Bacterial isolates from Drinking water river sources exhibit multi-drug resistant trait

Bacterial isolates from Drinking water river sources exhibit multi-drug resistant trait | 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 Bacterial isolates from Drinking water river sources exhibit multi-drug resistant trait Bukola Margaret Popoola, Jemimah Pearl Ogwerel, Oluwatosin Gbemisola Oladipo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4457954/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Oct, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted 4 You are reading this latest preprint version Abstract Freshwater habitat is a natural reservoir for antimicrobial resistance (AMR). AMR is known to pose serious human, animal, and environmental public health threats. This study aimed at evaluating the physicochemical and microbiological properties of five selected rivers (Apitipiti 1, Apitipiti 2, Apitipiti 3, Sogidi, and Aba Apa Akinmorin) in Oyo town, Nigeria, as well as the antibiotic resistance pattern of isolated bacterial species, using conventional methods. Most physicochemical parameters were within WHO and NIS permissible limits. Of the rivers, Sogidi had the highest microbial load (6.36 log CFU/mL) while Apititipiti 1 had the lowest (5.76 log CFU/mL). A total of thirty-three (32) bacterial species were isolated and identified as: Aeromonas (9) , Bacillus (2) , Corynebacterium (13) , Lactobacillus (1) , Pseudomonas (2) , Staphylococcus (4) , and Streptococcus (1). Pearson’s correlation matrix indicated that there were significant ( p <0.05) interactions among pH, electrical conductivity, temperature, sulphate and chloride salts, BOD and COD. Of all these, 81.8 % were multidrug-resistant, with Corynebacterium kutscheri and Aeromonas spp. isolated from Apitipiti 2 and Aba Apa Akinmorin rivers respectively, exhibiting a relatively high antibiotic resistance of 90.9 %. This study reveals that these rivers maybe unfit for consumption as multidrug-resistant bacteria of public health risk were associated with them. Antibiotic resistant bacteria antimicrobial resistance fresh water physicochemical parameters potable water public and human health Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. INTRODUCTION About 70% of the earth's surface is covered with water either as fresh water, brackish water or as saline water. Water, an important earth resource, is very vital to humans, plants and animals. Man utilizes this indispensable natural resource for various activities including domestic, industrial, agricultural and or recreational purposes. To the ecosystem, water serves as habitat for aquatic organisms such as fish, crocodile, snails, frogs, bacteria, fungi, protozoans, algae and viruses and these organisms interact with each other and form the aquatic ecosystem (Sen, 2019). Potable water is crucial to human existence. Unfortunately, some people struggle to have access to safe water. The microbial quality of water determines to a large extent its usefulness especially for domestic usage. Globally, bacterial contamination of water brings about serious public health threat (Hile et al ., 2023). Bacteria are one of the major pathogens responsible for waterborne diseases, and they have been implicated with many gastrointestinal outbreaks worldwide (Delgado-Gardea et al ., 2016). According to WHO (2017), every year, about 525,000 children under five come down with diarrhoea as a result of water borne diseases. Furthermore, in Nigeria, it has been reported that UNICEF recorded about 117,000 children’s death each year due to waterborne diseases (UNICEF, 2018). However, in spite of the continuous efforts to improve water quality worldwide, waterborne disease outbreaks are still reported (Hile et al ., 2023). Bacteria associated with waterborne diseases include Escherichia coli , Legionella spp., Pseudomonas aeruginosa , Aeromonas and Mycobacterium spp. Other waterborne pathogen are usually obligate pathogens, they multiply only in an infected host. They include: Campylobacter, Salmonella, Shigella, Escherichia coli, Acinetobacter sp., Clostridium spp., and Bacillus anthracis and Helicobacter pylori. (Delgado-Gardea et al. , 2016). When in the human hosts, these waterborne pathogens are usually eliminated by the use of antibiotics. Over the years and decades, antibiotics are the widely known antimicrobials used for the treatment of infectious diseases curbing, the rate of morbidity and mortality (Chukwu et al ., 2020). However, the misuse of antibiotics has led to the development of antimicrobial resistance. When consumed, antibiotics are broken down partially in the body, consequently, a large amount is excreted in their normal form or as active metabolites through urine and faeces, and these get into wastewater treatment plants. Unfortunately, wastewater treatment plants are not designed to eliminate antibiotics, hence, these antibiotics are eventually released into the environment including freshwaters (Grenni et al ., 2018) by erosion, sewage run off, or leaching (Wang et al ., 2022). The deposition of antibiotic residues into the environment, leads to the development of antimicrobial resistance in some bacteria. These bacterial species are referred to as Antibiotic Resistant Bacteria (ARB) and they contain Antibiotic Resistance Genes (ARGs) which are transferred horizontally or vertically to other bacteria (Peterson & Kaur, 2018). Antimicrobial Resistance (AMR) has become a serious global issue as it leads to a reduction in treatment options for bacterial infections thereby reducing clinical efficacy while increasing treatment costs and mortality (Oladipo et al ., 2019). In Nigeria, antimicrobial resistance is of top priority on the national public health agenda (Achi et al ., 2021). Hence, this study was designed to evaluate the physico-chemical parameters, microbial population, diversity and the antibiotic resistance pattern of heterotrophic bacteria in some selected rivers in Oyo town, Nigeria which serve as source of water supply to residents around the areas; for tourism (Sogidi), fishing (Apitipiti 1, Apitipiti 2, Apitipiti 3), and drinking (Sogidi, and Aba Apa Akinmorin). 2. MATERIALS AND METHODS 2.1 Sample collection sites Oyo State (7° 51̍ 09.25 N and 3° 55̍ 52.50 E) is one of the largest major states (Fig. 1 ) in Southwestern, Nigeria with a population of about 8 million. Five rivers were strategically sampled in one of the Local Government Areas (LGA) of the state – Atiba LGA. The five rivers studied are flowing water bodies namely Apitipiti 1, Apitipiti 2, Apitipiti 3, Sogidi, and Aba Apa Akinmorin. These rivers are mainly used for fishing (Apitipiti 1, Apitipiti 2, Apitipiti 3), tourism (Sogidi) and drinking (Sogidi, and Aba Apa Akinmorin). 2.2 Sample collection Sampling sites were purposely selected and some principal rivers within Oyo town were selected. The water samples were collected during dry season at a depth of 20 cm. While plastic bottles were used to collect water samples for physicochemical analysis, sterile McCartney bottles were used to collect water samples for microbiological analysis. The plastic bottles were rinsed with the river water thrice and water was collected. The sterile McCartney bottles were held with sterile forceps and dipped into the water and the water was collected and capped immediately. The forceps were sterilised intermittently with 70% ethanol. The bottles were labelled appropriately, and were transported to the laboratory for analyses in ice pack. 2.3 Water quality evaluation The physical and chemical properties of the various water samples were determined. Physical parameters such as pH, temperature, and electrical conductivity were determined in-situ using a hand digital metre (HI 9812-5, Hanna Instruments, Woonsocket, RI, USA). Chemical properties measured include heavy metal concentrations, biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), turbidity and anions concentration. The BOD of the samples was determined using Winkler’s method while COD was conducted using titration method (Schmitz, 2017). Nitrate concentration was determined using the brucine method (Greweling & Peech, 1968) and phosphate using the molybdenum blue method (Murphy and Riley, 1962). The Mohr Method was used to determine the chloride concentrations in the water samples while sulphate was determined using turbidimetric method. The concentrations of fluoride ion (F) and heavy metals such as; Iron (Fe), Copper (Cu), Zinc (Zn), Chromium (Cr), Cadmium (Cd), Lead (Pb), Nickel (Ni) and Manganese (Mn) were determined using atomic absorption spectrophotometer (Model 210/211 VGP Buck Scientific Atomic Absorption Spectrometer, East Norwalk, CT., USA) (Ayandiran et al. 2014). 2.4 Microbial analysis For the microbial analyses, samples were serially diluted into five test tubes that were labelled appropriately. One mL each of the water samples was dispensed into sterile test tubes containing 9 mL sterile distilled water, a serial dilution of five folds was carried out. One mL of the 3rd and 5th dilutions were pipette into sterile petri dishes using pour plate method. The media used were Nutrient Agar (NA) (NA, Oxoid Ltd., Basingstoke, UK), MacConkey Agar (MAC), Eosin Methylene Blue Agar (EMB), and Mannitol agar (MA), in order to mitigate possible biases. These were all prepared according to manufacturers’ instructions. The prepared MacConkey agar, Nutrient agar, EMB agar and Mannitol agar were allowed to cool to about 45 o C – 50 o C, the mouth of the flask was flamed, then about 15 ml of the agar medium was poured aseptically into the petri dishes containing the samples. The plates were rocked gently immediately after pouring the agar so the microorganisms could be evenly separated during growth. Solidification of these agars were followed by incubation of the plates invertedly at 37 o C for 24 hours. Three control plates of each agar were poured. The colonies were observed, counted and recorded after 24 hours. Distinct colonies were then sub-cultured until pure cultures were obtained and transferred onto slant bottles containing freshly prepared agars. Individual colonies were purified and identified by morphological and biochemical techniques (Dubey & Masheshwari, 2004). Using the biochemical characteristics of the isolates such as oxidase testing, catalase testing and substrate utilization tests, the probable identities of the organisms were carried out and further confirmed on the National Center for Biotechnology Information (NCBI) platform ( https://www.ncbi.nlm.nih.gov/ ). 2.5 Antibiotic susceptibility testing The antibiotic sensitivity test was carried out for each of the isolates using the Kirby Bauer Disc Diffusion method according to the guidelines of the Clinical Laboratory Standards Institute (CLSI, 2020). The antibiotics used were Imipenem (10 µg), Meropenem (10 µg), Ceftazidime (30 µg), Ciprofloxacin (5 µg), Gentamicin (10 µg), Chloramphenicol (12.5 µg), Tetracycline (30 µg), Oxacillin (1 µg), Cotrimoxazole (25 µg), Augmentin (20 µg), Vancomycin (30 µg) and Erythromycin (15 µg). The inoculum was prepared for each bacterial isolate by adjusting the turbidity to 0.5 McFarland standard and spread on Mueller-Hinton agar plates. The antibiotic discs were placed on the agar plates and incubated overnight at 37°C for 24 hours. The zones of inhibition (ZOI) were measured and the isolates were classified as sensitive, intermediate or resistant according to CLSI tables and guidelines (Bayot & Bragg, 2021). 2.6 Statistical analysis All statistical analysis of data obtained was done using one-way analysis of variance (ANOVA) at 5% level of significance using the Statistical Package for Social Sciences (SPSS) version 28 (IBM, Armonk, NY, USA). A post hoc test was afterwards performed using the Duncan’s New Multiple Range Test. Pearson’s correlation ( r ) and cluster ( r = 0.15 and 0.40, p < 0.05) analyses were used to determine interrelationships between the parameters. 3. RESULTS AND DISCUSSION 3.1 Physicochemical characteristics of sampled rivers The physicochemical characteristics of the sampled rivers were recorded and compared with World Health Organisation (WHO) standards and the Nigerian Institute Standard (NIS) acceptable levels in the guidelines for drinking water is reported (Table 1 ). 3.1.1 pH and temperature variations in sampled rivers The pH of the water samples varied from river to river. Samples obtained from rivers Apitipiti 1, Sogidi and Aba Apa Akinmorin, had pH values within WHO acceptable range (6.5–8.5). However, Apitipiti 2 recorded slightly higher value of 8.6 while Apitipiti 3 (9.5) exceeded the limit. According to UMA (2016), pH above 6.5-8.0 range negatively affects aquatic animals in rivers. Although, with reference to NIS, all the river samples were within the permissible range of 6.5–9.5, indicating that the water samples Table 1 Physicochemical analysis of the water samples from the selected rivers Parameters Water Samples WHO Standard (2007) NIS (2011) A B C D E Temperature ( O C) 36.00 ± 0.0 a 33.00 ± 0.0 a 34.00 ± 0.0 a 29.00 ± 0.0 a 29.00 ± 0.0 a NA Ambient pH 8.50 ± 0.0 a 9.30 ± 0.0 a 8.60 ± 0.0 a 6.70 ± 0.0 a 6.80 ± 0.0 a 6.5–8.5 6.5–9.5 EC (µS/cm) 100.00 ± 0.58 a 320.00 ± 0.58 d 540.00 ± 0.58 e 260.00 ± 0.58 c 130.00 ± 0.58 b 1000 1000 Iron (Mgl − 1 ) 40.011 ± 0.0017 e 1.411 ± 0.00058 c 0.611 ± 0.00 b 0.443 ± 0.00058 a 4.679 ± 0.00058 d 0.3 0.3 Copper (Mgl − 1 ) 0.110 ± 0.00058 d 0.074 ± 0.0 b 0.051 ± 0.00058 a 0.080 ± 0.0029 c 0.760 ± 0.00 bc 2.000 1.000 Zinc (Mgl − 1 ) 0.037 ± 0.0017 c 0.049 ± 0.0017 d 0.246 ± 0.0017 e 0.003 ± 0.00058 a 0.029 ± 0.0017 b NA 3.000 Chromium (Mgl − 1 ) 0 ± 0.0 0 ± 0.0 0 ± 0.0 0 ± 0.0 0 ± 0.0 0.05 0.05 Cadmium (Mgl − 1 ) 0 ± 0.0 0 ± 0.0 0 ± 0.0 0 ± 0.0 0 ± 0.0 0.003 0.003 Lead (Mgl − 1 ) 0.01 ± 0.0 c 0.0 ± 0.0 a 0.003 ± 0.00058 b 0.003 ± 0.00058 b 0.002 ± 0.00058 b 0.01 0.01 Nickel (Mgl − 1 ) 0.0 ± 0.0 a 0.0 ± 0.0 a 0.068 ± 0.02 c 0.041 ± 0.001 b 0.014 ± 0.0 a 2.0 1.0 Chloride (NTU) 43.20 ± 0.551 a 64.80 ± 2.354 c 93.27 ± 0.343 d 50.40 ± 0.064 b 43.20 ± 0.029 a 250 250 Sulphate (Mgl − 1 ) 3.355 ± 0.0 c 3.375 ± 0.00058 d 2.4280 ± 0.0055 b 0.0 ± 0.0 a 0.0 ± 0.0 a 500 100 Phosphate (Mgl − 1 ) 0.404 ± 0.002 b 0.209 ± 0.001 a 0.638 ± 0.013 c 0.204 ± 0.0 a 0.200 ± 0.0003 a 1.0 1.0 Turbidity (Mgl − 1 ) 8.533 ± 0.694 d 7.033 ± 0.186 c 5.967 ± 0.088 bc 5.0 ± 0.231 ab 4.0 ± 0.058 a 5 5 Nitrate (Mgl − 1 ) 0.0039 ± 0.00009 b 0.0 ± 0.0 a 0.0 ± 0.0 a 0.316 ± 0.0013 d 0.046 ± 0.00007 c 50 50 COD (Mgl − 1 ) 64.67 ± 0.333 e 24.33 ± 2.028 b 8.0 ± 0.577 a 32.33 ± 0.333 c 40.0 ± 1.732 d 10 NA BOD (Mgl − 1 ) 5.17 ± 0.088 b 2.60 ± 0.346 a 3.13 ± 0.606 a 2.37 ± 0.745 a 2.93 ± 0.606 a 5.0 NA Manganese (Mgl − 1 ) 0.20 ± 0.0115 c 0.06 ± 0.0058 b 0.10 ± 0.0 a 0.02 ± 0.0058 a 0.083 ± 0.0088 b 0.4 NA Fluoride (Mgl − 1 ) 0.110 ± 0.0058 c 0.06 ± 0.0115 b 0.037 ± 0.0088 ab 0.01 ± 0.0 a 0.04 ± 0.0115 b 1.5 NA WHO - World Health Organisation; NIS- Nigerian Industrial Standard; NA- Not Available Key A- Apitipiti 1; B- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin; COD- Chemical Oxygen Demand; BOD- Biological Oxygen Demand; EC- Electrical Conductivity. had acceptable pH values. This finding correlates with the study of Ayandiran et al . (2014), with river water sampled being within WHO and NIS permissible ranges. Furthermore, in this study, the average pH recorded across the five rivers during the dry season of sampling was 7.98. For the temperatures, there were no significant ( p > 0.05) differences among the sampled rivers in this study. In addition, the recorded temperature ranges (29.0–36.0 O C) of all the analysed water samples indicate a favourable environmental condition which supported the mesophilic bacteria isolated from the rivers. According to Hoorzook et al . (2021), the temperature of water is an essential environmental factor in aquatic ecosystems that affects the rate of metabolic activities of microorganisms. 