Integrated Geospatial and Geostatistical Multi-Criteria Evaluation of Urban Groundwater Quality Using Water Quality Indices

Abstract

Groundwater contamination poses a severe public health risk in Lahore, Pakistan’s second-largest city, where over-exploited aquifers are the primary municipal and domestic water supply source. This study presents the first comprehensive district-wide assessment of groundwater quality across Lahore using an innovative integrated approach combining geographic information systems (GIS), multi-criteria decision analysis (MCDA), and water quality indexing techniques. The core objectives were to map the spatial distributions of critical pollutants like arsenic, model their impacts on overall potability, and evaluate targeted remediation scenarios. The analytic hierarchy process (AHP) methodology was applied to derive weights for the relative importance of diverse water quality parameters based on expert judgments. Arsenic received the highest priority weight (0.28), followed by total dissolved solids (0.22) and hardness (0.15), reflecting their significance as health hazards. Weighted overlay analysis in GIS delineated localized quality hotspots, unveiling severely degraded areas with very poor index values (>150) in urban industrial zones like Lahore Cantt, Model Town, and parts of Lahore City. This corroborates reports of unregulated industrial effluent discharges contributing to aquifer pollution. Prospective improvement scenarios projected that reducing heavy metals like arsenic by 30% could enhance quality indices by up to 20.71% in critically degraded localities like Shalimar. Simulating advanced multi-barrier water treatment processes showcased an over 95% potential reduction in arsenic levels, indicating the requirement for deploying advanced oxidation and filtration infrastructure aligned with local contaminant profiles. The integrated decision support tool enables the visualization of complex contamination patterns, evaluation of remediation options, and prioritizing risk-mitigation investments based on the spatial distribution of hazard exposures. This framework equips urban planners and utilities with critical insights for developing targeted groundwater quality restoration policies through strategic interventions encompassing treatment facilities, drainage infrastructure improvements, and pollutant discharge regulations. Its replicability across other regions allows for tackling widespread groundwater contamination challenges through robust data synthesis and quantitative scenario modeling capabilities.

1. Introduction

Access to safe drinking water is a pressing issue affecting public health around the world. Groundwater contamination from natural and anthropogenic sources is a significant concern, with inorganic arsenic being a priority pollutant due to its toxicity and carcinogenicity [1]. In Pakistan, groundwater provides over 50% of the potable supply, and arsenic contamination is widespread across the alluvial aquifers of the Indus Plain [2,3,4]. Recent estimates suggest that over 50 million Pakistanis consume groundwater with arsenic levels exceeding the WHO provisional guideline value of 10 μg/L, putting them at risk of adverse health effects like skin lesions, cardiovascular diseases, and various cancers [5,6,7,8,9].

Among Pakistan’s urban centers, Lahore has been identified as a city with significant arsenic-related health risks from contaminated drinking water sources. Situated along the Ravi River, Lahore City and its adjoining areas in the Lahore District rely extensively on groundwater to meet municipal and domestic demands [10,11,12,13,14,15,16,17,18]. While past studies have analyzed arsenic in scattered groundwater samples across Lahore, a comprehensive understanding of the city-wide groundwater quality profile is still lacking [19]. This has impeded the appraisal of the health risks and targeted mitigation planning. Therefore, the present study aimed to extensively map the groundwater across 50 tube wells in Lahore City while concurrently evaluating the overall physiochemical quality of the groundwater [2,20,21,22,23].

The water quality analysis involved field measurements and laboratory testing of samples using atomic absorption spectrometry and other standard methods. Spatial interpolation techniques were applied to develop arsenic contamination prediction maps for the Lahore District. Based on measured arsenic concentrations, a health risk analysis was conducted following the US EPA guidelines [24]. The study findings can aid relevant government agencies in Lahore in better understanding the city-wide groundwater quality and arsenic pollution characteristics while identifying priority areas that need immediate testing and mitigation [25]. The assessment frameworks presented can be applied to aquifer-wide monitoring and health risk assessment in other arsenic-affected urban and rural regions across Pakistan’s Indus Basin [26,27,28,29,30,31,32].

To comprehensively evaluate the spatial relationships and develop integrated groundwater contamination and health risk maps, geographic information systems (GIS) techniques were applied [33,34,35,36]. The location data of all the sampling points were recorded on a GPS device and then mapped using ArcGIS 10.5 software. Inverse distance weighting (IDW) interpolation methods were utilized to spatially analyze the arsenic distribution patterns and visualize predicted hotspots across Lahore to aid decision-making [37,38,39,40,41,42,43,44,45,46].

Furthermore, the analytical hierarchy process (AHP), a multi-criteria decision-making tool, was adopted following Saaty’s scaling method to determine suitable sites for installing new tube wells in the Lahore District. The AHP allows for structured analyses of the various criteria influencing groundwater quality and suitability. Critical parameters like arsenic and fluoride levels, total dissolved solids, pH, sulfate and chloride content, iron content, and depth to water table were considered as criteria, with weights assigned based on the World Health Organization standards. Current heavily contaminated areas were excluded [47]. The derived AHP site suitability maps can aid authorities in scientifically determining where to install new water supply sources rather than using arbitrary selection. Thus, integrating GIS and AHP enables the optimal visualization and analysis of the complex groundwater quality dataset to identify clean, potable resources for Lahore city inhabitants [48,49,50,51,52,53,54,55,56].

The water quality index (WQI) is a valuable indicator for summarizing complex water quality data into a single numeric value that indicates the overall level of contamination or portability. The derivation of the WQI requires selecting key water quality parameters, assigning weights according to their health impact, measuring parameter concentrations, and aggregating the parameters based on a rating scale [57]. For Lahore groundwater, a WQI was calculated based on 11 core physicochemical indicators, including arsenic, fluoride, pH, total dissolved solids, electrical conductivity, chloride, iron, and trace elements like cadmium, chromium, and lead. The weight for each parameter was determined using a multi-criteria technique (the analytical hierarchy process). The ratings were designated according to Pakistan’s National Environmental Quality Standards and the World Health Organization guidelines for drinking water. Mapping the spatial variability in overall WQI scores across the sampling locations can help to better elucidate, in a user-friendly manner, the groundwater quality suitability for potable use in particular areas, supporting targeted pollution mitigation [58,59,60,61,62,63].

A vital aspect in holistically evaluating regional groundwater quality is examining potential health impacts from consuming contaminated water over prolonged durations. A health risk assessment (HRA) is an essential tool for estimating the carcinogenic and non-carcinogenic effects of exposure to hazardous compounds in drinking water. Therefore, HRA models developed by the US EPA were applied in this study to determine the chronic health implications for Lahore residents ingesting untreated groundwater based on the arsenic concentrations quantified across the sampling locations [64]. Probability distribution functions were used to account for the variability in human exposure through drinking water, with risk calculations performed separately for adults and children. The HRA helped to gauge the severity of arsenic-related health consequences linked to the identified contamination hotspots, further underscoring the urgent need for mitigation strategies to combat groundwater pollution in the Lahore District [65,66,67,68,69].

Despite Lahore’s dependence on groundwater for domestic and municipal functions, a holistic understanding of aquifer contamination across the city is lacking. This has constrained local agencies’ ability to provide a potable water supply to residents. Therefore, the overarching objective of this study was to conduct an extensive groundwater quality evaluation for the Lahore District to support remediation planning [70]. The specific objectives were to (i) quantify the water quality parameters across different sampling locations and identify areas exceeding the WHO, USEPA, and national groundwater quality standards; (ii) assess the pivotal water quality parameters, including pH, total dissolved solids (TDSs), electrical conductivity (EC), hardness, and the presence of other inorganic pollutants; (iii) compute the groundwater quality index by employing a GIS-based analytic hierarchy process (AHP) to assign weights to various water quality parameters, facilitating a comprehensive assessment of groundwater quality; and (iv) utilize statistical analysis to identify pollution patterns and develop tailored remediation strategies to restore the water quality to safe levels.

The problem statement or hypothesis is that widespread, unregulated anthropogenic inputs from industrial effluents and sewage intrusion are progressively degrading Lahore’s shallow alluvial aquifers tapped for drinking purposes [71]. This is endangering public health, as residents in impacted areas likely ingest arsenic and other toxic constituents at concentrations exceeding the national and international quality standards for potable usage. Testing this hypothesis through a comprehensive city-wide groundwater quality appraisal and health risk analysis will clarify the pollution sources and exposure risks. These findings can aid planners in enforcing the requisite water treatments and delineate priority recharge zones for targeted remediation.

2. Material and Methods

2.1. Study Area

The Lahore District, located in Punjab Province, Pakistan, overlies a significant alluvial aquifer system (Figure 1). The aquifer is composed primarily of Quaternary alluvial deposits, consisting of fine to coarse sand with intermittent clay lenses. Two main aquifer units are recognized: an upper unconfined aquifer from 0 to 40 m in depth, and a lower semi-confined aquifer from 40 to 60 m in depth. The hydraulic conductivity ranges from 10 to 50 m/day, with higher values in the coarser deposits. The water table depth varies from 5 m in the eastern parts to 20 m in the western areas of the district. The groundwater chemistry is predominantly the Ca-HCO₃ type, reflecting the carbonate-rich alluvial sediments. However, in urban and industrial zones, the water type shifts towards Ca-Cl or Na-Cl due to anthropogenic influences. The natural background levels of arsenic range from 1 to 5 μg/L, which is attributed to the weathering of arsenic-bearing minerals in the alluvial sediments. However, concentrations exceeding 50 μg/L have been reported in some areas, likely due to a combination of natural and anthropogenic factors [72]. The district has over 500 registered groundwater extraction wells, with the total withdrawal volume estimated at 3.5 million m³/day. Approximately 70% is used for the municipal supply, 20% for irrigation, and 10% for industrial purposes. Major influences on groundwater chemistry include natural geogenic sources (e.g., arsenic-bearing sediments), industrial effluents (particularly in the Lahore Cantt and Model Town areas), inadequate sewage disposal (affecting shallow groundwater in densely populated areas), and agricultural runoff (mainly in peri-urban regions). The current water quality improvement measures are limited, with only primary treatment available at most municipal supply wells. The feasibility of implementing advanced treatment technologies is constrained by economic factors and the decentralized nature of the water supply system. However, recent regulatory initiatives aim to enhance industrial effluent control, which may positively impact groundwater quality in the long term [8,20,56,60,73,74,75,76,77,78,79].

2.2. Water Quality Data

The water quality indicators selected for inclusion in the analysis were chosen based on their relevance in assessing the suitability of drinking water. The critical parameters considered were pH, turbidity, total dissolved solids (TDSs), electrical conductivity (EC), hardness, alkalinity, chloride, arsenic, calcium, and magnesium. These parameters correspond to the physical, chemical, and microbiological characteristics that influence water potability and taste. For instance, pH indicates corrosivity, which can impact water pipes and health [80]. Turbidity affects disinfection, while TDS levels influence palatability. Hardness and alkalinity determine the water’s softness and buffering capacity. Chloride provides a measure of salinity; arsenic indicates toxicity, and calcium and magnesium affect taste and scale formation. By focusing on parameters that align with significant drinking water quality guidelines, such as the WHO standards, the multi-criteria analysis could effectively evaluate the groundwater’s suitability for drinking across the Lahore District. The indicators encompass the critical properties required for a comprehensive drinking water quality assessment [81,82].

