Geospatial mapping of microbial contaminants and public health risks in Lahore’s municipal water
Abstract
Ensuring access to safe drinking water remains a significant public health challenge in developing countries. This study systematically investigates coliform contamination in Lahore’s municipal water supply, examining its public health implications, correlation with waterborne disease symptoms, and public perception of water quality. A systematic sampling approach was employed across 381 locations to assess water quality, with water samples analyzed for coliforms and Escherichia coli contamination. Coliform contamination was detected in several areas, including Chungi Amar Sidhu, Charar, Kirianwala, Kharas Mohalla, Shareefpura, Mustafaabad, and Kot Khawaja Saeed. To enhance data robustness, focused case studies with larger sample sizes were conducted in identified hotspots. The average coliform concentration and the percentage of affected households varied significantly across locations, with Shareefpura (3941.4 CFU/ml) and Mustafaabad (1668.2 CFU/ml) exhibiting the highest contamination levels. Mustafaabad also had the highest percentage of affected households (60.3%). Public perception of water quality varied significantly across areas, generally aligning with contamination levels. A Chi-Square Test for Independence showed a significant association between location and public perception (χ2 = 180.7, df = 20, P < 0.0001). Spearman’s Rank Correlation revealed a moderate negative correlation between contamination and perception scores, suggesting that sensory cues and historical knowledge influence perception. This study provides critical insights for public health interventions and urban water management policies, highlighting the need for systematic water quality monitoring, infrastructure upgrades, and community awareness programs to mitigate public health risks associated with microbial contamination in urbanizing regions.
Introduction
Although drinking water quality standards are often associated solely with water consumed directly, the World Health Organization (WHO) guidelines specify that the same standards apply to all domestic uses, including food preparation, cooking, and personal hygiene, to safeguard public health1. However, in developing countries, inadequate access to safe drinking water remains a critical public health challenge, contributing to a high burden of waterborne diseases2,3. According to WHO, at least 1.7 billion people worldwide consume drinking water contaminated with fecal matter, leading to approximately 1 million diarrheal deaths annually, particularly among vulnerable populations such as children under five4. Microbial contamination, primarily from fecal sources, poses the greatest risk to water safety by facilitating the transmission of diseases including diarrhea, cholera, dysentery, and typhoid. This results in an estimated 505,000 diarrheal deaths each year, underscoring the urgent need for effective water quality management and public health interventions to reduce the impact of waterborne diseases4.
Urban areas with high population densities and extensive commercial activities face significant challenges in maintaining water quality due to inadequate infrastructure for managing urban runoff. According to the U.S. Environmental Protection Agency (EPA), stormwater runoff from impervious surfaces carries pollutants, including industrial discharges, vehicular emissions, and solid waste, into streams, lakes, and groundwater, exacerbating contamination in urban environments5. Additionally, the close proximity of buildings and dense human activities intensify pollution levels, particularly in surrounding waterways. Although centralized water sources in urban areas are typically treated to meet disinfection standards, the rapid pace of urbanization often overwhelms these systems. These challenges persist even in regions with advanced wastewater treatment facilities, underscoring the need for improved urban water management and pollution control strategies6.
In developing countries like Pakistan, where urban planning and infrastructure may struggle to keep pace with rapid urbanization, these challenges are intensified. High levels of microbial pollution affecting both recreational water quality and drinking water sources have been increasingly reported 7,8,9. There is a growing interest in understanding the transport, fate, and survival of these pollutants in the environment10. However, further research is needed to understand the characteristics, sources, and variability of microbial pollution in urbanizing watersheds.
Microbial pollution of water is commonly assessed using fecal indicator bacteria such as Escherichia coli, coliforms, and enterococci11,12, which signal potential fecal contamination and the presence of pathogenic microorganisms. In urban areas, coliforms often enter water bodies through sewage overflows, stormwater runoff, and aging distribution systems13. The presence of coliforms in drinking water poses a public health risk, highlighting the need for effective monitoring and management.