3.1.2 Electrical conductivity and soluble salts in river water samples In this study, it was observed that the electrical conductivity (EC) and salt levels (chloride, fluorine, sulphate, phosphate and nitrate) of all the samples were within the WHO and NIS permissible limits. Although, the electrical conductivity (100–540 µS/cm) significantly ( p < 0.05) varied from one river to another, all the values were within the WHO and NIS permissible limit (1000 µS/cm). While Apitipiti 1 recorded the least (100 µS/cm) EC, Apitipiti 3, had the highest with 540 µS/cm. These findings correlate with the study of Hoorzook et al . (2021) where sampled rivers in South Africa, recorded an EC value below the permissible limit. Shoeb et al . (2022), reported that water conductivity influences the aquatic life of river animals. Likewise, a similar occurrence obtained for EC was recorded with salts of chloride, fluorine, sulphate, phosphate and nitrate salt levels of all the sampled rivers levels within the WHO and NIS permissible limit. This is in conformity with the findings of Egwuonwu et al . (2012), where the mineral content of the water samples studied were within the WHO standard. The similar trend recorded for EC and the soluble salts can be ascribed to the direct interrelationship between the parameters. Shoeb et al . (2022) reported that the level of electrical conductivity in water also indicates the presence of soluble ions. 3.1.3 Heavy metal contents in sampled river waters About 87.5% (7 out of 8) of the metals tested in the river water samples were within the WHO and NIS permissible limits. Only the iron concentrations in all analysed water samples exceeded both WHO and NIS permissible limit (0.3 Mgl − 1 ) with Apitipiti 1 (40.011 Mgl − 1 ) being significantly ( p < 0.05) higher than all the others. This may be ascribed to the ongoing road construction activity taking place at the time of sample collection. In addition, during sampling, it was observed that Apitipiti 1 River was surrounded by rocks. The elevated iron levels recorded across the rivers in this study correlates with that of Kumar et al . (2017), where all water samples contained elevated iron content higher than the permissible WHO and NIS range. Although, this trend is in contrast with a study conducted by Sarkar and Shekhar (2018), where the iron concentrations in sampled water recorded values below the WHO and NIS permissible limit. The rivers sampled in this study, maybe considered unfit as a result of the elevated iron content. Many factors may have contributed to the high Fe levels of recorded across all the samples. According to Khatri et al . (2017), iron gains entry into water bodies via geogenic sources, industrial effluents and dumping of domestic waste or pollution from iron and steel industries, iron metal corrosion and iron mining. High iron intake in humans has been linked to haemochromatosis (Kumar et al ., 2017), retinitis, conjunctivitis, choroiditis, cancer and heart diseases (Khatri et al ., 2017). Furthermore, for Cu (0.051–0.11 Mgl − 1 ) and Zn (0.003–0.245 Mgl − 1 ) contents in all the water samples, values recorded were within the WHO and NIS permissible range (1–2 and 3 Mgl − 1 ). According to Dietrich et al . (2004), surface and ground water usually contain low concentrations of copper. This finding correlates with that of Kusmana et al . (2018), where the sampled rivers in Indonesia had copper concentrations below the WHO and NIS permissible ranges. However, a more recent study by Khatib et al. (2023) obtained elevated levels of Cu in river samples which is at variance with the trend recorded in this study. For Zn concentrations, this study corroborates the findings of Khatib et al . (2023), where the sampled river waters in Japan, recorded zinc concentrations lower than the NIS permissible range. Heavy metals such as chromium (0.00 Mgl − 1 ), cadmium (0.00 Mgl − 1 ), lead (0.00-0.01 Mgl − 1 ), manganese (0.02–0.2 Mgl − 1 ) and nickel (< 0.2 Mgl − 1 ) were all within the WHO and NIS permissible range. This could indicate that these water samples were free from some major anthropogenic activities such as solid mineral mining and smelting which could trigger health hazards. Elevated levels of metal in water have been identified to pose both human health and environmental threat (Senze et al ., 2022). Human diseases such as cardiovascular disorders, neuronal damage, renal injuries and cancer have been reported as a result of heavy metal pollution (Rehman et al ., 2018). 3.1.4. Turbidity, Chemical Oxygen Demand and Biological Oxygen Demand With regards to the turbidity levels of the five rivers sampled in this study, only one river - Aba Apa Akinmorin River recorded a value (4.0. Mgl − 1 ) below the WHO and NIS – 5 Mgl − 1 permissible limit. Apitipiti 1, 2 and 3 had turbidity values (8.533, 7.033 and 5.967 Mgl − 1 respectively) that exceeded the permissible limit while Sogidi River recorded the same concentration as the permissible limit. A similar study on river turbidity levels by Dongsheng et al . (2022), recorded the same trend as observed in this study. The implication of high turbidity on water bodies is reduction in the aesthetic value and quality of such rivers, subsequently having a negative impact on tourism and recreation. The biological oxygen demand (BOD) of Apitipiti 2, Apitipiti 3, Sogidi and Aba Apa Akinmorin were below the permissible limit, while that of Apitipiti 1 was slightly above the permissible limit by 0.2 Mgl − 1 . The chemical oxygen demand (COD) of Apitipiti 1 (64.67 Mgl − 1 ), Apitipiti 2 (24.33 Mgl − 1 ), Sogidi (32.33 Mgl − 1 ), and Aba Apa Akinmorin (40.0 Mgl − 1 ) were way beyond the permissible limit (10 Mgl − 1 ). However, Apitipiti 3 recorded a COD level below the permissible limit. From the results, water samples from Apitipiti 1 had both its BOD and COD values being significantly ( p < 0.05) higher than all the other four rivers. The high COD as well as BOD values indicate an abundance of pollutants in the water. BOD is the measure of the amount of utilized oxygen by microbes, hence suggesting that these drinking river sources were probably polluted with improper disposal of domestic waste materials which could be through runoffs and most importantly they are polluted by organic matter such as faecal contamination and hence poor water quality similar report has been noted by Zhao et al . (2021). 3.2 Bacterial isolate counts and diversity from Atiba sampled rivers Four media namely - Nutrient Agar, MacConkey Agar, Eosin Methylene Blue Agar and Mannitol agar were used to determine the bacterial heterotrophic counts and diversity represented in the five sampled rivers in this study. 3.2.1 Bacteria Total Heterotrophic Counts in sampled rivers On nutrient agar, Sogidi river had significantly ( p 0.05) lower (0.00 Log CFU/mL) to that of the other rivers. However, on MacConkey agar, Aba Apa Akinmorin indicated significantly ( p < 0.05) higher microbial load than the other rivers (5.90 Log CFU/mL). While on EMB agar, Apititipiti 1 had significantly ( p < 0.05) higher (5.78 Log CFU/mL) microbial load compared to others. Overall, the total bacterial heterotrophic plate counts of all the water samples from the five rivers exceeded the WHO permissible limit for potable water (500 Cfu/mL) indicating that the rivers poor water quality that is unfit for drinking or domestic purposes. This finding corroborates with findings from various studies by Onuoha, (2017) and Ayandele et al . (2019). 3.2.2 Diversity of bacterial species in water samples of rivers A total of thirty-two ( 32 ) bacterial species were isolated from the five sampling rivers using different culture media. Twenty-one ( 21 ) of the isolates were Gram-positive while eleven ( 11 ) were Gram-negative. This study does not follow the trend of some previous studies. For instance, Onuoha, (2017) found only 7 Gram-positive bacteria and 19 Gram-negative bacteria being isolated from different water sources in South Eastern, Nigeria. In addition, another study conducted in Ghana by Odonkor et al . (2022), isolated more Gram-negative bacteria (83) compared to Gram-positive bacteria ( 27 ) from different water sources. In all, eight different genera (Fig. 3 ) of bacteria were isolated and identified as: Aeromonas ( 9 ), Bacillus ( 2 ), Corynebacterium ( 13 ), Lactobacillus ( 1 ), Pseudomonas ( 2 ), Staphylococcus ( 4 ), and Streptococcus ( 1 ). A study carried out by Ayandele et al . (2019) on a close by dam to where this study was conducted, isolated similar bacterial species of: Corynebacterium kutscheri, Streptococcus pyogenes, Shigella flexneri , Pseudomonas aeruginosa, Staphylococcus aureus , and Escherichia coli . Furthermore, Aeromonas were already isolated from sources like fresh water (Pessoa et al ., 2022). Although Escherichia coli was not detected in any of the five rivers sampled, this fact does not depict complete absence of the organisms or other enteric bacteria, the high level of observed BOD and COD implies possible presence of faecal contamination which probably was not just detected in the water at the time of sampling. This finding correlates with the findings of Odonkor and Addo (2018) in a study conducted in Ghana, where no E. coli strain was isolated from river water sources. Corynebacterium kutsceri had the highest frequency of occurrence of 11 while Aeromonas spp. followed closely with the second highest frequency ( 9 ). Lactobacillus and Streptococcus had the lowest frequency of occurrence at (3.0%) (Fig. 3 ). It is worth noting that some Aeromonas spp. have been identified as pathogenic microbe associated with water bodies and its presence in the rivers has been implicated as a major public health concern (Pessoa et al ., 2022). 3.3 Relationships among parameters using correlation coefficient and cluster analyses This study determined the correlation between different determined physicochemical, heavy metals and microbial parameters with the aid of correlation coefficient and the cluster analyses. 3.3.1 Interrelationship between physical, chemical and biological parameters For this study, the Pearson correlation analysis conducted indicated that there were significant relationships between the physicochemical and total heterotrophic counts variables. Sulphate exihibited significant to highly significant relationships with four key parameters (Supplementary table 1). This was reflected with temperature ( r = 0.93; p < 0.05), turbidity ( r = 0.90; p < 0.05) and pH ( r = 0.96; p < 0.01) having positive relationships and THB ( r =-0.90; p < 0.05) which had a negative significant relationship. Temperature and turbidity ( r = 0.91; p < 0.05) showed significant relationship with one another. With regards to EC, there was a highly significant relationship with chloride ( r = 0.98; p < 0.01) but a negative significant relationship with COD ( r =-0.92; p < 0.05) was obtained. In addition, chloride showed a significant negative relationship with COD ( r =-0.86; p < 0.05), while flouride showed a significant positive relationship with BOD ( r = 0.89; p < 0.05) and a negative significant correlation with THB ( r =-0.92; p < 0.05). Furthermore, the cluster analysis (Fig. 4 ) at 0.4, confirmed the trend observed in supplementary table 1. Three clusters were formed, the first cluster included nitrate and THB, the second cluster, turbidity, temperatures, pH, sulphate, COD, BOD and fluoride while the third cluster revealed that similarity existed among EC, chloride and phosphate. These results indicate the interrelationship that exists among physico-chemical and biological parameters. Previous studies (Saalidong et al. 2022) have established these findings. 3.3.2 Interrelationship between heavy metals and total heterotrophic bacterial counts Correlation existed only between selected heavy metals but not with THB (supplementary table 2). Iron revealed a significant relationship with lead ( r = 0.93; p < 0.05) and with manganese ( r = 0.91; p < 0.05). Hierarchical cluster analysis was performed to confirm the relatively homogeneous groups of heavy metals and their inter-relationship with bacterial counts (Fig. 5 ). At 0.15, the first cluster was formed. This result confirmed strong positive interactions among lead, iron and manganese. A second cluster was formed with only copper while a third cluster included zinc, nickel and THB. Similar study by Trueman et al. (2019) corroborate the findings of this study that positive interactions exist among heavy metals. 3.4 Exposure of isolated bacterial species from rivers to antibiotics Bacterial species isolated from the five rivers when subjected to antibiotic susceptibility testing revealed that most of the bacterial isolates (both Gram-positive and Gram-negative bacteria) were susceptible to Carbapenems (Imipenem and Meropenem) and Gentamicin (Fig. 6 ). Although, on exposure to Ceftazidime and Oxacillin, most isolates exhibited resistance. Of all the bacterial species isolated from the rivers sampled in this study, 81.8% showed multidrug-resistance (MDR) with a multi-antibiotic resistance (MAR) index > 0.2 (Tables 2 & 3 ). Several studies have previously reported similar trends. From Africa, Ayandiran et al . (2014), in a study conducted in another Southwestern State in Nigeria reported a prevalence of 100% MDR bacteria; Addae-Nuku et al . (2022), also reported the prevalence of 55.5% MDR bacteria isolates from a river in Accra, Ghana. Furthermore, a study carried out by Koumaré et al . (2022) in Mali on the Niger River, reported a prevalence of 81.8% MDR bacteria, being resistant to more than two antibiotics. In addition, other studies conducted in Malaysia by Salikan et al . (2020) on different rivers recorded a prevalence of 50% and 95.8% MDR bacteria while in Spain, Perez-Etayo et al . (2020) reported 96.4% isolates exhibited multi-drug resistance in some Northern Rivers. In this study, it was observed that one Gram-positive ( Corynebacterium sp.) and one Gram-negative ( Aeromonas sp.) specie revealed the highest MAR of 90.9% resistance to the antibiotics tested. This corroborates the findings of Ayandele et al . (2019) in Nigeria that reported the multidrug-resistant trait of Corynebacterium species isolated from a close by dam to where the current study was carried out. Furthermore, in Brazil, Conte et al . (2021) published the multidrug-resistance of Aeromonas species isolated from aquatic environments. The observed high frequency of bacterial resistance in this study is of huge health concern. This phenomenon may result in the therapeutic failure of the river fauna population. In addition, the human health of the community may be endangered by being at risk of infection from pathogens animals, coupled with the possibility of plasmid transfer of resistance to human pathogenic bacteria (Nwobodo et al ., 2022). The occurrence of antibiotic resistant bacteria in the rivers sampled for this study, reveals that antibiotics may be indiscriminately used by members of the communities. The resistance pattern by the isolated bacteria to most of the antibiotics tested may likely be through discharge of antibiotics in considerable amounts via human waste (Dahunsi et al ., 2014). Usually, indigenous bacteria in