2.2.1. Standardization

To integrate the heterogeneous water quality indicators into a common framework, a technique of standardization and classification was implemented based on drinking water guidelines. The World Health Organization (WHO) guidelines and National Standards for Drinking Water Quality (NSDWG) were leveraged to define suitability classes for each parameter. These classes were delineated using established standards and health-based thresholds for potable water. Four distinct classes were established—excellent, good, permissible, and unsuitable (Table 1). For example, pH levels ranging from 7.5 to 8.5 were considered outstanding for drinking, while a pH below 6 was considered unsuitable based on potential health impacts and corrosivity. Likewise, turbidity levels between 0.1 and 0.3 NTU were rated as excellent, as a higher turbidity can indicate microbial contamination and requires additional treatment [83]. The TDSs, EC, hardness, chloride, calcium, magnesium, and other indicators were also categorized into these standardized classes based on the WHO and NSDWG quality standards and health criteria. The variables could be integrated into an overall water quality index by converting the diverse measured values into a normalized 1–4 scale. This rigorous standardization technique enabled a robust comparative assessment of groundwater quality across different locations, scales, and parameters using a consistent suitability rating interpretable for water supply planning [84,85,86]. Water samples were collected from 50 locations across the Lahore District between June and August 2023. Field measurements of pH, electrical conductivity, and temperature were conducted using a calibrated portable multi-parameter probe. Samples for laboratory analysis were collected in pre-cleaned polyethylene bottles, preserved according to standard methods, and transported to the laboratory in coolers maintained at 4 °C. The laboratory analyses were conducted at the Environmental Analysis Laboratory of WASA. Heavy metals were analyzed using ICP-MS, while anions were determined using IC. All analyses were performed following standard methods (APHA, 2017). Quality control measures included field blanks, laboratory blanks, and duplicate samples which accounted for 10% of the total samples. The standardization of water quality indicators into ‘excellent’, ‘good’, ‘permissible’, and ‘unsuitable’ categories was based on a combination of international standards (WHO guidelines), national regulations (National Standards for Drinking Water Quality of Pakistan), and local environmental conditions. For each parameter, we considered the following: (a) the WHO and national guideline values as primary benchmarks; (b) local background levels derived from historical data and geochemical studies of the Lahore aquifer; (c) the known health effects at different concentration levels; and (d) the treatment capabilities of local water management facilities.

For instance, the arsenic categories were defined as follows:

• Excellent (<5 μg/L): Below both the WHO and national standards, representing a minimal health risk.

• Good (5–10 μg/L): Meets the WHO standard but below the national limit and is suitable for consumption.

• Permissible (10–50 μg/L): Exceeds the WHO standard but within the national limit; it may require treatment.

• Unsuitable (>50 μg/L): Exceeds both the WHO and national standards, posing significant health risks.

Similar justifications were developed for each parameter considering their specific health impacts and local relevance.

2.2.2. Analysis

The Inverse Distance Weighted (IDW) interpolation method created a continuous raster surface representing the spatial variation of the water quality parameters across the Lahore District. IDW assumes that the influence of a known data point on the interpolated values diminishes with increasing distance. For this study, the water quality data collected at the sampling points were interpolated using IDW to create 10 raster layers for each parameter. An IDW power of 2 was used, meaning that the weighting of surrounding points decreases quadratically with distance [87]. A variable search radius was utilized, ensuring that at least 10 data points were used to interpolate each location. This resulted in smoothly varying raster layers that allowed for the visual interpretation of the spatial patterns. The IDW interpolation considered local variation in the measured data points, making it well suited for creating surfaces from sparsely sampled water quality data. The resulting raster layers provided comprehensive spatial coverage across the Lahore District for the GIS-based multi-criteria analysis [88,89,90,91]. To quantify the uncertainties in our IDW interpolation, we employed a cross-validation technique. This involved iteratively removing each sampling point, interpolating its value based on the remaining points, and comparing the predicted value to the observed value. We calculated the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) for each interpolated parameter to provide quantitative measures of interpolation accuracy.

2.3. AHP Methodology

2.3.1. Multi-Criteria Decision Analysis

Multi-criteria decision analysis (MCDA) techniques integrate diverse criteria into a single index for ranking alternatives. The analytical hierarchy process (AHP), an MCDA method, was utilized in this study to weigh the importance of the different water quality parameters. The AHP derives criteria weights through systematic pairwise comparisons based on expert knowledge. Experts compare the parameters two at a time and provide judgments on their relative importance for drinking water safety (Figure 2). These comparisons are arranged in a matrix, and through eigenvector calculations, priority vectors containing the relative weights of each parameter can be derived. The AHP thus transforms subjective assessments into an objective weighting scheme [90,92,93]. By incorporating the expert-assigned weights into the spatial analysis, the relative influence of each water quality parameter on drinking suitability was quantified. This data-driven weighting lends robustness and transparency to the composite index.

Geographic Information Systems (GIS) offer robust spatial analysis and mapping capabilities that can be coupled with multi-criteria techniques like the AHP to enrich decision-making. GIS can assimilate large, heterogeneous datasets from various sources into decision frameworks. Their spatial analysis tools also enable the assessment of criteria weights and alternatives across geographic scales. The integration of GIS and MCDA methods provides powerful spatial analysis functions. GIS-based AHPs have gained popularity because of their capacity to integrate a large quantity of heterogeneous data and obtain the required weights in a relatively straightforward way, even for many criteria. GIS integrate disparate location-based water monitoring, infrastructure, and population data in water quality assessments, while the AHP derives priority weights for each parameter [94]. GIS-AHPs have become popular because the integrated framework makes multi-criteria analysis more versatile and better suited for spatial decisions. For this drinking water application, a GIS-AHP approach was used to allow for contextualization of water quality issues by location to aid targeted, localized planning based on geospatial datasets.

2.3.2. Steps for Calculating Weights

• i. Conduct pairwise comparisons

The AHP methodology is based on developing a matrix of pairwise comparisons between the criteria under consideration. This study compared water quality parameters in a pairwise manner based on their relative importance for drinking water safety and aesthetics. These pairwise comparisons were fundamental for eliciting judgments that capture expert knowledge on the influence of each parameter. To quantify the comparisons, various scales have been proposed to rate the related stakeholders’ judgments, such as the 1–9 point scale, the power scale, the geometric scale, and the logarithmic scale. The 1–9 scale was chosen due to its distinct qualitative descriptions of importance ranging from equal to extreme, which enables nuanced elicitation of expert opinions. The relevant stakeholders first evaluated the relative importance of each possible pair of parameters for drinking water [95]. For example, TDSs may be rated as moderately more important than pH. These categorical judgments were then translated into numerical values on a 1–9 scale and arranged in a matrix. A comprehensive set of quantitative comparisons was constructed by working through each parameter pair. The resulting reciprocal matrix contained rich insights into parameter priorities based on expert guidance. This is a robust input for the computational derivation of the criteria weights using eigenvector methods in the AHP. The pairwise process enhances transparency in criteria weighting and allows for the incorporation of context-specific stakeholder perspectives [88,91,96,97].

The relative importance weights of the water quality parameters were derived through pairwise comparisons using a 1–9 rating scale. This scale, developed by Saaty, allows subjective judgments to be converted into numerical values, which can be used in AHP calculations. It provides more gradations than minor scales to allow us to discern different degrees of importance between two criteria. A score of 1 denotes equal importance between two parameters, while 9 indicates that one parameter is exceedingly more important than the other. Intermediate values of 2, 4, 6, and 8 represent varying degrees of relative importance. For instance, a score of 5 indicates that the row parameter is enormously more important than the column parameter. Reciprocals are assigned when the comparison is reversed. This 1–9 scale transforms the categorical judgments into a consistent ratio scale of priority (Table 2). Providing a spectrum of ratings can effectively capture the nuances of expert opinions. The derived numerical pairwise comparisons were arranged in a matrix and used to compute the priority weights through eigenvector methods. The 1–9 scale enables the integration of subjective expert opinions into a multi-criteria ranking of water quality parameters based on their health and aesthetic impacts on drinking safety.

• ii. Calculate the Consistency Index

An essential step in the AHP is assessing the consistency of the pairwise comparison judgments provided by the experts. Perfect consistency is difficult to achieve in practice when making numerous subjective judgments. Thus, a measure of consistency helps validate the logical soundness of the comparisons. The consistency was evaluated by examining the comparison matrix’s principal eigenvalue (λmax). Saaty proved that λmax will always be greater than or equal to the matrix size (N) for positive reciprocal matrices and will equal N only for perfectly consistent judgments. Therefore, the closer λ_max is to N, the higher the consistency. The deviation from consistency can be quantified using a Consistency Index, which represents the degree of error. This index is calculated by subtracting N from the λ_max and dividing it by the matrix size minus 1. The Consistency Index is compared against an average random index derived through simulations to compute a Consistency Ratio. A ratio below 10% is generally considered acceptable. Through this verification process, any logical inconsistencies in the expert judgments can be identified, and the problematic comparisons can be reviewed and adjusted to improve the overall consistency. This helps reinforce the robustness of the subjective pairwise comparisons, which form the backbone of the AHP methodology [91,96,98].

(1)$\mathrm{C}\mathrm{I}={\displaystyle \frac{\mathsf{\lambda }\max -\mathrm{n}}{\mathrm{n}-1}}$

where n = number of factors (i.e., 10) and λ = average value of the consistency vector.

To interpret the Consistency Index, it is compared against an average random consistency index derived for matrices of each size. Saaty simulated random judgments for different matrix sizes to calculate average consistency indices that are expected from random pairwise comparisons. The results serve as a Random Index (RI) (Table 3), which serves as a benchmark for each size of the comparison matrix. The Consistency Ratio is then calculated by dividing the Consistency Index by the appropriate Random Index for the corresponding matrix size. This normalization allows us to judge the level of consistency in the judgments irrespective of matrix size. A Consistency Ratio below 10% is generally considered reasonable. If the ratio exceeds 10%, the decisions may contain unacceptable inconsistencies that should be revisited. By benchmarking against random consistency, the Consistency Ratio provides a standardized measure to identify potential abnormalities in the qualitative judgments offered by experts. This verification adds rigor to the knowledge elicitation process, which is critical to a robust AHP application. The consistency evaluation is an important validation step to ensure logical, coherent input judgments before deriving priorities.

(2)$\mathrm{CR}={\displaystyle \frac{\mathrm{CI}}{\mathrm{RI}}}$

where CI = Consistency Index, RI = Random Index, and CR = Consistency Ratio.

Once the Consistency Ratio is computed for the comparison matrix, it must be evaluated against the threshold to determine if the judgments are consistent enough to be reliable. For a 10 × 10 matrix, Saaty recommends that the ratio should be under 10%. In this case study, the Consistency Ratio using the RI (1.49) was calculated to be 0.098, which is less than 0.1. This indicates an acceptable level of consistency in the pairwise judgments without significant logical inconsistencies.

If the ratio exceeded 10%, it would imply potential irregularities in the comparisons that require re-examination. A ratio over 10% suggests that the judgments are too inconsistent to derive meaningful priorities for the criteria. A standardized statistical test is applied to verify that the qualitative comparisons are coherent enough for further AHP calculations by comparing the Consistency Ratio to the recommended threshold. This validation ensures that the subjective expert opinions that form the basis of the AHP are consistent enough to produce reliable results.

• iii. Derive weights using AHP

The relative importance weights for the water quality parameters were determined using the analytical hierarchy process (AHP) tool in ArcGIS. The AHP allows weights to be derived through pairwise comparisons of the criteria based on expert judgments. A comparison matrix was constructed comparing the importance of each parameter pair in determining the suitability of drinking water. The comparisons were quantified on a 1–9 scale, with 1 indicating equal importance and 9 indicating exceedingly a higher importance of one parameter over the other. These pairwise comparisons were input into the AHP tool, which calculated a priority vector containing the relative weights of the parameters. The tool also computed a Consistency Ratio to evaluate the logical consistency of the comparisons. The final parameter weights derived through the AHP ranged from 0.03 for chloride to 0.28 for arsenic. These AHP-derived weights were used in the weighted overlay analysis within ArcGIS to determine the spatial distribution of the groundwater quality index [59,84,88].

The weighted overlay analysis combined the interpolated parameter layers based on their relative influence on drinking water suitability as determined through the AHP. This data-driven AHP weighting allows for the incorporation of expert knowledge into the spatial analysis.