Lahore, the second-largest city in Pakistan, faces significant challenges in maintaining safe drinking water despite its relatively developed infrastructure. Previous studies have reported microbial contamination levels exceeding permissible limits, posing substantial public health risks (Table 1). However, these studies were sporadic and geographically limited, lacking systematic assessment across the city.
Table 1 Water quality data from Lahore.
This study systematically investigates coliform contamination in Lahore’s municipal water supply, mapping contamination hotspots and correlating contamination levels with gastrointestinal and dermatological symptoms. It also evaluates public perception of water quality and examines its alignment with actual contamination levels. Additionally, the study assesses the impact of environmental factors, particularly temperature, on coliform concentration. By adopting a systematic approach and providing comprehensive data, this research informs public health interventions, urban water management, and policy-making, addressing critical water safety challenges in urbanizing regions.
Methods
Study area and sampling sites
This study was conducted in Lahore, a major city in Pakistan with diverse water supply sources. The initial phase involved systematic random sampling to select 381 locations across the city (Fig. 1). Locations were chosen to ensure comprehensive geographic coverage and representation of different water supply sources, including the Water and Sewerage Authority (WASA), the Lahore Cantonment Board, and various central society systems.
Map of sample collection sites: a map of Lahore showing the 381 water sampling locations chosen through systematic randomization for the screening phase aimed at detecting coliform contamination. The map was generated using ArcGIS 10.8 software (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview).
GIS and Spatial Analysis
ArcGIS Pro v3.2 and 10.8 were used for selecting random pin locations during the initial sampling phase and for generating detailed maps. These maps were used to visualize sampling sites, coliform hotspots, and the spatial distribution of coliform contamination across different neighborhoods.
Sampling and data collection
Initial screening phase
During the screening phase, water samples were collected from 206 out of the 381 selected locations (from October 2022 to January 2023) with access to major water supply sources. The left-hand rule was employed at each selected location to ensure unbiased sample selection by choosing the nearest household on the left side of the path. Water samples were collected from household taps. The remaining 175 locations lacked access to such sources and were excluded from water sampling. Sample collection adhered to standard procedures to avoid contamination, with each sample collected in sterile containers and transported to the laboratory under refrigerated conditions within four hours of collection, following standard protocols for water sampling14.
Focused case studies
Based on initial findings, seven neighborhoods with coliform contamination were identified. To improve data reliability, the sample size was expanded in these areas, with the following distribution: Chungi Amar Sidhu (n = 90), Charar (n = 79), Kirianwala (n = 50), Kharas Mohalla (n = 53), Shareefpura (n = 32), Mustafaabad (n = 60), and Kot Khawaja Saeed (n = 56). Sampling locations within these areas were selected to ensure comprehensive residential and public water access points coverage.
Household surveys were conducted concurrently with water sample collection from the same households to assess the prevalence of waterborne disease symptoms and public perception of water quality. Survey data was collected using a structured questionnaire (Supplementary Table 1), which included sections on self-reported gastrointestinal and dermatological symptoms as well as perceptions of water quality. Trained data enumerators administered the questionnaire through face-to-face interviews to ensure accuracy and consistency in data collection. Public perception of water quality was assessed using a Likert scale, where respondents rated water quality on a scale from 1 (Very Poor) to 5 (Very Good). A total of N = 431 participants were surveyed. The study was conducted in compliance with ethical research standards. Ethical approval was obtained from the Bioethics and Safety Oversight Committee (BSOC) of the Action Research Collective (ARC). Verbal informed consent was obtained from all participants before data collection, and respondents were assured of the confidentiality and anonymity of their responses.
Temperature-dependent variation in coliform concentration
Water samples were collected daily from a household in Chungi Amar Sidhu, previously identified as a contamination site, to examine the effect of daily temperature on coliform concentrations. Sampling was conducted consistently each day from January to May 2024. Samples were transported under refrigerated conditions, and microbiological testing was performed. Daily maximum temperature data were obtained from the Pakistan Meteorological Department.