their natural environments develop resistance to antibiotics mostly when they persist in such environments, hence, this supports bacterial antibiotic resistance selection. This might also be due to the ineffectual regulation of using antibiotics to treat gastrointestinal infections, which results in lesser alternatives for therapeutic treatments (Delgado-Gardea et al ., 2016). Table 2 Antibiotics resistance pattern of Gram-positive bacterial isolates from water samples of rivers River Source Bacterial Isolates IMI MEM COT GEN OXA CAZ AUG CIP ERY VAN CHL MAR Index Resistance (%) A. Corynebacterium kutsceri S S S S R R S S R S R 0.4 36.4 Corynebacterium kutsceri S R S S R R S S R R R 0.5 54.5 Lactobacillus sp. S S S S S S S S S S S 0.0 0.0 Streptococcus sp. S S S S S S S S R R R 0.3 27.3 Staphylococcus aureus S S R S R R R S R R R 0.6 63.6 F. Corynebacterium kutsceri S S S S R R S S R R R 0.5 45.5 Staphylococcus aureus R S R S R R S S I S R 0.5 45.5 H. Corynebacterium kutsceri S R R S R R R S R R S 0.6 63.6 Corynebacterium kutsceri S S R I R R R R R R R 0.7 72.7 Corynebacterium kutsceri S S S S R R S S S S S 0.2 18.2 Bacillus sp. S S S S R R S S R I S 0.3 27.3 Corynebacterium xerosis S S S S I R S S S I R 0.2 18.2 Corynebacterium kutsceri I I R I R R R S R R R 0.6 63.6 Corynebacterium kutsceri S S S S I I S S S S R 0.1 9.1 Staphylococcus aureus S S I R R R R R R R R 0.7 72.7 Corynebacterium kutsceri S S S S R R S I R S S 0.3 27.3 Corynebacterium kutsceri S S S S R R S S S S R 0.3 27.3 Corynebacterium kutsceri R R R S R R R R R R R 0.9 90.9 S. Bacillus sp. S S S S I R S S R I R 0.3 27.3 T. Staphylococcus aureus S S I S R R S R R R R 0.5 54.5 Corynebacterium xerosis S S S S S R S S S I R 0.2 18.2 Key : A- Apitipiti 1; B- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin R – Resistant; I- Intermediate, S – Sensitive, and µ-Microgram. IMI- Imipenem (10 µg); MEM- Meropenem (10 µg); COT- Cotrimoxazole (25 µg); GEN- Gentamicin (10 µg); CAZ- Ceftazidime (30 µg); AUG- Augmentin (20 µg); CIP- Ciprofloxacin (5µg); ERY- Erythromycin (15 µg); VAN- Vancomycin (30 µg; OXA-Oxacillin (1 µg); CHL- Chloramphenicol (12.5 µg); MAR- Multi Antibiotics Resistance. Table 3 Antibiotics resistance pattern of Gram-negative bacteria isolated from water samples of rivers studied Sample Bacterial Isolates IMI MEM COT GEN OXA CAZ AUG CIP ERY TET VAN MAR Index Resistance (%) B. Aeromonas sp. S R R S R R R I R R R 0.7 72.7 Aeromonas sp. S S R R R S R R R R R 0.7 72.7 Aeromonas sp. I R R R R R R R R R R 0.9 90.9 Aeromonas sp. S S S S R R S S R S I 0.3 27.3 C. Aeromonas sp. I S R S R S R S R R R 0.5 54.5 Aeromonas sp. S S R R R R R S R R R 0.7 72.7 D. Aeromonas sp. S S R S R R R R R R R 0.7 72.7 E. Pseudomonas sp. S S R R R S R S R R R 0.6 63.6 Aeromonas sp. S S S S R R S S R S S 0.3 27.3 Pseudomonas sp. S R R S R S R S R R R 0.6 63.6 Aeromonas sp. S S S S R R S S S S I 0.2 18.2 Key B- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin; R – Resistant; I- Intermediate; S – Sensitive and µ-Microgram. IMI - Imipenem (10 µg); MEM - Meropenem (10 µg); COT - Cotrimoxazole (25 µg); GEN - Gentamicin (10 µg); CAZ - Ceftazidime (30 µg); AUG - Augmentin (20 µg); CIP - Ciprofloxacin (5µg); ERY - Erythromycin (15 µg); VAN - Vancomycin (30 µg); TET - Tetracycline (30 µg); OXA -Oxacillin (1 µg); MAR - Multi Antibiotics Resistance. Environmental factors play a crucial role in the development and spread of antibiotic resistant bacteria (ARB). Water used for agriculture, recreational, drinking, and other domestic purposes are risk sources to humans. Therefore, the frequent assessment of the antimicrobial resistance patterns in potable water and their sources is paramount for monitoring the spread, which can assist in the development of preventive strategies (Ateba et al ., 2020). Antibiotic resistance increases patient mortality and morbidity as well as increases the financial burden of disease especially in low-income countries like Nigeria. Hence, proper programmes to monitor antimicrobial usage and resistance in bacteria from aquatic environments is encouraged for the purpose of implementation in Nigeria. This is essential for the fact that acquired bacterial resistance to antibiotics is prevalent as seen in this study, which is usually due to the misuse and abuse of antibiotics (Hoorzook et al ., 2021). 4. CONCLUSION The physicochemical parameters, microbial identities and diversity as well as the antibiotics resistance profile of five rivers were assessed in this study. Each of the five rivers examined in this study has its peculiarity as regards the parameters tested in evaluating the standard quality of the water samples. Water samples revealed some level of pollution, microbial load and prevalence of multidrug-resistant bacteria in the rivers. The pH and temperature were of optimum values within the WHO and NIS permissible limits. On the other hand, EC, turbidity, COD and BOD exceeded the permissible limits. Generally, most heavy metals tested – Cu, Cd, Cr, Mn, Zn, Ni and Pb were within acceptable drinking limits across the five rivers except Fe. There were indications of interactions among the physical, chemical and biological parameters. With regards to microbial quality, all the river sampled were found unfit and not potable according to the WHO standard. This study also recorded one of the rivers having as high as 90.9 % MDR prevalence. This may pose a human health threat due to vertical/horizontal gene transfer. Overall, this study reveals that these rivers are unfit for consumption/domestic or recreation purposes. Proper treatment by chlorination and ultra-violet treatment are therefore suggested if these water sources are to be used for human consumption. This study is only preliminary; extensive research into each of these rivers and associated environmental conditions as well as investigation into the possible long-term risks of water borne diseases via the ingestion of these drug resistant bacteria is advocated. Declarations Author Contribution B. M. Popoola - Conceptualization and design of the manuscript.J. P. Ogwerel - Material preparation, data collection, analysis.O. G. Oladipo - Contribution to concept and design.All authors contributed towards manuscript writing and analysis. Funding Declaration This research was self-funded. Competing Interest Statement Authors declare that there are no competing interest. References Achi, C. R., Ayobami, O., Mark, G., Egwuenu, A., Ogbolu, D. & Kabir, J. 2021 Operationalising One Health in Nigeria: reflections from a high-level expert panel discussion commemorating the 2020 World Antibiotics Awareness Week. Frontiers in Public Health 9 (673504). https://doi.org/10.3389/fpubh.2021.673504. Addae-Nuku, D. S., Kotey, F. C., Dayie, N. T., Osei, M. M., Tette, E. M., Debrah, P. & Donkor, E. S. 2022 Multidrug-resistant bacteria in hospital wastewater of the Korle Bu teaching hospital in Accra, Ghana. Environmental Health Insights 16 , 11786302221130613. https://doi.org/10.1177/11786302221130613. Ateba, C. N., Tabi, N. M., Fri, J., Bissong, M. & Bezuidenhout, C. C. 2020 Occurrence of antibiotic-resistant bacteria & genes in two drinking water treatment & distribution systems in the North-West Province of South Africa. Antibiotics (Basel, & Switzerl), 9 (11), 745. https://doi.org/10.3390/antibiotics9110745. Ayandele, A. A., Ajala, O. O., Oyekemi, S. A., Awotunde M. O., Ajayi, O. M., Gbadamosi, A. B. & Adedugbe, J. 2019 Microbial & Physico-chemical Analysis of Water from Boreholes in Mosimi & Environs, Ogun State, Nigeria. British Journal of Applied Science & Technology 8 (2), 219-225. https://doi.org/2015/BJAST/2015/16941. Ayandiran, T. A., Ayandele, A. A., Dahunsi, S. O. & Ajala, O. O. 2014 Microbial Assessment & prevalence of Antibiotic Resistance in polluted Oluwa River, Nigeria. Egyptian Journal of Aquatic Research 40 , 291-299. https://doi.org/10.1016/j.ejar.2014.09.002. Bayot, M. L. & Bragg, B. N. 2021 Antimicrobial Susceptibility Testing. https://www.ncbi.nlm.nih.gov/books/NBK539714/. (Accessed on 30 March 2022). Chukwu, E. E., Oladele, D. A., Awoderu, O. B., Afocha, E. E., Lawal, R. G., Abdus-Salam, I., Ogunsola, F. & Audu, R. A. 2020 A national survey of public awareness of antimicrobial resistance in Nigeria. Antimicrobial Resistance & Infection Control 9 (1), 1-10. https://doi.org/10.1186/s13756-020-00739-0 Clinical and Laboratory Standards Institute (CLSI), 2020 Performance Standards for Antimicrobial Susceptibility Testing, 30th Edition, CLSI supplement M100, Wayne, PA, USA. Conte, D., Palmeiro, J. K., Bavaroski, A. A., Rodrigues, L. S., Cardozo, D., Tomaz, A. P., Camargo, J. & Dalla‐Costa, L. M. 2021 Antimicrobial resistance in Aeromonas species isolated from aquatic environments in Brazil. Journal of Applied Microbiology 131 (1), 169-181. https://doi.org/10.1111/jam.14965. Dahunsi, S. O., Owamal, H. I., Ayandiran, T. A. & Oranusi, U. S. 2014 Drinking water quality & public health of selected communities in South Western Nigeria. Water Quality and Experimental Health 6 , 143-153. Delgado-Gardea, M. C., Tamez-Guerra, P., Gomez-Flores, R., Zavala-Díaz de la Serna, F. J., Eroza-de la Vega, G., Nevárez-Moorillón, G. V., Pérez-Recoder, M. C., Sánchez-Ramírez, B., González-Horta, M. & Infante-Ramírez, R. 2016 Multidrug-Resistant Bacteria Isolated from Surface Water in Bassaseachic Falls National Park, Mexico . International Journal of Environmental Research & Public Health 13 (6), 597. https://doi.org/10.3390/ijerph13060597 Dietrich, A. M., Glindemann, D., Pizarro, F., Gidi, V., Olivares, M., Araya, Camper, A., Duncan, S., Dwyer, S., Whelton, A. J., Younos, T., Subramanian, S., Burlingame, G. A., Khiari, D., & Edwards, M. 2004 Health and aesthetic impacts of copper corrosion on drinking water. Water Science and Technology 49 (2), 55-62. https://doi.org/10.2166/wst.2004.0087 Dongsheng, W., Xiao, C., Kaiwei, M., Zhixuan, L. & Lianqing, D. 2022 Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model. Water Supply 22 (1), 1107–1119. https://doi.org/10.2166/ws.2021.213 Dubey, R. C. & Maheshwari, D. K. 2004 Practical microbiology. S. Chad & Company Ltd. New Delhi, pp. 162-301. Egwuonwu, C. C., Uzoije, A. P., Okafor, V. C., Ezeanya, N.C, Nwachukwu, M. U. 2012 Evaluation of the effects of industrial wastewater discharge on surface water (A case study of Nigeria Breweries Plc Enugu). Greener Journal of Physical Sciences , 2 (3), 056-063. Grenni, P., Ancona, V. & Barra Caracciolo, A. 2018 Ecological effects of antibiotics on natural ecosystems: a review. Microchemical Journal 136 , 25–39. https://doi.org/10.1016/j.microc.2017.02.006. Greweling, T. & Peech, M. 1968 Chemical soil tests. Bulletin 960 , Cornell Experiment Station, Ithaca. NY, pp. 23–24. Hile, T. D., Dunbarb, S. G. & Sinclair, R. G. 2023 Microbial contamination analysis of drinking water from bulk dispensers & fast-food restaurants in the Eastern Coachella Valley, California. Water Science & Technology Water Supply 1-19. DOI:10.2166/ws.2023.200. Hoorzook, K. B., Pieterse, A., Heine, L., Barnard, T. G. & van Rensburg, N. J. 2021 Soul of the Jukskei River: The extent of bacterial contamination in the Jukskei River in Gauteng Province, South Africa. International Journal of Environmental Research & Public Health 18 (16), 8537. https://doi.org/10.3390/ijerph18168537. Hutchings M. I., Truman A. W. & Wilkinson, B. 2019 Antibiotics: past, present & future. Current opinion in microbiology 51 , 72-80. https://doi.org/10.1016/j.mib.2019.10.008. Khatib, M., Daoud, M., Arairo, W., Saba, M. & Mortada, H. 2023 Water quality parameters assessment of Ras El-Ain Natural Ponds, Tyr, Lebanon. Water Supply 23 (6), 2575–2585. https://doi.org/10.2166/ws.2023.140. Khatri, N., Tyagi, S. & Rawtani, D. 2017 Recent strategies for the removal of iron from water: A review. Journal of Water Process Engineering 19 , 291-304. Koumaré, Y., Babana, A. H., Bah, A., Kassogué, A., Dao, S., Diallo, K. & Faradji, F. 2022 Bacteria isolated from Niger River water in Bamako showed multi-resistance to antibiotics. MedCrave Online Journal of Biology & Medicine 7 , 71-74. https://doi.org/10.15406/mojbm.2022.07.00168. Kumar, V., Bharti, P. K., Talwar, M., Tyagi, A. K. & Kumar, P. 2017 Studies on high iron content in water resources of Moradabad district (UP), India. Water Science 31 (1), 44-51. https://doi.org/10.1016/j.wsj.2017.02.003. Kusmana, C., Wahwakhi, S., Ghozali, A. A. & Iswantini, D. 2018 Cu metal concentration in the water & sediment of Surabaya’s flowed-Jagir River Estuary. IOP Conference Series: Earth & Environmental Science 203 . https://doi.org/10.1088/1755-1315/203/1/012014. Li, S., Zhang, C., Li, F., Hua, T., Zhou, Q. & Ho, S. H. 2021 Technologies towards antibiotic resistance genes (ARGs) removal from aquatic environment: A critical review. Journal of Hazardous Materials 411 , 125148 . https://doi.org/10.1016/j.jhazmat.2021.12514. Murphy, J. & Riley, J.P. 1962 A Modified single solution method for determination of phosphate in natural waters. Analytica Chimica Acta 27 , 31-36. https://doi.org/10.1016/S0003-2670(00)88444-5. Nigerian Standard for Drinking Water Quality. 2007 Nigerian Industrial Standards (NIS). Pp. 14-18. Nwobodo, D. C., Ugwu, M. C., Anie, C. O., S. Al-Ouqaili, M. T., Ikem, J. C., Chigozie, U. V., & Saki, M. 2022 Antibiotic resistance: The challenges and some emerging strategies for tackling a global menace. Journal of Clinical Laboratory Analysis 36 (9), e24655. https://doi.org/10.1002/jcla.24655 Odonkor, S. T. & Addo, K. K. 2018 Prevalence of multi-drug-resistant Escherichia coli isolated from drinking water sources. International journal of Microbiology 19 (3). Odonkor, S. T., Simpson, S. V., Morales Medina, W. R. & Fahrenfeld, N. L. 2022 Antibiotic-resistant bacteria & resistance genes in isolates from Ghanaian drinking water sources. Journal of Environmental & Public Health https://doi.org/10.1155/2022/2850165. Oladipo, A.O., Oladipo, O.G. & Bezuidenhout, C.C. 2019 Multi-drug resistance traits of methicillin-resistant Staphylococcus aureus & other Staphylococcal species from clinical & environmental sources. Journal of Water & Health , 17 (6), 930-943. https://doi.org/10.2166/wh.2019.177. Onuoha, S. C. 2017 The prevalence of antibiotic resistant diarrhogenic bacterial species in surface waters, South Eastern Nigeria. Ethiopian journal of health sciences 27 (4), 319-330. https://doi.org/10.4314/ejhs.v27i4.3. Pérez-Etayo, L., González, D., Leiva, J. & Vitas, A. I. 2020 Multi-drug-resistant bacteria isolated from different aquatic environments in the North of Spain & South of France. Microorganisms 8 (9), 1425. https://doi.org/10.3390/microorganisms8091425. Pessoa, R. B., Oliveira, W. F., Correia, M. T., Fontes, A., & Coelho, L. C. 2022 Aeromonas and human health disorders: Clinical approaches. Frontiers in Microbiology 13 , 868890. https://doi.org/10.3389/fmicb.2022.868890. Peterson E. & Kaur P. 2018 Antibiotic resistance mechanisms in bacteria: relationships between resistance determinants of antibiotic producers, environmental bacteria, & clinical pathogens. Frontiers in Microbiology 9 (2928). https://doi.org/10.3389/fmicb.2018.02928. Rehman, K., Fatima, F., Waheed, I. & Akash, M. 2018 Prevalence of exposure of heavy metals & their impact on health consequences. Journal of Cellular Biochemistry 119 (1), 157–184. https://doi.org/10.1002/jcb.26234. Saalidong, B.M., Aram, S.A., Out, S. & Lartey, P.O. 2022 Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems. PLoS ONE 17 (1): e0262117. https://doi.org/10.1371/journal.pone.0262117 Salikan, N. A., Zain, N. A. M. & Kam, K. A. R. 2020 Isolation of antibiotic resistant bacteria from rivers in Terengganu, Malaysia. International Journal of Life Sciences & Biotechnology 3 (3), 241-257. Sarkar, A. & Shekhar, S. 2018 Iron contamination in the waters of Upper Yamuna basin. Groundwater for Sustainable Development https://doi.org/10.1016/j.gsd.2017.12.011. Schmitz, K. S. 2017 Gibbs free energy, work, & equilibrium . Physical Chemistry, 99–157. https://doi.org/10.1016/b978-0-12-800514-9.00003-1. Sen, D. 2019 Types of Aquatic Ecosystems. https://sciencing.com/types-aquatic-ecosystems-6123685.html. (accessed on 17 March 2022). Senze, M., Kowalska-Góralska, M., Czyż, K. & Wondołowska-Grabowska, A. 2022 Possibility of metal accumulation in reed canary grass ( Phalaris arundinacea L.) in the aquatic environment of South-Western Polish Rivers. International Journal of Environmental Research & Public Health 19 (13), 7779. https://doi.org/10.3390/ijerph19137779. Shoeb, M., Sharmin, F., Islam, Md, Nahar, N.l., Islam, L. R. and Parvin N. 2022 Assessment of physico-chemical parameters of water samples collected from the southern part of Bangladesh. Dhaka University Journal of Science 70 (1): 49-57 DOI: https://doi.org/10.3329/dujs.v70i1.60381. Trueman, B.F., Gregory, B.S., McCormick, N.E., Gao, Y., Gora, S., Anaviapik-Soucie, T., L’Hérault, V. & Gagnon, G. A. 2019 Manganese increases lead release to drinking water. Environmental Science & Technology . 