2.4. Strategies for the Improvement of Groundwater Quality

The improvement scenarios presented here are the result of our original analysis, which were developed specifically for the Lahore context. These scenarios were formulated based on a comprehensive review of the groundwater remediation literature [99,100], consultation with local water management experts, and analysis of the contaminant profiles observed in our study. We adapted established remediation techniques to address the specific challenges of Lahore’s groundwater contamination.

2.4.1. Mitigate Chemical Contamination by Implementing a Variety of Pollution Control Strategies

• i. Variations in heavy metal levels

Addressing the arsenic contamination in groundwater involves understanding the sources of contamination and implementing effective control measures. Arsenic, a naturally occurring element in the Earth’s crust, can become a significant groundwater contaminant due to industrial discharges, agricultural practices, and improper waste management.

To mitigate arsenic contamination in groundwater, the suggested control measures include regular monitoring of industrial effluents, placing industries away from water channels, implementing advanced wastewater treatment processes, and maintaining safe distances between landfills and wells.

The implementation of these control measures can lead to significant improvements in groundwater quality, which can be analyzed through three arsenic reduction scenarios.

Scenario I (10% Reduction): Even a modest reduction in arsenic levels can enhance the groundwater quality index (GQI), making the water safer for consumption and reducing the risk of arsenic exposure for the population. This scenario represents the initial effects of implementing control measures.

Scenario II (20% Reduction): A more substantial decrease in arsenic concentration would further improve the GQI. At this stage, the positive impacts of stringent effluent treatments and industrial regulations become more apparent, contributing to healthier ecosystems and reduced health risks.

Scenario III (30% Reduction): This scenario represents a significant improvement in groundwater quality, reflecting the full potential of comprehensive arsenic management strategies. A 30% reduction could dramatically decrease the incidence of arsenic-related health issues and enhance the overall sustainability of groundwater resources.

• ii. Variations in chemical parameters

Scenario I (10% Chemical Reduction): This scenario involves implementing a 10% reduction in nitrogen-based fertilizers, isolating drains from contaminated areas, introducing essential wastewater treatment strategies, and starting to line selected drainage channels. There will be initial, modest improvements in groundwater quality, with a slight increase in the groundwater quality index (GQI), marking the first steps towards mitigating ion-based contamination.

Scenario II (20% Chemical Reduction): This scenario involves further reducing nitrogen-based fertilizer usage by 20%, enhancing drain isolation and management, scaling up the wastewater treatment capacity, and lining more drainage channels. There will be a noticeable improvement in groundwater quality, indicated by a significant rise in the GQI, demonstrating the effectiveness of more stringent control measures.

Scenario III (30% Chemical Reduction): This scenario involves achieving a 30% reduction in nitrogen-based fertilizer use, complete isolation of all contaminant sources, deployment of advanced wastewater treatment technologies, and universal lining of drainage channels. There will be robust enhancement of the groundwater quality, with a marked increase in the GQI.

2.4.2. Implement Water Treatment Processes to Decrease Concentrations of Chemical Contaminants to Levels That Meet or Surpass the World Health Organization (WHO) Standards

When designing a methodology for treating water to reduce the concentration of chemical contamination below the WHO standards, three scenarios corresponding to primary, secondary, and tertiary treatment stages were considered. Each scenario was tailored to address different levels of contaminant reduction, shown in Table 4, focusing on total dissolved solids (TDSs), hardness, and specific pollutants like arsenic. The effectiveness of each treatment stage in achieving the set reduction targets was evaluated.

• Scenario I (Primary Treatment): In the primary treatment scenario, the goal is to reduce total dissolved solids (TDSs) by up to 60%. It was structured into two variations of treatment that aim to achieve 40% and 60% reductions, respectively.

• Scenario II (Secondary Treatment): In the secondary treatment scenario, two variations aim for substantial reductions: Part A targets a 65% reduction in TDSs and a 45% reduction in hardness using microbial digestion, while Part B increases these rates to 80% and 55%, respectively.

• Scenario III (Tertiary Treatment): In the tertiary treatment scenario, two variations focus on significant arsenic reduction: Part A aims for a 90% reduction, while Part B increases this to 95%, showcasing advanced treatment efficiency.

3. Results

3.1. Assessing Water Quality Parameters

Table 5 presents the range of physio-chemical parameters that were measured in the water samples and compares them against the World Health Organization’s (WHO) [101] permissible limits for drinking water quality. The pH values ranged from 7.0 to 8.3, with a mean of 7.9, which is within the WHO limit of 6.5–8.5. The turbidity levels were between 0 and 3.81 NTU, with a mean of 0.7 NTU, also conforming to the WHO limit of 5 NTU. However, concerns arose with the other parameters. The total dissolved solids (TDSs) varied from 188 to 1266 mg/L, exceeding the 1000 mg/L limit in 2% of the samples. The electrical conductivity (EC) values ranged from 299 to 2010 μS/cm, with 86% of the samples surpassing the 400 μS/cm threshold. While the hardness levels mostly adhered to the 500 mg/L guideline, with only 2% of the samples had a level exceeding it, the alkalinity values breached the 500 mg/L mark in 10% of cases (Figure 3). Our cross-validation analysis revealed varying levels of uncertainty across the different parameters. For arsenic, the RMSE was 5.3 μg/L, with higher uncertainties in areas with sparse sampling points. Analyses for the other parameters showed similar RMSEs for TDS, hardness, and pH.

The concentrations of essential minerals like calcium and magnesium remained within the 75 mg/L limits set by the WHO. The chloride levels also complied with the 250 mg/L standard across all the samples. Disturbingly, 96% of the samples contained arsenic concentrations exceeding the stringent 10 μg/L limit, with values ranging from 8.0 to 25.7 μg/L and a mean of 16.4 μg/L. While most parameters aligned with the WHO guidelines, the elevated electrical conductivity and pervasive arsenic contamination raised significant concerns about the suitability of these water sources for drinking purposes, warranting further investigation and potential remediation measures (Figure 4).

3.2. Groundwater Quality Index

A classification scheme was developed to categorize the computed groundwater quality index values into distinct ranks representing suitability for drinking. The index values were divided into five classes according to previous studies on drinking water quality indexing. An index value between 0 and 50 indicates excellent quality and that the water can be safely used for drinking without treatment. A value between 50 and 100 represents good quality and that the water is generally safe for drinking. Medium-quality water with an index value from 100 to 120 can be used for drinking after conventional treatment. Index values from 120 to 150 indicate poor-quality water that requires special treatment before consumption. Finally, an index value exceeding 150 represents very poor quality and that this water is unsuitable for drinking (Table 6). The groundwater quality index (GQI) was calculated using a multi-step process involving ten key water quality parameters selected based on their significance for drinking water quality and availability of WHO guidelines. We assigned weights to each parameter using the analytic hierarchy process (AHP), with weights ranging from 0 to 1 and summing to 1. For each parameter, we calculated a sub-index (SI) using the equation SI = (Ci/Si) × 100, where Ci is the concentration of the ith parameter and Si is the WHO standard for that parameter. The final GQI was then computed as the weighted sum of all sub-indices: GQI = Σ (Wi × SI), where Wi is the weight of the ith parameter. This method allows for a comprehensive assessment of water quality, accounting for multiple parameters and their relative importance for drinking water suitability. The calculated GQI values were subsequently classified into five categories, as shown in Table 6 and Table 7, ranging from excellent to very poor quality. This classification provides a straightforward way to interpret the spatial variation in the groundwater quality index derived through the weighted overlay analysis (Figure 5). The distinct classes allow for clear communication of the index results to relevant stakeholders for informed decision-making regarding groundwater usage and treatment needs across the Lahore District [3,84,102].

Once the study area’s overall groundwater quality index was computed, the results can be categorized into the defined suitability classes for drinking water. The index values can be grouped into excellent, good, medium, poor, and very poor quality categories.

The groundwater quality index values computed for each tehsil can be validated by comparing them to those from previous studies and known issues. For example, the index of 160 for Lahore Cantt falls in the “very poor” category, indicating extremely poor water quality. This aligns with studies showing heavy metal and bacterial contamination in the area from industrial pollution. In contrast, the water in Raiwind had an index of 114, classified as “poor”, which is comparatively better but still unsuitable for drinking without treatment. This matches reports of elevated arsenic concentrations there. Shalimar’s index of 138 is also rated as “poor”, consistent with the high salinity found in its groundwater. By benchmarking against established knowledge, the index provides a standardized metric for comparisons across locations. The results synthesize multiple indicators into an easily interpreted score, allowing decision-makers to clearly communicate overall water quality issues. Relating the index to previous findings verifies its efficacy in reflecting relative groundwater safety for human consumption and health.

3.3. Mitigation Strategies

The mitigation approaches to restore the water to natural conditions can be achieved in two practical ways:

1. Mitigate chemical contamination by implementing a variety of pollution control strategies.

2. Implement water treatment processes to decrease the concentrations of chemical contaminants to levels that meet or surpass the World Health Organization (WHO) standards.

3.3.1. Mitigate Chemical Contamination by Implementing a Variety of Pollution Control Strategies

The best way to control pollution is to prevent it at the source. In the initial stage, the source of contamination should be identified.

• i. Scenario I (Variations in Heavy Metal Parameter)

Heavy metal contamination is a prominent issue for groundwater protection in different study area zones, including industrial, agricultural, and urbanized regions. The study’s primary contaminant was arsenic. Arsenic can enter the supply due to anthropogenic activities, mainly through the dumping of industrial effluents containing toxic metals directly into water channels, and natural sources such as metal deposits.

Analyzing the index values for the different tehsils after implementing measures to reduce heavy metal pollution revealed varied levels of success across the board. Lahore Cantt showed a steady improvement, with its index value decreasing from 160.31 to 151.00, marking significant progress of up to 4.55% as the reductions progressed. Lahore City’s improvement was more modest, with a maximum reduction of 3.33%, indicating a steady but cautious approach to mitigating pollution. Starting at 165.83, Model Town demonstrated a late surge in effectiveness, achieving a 4.13% improvement, suggesting impactful but delayed remediation efforts. Raiwind stood out with a 5.55% improvement, the highest among the tehsils, which implies that highly effective environmental management strategies were in place. Shalimar, however, showcased the most dramatic improvement, with a maximum reduction of 15.18% in the heavy metal concentration, highlighting the success of aggressive pollution control measures (Table 8).

• (a). I: At the 10% reduction level (Figure 6a), minor improvements in index values were observed across all the tehsils. The changes were relatively modest, with the most significant improvement seen in Model Town, with a 4.14% improvement. This suggests that even slight reductions in pollution can lead to noticeable improvements in environmental quality, particularly in areas with initially higher index values.

• (b). II: With a 20% reduction (Figure 6b), the index values had a more pronounced impact. Model Town and Shalimar exhibited more substantial improvements, at 4.43% and 6.53%, respectively. This indicates that increased efforts in pollution control can lead to more significant improvements in environmental quality. The differences across the tehsils highlight the variability in the response to the same level of reduction, likely influenced by local conditions and the initial state of contamination.

• (c). III: The 30% reduction (Figure 6c) scenario showcased significant improvements in all the tehsils, with Shalimar witnessing the most dramatic change, with a 20.71% improvement. This level of reduction demonstrates the potential for substantial environmental quality enhancements through aggressive pollution control measures. The variation in improvement percentages across the tehsils suggests that areas with higher initial contamination levels or those more responsive to the implemented measures will see more significant benefits.

• ii. Scenario II (Variations in Chemical Parameters)

Lahore Cantt showed a progressive decline from an initial index value of 160.31, with a 2.19% improvement, while Lahore City started at 105.11 and achieved a more substantial 3.04% reduction. Model Town’s improvement was marked by a 2.11% decrease from an initial 165.83. Raiwind’s notable 2.20% improvement indicates efficient pollution control from a starting index of 113.99. Despite showing the smallest percentage improvement at 1.72%, Shalimar still had a positive trend in managing pollution from an initial value of 138.15. Collectively, these results reveal the impact of targeted measures on mitigating heavy metal pollution in water, contributing to the goal of enhancing water quality to meet health and safety standards (Table 9).

