Assessing Hygiene Practices and Microbial Risks in Groundwater Sources: A Case Study of Kumbotso, Nigeria

Assessing Hygiene Practices and Microbial Risks in Groundwater Sources: A Case Study of Kumbotso, Nigeria | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Case Report Assessing Hygiene Practices and Microbial Risks in Groundwater Sources: A Case Study of Kumbotso, Nigeria Ibrahim Ibrahim Shu'aibu, Bello Gwadabe Ahmad, Awoke Guadie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7376778/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated the link between hygiene practices and groundwater quality in Kumbotso, Nigeria, where groundwater serves as a primary drinking water source. To assess the quality of groundwater, estimate contamination from sanitation, and determine socio-demographic determinants of health risk, informing targeted interventions. Conducted in August 2023, during the rainy season, 2 water samples obtained from Dug Wells (DW), Hand Pump Boreholes (HB), and Mechanized Boreholes (MB) in Kumbotso were analyzed. Physicochemical parameters (turbidity, conductivity, pH, temperature) and E. coli counts were ascertained by standard methods, and volumes were approximated using the manual string method. Socio-demographic data were obtained by questionnaires, while statistical tests (Chi-square, logistic regression) validated health correlations. Turbidity was 8.2-1914.0 NTU and E. coli 0-1305 cfu/100 mL, with DW most contaminated (median >100 cfu/100 mL). Volumes were variable (0.002-7.127 m³), with a turbidity-E. coli correlation (r = 0.38). Females (65.4%) and youth 12-17 years (50.0%) were most affected, with DW users self-reporting 50% illness (χ² = 41.73, p = 8.66E-10), though E. coli’s illness link was non-significant (odds ratio = 1.47, p = 0.29). The study concludes that improving groundwater safety in Kumbotso requires both infrastructure development and behavioral change. Targeted WASH interventions, source protection, and public education are essential for mitigating health risks and achieving sustainable access to safe drinking water. Health Economics & Outcomes Research Groundwater Sanitation Hygiene Pollution Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Highlights • Groundwater in Kumbotso is highly contaminated, especially dug wells, posing significant public health risks. • Dug well users self-reported 50% illness; females and youth age (–) are most affected. • Turbidity correlates with E. coli (r = 0.38); deeper boreholes offer superior microbial protection. • Targeted WASH interventions, source protection, and public education are critical for safe groundwater. 1. Introduction Groundwater is an essential resource for drinking water and irrigation, especially in areas of low surface water resources. Over 2.5 billion people throughout the world are believed to utilize groundwater for their daily hygiene and sanitation needs (Citaristi, 2022). Hygiene and sanitation are crucial for human health, community wellness, and sustainable living (Angelakis et al., 2023). However, groundwater pollution due to poor home sanitation and improper waste management has serious health along with environmental impacts, particularly in the developing world (Alao et al., 2025). The increasing stresses of urbanization, population, and lack of effective sanitary infrastructure have subjected groundwater resources to increased pollution from industrial, agricultural, and domestic sources (Victor et al., 2025). Groundwater pollution in the majority of regions is strongly impacted by human activities such as poor waste disposal, use of vault latrines, and agricultural runoff with high levels of fertilizers and pesticides (Graham & Polizzotto, 2013). Kano state, a land with issues of maintaining sanitary conditions and groundwater quality. It has experienced fast urbanization and absence of waste treatment, causing environmental degradation and water contamination. The region has tropical wet and dry climatic conditions, with the wet season usually occurring for four to five months within the period from April to October (Seth, 2003). In low-income suburbs, due to a shortage of public sanitation facilities, there is open defecation and improper waste disposal, hence increasing the microbial and chemical contamination of groundwater (Gwenzi et al., 2023). The introduction of pathogens such as Escherichia coli and fecal coliforms into groundwater has been strongly linked to the use of unimproved sanitation infrastructure, where human feces enter the aquifers (Aydamo et al., 2024). In sub-Saharan Africa's highly populated areas, the groundwater contamination is high (Islam et al., 2025) because of the short distance between the water points and the sanitation facilities. Similarly, in Nigeria, widespread utilization of poorly constructed pit latrines and insufficient wastewater treatment plants have been major contributors to groundwater pollution (Barati et al, 2022). The health consequences of consuming contaminated groundwater are colossal. Water-borne diseases such as diarrhea, cholera, and dysentery are rampant among populations that rely on contaminated water sources (Jabbar, 2020). Children under the age of five years, especially in rural and peri-urban populations, are most vulnerable, accounting for a tremendous proportion of childhood morbidity and mortality due to these infections (Tariq, 2022). To improve access to drinkable drinking water and enhanced public health outcomes in Nigeria, effective implementation of water, sanitation, and hygiene (WASH) programs is required (Wada et al., 2022). It has been revealed by studies that residual chlorine plays an important role in shaping microbial populations by enabling bacterial colonization with poor management, indicating the complexity of groundwater contamination (Javed et al., 2025). Groundwater quality has been found to be affected by local sanitation practices and geographical considerations in different parts of Nigeria. For instance, research has shown that there are high concentrations of ammonia and dissolved organics in borehole water located in proximity of waste deposition areas showing the risk for contamination due to some human activities around waste disposal areas (Dey et al, 2024). Microbiological pollutants like coliform are nil in some cases while chemical factors like pH, hardness and heavy metals surpass the maximum allowable levels, indicating the impact of natural and man-made activities (Amadi et al., 2020: Organization, 2010). The trends also highlight the importance of context-dependent studies such as the present work in Kumbotso to examine how groundwater microbiological and physicochemical quality is caused by daily cleanliness habits, sanitation facilities, and water handling patterns. The current study aims to find out groundwater pollution in Kumbotso, Nigeria, through an analysis of the physicochemical and microbial water quality of borehole water and examination of hygiene and sanitation in the home. The study, to this end, carried out socio-demographic interviews and examined water samples against the parameters of E. coli , turbidity, conductivity, pH, and temperature and also examined behavior around water storage, container, and environmental sanitation. 2. Materials and methods 2.1. Study area Kumbotso is one of the forty-four local government area of Kano State, with an area coverage of about 152 km 2 . The area lies between Latitudes 12°21'6.457"N to 12°6'7.808"N and Longitudes 9°28'0.944"E to 10°15'28.924"E (Fig. 2 ). According to the National Bureau of Statistics (2019) census, Kano state has a population of over 14 million, of which 409,500 resides in Kumbotso. The majority of the study area is dominated by residential buildings, and other activities (agricultural, institutional, commercial, and educational). 2.2. Sampling points The criteria for selecting sampling points were based on population density, areas with low sanitary reports or anthropogenic activities, and a historic record of water-borne diseases in the areas. As mentioned earlier that Kumbotso was famous for its manufacturing industries at Challawa, and rapid population growth, therefore it was significant to see the water quality in such areas. The Google map was scanned and digita99999lized to create the research area's base map. Data mapping and analysis for the assessment of groundwater quality are done using QGIS ~ 10. In order to facilitate the collection of groundwater samples from the area and to evaluate the sanitary methods employed in the retrieval, conveyance, and storage of drinking and household water, the territory was divided into 22 grids (Table 1 ). Communities were selected and 22 water sources were sampled (including boreholes, dug wells and drilled wells) in August 2023, which corresponded to the rainy season (Fig. 1 ). The vials used to collect the samples were made of high-density polyethylene due to chemically resistant, non-reactivity and prevention of contamination and ensuring accurate sample integrity (Rurouni, 2024). Parallel with ground water sample collection, socio-demographic data including interviews, observations and focus group discussions (FGDs) were done. The interviews provided in-depth insights into individual behaviors and attitudes toward water usage and sanitation. The FGDs were designed to be interactive discussions among community members, revealing collective practices, challenges, and environmental attitudes. 2.3. Sample analysis 2.3.1. Physicochemical and microbial analysis Conductivity, pH, temperature and turbidity measurements were carried out on-site at the sample collection location. The conductivity, turbidity, and temperature of the water samples were measured using conductivity meter (PT157, United Kingdom), portable turbidity meter (PTH092, Ireland), and pH (PT155, United Kingdom), respectively. Prior to conducting measurements, the pH meter was calibrated using three standard solutions, with pH values of 4.0, 6.0, and 10.0. After dipping the pH probe into the water sample and holding it for a few minutes to obtain a stable reading, the pH of each sample was determined. The process is applied for conductivity and temperature for all sample readings. The research employed a manual string method to estimate water source volumes in Kumbotso, Kano State, Nigeria. This involved lowering a weighted string into each well or borehole (Dug Well, Hand Pump Borehole, Mechanized Borehole) to measure the Total Drilled Depth (TDD) and Static Water Level (SWL) using a calibrated tape measure as the measuring device. The diameter was measured directly at the wellhead using a vernier caliper as the diameter measuring device. \(\:h=TDD-SWL\) 1 \(\:V=\pi\:\times\:{r}^{2}Xh\) 2 \(\:Volume\:in\:gallons=V\:\left({m}^{3}\right)\times\:264.172\) 3 V Volume h Water Column Height TDD Total Drilled Depth SWL Static Water Level The Membrane Filtration (MF) method was employed to establish Escherichia coli ( E. coli ) levels in water samples. Field sterilization is attained using Methanol (Methyl Alcohol). The processes involve pouring methanol into the stainless-steel sampling cup, igniting it to emit formaldehyde gas, and subsequently inverting the filter funnel and silicone rubber base components into the sampling cup to subject them to the sterilizing gas. This process was repeated whenever there is to be an analysis of a new sample of water. An accurately measured amount of the sample (100 mL or less if the source is highly contaminated) was filtered through a Membrane Filtration Unit (MFU) (Potatest 2, 2011). The MFU was connected to a vacuum hand pump, which created suction to pass the sample through a sterile filter having pores of 0.45-µm. This filter allowed water to pass through while retaining bacteria on its surface. On filtration, the membrane filter was carefully placed over an absorbent pad in a sterile petri dish. This pad had earlier been saturated with Membrane