Water quality analysis
All collected water samples were analyzed for pH, odor, and transparency. pH levels were measured using test strips, with all readings recorded within the range of 6 to 8. Odor assessment was conducted through sensory evaluation, and transparency was visually assessed.
The microbiological quality of water samples was evaluated using selective and differential agar plates to detect coliform bacteria, including E. coli. Samples were directly plated on MacConkey agar and incubated at 37 °C for 24 h. This method was selected based on preliminary experiments demonstrating the high concentration of coliform bacteria, allowing for clear visibility of colonies without the need for enrichment or filtration methods. This direct plating approach also facilitated the efficient processing of a large number of samples.
After incubation, colony-forming units (CFU) were counted to quantify bacterial concentration. Presumptive coliforms and E. coli colonies were confirmed by subculturing on Eosin methylene blue (EMB) agar, followed by identification using standard biochemical tests3.
Molecular identification
To confirm the identity of coliforms, selected colonies were subjected to 16S rRNA gene sequencing, performed by Macrogen in Seoul, Korea. Genomic DNA was extracted from the bacterial colonies, and the full-length 16S rRNA gene (~ 1,500 bp) was amplified using universal primers 27F (5′-AGAGTTTGATCMTGGCTCAG-3′) and 1492R (5′-TACGGYTACCTTGTTACGACTT-3′). The amplified PCR products were detected by electrophoresis on 1.5% agarose gel. The fragments were then purified and sequenced using the ABI 3100 automated sequencer (Applied Biosystems) with the Big Dye Terminator Kit v. 3.1 (Applied Biosystems). Sequencing was performed using primers 518 F (5′-CCAGCAGCCGCGGTAATACG-3′) and 800 R (5′-TACCAGGGTATCTAATCC-3′). The obtained sequences were edited using Vector NTI Suite 9 and compared with the NCBI database through BLAST searches.
Statistical analysis
Coliform concentrations in water samples were calculated as CFU/mL. Geometric mean (GM) was used to calculate microbial concentrations to accurately represent the central tendency of log-normally distributed data and minimize the influence of extreme values15. The GM was calculated using the formula:
where:
N = Number of samples. x_i = Individual coliform concentration (CFU/mL).
To measure variability, the Geometric Standard Deviation (GSD) was calculated using the following formula:
Error bars representing variability were calculated using the GSD as follows:
Descriptive statistics summarized coliform concentrations and the percentage of coliform-positive samples across the study areas. One-way ANOVA followed by Tukey’s multiple comparison tests were conducted to determine statistically significant differences in coliform concentrations between study locations. Linear regression analysis was used to evaluate the association between coliform-positive households and symptom prevalence. To assess differences in public perception of water quality between locations, a Chi-Square Test for Independence was conducted. The null hypothesis was that public perception is independent of location. Spearman’s Rank Correlation was applied to evaluate the relationship between coliform contamination prevalence and public perception scores, as the data were ordinal and exhibited a monotonic relationship.
Public perception scores were calculated using a weighted average method:
where:
P_i = Percentage of respondents selecting each Likert scale category. W_i = Corresponding weight for each category (1 to 5). k = Number of Likert scale categories.
All statistical analyses were performed using Microsoft Excel and GraphPad Prism version 10.
Results
Sampling locations, process, and coliform detection
In the screening phase, we systematically selected 381 locations in Lahore (Fig. 1). Of these, 206 (54.7%) had access to water from major supply boards or the central society system: 69 from the Water and Sewerage Authority (WASA), 67 from the Lahore Cantonment Board, and 70 from their respective society’s central boring water supply system. The remaining 175 (45.93%) lacked access to any central supply entities (Fig. 2a and b).