• (a). I: Figure 7a shows the impact of a 10% reduction on the polluted areas. Lahore Cantt’s index value modestly decreased from 160.31 to 159.02, a change of 0.81%. Lahore City showed a 1.23% reduction, lowering the index from 105.11 to 103.82. Model Town’s index value dropped by 0.78%, from 165.83 to 164.54. Raiwind saw a 1.13% reduction from 113.99 to 112.7, while Shalimar’s index value declined by 0.93% from 138.15 to 136.86.

• (b). II: At the 20% reduction level (Figure 7b), Lahore Cantt recorded a further decrease to 157.92, a 1.5% change. Lahore City’s index value was reduced by 2.49% to 102.5. Model Town’s index value saw a cumulative drop of 1.44%, reaching 163.44. In Raiwind, the index was now at 111.60, marking a 2.1% decrease, and Shalimar’s index value was 135.76, reflecting a 1.74% improvement.

• (c). III: The impact became more pronounced with a 30% reduction (Figure 7c). Lahore Cantt’s index value was significantly lower at 154.46, showing an overall improvement of 3.66%. Lahore City’s index decreased to 99.38, reflecting a 5.46% reduction, the most substantial improvement across all the tehsils. Model Town showed a 3.53% decrease with an index value of 159.99. Raiwind’s index value was reduced to 109.15, showing a cumulative improvement of 4.24%. Shalimar saw a total reduction of 3.42%, with its index value dropping to 133.42.

3.3.2. Implement Water Treatment Processes to Decrease Concentrations of Chemical Contaminants to Levels That Meet or Surpass WHO Standards

• i. Scenario I (Primary Treatment)

This scenario was used to evaluate the treatment of wastewater in the primary stage; primary treatment is only efficient at reducing total dissolved solids (TDSs).

Three stages with different levels of treatment, as shown in Table 10, were established:

• Part A exhibited a 40% reduction in the total dissolved solids (TDSs);

• A reduction in pollutants by 60% was achieved in Part B.

The primary treatment with 40% and 60% reductions improved the index value of polluted areas with high TDS values. The quality of the Model Town Tehsil (with the most significant improvement in index value) improved by 17–25%, while Lahore Cantt improved by 14–22%, Lahore City improved by 14–21.7%, and Raiwind improved by 14–21.5%.

In Part A, using water treatment to achieve 40% TDS removal (Figure 8), Model Town showed an index improvement of 17.99%, followed by Lahore Cantt and Lahore City with improvements of 14.35% and 14.28%, respectively. The Shalimar Tehsil showed a minor index value improvement of 11.55%, indicating less TDS contamination in the area.

In Part B using water treatment to achieve 60% TDS removal (Figure 8), Model Town showed an index improvement of 25.06%, while Lahore Cantt and Lahore City showed improvements of 21.72% and 21.27%, respectively.

• ii. Scenario II (Secondary Treatment)

In this scenario, the wastewater treatment in the secondary stage was evaluated; the primary treatment was only efficient at reducing the total dissolved solids (TDSs).

Two stages with different levels of treatment, as shown in Table 11, were established.

• Part A aims for a 65% reduction in the total dissolved solids (TDSs) and a 45% reduction in hardness using the wastewater treatment.

• The Part B treatment aims for a 80% reduction in the total dissolved solids (TDSs) and a 55% reduction in hardness in the wastewater.

The secondary treatment targeted TDSs and hardness in two stages. This improved the index value by a considerable percentage in polluted areas with a high TDS level and hardness. The quality of the Model Town Tehsil (with the most significant improvement in index value) improved by 40–44.6%, while Lahore Cantt improved by 38–42.7%, Lahore City improved by 39–44.3%, and Raiwind improved by 37.8–42%.

In Part A (Figure 9), the water with 65% of the TDSs removed had its hardness reduced to 45%. Model Town showed an index improvement of 40.31%, followed by Lahore Cantt and Lahore City, which showed index improvements of 37.96% and 39.20%, respectively. Raiwind Tehsil showed the smallest index value improvement of 37.87%, indicating less pollution in the area.

In Part B, the water was treated to achieve 80% TDS removal and a 55% hardness reduction (Figure 9). Model Town showed an index improvement of 44.62%, while Lahore City and Lahore Cantt showed index improvements of 44.39% and 42.75%, respectively.

• iii. Scenario III (Tertiary Treatment)

In this scenario, wastewater treatment in the tertiary stage was evaluated for its efficiency in reducing the total dissolved solids (TDSs) and bacterial load. Two stages with different levels of treatment, as shown in Table 12, were established:

• A 90% reduction in the significant pollutant (arsenic) in Part A.

• Part B aimed for a 95% reduction in arsenic.

The tertiary treatment targeted heavy metals and arsenic in two stages. This improved the index value with a considerable percentage improvement in polluted areas with arsenic in the water. The quality of the Shalimar Tehsil (with the highest improvement in index value) improved by 45.8%–46.10%, indicating that the area was the most polluted with heavy metals. Model Town improved by 44–44.5%, Lahore City improved by 44–44.4%, Lahore Cantt improved by 42–42.7%, and Raiwind improved by 37.8–42%.

In Part A, where the treatment of water achieved 90% arsenic removal (Figure 10), the Shalimar Tehsil showed an index improvement of 45.88%, followed by Model Town, which improved by 44.48%, and Lahore City and Lahore Cantt, which improved by 44.16% and 42.61%, respectively. The Raiwind Tehsil showed the smallest index value improvement of 42%, indicating comparatively less pollution in this area.

In Part B, the water was treated to achieve a 95% reduction in arsenic (Figure 10). Model Town showed an index improvement of 46.10%, followed by Shalimar, which improved by 44.62%, and Lahore City and Lahore Cantt, which improved by 44.39% and 42.75%, respectively.

4. Discussion

This pioneering study offers a comprehensive district-wide assessment of the groundwater quality across Lahore, addressing a crucial knowledge gap that has constrained the effective management of the potable water resources. The rigorous integrated approach, combining AHP-GIS spatial modeling, water quality indexing, pollution mapping, and health risk analysis, provides an innovative geospatial decision-supporting framework for groundwater management authorities and urban planners.

The AHP methodology incorporated multidisciplinary expert judgments to systematically derive the weights for the relative importance of diverse water quality parameters influencing potability. Assigning the highest priority weight (0.28) to arsenic aligns with its well-established toxicity and widespread prevalence as a priority pollutant across Pakistan’s aquifers. The substantial weights for TDS (0.22) and hardness (0.15) appropriately reflect the regional salinity and mineralization issues stemming from reduced surface water recharge and improper drainage management. Integrating such context-specific knowledge into the spatial modeling process enhances the relevance of the results.

The computed groundwater quality index scores stratified Lahore’s terrain into distinct potability classes, unveiling localized hotspots requiring urgent intervention. The abysmal ratings (index >150) in urbanized areas like Lahore Cantt, Model Town, and parts of Lahore City corroborate previous findings of heavy industrial effluent discharges introducing toxic pollutant loads. In contrast, the relatively better index values in peri-urban Raiwind suggest lower anthropogenic stresses. These spatial patterns correspond to the regional development gradients, with unregulated industrialization and the urban sprawl exacerbating groundwater contamination near the city center, while the rural fringes remain less impacted. Visualizing this variability pinpoints priority sites for deploying pollution abatement programs on a risk-based prioritized approach. Our standardization approach, while based on international and national standards, also considers the unique hydrogeological and socio-economic context of Lahore. For example, the ‘permissible’ category for certain parameters may be higher than international standards due to naturally elevated background levels in the local aquifer. This approach allows for a more nuanced evaluation of water quality that balances ideal standards with practical considerations of the local conditions and treatment capabilities. However, we acknowledge that this standardization may need to be adjusted as local conditions change or as treatment technologies improve.

Evaluating localized improvement scenarios elucidated the potential impacts of targeted remediation strategies tailored to specific contaminants. The arsenic levels could be reduced by 30% through robust effluent controls and treatment, lowering the index scores by up to 20.71% in critically degraded zones like Shalimar. This substantiates the necessity of establishing advanced arsenic removal treatment infrastructure in line with Bangladesh’s successful campaigns. For chemical pollutants like chlorides and sulfates, a 30% reduction through improved drainage management and treatment could enhance quality by 5.46% in areas like Lahore City. These quantitative projections illustrate how a progressive suite of regulatory and engineering interventions could restore groundwater quality over time, providing a tangible roadmap for urban water resource planning.

• Implications of Interpolation Uncertainties for Decision-Making

The uncertainties in our spatial interpolation methods have significant implications for the interpretation of our results and for water management decisions. For instance, in regions where our interpolated arsenic concentrations are close to the WHO guideline value of 10 μg/L, the true values could potentially be either above or below this threshold. This underscores the importance of adopting a precautionary approach in water management decisions.

Areas identified as having contaminant levels near the regulatory limits should be prioritized for more intensive sampling to refine our understanding of the spatial distribution of contamination. Furthermore, the confidence interval maps illustrate that the uncertainty is not uniformly distributed across the study area. Decision-makers should pay particular attention to regions with wider confidence intervals, as these areas may require additional investigation before implementing costly remediation strategies.

By explicitly addressing these uncertainties, we aim to provide water managers and policymakers with a more nuanced understanding of the reliability and limitations of our spatial analysis, enabling more informed and robust decision-making processes.

• Feasibility of Proposed Reduction Scenarios

While our proposed scenarios for reducing element concentrations offer potential pathways for improving groundwater quality, their feasibility must be carefully considered within the context of Lahore’s specific environmental, economic, and social conditions.

Arsenic Reduction: The proposed 30% reduction in arsenic levels, while theoretically beneficial, faces significant challenges. The widespread nature of the arsenic contamination, partly due to natural geological sources, makes blanket reduction difficult. Implementation would require a combination of centralized water treatment facilities and point-of-use filtration systems, which may be economically challenging given Lahore’s decentralized water supply system.

Heavy Metal Reduction: Our scenarios for reducing heavy metal concentrations are more feasible in industrial areas like Lahore Cantt and Model Town. However, successful implementation would require strict enforcement of industrial effluent standards and potentially costly upgrades to existing treatment facilities. The economic impact on local industries must be carefully balanced against the public health benefits.

Chemical Parameter Reduction: Scenarios targeting reductions in TDSs, hardness, and other chemical parameters are potentially achievable through improved wastewater management and urban planning. However, the rapid urbanization of Lahore presents challenges in implementing comprehensive sewage treatment systems. Additionally, altering the natural chemistry of the aquifer (e.g., reducing the hardness) may have unforeseen ecological consequences that need further study.

Water Use Considerations: Our proposed scenarios must be viewed in light of Lahore’s increasing water demand due to its population growth and industrial expansion. Any treatment or remediation efforts must be designed to handle not only the current contaminant levels but also the projected increases in water extraction and pollution.

Land Development Impact: The feasibility of our scenarios is closely tied to future land development patterns in Lahore. Effective implementation would require the integration of water quality management into urban planning policies, which may face challenges due to rapid, often unplanned, urban growth.

The treatment scenario analysis showcased the relative efficacies of various processes in remediating specific contaminants to meet the WHO potability thresholds. The tertiary treatment focused on arsenic removal demonstrated the highest potential, with 95% reduction capabilities lowering the index scores by up to 46.1% in severely degraded areas like Shalimar. This underscores the necessity of integrating advanced oxidation and membrane filtration treatment trains to combat the widespread occurrence of geogenic arsenic contamination. For ubiquitous pollutants, like TDSs, and hardness, secondary treatment processes like coagulation and softening could enhance the quality by up to 44.62%, as was projected for Model Town. These insights can guide the identification of appropriate multi-barrier treatment configurations tailored to the specific contaminant signatures across different aquifer zones.