Lauryl Sulphate Broth (MLSB), the liquid culture medium used to provide nutrients for the growth of the bacteria without allowing non-target organisms. The petri dish was then incubated for 20–24 hours at 37°C in a portable incubator. After incubation, the count of blue-colored colonies, each with a small gas bubble, was noted. Results were expressed as cfu/mL per 100 mL. The procedure also enabled measurement of the MPN of E. coli in 100 mL of water (Potatest 2, 2011: Environment Agency, 2009: Ayres, & Mara, 1996). Table 1 WHO Classification of Drinking Water Quality Based on E. coli Contamination Risk Levels. (Odonkor & Mahami, 2020) Risk Level E. coli Count (cfu/100 mL) Description Safe 0 No E. coli detected; water is safe for drinking Low Risk 1–10 Low probability of contamination Medium Risk 11–100 Increased likelihood of contamination High Risk > 100 High risk; unsafe for consumption without treatment 2.3.2. Statistical analysis 3. Results and discussion 3.1. Physicochemical and Biological Characteristics of Water Sources Physicochemical and microbial examination of groundwater water quality at Kumbotso revealed widespread variation in physicochemical and microbiological characteristics across various water sources like Dug wells (DW), Hand Pump Boreholes (HB), and Mechanized Boreholes (MB). Parameters analyzed include turbidity, conductivity, pH, temperature, and levels of Escherichia coli (E. coli). These differences align with differences in heterogeneity of environmental sanitation, handling modes of water, and wellhead protection conditions observed in the field. Table 2. Water quality parameters, microbial levels, source types, and estimated water volumes across sampling stations. Samples Physicochemical parameters Source type Volume (m³) Turbidity (NTU) Conductivity pH (-) Temp ( 0 C) E. coli (cfu/100 mL) Minimum 8.2 0.26 5.92 27 Maximum 1914.0 524.00 7.48 34 Mean 851.009 29.8986 6.7932 29.25 Std. Deviation 464.4842 110.70995 0.44181 1.616 Station 1 2.77 819 6.74 30.67 1 HB 0.029 Station 2 1.78 894 7.2 28.65 2 DW 1.095 Station 3 1.18 1155 7.21 30.57 104 DW 1.762 Station 4 2.13 921 7.08 33.1 861 DW 0.002 Station 5 2.11 678 7.16 33.9 120 DW 6.494 Station 6 3.6 1041 7.05 27.11 23 DW 2.960 Station 7 16.4 1914 6.93 28.0 0 HB 0.067 Station 8 38 1202 7.03 28.4 0 MB 0.150 Station 9 1.66 384 6.28 29.0 1 HB 0.040 Station 10 11.27 577 7.03 28.3 101 DW 0.566 Station 11 1.98 1541 7.34 28.31 2 HB 0.054 Station 12 2.2 501 6.97 29.53 23 DW 0.703 Station 13 524 233 7.48 27.8 920 DW 2.456 Station 14 2.54 634 6.77 29.0 92 DW 7.127 Station 15 1.44 1086 6.42 29.5 936 MB 0.060 Station 16 5.16 774 6.52 28.5 10 DW 2.033 Station 17 15.8 1221 6.92 29.1 135 DW 0.923 Station 18 0.26 263 6.4 29.3 62 HB 0.078 Station 19 3.83 8.2 5.92 29.1 354 DW 1.931 Station 20 1.48 387 6.18 28.9 4 HB 0.056 Station 21 17.1 1169 5.93 27.67 34 HB 0.064 Station 22 1.08 1320 6.89 29.0 1304 DW 0.537 NTU=Nephelometric turbidity units, Temp=Temperature, (-)=No unit, cfu=Colony-forming uni Turbidity values varied from 1.08 NTU to 524 NTU, greater than WHO's highest acceptable turbidity of 5 NTU in most cases, especially from dug wells. High turbidity is typical with suspended solids, poor well cover, or surface runoff contamination (Dey et al., 2024). Station 13 had the highest turbidity value at 524 NTU and also had abnormally high E. coli counts (920 cfu/100 mL), where it was evident that there existed a strong association between appearance and microbial risk. This evidence is corroborated by the findings from groundwater analysis in densely populated Nigerian suburbs (Aydamo et al., 2024). Conductivity ranged from 8.2 µS/cm to 1914 µS/cm, indicating extensive fluctuation in dissolved ionic concentration. Elevated conductivity, especially in hand pump and mechanized boreholes, indicates possible contamination with anthropogenic and inorganic salt sources and activities in the environs (Hu et al., 2023). Elevated conductivity at Stations 7 and 11 indicates possible leaching from material below ground or domestic effluent. pH values at all the stations were between 5.92 and 7.48. Although the range is, on average, within the acceptable WHO range of 6.5–8.5, some slight acidity in some locations—namely, dug wells—can influence microbial survival and mobility of metals. Slight acidity of pH 5.92 at Station 19, together with high concentration of E. coli (354 cfu/100 mL), can facilitate microbial survival and indicates the importance of pH in microbial ecology (Angelakis et al., 2023). The temperatures were also very similar, ranging from 27°C to 34°C, and averaging 29.25°C. Although temperature in itself may not physically affect potability, it has an influence on the proliferation of microbes as well as on oxygen solubility. The high temperature of all samples, characteristic of the tropical climate of the area, may enhance the proliferation of microbes in optimal conditions (Victor et al., 2025). E. coli contamination, a test for determining microbial safety, was beyond astronomical in all but one of the stations. It ranged from 0 cfu/100 mL to more than 1300 cfu/100 mL (Station 22), and put most of the sources into WHO's "high risk" category. Thirteen of 22 sources had counts greater than 100 cfu/100 mL, i.e., dug wells with a propensity to be contaminated by adjacent latrines, open defecation sites, and animal droppings. The findings conform to rural Ethiopia and Ghana where surface contact and poor sanitation are linked with microbial contamination (Birhan et al., 2023; Saalidong et al., 2022). The scatter plot (Figure 2) confirms a positive correlation between turbidity and E. coli concentration by sources. The dug wells clustered high on turbidity and on high levels of E. coli, and boreholes (especially MB) were low for both, which means that construction type is a factor that impacts microbial penetration. This is in agreement with earlier studies conducted in Adada, Nigeria, and Limpopo, South Africa, that had established structural design as a factor for microbial ingress (Amadi et al., 2020; Durowoju, et al., 2018). Correlation analysis (Figure 4) showed a moderate positive correlation between turbidity and E. coli (r = 0.38), validating turbidity as a good surrogate for microbial risk under field conditions. In addition, the low positive correlation values for conductivity vs. pH (r = 0.34), temperature vs. E. coli (r = 0.17) affirm that even though such parameters are equally used, turbidity is a more common indicator. Conductivity was not correlated with E. coli (r = 0) affirms that ion composition is not a predictor of microbial contamination and is consistent with findings from multi-country studies (Brima, 2017). A boxplot (Figure 3) indicated the range of E. coli by source type. Dug wells contained the greatest median contamination, followed by hand pump boreholes. Mechanized boreholes contained minimal or no contamination, as this illustrates the protective advantage of deeper and closed systems. The same results were observed from boreholes in Ghana and Indian groundwater wells (Kumar & Sinha, 2010). Volume analysis (Table 2) indicated that the volume of water in stored dug wells was also greatest at a maximum of 7.127 m³ and thus potentially contain larger storage reservoirs of contaminants. High volume combined with high microbial load suggests that the dug wells might be both exposure points and reservoirs of pathogens, especially for peri-urban dwellings. Table 3. Range of water quality parameters and E. coli levels across different source types, sample counts and volumes. Water source Sample analyzed (number) Physicochemical parameters E.coli (cfu/100 mL) Volume (m³) Turbidity (NTU) Conductivity (µS/cm) pH (-) Temp ( ) Dug well 13 1.08-524.0 8-1155 5.90-7.48 7.11-33.90 0-1305 0.002 - 7.127 HB 7 1.66-17.1 263-1914 5.93-7.34 27.67-30.67 0-61 0.029 - 0.078 MB 2 1.44-38.0 1086-1202 6.42-7.03 28.40-29.10 0-935 0.060 - 0.150 HB=Hand pump borehole, MB=Mechanized borehole, NTU=Nephelometric turbidity units, Temp=Temperature, (-) =No unit Table 4 presents the range of variability for water quality parameters by type of source. Dug wells showed the most variability and highest values across the majority of the parameters, indicating exposure to more than one path of contamination. Hand pump boreholes, though better protected, exhibited variable turbidity and conductivity, a reaction possibly of poor maintenance and positioning nearer the sources of contamination. Mechanized boreholes were least contaminated, supporting the worth of technological designs and greater abstraction from aquifers for purer drinking water (Wada et al., 2022). Cross-tabulation with global data (Table 5) showed that turbidity at Kumbotso and E. coli concentration are greater than those in surface and groundwater bodies in countries such as Sweden, Ethiopia, and India. Extremely polluted rivers in Ghana and Poland alone matched or even exceeded such levels of contamination (Lenart-Boroń et al., 2016; Saalidong et al., 2022). Such comparisons place the groundwater quality at Kumbotso in a perilous zone that calls for urgent action. Lastly, physicochemical and microbiological quality of groundwater in Kumbotso indicates that the quality of water differs considerably by source type, with the most compromise being in dug wells. The findings indicate that water safety depends on infrastructure, protection, and proximity to contaminant sources. They also emphasize that local conditions should be included in groundwater management planning and that emerging technologies for boreholes have solutions to reducing microbial exposure and health threats in peri-urban African settings. Kumbotso Table 4. Comparison of quality parameters of this study with other studies. Water Sources Country Physicochemical parameters References Temperature (℃) pH Turbidity (NTU) Conductivity (µS/cm) E. coli (cfu/100 mL) Ground water Ghana, Tarkwa - 5.24 – 7.85 0.01 - 1540 35 – 2751 0-8220 (Saalidong et al., 2022) Surface water Ghana, Tarkwa - 4.16 - 9.95 0.01 - 1540 – 821 0- 870 (Saalidong et al., 2022) River Poland, Trybsz 1.5 -6.20 3.8 - 7.9 - 20.6 - 233.5 0-3340 Lenart-Boroń et al., 2016 River Brazil, CP 0- 21.08 1.0 - 6.93 20.1 - 50.76 52.69 - 67.93 - (Piana & Moura, 2014) Wells Ethiopia, SG - 1.03 - 5.9 2.21 - 6.7 59.0 - 122.0 221 -750 (Birhan et al., 2023) Surface water Sweden, Göta älv. 0.8 - 18.70 - 3.3 - 16.4 - 10-567 (Sokolova et al., 2022) River Nigeria, Adada 18.0 – 37 4.66 -6.17 - 8.65 - 32.50 0 -1920 (Amadi et al., 2020) Stream Nigeria, E-N 23.8 - 28.3 7.32 - 10.2 5.0 - 44.0 11 – 352 21-269 (Tariq, 2022) Lake Iran, Urmia 8.0 - 10.0 6.56 – 7.40 0.1 - 0.8 - - (Malakootian et al., 2020) Ground water India, Moradabad 18.0 - 25.5 6.5 - 7.85 - 0.0 - 0.82 - (Kumar & Sinha, 2010) Tap water Ethiopia, shambu 7.66 - 16.26 6.33 – 8.63 - 119 – 212 4 – 33 (Garoma et al., 2018) Groundwater KSA, Najran - 7.12 - 7.82 - 339.0 - 536.0 - (Brima, 2017) River South Africa, Limpopo - 7.76 - 0.25 0.1 - 0.723 83.47 - 8.92 0 – 1 ( Durowoju, et al., 2018) Ground water India, Jharkhand 27.0 - 28.6 6.5 - 6.8 - 176.5 – 371 - (Kerketta et al., 2013) River India, Jharkhand 26.0 – 31.0 6.5 - 6.8 - 0.0 – 397 - (Kerketta et al., 2013) Tap water India, Ranchi - 27.2 6.5 - 7.1 - 0.0 – 115 - (Kerketta et al., 2013) Tap water Ethiopia, Nekemte, 15.0 - 20.5 6.5 - 6.82 0.0 - 12 0.0 - 70.0 0 – 12 (Duressa et al., 2019) Ground water Ethiopia, Jimma 22.79 – 24 6.5 - 7.85 0.0 - 1.87 46.42 - 366.93 9 – 424 (Yasin et al., 2015) Ground water Anambra, Nigeria 0 - 28.0 6.5 - 7.174 0.0 - 1.349 0.0 - 10.185 - (Eboagu et al., 2019) Spring Malaysia, Pahang 0 - 27.19 7.95 - 8.02 0.77 - 4.94 0.5 - 0.13 - (Sulaiman et al., 2016) SG=South Gondar, CP=Cascavel, Paraná, E-N=Eggon, Nasarawa, KSA=Kingdom of Saudi Araba 3.2. Socio-demographic data Socio-demographic household attributes of Kano State, Nigeria, Kumbotso households are critical to the delivery of background information on groundwater consumption patterns