53 (9): 4803-4812. doi: 10.1021/acs.est.9b00317 UMA (University of Massachusetts Amherst), 2016 Protocols for river, Massachusetts water watch partnership, https://www.umass.edu/mwwp/protocols/rivers/ph_alkalinity_ river .html. UNICEF. 2018 Water, Sanitation and Hygiene. https://www.unicef.org/nigeria/water-sanitation-and-hygiene (accessed 23 September 2023). Wang, Z., Yang, Y., Gomes, M. P. & Suthar, S. 2022 Antibiotics & Antibiotic Resistance Genes in Waters: Pollution, Risks, & Control. Frontiers in Environmental Science 10 , 967118. https://doi.org/10.3389/fenvs.2022.967118. World Health Organization (WHO). 2017 Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum. World Health Organization, Geneva. https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease. (Accessed 12 Aug 2023). Zhao, Y., Zhang, L., Zhang, M., Wu, J., Li, S., Ran, D., Sun, L. & Zhu, G. 2021 Effect of chemical oxygen demand concentration on nutrient removal in simultaneous nitrification, denitrification and phosphorus removal system in high-altitude areas. Water 13 (19), 2656. https://doi.org/10.3390/w13192656. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Published Journal Publication published 15 Oct, 2024 Read the published version in Environmental Monitoring and Assessment → Version 1 posted Editorial decision: Revision requested 13 Jun, 2024 Editor assigned by journal 04 Jun, 2024 Submission checks completed at journal 04 Jun, 2024 First submitted to journal 21 May, 2024 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-4457954","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":314196023,"identity":"4b91ab76-cd50-4df8-b2b2-882080dc9b86","order_by":0,"name":"Bukola Margaret Popoola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBACAxiDj4H5AIR1gFgtbAxsCSRr4TEgTou52AG2Bx932MixsZ/5+OFnG4Mc340Etodf8GixnJ3AbjjzTJoxG0/uZsneNgZjyRsJ7MYy+Bx2O4FNmrftcGIbQ+4GacY2hsQNQFukJYjSwv/m8W+glnoStEjksIFsSTAAapH8gNcvie0Qv0g8M7PsOScB5Dxsk8ajg8FcOvkYOMT4+ZMf3/hRZiPPdzz5mOQPfHoYgI5hbIDzQJ5gbGDmwasFGIlIWqDG4LdlFIyCUTAKRhgAAAIoSYnw8UVnAAAAAElFTkSuQmCC","orcid":"","institution":"Ajayi Crowther University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Bukola","middleName":"Margaret","lastName":"Popoola","suffix":""},{"id":314196025,"identity":"05b93d77-e0ef-415b-b94e-2707d7001736","order_by":1,"name":"Jemimah Pearl Ogwerel","email":"","orcid":"","institution":"Ajayi Crowther University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jemimah","middleName":"Pearl","lastName":"Ogwerel","suffix":""},{"id":314196027,"identity":"caea3440-2fd4-443d-a543-35495ed12ab5","order_by":2,"name":"Oluwatosin Gbemisola Oladipo","email":"","orcid":"","institution":"First Technical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Oluwatosin","middleName":"Gbemisola","lastName":"Oladipo","suffix":""}],"badges":[],"createdAt":"2024-05-22 03:36:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4457954/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4457954/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10661-024-13117-9","type":"published","date":"2024-10-15T15:57:31+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":58551432,"identity":"b8b4ab77-7a19-4706-a2bc-471dbcf811fd","added_by":"auto","created_at":"2024-06-18 06:55:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140584,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMap of Oyo State, Nigeria\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/a48b032aec9c8afc8699766f.png"},{"id":58552068,"identity":"98aaf959-7122-4ce3-888f-e8fb7c8f054b","added_by":"auto","created_at":"2024-06-18 07:03:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHeterotrophic bacterial plate counts from sampled rivers using different culture media\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKey\u003c/strong\u003e: Nutrient Agar -NA; MacConkey Agar- MAC; Eosin Methylene Blue Agar- EMB; and Mannitol agar- MA. A- Apitipiti 1; B- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/119a85ad63a23290360eb270.png"},{"id":58551429,"identity":"d7d083ce-bc07-4b94-b2a9-44a6bf1e486f","added_by":"auto","created_at":"2024-06-18 06:55:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":37185,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDiversity of occurrence of bacterial isolates isolated from rivers\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/ce7123548487fab0ee25f428.png"},{"id":58551431,"identity":"c6615862-ba2d-44c9-b087-9867b7095e08","added_by":"auto","created_at":"2024-06-18 06:55:30","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":16182,"visible":true,"origin":"","legend":"\u003cp\u003eHierarchical dendogram of physicochemical, biological and microbial parameters obtained on rivers samples by the clustering method\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/3a955c98e99432491ee0ed81.png"},{"id":58551428,"identity":"00ed5787-3531-4209-b12d-31a0644742e2","added_by":"auto","created_at":"2024-06-18 06:55:29","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14072,"visible":true,"origin":"","legend":"\u003cp\u003eCluster analysis of heavy metals and total bacterial counts in rivers sampled\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/0d1ea3bc2ca634af9b04425a.png"},{"id":58551430,"identity":"4466bfd6-dec4-4cda-ba3a-c24d8a612dc0","added_by":"auto","created_at":"2024-06-18 06:55:30","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":20854,"visible":true,"origin":"","legend":"\u003cp\u003eAntibiotic sensitivity pattern of the Gram positive and Gram-negative bacterial isolates from the selected river\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/e196e38baa75c792f30af38a.png"},{"id":67149716,"identity":"d7e1220b-cf42-4b50-9ffe-fa6a116842f2","added_by":"auto","created_at":"2024-10-21 16:13:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1513509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/c21f5be9-1266-4f69-b473-641828a81d3d.pdf"},{"id":58551426,"identity":"62335785-3315-4baa-abd0-5b190e178cdb","added_by":"auto","created_at":"2024-06-18 06:55:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19249,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4457954/v1/5ff1d2cc6633b111fb3b68d2.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bacterial isolates from Drinking water river sources exhibit multi-drug resistant trait","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAbout 70% of the earth's surface is covered with water either as fresh water, brackish water or as saline water. Water, an important earth resource, is very vital to humans, plants and animals. Man utilizes this indispensable natural resource for various activities including domestic, industrial, agricultural and or recreational purposes. To the ecosystem, water serves as habitat for aquatic organisms such as fish, crocodile, snails, frogs, bacteria, fungi, protozoans, algae and viruses and these organisms interact with each other and form the aquatic ecosystem (Sen, 2019).\u003c/p\u003e \u003cp\u003ePotable water is crucial to human existence. Unfortunately, some people struggle to have access to safe water. The microbial quality of water determines to a large extent its usefulness especially for domestic usage. Globally, bacterial contamination of water brings about serious public health threat (Hile \u003cem\u003eet al\u003c/em\u003e., 2023). Bacteria are one of the major pathogens responsible for waterborne diseases, and they have been implicated with many gastrointestinal outbreaks worldwide (Delgado-Gardea \u003cem\u003eet al\u003c/em\u003e., 2016). According to WHO (2017), every year, about 525,000 children under five come down with diarrhoea as a result of water borne diseases. Furthermore, in Nigeria, it has been reported that UNICEF recorded about 117,000 children\u0026rsquo;s death each year due to waterborne diseases (UNICEF, 2018).\u003c/p\u003e \u003cp\u003eHowever, in spite of the continuous efforts to improve water quality worldwide, waterborne disease outbreaks are still reported (Hile \u003cem\u003eet al\u003c/em\u003e., 2023). Bacteria associated with waterborne diseases include \u003cem\u003eEscherichia coli\u003c/em\u003e, \u003cem\u003eLegionella\u003c/em\u003e spp., \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e, \u003cem\u003eAeromonas\u003c/em\u003e and \u003cem\u003eMycobacterium\u003c/em\u003e spp. Other waterborne pathogen are usually obligate pathogens, they multiply only in an infected host. They include: \u003cem\u003eCampylobacter, Salmonella, Shigella, Escherichia coli, Acinetobacter\u003c/em\u003e sp., \u003cem\u003eClostridium\u003c/em\u003e spp., \u003cem\u003eand Bacillus anthracis\u003c/em\u003e and \u003cem\u003eHelicobacter pylori.\u003c/em\u003e (Delgado-Gardea \u003cem\u003eet al.\u003c/em\u003e, 2016). When in the human hosts, these waterborne pathogens are usually eliminated by the use of antibiotics.\u003c/p\u003e \u003cp\u003eOver the years and decades, antibiotics are the widely known antimicrobials used for the treatment of infectious diseases curbing, the rate of morbidity and mortality (Chukwu \u003cem\u003eet al\u003c/em\u003e., 2020). However, the misuse of antibiotics has led to the development of antimicrobial resistance. When consumed, antibiotics are broken down partially in the body, consequently, a large amount is excreted in their normal form or as active metabolites through urine and faeces, and these get into wastewater treatment plants. Unfortunately, wastewater treatment plants are not designed to eliminate antibiotics, hence, these antibiotics are eventually released into the environment including freshwaters (Grenni \u003cem\u003eet al\u003c/em\u003e., 2018) by erosion, sewage run off, or leaching (Wang \u003cem\u003eet al\u003c/em\u003e., 2022).\u003c/p\u003e \u003cp\u003eThe deposition of antibiotic residues into the environment, leads to the development of antimicrobial resistance in some bacteria. These bacterial species are referred to as Antibiotic Resistant Bacteria (ARB) and they contain Antibiotic Resistance Genes (ARGs) which are transferred horizontally or vertically to other bacteria (Peterson \u0026amp; Kaur, 2018). Antimicrobial Resistance (AMR) has become a serious global issue as it leads to a reduction in treatment options for bacterial infections thereby reducing clinical efficacy while increasing treatment costs and mortality (Oladipo \u003cem\u003eet al\u003c/em\u003e., 2019).\u003c/p\u003e \u003cp\u003eIn Nigeria, antimicrobial resistance is of top priority on the national public health agenda (Achi \u003cem\u003eet al\u003c/em\u003e., 2021). Hence, this study was designed to evaluate the physico-chemical parameters, microbial population, diversity and the antibiotic resistance pattern of heterotrophic bacteria in some selected rivers in Oyo town, Nigeria which serve as source of water supply to residents around the areas; for tourism (Sogidi), fishing (Apitipiti 1, Apitipiti 2, Apitipiti 3), and drinking (Sogidi, and Aba Apa Akinmorin).\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Sample collection sites\u003c/h2\u003e \u003cp\u003eOyo State (7\u0026deg; 51̍ 09.25 N and 3\u0026deg; 55̍ 52.50 E) is one of the largest major states (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) in Southwestern, Nigeria with a population of about 8\u0026nbsp;million. Five rivers were strategically sampled in one of the Local Government Areas (LGA) of the state \u0026ndash; Atiba LGA. The five rivers studied are flowing water bodies namely Apitipiti 1, Apitipiti 2, Apitipiti 3, Sogidi, and Aba Apa Akinmorin. These rivers are mainly used for fishing (Apitipiti 1, Apitipiti 2, Apitipiti 3), tourism (Sogidi) and drinking (Sogidi, and Aba Apa Akinmorin).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Sample collection\u003c/h2\u003e \u003cp\u003eSampling sites were purposely selected and some principal rivers within Oyo town were selected. The water samples were collected during dry season at a depth of 20 cm. While plastic bottles were used to collect water samples for physicochemical analysis, sterile McCartney bottles were used to collect water samples for microbiological analysis. The plastic bottles were rinsed with the river water thrice and water was collected. The sterile McCartney bottles were held with sterile forceps and dipped into the water and the water was collected and capped immediately. The forceps were sterilised intermittently with 70% ethanol. The bottles were labelled appropriately, and were transported to the laboratory for analyses in ice pack.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Water quality evaluation\u003c/h2\u003e \u003cp\u003eThe physical and chemical properties of the various water samples were determined. Physical parameters such as pH, temperature, and electrical conductivity were determined \u003cem\u003ein-situ\u003c/em\u003e using a hand digital metre (HI 9812-5, Hanna Instruments, Woonsocket, RI, USA). Chemical properties measured include heavy metal concentrations, biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), turbidity and anions concentration. The BOD of the samples was determined using Winkler\u0026rsquo;s method while COD was conducted using titration method (Schmitz, 2017). Nitrate concentration was determined using the brucine method (Greweling \u0026amp; Peech, 1968) and phosphate using the molybdenum blue method (Murphy and Riley, 1962). The Mohr Method was used to determine the chloride concentrations in the water samples while sulphate was determined using turbidimetric method. The concentrations of fluoride ion (F) and heavy metals such as; Iron (Fe), Copper (Cu), Zinc (Zn), Chromium (Cr), Cadmium (Cd), Lead (Pb), Nickel (Ni) and Manganese (Mn) were determined using atomic absorption spectrophotometer (Model 210/211 VGP Buck Scientific Atomic Absorption Spectrometer, East Norwalk, CT., USA) (Ayandiran \u003cem\u003eet al.\u003c/em\u003e 2014).