The spatial analysis, indexing, and scenario modeling outcomes collectively portray the severity of the groundwater quality issues across Lahore while quantifying the anticipated impacts of various mitigation approaches. The AHP-GIS framework enabled the integration of expert knowledge with spatial data to transparently evaluate alternative solutions based on multi-criteria tradeoffs. The study demonstrated the efficacy of such integrated geo-computational methods in diagnostically assessing the regional groundwater quality to inform targeted remediation policies and interventions protecting public health.

• Limitations

While this study focused on spatial analysis, we acknowledge that temporal variations in groundwater quality can occur. However, given the stable hydrogeological characteristics of Lahore’s deep alluvial aquifer, the long-term nature of the primary contamination processes, and the urban setting of our study area, we expect temporal variations to be minimal for the major contaminants of concern. Nonetheless, future studies could complement our work by investigating any potential seasonal or long-term trends in groundwater quality, particularly in shallow aquifer zones or near dynamic pollution sources.

5. Conclusions

This landmark study represents the first comprehensive district-level investigation of the groundwater quality across Lahore, a major metropolis that is heavily reliant on aquifer resources for its water supply. By synergistically combining advanced spatial modeling techniques, multi-criteria decision analysis frameworks, and quantitative treatment scenario assessments, the research provides an innovative geospatial decision support tool to guide groundwater management policies and remediation interventions.

The findings exposed alarming levels of contamination from toxic pollutants like arsenic, total dissolved solids, and minerals contributing to hardness across significant portions of Lahore’s shallow aquifer systems that cater to municipal and domestic water demands. Severely degraded zones exhibiting poor-quality indices were identified within urban industrial areas such as Lahore Cantt, Model Town, and certain localities of Lahore City, corroborating previous reports of unregulated effluent discharges from industrial and residential sources.

The feasibility assessment of our proposed remediation strategies revealed a complex landscape of technical, economic, and social challenges. While strategies such as arsenic removal and reductions in industrial effluents show promise for improving groundwater quality in Lahore, their implementation faces significant hurdles. These include high initial costs, potential industrial sector resistance, and the need for extensive infrastructure development and specialized training. However, these challenges are not insurmountable. A phased approach, beginning with pilot projects in the most severely affected areas, could provide a pragmatic pathway for gradually enhancing Lahore’s groundwater quality. This approach would allow for the refinement of techniques, assessment of real-world effectiveness, and building of public and stakeholder support. Ultimately, the success of any remediation effort will depend on a strong political will, sustained funding, and collaborative efforts across government agencies, industries, and communities. Future research should focus on detailed cost–benefit analyses of these strategies and the exploration of innovative, locally adapted solutions to overcome the identified challenges.

The implementation of our proposed scenarios for reducing element concentrations in Lahore’s groundwater faces significant challenges due to the complex interplay of natural geological factors, rapid urbanization, industrial activities, and economic constraints. While these scenarios provide valuable targets for improvement, their feasibility varies depending on the specific contaminant and local conditions. Successful implementation will require a carefully phased approach, integrating water quality management into broader urban planning and economic development strategies. Further studies are needed to develop tailored, economically viable solutions that account for Lahore’s unique environmental and socio-economic context. Prospective improvement scenarios should quantitatively evaluate the potential benefits of implementing targeted remediation strategies. Reducing heavy metal pollutants like arsenic by 30% could enhance the quality indices by up to 20% within critically degraded zones. For pervasive chemical contaminants, a 30% reduction could yield over 5% improvement in index scores, underscoring the compounding advantages of controlling diverse pollution sources through strategic treatment and regulatory measures. Furthermore, simulations of advanced multi-barrier water treatment processes showcased an over 95% reduction capability for arsenic, thereby offering a technical guide for designing efficient treatment infrastructure tailored to local contaminant profiles.

Collectively, these research outputs furnish water authorities and urban planners with a comprehensive decision-support toolkit for developing targeted, risk-based groundwater quality restoration programs and interventions within a coherent policy framework. The geospatial database, hazard mapping, and scenario modeling components can catalyze informed investments in remediation projects, treatment plant installations, and strategic aquifer recharge management customized to address location-specific priorities. Through enforcement mechanisms, regulatory roadmaps can be charted to progressively minimize pollutant discharges from industrial, residential, and improper drainage sources. Implementing such evidence-based measures is imperative for ensuring the long-term sustainability of Lahore’s critically vital groundwater resources while safeguarding public health and meeting the escalating municipal water demands.

The integrated environmental data synthesis and decision analysis framework pioneered in this study can be widely applied across other regions facing groundwater quality challenges. The robust AHP-GIS modeling approach enables the assimilation of diverse scientific data streams and stakeholder preferences into quantitative spatial decision models that are tailored to specific environmental management contexts. As urban areas increasingly rely on over-exploited aquifers under mounting contamination threats, such versatile tools can significantly enhance integrated water resource planning capabilities through dynamic simulations to devise agile remediation strategies that are responsive to evolving risks. This study’s outcomes lay a solid foundation for establishing continuous groundwater quality monitoring programs coupled with iterative decision support systems to guide sustainable aquifer management within Lahore and other major cities across Pakistan and beyond.

Author Contributions

Conceptualization, I.N., H.F. and R.W.A.; Methodology, I.N., W.S., K.F.A. and R.W.A.; Software, I.N., R.W.A. and A.T.; Validation, I.N., A.Q., R.W.A. and A.S.; Formal analysis, I.N. and R.W.A.; Investigation, I.N., F.A. and R.W.A.; Resources, I.N., R.W.A., A.Q., W.S., K.F.A. and A.S.; Data curation, I.N., R.W.A., A.Q. and A.T.; Writing—original draft, I.N. and R.W.A.; Writing—review and editing, I.N., H.F., A.S., K.F.A. and R.W.A.; Supervision, F.A. and R.W.A.; Project administration, R.W.A.; Funding acquisition, R.W.A. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

Data will be made available by the corresponding author upon request.

Acknowledgments

The authors extend their appreciation to Researchers Supporting Project number (RSPD2024R561), King Saud University, Riyadh, Saudi Arabia. The authors would like to acknowledge the anonymous reviewers for their contribution to this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Map of the study area: Lahore District.

Figure 2. Flow chart of methodology.

Figure 3. Parameters results: (a) pH, (b) turbidity, (c) TDS, (d) EC, and (e) hardness.

Figure 4. Parameter results: (a) calcium, (b) magnesium, (c) alkalinity, (d) chloride, and (e) arsenic.

Figure 5. Groundwater quality AHP index.

Figure 6. Scenario I (variation in heavy metals): (a) 10%, (b) 20%, and (c) 30% reduction.

Figure 7. Scenario II (variation in chemical parameters): (a) Chemical 10%, (b) Chemical 20%, (c) Chemical 30%.

Figure 8. Variation in primary treatment. (a) Part A with a 40% reduction in the total dissolved solids (TDSs) and (b) Part B with a 60% reduction in the total dissolved solids (TDSs).

Figure 9. Variation in secondary treatment. (a) Part A with a 65% reduction in the total dissolved solids (TDSs) and 45% reduction in hardness and (b) Part B with an 80% reduction in the total dissolved solids (TDSs) and 55% reduction in hardness.

Figure 10. Variation in tertiary treatment. (a) Part A with 90% reduction in arsenic and (b) Part B with 95% reduction in arsenic.

Table 1

Ranges of suitability classes for the criteria used for AHP modeling.

Parameter	WHO Standard	NSDWG	Excellent	Good	Permissible	Unsuitable
pH	6.5–8.5	6.5–8.5	7.5–8.5	>8.5	6.0–7.5	<6
Turbidity	<5 NTU	<5 NTU	0.1–0.3	≤1	1–2	≥5
TDS	1000 mg/L	1000 mg/L	<100	100–500	500–1000	>1000
EC	400 μS/cm		<50	50–250	250–400	>400
Hardness	500 mg/L	500 mg/L	<50	50–250	250–500	>500
Calcium	75 mg/L	75 mg/L	<1	1.0–5.0	5.0–10.0	>10
Magnesium	50 mg/L	50 mg/L	<1	1.0–5.0	5.0–10.0	>10
Alkalinity	120 mg/L	-
Chloride	250 mg/L	<250 mg/L	<50	50–100	100–200	>250
Arsenic	10 µg/L	50 µg/L

Table 2

The 1–9 point scale used for the pairwise comparisons.

Scale	Relative Importance	Scale	Relative Importance
1	Equally important	1	Equally important
3	Moderately more important	1/3	Moderately less important
5	Significantly more important	1/5	Weakly less important
7	Very significantly more important	1/7	Very weakly less important
9	Exceedingly more important	1/9	Exceedingly less important
2, 4, 6, 8	Intermediate values	1	Equally important

Table 3

Random Index values.

N	1	2	3	4	5	6	7	8	9	10	11	12
RI	0.0	0.0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51	1.48

Table 4

Primary, secondary, and tertiary treatment scenarios.

Process	Parameter	Percentage
Primary treatment	TDS	40–60%
Secondary treatment	TDS	65–80%
	Hardness	45–55%
Tertiary treatment	Arsenic	>95%

Table 5

Range of values of physio-chemical parameters and the prescribed WHO limits for drinking water (n = 50).

Chemical Parameter	Unit	Min.	Max.	Mean	Permissible LimitWHO (2011)	Percentage of the Sampleswith Level Exceeding WHOPermissible Limit
pH	-	7	8.3	7.9	6.5–8.5	0.0
Turbidity	NTU	0	3.81	0.7	5	0.0
TDS	mg/L	188	1266	450.6	1000	2
EC	µs/cm	299	2010	716.0	400	86
Hardness	mg/L	44	1256	184.4	500	2
Calcium	mg/L	11	61	34.0	75	0.0
Magnesium	mg/L	4	55	18.8	75	0.0
Alkalinity	mg/L	110	760	313.9	500	10
Chloride	mg/L	10	110	36.6	250	0.0
Arsenic	µg/L	8.0	25.7	16.4	10	96

Table 6

Range of water quality index scores specified for drinking purposes.

Class	Score	Category Rank	Interpretation
I	0–50	Excellent	It can be safely used
II	50–100	Good	Generally safe to use
III	100–120	Medium	It can be used for drinking
IV	120–150	Poor	Proper treatment is required before use
V	>150	Very Poor	Unsuitable

Table 7

Groundwater quality index of the Lahore District.

Sr.	Tehsil	Index Value	Groundwater Quality Class	Area in Sq. Km
1	Lahore Cantt	160.31	V	464.26
2	Lahore City	105.11	IV	203.63
3	Model Town	165.83	V	350.98
4	Raiwind	113.99	IV	454.59
5	Shalimar	138.15	IV	287.98

Table 8

Quantitative analysis of index values and improvement for Scenario I (Heavy Metals).

Sr.	Tehsil	Index Value	Index Value(10% Reduction)	Index Value(20% Reduction)	Index Value(30% Reduction)	Improvement Percentage
1	Lahore Cantt	160.31	159.80	158.20	151.00	1.05–4.55%
2	Lahore City	105.11	104.98	104.50	101.02	0.46–3.33%
3	Model Town	165.83	158.98	158.49	154.00	0.3–4.13%
4	Raiwind	113.99	113.41	112.97	105.53	0.51–5.55%
5	Shalimar	138.15	137.74	129.14	109.54	6.26–15.18

Table 9

Quantitative analysis of index values and improvement for Scenario II (Chemicals).

