Multivariate Assessment of Waterborne Bacterial Contamination across Boreholes, Wells, and Streams in Kuje Agro-Ecological Area, Nigeria

Multivariate Assessment of Waterborne Bacterial Contamination across Boreholes, Wells, and Streams in Kuje Agro-Ecological Area, 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 Research Article Multivariate Assessment of Waterborne Bacterial Contamination across Boreholes, Wells, and Streams in Kuje Agro-Ecological Area, Nigeria Musa Peter, Olufunmilayo Iyadunni Ndububa, Toochukwu Chibueze Ogwueleka, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8956067/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 Waterborne bacterial contamination remains a major public health concern in agro-ecological communities that rely on multiple water sources for domestic and agricultural use. This study assessed the distribution and interrelationships of selected waterborne bacteria across boreholes, hand-dug wells, and streams in the Kuje agro-ecological area of the Federal Capital Territory, Nigeria, using multivariate statistical techniques. Water samples were collected from boreholes, wells, streams, and a treated water control source and analyzed for Escherichia coli , Salmonella spp., Shigella spp., and Pseudomonas spp. using standard microbiological methods. Multivariate analysis of variance (MANOVA) was applied to evaluate differences in bacterial distributions across water sources, supported by Box’s M test, Dunnett’s post-hoc comparisons, and Pearson correlation analysis. MANOVA results showed no statistically significant differences using Wilks’ Lambda and Pillai’s Trace (p > 0.05); however, Roy’s Largest Root revealed a significant multivariate effect (p < 0.05), indicating subtle but meaningful variations in bacterial contamination patterns. Correlation analysis demonstrated significant positive associations among key bacterial indicators, suggesting shared contamination pathways linked to fecal pollution and environmental exposure. The findings highlight the value of multivariate statistical tools in water quality assessment and underscore the need for routine microbiological monitoring, improved sanitation practices, and integrated water safety planning in agro-ecological communities. Waterborne bacteria environmental monitoring MANOVA microbial water quality Nigeria Figures Figure 1 Figure 2 1. Introduction Access to safe drinking water is essential for human health; however, microbial contamination of water sources remains a persistent challenge in many low- and middle-income countries. In agro-ecological areas, reliance on boreholes, hand-dug wells, and surface waters increases vulnerability to contamination arising from agricultural runoff, open defecation, livestock activities, and inadequate waste management systems. Bacterial indicators such as Escherichia coli are widely used to assess fecal contamination, while pathogens including Salmonella spp., Shigella spp., and Pseudomonas spp. pose direct public health risks. The presence of these organisms in drinking water sources has been associated with outbreaks of waterborne diseases, particularly in rural and peri-urban communities. Although several studies have evaluated microbial water quality in Nigeria and sub-Saharan Africa, many rely on univariate analytical approaches that may not adequately capture the complex interrelationships among multiple microbial indicators. Multivariate statistical techniques, such as multivariate analysis of variance (MANOVA), allow for the simultaneous evaluation of multiple dependent variables and provide a more comprehensive understanding of contamination patterns. This study therefore aimed to assess the distribution and interrelationships of selected waterborne bacteria across borehole, well, and stream water sources in the Kuje agro-ecological area using multivariate statistical methods. The findings are intended to support evidence-based environmental monitoring and water quality management in similar settings. 2. Materials and Methods 2.1 Study Area The study was conducted in the Kuje agro-ecological area of the Federal Capital Territory (FCT), Nigeria. Kuje Area Council is one of the six Area Councils of the Federal Capital Territory, with approximately between latitude 8 o 45’N and longitude 7 o 15’E, located in the southern part of FCT and shares boundaries with several states and local government areas. (Figure: 1). The area is characterized by a tropical savannah climate, mixed subsistence and commercial farming activities, and dispersed rural settlements. Residents primarily depend on boreholes, hand-dug wells, and surface water (streams) for domestic and agricultural purposes. These water sources are potentially exposed to contamination from agricultural runoff, open defecation, livestock activities, and inadequate waste disposal systems. 2.2 Sample Collection A total number of 30 samples were randomly collected, 3 replicates of samples were collected in each area comprising of, 9 borehole samples, 9 hand-dug well samples, 9 stream samples, and 3 treated water control samples using sterile 500 mL polyethylene bottles. Sample collection area coordinates are as follows; Gaube well (Lat: 8° 49' 08.094" N, Long: 7° 20' 32.036"E); Gaube stream (Lat: 8° 49' 10.669" N, Long: 7° 20' 53.927" E); Gaube borehole (Lat: 8° 49' 16.019" N, Long: 7° 20' 21.048" E); Passali well (Lat: 8°50'59.952"N, Long: 7°18'18.550"E); Passali stream (Lat: 8°50'56.271"N, Long: 7°18'28.158"E); Passali borehole (Lat: 8°50'56.271"N, Long: 7°18'28.158); Kasada well (Lat: 8°47'47.468"N, Long: 7°18'18.550"E); Kasada borehole (Lat: 8°47'54.686"N, Long: 7°18'18.550"E); Kasada stream (Lat: 8°47'59.016"N, Long: 7°18'18.550"E). Sampling was conducted following standard procedures to avoid cross-contamination. For boreholes and wells, water was allowed to run for 2–3 minutes before collection. Stream samples were collected midstream at approximately 30 cm depth. All samples were transported in ice-packed containers to the laboratory and analyzed within 6 hours of collection. 