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Microbial analysis\u003c/h2\u003e \u003cp\u003eFor the microbial analyses, samples were serially diluted into five test tubes that were labelled appropriately. One mL each of the water samples was dispensed into sterile test tubes containing 9 mL sterile distilled water, a serial dilution of five folds was carried out. One mL of the 3rd and 5th dilutions were pipette into sterile petri dishes using pour plate method. The media used were Nutrient Agar (NA) (NA, Oxoid Ltd., Basingstoke, UK), MacConkey Agar (MAC), Eosin Methylene Blue Agar (EMB), and Mannitol agar (MA), in order to mitigate possible biases. These were all prepared according to manufacturers\u0026rsquo; instructions.\u003c/p\u003e \u003cp\u003eThe prepared MacConkey agar, Nutrient agar, EMB agar and Mannitol agar were allowed to cool to about 45 \u003csup\u003eo\u003c/sup\u003eC \u0026ndash; 50 \u003csup\u003eo\u003c/sup\u003eC, the mouth of the flask was flamed, then about 15 ml of the agar medium was poured aseptically into the petri dishes containing the samples. The plates were rocked gently immediately after pouring the agar so the microorganisms could be evenly separated during growth. Solidification of these agars were followed by incubation of the plates invertedly at 37 \u003csup\u003eo\u003c/sup\u003eC for 24 hours. Three control plates of each agar were poured. The colonies were observed, counted and recorded after 24 hours. Distinct colonies were then sub-cultured until pure cultures were obtained and transferred onto slant bottles containing freshly prepared agars. Individual colonies were purified and identified by morphological and biochemical techniques (Dubey \u0026amp; Masheshwari, 2004). Using the biochemical characteristics of the isolates such as oxidase testing, catalase testing and substrate utilization tests, the probable identities of the organisms were carried out and further confirmed on the National Center for Biotechnology Information (NCBI) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Antibiotic susceptibility testing\u003c/h2\u003e \u003cp\u003eThe antibiotic sensitivity test was carried out for each of the isolates using the Kirby Bauer Disc Diffusion method according to the guidelines of the Clinical Laboratory Standards Institute (CLSI, 2020). The antibiotics used were Imipenem (10 \u0026micro;g), Meropenem (10 \u0026micro;g), Ceftazidime (30 \u0026micro;g), Ciprofloxacin (5 \u0026micro;g), Gentamicin (10 \u0026micro;g), Chloramphenicol (12.5 \u0026micro;g), Tetracycline (30 \u0026micro;g), Oxacillin (1 \u0026micro;g), Cotrimoxazole (25 \u0026micro;g), Augmentin (20 \u0026micro;g), Vancomycin (30 \u0026micro;g) and Erythromycin (15 \u0026micro;g). The inoculum was prepared for each bacterial isolate by adjusting the turbidity to 0.5 McFarland standard and spread on Mueller-Hinton agar plates. The antibiotic discs were placed on the agar plates and incubated overnight at 37\u0026deg;C for 24 hours. The zones of inhibition (ZOI) were measured and the isolates were classified as sensitive, intermediate or resistant according to CLSI tables and guidelines (Bayot \u0026amp; Bragg, 2021).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analysis of data obtained was done using one-way analysis of variance (ANOVA) at 5% level of significance using the Statistical Package for Social Sciences (SPSS) version 28 (IBM, Armonk, NY, USA). A post hoc test was afterwards performed using the Duncan\u0026rsquo;s New Multiple Range Test. Pearson\u0026rsquo;s correlation (\u003cem\u003er\u003c/em\u003e) and cluster (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15 and 0.40, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) analyses were used to determine interrelationships between the parameters.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Physicochemical characteristics of sampled rivers\u003c/h2\u003e\n \u003cp\u003eThe physicochemical characteristics of the sampled rivers were recorded and compared with World Health Organisation (WHO) standards and the Nigerian Institute Standard (NIS) acceptable levels in the guidelines for drinking water is reported (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003c/div\u003e\n \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.1 pH and temperature variations in sampled rivers\u003c/h2\u003e\n \u003cp\u003eThe pH of the water samples varied from river to river. Samples obtained from rivers Apitipiti 1, Sogidi and Aba Apa Akinmorin, had pH values within WHO acceptable range (6.5\u0026ndash;8.5). However, Apitipiti 2 recorded slightly higher value of 8.6 while Apitipiti 3 (9.5) exceeded the limit. According to UMA (2016), pH above 6.5-8.0 range negatively affects aquatic animals in rivers. Although, with reference to NIS, all the river samples were within the permissible range of 6.5\u0026ndash;9.5, indicating that the water samples\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePhysicochemical analysis of the water samples from the selected rivers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eWater Samples\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eWHO\u003c/p\u003e\n \u003cp\u003eStandard (2007)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNIS (2011)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eB\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTemperature (\u003csup\u003eO\u003c/sup\u003eC)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e36.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmbient\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003epH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.5\u0026ndash;9.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC (\u0026micro;S/cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e320.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e540.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e260.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIron (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.011\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0017\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.411\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.611\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.443\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.679\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCopper (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.074\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.051\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.080\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0029\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.760\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZinc (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0017\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.049\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0017\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.246\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0017\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.029\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0017\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChromium (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCadmium (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLead (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNickel (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.041\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.014\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChloride (NTU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.551\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.80\u0026thinsp;\u0026plusmn;\u0026thinsp;2.354\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.343\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50.40\u0026thinsp;\u0026plusmn;\u0026thinsp;0.064\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.029\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSulphate (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.355\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.375\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00058\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4280\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0055\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePhosphate (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.404\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.209\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.638\u0026thinsp;\u0026plusmn;\u0026thinsp;0.013\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.200\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0003\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTurbidity (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.533\u0026thinsp;\u0026plusmn;\u0026thinsp;0.694\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.033\u0026thinsp;\u0026plusmn;\u0026thinsp;0.186\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.967\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.231\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.058\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNitrate (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0039\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00009\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.316\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0013\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046\u0026thinsp;\u0026plusmn;\u0026thinsp;0.00007\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOD (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.333\u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24.33\u0026thinsp;\u0026plusmn;\u0026thinsp;2.028\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.577\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32.33\u0026thinsp;\u0026plusmn;\u0026thinsp;0.333\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;1.732\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBOD (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.088\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.346\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.606\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.745\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.606\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eManganese (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0115\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0058\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0058\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.083\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0088\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFluoride (Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0058\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0115\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.037\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0088\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0115\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNA\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\u003eWHO - World Health Organisation; NIS- Nigerian Industrial Standard; NA- Not Available\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eKey\u0026nbsp;\u003c/strong\u003eA- Apitipiti 1; B- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin; COD- Chemical Oxygen Demand; BOD- Biological Oxygen Demand; EC- Electrical Conductivity.\u003c/p\u003e\n \u003cp\u003ehad acceptable pH values. This finding correlates with the study of Ayandiran \u003cem\u003eet al\u003c/em\u003e. (2014), with river water sampled being within WHO and NIS permissible ranges. Furthermore, in this study, the average pH recorded across the five rivers during the dry season of sampling was 7.98. For the temperatures, there were no significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) differences among the sampled rivers in this study. In addition, the recorded temperature ranges (29.0\u0026ndash;36.0 \u003csup\u003eO\u003c/sup\u003eC) of all the analysed water samples indicate a favourable environmental condition which supported the mesophilic bacteria isolated from the rivers. According to Hoorzook \u003cem\u003eet al\u003c/em\u003e. (2021), the temperature of water is an essential environmental factor in aquatic ecosystems that affects the rate of metabolic activities of microorganisms.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.2 Electrical conductivity and soluble salts in river water samples\u003c/h2\u003e\n \u003cp\u003eIn this study, it was observed that the electrical conductivity (EC) and salt levels (chloride, fluorine, sulphate, phosphate and nitrate) of all the samples were within the WHO and NIS permissible limits. Although, the electrical conductivity (100\u0026ndash;540 \u0026micro;S/cm) significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) varied from one river to another, all the values were within the WHO and NIS permissible limit (1000 \u0026micro;S/cm). While Apitipiti 1 recorded the least (100 \u0026micro;S/cm) EC, Apitipiti 3, had the highest with 540 \u0026micro;S/cm. These findings correlate with the study of Hoorzook \u003cem\u003eet al\u003c/em\u003e. (2021) where sampled rivers in South Africa, recorded an EC value below the permissible limit. Shoeb \u003cem\u003eet al\u003c/em\u003e. (2022), reported that water conductivity influences the aquatic life of river animals.\u003c/p\u003e\n \u003cp\u003eLikewise, a similar occurrence obtained for EC was recorded with salts of chloride, fluorine, sulphate, phosphate and nitrate salt levels of all the sampled rivers levels within the WHO and NIS permissible limit. This is in conformity with the findings of Egwuonwu \u003cem\u003eet al\u003c/em\u003e. (2012), where the mineral content of the water samples studied were within the WHO standard. The similar trend recorded for EC and the soluble salts can be ascribed to the direct interrelationship between the parameters. Shoeb \u003cem\u003eet al\u003c/em\u003e. (2022) reported that the level of electrical conductivity in water also indicates the presence of soluble ions.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.3 Heavy metal contents in sampled river waters\u003c/h2\u003e\n \u003cp\u003eAbout 87.5% (7 out of 8) of the metals tested in the river water samples were within the WHO and NIS permissible limits. Only the iron concentrations in all analysed water samples exceeded both WHO and NIS permissible limit (0.3 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with Apitipiti 1 (40.011 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) being significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher than all the others. This may be ascribed to the ongoing road construction activity taking place at the time of sample collection.\u003c/p\u003e\n \u003cp\u003eIn addition, during sampling, it was observed that Apitipiti 1 River was surrounded by rocks. The elevated iron levels recorded across the rivers in this study correlates with that of Kumar \u003cem\u003eet al\u003c/em\u003e. (2017), where all water samples contained elevated iron content higher than the permissible WHO and NIS range. Although, this trend is in contrast with a study conducted by Sarkar and Shekhar (2018), where the iron concentrations in sampled water recorded values below the WHO and NIS permissible limit. The rivers sampled in this study, maybe considered unfit as a result of the elevated iron content. Many factors may have contributed to the high Fe levels of recorded across all the samples. According to Khatri \u003cem\u003eet al\u003c/em\u003e. (2017), iron gains entry into water bodies via geogenic sources, industrial effluents and dumping of domestic waste or pollution from iron and steel industries, iron metal corrosion and iron mining. High iron intake in humans has been linked to haemochromatosis (Kumar \u003cem\u003eet al\u003c/em\u003e., 2017), retinitis, conjunctivitis, choroiditis, cancer and heart diseases (Khatri \u003cem\u003eet al\u003c/em\u003e., 2017).