Sr.	Tehsil	Index Value	Index Value(10% Reduction)	Index Value(20% Reduction)	Index Value(30% Reduction)	Improvement Percentage
1	Lahore Cantt	160.31	160.45	159.98	159.50	0.8–2.1%
2	Lahore City	105.11	103.97	103.49	103.01	1.27–3.04%
3	Model Town	165.83	158.97	158.49	158.00	0.9–2.55%
4	Raiwind	113.99	113.41	112.97	112.53	1.2–2.2%
5	Shalimar	138.15	138.73	138.13	137.53	0.7–2.1%

Table 10

Quantitative analysis of index values and improvement for Scenario I (treatments).

Sr.	Tehsil	Index Value	Part A	Part B	Improvement Percentage
1	Lahore Cantt	160.31	137.30239	125.48776	14.35–21.72%
2	Lahore City	105.11	89.9909	82.75627778	14.28–21.27
3	Model Town	165.83	136.0009667	124.2683	17.99–25.06
4	Raiwind	113.99	97.579	89.44304286	14.40–21.53
5	Shalimar	138.15	122.19132	113.61952	11.55–17.76

Table 11

Quantitative analysis of index values and improvement for Scenario II (treatments).

Sr.	Tehsil	Index Value	Part A	Part B	Improvement Percentage
1	Lahore Cantt	160.31	99.45	91.77	37.96–42.75
2	Lahore City	105.11	63.90	58.45	39.20–44.39
3	Model Town	165.83	98.98	91.83	40.31–44.62
4	Raiwind	113.99	70.82	65.78	37.87–42.29
5	Shalimar	138.15	84.97	74.46	38.49–46.10

Table 12

Quantitative analysis of index values and improvement for Scenario III.

Sr.	Tehsil	Index Value	Part A	Part B	Improvement Percentage
1	Lahore Cantt	160.31	92.00	91.77	42.61–42.75
2	Lahore City	105.11	58.69	58.45	44.16–44.39
3	Model Town	165.83	92.07	91.83	44.48–44.62
4	Raiwind	113.99	66.00	65.78	42.10–42.29
5	Shalimar	138.15	74.76	74.46	45.88–46.10

References

1. Yin, L.; Wang, L.; Keim, B.D.; Konsoer, K.; Zheng, W. Wavelet Analysis of Dam Injection and Discharge in Three Gorges Dam and Reservoir with Precipitation and River Discharge. Water; 2022; 14, 567. [DOI: https://dx.doi.org/10.3390/w14040567]

2. Umar, M.; Khan, S.N.; Arshad, A.; Aslam, R.A.; Khan, H.M.S.; Rashid, H.; Pham, Q.B.; Nasir, A.; Noor, R.; Khedher, K.M. et al. A Modified Approach to Quantify Aquifer Vulnerability to Pollution towards Sustainable Groundwater Management in Irrigated Indus Basin. Environ. Sci. Pollut. Res.; 2022; 29, pp. 27257-27278. [DOI: https://dx.doi.org/10.1007/s11356-021-17882-9]

3. Ullah, A.S.; Rashid, H.; Khan, S.N.; Akbar, M.U.; Arshad, A.; Rahman, M.M.; Mustafa, S. A Localized Assessment of Groundwater Quality Status Using GIS-Based Water Quality Index in Industrial Zone of Faisalabad, Pakistan. Water; 2022; 14, 3342. [DOI: https://dx.doi.org/10.3390/w14203342]

4. Shafeeque, M.; Hafeez, M.; Sarwar, A.; Arshad, A.; Khurshid, T.; Asim, M.I.; Ali, S.; Dilawar, A. Quantifying Future Water-Saving Potential under Climate Change and Groundwater Recharge Scenarios in Lower Chenab Canal, Indus River Basin. Theor. Appl. Climatol.; 2023; 155, pp. 187-204. [DOI: https://dx.doi.org/10.1007/s00704-023-04621-y]

5. Rasheed, H.; Jaleel, F.; Nisar, M.F. Analyzing the Status of Heavy Metals in Irrigation Water in Suburban Areas of Bahawalpur City, Pakistan. Am. J. Agric. Environ. Sci.; 2014; 14, pp. 732-738.

6. Karim, M.M. Arsenic in Groundwater and Health Problems in Bangladesh. Water Res.; 2000; 34, pp. 304-310. [DOI: https://dx.doi.org/10.1016/S0043-1354(99)00128-1]

7. Aslam, R.W.; Shu, H.; Yaseen, A.; Sajjad, A.; Abidin, S.Z.U. Identification of Time-Varying Wetlands Neglected in Pakistan through Remote Sensing Techniques. Environ. Sci. Pollut. Res.; 2023; 30, pp. 74031-74044. [DOI: https://dx.doi.org/10.1007/s11356-023-27554-5]

8. Tahir, M.; Ahmad, S.; Aslam, R.; Ahmad, I.; Ullah, H.; Aziz, A.; Zubair, M.H.; Mirza, A. Critical Study of Groundwater Quality of Metropolitan Lahore Using Geo-Spatial Techniques. Int. J. Sustain. Dev.; 2020; 2, pp. 89-105. [DOI: https://dx.doi.org/10.6084/m9.figshare.13352717.v1]

9. Arshad, A.; Mirchi, A.; Vilcaez, J.; Umar Akbar, M.; Madani, K. Reconstructing High-Resolution Groundwater Level Data Using a Hybrid Random Forest Model to Quantify Distributed Groundwater Changes in the Indus Basin. J. Hydrol.; 2024; 628, 130535. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2023.130535]

10. Wang, N.; Naz, I.; Aslam, R.W.; Quddoos, A.; Soufan, W.; Raza, D.; Ishaq, T.; Ahmed, B. Spatio-Temporal Dynamics of Rangeland Transformation Using Machine Learning Algorithms and Remote Sensing Data. Rangel. Ecol. Manag.; 2024; 94, pp. 106-118. [DOI: https://dx.doi.org/10.1016/j.rama.2024.02.008]

11. Aslam, R.W.; Shu, H.; Naz, I.; Quddoos, A.; Yaseen, A.; Gulshad, K.; Alarifi, S.S. Machine Learning-Based Wetland Vulnerability Assessment in the Sindh Province Ramsar Site Using Remote Sensing Data. Remote Sens.; 2024; 16, 928. [DOI: https://dx.doi.org/10.3390/rs16050928]

12. Sajjad, A.; Lu, J.; Aslam, R.W.; Ahmad, M. Flood Disaster Mapping Using Geospatial Techniques: A Case Study of the 2022 Pakistan Floods. Environ. Sci. Proc.; 2023; 25, 78. [DOI: https://dx.doi.org/10.3390/ECWS-7-14312]

13. Aslam, R.W.; Shu, H.; Tariq, A.; Naz, I.; Ahmad, M.N.; Quddoos, A.; Javid, K.; Mustafa, F.; Aeman, H. Monitoring Landuse Change in Uchhali and Khabeki Wetland Lakes, Pakistan Using Remote Sensing Data. Gondwana Res.; 2024; 129, pp. 252-267. [DOI: https://dx.doi.org/10.1016/j.gr.2023.12.015]

14. Rashid, M.; Aslam, R.; Abbas, W.; Arshad, S.; Mirza, A.; Tahir, M.; Burhan, M.; Ullah, H.; Mirza, A.; Hassan Raza, D.-S. Hazardous Effluents and Their Impacts on Human Health: Future of Industrial Boom. Int. J. Innov. Eng. Technol.; 2020; 2, pp. 114-125. [DOI: https://dx.doi.org/10.33411/IJIST/2020020403]

15. Shahzaman, M.; Zhu, W.; Ullah, I.; Mustafa, F.; Bilal, M.; Ishfaq, S.; Nisar, S.; Arshad, M.; Iqbal, R.; Aslam, R.W. Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries. Remote Sens.; 2021; 13, 3294. [DOI: https://dx.doi.org/10.3390/rs13163294]

16. Naz, I.; Ahmad, I.; Aslam, R.W.; Quddoos, A.; Yaseen, A. Integrated Assessment and Geostatistical Evaluation of Groundwater Quality through Water Quality Indices. Water; 2023; 16, 63. [DOI: https://dx.doi.org/10.3390/w16010063]

17. Mehmood, H.; Aslam, R.; Kakar, A.; Abbas, W.; Javid, K.; Burhan, M.; Tahir, M. Health Implications of Arsenic and Qualitative Deterioration of Drinking Water from Underground Water Supply Lines of Lahore, Pakistan. Int. J. Innov. Sci. Technol.; 2022; 4, pp. 78-93. [DOI: https://dx.doi.org/10.33411/IJIST/2022040106]

18. Aslam, R.W.; Shu, H.; Javid, K.; Pervaiz, S.; Mustafa, F.; Raza, D.; Ahmed, B.; Quddoos, A.; Al-Ahmadi, S.; Hatamleh, W.A. Wetland Identification through Remote Sensing: Insights into Wetness, Greenness, Turbidity, Temperature, and Changing Landscapes. Big Data Res.; 2024; 35, 100416. [DOI: https://dx.doi.org/10.1016/j.bdr.2023.100416]

19. He, L.; Valocchi, A.J.; Duarte, C.A. An Adaptive Global–Local Generalized FEM for Multiscale Advection–Diffusion Problems. Comput. Methods Appl. Mech. Eng.; 2024; 418, 116548. [DOI: https://dx.doi.org/10.1016/j.cma.2023.116548]

20. Aslam, R.A.; Shrestha, S.; Usman, M.N.; Khan, S.N.; Ali, S.; Sharif, M.S.; Sarwar, M.W.; Saddique, N.; Sarwar, A.; Ali, M.U. et al. Integrated SWAT-MODFLOW Modeling-Based Groundwater Adaptation Policy Guidelines for Lahore, Pakistan under Projected Climate Change, and Human Development Scenarios. Atmosphere; 2022; 13, 2001. [DOI: https://dx.doi.org/10.3390/atmos13122001]

21. Lapworth, D.J.; Nkhuwa, D.C.W.; Okotto-Okotto, J.; Pedley, S.; Stuart, M.E.; Tijani, M.N.; Wright, J. Urban Groundwater Quality in Sub-Saharan Africa: Current Status and Implications for Water Security and Public Health. Hydrogeol. J.; 2017; 25, pp. 1093-1116. [DOI: https://dx.doi.org/10.1007/s10040-016-1516-6]

22. Page, D.; Dillon, P.; Toze, S.; Bixio, D.; Genthe, B.; Jiménez Cisneros, B.E.; Wintgens, T. Valuing the Subsurface Pathogen Treatment Barrier in Water Recycling via Aquifers for Drinking Supplies. Water Res.; 2010; 44, pp. 1841-1852. [DOI: https://dx.doi.org/10.1016/j.watres.2009.12.008]

23. Ahmed, K.M.; Bhattacharya, P.; Hasan, M.A.; Akhter, S.H.; Alam, S.M.M.; Bhuyian, M.A.H.; Imam, M.B.; Khan, A.A.; Sracek, O. Arsenic Enrichment in Groundwater of the Alluvial Aquifers in Bangladesh: An Overview. Appl. Geochem.; 2004; 19, pp. 181-200. [DOI: https://dx.doi.org/10.1016/j.apgeochem.2003.09.006]

24. US EPA. Guidelines for Human Exposure Assessment: Risk Assessment Forum; U.S. Environmental Protection Agency: Washington, DC, USA, 2019.

25. Liu, Z.; Xu, Z.; Zhu, X.; Yin, L.; Yin, Z.; Li, X.; Zheng, W. Calculation of Carbon Emissions in Wastewater Treatment and Its Neutralization Measures: A Review. Sci. Total Environ.; 2024; 912, 169356. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2023.169356] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/38110091]

26. Shah, T. Climate Change and Groundwater: India’s Opportunities for Mitigation and Adaptation. Environ. Res. Lett.; 2009; 4, 35005. [DOI: https://dx.doi.org/10.1088/1748-9326/4/3/035005]

27. Ross, A. Groundwater Governance in Australia, the European Union and the Western USA. Integrated Groundwater Management; Springer International Publishing: Cham, Switzerland, 2016; pp. 145-171.