2.3 Microbiological Analysis Microbiological analyses were conducted using standard culture-based methods. Water samples were analyzed for E. coli , Salmonella spp., Shigella spp., and Pseudomonas spp. using selective and differential media following procedures recommended by the American Public Health Association. Presumptive isolates were identified based on colony morphology, Gram staining, and biochemical tests. Bacterial concentrations were expressed as colony-forming units per 100 mL (CFU/100 mL). 2.4 Statistical Analysis Descriptive statistics were used to summarize bacterial concentrations across water sources. Multivariate analysis of variance (MANOVA) was applied to assess differences in bacterial distributions among water sources, with water source type as the independent variable and bacterial indicators as dependent variables. Box’s M test was used to evaluate the assumption of covariance homogeneity. Where appropriate, Dunnett’s post-hoc test was applied to compare each water source with the treated control. Pearson correlation analysis was used to examine interrelationships among bacterial indicators. Statistical significance was set at p < 0.05. Roy’s Largest Root was emphasized due to its sensitivity in detecting dominant multivariate effects when dependent variables are correlated. Analyses were performed using SPSS software. 3. Results 3.1 Distribution of Waterborne Bacteria All sampled water sources exhibited detectable levels of bacterial contamination. Escherichia coli was detected across boreholes, wells, and streams, with higher mean counts observed in surface water sources. Salmonella spp. and Shigella spp. were more frequently isolated from wells and streams, while Pseudomonas spp. was consistently detected across all water sources, including boreholes. The bacterial growths exhibited distinct morphological characteristics across the selective and differential media. (Figure: 2). 3.2 Multivariate Analysis of Variance MANOVA results indicated no statistically significant differences across most microbial parameters using Wilks’ Lambda and Pillai’s Trace (p > 0.05). However, Roy’s Largest Root test revealed a significant multivariate effect (p < 0.05), suggesting that at least one linear combination of bacterial indicators differed among water sources. Box’s M test confirmed homogeneity of covariance matrices, validating the MANOVA assumptions. (Table 1 ). Table 1 Multivariate Tests Microbial Analysis Multivariate Tests Effect Value F Hypothesis df Error df Sig. Intercept Pillai's Trace .144 2.182 b 4.000 52.000 .084 Wilks' Lambda .856 2.182 b 4.000 52.000 .084 Hotelling's Trace .168 2.182 b 4.000 52.000 .084 Roy's Largest Root .168 2.182 b 4.000 52.000 .084 Water Source Pillai's Trace .301 1.507 12.000 162.000 .126 ns Wilks' Lambda .711 1.577 12.000 137.871 .105 ns Hotelling's Trace .388 1.637 12.000 152.000 .087 ns Roy's Largest Root .334 4.513 c 4.000 54.000 .003* b. Exact statistic *.The Test is significant at the .05 level. ns . The Test is not significant at the .05 level. c. The statistic is an upper bound on F that yields a lower bound on the significance level. Box's Test of Equality of Covariance Matrices a Box's M 77.878 F 6.862 df1 10 df2 6173.992 Sig. .000 * Tests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups. a. Design: Intercept + Water Source 3.3 Post-Hoc Comparisons Dunnett’s post-hoc test showed no statistically significant differences between individual water sources and the treated control for most bacterial indicators, although stream water consistently exhibited higher bacterial counts compared to boreholes and treated control water. (Table 2 ). Table 2 Dunnett Test for Multiple Comparisons Dunnett Test for Multiple Comparisons Dependent Variable (I) Water Source (J) Water Source Mean Difference (I-J) Std. Error Sig. 95% Confidence Interval Lower Bound Upper Bound Salmonella Borehole Control .0000 ns 7.13857 1.000 -15.9595 15.9595 Well Control .3333 ns 7.13857 1.000 -15.6262 16.2928 Stream Control 9.0476 ns 7.08739 .306 -6.7975 24.8927 E. coli Borehole Control .7778 ns 3.96456 .979 -8.0857 9.6412 Well Control 3.6111 ns 3.96456 .511 -5.2523 12.4746 Stream Control 4.3333 ns 3.93614 .397 -4.4666 13.1332 Pseudomonas Borehole Control 5.1667 ns 16.63639 .930 -32.0268 42.3602 Well Control 9.7778 ns 16.63639 .740 -27.4157 46.9713 Stream Control 17.1429 ns 16.51713 .433 -19.7840 54.0697 Shigella Borehole Control .7778 ns 17.32979 1.000 -37.9659 39.5215 Well Control 5.8889 ns 17.32979 .914 -32.8548 44.6326 Stream Control 19.6190 ns 17.20556 .375 -18.8469 58.0850 Based on observed means. The error term is Mean Square (Error) = 540.579. *. The mean difference is significant at the .05 level. ns . The mean difference is not significant at the .05 level. a. Dunnett t-tests treat one group as a control, and compare all other groups against it. NB: All the microbial parameters shows not a significant mean difference in water source; most of the MANOVA Tests criteria showed not significant except the Roy's Largest Root Test! 3.4 Correlation Analysis Pearson correlation analysis revealed significant positive correlations between E. coli and Salmonella spp ., as well as between Shigella spp. and E. coli (p < 0.05). These correlations indicate shared contamination pathways, likely linked to fecal pollution and environmental exposure. (Table 3 ). Table 3 Correlation Matrix a Correlation Matrix a Salmonella E. coli Pseudomonas Shigella Correlation Salmonella 1.000 .017 .443 .347 E. coli .017 1.000 − .111 .272 Pseudomonas .443 − .111 1.000 .354 Shigella .347 .272 .354 1.000 Sig. (1-tailed) Salmonella .448 .000 .004 E. coli .448 ns .201 .019 Pseudomonas .000* .201 ns .003 Shigella .004* .019* .003* a. Determinant = .585 *. The correlation is significant at the .05 level. ns . The correlation is not significant at the .05 level. NB: All the correlation among microbial parameters shows significance except between E. coli / Salmonella and Pseudomonas / E. coli . 