Water access distribution in Lahore: (a) a map of Lahore indicating sites with and without access to water from major water supply boards or societies’ central water supply systems. (b) A pie chart illustrating the percentage of sites without access to water from any central supply entity and those with access to water from the Water and Sewerage Authority (WASA), the Cantonment Board, or societies’ central water supply systems. The map was generated using ArcGIS 10.8 software (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview).
Water samples from 206 locations with major supply sources were collected and analyzed. All samples had pH levels between 6 and 8, were odorless, and transparent. Coliforms were detected in samples from Chungi Amar Sidhu, Charar, Kirianwala, Kharas Mohalla, Shareefpura, Mustafaabad, and Kot Khawaja Saeed (Fig. 3a). Coliform concentrations were highest in Kharas Mohalla (6000 CFU/ml) and lowest in Charar (70 CFU/ml) (Fig. 3b). E. coli was found in all areas where coliforms were detected, indicating significant health risks associated with the water supply in these neighborhoods.
Coliform contamination sites: (a) a map of Lahore indicating sites where coliforms were detected during the screening phase of the study. (b) A bar graph illustrating the coliform concentration (CFU/ml) detected in samples collected from the respective areas. The map was generated using ArcGIS 10.8 software (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview).
Focused case studies in identified areas
Building upon the screening phase, we expanded our research in the seven areas with coliform contamination to confirm initial findings. Increasing the sample size aimed to enhance data robustness and reliability. A larger number of water samples were collected from each area: Chungi Amar Sidhu (n = 90), Charar (n = 79), Kirianwala (n = 50), Kharas Mohalla (n = 53), Shareefpura (n = 32), Mustafaabad (n = 60), and Kot Khawaja Saeed (n = 56). These samples were tested for coliforms and E. coli contamination.
Maps in Fig. 4a display sampling locations, highlighting where coliform contamination was detected. Figure 4b shows the percentage of affected households, with Mustafaabad (60.3%) and Charar (48.7%) having the highest rates. Shareefpura (48.4%), Chungi Amar Sidhu (46.1%), Kharas Mohalla (34.6%), and Kot Khawaja Saeed (30.9%) follow. Kirianwala shows the lowest contamination rate, with only 7.7% of households affected. This lower percentage may be attributed to residents opting for alternative water sources, such as borehole water, due to prior knowledge of contamination in the water supply.
Sampling locations and contamination levels in coliform hotspots: (a) maps illustrating the sampling locations and contaminated sites in Mustafabad, Kot Khawaja Saeed, Shareefpura, Kharas Mohallah, Charar, Chungi Amar Sidhu, and Kirainwala. (b) Bar graph showing the percentage of affected households in each identified coliform hotspot. (c) Bar graph depicting the geometric mean coliform concentration (CFU/mL) in each area, with error bars representing the geometric standard deviation (GSD), illustrating variability in contamination levels within coliform hotspots. (d) Scatter dot plot displaying coliform concentration (CFU/ml) for samples testing positive for coliforms, particularly emphasizing contamination levels within coliform hotspots. (e) Bar graph showing the percentage of E. coli-positive water samples in each area. Significance was determined by ordinary One-Way ANOVA multiple comparison test, P-values are indicated with asterisks denoting statistically significant differences (* p < 0.05, ** p < 0.01, *** p < 0.001, n.s not significant p > 0.05). The map was generated using ArcGIS 10.8 software (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview).
The geometric mean coliform concentration (CFU/ml) varied significantly across the study areas (Fig. 4c). Shareepura exhibited the highest geometric mean concentration at 3941.4 CFU/ml, followed by Mustafabad at 1668.2 CFU/ml. In contrast, Chungi Amar Sidhu showed the lowest concentration at 374.1 CFU/ml. The error bars represent the geometric standard deviations, reflecting substantial variability in contamination levels across samples. Notably, Chungi Amar Sidhu and Charar displayed lower geometric standard deviations, indicating more consistent contamination levels across samples within these areas. This suggests a relatively uniform source or pattern of contamination in these locations. Conversely, Shareepura, Kot Khawaja Saeed and Kirianwala showed higher standard deviations, reflecting greater variability in contamination levels, which may be due to fluctuating sources of contamination or environmental factors. A scatter dot plot (Fig. 4d) further illustrates these differences, highlighting the significant fluctuations in coliform concentrations among the surveyed areas.