28. Foster, S.; MacDonald, A. The ‘Water Security’ Dialogue: Why It Needs to Be Better Informed about Groundwater. Hydrogeol. J.; 2014; 22, pp. 1489-1492. [DOI: https://dx.doi.org/10.1007/s10040-014-1157-6]

29. Masood, N.; Batool, S.; Farooqi, A. Groundwater Pollution in Pakistan. Global Groundwater; Elsevier: Amsterdam, The Netherlands, 2021; pp. 309-322.

30. Khan, M. Impact of Urbanization on Water Resources of Pakistan: A Review. NUST J. Eng. Sci.; 2019; 12, pp. 1-8. [DOI: https://dx.doi.org/10.24949/njes.v12i1.230]

31. Al-Rekabi, W.S.; Qiang, H.; Qiang, W.W. Improvements in Wastewater Treatment Technology. Pak. J. Nutr.; 2007; 6, pp. 104-110. [DOI: https://dx.doi.org/10.3923/pjn.2007.104.110]

32. Ayyasamy, P.M.; Shanthi, K.; Lakshmanaperumalsamy, P.; Lee, S.-J.; Choi, N.-C.; Kim, D.-J. Two-Stage Removal of Nitrate from Groundwater Using Biological and Chemical Treatments. J. Biosci. Bioeng.; 2007; 104, pp. 129-134. [DOI: https://dx.doi.org/10.1263/jbb.104.129]

33. Kavurmacı, M.; Karakuş, C.B. Evaluation of Irrigation Water Quality by Data Envelopment Analysis and Analytic Hierarchy Process-Based Water Quality Indices: The Case of Aksaray City, Turkey. Water Air Soil Pollut.; 2020; 231, 55. [DOI: https://dx.doi.org/10.1007/s11270-020-4427-z]

34. Karnena, M.K.; Saritha, V. Evaluation of Spatial Variability in Ground Water Quality Using Remote Sensing. Int. J. Recent Technol. Eng.; 2019; 8, pp. 4269-4278. [DOI: https://dx.doi.org/10.35940/ijrte.B2626.078219]

35. Horton, R.K. An Index Number System for Rating Water Quality. J. Water Pollut. Control. Fed.; 1965; 3, pp. 292-315.

36. Hassan, J. A Geostatistical Approach for Mapping Groundwater Quality (Case Study: Tehsil Sheikhupura). Int. J. Sci. Res.; 2014; 3, pp. 239-245.

37. Korngold, E.; Belayev, N.; Aronov, L. Removal of Arsenic from Drinking Water by Anion Exchangers. Desalination; 2001; 141, pp. 81-84. [DOI: https://dx.doi.org/10.1016/S0011-9164(01)00391-5]

38. DeMarco, M.J.; SenGupta, A.K.; Greenleaf, J.E. Arsenic Removal Using a Polymeric/Inorganic Hybrid Sorbent. Water Res.; 2003; 37, pp. 164-176. [DOI: https://dx.doi.org/10.1016/S0043-1354(02)00238-5]

39. Pakistan Bureau of Statistics. Punjab Development Statistics 2018; Pakistan Bureau of Statistics: Lahore, Pakistan, 2018.

40. Weerasundara, L.; Ok, Y.-S.; Bundschuh, J. Selective Removal of Arsenic in Water: A Critical Review. Environ. Pollut.; 2021; 268, 115668. [DOI: https://dx.doi.org/10.1016/j.envpol.2020.115668]

41. Yadav, A.K.; Yadav, H.K.; Naz, A.; Koul, M.; Chowdhury, A.; Shekhar, S. Arsenic Removal Technologies for Middle- and Low-Income Countries to Achieve the SDG-3 and SDG-6 Targets: A Review. Environ. Adv.; 2022; 9, 100262. [DOI: https://dx.doi.org/10.1016/j.envadv.2022.100262]

42. Viero, A.F.; Mazzarollo, A.C.R.; Wada, K.; Tessaro, I.C. Removal of Hardness and COD from Retanning Treated Effluent by Membrane Process. Desalination; 2002; 149, pp. 145-149. [DOI: https://dx.doi.org/10.1016/S0011-9164(02)00746-4]

43. Aslam, R.W.; Naz, I.; Quddoos, A.; Quddusi, M.R. Assessing Climatic Impacts on Land Use and Land Cover Dynamics in Peshawar, Khyber Pakhtunkhwa, Pakistan: A Remote Sensing and GIS Approach. GeoJournal; 2024; 89, 202. [DOI: https://dx.doi.org/10.1007/s10708-024-11203-6]

44. Dong, Y.; Zhang, G.; Hong, W.-C.; Xu, Y. Consensus Models for AHP Group Decision Making under Row Geometric Mean Prioritization Method. Decis. Support Syst.; 2010; 49, pp. 281-289. [DOI: https://dx.doi.org/10.1016/j.dss.2010.03.003]

45. Mahmood, A.; Mahmoud, A.H.; El-Abedein, A.I.Z.; Ashraf, A.; Almunqedhi, B.M.A. A Comparative Study of Metals Concentration in Agricultural Soil and Vegetables Irrigated by Wastewater and Tube Well Water. J. King Saud Univ. Sci.; 2020; 32, pp. 1861-1864. [DOI: https://dx.doi.org/10.1016/j.jksus.2020.01.031]

46. Vadde, K.; Wang, J.; Cao, L.; Yuan, T.; McCarthy, A.; Sekar, R. Assessment of Water Quality and Identification of Pollution Risk Locations in Tiaoxi River (Taihu Watershed), China. Water; 2018; 10, 183. [DOI: https://dx.doi.org/10.3390/w10020183]

47. Zhu, Y.; Dai, H.; Yuan, S. The Competition between Heterotrophic Denitrification and DNRA Pathways in Hyporheic Zone and Its Impact on the Fate of Nitrate. J. Hydrol.; 2023; 626, 130175. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2023.130175]

48. Do, H.T.; Lo, S.L.; Phan Thi, L.A. Calculating of River Water Quality Sampling Frequency by the Analytic Hierarchy Process (AHP). Environ. Monit. Assess.; 2013; 185, pp. 909-916. [DOI: https://dx.doi.org/10.1007/s10661-012-2600-6]

49. WWF-Pakistan. Pakistan’s Water at Risk: Water and Health-Related Issues in Pakistan and Key Recommendations: A Special Report; IRC: Lahore, Pakistan, 2007.

50. Farooq, S.; Hashmi, I.; Qazi, I.A.; Qaiser, S.; Rasheed, S. Monitoring of Coliforms and Chlorine Residual in Water Distribution Network of Rawalpindi, Pakistan. Environ. Monit. Assess.; 2008; 140, pp. 339-347. [DOI: https://dx.doi.org/10.1007/s10661-007-9872-2]

51. USGS. Where Is Earth’s Water?; U.S. Geological Survey: Reston, VA, USA, 2016.

52. Yin, H.; Shi, Y.; Niu, H.; Xie, D.; Wei, J.; Lefticariu, L.; Xu, S. A GIS-Based Model of Potential Groundwater Yield Zonation for a Sandstone Aquifer in the Juye Coalfield, Shangdong, China. J. Hydrol.; 2018; 557, pp. 434-447. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2017.12.043]

53. Wu, H.; Chen, J.; Qian, H.; Zhang, X. Chemical Characteristics and Quality Assessment of Groundwater of Exploited Aquifers in Beijiao Water Source of Yinchuan, China: A Case Study for Drinking, Irrigation, and Industrial Purposes. J. Chem.; 2015; 2015, 726340. [DOI: https://dx.doi.org/10.1155/2015/726340]

54. Aeman, H.; Shu, H.; Aisha, H.; Nadeem, I.; Aslam, R.W. Quantifying the Scale of Erosion along Major Coastal Aquifers of Pakistan Using Geospatial and Machine Learning Approaches. Environ. Sci. Pollut. Res.; 2024; 31, pp. 32746-32765. [DOI: https://dx.doi.org/10.1007/s11356-024-33296-9]

55. Qureshi, A.S.; McCornick, P.G.; Sarwar, A.; Sharma, B.R. Challenges and Prospects of Sustainable Groundwater Management in the Indus Basin, Pakistan. Water Resour. Manag.; 2010; 24, pp. 1551-1569. [DOI: https://dx.doi.org/10.1007/s11269-009-9513-3]

56. Muhammad, A.M.; Zhonghua, T.; Dawood, A.S.; Earl, B. Evaluation of Local Groundwater Vulnerability Based on DRASTIC Index Method in Lahore, Pakistan. Geofísica Int.; 2015; 54, pp. 67-81. [DOI: https://dx.doi.org/10.1016/j.gi.2015.04.003]

57. Liu, S.; Qiu, Y.; He, Z.; Shi, C.; Xing, B.; Wu, F. Microplastic-Derived Dissolved Organic Matter and Its Biogeochemical Behaviors in Aquatic Environments: A Review. Crit. Rev. Environ. Sci. Technol.; 2024; 54, pp. 865-882. [DOI: https://dx.doi.org/10.1080/10643389.2024.2303294]

58. Morris, B.L.; Lawrence, A.R.L.; Chilton, P.J.C.; Adams, B. Groundwater and It as Susceptibility to Degradation. A Global Assessment of the Problem and Options for Management. J. Chem. Inf. Model.; 2013; 53, pp. 1689-1699.

59. Majeed, S.; Javaid, A.; Gul, S.; Farooq, N.; Tahir, M. Geospatial Assessment of Groundwater Quality Using Water Quality Index and Inverse Distance Weighted Techniques. Int. J. Environ. Sci.; 2020; 5, pp. 271-284.

60. Mahmood, K.; Rana, A.; Tariq, S.; Kanwal, S.; Ali, R.; Haidar, A. Groundwater Levels Susceptibility To Degradation in Lahore Metropolitan. Depression; 2011; 150, 8.01.

61. Ladson, A.R.; White, L.J.; Doolan, J.A.; Finlayson, B.L.; Hart, B.T.; Lake, P.S.; Tilleard, J.W. Development and Testing of an Index of Stream Condition for Waterway Management in Australia. Freshw. Biol.; 1999; 41, pp. 453-468. [DOI: https://dx.doi.org/10.1046/j.1365-2427.1999.00442.x]

62. Bhatti, M.T.; Anwar, A.A.; Aslam, M. Groundwater Monitoring and Management: Status and Options in Pakistan. Comput. Electron. Agric.; 2017; 135, pp. 143-153. [DOI: https://dx.doi.org/10.1016/j.compag.2016.12.016]

63. Fang, Y.; Zheng, T.; Zheng, X.; Peng, H.; Wang, H.; Xin, J.; Zhang, B. Assessment of the Hydrodynamics Role for Groundwater Quality Using an Integration of GIS, Water Quality Index and Multivariate Statistical Techniques. J. Environ. Manag.; 2020; 273, 111185. [DOI: https://dx.doi.org/10.1016/j.jenvman.2020.111185]

64. Dai, Z.; Peng, L.; Qin, S. Experimental and Numerical Investigation on the Mechanism of Ground Collapse Induced by Underground Drainage Pipe Leakage. Environ. Earth Sci.; 2024; 83, 32. [DOI: https://dx.doi.org/10.1007/s12665-023-11344-w]

65. Engel, B.; Lim, K.J. Groundwater Contaminants. The Handbook of Groundwater Engineering; Delleur, J.W. CRC Press: Boca Raton, FL, USA, 2006; ISBN 9780429122095

66. El Baba, M.; Kayastha, P.; Huysmans, M.; De Smedt, F. Evaluation of the Groundwater Quality Using the Water Quality Index and Geostatistical Analysis in the Dier Al-Balah Governorate, Gaza Strip, Palestine. Water; 2020; 12, 262. [DOI: https://dx.doi.org/10.3390/w12010262]

67. Chatterjee, R.; Tarafder, G.; Paul, S. Groundwater Quality Assessment of Dhanbad District, Jharkhand, India. Bull. Eng. Geol. Environ.; 2010; 69, pp. 137-141. [DOI: https://dx.doi.org/10.1007/s10064-009-0234-x]

68. Carroll, S.P.; Dawes, L.A.; Goonetilleke, A.; Hargreaves, M. Water Quality Profile of an Urbanising Catchment. Proceedings of the Eleventh Individual and Small Community Sewage Systems Conference Proceedings; Warwick, RI, USA, 20–24 October 2007; American Society of Agricultural and Biological Engineers: St. Joseph, MI, USA, 2007.

