British Journal of Applied Science & Technology 10(6): 1-18, 2015, Article no.BJAST.12452 ISSN: 2231-0843 SCIENCEDOMAIN international www.sciencedomain.org Assessment of Hydrological Properties and Proximate Impact of Septic Tank Leachate on Well-water Quality in Two Residential Areas in Ibadan, South-western Nigeria Roseangela I. Nwuba1* and Amire O. Philips2 1 Department of Zoology, Cellular Parasitology Programme, Cellular Biology and Genetics Unit, University of Ibadan, Nigeria. 2 Department of Zoology, Ecology and Environmental Biology Unit, University of Ibadan, Nigeria. Authors’ contributions This work was carried out in collaboration between both authors. Author RIN designed the study, wrote the protocol, corrected the manuscript and managed literature searches; while author AOP carried out the work, performed the statistical analysis, managed literature searches and wrote the first draft of the manuscript. Both authors read and approved the final manuscript. Article Information DOI: 10.9734/BJAST/2015/12452 Editor(s): (1) Jian Guo Zhou, Centre for Engineering Sustainability, School of Engineering, University of Liverpool, UK. (2) Ahmed Fawzy Yousef, Geology Department, Desert Research Center, Egypt. Reviewers: (1) K. Arumugam, Department of Civil Engineering, Anna University, India. (2) Anonymous, University of Illinois, USA. (3) Anonymous, Autonomous University of Morelos State, México. (4) Anonymous, Al al-Bayt University, Jordan. Complete Peer review History: http://sciencedomain.org/review-history/10280 Original Research Article Received 2nd July 2014 th Accepted 10 July 2015 Published 23rd July 2015 ABSTRACT Aims: To determine the impact of leachate from septic tank on proximate well-water quality in two different residential areas and the variation in the physico-chemical parameters of the well-water that are associated with spatial geographical location. Study Design: Randomized monthly collection and analysis of well-water over four months in two chosen residential areas. Place and Duration of Study: Residential houses at Agbowo and Akobo, Ibadan, Nigeria between April and August, 2012. _____________________________________________________________________________________________________ *Corresponding author: E-mail: r.nwuba@mail.ui.edu.ng, rnwuba@yahoo.com; Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 Methodology: The well-water samples were collected from 30 sites once every month; from different wells located at various perimeters from the septic tanks. The distance between the septic tanks and the wells were measured and the water subjected to physico-chemical analysis and bacteriological assessment to evaluate their qualitative, spatial and temporal variations. Results: A significant increase (p<0.01) was found in results from coliform counts between dry and wet seasons, while significant decrease (p<0.05) was recorded in the concentration of phosphate, salinity, total dissolved solids and potassium. The distance from the well to the septic tanks exhibited a negative correlation with coliform count (p<0.05), as well as for phosphate, nitrate, 2 chlorine and ammonia (p>0.05). The Discriminant Chi square (X = 62.526, p<0.01) and Wilk’s Lambda (0.058) revealed a significant discrimination between the two study sites. Partial Eta Squared value of 0.740, 0.382 and 0.137 were reported for location, proximity of septic tank to wellwater site and well depth respectively, showing their degree of contribution to variation in parameters measured. Conclusion: The results stressed the need to set standards concerning the distance and location of wells from septic tanks/septic tank, while considering spatial and temporal variations in hydrological environment of well-water sites. Keywords: Well-water quality; water quality index; physico-chemical variation; septic tank; bacteriological assessment. 1. INTRODUCTION resulted in a dramatic increase in the potential infiltration of extraneous materials into the groundwater environment [10]. In recent times, the impact of leachates on groundwater and other water sources has attracted a lot of attention because of its overwhelming environmental significance; hence, protection of groundwater is now an important environmental issue [11-12]. Leachates from septic tanks and other municipal sewage contain variety of chemicals like detergents, germicides, complex organic compounds and metals. Besides, human effluent composite and water contaminated with these sewage effluents may contain pathogenic organisms, high load of nitrate, phosphate, ammonia, total dissolve solid and associated complex organic substances