and the subsequent health hazards, such as in Table 5. In this study, 162 respondents were interviewed, of which 56 (34.6%) were male and 106 (65.4%) were female, depicting increased reliance on female respondents, most likely due to their hospitality nature in household management and water procurement. Age distribution showed a leading cluster of young, with 81 (50.0%) aged 12-17 years, 51 (31.5%) aged 18-28 years, and 30 (18.5%) aged 28-50 years, indicating youth and children to predominate in water activities. Evidence of the strong demographic factors influencing the selection of water sources and susceptibility to contact with contamination is revealed in the highly significant p-values (0.0000) for sex and age groups, a pattern that reflects household practice in sub-Saharan Africa (Graham et al., 2021). Table 5. Demographic distribution of household characteristics with statistical significance. Household Characteristics Overall p- Value Alternatives Frequency % Gender Male 56 34.6% 0.0000 Female 106 65.4% Age 28-50 30 18.5% 0.0000 18-28 51 31.5% 12-17 81 50.0% The gender imbalance, where the female population comprises two-thirds of the sample, is in line with the culture in which girls and women are conventionally occupied in fetching water, thus exposing them to more polluted sources such as Dug Wells (DW) (Wada et al., 2022). This is particularly the case when E. coli numbers in DW (up to 1304 cfu/100 mL), as indicated in Table 2. Dominance of respondents aged 12-17 also indicates the vulnerability of school-age children, which may be unaware of water safety hygiene, hence increasing the tendency towards health risk like diarrhea and cholera (Victor et al., 2025). The statistical validity of such demographic measures means interventions are to be directed at such populations in order to stem the transmission of waterborne diseases. Figure 5, a bar graph of the disease proportions, illustrates the health consequence that results from the utilization of water points, where DW users have the highest incidence of water-borne disease (approximately 50%), followed by Hand Pump Boreholes (HB) (approximately 25%), and Mechanized Boreholes (MB) (below 10%). This gradient also supports the postulate that deeper sources like MB are safer because of reduced contact with surface contamination, an outcome also consistent with reduced E. coli medians for HB and MB (Figure 3). The Chi-square test result in Table 6 also confirms a significant association between main water source and waterborne disease (χ² = 41.73, p = 8.66E-10), that DW use increases risk of sickness significantly. This association is also likely to be attributed to DW being near sanitation facilities, i.e., pit latrines, that are prevalent in Kumbotso poor communities (Gwenzi et al., 2023). Table 6: Chi-square test results for associations between water quality factors and health outcomes. Test Chi2_Statistic DF p_value Water_Source_Protected vs Toilet_Facility 0.52597403 2 0.76875188 Primary_Water_Source vs Water_Related_Illness 41.7338997 2 8.6616E-10 Logistic regression analysis (Table 7) provides additional data on predictors of water-related illness. coli had an odds ratio of 1.47 (p = 0.29), which represents a positive but non-significant association with disease, perhaps as a consequence of sample size constraint or residual confounding variables such as the distance to a sanitation facility (Victor et al., 2025). Temperature and conductivity contributed marginally (odds ratios 0.20 and 0.15, respectively), with non-significant p-values, which represents their contribution secondary to microbial contamination. The Chi-square test between Water_Source_Protected and Toilet_Facility (χ² = 0.53, p = 0.77) is not significant, showing water source protection might not be affected by type of toilet facility but requiring more studies with larger sample sizes. Table 7: Logistic Regression Analysis of Water Quality Parameters as Predictors of Water-Related Illness Incidence Estimate Std. Error z value Pr(>|z|) Odds_Ratio -1.92150491 10.8567846 -0.17698656 0.85951895 0.1463865 0.01396281 0.01117733 1.24920743 0.21158922 1.01406074 0.00200915 0.00153591 1.3081147 0.19083441 1.00201117 -1.58491553 1.60426111 -0.98794113 0.32318148 0.2049651 0.38268002 0.36352317 1.05269775 0.29247954 1.4662088 -0.00153347 0.00148774 -1.03073965 0.30266293 0.9984677 The correlation of socio-demographic information to results on water quality (Table 2) shows strong agreement between household characteristics and exposure to contamination. The predominant percentage of females (65.4%) is linked to their greater frequency of consumption of DW, which recorded the widest range in E. coli (0-1305 cfu/100 mL) and turbidity (1.08-524.0 NTU), as indicated in Table 3. This exposure is extremely high in the age group 12-17 years (50.0%), which tends to fetch water from such polluted sources on a daily basis, thereby exposing them to waterborne disease. The elevated turbidity-E. Escherichia coli correlation (r = 0.38, Fig. 4) further deserves this because particulate matter in DW probably supports pathogen carriage, as uncovered under rainy season sampling (Sokolova et al., 2022). Comparative studies with other studies aligns with these findings. Incidence of illnesses among DW's users (50%) is higher than a reported incidence in peri-urban Ethiopia, where seasonal patterns of water use previously contaminated water (Aydamo et al., 2024). The demographic skew toward younger members indicates a pattern observed in flood-prone Northwest Ethiopia, in which children dominated as water fetchers and were being exposed to health risk more (Birhan et al., 2023). Absence of dramatic toilet facility effect is a deviation from Hosanna Town research, where proximity to sanitation facilities was a major determinant of water quality (Aydamo et al., 2024), which suggests that Kumbotso's contamination is more likely to be by open defecation and runoff than facility design. Marriage of socio-demographic information with physicochemical and biological results provides an additional sketch of Kumbotso groundwater quality concerns. Station-specific data in Table 2, for example, Station 4 (DW) 0.002 m³ and 861 cfu/100 mL, illustrates that low-volume wells are highly susceptible to contamination due to inadequate dilution and the position of surrounding contaminated zones (Dey et al., 2024). Station 14 (DW) 7.127 m³ and 92 cfu/100 mL, however, illustrates that more volumes can minimize but not eradicate damage. Figure 2 scatter plot and Figure 4 heatmap verify the turbidity-E. coli relationship, where higher colour temperatures represent higher associations since necessarily there would be with microbial particle adsorption (Sokolova et al., 2022). Figure 3 box plot illustrates DW degree of contamination, while comparisons in Table 4 outline Kumbotso's turbidity (8.2-1914.0 NTU) and E. coli (0-1305 cfu/100 mL) was above most of the world ranges and suggests serious local contamination (Birhan et al., 2023). Statistical calculations (Tables 7 and 8) show the health burden, where ingestion of DW drives disease occurrence, particularly in vulnerable populations. Non-significant logistic regression results for E. coli (p = 0.29) suggest that further sampling is necessary to uncover sanitation practice variation (Victor et al., 2025). Moderate turbidity-E. Coli correlation (r = 0.38) and weak conductivity-turbidity correlation (r = -0.27) concur with rainy season dilution effects (Saalidong et al., 2022). They validate sub-Saharan Africa's urbanization-driven pollution dynamics, in which pit latrines and waste disposal accelerate groundwater contamination (Back et al., 2018). Overall, the presentation and findings document a multifaceted groundwater quality crisis in Kumbotso, driven by high turbidity (8.2-1914.0 NTU), changing E. coli levels (0-1305 cfu/100 mL), and variable amounts (0.002-7.127 m³) with highest contamination measured by DW. Socio-demographic data highlight the susceptibility of women (65.4%) and adolescents aged 12-17 years (50.0%), who are disproportionately exposed to DW, with a connected 50% disease rate. Statistical inference confirms a highly significant water source-disease association (χ² = 41.73, p = 8.66E-10), yet the E. coli health impact remains to be established through further inquiry. The result highlights the necessity of WASH interventions, including well sealing, sanitation system upgrading, and education, to protect public health and promote sustainable development goals in this rapidly growing urban population. Conclusion The study has thoroughly explored the physicochemical, biological, and socio-demographic factors influencing the use of groundwater in Kumbotso, identifying the relevant issues on safety and sustainability of obtainable water sources. Collecting laboratory analysis and survey results at the community level underscored the frequency of dug well contamination, and to some extent, hand pump boreholes. Physicochemical turbidity and conductivity measurements and E. coli counts, were more likely to exceed WHO recommendations in the majority of instances, particularly from unprotected and common sources. Results reaffirm that microbial risks are inevitably associated with filthy conditions, poor infrastructure, and unhygienic handling of water. Socio-demographic determinants of low literacy level, large household size, and limited access to toilets were significantly linked with contaminant pathways. Open defecation, open dumping, and poor environmental sanitation were population practices that were differentiated with high waterborne disease rates, which were led by diarrhea and typhoid. The gap between self-reported water quality and laboratory results suggests the necessity for regular awareness campaigns and behavior change. The turbidity-microbial contamination correlation supports the validity of the use of visual signs as a field-level health hazard indicator, especially in resource-constrained environments. Logistic regression, though not statistically significant, showed that turbidity and E. coli are predictors of the health hazard. The volumetric data further showed that dug wells, though possess large storage volume, are long-term safety risks where there is neither community nor structural management. Generally, improving groundwater security in Kumbotso will entail a combination of technical improvement i.e., upgrading to improved boreholes and behavior-change-enabling WASH interventions. Risk communication, education, and enforcement of sanitation requirements will take center stage in the future. Such a hybrid strategy is desirable not only to reduce disease burden but also to ensure long-term availability of safe water, in line with national policy and the UN Sustainable Development Goals. Declarations Ethics approval and consent to participate The study was reviewed and approved by the Kano State Ministry for Water resources, Rural Water Supply and Sanitation Agency Kano State (RUWASA) Research Ethics Committee prior to data collection. Informed consent was obtained from all participants involved in the study. Consent for publication All participants gave their consent for the publication of anonymized data and results derived from the study. Availability of data and materials All data supporting the findings of this study are fully included within the manuscript. Clinical trial number Not applicable. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Author contributions Shu’aibu Ibrahim Ibrahim: Method, Formal analysis, Project administration, Data curation, Writing - review & editing. Bello Gwadabe Ahmad: Methodology, Supervision, Software, Investigation, Writing - original draft. Awoke Guadie: Writing - review & editing References Angelakis AN, Capodaglio AG, Passchier CW, Valipour M, Krasilnikoff J, Tzanakakis VA, Dercas N (2023) Sustainability of water, sanitation, and hygiene: from prehistoric times to the present times and the future. Water 15(8):1614. https://doi.org/10.3390/w15081614 Aydamo AA, Gari R, S., Mereta ST (2024) Seasonal variations in household water use, microbiological water quality, and challenges to the provision of adequate drinking water: A case of peri-urban and informal settlements of Hosanna Town, Southern Ethiopia. Environ Health Insights 18. https://doi.org/10.1177/11786302241238940 Back JO, Rivett MO, Hinz LB, Mackay N, Wanangwa GJ, Phiri OL, Kalin RM (2018) Risk assessment to groundwater of pit latrine rural sanitation policy in developing country settings. 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California State University, Sacramento Saalidong BM, Aram SA, Otu S, Lartey PO (2022) Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems. PLoS ONE 17(11):1–17. https://doi.org/10.1371/journal.pone.0262117 Seth SM (2003) Human impacts and management issues in arid and semi-arid regions. Int Contrib Hydrogeol 23:289–341. https://doi.org/10.1007/978-3-642-59354-0_14 Sokolova E, Ivarsson O, Lilliestrom A et al (2022) Data-driven models for predicting microbial water quality in the drinking water source using E. coli monitoring and hydrometeorological data. Sci Total Environ 802:149798. https://doi.org/10.1016/j.scitotenv.2021.149798 Alao JO, Otorkpa OJ, Ayejoto DA, Saqr AM (2025) Assessing the Community Knowledge on Waste Management Practices, Drinking Water Source Systems, and the Possible Implications on Public Health Systems. Clean Waste Syst 100295. https://doi.org/10.1016/j.clwas.2025.100295 Tariq M (2022) Assessing the impact of water contamination events and socio-demographic drivers on the willingness to pay for improved water quality . https://doi.org/10.1080/09593330.2020.1815860 Victor R, Adebayo A, Okeke C (2025) Urbanization and groundwater pollution in sub-Saharan Africa: A multi-country analysis. Water Resour Res 61(2):123–135. https://doi.org/10.1002/wrcr.2025.123 Wada F, Sulaimon A, Mohammed K (2022) WASH practices and their impact on water quality in North-Central Nigeria. J Water Sanitation Hygiene Dev 12(4):598–612. https://doi.org/10.2166/washdev.2022.040 Wada OZ, Olawade DB, Oladeji EO, Amusa AO, Oloruntoba EO (2022) School water, sanitation, and hygiene inequalities: A bane of sustainable development goal six in Nigeria. Can J Public Health 113(4):622–635. https://doi.org/10.17269/s41997-022-00633-9 Additional Declarations The authors declare no competing interests. Supplementary Files image1.png Graphical Abstract Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7376778","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Case Report","associatedPublications":[],"authors":[{"id":500728605,"identity":"64c54e2d-61d5-4905-8b5c-11ef2f064252","order_by":0,"name":"Ibrahim Ibrahim Shu'aibu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYBACNgYeIGlwgIFNgoGBmcGAQQ4keuABKVqMwVoS8NrDA1bDwADWwsCQ2ADi49PCx3/2mMSPgjuJfdK9Dz8XFGxLnx92+CHQFjs53QYcDpPIS5PsMXiW2CZz3Fh6hsHt3I230wyAWpKNzQ7g0sJjJsFjcDixTSKNQZoHpGV2AkjLgcRtuLTwnzGT/APRwvwbqCXdcHb6B/xaGHLMpKG2sIFsSZCXziFgi0SOsbWMwTPjNpljbNZALYYbpHMKDiQY4PaLfP8Zw5tv/tyRnT+7jfk2z5/b8vKz0zd/+FBhJ4dLCyYwAKs0IFY52N4GUlSPglEwCkbBSAAA2ntdADul3HsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0009-0005-9866-4466","institution":"Water Policy, Institute for Water and Energy Sciences including Climate Change, Pan African University B.P. 119, 13000 Tlemcen, Algeria","correspondingAuthor":true,"prefix":"","firstName":"Ibrahim","middleName":"Ibrahim","lastName":"Shu'aibu","suffix":""},{"id":500731597,"identity":"5a3e0157-bd3c-4a9c-a9d0-04013e273319","order_by":1,"name":"Bello Gwadabe Ahmad","email":"","orcid":"","institution":"Department of Industrial Chemistry, Faculty of Natural Sciences, Bayero University Kano, PMB 3011 Kano, Nigeria","correspondingAuthor":false,"prefix":"","firstName":"Bello","middleName":"Gwadabe","lastName":"Ahmad","suffix":""},{"id":500731598,"identity":"f03fa8f1-7ce8-4a26-b589-8d34c34174f5","order_by":2,"name":"Awoke Guadie","email":"","orcid":"https://orcid.org/0000-0003-2753-4216","institution":"Department of Biology, College of Natural Sciences, Arba Minch University, 21 Arba Minch, Ethiopia","correspondingAuthor":false,"prefix":"","firstName":"Awoke","middleName":"","lastName":"Guadie","suffix":""}],"badges":[],"createdAt":"2025-08-14 20:37:26","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7376778/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7376778/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89386427,"identity":"1cb266b1-791f-425e-93f2-020a1e6d1415","added_by":"auto","created_at":"2025-08-19 12:35:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":245258,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the study area (A) Africa, (B) Nigeria, and (C) Kumbotso local government area.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/9c42b783d7dd4a64685574e2.png"},{"id":89389875,"identity":"2751b249-8cf7-4bed-99c4-7f2f2ad5ca94","added_by":"auto","created_at":"2025-08-19 12:51:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63936,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot with trend line showing E. coli levels (cfu/100mL) versus turbidity (NTU) across Dug well, Hand pump borehole, and Mechanized borehole sources.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/8eec8630eaea3646eb7afa93.png"},{"id":89386425,"identity":"e310a1b9-0c72-41cd-b7bf-5240a44feb87","added_by":"auto","created_at":"2025-08-19 12:35:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 4\u003c/strong\u003e Correlation heatmap of water quality parameters (Turbidity, Conductivity, pH, Temperature, E. coli) in Kano\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/25132db41881396eac9e7865.png"},{"id":89386424,"identity":"e2aad905-f1f3-4c45-8df0-b2524122dbc4","added_by":"auto","created_at":"2025-08-19 12:35:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":62607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFig. 3\u003c/strong\u003e Box plot illustrating E. coli concentrations (cfu/100mL) across primary water sources, including Dug well, Hand pump borehole, and Mechanized borehole, highlighting contamination variations.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/7b4ac632f8ac0f1540745e8c.png"},{"id":89386433,"identity":"5b866111-877c-4404-b708-d52ae2e6b1b6","added_by":"auto","created_at":"2025-08-19 12:35:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":70663,"visible":true,"origin":"","legend":"\u003cp\u003eBar chart displaying the proportion of water-related illness (Yes/No) across Dug well, Hand pump borehole, and Mechanized borehole primary water sources, highlighting health impacts.\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/7bca239e04b148df77301f84.png"},{"id":89391468,"identity":"f4b09cf3-091d-446f-8a46-e96a34db8d61","added_by":"auto","created_at":"2025-08-19 13:08:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1495886,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/ecdd66ae-da81-4dcf-aadd-828a08d897b2.pdf"},{"id":89386423,"identity":"158ddbec-3198-47e6-939a-214d49b407ea","added_by":"auto","created_at":"2025-08-19 12:35:56","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":233255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Abstract\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7376778/v1/e23a3a3cd48970ba9a2aa006.png"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eAssessing Hygiene Practices and Microbial Risks in Groundwater Sources: A Case Study of Kumbotso, Nigeria\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Highlights","content":"\u003cp\u003e\u0026bull; Groundwater in Kumbotso is highly contaminated, especially dug wells, posing significant public health risks.\u003c/p\u003e\u003cp\u003e\u0026bull; Dug well users self-reported 50% illness; females and youth age (\u0026ndash;) are most affected.\u003c/p\u003e\u003cp\u003e\u0026bull; Turbidity correlates with E. coli (r\u0026thinsp;=\u0026thinsp;0.38); deeper boreholes offer superior microbial protection.\u003c/p\u003e\u003cp\u003e\u0026bull; Targeted WASH interventions, source protection, and public education are critical for safe groundwater.\u003c/p\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eGroundwater is an essential resource for drinking water and irrigation, especially in areas of low surface water resources. Over 2.5\u0026nbsp;billion people throughout the world are believed to utilize groundwater for their daily hygiene and sanitation needs (Citaristi, 2022). Hygiene and sanitation are crucial for human health, community wellness, and sustainable living (Angelakis et al., 2023). However, groundwater pollution due to poor home sanitation and improper waste management has serious health along with environmental impacts, particularly in the developing world (Alao et al., 2025).\u003c/p\u003e\u003cp\u003eThe increasing stresses of urbanization, population, and lack of effective sanitary infrastructure have subjected groundwater resources to increased pollution from industrial, agricultural, and domestic sources (Victor et al., 2025). Groundwater pollution in the majority of regions is strongly impacted by human activities such as poor waste disposal, use of vault latrines, and agricultural runoff with high levels of fertilizers and pesticides (Graham \u0026amp; Polizzotto, 2013).\u003c/p\u003e\u003cp\u003eKano state, a land with issues of maintaining sanitary conditions and groundwater quality. It has experienced fast urbanization and absence of waste treatment, causing environmental degradation and water contamination. The region has tropical wet and dry climatic conditions, with the wet season usually occurring for four to five months within the period from April to October (Seth, 2003). In low-income suburbs, due to a shortage of public sanitation facilities, there is open defecation and improper waste disposal, hence increasing the microbial and chemical contamination of groundwater (Gwenzi et al., 2023). The introduction of pathogens such as \u003cem\u003eEscherichia coli\u003c/em\u003e and fecal coliforms into groundwater has been strongly linked to the use of unimproved sanitation infrastructure, where human feces enter the aquifers (Aydamo et al., 2024). In sub-Saharan Africa's highly populated areas, the groundwater contamination is high (Islam et al., 2025) because of the short distance between the water points and the sanitation facilities. Similarly, in Nigeria, widespread utilization of poorly constructed pit latrines and insufficient wastewater treatment plants have been major contributors to groundwater pollution (Barati et al, 2022).\u003c/p\u003e\u003cp\u003eThe health consequences of consuming contaminated groundwater are colossal. Water-borne diseases such as diarrhea, cholera, and dysentery are rampant among populations that rely on contaminated water sources (Jabbar, 2020). Children under the age of five years, especially in rural and peri-urban populations, are most vulnerable, accounting for a tremendous proportion of childhood morbidity and mortality due to these infections (Tariq, 2022). To improve access to drinkable drinking water and enhanced public health outcomes in Nigeria, effective implementation of water, sanitation, and hygiene (WASH) programs is required (Wada et al., 2022). It has been revealed by studies that residual chlorine plays an important role in shaping microbial populations by enabling bacterial colonization with poor management, indicating the complexity of groundwater contamination (Javed et al., 2025).