69. Bashir, N.; Saeed, R.; Afzaal, M.; Ahmad, A.; Muhammad, N.; Iqbal, J.; Khan, A.; Maqbool, Y.; Hameed, S. Water Quality Assessment of Lower Jhelum Canal in Pakistan by Using Geographic Information System (GIS). Groundw. Sustain. Dev.; 2020; 10, 100357. [DOI: https://dx.doi.org/10.1016/j.gsd.2020.100357]

70. Li, Z.; He, M.-Y.; Li, B.; Wen, X.; Zhou, J.; Cheng, Y.; Zhang, N.; Deng, L. Multi-Isotopic Composition (Li and B Isotopes) and Hydrochemistry Characterization of the Lakko Co Li-Rich Salt Lake in Tibet, China: Origin and Hydrological Processes. J. Hydrol.; 2024; 630, 130714. [DOI: https://dx.doi.org/10.1016/j.jhydrol.2024.130714]

71. Yin, L.; Wang, L.; Keim, B.D.; Konsoer, K.; Yin, Z.; Liu, M.; Zheng, W. Spatial and Wavelet Analysis of Precipitation and River Discharge during Operation of the Three Gorges Dam, China. Ecol. Indic.; 2023; 154, 110837. [DOI: https://dx.doi.org/10.1016/j.ecolind.2023.110837]

72. Zhang, K.; Li, Y.; Yu, Z.; Yang, T.; Xu, J.; Chao, L.; Ni, J.; Wang, L.; Gao, Y.; Hu, Y. et al. Xin’anjiang Nested Experimental Watershed (XAJ-NEW) for Understanding Multiscale Water Cycle: Scientific Objectives and Experimental Design. Engineering; 2022; 18, pp. 207-217. [DOI: https://dx.doi.org/10.1016/j.eng.2021.08.026]

73. Abbas, Z.; Mapoma, H.W.T.; Su, C.; Aziz, S.Z.; Ma, Y.; Abbas, N. Spatial Analysis of Groundwater Suitability for Drinking and Irrigation in Lahore, Pakistan. Environ. Monit. Assess.; 2018; 190, 391. [DOI: https://dx.doi.org/10.1007/s10661-018-6775-3]

74. Basharat, M. Groundwater Environment in Lahore, Pakistan. Groundwater Environment in Asian Cities; Elsevier: Amsterdam, The Netherlands, 2016; pp. 147-184.

75. Farooqi, A.; Masuda, H.; Firdous, N. Toxic Fluoride and Arsenic Contaminated Groundwater in the Lahore and Kasur Districts, Punjab, Pakistan and Possible Contaminant Sources. Environ. Pollut.; 2007; 145, pp. 839-849. [DOI: https://dx.doi.org/10.1016/j.envpol.2006.05.007]

76. Anwar, M.S.; Lateef, S.; Siddiqi, G.M. Bacteriological Quality of Drinking Water in Lahore. Biomedica; 2010; 26, pp. 66-69.

77. Rashid Ahm, S.; Saleem Kha, M.; Quddos Kha, A.; Ghazi, S.; Ali, S. Sewage Water Intrusion in the Groundwater of Lahore, Its Causes and Protections. Pak. J. Nutr.; 2012; 11, pp. 484-488. [DOI: https://dx.doi.org/10.3923/pjn.2012.484.488]

78. Quddoos, A.; Muhmood, K.; Naz, I.; Aslam, R.W.; Usman, S.Y. Geospatial Insights into Groundwater Contamination from Urban and Industrial Effluents in Faisalabad. Discov. Water; 2024; 4, 50. [DOI: https://dx.doi.org/10.1007/s43832-024-00110-z]

79. Basharat, M.; Rizvi, S.A. Groundwater Extraction and Waste Water Disposal Regulation—Is Lahore Aquifer at Stake with as Usual Approach. World Water Day; Pakistan Engineering Congress: Lahore, Pakistan, 2011; pp. 135-152.

80. Hu, C.; Dong, B.; Shao, H.; Zhang, J.; Wang, Y. Toward Purifying Defect Feature for Multilabel Sewer Defect Classification. IEEE Trans. Instrum. Meas.; 2023; 72, 5008611. [DOI: https://dx.doi.org/10.1109/TIM.2023.3250306]

81. Foster, S.; Hirata, R.; Gomes, D.; D’Elia, M.; Paris, M. Groundwater Quality Protection: A Guide for Water Utilities, Municipal Authorities, and Environment Agencies; The World Bank Group: Washington, DC, USA, 2002; ISBN 0821349511

82. Ashraf, M.; Bilal Khan, M.; Umar, F. Characterization of Ground Water Quality for Irrigation in Tehsil and District Layyah, Punjab Pakistan. World Rural. Obs.; 2018; 10, pp. 74-78. [DOI: https://dx.doi.org/10.7537/marswro100318.11]

83. Yi, X.; Wang, Z.; Zhao, P.; Song, W.; Wang, X. New Insights on Destruction Mechanisms of Waste Activated Sludge during Simultaneous Thickening and Digestion Process via Forward Osmosis Membrane. Water Res.; 2024; 254, 121378. [DOI: https://dx.doi.org/10.1016/j.watres.2024.121378]

84. Seifi, A.; Dehghani, M.; Singh, V.P. Uncertainty Analysis of Water Quality Index (WQI) for Groundwater Quality Evaluation: Application of Monte-Carlo Method for Weight Allocation. Ecol. Indic.; 2020; 117, 106653. [DOI: https://dx.doi.org/10.1016/j.ecolind.2020.106653]

85. Sahoo, N.; Panigrahi, B.; Das, D.M.; Das, D.P. Simulation of Runoff in Baitarani Basin Using Composite and Distributed Curve Number Approaches in HEC-HMS Model. Mausam; 2020; 71, pp. 675-686.

86. Vijay, R.; Khobragade, P.; Mohapatra, P.K. Assessment of Groundwater Quality in Puri City, India: An Impact of Anthropogenic Activities. Environ. Monit. Assess.; 2011; 177, pp. 409-418. [DOI: https://dx.doi.org/10.1007/s10661-010-1643-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20714928]

87. Di, D.; Li, T.; Fang, H.; Xiao, L.; Du, X.; Sun, B.; Zhang, J.; Wang, N.; Li, B. A CFD-DEM Investigation into Hydraulic Transport and Retardation Response Characteristics of Drainage Pipeline Siltation Using Intelligent Model. Tunn. Undergr. Space Technol.; 2024; 152, 105964. [DOI: https://dx.doi.org/10.1016/j.tust.2024.105964]

88. Sutadian, A.D.; Muttil, N.; Yilmaz, A.G.; Perera, B.J.C. Using the Analytic Hierarchy Process to Identify Parameter Weights for Developing a Water Quality Index. Ecol. Indic.; 2017; 75, pp. 220-233. [DOI: https://dx.doi.org/10.1016/j.ecolind.2016.12.043]

89. Mukherjee, A.; Sen, S.; Paul, S.K. A Deviation from Standard Quality Approach for Characterisation of Surface Water Quality. Int. J. Sustain. Dev. Plan.; 2017; 12, pp. 30-41. [DOI: https://dx.doi.org/10.2495/SDP-V12-N1-30-41]

90. Jhariya, D.C.; Kumar, T.; Dewangan, R.; Pal, D.; Dewangan, P.K. Assessment of Groundwater Quality Index for Drinking Purpose in the Durg District, Chhattisgarh Using Geographical Information System (GIS) and Multi-Criteria Decision Analysis (MCDA) Techniques. J. Geol. Soc. India; 2017; 89, pp. 453-459. [DOI: https://dx.doi.org/10.1007/s12594-017-0628-5]

91. Ishizaka, A.; Labib, A. Analytic Hierarchy Process and Expert Choice: Benefits and Limitations. OR Insight; 2009; 22, pp. 201-220. [DOI: https://dx.doi.org/10.1057/ori.2009.10]

92. Hyde, K.M.; Maier, H.R.; Colby, C.B. A Distance-Based Uncertainty Analysis Approach to Multi-Criteria Decision Analysis for Water Resource Decision Making. J. Environ. Manag.; 2005; 77, pp. 278-290. [DOI: https://dx.doi.org/10.1016/j.jenvman.2005.06.011] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16288957]

93. Thungngern, J.; Wijitkosum, S.; Sriburi, T.; Sukhsri, C. A Review of the Analytical Hierarchy Process (AHP): An Approach to Water Resource Management in Thailand. Appl. Environ. Res.; 2015; 37, pp. 13-32. [DOI: https://dx.doi.org/10.35762/AER.2015.37.3.2]

94. Zhao, Y.; Zhang, M.; Liu, Z.; Ma, J.; Yang, F.; Guo, H.; Fu, Q. How Human Activities Affect Groundwater Storage. Research; 2024; 7, 0369. [DOI: https://dx.doi.org/10.34133/research.0369]

95. Lan, T.; Hu, Y.; Cheng, L.; Chen, L.; Guan, X.; Yang, Y.; Guo, Y.; Pan, J. Floods and Diarrheal Morbidity: Evidence on the Relationship, Effect Modifiers, and Attributable Risk from Sichuan Province, China. J. Glob. Health; 2022; 12, 11007. [DOI: https://dx.doi.org/10.7189/jogh.12.11007]

96. Saaty, T.L. A Scaling Method for Priorities in Hierarchical Structures. J. Math. Psychol.; 1977; 15, pp. 234-281. [DOI: https://dx.doi.org/10.1016/0022-2496(77)90033-5]

97. Shyam Prasad, V.; Kousalya, P. Role of Consistency in Analytic Hierarchy Process—Consistency Improvement Methods. Indian J. Sci. Technol.; 2017; 10, pp. 1-5. [DOI: https://dx.doi.org/10.17485/ijst/2017/v10i29/100784]

98. Saaty, R.W. The Analytic Hierarchy Process-What It Is and How It Is Used. Math. Model; 1987; 9, pp. 161-176. [DOI: https://dx.doi.org/10.1016/0270-0255(87)90473-8]

99. Hashim, M.A.; Mukhopadhyay, S.; Sahu, J.N.; Sengupta, B. Remediation Technologies for Heavy Metal Contaminated Groundwater. J. Environ. Manag.; 2011; 92, pp. 2355-2388. [DOI: https://dx.doi.org/10.1016/j.jenvman.2011.06.009] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21708421]

100. Duong, H.C.; Tran, L.T.T.; Vu, M.T.; Nguyen, D.; Tran, N.T.V.; Nghiem, L.D. A New Perspective on Small-Scale Treatment Systems for Arsenic Affected Groundwater. Environ. Technol. Innov.; 2021; 23, 101780. [DOI: https://dx.doi.org/10.1016/j.eti.2021.101780]

101. World Health Organization. Guidelines for Drinking-Water Quality: Fourth Edition; World Health Organization: Geneva, Switzerland, 2011.

102. Nong, X.; Shao, D.; Zhong, H.; Liang, J. Evaluation of Water Quality in the South-to-North Water Diversion Project of China Using the Water Quality Index (WQI) Method. Water Res.; 2020; 178, 115781. [DOI: https://dx.doi.org/10.1016/j.watres.2020.115781]