4. Discussion The presence of multiple bacterial indicators across all water sources demonstrates widespread microbial contamination within the Kuje agro-ecological area. The detection of E. coli confirms fecal pollution, consistent with previous studies in rural Nigerian and sub-Saharan African settings. (Adefisoye & Okoh, 2017 ; Edokpayi et al., 2018 ). Although univariate differences among water sources were limited, the significant result obtained using Roy’s Largest Root highlights the utility of multivariate approaches in detecting subtle contamination patterns that may otherwise be overlooked. Roy’s Largest Root was emphasized due to its sensitivity in detecting dominant multivariate effects when dependent variables are correlated. This supports the use of multivariate techniques in water quality assessment, as they capture complex interactions among multiple microbial indicators that may be overlooked by single-parameter analyses (Shrestha & Kazama, 2007 ). The observed correlations among bacterial indicators suggest common contamination sources, including agricultural runoff, livestock waste, and inadequate sanitation infrastructure. The relatively uniform distribution of bacteria across water sources indicates diffuse environmental contamination, underscoring the need for integrated water safety planning rather than source-specific interventions alone. 5. Conclusion This study demonstrates that water sources; boreholes, hand-dug wells, and streams in the Kuje agro-ecological area are contaminated with multiple waterborne bacterial indicators. While conventional univariate analyses showed limited differences, multivariate analysis revealed meaningful interrelationships among microbial parameters. The findings support the incorporation of multivariate analytical tools into routine environmental monitoring frameworks and highlight the importance of continuous microbiological surveillance, improved sanitation practices, and integrated water resource management to reduce public health risks. Declarations Acknowledgements The authors acknowledge the support of laboratory staff and community members in the Kuje area who facilitated sample collection and analysis. Ethics Statement Ethical approval was not required for this study as it involved environmental water sampling and did not include human or animal subjects. Conflict of Interest: The authors declare no conflict of interest. Funding Declaration: There was no external Funding for this research. References Adefisoye, M. A., & Okoh, A. I. (2017). Identification and antimicrobial resistance prevalence of pathogenic Escherichia coli strains from treated wastewater effluents in Eastern Cape, South Africa. MicrobiologyOpen, 6(3), e00470. https://doi.org/10.1002/mbo3.470 APHA. (2017). Standard methods for the examination of water and wastewater (23rd ed.). American Public Health Association. Bain, R., Cronk, R., Wright, J., Yang, H., Slaymaker, T., & Bartram, J. (2014). Fecal contamination of drinking-water in low- and middle-income countries: A systematic review. PLoS Medicine, 11(5), e1001644. Cabral, J. P. S. (2010). Water microbiology: Bacterial pathogens and water. International Journal of Environmental Research and Public Health, 7(10), 3657–3703. Douterelo, I., Fish, K. E., & Boxall, J. B. (2018). Succession of bacterial communities in drinking water distribution systems. Water Research, 141, 475–484. Edokpayi, J. N., Rogawski, E. T., Kahler, D. M., Hill, C. L., Reynolds, C., Nyathi, E., & Dillingham, R. (2018). Challenges to sustainable safe drinking water: A case study of rural communities in South Africa. Water, 10(2), 159. https://doi.org/10.3390/w10020159 Eze, V. C., & Nwagwe, N. C. (2016). Microbiological and physicochemical characteristics of water sources in rural communities of Anambra State, Nigeria. Journal of Applied Sciences and Environmental Management, 20(2), 393–399. Leclerc, H., Schwartzbrod, L., & Dei-Cas, E. (2002). Microbial agents associated with waterborne diseases. Critical Reviews in Microbiology, 28(4), 371–409. Odonkor, S. T., & Ampofo, J. K. (2013). Escherichia coli as an indicator of bacteriological quality of water: An overview. Microbiology Research, 4(1), e2. Olasoji, S. O., Oyewole, N. O., Abiola, B., & Edokpayi, J. N. (2019). Water quality assessment of surface and groundwater sources in rural areas of Ogun State, Nigeria. Environmental Monitoring and Assessment, 191, 281. Oloruntoba, E. O., & Sridhar, M. K. C. (2017). Bacteriological quality of drinking water in rural communities of Nigeria. African Journal of Biomedical Research, 20(3), 285–292. Ojekunle, Z. O., Ojekunle, O. V., Adeyemi, A. A., Taiwo, A. G., Sangowusi, O. R., & Taiwo, A. M. (2020). Evaluation of surface water quality indices and ecological risk assessment in Nigeria. Environmental Science and Pollution Research, 27, 31617–31628. Okoh, A. I., Odjadjare, E. E., Igbinosa, E. O., & Osode, A. N. (2007). Wastewater treatment plants as a source of microbial pathogens in receiving watersheds. African Journal of Biotechnology, 6(25), 2932–2944. Shrestha, S., & Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji River Basin, Japan. Environmental Modelling & Software, 22(4), 464–475. https://doi.org/10.1016/j.envsoft.2006.02.001 WHO. (2017). Guidelines for drinking-water quality (4th ed.). World Health Organization. World Health Organization. (2022). Guidelines for drinking-water quality (4th ed., incorporating 1st–3rd addenda). WHO. WHO & UNICEF. (2023). Progress on household drinking water, sanitation and hygiene 2000–2022. WHO/UNICEF Joint Monitoring Programme. Additional Declarations No competing interests reported. 