\u003c/p\u003e\u003cp\u003eGroundwater quality has been found to be affected by local\u0026ensp;sanitation practices and geographical considerations in different parts of Nigeria. For instance, research has shown that there are high concentrations of ammonia and dissolved organics in borehole water located in proximity of waste deposition areas showing the risk\u0026ensp;for contamination due to some human activities around waste disposal areas (Dey et al, 2024). Microbiological pollutants like coliform are nil in some cases while chemical factors like pH, hardness and heavy metals surpass the maximum allowable levels, indicating the impact of natural\u0026ensp;and man-made activities (Amadi et al., 2020: Organization, 2010). The trends also highlight the importance of context-dependent studies such as the present work in Kumbotso to examine how groundwater microbiological and physicochemical quality is caused by daily cleanliness habits, sanitation facilities, and water handling patterns.\u003c/p\u003e\u003cp\u003eThe current study aims to find out groundwater pollution in Kumbotso, Nigeria, through an analysis of the physicochemical and microbial water quality of borehole water and examination of hygiene and sanitation in the home. The study, to this end, carried out socio-demographic interviews and examined water samples against the parameters of \u003cem\u003eE. coli\u003c/em\u003e, turbidity, conductivity, pH, and temperature and also examined behavior around water storage, container, and environmental sanitation.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Study area\u003c/h2\u003e\u003cp\u003eKumbotso is one of the forty-four local government area of Kano State, with an area coverage of about 152 km\u003csup\u003e2\u003c/sup\u003e. The area lies between Latitudes 12\u0026deg;21'6.457\"N to 12\u0026deg;6'7.808\"N and Longitudes 9\u0026deg;28'0.944\"E to 10\u0026deg;15'28.924\"E (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the National Bureau of Statistics (2019) census, Kano state has a population of over 14\u0026nbsp;million, of which 409,500 resides in Kumbotso. The majority of the study area is dominated by residential buildings, and other activities (agricultural, institutional, commercial, and educational).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Sampling points\u003c/h2\u003e\u003cp\u003eThe criteria for selecting sampling points were based on population density, areas with low sanitary reports or anthropogenic activities, and a historic record of water-borne diseases in the areas. As mentioned earlier that Kumbotso was famous for its manufacturing industries at Challawa, and rapid population growth, therefore it was significant to see the water quality in such areas.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Google map was scanned and digita99999lized to create the research area's base map. Data mapping and analysis for the assessment of groundwater quality are done using QGIS\u0026thinsp;~\u0026thinsp;10. In order to facilitate the collection of groundwater samples from the area and to evaluate the sanitary methods employed in the retrieval, conveyance, and storage of drinking and household water, the territory was divided into 22 grids (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCommunities were selected and 22 water sources were sampled (including boreholes, dug wells and drilled wells) in August 2023, which corresponded to the rainy season (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The vials used to collect the samples were made of high-density polyethylene due to chemically resistant, non-reactivity and prevention of contamination and ensuring accurate sample integrity (Rurouni, 2024). Parallel with ground water sample collection, socio-demographic data including interviews, observations and focus group discussions (FGDs) were done. The interviews provided in-depth insights into individual behaviors and attitudes toward water usage and sanitation. The FGDs were designed to be interactive discussions among community members, revealing collective practices, challenges, and environmental attitudes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Sample analysis\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Physicochemical and microbial analysis\u003c/h2\u003e\u003cp\u003eConductivity, pH, temperature and turbidity measurements were carried out on-site at the sample collection location. The conductivity, turbidity, and temperature of the water samples were measured using conductivity meter (PT157, United Kingdom), portable turbidity meter (PTH092, Ireland), and pH (PT155, United Kingdom), respectively. Prior to conducting measurements, the pH meter was calibrated using three standard solutions, with pH values of 4.0, 6.0, and 10.0. After dipping the pH probe into the water sample and holding it for a few minutes to obtain a stable reading, the pH of each sample was determined. The process is applied for conductivity and temperature for all sample readings. The research employed a manual string method to estimate water source volumes in Kumbotso, Kano State, Nigeria. This involved lowering a weighted string into each well or borehole (Dug Well, Hand Pump Borehole, Mechanized Borehole) to measure the Total Drilled Depth (TDD) and Static Water Level (SWL) using a calibrated tape measure as the measuring device. The diameter was measured directly at the wellhead using a vernier caliper as the diameter measuring device.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:h=TDD-SWL\\)\u003c/span\u003e\u003c/span\u003e 1\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V=\\pi\\:\\times\\:{r}^{2}Xh\\)\u003c/span\u003e\u003c/span\u003e 2\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Volume\\:in\\:gallons=V\\:\\left({m}^{3}\\right)\\times\\:264.172\\)\u003c/span\u003e\u003c/span\u003e 3\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eV\u003c/strong\u003e\u003cp\u003eVolume\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eh\u003c/strong\u003e\u003cp\u003eWater Column Height\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTDD\u003c/strong\u003e\u003cp\u003eTotal Drilled Depth\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eSWL\u003c/strong\u003e\u003cp\u003eStatic Water Level\u003c/p\u003e\u003c/p\u003e\u003cp\u003eThe Membrane Filtration (MF) method was employed to establish Escherichia coli (\u003cem\u003eE. coli\u003c/em\u003e) levels in water samples. Field sterilization is attained using Methanol (Methyl Alcohol). The processes involve pouring methanol into the stainless-steel sampling cup, igniting it to emit formaldehyde gas, and subsequently inverting the filter funnel and silicone rubber base components into the sampling cup to subject them to the sterilizing gas. This process was repeated whenever there is to be an analysis of a new sample of water. An accurately measured amount of the sample (100 mL or less if the source is highly contaminated) was filtered through a Membrane Filtration Unit (MFU) (Potatest 2, 2011). The MFU was connected to a vacuum hand pump, which created suction to pass the sample through a sterile filter having pores of 0.45-\u0026micro;m. This filter allowed water to pass through while retaining bacteria on its surface. On filtration, the membrane filter was carefully placed over an absorbent pad in a sterile petri dish. This pad had earlier been saturated with Membrane Lauryl Sulphate Broth (MLSB), the liquid culture medium used to provide nutrients for the growth of the bacteria without allowing non-target organisms. The petri dish was then incubated for 20\u0026ndash;24 hours at 37\u0026deg;C in a portable incubator. After incubation, the count of blue-colored colonies, each with a small gas bubble, was noted. Results were expressed as cfu/mL per 100 mL. The procedure also enabled measurement of the MPN of \u003cem\u003eE. coli\u003c/em\u003e in 100 mL of water (Potatest 2, 2011: Environment Agency, 2009: Ayres, \u0026amp; Mara, 1996).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eWHO Classification of Drinking Water Quality Based on \u003cem\u003eE. coli\u003c/em\u003e Contamination Risk Levels. (Odonkor \u0026amp; Mahami, 2020)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRisk Level\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e Count (cfu/100 mL)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSafe\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo \u003cem\u003eE. coli\u003c/em\u003e detected; water is safe for drinking\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLow Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow probability of contamination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedium Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIncreased likelihood of contamination\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHigh Risk\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh risk; unsafe for consumption without treatment\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Statistical analysis\u003c/h2\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1. Physicochemical and Biological Characteristics of Water Sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePhysicochemical and microbial examination of groundwater water quality at Kumbotso revealed widespread variation in physicochemical and microbiological characteristics across various water sources like Dug wells (DW), Hand Pump Boreholes (HB), and Mechanized Boreholes (MB). Parameters analyzed include turbidity, conductivity, pH, temperature, and levels of Escherichia coli (E. coli). These differences align with differences in heterogeneity of environmental sanitation, handling modes of water, and wellhead protection conditions observed in the field.\u003c/p\u003e\n\u003cp\u003eTable 2. Water quality parameters, microbial levels, source types, and estimated water volumes across sampling stations.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSamples\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePhysicochemical parameters\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSource type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"6\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eVolume (m\u0026sup3;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurbidity (NTU)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eConductivity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003epH (-)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTemp (\u003csup\u003e0\u003c/sup\u003eC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eE. coli\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(cfu/100 mL)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1914.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e524.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e851.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.8986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.7932\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e464.4842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e110.70995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.44181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.616\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.762\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e577\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.456\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.060\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.923\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e354\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.931\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStation 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNTU=Nephelometric turbidity units, Temp=Temperature, (-)=No unit, cfu=Colony-forming uni\u003c/p\u003e\n\u003cp\u003eTurbidity values varied from 1.08 NTU to 524 NTU, greater than WHO\u0026apos;s highest acceptable turbidity of 5 NTU in most cases, especially from dug wells. High turbidity is typical with suspended solids, poor well cover, or surface runoff contamination (Dey et al., 2024). Station 13 had the highest turbidity value at 524 NTU and also had abnormally high E. coli counts (920 cfu/100 mL), where it was evident that there existed a strong association between appearance and microbial risk. This evidence is corroborated by the findings from groundwater analysis in densely populated Nigerian suburbs (Aydamo et al., 2024).