We next assessed the distribution of coliform contamination across the study areas (Supplementary Fig. S1). Mustafaabad exhibited the highest contamination, with a substantial number of samples falling within the 2000–5000 CFU/ml range and exceeding this threshold. Significant contamination was also observed in Charar and Shareefpura, while Chungi Amar Sidhu displayed comparatively lower levels. In contrast, Kharas Mohalla, Kot Khawaja Saeed, and Kirianwala showed high contamination levels, predominantly within the 2000–5000 CFU/ml range and higher categories.
E. coli positivity rates in water samples were: Charar (39.7%), Shareefpura (25.8%), Chungi Amar Sidhu (25.8%), Kot Khawaja Saeed (21.8%), Kharas Mohalla (13.5%), Mustafaabad (10.3%), and Kirianwala (7.7%) (Fig. 4e). 16S rRNA sequencing confirmed the identity of coliform isolates, revealing species within the Enterobacteriaceae family, including Escherichia fergusonii, Enterobacter cloacae, and Klebsiella pneumoniae (Supplementary Figs. S2–S11).
A heatmap (Fig. 5) depicts coliform contamination intensity across different areas, providing a spatial representation of contamination levels and aiding in identifying regions with higher contamination intensity. Additionally, we collected water samples from the central tube wells in each area, which supply water to entire neighborhoods. Our analysis revealed that fecal coliforms were not detected in the tube well water, suggesting an absence of direct fecal contamination at the source. However, the absence of fecal coliforms does not guarantee the absence of all pathogens, as they are limited indicators that do not detect viruses, protozoa, and other non-bacterial pathogens. The contamination observed in household water indicates that the distribution infrastructure of the water supply is compromised, potentially leading to contamination during transit.
Spatial representation of coliform contamination intensity: maps illustrating the intensity of coliform contamination across the studied areas, facilitating the identification of regions with higher contamination intensity through spatial representation. The map was generated using ArcGIS 10.8 software (https://www.esri.com/en-us/arcgis/products/arcgis-desktop/overview).
Linking coliform contamination to waterborne disease symptoms
In the identified areas, we surveyed households for symptoms of waterborne illnesses, focusing on gastrointestinal and dermatological infections. Mustafabad had the highest prevalence at 84.3%, followed by Charar (59.6%), Shareepura (50.2%), Chungi Amar Sidhu (37.0%), Kharas Mohalla (20.0%), and Kot Khawaja Saeed (3.8%). Kirianwala, despite low coliform contamination, had 32.0% of households reporting symptoms (Fig. 6a).
Analysis of coliform contamination and health symptoms: (a) a bar graph showing the percentage of coliform-positive households, alongside a grey line graph indicating the percentage of survey respondents reporting family members with waterborne illness symptoms in the last two weeks, and a blue line graph showing the percentage of households with access to filtration plants. (b) A multiple-variable bubble plot illustrating the relationship between the percentage of affected households and the prevalence of symptoms in each area, with the size of each bubble representing the total number of households in each area.
A clear trend emerged: higher coliform contamination levels corresponded with a higher prevalence of waterborne illness symptoms (Fig. 6a). The linear regression analysis quantified this relationship with the equation y = 1.05x − 0.5256, indicating that for every 1% increase in coliform-positive households, the prevalence of symptoms increases by approximately 1.05% (Supplementary Fig. S12a). The R-squared value of 0.4562 suggests that coliform contamination levels can explain about 45.62% of the variability in symptom prevalence.