which may be hazardous to human health if such water is consumed without treatment [13,14]. Additionally, uncontrolled microbial actions may result in the release of more toxic substances due to cumulative or synergistic effect of previously free or non-reactive component of the waste. Hence, the discharge of wastes from municipal sewers is one of the most important water quality issues worldwide and it is of particular significance to drinking water sourced from shallow water table [13,15]. During leachate percolation, water present in the waste and those generated by biodegradation serve as vehicle for leachate’s vertical and horizontal migration finding its way into the groundwater environment thereby contaminating the groundwater. Survival and dispersal of microbes in the groundwater environment also varies, for instance Escherichia coli have been reported to have moved 46 m As population grows and urbanization increases, more water is required and greater demand would be placed on ground and surface water [1]. The rate of urbanization in Nigeria is alarming as the major cities are growing at rates between 10-15% per annum [2]. The human activities associated with such growth (including soil fertility remediation, indiscriminate refuse and waste disposal, and the use of septic tanks, soak-away pits and pit latrines) are on the increase, with concurrent increase in underground water pollution. Groundwater contributes only 0.6% of the total water resources on earth, yet, it remains the major source of drinking water in Nigeria, as in every part of the world [3]. Ideally, potable water sources must be highly pure and free from chemical and microbial contamination. However, this useful resource is under threat of pollution from human activities as evident in low level of hygiene practices [4-6], and lack of recommended ‘safe distance’ between wellwater/borehole and septic tank sites in Nigeria. In view of this, monitoring the chemical, physical, and microbiological quality of groundwater is as important as assessing its quantity [7]. The two main contributors to the variations in the biogeochemical composition of groundwater are weathering processes and anthropogenic inputs [8]. Rapid increase in population, changes in life style, changes in living conditions and domestic waste management system [9] in Nigeria have 2 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 The climate of the study areas is tropically wet and dry, with the wet season from mid-March to October and dry period from November to March. The study was conducted within the Precambrian Basement Complex terrain of south-western Nigeria [17]. The main lithology of the rock unit include the amphibolites, migmatite gneisses, granites and pegmatites. Other important rock units are the schists, made up of biotite schist, quartzite schist talk-tremolite schist, and the muscovite schists [18]. vertically and 70 m horizontally in an aquifer under favourable conditions, while enteroviruses have been reported in groundwater 183 m from a wastewater point source [15]. Most chemicals found in drinking water are of health concern, though only few of these chemical contaminants have been shown to demonstrate adverse health effects in humans as a consequence of prolonged exposure through consumption [16]. Consequently, evaluation of the microbial, physical and chemical quality of water is an important means of securing potable water for daily consumption. This study seeks to assess the proximate impact of septic tank leachates on the physico-chemical and microbial quality of groundwater in different hydrological settings viz: Agbowo (densely populated) and Akobo (sparsely populated) residential areas of Ibadan, Nigeria. This will provide a baseline information towards exploring a possible minimum distance that could be recommended for Nigeria, where well-water is the most common water source within the cities. The topography of the city is characterized by undulating terrain with general elevation between 180 m and 210 m above sea level, and is drained by rivers Omi, among others. Occurrence of groundwater is dependent on the extent of weathered units and development of secondary porosity, e.g. fractures, faults, joints [19]. Consequently, the weathered aquifers are generally discontinuous, with groundwater occurrences in localized disconnected phreatic weathered regolith aquifers, essentially under unconfined to semi-confined conditions [20]. 