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-8956067","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":597882081,"identity":"158a4719-1345-4744-a67d-e06dd25d274f","order_by":0,"name":"Musa Peter","email":"","orcid":"","institution":"University of Abuja","correspondingAuthor":false,"prefix":"","firstName":"Musa","middleName":"","lastName":"Peter","suffix":""},{"id":597882084,"identity":"ddec7435-b082-42d7-a460-acf4ac1edb96","order_by":1,"name":"Olufunmilayo Iyadunni Ndububa","email":"","orcid":"","institution":"University of Abuja","correspondingAuthor":false,"prefix":"","firstName":"Olufunmilayo","middleName":"Iyadunni","lastName":"Ndububa","suffix":""},{"id":597882089,"identity":"c16203ee-276c-498e-9396-380587b98d3c","order_by":2,"name":"Toochukwu Chibueze Ogwueleka","email":"","orcid":"","institution":"University of Abuja","correspondingAuthor":false,"prefix":"","firstName":"Toochukwu","middleName":"Chibueze","lastName":"Ogwueleka","suffix":""},{"id":597882090,"identity":"7188445e-2cd1-445d-a4a8-dea55731d3bd","order_by":3,"name":"Adaeze Joy Alu","email":"data:image/png;base64,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","orcid":"","institution":"University of Abuja","correspondingAuthor":true,"prefix":"","firstName":"Adaeze","middleName":"Joy","lastName":"Alu","suffix":""}],"badges":[],"createdAt":"2026-02-24 10:23:53","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8956067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8956067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103709813,"identity":"64912e50-4345-49db-9893-cd5fefd7a07d","added_by":"auto","created_at":"2026-03-02 03:21:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1085795,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of the Study Area\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8956067/v1/2283a4b6de1d653a7ded3d97.png"},{"id":103709812,"identity":"6291f33c-0778-4782-9400-5a6ff3129691","added_by":"auto","created_at":"2026-03-02 03:21:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1164149,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBacterial growths on selective and differential media (\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eShigella \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003esp, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eSalmonella\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e spp, \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ePseudomonas\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e spp and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eE. coli\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8956067/v1/d74e48e973f031ce7e67ba8a.png"},{"id":104779377,"identity":"10555645-9504-4f44-b902-ca400589792e","added_by":"auto","created_at":"2026-03-17 07:39:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3356910,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8956067/v1/143aa183-d9fa-4b79-9746-b31bb29e40ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Multivariate Assessment of Waterborne Bacterial Contamination across Boreholes, Wells, and Streams in Kuje Agro-Ecological Area, Nigeria","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAccess to safe drinking water is essential for human health; however, microbial contamination of water sources remains a persistent challenge in many low- and middle-income countries. In agro-ecological areas, reliance on boreholes, hand-dug wells, and surface waters increases vulnerability to contamination arising from agricultural runoff, open defecation, livestock activities, and inadequate waste management systems.\u003c/p\u003e \u003cp\u003eBacterial indicators such as \u003cem\u003eEscherichia coli\u003c/em\u003e are widely used to assess fecal contamination, while pathogens including \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and \u003cem\u003ePseudomonas\u003c/em\u003e spp. pose direct public health risks. The presence of these organisms in drinking water sources has been associated with outbreaks of waterborne diseases, particularly in rural and peri-urban communities.\u003c/p\u003e \u003cp\u003eAlthough several studies have evaluated microbial water quality in Nigeria and sub-Saharan Africa, many rely on univariate analytical approaches that may not adequately capture the complex interrelationships among multiple microbial indicators. Multivariate statistical techniques, such as multivariate analysis of variance (MANOVA), allow for the simultaneous evaluation of multiple dependent variables and provide a more comprehensive understanding of contamination patterns.\u003c/p\u003e \u003cp\u003eThis study therefore aimed to assess the distribution and interrelationships of selected waterborne bacteria across borehole, well, and stream water sources in the Kuje agro-ecological area using multivariate statistical methods. The findings are intended to support evidence-based environmental monitoring and water quality management in similar settings.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Area\u003c/h2\u003e \u003cp\u003eThe study was conducted in the Kuje agro-ecological area of the Federal Capital Territory (FCT), Nigeria. Kuje Area Council is one of the six Area Councils of the Federal Capital Territory, with approximately between latitude 8\u003csup\u003eo\u003c/sup\u003e45\u0026rsquo;N and longitude 7\u003csup\u003eo\u003c/sup\u003e15\u0026rsquo;E, located in the southern part of FCT and shares boundaries with several states and local government areas. (Figure: 1). The area is characterized by a tropical savannah climate, mixed subsistence and commercial farming activities, and dispersed rural settlements. Residents primarily depend on boreholes, hand-dug wells, and surface water (streams) for domestic and agricultural purposes. These water sources are potentially exposed to contamination from agricultural runoff, open defecation, livestock activities, and inadequate waste disposal systems.