Interestingly, when Kirianwala, which had the lowest coliform contamination rate but a significant prevalence of symptoms (32.0%), is removed from the analysis, the R-squared value increases to 0.9633 (Supplementary Fig. S12b). This suggests that coliform contamination levels can explain approximately 96.33% of the variability in symptom prevalence when Kirianwala is excluded. It should be noted that most residents in Kirianwala use bore water instead of municipal water. Kirianwala’s deviation from the trend indicates that factors other than coliform contamination, such as other pathogenic organisms in bore water, personal hygiene practices, or other environmental factors, may significantly impact health outcomes in that area.
The surveys also gathered data on the percentage of households with access to alternative clean water sources, such as boreholes or filtration plants. Despite access to alternative clean water sources, the prevalence of waterborne illnesses remained high in some areas; for example, in , Charar, 94.7% had access to clean water, but symptoms were still prevalent (Fig. 6a). We observed that despite having access to alternative clean drinking water sources, many households continued using piped water for other domestic purposes, which may be contributing to the prevalence of the illness.
Figure 6b displays a multiple-variable bubble plot illustrating the relationship between the percentage of affected households and the prevalence of symptoms for each area. The size of each bubble represents the total number of households in each area, based on the Pakistan Election Commission’s polling scheme for the 2024 elections in Pakistan.
Impact of maximum daily temperature on coliform concentration
To investigate the impact of daily temperature on coliform concentration, we selected a household in Chungi Amar Sidhu where water contamination was previously identified and commenced daily water collection from this location. The analysis of the relationship between maximum daily temperature and coliform concentration from January to May 2024 reveals a complex and non-linear trend (Fig. 7). During colder days, with temperatures below 12 °C, coliform concentrations were generally low. As temperatures increased to moderate levels (between 12 and 19 °C), significant spikes in coliform concentrations were observed, suggesting that these temperatures may create a conducive environment for bacterial growth. However, the data exhibited variability, with some warmer days still showing low coliform levels. On the hottest days, with temperatures reaching up to 40 °C, coliform concentrations varied widely, including both high and low values. This variability indicates that while temperature may influence coliform presence, it is not the sole determining factor.
Impact of maximum daily temperature on coliform concentration: A bar graph (left y-axis) showing the coliform concentration (CFU/ml) across multiple days at a single location, alongside an area graph (right y-axis) displaying the maximum temperature recorded on the day of sample collection.
Public perception of water quality and its association with coliform contamination
Public perception of water quality was assessed using a Likert scale ranging from 1 (very poor) to 5 (very good) (Fig. 8a). Significant variations in public perception were observed across the six study locations. Perception scores were calculated using a weighted average method (Table 2). A Chi-Square Test for Independence revealed a significant association between location and public perception of water quality (χ2 = 180.7, df = 20, P < 0.0001), indicating that public perception varied considerably between the study locations.
Public perception of water quality and its association with coliform contamination. (a) Distribution of public perception of water quality across six study locations. Perceptions were measured using a Likert scale ranging from 1 (Very Poor) to 5 (Very Good). The stacked bar chart illustrates the percentage of respondents in each category for each location. (b) Scatter plot showing the relationship between public perception scores and the percentage of coliform-positive households in each location.
Table 2 Public perception of water quality.
Spearman’s Rank Correlation analysis revealed a moderate negative correlation between coliform contamination prevalence and public perception scores (ρ = − 0.600, P = 0.2417) (Fig. 8b). Although the negative correlation suggests that higher contamination levels are associated with more negative public perceptions of water quality, the relationship was not statistically significant. This indicates that factors other than contamination, such as historical knowledge, local experiences, and sensory cues (e.g., odor, taste, and appearance of water), may also influence residents’ perceptions.
Discussion
This study systematically maps coliform contamination hotspots in Lahore’s municipal water supply, revealing significant public health risks associated with microbial pollution in densely populated urban neighborhoods. The highest contamination levels were observed in Chungi Amar Sidhu, Kirianwala, Kharas Mohalla, Shareefpura, Mustafaabad, and Kot Khawaja Saeed, areas characterized by aging infrastructure and lower socio-economic status. These findings highlight the vulnerability of urban communities to water contamination due to inadequate infrastructure and maintenance. By systematically identifying contamination hotspots and correlating them with gastrointestinal and dermatological symptoms, this study provides critical insights into the public health impact of waterborne diseases in urban environments. Unlike previous studies that provided sporadic data, this investigation offers a comprehensive and systematic assessment, informing targeted public health interventions and water management policies.