\u003c/p\u003e\n \u003cp\u003eFurthermore, for Cu (0.051\u0026ndash;0.11 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and Zn (0.003\u0026ndash;0.245 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) contents in all the water samples, values recorded were within the WHO and NIS permissible range (1\u0026ndash;2 and 3 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). According to Dietrich \u003cem\u003eet al\u003c/em\u003e. (2004), surface and ground water usually contain low concentrations of copper. This finding correlates with that of Kusmana \u003cem\u003eet al\u003c/em\u003e. (2018), where the sampled rivers in Indonesia had copper concentrations below the WHO and NIS permissible ranges. However, a more recent study by Khatib \u003cem\u003eet al.\u003c/em\u003e (2023) obtained elevated levels of Cu in river samples which is at variance with the trend recorded in this study. For Zn concentrations, this study corroborates the findings of Khatib \u003cem\u003eet al\u003c/em\u003e. (2023), where the sampled river waters in Japan, recorded zinc concentrations lower than the NIS permissible range.\u003c/p\u003e\n \u003cp\u003eHeavy metals such as chromium (0.00 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), cadmium (0.00 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), lead (0.00-0.01 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), manganese (0.02\u0026ndash;0.2 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and nickel (\u0026lt;\u0026thinsp;0.2 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were all within the WHO and NIS permissible range. This could indicate that these water samples were free from some major anthropogenic activities such as solid mineral mining and smelting which could trigger health hazards. Elevated levels of metal in water have been identified to pose both human health and environmental threat (Senze \u003cem\u003eet al\u003c/em\u003e., 2022). Human diseases such as cardiovascular disorders, neuronal damage, renal injuries and cancer have been reported as a result of heavy metal pollution (Rehman \u003cem\u003eet al\u003c/em\u003e., 2018).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.1.4. Turbidity, Chemical Oxygen Demand and Biological Oxygen Demand\u003c/h2\u003e\n \u003cp\u003eWith regards to the turbidity levels of the five rivers sampled in this study, only one river - Aba Apa Akinmorin River recorded a value (4.0. Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) below the WHO and NIS \u0026ndash; 5 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e permissible limit. Apitipiti 1, 2 and 3 had turbidity values (8.533, 7.033 and 5.967 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e respectively) that exceeded the permissible limit while Sogidi River recorded the same concentration as the permissible limit. A similar study on river turbidity levels by Dongsheng \u003cem\u003eet al\u003c/em\u003e. (2022), recorded the same trend as observed in this study. The implication of high turbidity on water bodies is reduction in the aesthetic value and quality of such rivers, subsequently having a negative impact on tourism and recreation.\u003c/p\u003e\n \u003cp\u003eThe biological oxygen demand (BOD) of Apitipiti 2, Apitipiti 3, Sogidi and Aba Apa Akinmorin were below the permissible limit, while that of Apitipiti 1 was slightly above the permissible limit by 0.2 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e. The chemical oxygen demand (COD) of Apitipiti 1 (64.67 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Apitipiti 2 (24.33 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), Sogidi (32.33 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), and Aba Apa Akinmorin (40.0 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) were way beyond the permissible limit (10 Mgl\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). However, Apitipiti 3 recorded a COD level below the permissible limit. From the results, water samples from Apitipiti 1 had both its BOD and COD values being significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher than all the other four rivers. The high COD as well as BOD values indicate an abundance of pollutants in the water. BOD is the measure of the amount of utilized oxygen by microbes, hence suggesting that these drinking river sources were probably polluted with improper disposal of domestic waste materials which could be through runoffs and most importantly they are polluted by organic matter such as faecal contamination and hence poor water quality similar report has been noted by Zhao \u003cem\u003eet al\u003c/em\u003e. (2021).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Bacterial isolate counts and diversity from Atiba sampled rivers\u003c/h2\u003e\n \u003cp\u003eFour media namely - Nutrient Agar, MacConkey Agar, Eosin Methylene Blue Agar and Mannitol agar were used to determine the bacterial heterotrophic counts and diversity represented in the five sampled rivers in this study.\u003c/p\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Bacteria Total Heterotrophic Counts in sampled rivers\u003c/h2\u003e\n \u003cp\u003eOn nutrient agar, Sogidi river had significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher microbial load (6.36 Log CFU/mL) compared to the other rivers (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Further, on incubation in mannitol agar, Apititipiti 1 and Aba Apa Akinmorin were significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) lower (0.00 Log CFU/mL) to that of the other rivers. However, on MacConkey agar, Aba Apa Akinmorin indicated significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher microbial load than the other rivers (5.90 Log CFU/mL). While on EMB agar, Apititipiti 1 had significantly (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) higher (5.78 Log CFU/mL) microbial load compared to others. Overall, the total bacterial heterotrophic plate counts of all the water samples from the five rivers exceeded the WHO permissible limit for potable water (500 Cfu/mL) indicating that the rivers poor water quality that is unfit for drinking or domestic purposes. This finding corroborates with findings from various studies by Onuoha, (2017) and Ayandele \u003cem\u003eet al\u003c/em\u003e. (2019).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.2 Diversity of bacterial species in water samples of rivers\u003c/h2\u003e\n \u003cp\u003eA total of thirty-two (\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e) bacterial species were isolated from the five sampling rivers using different culture media. Twenty-one (\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e) of the isolates were Gram-positive while eleven (\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e) were Gram-negative. This study does not follow the trend of some previous studies. For instance, Onuoha, (2017) found only 7 Gram-positive bacteria and 19 Gram-negative bacteria being isolated from different water sources in South Eastern, Nigeria. In addition, another study conducted in Ghana by Odonkor \u003cem\u003eet al\u003c/em\u003e. (2022), isolated more Gram-negative bacteria (83) compared to Gram-positive bacteria (\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e) from different water sources.\u003c/p\u003e\n \u003cp\u003eIn all, eight different genera (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) of bacteria were isolated and identified as: \u003cem\u003eAeromonas\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e), \u003cem\u003eBacillus\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e), \u003cem\u003eCorynebacterium\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e), \u003cem\u003eLactobacillus\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e), \u003cem\u003ePseudomonas\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e), \u003cem\u003eStaphylococcus\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e), and \u003cem\u003eStreptococcus\u003c/em\u003e (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e). A study carried out by Ayandele \u003cem\u003eet al\u003c/em\u003e. (2019) on a close by dam to where this study was conducted, isolated similar bacterial species of: \u003cem\u003eCorynebacterium kutscheri, Streptococcus pyogenes, Shigella flexneri\u003c/em\u003e, \u003cem\u003ePseudomonas aeruginosa, Staphylococcus aureus\u003c/em\u003e, and \u003cem\u003eEscherichia coli\u003c/em\u003e. Furthermore, \u003cem\u003eAeromonas\u003c/em\u003e were already isolated from sources like fresh water (Pessoa \u003cem\u003eet al\u003c/em\u003e., 2022).\u003c/p\u003e\n \u003cp\u003eAlthough \u003cem\u003eEscherichia coli\u003c/em\u003e was not detected in any of the five rivers sampled, this fact does not depict complete absence of the organisms or other enteric bacteria, the high level of observed BOD and COD implies possible presence of faecal contamination which probably was not just detected in the water at the time of sampling. This finding correlates with the findings of Odonkor and Addo (2018) in a study conducted in Ghana, where no \u003cem\u003eE. coli\u003c/em\u003e strain was isolated from river water sources.\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e had the highest frequency of occurrence of 11 while \u003cem\u003eAeromonas\u003c/em\u003e spp. followed closely with the second highest frequency (\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e). \u003cem\u003eLactobacillus\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e had the lowest frequency of occurrence at (3.0%) (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). It is worth noting that some \u003cem\u003eAeromonas\u003c/em\u003e spp. have been identified as pathogenic microbe associated with water bodies and its presence in the rivers has been implicated as a major public health concern (Pessoa \u003cem\u003eet al\u003c/em\u003e., 2022).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Relationships among parameters using correlation coefficient and cluster analyses\u003c/h2\u003e\n \u003cp\u003eThis study determined the correlation between different determined physicochemical, heavy metals and microbial parameters with the aid of correlation coefficient and the cluster analyses.\u003c/p\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.1 Interrelationship between physical, chemical and biological parameters\u003c/h2\u003e\n \u003cp\u003eFor this study, the Pearson correlation analysis conducted indicated that there were significant relationships between the physicochemical and total heterotrophic counts variables. Sulphate exihibited significant to highly significant relationships with four key parameters (Supplementary table 1). This was reflected with temperature (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), turbidity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and pH (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.96; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) having positive relationships and THB (\u003cem\u003er\u003c/em\u003e=-0.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) which had a negative significant relationship. Temperature and turbidity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) showed significant relationship with one another. With regards to EC, there was a highly significant relationship with chloride (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.98; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) but a negative significant relationship with COD (\u003cem\u003er\u003c/em\u003e=-0.92; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) was obtained. In addition, chloride showed a significant negative relationship with COD (\u003cem\u003er\u003c/em\u003e=-0.86; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while flouride showed a significant positive relationship with BOD (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.89; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and a negative significant correlation with THB (\u003cem\u003er\u003c/em\u003e=-0.92; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cp\u003eFurthermore, the cluster analysis (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) at 0.4, confirmed the trend observed in supplementary table 1. Three clusters were formed, the first cluster included nitrate and THB, the second cluster, turbidity, temperatures, pH, sulphate, COD, BOD and fluoride while the third cluster revealed that similarity existed among EC, chloride and phosphate. These results indicate the interrelationship that exists among physico-chemical and biological parameters. Previous studies (Saalidong \u003cem\u003eet al.\u003c/em\u003e 2022) have established these findings.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.3.2 Interrelationship between heavy metals and total heterotrophic bacterial counts\u003c/h2\u003e\n \u003cp\u003eCorrelation existed only between selected heavy metals but not with THB (supplementary table 2). Iron revealed a significant relationship with lead (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.93; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and with manganese (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.91; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Hierarchical cluster analysis was performed to confirm the relatively homogeneous groups of heavy metals and their inter-relationship with bacterial counts (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). At 0.15, the first cluster was formed. This result confirmed strong positive interactions among lead, iron and manganese. A second cluster was formed with only copper while a third cluster included zinc, nickel and THB. Similar study by Trueman \u003cem\u003eet al.\u003c/em\u003e (2019) corroborate the findings of this study that positive interactions exist among heavy metals.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Exposure of isolated bacterial species from rivers to antibiotics\u003c/h2\u003e\n \u003cp\u003eBacterial species isolated from the five rivers when subjected to antibiotic susceptibility testing revealed that most of the bacterial isolates (both Gram-positive and Gram-negative bacteria) were susceptible to Carbapenems (Imipenem and Meropenem) and Gentamicin (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Although, on exposure to Ceftazidime and Oxacillin, most isolates exhibited resistance.\u003c/p\u003e\n \u003cp\u003eOf all the bacterial species isolated from the rivers sampled in this study, 81.8% showed multidrug-resistance (MDR) with a multi-antibiotic resistance (MAR) index\u0026thinsp;\u0026gt;\u0026thinsp;0.2 (Tables \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Several studies have previously reported similar trends. From Africa, Ayandiran \u003cem\u003eet al\u003c/em\u003e. (2014), in a study conducted in another Southwestern State in Nigeria reported a prevalence of 100% MDR bacteria; Addae-Nuku \u003cem\u003eet al\u003c/em\u003e. (2022), also reported the prevalence of 55.5% MDR bacteria isolates from a river in Accra, Ghana. Furthermore, a study carried out by Koumar\u0026eacute; \u003cem\u003eet al\u003c/em\u003e. (2022) in Mali on the Niger River, reported a prevalence of 81.8% MDR bacteria, being resistant to more than two antibiotics. In addition, other studies conducted in Malaysia by Salikan \u003cem\u003eet al\u003c/em\u003e. (2020) on different rivers recorded a prevalence of 50% and 95.8% MDR bacteria while in Spain, Perez-Etayo \u003cem\u003eet al\u003c/em\u003e. (2020) reported 96.4% isolates exhibited multi-drug resistance in some Northern Rivers.