\u003c/p\u003e\n\u003cp\u003eConductivity ranged from 8.2 \u0026micro;S/cm to 1914 \u0026micro;S/cm, indicating extensive fluctuation in dissolved ionic concentration. Elevated conductivity, especially in hand pump and mechanized boreholes, indicates possible contamination with anthropogenic and inorganic salt sources and activities in the environs (Hu et al., 2023). Elevated conductivity at Stations 7 and 11 indicates possible leaching from material below ground or domestic effluent. pH values at all the stations were between 5.92 and 7.48. Although the range is, on average, within the acceptable WHO range of 6.5\u0026ndash;8.5, some slight acidity in some locations\u0026mdash;namely, dug wells\u0026mdash;can influence microbial survival and mobility of metals. Slight acidity of pH 5.92 at Station 19, together with high concentration of E. coli (354 cfu/100 mL), can facilitate microbial survival and indicates the importance of pH in microbial ecology (Angelakis et al., 2023). The temperatures were also very similar, ranging from 27\u0026deg;C to 34\u0026deg;C, and averaging 29.25\u0026deg;C. Although temperature in itself may not physically affect potability, it has an influence on the proliferation of microbes as well as on oxygen solubility. The high temperature of all samples, characteristic of the tropical climate of the area, may enhance the proliferation of microbes in optimal conditions (Victor et al., 2025).\u003c/p\u003e\n\u003cp\u003eE. coli contamination, a test for determining microbial safety, was beyond astronomical in all but one of the stations. It ranged from 0 cfu/100 mL to more than 1300 cfu/100 mL (Station 22), and put most of the sources into WHO\u0026apos;s \u0026quot;high risk\u0026quot; category. Thirteen of 22 sources had counts greater than 100 cfu/100 mL, i.e., dug wells with a propensity to be contaminated by adjacent latrines, open defecation sites, and animal droppings. The findings conform to rural Ethiopia and Ghana where surface contact and poor sanitation are linked with microbial contamination (Birhan et al., 2023; Saalidong et al., 2022).\u003c/p\u003e\n\u003cp\u003eThe scatter plot (Figure 2) confirms a positive correlation between turbidity and E. coli concentration by sources. The dug wells clustered high on turbidity and on high levels of E. coli, and boreholes (especially MB) were low for both, which means that construction type is a factor that impacts microbial penetration. This is in agreement with earlier studies conducted in Adada, Nigeria, and Limpopo, South Africa, that had established structural design as a factor for microbial ingress (Amadi et al., 2020; Durowoju, et al., 2018).\u003c/p\u003e\n\u003cp\u003eCorrelation analysis (Figure 4) showed a moderate positive correlation between turbidity and E. coli (r = 0.38), validating turbidity as a good surrogate for microbial risk under field conditions. In addition, the low positive correlation values for conductivity vs. pH (r = 0.34), temperature vs. E. coli (r = 0.17) affirm that even though such parameters are equally used, turbidity is a more common indicator. Conductivity was not correlated with E. coli (r = 0) affirms that ion composition is not a predictor of microbial contamination and is consistent with findings from multi-country studies (Brima, 2017).\u003c/p\u003e\n\u003cp\u003eA boxplot (Figure 3) indicated the range of E. coli by source type. Dug wells contained the greatest median contamination, followed by hand pump boreholes. Mechanized boreholes contained minimal or no contamination, as this illustrates the protective advantage of deeper and closed systems. The same results were observed from boreholes in Ghana and Indian groundwater wells (Kumar \u0026amp; Sinha, 2010).\u003c/p\u003e\n\u003cp\u003eVolume analysis (Table 2) indicated that the volume of water in stored dug wells was also greatest at a maximum of 7.127 m\u0026sup3; and thus potentially contain larger storage reservoirs of contaminants. High volume combined with high microbial load suggests that the dug wells might be both exposure points and reservoirs of pathogens, especially for peri-urban dwellings.\u003c/p\u003e\n\u003cp\u003eTable 3. Range of water quality parameters and E. coli levels across different source types, sample counts and volumes.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWater source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eSample analyzed\u003c/p\u003e\n \u003cp\u003e(number)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\"\u003e\n \u003cp\u003ePhysicochemical parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eE.coli\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(cfu/100 mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eVolume (m\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003cp\u003e(NTU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConductivity\u003c/p\u003e\n \u003cp\u003e(\u0026micro;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003cp\u003e(-)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTemp\u003c/p\u003e\n \u003cp\u003e(\u0026nbsp;)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDug well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.08-524.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8-1155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.90-7.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.11-33.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-1305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.002 - 7.127\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.66-17.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e263-1914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.93-7.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.67-30.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.029 - 0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.44-38.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1086-1202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.42-7.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.40-29.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.060 - 0.150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eHB=Hand pump borehole, MB=Mechanized borehole, NTU=Nephelometric turbidity units, Temp=Temperature, (-) =No unit\u003c/p\u003e\n\u003cp\u003eTable 4 presents the range of variability for water quality parameters by type of source. Dug wells showed the most variability and highest values across the majority of the parameters, indicating exposure to more than one path of contamination. Hand pump boreholes, though better protected, exhibited variable turbidity and conductivity, a reaction possibly of poor maintenance and positioning nearer the sources of contamination. Mechanized boreholes were least contaminated, supporting the worth of technological designs and greater abstraction from aquifers for purer drinking water (Wada et al., 2022).\u003c/p\u003e\n\u003cp\u003eCross-tabulation with global data (Table 5) showed that turbidity at Kumbotso and E. coli concentration are greater than those in surface and groundwater bodies in countries such as Sweden, Ethiopia, and India. Extremely polluted rivers in Ghana and Poland alone matched or even exceeded such levels of contamination (Lenart-Boroń et al., 2016; Saalidong et al., 2022). Such comparisons place the groundwater quality at Kumbotso in a perilous zone that calls for urgent action.\u003c/p\u003e\n\u003cp\u003eLastly, physicochemical and microbiological quality of groundwater in Kumbotso indicates that the quality of water differs considerably by source type, with the most compromise being in dug wells. The findings indicate that water safety depends on infrastructure, protection, and proximity to contaminant sources. They also emphasize that local conditions should be included in groundwater management planning and that emerging technologies for boreholes have solutions to reducing microbial exposure and health threats in peri-urban African settings. Kumbotso\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Comparison of quality parameters of this study with other studies.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eWater Sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" valign=\"top\"\u003e\n \u003cp\u003ePhysicochemical parameters\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTemperature\u003c/p\u003e\n \u003cp\u003e(℃)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTurbidity\u003c/p\u003e\n \u003cp\u003e(NTU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eConductivity\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(\u0026micro;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e(cfu/100 mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGround water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGhana, Tarkwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.24 \u0026ndash; 7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01 - 1540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e35 \u0026ndash; 2751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-8220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Saalidong et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurface water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGhana, Tarkwa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.16 - 9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.01 - 1540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col start=\"39\"\u003e\n \u003cli\u003e\u0026ndash; 821\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0- 870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Saalidong et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePoland, Trybsz\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.5 -6.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8 - 7.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.6 - 233.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0-3340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLenart-Boroń et al., 2016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBrazil, CP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0- 21.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.0 - 6.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e20.1 - 50.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e52.69 - 67.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Piana \u0026amp; Moura, 2014)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthiopia, SG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.03 - 5.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.21 - 6.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e59.0 - 122.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e221 -750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Birhan et al., 2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSurface water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSweden, G\u0026ouml;ta \u0026auml;lv.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.8 - 18.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.3 - 16.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10-567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Sokolova et al., 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNigeria, Adada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.0 \u0026ndash; 37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4.66 -6.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.65 - 32.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 -1920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Amadi et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStream\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNigeria, E-N\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e23.8 - 28.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.32 - 10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e5.0 - 44.