vomiting, abdominal pain, and diarrhoea, which can result from the ingestion of contaminated water. Gastrointestinal issues are consistent with the known health risks associated with contaminated water and food sources. Waterborne and foodborne pathogens, as well as chemical contaminants, pose significant threats to gastrointestinal health, leading to diarrheal diseases, food poisoning, and gastrointestinal disorders⁶⁴. Inadequate sanitation infrastructure and poor hygiene practices exacerbate the transmission of microbial pathogens, resulting in a higher incidence of gastrointestinal infections, particularly in regions with limited access to safe drinking water and sanitation facilities. Moreover, chemical contaminants such as heavy metals, pesticides, and industrial pollutants can contaminate food crops and water sources, further increasing the risk of gastrointestinal illnesses⁸².

Following gastrointestinal issues, dermatological issues emerged as the second most prevalent health issue with 17% of respondents reporting experiencing such issues. Dermatological issues manifest in various forms, including itching, rashes, and dermatitis, and may be indicative of exposure to environmental pollutants, such as heavy metals, present in water sources or industrial components. Numerous studies have demonstrated the association between exposure to chemical contaminants and various dermatological conditions. For example, heavy metals like lead, cadmium, and arsenic, commonly found in industrial effluents and contaminated soil, have been linked to allergic dermatitis, eczema, and contact dermatitis⁸³.

Following dermatological issues, respiratory problems emerge as the third most prevalent health issue, with 11% of respondents reporting experiencing respiratory symptoms. These include coughing, wheezing, shortness of breath, and chest tightness, suggestive of respiratory tract inflammation or irritation possibly triggered by ingestion of contaminated water or by inhalation of airborne pollutants, including particulate matter and heavy metal particles. Respiratory problems reported by respondents are consistent with extensive research demonstrating the adverse respiratory effects of air pollution. Exposure to ambient air pollutants has been associated with a wide range of respiratory conditions, including asthma, chronic obstructive pulmonary disease (COPD), bronchitis, and respiratory infections. Particulate matter, ozone, nitrogen dioxide, and sulfur dioxide are among the key pollutants known to exacerbate respiratory symptoms and impair lung function, with vulnerable populations such as children, the elderly, and individuals with pre-existing respiratory conditions being particularly susceptible. Additionally, indoor air pollution from sources such as biomass combustion, tobacco smoke, and indoor cooking fuels contributes significantly to the burden of respiratory diseases, especially in low- and middle-income countries^84,85. The significant prevalence of respiratory issues underscores the importance of implementing measures to mitigate water and air pollution and enhance respiratory health outcomes among residents of Gujranwala.

Neurological symptoms are also reported, albeit less frequently, with 6% of respondents, indicating experiencing such health problems. Similarly, neurological symptoms, such as headaches, dizziness, and cognitive impairments, may be indicative of neurotoxic effects associated with exposure to heavy metals, highlighting the need for further investigation into the sources and pathways of heavy metal contamination in the study area.

Additionally, Fig. 6 acknowledges the presence of other reported health issues, with 4% of respondents indicating experiencing miscellaneous symptoms not categorized under the predefined health issue categories. While the specific nature of these health concerns may vary, their inclusion underscores the multifaceted nature of health impacts associated with environmental pollution and underscores the importance of holistic approaches to address public health challenges in Gujranwala. These findings corroborate existing literature on environmental health risks associated with water pollution and poor sanitation, emphasizing the need for targeted interventions to mitigate adverse health outcomes.

Conclusion

In conclusion, this study provides comprehensive information on quantification, correlation, spatial distribution, compliance requirements and health risk assessment of heavy metals in drinking water of 5 different zones of Gujranwala. The findings highlight regional differences in pollution levels, influencing factors, and compliance status, suggesting the need for targeted interventions to address more polluted areas and ensure water quality. The mean concentrations of Heavy metals in drinking water i.e. Cd, Pb, Fe, Ni, Cr, Co, As, Hg and Al were above the regulatory standards of WHO and PSQCA in the industrial areas (sampled zones) of Gujranwala. Cd, Pb and Al are highest in Zone 2 (GT road towards Rawalpindi). Fe and Hg is highest in Zone 4(Sialkot Road). Cu, Mn and Zn were below the permissible limits of WHO and PSQCA. Overall drinking water heavy metal contamination trend is Zone 2 (GT road towards Rawalpindi) > Zone 4 (Sialkot Road) > Zone 5(Sheikhupura Road) > Zone 1(Main City) > Zone 3 (GT Road towards Lahore). Industrial activities contribute, specifically vehicular manufacturing in Zone 2 and steel and iron industries in Zone 4, to higher contamination levels in these zones. The findings of Zone 2 and 4 drinking water give an alert that precautionary measures should be taken immediately.

Health risk index calculation showed that 86% of the water samples from industrial areas are unsafe for human consumption. Only 14% of the obtained samples were safe for human use. Respondents reported acute and chronic health problems. 34% of respondents indicated experiencing gastrointestinal issues, and 17% of respondents reported experiencing dermatological issues. 11% of respondents reported respiratory problems and 6% of respondents reported neurological symptoms. Additionally, 4% of respondents indicated experiencing miscellaneous symptoms. Correlation analysis underscores the influence of water quality parameters like electrical conductivity and total dissolved solids on pollution levels. Urgent remedial measures are needed to reduce sources of pollution and reduce health risks associated with exposure to heavy metals in drinking water.

Recommendations

Based on the results, several recommendations can be made to improve water quality management in Gujranwala:

• Strengthen monitoring and enforcement to ensure that heavy metals in drinking water meet WHO regulatory standards. Conduct routine testing and review of water quality parameters to monitor changes over time and provide information on adaptive management strategies.

• Take source control measures to identify and reduce sources of pollution, especially high-risk areas identified through site analysis

• Improving education programs and public awareness to promote water conservation methods and reduce the use of potentially polluting materials in homes and industries.

• Invest in infrastructure development and treatment technology to improve water quality and ensure access to drinking water for all residents.

Limitations

The study acknowledges the limitation of not having a control group, such as non-industrial areas, which would have strengthened comparisons and conclusions. The focus on industrial zones was aimed at providing a preliminary understanding of water contamination in high-risk areas. Additionally, seasonal variations, which are important for water quality, could not be incorporated due to time and resource constraints. Future studies should address these limitations for a more comprehensive assessment.

Author contributions

I.A. conducted and excecuted the study, S.B. and S.I. supervised, finalized methodology and reviewed, R.S. helped in G.I.S. software use for maps making, A.S. and T.J. helped with review and finances.

Data availability

Raw data would be made available upon request from the corresponding author for scientific research purposes in accordance with data sharing policies and regulations.

Declarations

Competing interests

The authors declare no competing interests.

Ethical approval

This research was conducted following ethical standards and scientific integrity.