2. METHODOLOGY 2.2 Sample Collection 2.1 Location and Geology of the Study Area This study was carried out between April and August, 2012, across wet and dry seasons. Wellwater samples were collected from thirty-one locations in the study site. Hand held Global Positioning System (GPS) receiver device with 12-channel tracking and differential correction capability was used to determine and identify location where the water samples were collected. This study was carried out in Agbowo and Akobo residential areas of Ibadan. Ibadan is located in south-western Nigeria, 160 km inland northeast of Atlantic coast of Africa, situated in-between rainforest and savanna. The study areas lie within latitudes 7°25’37”N and 7°26’26” N and Longitude 3°54’25” E and 3°56’50” E (Fig. 1). Fig. 1. Map of the study area [A: Agbowo (a densely populated area) and B: Akobo (a sparsely populated area)] 3 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 2.3 Quality Assurance Procedures Bromocresol purple as indicator (LAB M Ltd. UK) was used as the growth media. Sterile syringes were used to introduce 5 mL of the broth media (3.5% w/v) into 160/15 mm test-tube and small Durham tube inverted inside the test tube (totally submerge) to determine gas production in the sterilized fermentation tube. Series of 100%, 10% 1% and 0.1% of the stock solution of wellwater samples (1 mL) were prepared with distilled water. The prepared concentrations were introduced into sterilized fermentation tubes. Samples were collected in plastics that were prewashed with detergent water solution, rinsed with distilled water and soaked for 48 h in 50% HNO3, then rinsed thoroughly with distilled deionised water before air-drying. As part of the quality control measures, samples’ bottles were rinsed with sample well-water before filling. Samples for 2+ 2+ Calcium (Ca ), Magnesium (Mg ), Sodium + + (Na ), Potassium (K ), Ammonia (NH4+), Nitrate 3(NO3 ), Chloride (Cl ) and Phosphate (PO4 ) were refrigerated at 4°C prior to analysis. 2.4 Well Depth Determination and Water Most Probable Number (MPN)/100 mL were estimated using MPN Index of Standard methods 9221 Standard Total Coliform fermentation technique [21]. A multiple-tube fermentation technique was used for total coliforms (McConkey Broth Medium) and various combinations of positive results from five tubes per dilution were used (10 ml, 1.0 ml and 0.l ml sample portions). Level Well depths were determined using graduated tape with added weight and a similar procedure was employed for water level determination. 2.5 Determination of Physical Chemical Parameters and 2.7 Data Analysis The bacteriological data were log-transformed to meet the assumption of equal variance which were then subjected to student T-test to evaluate the impact of seasonal variation on coliform density. Correlational analysis were performed to evaluate the extent of the relationship between coliform density, well depth and distance between well and the nearest septic tank point for dry and wet season. Correlation analysis was performed on the parameters measured to evaluate their inter-relatedness. Principal Component Analysis (PCA) was also used to assess the concentration of physicochemical and bacteriological data from well-water samples, quantitatively determining the minimum number of new variables necessary to reproduce various attributes of the data. Discriminant analysis was performed on the parameters to identify variables that best differentiate the two sampling zones (Agbowo and Akobo). The Water Quality Index (WQI) was determined using the Weighted Arithmetic Index method [22]. Non-conservable parameters like, pH, Salinity and other parameters like Total Dissolved Solid (TDS), conductivity, temperature (°C), Dissolve Oxygen (DO) and percentage Oxygen (%O2) were taken immediately at the sampling point using Consort CS-C933 Multi-Parameters Portable meter, Topac Instrument, Inc. USA. Total hardiness, Calcium (Ca) and Magnesium were determined by Ethylene Diamine Tetraacetic Acid (EDTA) method. Flame photometric method was employed in the determination of Sodium (Na+) and Potassium (K+); Chloride (Cl-) was measured by potassium chromate method. Nitrate (NO-3) by Phenoldisulphonic acid method, -4 Phosphate (PO3 ) by Ascorbic Acid Method and Ammonia by Indophenols (Colorimetric). 