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u003c/div\u003e\n \u003cp\u003eIn this study, it was observed that one Gram-positive (\u003cem\u003eCorynebacterium\u003c/em\u003e sp.) and one Gram-negative (\u003cem\u003eAeromonas\u003c/em\u003e sp.) specie revealed the highest MAR of 90.9% resistance to the antibiotics tested. This corroborates the findings of Ayandele \u003cem\u003eet al\u003c/em\u003e. (2019) in Nigeria that reported the multidrug-resistant trait of \u003cem\u003eCorynebacterium\u003c/em\u003e species isolated from a close by dam to where the current study was carried out. Furthermore, in Brazil, Conte \u003cem\u003eet al\u003c/em\u003e. (2021) published the multidrug-resistance of \u003cem\u003eAeromonas\u003c/em\u003e species isolated from aquatic environments. The observed high frequency of bacterial resistance in this study is of huge health concern. This phenomenon may result in the therapeutic failure of the river fauna population. In addition, the human health of the community may be endangered by being at risk of infection from pathogens animals, coupled with the possibility of plasmid transfer of resistance to human pathogenic bacteria (Nwobodo \u003cem\u003eet al\u003c/em\u003e., 2022).\u003c/p\u003e\n \u003cp\u003eThe occurrence of antibiotic resistant bacteria in the rivers sampled for this study, reveals that antibiotics may be indiscriminately used by members of the communities. The resistance pattern by the isolated bacteria to most of the antibiotics tested may likely be through discharge of antibiotics in considerable amounts via human waste (Dahunsi \u003cem\u003eet al\u003c/em\u003e., 2014). Usually, indigenous bacteria in their natural environments develop resistance to antibiotics mostly when they persist in such environments, hence, this supports bacterial antibiotic resistance selection. This might also be due to the ineffectual regulation of using antibiotics to treat gastrointestinal infections, which results in lesser alternatives for therapeutic treatments (Delgado-Gardea \u003cem\u003eet al\u003c/em\u003e., 2016).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAntibiotics resistance pattern of Gram-positive bacterial isolates from water samples of rivers\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRiver Source\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBacterial Isolates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMEM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOXA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCAZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eERY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCHL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAR Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResistance (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eA.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eLactobacillus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStreptococcus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eF.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"11\"\u003e\n \u003cp\u003eH.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium xerosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium kutsceri\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eBacillus\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eT.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eCorynebacterium xerosis\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"15\"\u003e\u003cstrong\u003eKey\u003c/strong\u003e: A- Apitipiti 1; B- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eR \u0026ndash; Resistant; I- Intermediate, S \u0026ndash; Sensitive, and \u0026micro;-Microgram. IMI- Imipenem (10 \u0026micro;g); MEM- Meropenem (10 \u0026micro;g); COT- Cotrimoxazole (25 \u0026micro;g); GEN- Gentamicin (10 \u0026micro;g); CAZ- Ceftazidime (30 \u0026micro;g); AUG- Augmentin (20 \u0026micro;g); CIP- Ciprofloxacin (5\u0026micro;g); ERY- Erythromycin (15 \u0026micro;g); VAN- Vancomycin (30 \u0026micro;g; OXA-Oxacillin (1 \u0026micro;g); CHL- Chloramphenicol (12.5 \u0026micro;g); MAR- Multi Antibiotics Resistance.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAntibiotics resistance pattern of Gram-negative bacteria isolated from water samples of rivers studied\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"15\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBacterial Isolates\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIMI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMEM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCOT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eGEN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOXA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCAZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUG\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCIP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eERY\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTET\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVAN\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAR\u003c/p\u003e\n \u003cp\u003eIndex\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResistance\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e90.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003eC.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eD.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eE.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003ePseudomonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eAeromonas\u003c/em\u003e sp.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.2\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\u003cstrong\u003eKey\u0026nbsp;\u003c/strong\u003eB- Apitipiti 2; C- Apitipiti 3; D- Sogidi; E- Aba Apa Akinmorin; R \u0026ndash; Resistant; I- Intermediate; S \u0026ndash; Sensitive and \u0026micro;-Microgram. \u003cstrong\u003eIMI\u003c/strong\u003e- Imipenem (10 \u0026micro;g); \u003cstrong\u003eMEM\u003c/strong\u003e- Meropenem (10 \u0026micro;g); \u003cstrong\u003eCOT\u003c/strong\u003e- Cotrimoxazole (25 \u0026micro;g); \u003cstrong\u003eGEN\u003c/strong\u003e- Gentamicin (10 \u0026micro;g); \u003cstrong\u003eCAZ\u003c/strong\u003e- Ceftazidime (30 \u0026micro;g); \u003cstrong\u003eAUG\u003c/strong\u003e- Augmentin (20 \u0026micro;g); \u003cstrong\u003eCIP\u003c/strong\u003e- Ciprofloxacin (5\u0026micro;g); \u003cstrong\u003eERY\u003c/strong\u003e- Erythromycin (15 \u0026micro;g); \u003cstrong\u003eVAN\u003c/strong\u003e- Vancomycin (30 \u0026micro;g); \u003cstrong\u003eTET\u003c/strong\u003e- Tetracycline (30 \u0026micro;g); \u003cstrong\u003eOXA\u003c/strong\u003e-Oxacillin (1 \u0026micro;g); \u003cstrong\u003eMAR\u003c/strong\u003e- Multi Antibiotics Resistance.\u003c/p\u003e\n \u003cp\u003eEnvironmental factors play a crucial role in the development and spread of antibiotic resistant bacteria (ARB). Water used for agriculture, recreational, drinking, and other domestic purposes are risk sources to humans. Therefore, the frequent assessment of the antimicrobial resistance patterns in potable water and their sources is paramount for monitoring the spread, which can assist in the development of preventive strategies (Ateba \u003cem\u003eet al\u003c/em\u003e., 2020). Antibiotic resistance increases patient mortality and morbidity as well as increases the financial burden of disease especially in low-income countries like Nigeria. Hence, proper programmes to monitor antimicrobial usage and resistance in bacteria from aquatic environments is encouraged for the purpose of implementation in Nigeria. This is essential for the fact that acquired bacterial resistance to antibiotics is prevalent as seen in this study, which is usually due to the misuse and abuse of antibiotics (Hoorzook \u003cem\u003eet al\u003c/em\u003e., 2021).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. CONCLUSION","content":"\u003cp\u003eThe physicochemical parameters, microbial identities and diversity as well as the antibiotics resistance profile of five rivers were assessed in this study. Each of the five rivers examined in this study has its peculiarity as regards the parameters tested in evaluating the standard quality of the water samples. Water samples revealed some level of pollution, microbial load and prevalence of multidrug-resistant bacteria in the rivers. The pH and temperature were of optimum values within the WHO and NIS permissible limits. On the other hand, EC, turbidity, COD and BOD exceeded the permissible limits. Generally, most heavy metals tested – Cu, Cd, Cr, Mn, Zn, Ni and Pb were within acceptable drinking limits across the five rivers except Fe. There were indications of interactions among the physical, chemical and biological parameters. With regards to microbial quality, all the river sampled were found unfit and not potable according to the WHO standard. \u0026nbsp;This study also recorded one of the rivers having as high as 90.9 % MDR prevalence. This may pose a human health threat due to vertical/horizontal gene transfer. Overall, this study reveals that these rivers are unfit for consumption/domestic or recreation purposes. Proper treatment by chlorination and ultra-violet treatment are therefore suggested if these water sources are to be used for human consumption. This study is only preliminary; extensive research into each of these rivers and associated environmental conditions as well as investigation into the possible long-term risks of water borne diseases via the ingestion of these drug resistant bacteria is advocated.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eB. M. Popoola - Conceptualization and design of the manuscript.J. P. Ogwerel - Material preparation, data collection, analysis.O. G. Oladipo - Contribution to concept and design.All authors contributed towards manuscript writing and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was self-funded.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interest Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare that there are no competing interest.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAchi, C. R., Ayobami, O., Mark, G., Egwuenu, A., Ogbolu, D. \u0026amp; Kabir, J. 2021 Operationalising One Health in Nigeria: reflections from a high-level expert panel discussion commemorating the 2020 World Antibiotics Awareness Week. \u003cem\u003eFrontiers in Public Health\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e(673504). https://doi.org/10.3389/fpubh.2021.673504.\u003c/li\u003e\n\u003cli\u003eAddae-Nuku, D. S., Kotey, F. C., Dayie, N. T., Osei, M. M., Tette, E. M., Debrah, P. \u0026amp; Donkor, E. S. 2022 Multidrug-resistant bacteria in hospital wastewater of the Korle Bu teaching hospital in Accra, Ghana. \u003cem\u003eEnvironmental Health Insights \u003c/em\u003e\u003cstrong\u003e16\u003c/strong\u003e, 11786302221130613. https://doi.org/10.1177/11786302221130613. \u003c/li\u003e\n\u003cli\u003eAteba, C. N., Tabi, N. M., Fri, J., Bissong, M. \u0026amp; Bezuidenhout, C. C. 2020 Occurrence of antibiotic-resistant bacteria \u0026amp; genes in two drinking water treatment \u0026amp; distribution systems in the North-West Province of South Africa. \u003cem\u003eAntibiotics\u003c/em\u003e (Basel, \u0026amp; Switzerl), \u003cstrong\u003e9\u003c/strong\u003e(11), 745. https://doi.org/10.3390/antibiotics9110745.\u003c/li\u003e\n\u003cli\u003eAyandele, A. A., Ajala, O. O., Oyekemi, S. A., Awotunde M. O., Ajayi, O. M., Gbadamosi, A. B. \u0026amp; Adedugbe, J. 2019 Microbial \u0026amp; Physico-chemical Analysis of Water from Boreholes in Mosimi \u0026amp; Environs, Ogun State, Nigeria. \u003cem\u003eBritish Journal of Applied Science \u0026amp; Technology\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e(2), 219-225. https://doi.org/2015/BJAST/2015/16941. \u003c/li\u003e\n\u003cli\u003eAyandiran, T. A., Ayandele, A. A., Dahunsi, S. O. \u0026amp; Ajala, O. O. 2014 Microbial Assessment \u0026amp; prevalence of Antibiotic Resistance in polluted Oluwa River, Nigeria. \u003cem\u003eEgyptian Journal of Aquatic Research\u003c/em\u003e\u003cstrong\u003e40\u003c/strong\u003e, 291-299. https://doi.org/10.1016/j.ejar.2014.09.002. \u003c/li\u003e\n\u003cli\u003eBayot, M. L. \u0026amp; Bragg, B. N. 2021 Antimicrobial Susceptibility Testing. https://www.ncbi.nlm.nih.gov/books/NBK539714/. (Accessed on 30 March 2022). \u003c/li\u003e\n\u003cli\u003eChukwu, E. E., Oladele, D. A., Awoderu, O. B., Afocha, E. E., Lawal, R. G., Abdus-Salam, I., Ogunsola, F. \u0026amp; Audu, R. A. 2020 A national survey of public awareness of antimicrobial resistance in Nigeria. \u003cem\u003eAntimicrobial Resistance \u0026amp; Infection Control\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e(1), 1-10. https://doi.org/10.1186/s13756-020-00739-0\u003c/li\u003e\n\u003cli\u003eClinical and Laboratory Standards Institute (CLSI), 2020 Performance Standards for Antimicrobial Susceptibility Testing, 30th Edition, CLSI supplement M100, Wayne, PA, USA. \u003c/li\u003e\n\u003cli\u003eConte, D., Palmeiro, J. K., Bavaroski, A. A., Rodrigues, L. S., Cardozo, D., Tomaz, A. P., Camargo, J. \u0026amp; Dalla‐Costa, L. M. 2021 Antimicrobial resistance in \u003cem\u003eAeromonas\u003c/em\u003e species isolated from aquatic environments in Brazil. \u003cem\u003eJournal of Applied Microbiology\u003c/em\u003e\u003cstrong\u003e131\u003c/strong\u003e(1), 169-181. https://doi.org/10.1111/jam.14965. \u003c/li\u003e\n\u003cli\u003eDahunsi, S. O., Owamal, H. I., Ayandiran, T. A. \u0026amp; Oranusi, U. S. 2014 Drinking water quality \u0026amp; public health of selected communities in South Western Nigeria. \u003cem\u003eWater Quality and Experimental Health\u003c/em\u003e\u003cstrong\u003e6\u003c/strong\u003e, 143-153.\u003c/li\u003e\n\u003cli\u003eDelgado-Gardea, M. C., Tamez-Guerra, P., Gomez-Flores, R., Zavala-D\u0026iacute;az de la Serna, F. J., Eroza-de la Vega, G., Nev\u0026aacute;rez-Moorill\u0026oacute;n, G. V., P\u0026eacute;rez-Recoder, M. C., S\u0026aacute;nchez-Ram\u0026iacute;rez, B., Gonz\u0026aacute;lez-Horta, M. \u0026amp; Infante-Ram\u0026iacute;rez, R. 2016 Multidrug-Resistant Bacteria Isolated from Surface Water in Bassaseachic Falls National Park, Mexico\u003cem\u003e. International Journal of Environmental Research \u0026amp; Public Health\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e(6), 597. https://doi.org/10.3390/ijerph13060597\u003c/li\u003e\n\u003cli\u003eDietrich, A. M., Glindemann, D., Pizarro, F., Gidi, V., Olivares, M., Araya, Camper, A., Duncan, S., Dwyer, S., Whelton, A. J., Younos, T., Subramanian, S., Burlingame, G. A., Khiari, D., \u0026amp; Edwards, M. 2004 Health and aesthetic impacts of copper corrosion on drinking water. \u003cem\u003eWater Science and Technology\u003c/em\u003e\u003cstrong\u003e49\u003c/strong\u003e(2), 55-62. https://doi.org/10.2166/wst.2004.0087\u003c/li\u003e\n\u003cli\u003eDongsheng, W., Xiao, C., Kaiwei, M., Zhixuan, L. \u0026amp; Lianqing, D. 2022 Estimating effluent turbidity in the drinking water flocculation process with an improved random forest model. \u003cem\u003eWater Supply\u003c/em\u003e\u003cstrong\u003e22\u003c/strong\u003e (1), 1107\u0026ndash;1119. https://doi.org/10.2166/ws.2021.213\u003c/li\u003e\n\u003cli\u003eDubey, R. C. \u0026amp; Maheshwari, D. K. 2004 Practical microbiology. S. Chad \u0026amp; Company Ltd. New Delhi, pp. 162-301.