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample Collection\u003c/h2\u003e \u003cp\u003eA total number of 30 samples were randomly collected, 3 replicates of samples were collected in each area comprising of, 9 borehole samples, 9 hand-dug well samples, 9 stream samples, and 3 treated water control samples using sterile 500 mL polyethylene bottles. Sample collection area coordinates are as follows; Gaube well (Lat: 8\u0026deg; 49' 08.094\" N, Long: 7\u0026deg; 20' 32.036\"E); Gaube stream (Lat: 8\u0026deg; 49' 10.669\" N, Long: 7\u0026deg; 20' 53.927\" E); Gaube borehole (Lat: 8\u0026deg; 49' 16.019\" N, Long: 7\u0026deg; 20' 21.048\" E); Passali well (Lat: 8\u0026deg;50'59.952\"N, Long: 7\u0026deg;18'18.550\"E); Passali stream (Lat: 8\u0026deg;50'56.271\"N, Long: 7\u0026deg;18'28.158\"E); Passali borehole (Lat: 8\u0026deg;50'56.271\"N, Long: 7\u0026deg;18'28.158); Kasada well (Lat: 8\u0026deg;47'47.468\"N, Long: 7\u0026deg;18'18.550\"E); Kasada borehole (Lat: 8\u0026deg;47'54.686\"N, Long: 7\u0026deg;18'18.550\"E); Kasada stream (Lat: 8\u0026deg;47'59.016\"N, Long: 7\u0026deg;18'18.550\"E).\u003c/p\u003e \u003cp\u003eSampling was conducted following standard procedures to avoid cross-contamination. For boreholes and wells, water was allowed to run for 2\u0026ndash;3 minutes before collection. Stream samples were collected midstream at approximately 30 cm depth. All samples were transported in ice-packed containers to the laboratory and analyzed within 6 hours of collection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Microbiological Analysis\u003c/h2\u003e \u003cp\u003eMicrobiological analyses were conducted using standard culture-based methods. Water samples were analyzed for \u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and \u003cem\u003ePseudomonas\u003c/em\u003e spp. using selective and differential media following procedures recommended by the American Public Health Association. Presumptive isolates were identified based on colony morphology, Gram staining, and biochemical tests. Bacterial concentrations were expressed as colony-forming units per 100 mL (CFU/100 mL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to summarize bacterial concentrations across water sources. Multivariate analysis of variance (MANOVA) was applied to assess differences in bacterial distributions among water sources, with water source type as the independent variable and bacterial indicators as dependent variables. Box\u0026rsquo;s M test was used to evaluate the assumption of covariance homogeneity. Where appropriate, Dunnett\u0026rsquo;s post-hoc test was applied to compare each water source with the treated control. Pearson correlation analysis was used to examine interrelationships among bacterial indicators. Statistical significance was set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Roy\u0026rsquo;s Largest Root was emphasized due to its sensitivity in detecting dominant multivariate effects when dependent variables are correlated. Analyses were performed using SPSS software.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution of Waterborne Bacteria\u003c/h2\u003e \u003cp\u003eAll sampled water sources exhibited detectable levels of bacterial contamination. \u003cem\u003eEscherichia coli\u003c/em\u003e was detected across boreholes, wells, and streams, with higher mean counts observed in surface water sources. \u003cem\u003eSalmonella\u003c/em\u003e spp. and \u003cem\u003eShigella\u003c/em\u003e spp. were more frequently isolated from wells and streams, while \u003cem\u003ePseudomonas\u003c/em\u003e spp. was consistently detected across all water sources, including boreholes. The bacterial growths exhibited distinct morphological characteristics across the selective and differential media. (Figure: 2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Multivariate Analysis of Variance\u003c/h2\u003e \u003cp\u003eMANOVA results indicated no statistically significant differences across most microbial parameters using Wilks\u0026rsquo; Lambda and Pillai\u0026rsquo;s Trace (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). However, Roy\u0026rsquo;s Largest Root test revealed a significant multivariate effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that at least one linear combination of bacterial indicators differed among water sources. Box\u0026rsquo;s M test confirmed homogeneity of covariance matrices, validating the MANOVA assumptions. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eMultivariate Tests\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eMicrobial Analysis Multivariate Tests\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHypothesis df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eError df\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePillai's Trace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.182\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilks' Lambda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.182\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotelling's Trace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.182\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoy's Largest Root\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.182\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e52.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eWater Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePillai's Trace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e162.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.126 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWilks' Lambda\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e137.871\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.105 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHotelling's Trace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.637\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e152.