2.6 Total Coliform Assessment Sterile corning tubes were used to collect wellwater samples from various sampling sites and were preserved at 4°C prior to microbial analyses. Microbial analyses were carried out within 24 hours of collection. Borosilicate glassware were used in microbiological testing in conformity with Section 9000 of “Standard Methods for the Examination of Water and Wastewater”. Dilution bottles were made of resistant Borosilicate glass, with plastic screw caps which were equipped with liners that do not produce toxic or inhibitory compounds when sterilized. McConkey broth with incorporated 3. RESULTS AND DISCUSSION 3.1 Variable Samples Analysis of Well-water The descriptive statistical analysis data presented in Table 1 show the univariate summary of well-water sample analysed during the study period between the month of April and 4 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 August, 2012. In this study, the distance between well and septic tank ranges from 4 to 30.5 m and well depth ranges from 0.25-15 m. Well depth in Akobo study area ranges from 4 to 15 m, while Agbowo recorded depth range of 0.25 to 5.5 m, indicating an area with high aquifer level. Results for Water Hardness, coliform count, TDS, conductivity and chloride recorded large variances. Agbowo and Akobo as shown in Table 1. The mean values for TDS, conductivity, salinity, water hardness recorded for Agbowo (408.01, 766.79, 0.35, 377.25 respectively) were significantly higher when compared with the mean values recorded for Akobo, a low density area (97.58, 181.98, 0.1, 111.50 respectively). The overall evaluation of most parameters however fell below the permissible limit of NESREA; except coliform count and chloride concentration [28]. Generally, underground water is believed to be the purest source of water [23] because of the purification properties of soil [24]. However, the amount and variation in the level of dissolved materials in underground water is a complex function of interaction between climate, land use patterns, human activity, weathering and the geologic make-up of the hydrologic environment. These functions vary from one geographical area to the other [25]. The result presented in Table 1 shows that the overall water hardness, DO, coliform, distance from nearest septic tank, salinity, TDS, conductivity, ammonia, nitrate, phosphate cluster less around the mean, while, temperature, pH, potassium, calcium, well depth and Percentage Oxygen clustered more. The degree of variation observed in the data structure is in agreement with previous studies [26-27]. A wide variation was observed in the physicochemical variables of well-water collected from 3.2 Coliform Count in the Dry and Rainy Seasons The presence of coliform was confirmed by lactose fermentation and concurrent gas production. There was a strong negative correlation (r = -0.51, P < 0.05) between coliform count and distance from septic tank for the dry season (Table 2) and the strength of correlation coefficient increased during the raining season (r = - 0.58, P < 0.05) compared to dry season (r = - 0.51, P < 0.01). The correlation between coliform count and well depth was not significant (r = - 0.16, P > 0.05) but there was an indication of a negative relationship between coliform count and that of well depth in wet season (r= - 0.16, P>0.05). Table 1. Summary of the descriptive statistics of the entire variable evaluated in Akobo and Agbowo sites Minimum Maximum Mean Temperature °C 3PO4 mg/L NO3 mg/L + NH4 mg/L Cl mg/L pH + Na mg/L 2+ Mg mg/L + K mg/L 2+ Ca mg/L Conductivity (µs/cm) TDS mg/L Salinity % Hardness mg/L DO mg/L %O2 Coliform MPN/100 mL Distance m Average well depth m 28.6 7.0 10.1 11.6 75.0 6.0 0.8 0.8 0.9 2.0 88.1 31.2 24.0 60.5 80.9 475.0 7.6 4.1 3.5 10.5 37.1 1340.4 29.3 17.2 30.3 42.4 2.2 7.0 2.3 1.8 2.6 10.2 559.0 Std. deviation 0.5 4.2 12.6 19.7 126.9 0.4 0.8 0.6 1.7 6.9 340.2 51.1 0.1 50.0 0.1 1.1 1.0 716.0 0.6 610.0 0.7 10.3 1600.0 298.0 0.3 283.0 0.4 4.4 890.0 180.8 0.2 154.7 0.2 2.0 633.9 32700.0 0.02 23900.0 0.03 4.1 402000.0 0.3 0.4 0.3 0.7 1.0 -0.2 -0.6 -0.7 -0.7 -0.8 1.2 -1.6 4.0 0.3 30.5 15.0 16.0 3.9 8.0 3.2 63.3 10.1 0.5 2.2 -0.8 5.9 5 Variance Skewness Kurtosis 0.2 18.0 158.8 386.6 16100.0 0.2 0.7 0.4 2.8 47.0 116000.0 1.8 -0.4 0.3 0.2 0.4 -0.8 0.3 0.9 3.7 2.0 0.3 6.5 -0.6 -0.6 -1.1 -0.8 1.2 -0.3 1.5 17.7 6.9 -0.7 NESREA 45.0 200.0 6.5-8.5 200.0 150.0 200.0 200.0 1000.0 500.0 500.0 4.0 10.0 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 On the other hand, slightly positive but insignificant correlation (r = 0.1, P>0.05) existed between coliform count and well depth during dry season (Table 2). Well-water in Akobo study area recorded a low mean coliform count of MPN 674.14/100 mL with higher kurtosis (-1.85) compared with Agbowo which recorded a mean of MPN 1008.00/100 mL and -1.20 for kurtosis indicating that well samples coliform count in Akobo clustered less around high MPN value than Agbowo samples. agreement with the fact that soil filtration potential increased with distance both vertically and horizontally. However, the