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e11 \u0026ndash; 352\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e21-269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Tariq, 2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLake\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIran, Urmia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.0 - 10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.56 \u0026ndash; 7.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1 - 0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Malakootian et al., 2020)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGround water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia, Moradabad\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e18.0 - 25.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 - 0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Kumar \u0026amp; Sinha, 2010)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTap water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthiopia, shambu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.66 - 16.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.33 \u0026ndash; 8.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e119 \u0026ndash; 212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e4 \u0026ndash; 33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Garoma et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGroundwater\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKSA, Najran\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.12 - 7.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e339.0 - 536.0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Brima, 2017)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSouth Africa, Limpopo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.76 - 0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.1 - 0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e83.47 - 8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 \u0026ndash; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e( Durowoju,\u0026nbsp;et al., 2018)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGround water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia, Jharkhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27.0 - 28.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e176.5 \u0026ndash; 371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Kerketta et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRiver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia, Jharkhand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e26.0 \u0026ndash; 31.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 6.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 \u0026ndash; 397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Kerketta et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTap water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIndia, Ranchi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003col start=\"26\"\u003e\n \u003cli\u003e- 27.2\u003c/li\u003e\n \u003c/ol\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 7.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 \u0026ndash; 115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Kerketta et al., 2013)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTap water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthiopia, Nekemte,\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e15.0 - 20.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 6.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 - 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 - 70.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 \u0026ndash; 12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Duressa et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGround water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEthiopia, Jimma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e22.79 \u0026ndash; 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 - 1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e46.42 - 366.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9 \u0026ndash; 424\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Yasin et al., 2015)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGround water\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAnambra, Nigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 - 28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.5 - 7.174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 - 1.349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.0 - 10.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Eboagu et al., 2019)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMalaysia, Pahang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0 - 27.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.95 - 8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.77 - 4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e0.5 - 0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e(Sulaiman et al., 2016)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSG=South Gondar, CP=Cascavel, Paran\u0026aacute;, E-N=Eggon, Nasarawa, KSA=Kingdom of Saudi Araba\u003c/p\u003e\n\u003ch2\u003e3.2. Socio-demographic data\u003c/h2\u003e\n\u003cp\u003eSocio-demographic household attributes of Kano State, Nigeria, Kumbotso households are critical to the delivery of background information on groundwater consumption patterns and the subsequent health hazards, such as in Table 5. In this study, 162 respondents were interviewed, of which 56 (34.6%) were male and 106 (65.4%) were female, depicting increased reliance on female respondents, most likely due to their hospitality nature in household management and water procurement. Age distribution showed a leading cluster of young, with 81 (50.0%) aged 12-17 years, 51 (31.5%) aged 18-28 years, and 30 (18.5%) aged 28-50 years, indicating youth and children to predominate in water activities. Evidence of the strong demographic factors influencing the selection of water sources and susceptibility to contact with contamination is revealed in the highly significant p-values (0.0000) for sex and age groups, a pattern that reflects household practice in sub-Saharan Africa (Graham et al., 2021).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5.\u003c/strong\u003e Demographic distribution of household characteristics with statistical significance.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eHousehold Characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 383px;\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u003cem\u003ep- Value\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAlternatives\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eFrequency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e34.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e65.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e28-50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e18-28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e31.5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e12-17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e50.0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 128px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe gender imbalance, where the female population comprises two-thirds of the sample, is in line with the culture in which girls and women are conventionally occupied in fetching water, thus exposing them to more polluted sources such as Dug Wells (DW) (Wada et al., 2022). This is particularly the case when E. coli numbers in DW (up to 1304 cfu/100 mL), as indicated in Table 2. Dominance of respondents aged 12-17 also indicates the vulnerability of school-age children, which may be unaware of water safety hygiene, hence increasing the tendency towards health risk like diarrhea and cholera (Victor et al., 2025). The statistical validity of such demographic measures means interventions are to be directed at such populations in order to stem the transmission of waterborne diseases.\u003c/p\u003e\n\u003cp\u003eFigure 5, a bar graph of the disease proportions, illustrates the health consequence that results from the utilization of water points, where DW users have the highest incidence of water-borne disease (approximately 50%), followed by Hand Pump Boreholes (HB) (approximately 25%), and Mechanized Boreholes (MB) (below 10%). This gradient also supports the postulate that deeper sources like MB are safer because of reduced contact with surface contamination, an outcome also consistent with reduced E. coli medians for HB and MB (Figure 3). The Chi-square test result in Table 6 also confirms a significant association between main water source and waterborne disease (\u0026chi;\u0026sup2; = 41.73, p = 8.66E-10), that DW use increases risk of sickness significantly. This association is also likely to be attributed to DW being near sanitation facilities, i.e., pit latrines, that are prevalent in Kumbotso poor communities (Gwenzi et al., 2023).\u003c/p\u003e\n\u003cp\u003eTable 6: Chi-square test results for associations between water quality factors and health outcomes.\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 295px;\"\u003e\n \u003cp\u003eTest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eChi2_Statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003ep_value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 295px;\"\u003e\n \u003cp\u003eWater_Source_Protected vs Toilet_Facility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.52597403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.76875188\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 295px;\"\u003e\n \u003cp\u003ePrimary_Water_Source vs Water_Related_Illness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e41.7338997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e8.6616E-10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis (Table 7) provides additional data on predictors of water-related illness. coli had an odds ratio of 1.47 (p = 0.29), which represents a positive but non-significant association with disease, perhaps as a consequence of sample size constraint or residual confounding variables such as the distance to a sanitation facility (Victor et al., 2025). Temperature and conductivity contributed marginally (odds ratios 0.20 and 0.15, respectively), with non-significant p-values, which represents their contribution secondary to microbial contamination. The Chi-square test between Water_Source_Protected and Toilet_Facility (\u0026chi;\u0026sup2; = 0.53, p = 0.77) is not significant, showing water source protection might not be affected by type of toilet facility but requiring more studies with larger sample sizes.\u003c/p\u003e\n\u003cp\u003eTable 7: Logistic Regression Analysis of Water Quality Parameters as Predictors of Water-Related Illness Incidence\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"413\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eEstimate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eStd. Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003ez value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003ePr(\u0026gt;|z|)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003eOdds_Ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.92150491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e10.8567846\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.17698656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.85951895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.1463865\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.01396281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.01117733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.24920743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.21158922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.01406074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00200915\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00153591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.3081147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.19083441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.00201117\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.58491553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.60426111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.98794113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.32318148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.2049651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.38268002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.36352317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.05269775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.29247954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e1.4662088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-0.00153347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.00148774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e-1.03073965\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.30266293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e0.9984677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eThe correlation of socio-demographic information to results on water quality (Table 2) shows strong agreement between household characteristics and exposure to contamination. The predominant percentage of females (65.4%) is linked to their greater frequency of consumption of DW, which recorded the widest range in E. coli (0-1305 cfu/100 mL) and turbidity (1.08-524.0 NTU), as indicated in Table 3. This exposure is extremely high in the age group 12-17 years (50.0%), which tends to fetch water from such polluted sources on a daily basis, thereby exposing them to waterborne disease. The elevated turbidity-E. Escherichia coli correlation (r = 0.38, Fig. 4) further deserves this because particulate matter in DW probably supports pathogen carriage, as uncovered under rainy season sampling (Sokolova et al., 2022).\u003c/p\u003e\n\u003cp\u003eComparative studies with other studies aligns with these findings. Incidence of illnesses among DW\u0026apos;s users (50%) is higher than a reported incidence in peri-urban Ethiopia, where seasonal patterns of water use previously contaminated water (Aydamo et al., 2024). The demographic skew toward younger members indicates a pattern observed in flood-prone Northwest Ethiopia, in which children dominated as water fetchers and were being exposed to health risk more (Birhan et al., 2023). Absence of dramatic toilet facility effect is a deviation from Hosanna Town research, where proximity to sanitation facilities was a major determinant of water quality (Aydamo et al., 2024), which suggests that Kumbotso\u0026apos;s contamination is more likely to be by open defecation and runoff than facility design.\u003c/p\u003e\n\u003cp\u003eMarriage of socio-demographic information with physicochemical and biological results provides an additional sketch of Kumbotso groundwater quality concerns. Station-specific data in Table 2, for example, Station 4 (DW) 0.002 m\u0026sup3; and 861 cfu/100 mL, illustrates that low-volume wells are highly susceptible to contamination due to inadequate dilution and the position of surrounding contaminated zones (Dey et al., 2024). Station 14 (DW) 7.127 m\u0026sup3; and 92 cfu/100 mL, however, illustrates that more volumes can minimize but not eradicate damage. Figure 2 scatter plot and Figure 4 heatmap verify the turbidity-E. coli relationship, where higher colour temperatures represent higher associations since necessarily there would be with microbial particle adsorption (Sokolova et al., 2022). Figure 3 box plot illustrates DW degree of contamination, while comparisons in Table 4 outline Kumbotso\u0026apos;s turbidity (8.2-1914.0 NTU) and E. coli (0-1305 cfu/100 mL) was above most of the world ranges and suggests serious local contamination (Birhan et al., 2023). Statistical calculations (Tables 7 and 8) show the health burden, where ingestion of DW drives disease occurrence, particularly in vulnerable populations. Non-significant logistic regression results for E. coli (p = 0.29) suggest that further sampling is necessary to uncover sanitation practice variation (Victor et al., 2025). Moderate turbidity-E. Coli correlation (r = 0.38) and weak conductivity-turbidity correlation (r = -0.27) concur with rainy season dilution effects (Saalidong et al., 2022). They validate sub-Saharan Africa\u0026apos;s urbanization-driven pollution dynamics, in which pit latrines and waste disposal accelerate groundwater contamination (Back et al., 2018).\u003c/p\u003e\n\u003cp\u003eOverall, the presentation and findings document a multifaceted groundwater quality crisis in Kumbotso, driven by high turbidity (8.2-1914.0 NTU), changing E. coli levels (0-1305 cfu/100 mL), and variable amounts (0.002-7.127 m\u0026sup3;) with highest contamination measured by DW. Socio-demographic data highlight the susceptibility of women (65.4%) and adolescents aged 12-17 years (50.0%), who are disproportionately exposed to DW, with a connected 50% disease rate. Statistical inference confirms a highly significant water source-disease association (\u0026chi;\u0026sup2; = 41.73, p = 8.66E-10), yet the E. coli health impact remains to be established through further inquiry. The result highlights the necessity of WASH interventions, including well sealing, sanitation system upgrading, and education, to protect public health and promote sustainable development goals in this rapidly growing urban population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe study has thoroughly explored the physicochemical, biological, and socio-demographic factors influencing the use of groundwater in Kumbotso, identifying the relevant issues on safety and sustainability of obtainable water sources. Collecting laboratory analysis and survey results at the community level underscored the frequency of dug well contamination, and to some extent, hand pump boreholes. Physicochemical turbidity and conductivity measurements and E. coli counts, were more likely to exceed WHO recommendations in the majority of instances, particularly from unprotected and common sources. Results reaffirm that microbial risks are inevitably associated with filthy conditions, poor infrastructure, and unhygienic handling of water. Socio-demographic determinants of low literacy level, large household size, and limited access to toilets were significantly linked with contaminant pathways. Open defecation, open dumping, and poor environmental sanitation were population practices that were differentiated with high waterborne disease rates, which were led by diarrhea and typhoid. The gap between self-reported water quality and laboratory results suggests the necessity for regular awareness campaigns and behavior change. The turbidity-microbial contamination correlation supports the validity of the use of visual signs as a field-level health hazard indicator, especially in resource-constrained environments. Logistic regression, though not statistically significant, showed that turbidity and E. coli are predictors of the health hazard. The volumetric data further showed that dug wells, though possess large storage volume, are long-term safety risks where there is neither community nor structural management. Generally, improving groundwater security in Kumbotso will entail a combination of technical improvement i.e., upgrading to improved boreholes and behavior-change-enabling WASH interventions. Risk communication, education, and enforcement of sanitation requirements will take center stage in the future. Such a hybrid strategy is desirable not only to reduce disease burden but also to ensure long-term availability of safe water, in line with national policy and the UN Sustainable Development Goals.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was reviewed and approved by the \u003cem\u003eKano State Ministry for Water resources, Rural Water Supply and\u0026nbsp;\u003c/em\u003e\u003cem\u003eSanitation Agency Kano State (RUWASA) Research Ethics Committee\u0026nbsp;\u003c/em\u003eprior to data collection. Informed consent was obtained from all participants involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants gave their consent for the publication of anonymized data and results derived from the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data supporting the findings of this study are fully included within the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShu\u0026rsquo;aibu Ibrahim Ibrahim: Method, Formal analysis, Project administration, Data curation, Writing - review \u0026amp; editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBello Gwadabe Ahmad: Methodology, Supervision, Software, Investigation, Writing - original draft.\u003c/p\u003e\n\u003cp\u003eAwoke Guadie: Writing - review \u0026amp; editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAngelakis AN, Capodaglio AG, Passchier CW, Valipour M, Krasilnikoff J, Tzanakakis VA, Dercas N (2023) Sustainability of water, sanitation, and hygiene: from prehistoric times to the present times and the future. 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Can J Public Health 113(4):622\u0026ndash;635. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17269/s41997-022-00633-9\u003c/span\u003e\u003cspan address=\"10.17269/s41997-022-00633-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Groundwater, Sanitation, Hygiene, Pollution","lastPublishedDoi":"10.21203/rs.3.rs-7376778/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7376778/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated the link between hygiene practices and groundwater quality in Kumbotso, Nigeria, where groundwater serves as a primary drinking water source. To assess the quality of groundwater, estimate contamination from sanitation, and determine socio-demographic determinants of health risk, informing targeted interventions. Conducted in August 2023, during the rainy season, 2 water samples obtained from Dug Wells (DW), Hand Pump Boreholes (HB), and Mechanized Boreholes (MB) in Kumbotso were analyzed. Physicochemical parameters (turbidity, conductivity, pH, temperature) and E. coli counts were ascertained by standard methods, and volumes were approximated using the manual string method. Socio-demographic data were obtained by questionnaires, while statistical tests (Chi-square, logistic regression) validated health correlations. Turbidity was 8.2-1914.0 NTU and E. coli 0-1305 cfu/100 mL, with DW most contaminated (median \u0026gt;100 cfu/100 mL). Volumes were variable (0.002-7.127 m³), with a turbidity-E. coli correlation (r = 0.38). Females (65.4%) and youth 12-17 years (50.0%) were most affected, with DW users self-reporting 50% illness (χ² = 41.73, p = 8.66E-10), though E. coli’s illness link was non-significant (odds ratio = 1.47, p = 0.29). The study concludes that improving groundwater safety in Kumbotso requires both infrastructure development and behavioral change. Targeted WASH interventions, source protection, and public education are essential for mitigating health risks and achieving sustainable access to safe drinking water.\u003c/p\u003e","manuscriptTitle":"Assessing Hygiene Practices and Microbial Risks in Groundwater Sources: A Case Study of Kumbotso, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-19 12:35:51","doi":"10.21203/rs.3.rs-7376778/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6ba43e73-fb0f-4f20-b875-de8acc840b2a","owner":[],"postedDate":"August 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53197527,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2025-08-19T12:35:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-19 12:35:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7376778","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7376778","identity":"rs-7376778","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}