\u003c/li\u003e\n\u003cli\u003eEgwuonwu, C. C., Uzoije, A. P., Okafor, V. C., Ezeanya, N.C, Nwachukwu, M. U. 2012 Evaluation of the effects of industrial wastewater discharge on surface water (A case study of Nigeria Breweries Plc Enugu). \u003cem\u003eGreener Journal of Physical Sciences\u003c/em\u003e, \u003cstrong\u003e2\u003c/strong\u003e(3), 056-063. \u003c/li\u003e\n\u003cli\u003eGrenni, P., Ancona, V. \u0026amp; Barra Caracciolo, A. 2018 Ecological effects of antibiotics on natural ecosystems: a review. \u003cem\u003eMicrochemical Journal \u003c/em\u003e\u003cstrong\u003e136\u003c/strong\u003e, 25\u0026ndash;39. https://doi.org/10.1016/j.microc.2017.02.006.\u003c/li\u003e\n\u003cli\u003eGreweling, T. \u0026amp; Peech, M. 1968 Chemical soil tests. \u003cem\u003eBulletin \u003c/em\u003e960\u003cem\u003e, \u003c/em\u003eCornell Experiment Station, Ithaca. NY, pp. 23\u0026ndash;24.\u003c/li\u003e\n\u003cli\u003eHile, T. D., Dunbarb, S. G. \u0026amp; Sinclair, R. G. 2023 Microbial contamination analysis of drinking water from bulk dispensers \u0026amp; fast-food restaurants in the Eastern Coachella Valley, California. \u003cem\u003eWater Science \u0026amp; Technology Water Supply \u003c/em\u003e1-19. DOI:10.2166/ws.2023.200. \u003c/li\u003e\n\u003cli\u003eHoorzook, K. B., Pieterse, A., Heine, L., Barnard, T. G. \u0026amp; van Rensburg, N. J. 2021 Soul of the Jukskei River: The extent of bacterial contamination in the Jukskei River in Gauteng Province, South Africa. \u003cem\u003eInternational Journal of Environmental Research \u0026amp; Public Health\u003c/em\u003e\u003cstrong\u003e18\u003c/strong\u003e(16), 8537. https://doi.org/10.3390/ijerph18168537.\u003c/li\u003e\n\u003cli\u003eHutchings M. I., Truman A. W. \u0026amp; Wilkinson, B. 2019 Antibiotics: past, present \u0026amp; future. \u003cem\u003eCurrent opinion in microbiology \u003c/em\u003e\u003cstrong\u003e51\u003c/strong\u003e, 72-80. https://doi.org/10.1016/j.mib.2019.10.008.\u003c/li\u003e\n\u003cli\u003eKhatib, M., Daoud, M., Arairo, W., Saba, M. \u0026amp; Mortada, H. 2023 Water quality parameters assessment of Ras El-Ain Natural Ponds, Tyr, Lebanon. \u003cem\u003eWater Supply\u003c/em\u003e\u003cstrong\u003e23\u003c/strong\u003e(6), 2575\u0026ndash;2585. https://doi.org/10.2166/ws.2023.140.\u003c/li\u003e\n\u003cli\u003eKhatri, N., Tyagi, S. \u0026amp; Rawtani, D. 2017 Recent strategies for the removal of iron from water: A review. \u003cem\u003eJournal of Water Process Engineering\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e, 291-304.\u003c/li\u003e\n\u003cli\u003eKoumar\u0026eacute;, Y., Babana, A. H., Bah, A., Kassogu\u0026eacute;, A., Dao, S., Diallo, K. \u0026amp; Faradji, F. 2022 Bacteria isolated from Niger River water in Bamako showed multi-resistance to antibiotics. \u003cem\u003eMedCrave Online Journal of Biology \u0026amp; Medicine\u003c/em\u003e\u003cstrong\u003e7\u003c/strong\u003e, 71-74. https://doi.org/10.15406/mojbm.2022.07.00168. \u003c/li\u003e\n\u003cli\u003eKumar, V., Bharti, P. K., Talwar, M., Tyagi, A. K. \u0026amp; Kumar, P. 2017 Studies on high iron content in water resources of Moradabad district (UP), India. \u003cem\u003eWater Science\u003c/em\u003e\u003cstrong\u003e31\u003c/strong\u003e(1), 44-51. https://doi.org/10.1016/j.wsj.2017.02.003.\u003c/li\u003e\n\u003cli\u003eKusmana, C., Wahwakhi, S., Ghozali, A. A. \u0026amp; Iswantini, D. 2018 Cu metal concentration in the water \u0026amp; sediment of Surabaya\u0026rsquo;s flowed-Jagir River Estuary. \u003cem\u003eIOP Conference Series: Earth \u0026amp; Environmental Science \u003c/em\u003e\u003cstrong\u003e203\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003ehttps://doi.org/10.1088/1755-1315/203/1/012014. \u003c/li\u003e\n\u003cli\u003eLi, S., Zhang, C., Li, F., Hua, T., Zhou, Q. \u0026amp; Ho, S. H. 2021 Technologies towards antibiotic resistance genes (ARGs) removal from aquatic environment: A critical review. \u003cem\u003eJournal of Hazardous Materials \u003c/em\u003e\u003cstrong\u003e411\u003c/strong\u003e, 125148\u003cem\u003e.\u003c/em\u003ehttps://doi.org/10.1016/j.jhazmat.2021.12514.\u003c/li\u003e\n\u003cli\u003eMurphy, J. \u0026amp; Riley, J.P. 1962 A Modified single solution method for determination of phosphate in natural waters. \u003cem\u003eAnalytica Chimica Acta\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e, 31-36. https://doi.org/10.1016/S0003-2670(00)88444-5.\u003c/li\u003e\n\u003cli\u003eNigerian Standard for Drinking Water Quality. 2007 Nigerian Industrial Standards (NIS). Pp. 14-18.\u003c/li\u003e\n\u003cli\u003eNwobodo, D. C., Ugwu, M. C., Anie, C. O., S. Al-Ouqaili, M. T., Ikem, J. C., Chigozie, U. V., \u0026amp; Saki, M. 2022 Antibiotic resistance: The challenges and some emerging strategies for tackling a global menace. \u003cem\u003eJournal of Clinical Laboratory Analysis\u003c/em\u003e\u003cstrong\u003e36\u003c/strong\u003e(9), e24655. https://doi.org/10.1002/jcla.24655 \u003c/li\u003e\n\u003cli\u003eOdonkor, S. T. \u0026amp; Addo, K. K. 2018 Prevalence of multi-drug-resistant Escherichia coli isolated from drinking water sources. \u003cem\u003eInternational journal of Microbiology\u003c/em\u003e\u003cstrong\u003e 19\u003c/strong\u003e(3).\u003c/li\u003e\n\u003cli\u003eOdonkor, S. T., Simpson, S. V., Morales Medina, W. R. \u0026amp; Fahrenfeld, N. L. 2022 Antibiotic-resistant bacteria \u0026amp; resistance genes in isolates from Ghanaian drinking water sources. \u003cem\u003eJournal of Environmental \u0026amp; Public Health\u003c/em\u003ehttps://doi.org/10.1155/2022/2850165.\u003c/li\u003e\n\u003cli\u003eOladipo, A.O., Oladipo, O.G. \u0026amp; Bezuidenhout, C.C. 2019 Multi-drug resistance traits of methicillin-resistant \u003cem\u003eStaphylococcus aureus\u003c/em\u003e \u0026amp; other Staphylococcal species from clinical \u0026amp; environmental sources. \u003cem\u003eJournal of Water \u0026amp; Health\u003c/em\u003e, \u003cstrong\u003e17\u003c/strong\u003e(6), 930-943. https://doi.org/10.2166/wh.2019.177. \u003c/li\u003e\n\u003cli\u003eOnuoha, S. C. 2017 The prevalence of antibiotic resistant diarrhogenic bacterial species in surface waters, South Eastern Nigeria. \u003cem\u003eEthiopian journal of health sciences\u003c/em\u003e\u003cstrong\u003e27\u003c/strong\u003e(4), 319-330. https://doi.org/10.4314/ejhs.v27i4.3.\u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Etayo, L., Gonz\u0026aacute;lez, D., Leiva, J. \u0026amp; Vitas, A. I. 2020 Multi-drug-resistant bacteria isolated from different aquatic environments in the North of Spain \u0026amp; South of France. \u003cem\u003eMicroorganisms\u003c/em\u003e\u003cstrong\u003e8\u003c/strong\u003e(9), 1425. https://doi.org/10.3390/microorganisms8091425.\u003c/li\u003e\n\u003cli\u003ePessoa, R. B., Oliveira, W. F., Correia, M. T., Fontes, A., \u0026amp; Coelho, L. C. 2022 Aeromonas and human health disorders: Clinical approaches. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e, 868890. https://doi.org/10.3389/fmicb.2022.868890.\u003c/li\u003e\n\u003cli\u003ePeterson E. \u0026amp; Kaur P. 2018 Antibiotic resistance mechanisms in bacteria: relationships between resistance determinants of antibiotic producers, environmental bacteria, \u0026amp; clinical pathogens. \u003cem\u003eFrontiers in Microbiology\u003c/em\u003e\u003cstrong\u003e9\u003c/strong\u003e(2928). https://doi.org/10.3389/fmicb.2018.02928. \u003c/li\u003e\n\u003cli\u003eRehman, K., Fatima, F., Waheed, I. \u0026amp; Akash, M. 2018 Prevalence of exposure of heavy metals \u0026amp; their impact on health consequences. \u003cem\u003eJournal of Cellular Biochemistry\u003c/em\u003e\u003cstrong\u003e119\u003c/strong\u003e(1), 157\u0026ndash;184. https://doi.org/10.1002/jcb.26234. \u003c/li\u003e\n\u003cli\u003eSaalidong, B.M., Aram, S.A., Out, S. \u0026amp; Lartey, P.O. 2022 Examining the dynamics of the relationship between water pH and other water quality parameters in ground and surface water systems. PLoS ONE \u003cstrong\u003e17\u003c/strong\u003e(1): e0262117. https://doi.org/10.1371/journal.pone.0262117 \u003c/li\u003e\n\u003cli\u003eSalikan, N. A., Zain, N. A. M. \u0026amp; Kam, K. A. R. 2020 Isolation of antibiotic resistant bacteria from rivers in Terengganu, Malaysia. \u003cem\u003eInternational Journal of Life Sciences \u0026amp; Biotechnology\u003c/em\u003e\u003cstrong\u003e3\u003c/strong\u003e(3), 241-257.\u003c/li\u003e\n\u003cli\u003eSarkar, A. \u0026amp; Shekhar, S. 2018 Iron contamination in the waters of Upper Yamuna basin.\u003cem\u003e Groundwater for Sustainable Development \u003c/em\u003ehttps://doi.org/10.1016/j.gsd.2017.12.011. \u003c/li\u003e\n\u003cli\u003eSchmitz, K. S. 2017 Gibbs free energy, work, \u0026amp; equilibrium\u003cem\u003e. Physical Chemistry, 99\u0026ndash;157.\u003c/em\u003ehttps://doi.org/10.1016/b978-0-12-800514-9.00003-1. \u003c/li\u003e\n\u003cli\u003eSen, D. 2019 Types of Aquatic Ecosystems. https://sciencing.com/types-aquatic-ecosystems-6123685.html. (accessed on 17 March 2022).\u003c/li\u003e\n\u003cli\u003eSenze, M., Kowalska-G\u0026oacute;ralska, M., Czyż, K. \u0026amp; Wondołowska-Grabowska, A. 2022 Possibility of metal accumulation in reed canary grass (\u003cem\u003ePhalaris arundinacea\u003c/em\u003e L.) in the aquatic environment of South-Western Polish Rivers. \u003cem\u003eInternational Journal of Environmental Research \u0026amp; Public Health\u003c/em\u003e\u003cstrong\u003e19\u003c/strong\u003e(13), 7779. https://doi.org/10.3390/ijerph19137779.\u003c/li\u003e\n\u003cli\u003eShoeb, M., Sharmin, F., Islam, Md, Nahar, N.l., Islam, L. R. and Parvin N. 2022 Assessment of physico-chemical parameters of water samples collected from the southern part of Bangladesh. \u003cem\u003eDhaka University Journal of Science\u003c/em\u003e\u003cstrong\u003e70\u003c/strong\u003e(1): 49-57 DOI: https://doi.org/10.3329/dujs.v70i1.60381. \u003c/li\u003e\n\u003cli\u003eTrueman, B.F., Gregory, B.S., McCormick, N.E., Gao, Y., Gora, S., Anaviapik-Soucie, T., L\u0026rsquo;Hérault, V. \u0026amp; Gagnon, G. A. 2019 Manganese increases lead release to drinking water. \u003cem\u003eEnvironmental Science \u0026amp; Technology\u003c/em\u003e. \u003cstrong\u003e53\u003c/strong\u003e(9): 4803-4812. doi: 10.1021/acs.est.9b00317\u003c/li\u003e\n\u003cli\u003eUMA (University of Massachusetts Amherst), 2016 Protocols for river, Massachusetts water watch partnership, https://www.umass.edu/mwwp/protocols/rivers/ph_alkalinity_ river .html.\u003c/li\u003e\n\u003cli\u003eUNICEF. 2018 Water, Sanitation and Hygiene. https://www.unicef.org/nigeria/water-sanitation-and-hygiene (accessed 23 September 2023).\u003c/li\u003e\n\u003cli\u003eWang, Z., Yang, Y., Gomes, M. P. \u0026amp; Suthar, S. 2022 Antibiotics \u0026amp; Antibiotic Resistance Genes in Waters: Pollution, Risks, \u0026amp; Control. \u003cem\u003eFrontiers in Environmental Science\u003c/em\u003e\u003cstrong\u003e10\u003c/strong\u003e, 967118. https://doi.org/10.3389/fenvs.2022.967118.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). 2017 Guidelines for Drinking-Water Quality: Fourth Edition Incorporating the First Addendum. World Health Organization, Geneva. https://www.who.int/news-room/fact-sheets/detail/diarrhoeal-disease. (Accessed 12 Aug 2023).\u003c/li\u003e\n\u003cli\u003eZhao, Y., Zhang, L., Zhang, M., Wu, J., Li, S., Ran, D., Sun, L. \u0026amp; Zhu, G. 2021 Effect of chemical oxygen demand concentration on nutrient removal in simultaneous nitrification, denitrification and phosphorus removal system in high-altitude areas. \u003cem\u003eWater\u003c/em\u003e\u003cstrong\u003e13\u003c/strong\u003e(19), 2656. https://doi.org/10.3390/w13192656. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Antibiotic resistant bacteria, antimicrobial resistance, fresh water, physicochemical parameters, potable water, public and human health","lastPublishedDoi":"10.21203/rs.3.rs-4457954/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4457954/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFreshwater habitat is a natural reservoir for antimicrobial resistance (AMR). AMR is known to pose serious human, animal, and environmental public health threats. This study aimed at evaluating the physicochemical and microbiological properties of five selected rivers (Apitipiti 1, Apitipiti 2, Apitipiti 3, Sogidi, and Aba Apa Akinmorin) in Oyo town, Nigeria, as well as the antibiotic resistance pattern of isolated bacterial species, using conventional methods. Most physicochemical parameters were within WHO and NIS permissible limits. Of the rivers, Sogidi had the highest microbial load (6.36 log CFU/mL) while Apititipiti 1 had\u003cem\u003e \u003c/em\u003ethe lowest (5.76 log CFU/mL). A total of thirty-three (32) bacterial species were isolated and identified as: \u003cem\u003eAeromonas \u003c/em\u003e(9)\u003cem\u003e, Bacillus \u003c/em\u003e(2)\u003cem\u003e, Corynebacterium \u003c/em\u003e(13)\u003cem\u003e, Lactobacillus \u003c/em\u003e(1)\u003cem\u003e, Pseudomonas \u003c/em\u003e(2)\u003cem\u003e, Staphylococcus \u003c/em\u003e(4)\u003cem\u003e, \u003c/em\u003eand\u003cem\u003e Streptococcus \u003c/em\u003e(1). Pearson’s correlation matrix indicated that there were significant (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) interactions among pH, electrical conductivity, temperature, sulphate and chloride salts, BOD and COD. Of all these, 81.8 % were multidrug-resistant, with \u003cem\u003eCorynebacterium kutscheri\u003c/em\u003e and \u003cem\u003eAeromonas \u003c/em\u003espp. isolated from Apitipiti 2 and Aba Apa Akinmorin rivers respectively, exhibiting a relatively high antibiotic resistance of 90.9 %. This study reveals that these rivers maybe unfit for consumption as multidrug-resistant bacteria of public health risk were associated with them.\u003c/p\u003e","manuscriptTitle":"Bacterial isolates from Drinking water river sources exhibit multi-drug resistant trait","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-18 06:55:25","doi":"10.21203/rs.3.rs-4457954/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-06-13T20:25:52+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-06-04T15:15:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-04T15:12:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Environmental Monitoring and Assessment","date":"2024-05-22T03:32:46+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"environmental-monitoring-and-assessment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"emas","sideBox":"Learn more about [Environmental Monitoring and Assessment](http://link.springer.com/journal/10661)","snPcode":"10661","submissionUrl":"https://submission.nature.com/new-submission/10661/3","title":"Environmental Monitoring and Assessment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d60a2495-ae91-410c-88d6-a1d3c05c1dd2","owner":[],"postedDate":"June 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-10-21T16:10:26+00:00","versionOfRecord":{"articleIdentity":"rs-4457954","link":"https://doi.org/10.1007/s10661-024-13117-9","journal":{"identity":"environmental-monitoring-and-assessment","isVorOnly":false,"title":"Environmental Monitoring and Assessment"},"publishedOn":"2024-10-15 15:57:31","publishedOnDateReadable":"October 15th, 2024"},"versionCreatedAt":"2024-06-18 06:55:25","video":"","vorDoi":"10.1007/s10661-024-13117-9","vorDoiUrl":"https://doi.org/10.1007/s10661-024-13117-9","workflowStages":[]},"version":"v1","identity":"rs-4457954","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4457954","identity":"rs-4457954","version":["v1"]},"buildId":"cBFmMYwuxLRRLfASyISRj","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}