vulnerability of groundwater to particulate pollution is a function of the ease with which particulate and dissolve solutes can move with water and the attenuation capacity of the intervening materials [31]. The filtering capacity of soil as intervening materials will therefore depend on the soil properties and the distance between pollutant source and receiving water and also the properties of the pollutant. Previous studies [31] revealed that coliform could travel distance of 70.7 m from sewage trenches intersecting groundwater while covering a distance of 30.5 m within 35 hrs when travelling through sand and pea gravel aquifer. Francy et al. [32] also confirm an association between proximity of septic tank to well water site, well depth and coliform density. Table 2. Correlation analysis showing relationship between septic tank and well distance and corresponding seasonal changes in coliform density Distance from well to septic tank Well depth in dry Well depth in wet Coliform count in wet season Coliform count in dry season -0.51** -0.58** 0.1 - - -0.16 3.3 Variation in Well Water chemical Properties Physico- A paired sample Student T-test (Two tailed) comparing mean differences that exist in microbial and physico-chemical parameters of well-water sample in the dry season (April) and wet season (Average of value obtained between May and August) was determined (Table 3). The result shows that there was significant increase of coliform count between the dry season and wet season. (T = -3.401, P < 0.01) and pH (T= 3.566, P < 0.01), while there was a significant decrease in the concentration of phosphate (p < 0.01), salinity (P < 0.05), TDS (P < 0.01), potassium (P < 0.01) and sodium (P <0.01), conductivity (P <0.01) and ammonia (P <0.01). **. Correlation is significant at the 0.01 level (2-tailed). The result of the microbial analysis shows high levels of coliform contamination in well-water collected in Agbowo. The low well-depth range recorded in Agbowo could account for the high level of groundwater contamination as against what recorded in Akobo. The low sanitation level and hygiene practices [28] and other environmental factors observed in the community [1] may have contributed to the high microbial assessment of Agbowo wells. It was observed that the layout of the buildings in Agbowo were highly clustered and not well planned, resulting in the close distance of well sites to septic tanks and refuse dumps. An extreme observation involved cases in fenced neighbourhoods where wells were ignorantly sited at close proximity to the septic tank of the neighbouring house. This might have affected the reported high value of Coliform density observed in site AG2 with > MPN1600/100 mL and it supports the report on human waste contamination of groundwater by USEPA [29]. The result of monthly variation observed in salinity, TDS and conductivity were as shown in Figs. 2, 3 and 4 respectively. There was a significant difference in Salinity (t = 8.234, df = 29, P <0.01) between samples collected from Agbowo to that of Akobo. The salinity of wellwater in Agbowo increased slightly from April to May and later declined through June and July and a slight increase in salinity was later observed in August. In Akobo, there was no obvious fluctuation in the salinity of the wellwater (Fig. 2). A significant difference was also observed in the TDS (t = 8.175, df = 29, P< 0.01) and conductivity (t = 8.206, df = 29, P <0.01) between Agbowo and Akobo study areas. The monthly variation in TDS and conductivity of Agbowo study area followed a similar pattern as that of Salinity. However, TDS and conductivity of well water in Akobo showed a slight decrease The negative correlation obtained between total coliform count and distance from the nearest septic tank and well-depth (Table 2) suggests that the total coliform decrease with increasing well distance and well depth. This is in agreement with the findings of Adekunle et al. [30] and Adetunji and Odetokun [1] and also in 6 Nwuba and Philips; BJAST, 10(6): 1-18,, 2015; Article no.BJAST.12452 no. from the month of April through July before gaining stability (Figs. 3 and 4). be enriched with high load of soluble salt [25] septic and consequently enhance the leaching of septi tank scum into the groundwater environment. The subsequent reduction in salinity, TDS and Conductivity, may have resulted from increased dilution from continuous rainfall during the raining season [26] compounded by human withdrawal of previous solute laden den water for domestic use. The increased concentration of Salinity, TDS and M Conductivity between the