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.087 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoy's Largest Root\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.513\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.003*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eb. Exact statistic *.The Test is significant at the .05 level. \u003csup\u003ens\u003c/sup\u003e. The Test is not significant at the .05 level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003ec. The statistic is an upper bound on F that yields a lower bound on the significance level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBox's Test of Equality of Covariance Matrices\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBox's M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.878\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.862\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003edf2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6173.992\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.000\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eTests the null hypothesis that the observed covariance matrices of the dependent variables are equal across groups.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003ea. Design: Intercept\u0026thinsp;+\u0026thinsp;Water Source\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Post-Hoc Comparisons\u003c/h2\u003e \u003cp\u003eDunnett\u0026rsquo;s post-hoc test showed no statistically significant differences between individual water sources and the treated control for most bacterial indicators, although stream water consistently exhibited higher bacterial counts compared to boreholes and treated control water. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDunnett Test for Multiple Comparisons\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eDunnett Test for Multiple Comparisons\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(I) Water Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(J) Water Source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean Difference\u003c/p\u003e \u003cp\u003e(I-J)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% Confidence Interval\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower Bound\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper Bound\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorehole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.0000 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.13857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-15.9595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e15.9595\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.3333 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.13857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-15.6262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e16.2928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.0476 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.08739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-6.7975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.8927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorehole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7778 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.96456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-8.0857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.6412\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6111 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.96456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-5.2523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e12.4746\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3333 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.93614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-4.4666\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.1332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorehole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.1667 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.63639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.930\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-32.0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e42.3602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.7778 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.63639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-27.4157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46.9713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.1429 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.51713\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-19.7840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e54.0697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBorehole\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.7778 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.32979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-37.9659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.5215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8889 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.32979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-32.8548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44.6326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStream\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.6190 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.20556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-18.8469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58.0850\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003eBased on observed means. The error term is Mean Square (Error)\u0026thinsp;=\u0026thinsp;540.579.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e*. The mean difference is significant at the .05 level. \u003csup\u003ens\u003c/sup\u003e. The mean difference is not significant at the .05 level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003ea. Dunnett t-tests treat one group as a control, and compare all other groups against it.