The contrasting contamination levels between Charar and the neighboring economically advantaged Defense Housing Authority (DHA) highlight the critical role of local infrastructure integrity and socioeconomic disparities in influencing water quality. Although both areas source water from the same supply, Charar exhibited significantly higher coliform contamination, likely due to aging distribution systems, inadequate maintenance, and lower household plumbing standards. These findings emphasize the need for equitable access to safe water through targeted infrastructure improvements in underserved communities. Addressing these socio-economic disparities is crucial for achieving public health equity and disease prevention, particularly in rapidly urbanizing cities like Lahore.
The study revealed a significant correlation between coliform contamination and the prevalence of gastrointestinal and dermatological symptoms, particularly in Mustafaabad, Charar, and Shareepura, which had the highest contamination rates. Linear regression analysis confirmed that 1% increase in coliform-positive households corresponded to a 1.05% increase in symptom prevalence. Previous studies have highlighted the limitations of using fecal indicator bacteria (FIB) as proxies for enteric pathogens 10,16. These indicators infer exposure based on assumptions about individuals’ interactions with contaminated environments rather than directly measuring the ingestion of enteric pathogens. Consequently, the inferred exposure from these proxy measures can differ substantially from actual exposure. The strong association between FIB levels and symptoms of waterborne illness in our study is therefore concerning. In environments with consistently high levels of fecal contamination, both FIB and enteric pathogens are likely to be present in significant amounts, resulting in a strong correlation between FIB levels and disease prevalence. Frequent and intense exposure to contaminated environments increases the likelihood of ingesting pathogens along with FIB, further reinforcing this correlation.
The analysis of maximum daily temperature and coliform concentration revealed a complex and non-linear relationship, indicating that temperature alone does not fully explain contamination patterns. While moderate temperatures were associated with increased coliform levels, persistent contamination from infrastructure leaks and cross-connections with sewage lines contributed to consistent microbial presence, irrespective of temperature changes. Despite the observed relationship between temperature and coliform concentration, coliform levels are known to vary due to additional factors such as seasonal influences, including rainfall patterns and agricultural runoff17,18, as well as periodic water treatments conducted by the water boards19. These findings underscore the need for comprehensive water quality monitoring that accounts for a broader range of environmental variables and longer observation periods to fully understand the factors influencing coliform contamination. Further research should focus on these environmental dynamics to develop adaptive water management strategies that effectively mitigate contamination risks.
The analysis of public perception of water quality revealed significant variations across the study locations, with higher contamination areas corresponding to more negative perceptions. Spearman’s Rank Correlation showed a moderate negative relationship between coliform contamination prevalence and perception scores, although the correlation was not statistically significant. This suggests that factors such as historical knowledge, local experiences, and sensory cues (e.g., odor, taste, and appearance of water) also influence public perception. These findings underscore the need for public health communication strategies that address both actual water quality and perceived risks to improve community awareness and trust in municipal water safety.
This study highlights the public health risks associated with coliform contamination in Lahore’s municipal water supply, particularly in low-income neighborhoods with aging infrastructure. By systematically mapping contamination hotspots and linking them to waterborne disease symptoms and public perception scores, the study provides a comprehensive understanding of the socio-environmental dynamics influencing water quality. These findings emphasize the need for policy interventions, including infrastructure upgrades, regular water quality monitoring, and public awareness campaigns. Future research should investigate other pathogenic contaminants and environmental variables over extended periods to fully understand the dynamics of urban water contamination.
Data availability
The datasets supporting the conclusions of this article are included within the article.
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