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNB: All the microbial parameters shows not a significant mean difference in water source; most of the MANOVA Tests criteria showed not significant except the Roy's Largest Root Test!\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation Analysis\u003c/h2\u003e \u003cp\u003ePearson correlation analysis revealed significant positive correlations between \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003eSalmonella spp\u003c/em\u003e., as well as between \u003cem\u003eShigella\u003c/em\u003e spp. and \u003cem\u003eE. coli\u003c/em\u003e (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These correlations indicate shared contamination pathways, likely linked to fecal pollution and environmental exposure. (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Matrix\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCorrelation Matrix\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eCorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.347\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.272\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSig. (1-tailed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.448\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eE. coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.448 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.000*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.201 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eShigella\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.019*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003ea. Determinant = .585 *. The correlation is significant at the .05 level. \u003csup\u003ens\u003c/sup\u003e. The correlation is not significant at the .05 level.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNB: All the correlation among microbial parameters shows significance except between \u003cem\u003eE. coli\u003c/em\u003e/ \u003cem\u003eSalmonella\u003c/em\u003e and \u003cem\u003ePseudomonas\u003c/em\u003e/ \u003cem\u003eE. coli\u003c/em\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe presence of multiple bacterial indicators across all water sources demonstrates widespread microbial contamination within the Kuje agro-ecological area. The detection of \u003cem\u003eE. coli\u003c/em\u003e confirms fecal pollution, consistent with previous studies in rural Nigerian and sub-Saharan African settings. (Adefisoye \u0026amp; Okoh, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Edokpayi et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough univariate differences among water sources were limited, the significant result obtained using Roy\u0026rsquo;s Largest Root highlights the utility of multivariate approaches in detecting subtle contamination patterns that may otherwise be overlooked. Roy\u0026rsquo;s Largest Root was emphasized due to its sensitivity in detecting dominant multivariate effects when dependent variables are correlated. This supports the use of multivariate techniques in water quality assessment, as they capture complex interactions among multiple microbial indicators that may be overlooked by single-parameter analyses (Shrestha \u0026amp; Kazama, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe observed correlations among bacterial indicators suggest common contamination sources, including agricultural runoff, livestock waste, and inadequate sanitation infrastructure. The relatively uniform distribution of bacteria across water sources indicates diffuse environmental contamination, underscoring the need for integrated water safety planning rather than source-specific interventions alone.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study demonstrates that water sources; boreholes, hand-dug wells, and streams in the Kuje agro-ecological area are contaminated with multiple waterborne bacterial indicators. While conventional univariate analyses showed limited differences, multivariate analysis revealed meaningful interrelationships among microbial parameters. The findings support the incorporation of multivariate analytical tools into routine environmental monitoring frameworks and highlight the importance of continuous microbiological surveillance, improved sanitation practices, and integrated water resource management to reduce public health risks.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the support of laboratory staff and community members in the Kuje area who facilitated sample collection and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was not required for this study as it involved environmental water sampling and did not include human or animal subjects.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u0026nbsp;\u0026nbsp;\u003c/strong\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eThere was no external Funding for this research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdefisoye, M. A., \u0026amp; Okoh, A. I. (2017). Identification and antimicrobial resistance prevalence of pathogenic Escherichia coli strains from treated wastewater effluents in Eastern Cape, South Africa. MicrobiologyOpen, 6(3), e00470. https://doi.org/10.1002/mbo3.470\u003c/li\u003e\n \u003cli\u003eAPHA. (2017). Standard methods for the examination of water and wastewater (23rd ed.). American Public Health Association.\u003c/li\u003e\n \u003cli\u003eBain, R., Cronk, R., Wright, J., Yang, H., Slaymaker, T., \u0026amp; Bartram, J. (2014). Fecal contamination of drinking-water in low- and middle-income countries: A systematic review. PLoS Medicine, 11(5), e1001644.\u003c/li\u003e\n \u003cli\u003eCabral, J. P. S. (2010). Water microbiology: Bacterial pathogens and water. International Journal of Environmental Research and Public Health, 7(10), 3657\u0026ndash;3703.\u003c/li\u003e\n \u003cli\u003eDouterelo, I., Fish, K. E., \u0026amp; Boxall, J. B. (2018). Succession of bacterial communities in drinking water distribution systems. Water Research, 141, 475\u0026ndash;484.\u003c/li\u003e\n \u003cli\u003eEdokpayi, J. N., Rogawski, E. T., Kahler, D. M., Hill, C. L., Reynolds, C., Nyathi, E., \u0026amp; Dillingham, R. (2018). Challenges to sustainable safe drinking water: A case study of rural communities in South Africa. Water, 10(2), 159. https://doi.org/10.3390/w10020159\u003c/li\u003e\n \u003cli\u003eEze, V. C., \u0026amp; Nwagwe, N. C. (2016). Microbiological and physicochemical characteristics of water sources in rural communities of Anambra State, Nigeria. Journal of Applied Sciences and Environmental Management, 20(2), 393\u0026ndash;399.\u003c/li\u003e\n \u003cli\u003eLeclerc, H., Schwartzbrod, L., \u0026amp; Dei-Cas, E. (2002). Microbial agents associated with waterborne diseases. Critical Reviews in Microbiology, 28(4), 371\u0026ndash;409.\u003c/li\u003e\n \u003cli\u003eOdonkor, S. T., \u0026amp; Ampofo, J. K. (2013). Escherichia coli as an indicator of bacteriological quality of water: An overview. Microbiology Research, 4(1), e2.\u003c/li\u003e\n \u003cli\u003eOlasoji, S. O., Oyewole, N. O., Abiola, B., \u0026amp; Edokpayi, J. N. (2019). Water quality assessment of surface and groundwater sources in rural areas of Ogun State, Nigeria. Environmental Monitoring and Assessment, 191, 281.\u003c/li\u003e\n \u003cli\u003eOloruntoba, E. O., \u0026amp; Sridhar, M. K. C. (2017). Bacteriological quality of drinking water in rural communities of Nigeria. African Journal of Biomedical Research, 20(3), 285\u0026ndash;292.\u003c/li\u003e\n \u003cli\u003eOjekunle, Z. O., Ojekunle, O. V., Adeyemi, A. A., Taiwo, A. G., Sangowusi, O. R., \u0026amp; Taiwo, A. M. (2020). Evaluation of surface water quality indices and ecological risk assessment in Nigeria. Environmental Science and Pollution Research, 27, 31617\u0026ndash;31628.\u003c/li\u003e\n \u003cli\u003eOkoh, A. I., Odjadjare, E. E., Igbinosa, E. O., \u0026amp; Osode, A. N. (2007). Wastewater treatment plants as a source of microbial pathogens in receiving watersheds. African Journal of Biotechnology, 6(25), 2932\u0026ndash;2944.\u003c/li\u003e\n \u003cli\u003eShrestha, S., \u0026amp; Kazama, F. (2007). Assessment of surface water quality using multivariate statistical techniques: A case study of the Fuji River Basin, Japan. Environmental Modelling \u0026amp; Software, 22(4), 464\u0026ndash;475. https://doi.org/10.1016/j.envsoft.2006.02.001\u003c/li\u003e\n \u003cli\u003eWHO. (2017). Guidelines for drinking-water quality (4th ed.). World Health Organization.\u003c/li\u003e\n \u003cli\u003eWorld Health Organization. (2022). Guidelines for drinking-water quality (4th ed., incorporating 1st\u0026ndash;3rd addenda). WHO.\u003c/li\u003e\n \u003cli\u003eWHO \u0026amp; UNICEF. (2023). Progress on household drinking water, sanitation and hygiene 2000\u0026ndash;2022. WHO/UNICEF Joint Monitoring Programme.\u003c/li\u003e\n\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":"Waterborne bacteria, environmental monitoring, MANOVA, microbial water quality, Nigeria","lastPublishedDoi":"10.21203/rs.3.rs-8956067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8956067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWaterborne bacterial contamination remains a major public health concern in agro-ecological communities that rely on multiple water sources for domestic and agricultural use. This study assessed the distribution and interrelationships of selected waterborne bacteria across boreholes, hand-dug wells, and streams in the Kuje agro-ecological area of the Federal Capital Territory, Nigeria, using multivariate statistical techniques. Water samples were collected from boreholes, wells, streams, and a treated water control source and analyzed for \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eSalmonella\u003c/em\u003e spp., \u003cem\u003eShigella\u003c/em\u003e spp., and \u003cem\u003ePseudomonas\u003c/em\u003e spp. using standard microbiological methods. Multivariate analysis of variance (MANOVA) was applied to evaluate differences in bacterial distributions across water sources, supported by Box\u0026rsquo;s M test, Dunnett\u0026rsquo;s post-hoc comparisons, and Pearson correlation analysis. MANOVA results showed no statistically significant differences using Wilks\u0026rsquo; Lambda and Pillai\u0026rsquo;s Trace (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05); however, Roy\u0026rsquo;s Largest Root revealed a significant multivariate effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating subtle but meaningful variations in bacterial contamination patterns. Correlation analysis demonstrated significant positive associations among key bacterial indicators, suggesting shared contamination pathways linked to fecal pollution and environmental exposure. The findings highlight the value of multivariate statistical tools in water quality assessment and underscore the need for routine microbiological monitoring, improved sanitation practices, and integrated water safety planning in agro-ecological communities.\u003c/p\u003e","manuscriptTitle":"Multivariate Assessment of Waterborne Bacterial Contamination across Boreholes, Wells, and Streams in Kuje Agro-Ecological Area, Nigeria","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-02 03:20:56","doi":"10.21203/rs.3.rs-8956067/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":"afd56dfc-8625-4407-bf08-64291fc7e8ee","owner":[],"postedDate":"March 2nd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-02T21:24:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-02 03:20:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8956067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8956067","identity":"rs-8956067","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}