month of April to May as shown in Figs.. 2, 3, and 4 may have resulted from precipitation enhancing leaching of long ionic particles term accumulated ionic and non-ionic (during dry season) on surface soil [26]. Precipitation also enhanced dissolution of materials naturally present in soil and the introduced water dissolved ionic and non-ionic non minerals. The resulting water that moves through the soil to the underlying groundwater will then The trend in variation of average Salinity, TDS and Conductivity was taken as the prime parameter to focus on monthly variations [33] for Agbowo and Akobo study areas. chemical and microbial Table 3. Seasonal (Dry and wet season) changes in physico-chemical parameters Parameters compared Coliform count Percent oxygen Dissolve oxygen Hardness Salinity TDS Conductivity Calcium dry Potassium Magnesium Sodium pH Chloride Ammonia Nitrate Phosphate Temperature Mean -328.27 -0.73 -0.07 14.61 0.02 25.15 44.54 2.72 0.90 0.00 0.57 -0.15 18.45 8.33 1.56 2.76 -0.07 T -3.40** -1.11 -1.38 1.06 2.50* 3.16** 2.86** 1.76 8.11** 0.02 4.40** -3.57** 1.95 2.33* 0.86 7.23** -0.28 Df 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 30 Sig. (2-tailed) (2 0.00 0.28 0.18 0.30 0.02 0.0 0.00 0.01 0.09 0.00 0.99 0.00 0.00 0.06 0.03 0.40 0.00 0.78 * P< 0.05 and ** P< 0.01 indicating significant differences between dry season and wet season Fig. 2. Monthly variations in salinity of well water samples in Agbowo and Akobo 7 Nwuba and Philips; BJAST, 10(6): 1-18,, 2015; Article no.BJAST.12452 no. Fig.. 3. Monthly variations in total dissolve solid (TDS) of well water samples in Akobo and Agbowo Fig. 4. Monthly variations in conductivity of well water samples of Akobo and Agbowo 3.4 Inter-relationships relationships of Microbial, Physical and Chemical Parameters like phosphate, nitrate, ammonia, sodium, conductivity, TDS and salinity. Well distance from septic tank had a significant negative correlation (P<0.05) with average coliform count, and negative insignificant correlation on (P>0.05) with phosphate, nitrate and ammonia ((Table 4). Well depth recorded a significant ficant negative correlation 0.05) with parameters like phosphate, nitrate, (P<0.05) ammonia, chloride, sodium, TDS, salinity and hardness (Table 4a & b). The result of the correlation analysis performed to evaluate the extent of the interrelationship that exists among the assessed variables is shown in Table 4. The correlation matrix revealed that strong positive relationship (P<0.01) existed between phosphate and the following parameters; nitrate, ammonia, chloride, sodium, magnesium, calcium, conductivity, TDS, salinity, and hardness (Table 4). In addition, strong positive correlation (P<0.01) also existed en nitrate and the following; ammonia, between chloride, sodium, magnesium, calcium, conductivity, TDS and salinity. The positive correlation ion observed between Coliform count, Phosphate, Nitrate, Chloride and Ammonia in ground water, was in line with previous work that had associated leachate from septic tank impacting on the concentrations of [34-35]. The these variables concurrently [34 presence e of coliform in the groundwater had been reported to indicate contamination by human and animal faeces [22,36]. These pathogens may pose a health hazard to infants Conductivity and TDS were perfectly correlated (r = 1.00) (Table 4). A significant positive positiv correlation (P<0.05) 0.05) was found between log transformed d coliform count and other parameters 8 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 and people with weak immune systems [36]. The significant variations in coliform count during wet and dry seasons; in relation to distance of well from septic tank and well depth show that run-off and weathering resulting from precipitation during raining season had a positive impact on the migration of coliform from different pollution sources to the receiving underground water. phosphate and ammonia concentrations may suggest that leachate from septic tank contributed to the increased concentrations of these parameters [37-39]. The strong negative correlation reported for nitrate concentration and well depth agrees with Piskin [40] whose findings indicates that Nitrate concentration decrease with increase in well-depth. Nitrate and Ammonia are by-products of natural break-down of Nitrogen containing organic compounds from human and domestic wastes that accumulate in the septic tank and this is reflected in the observed strong positive correlation between the above two variables. The strong co-linearity that existed between salinity, TDS and conductivity as expressed in the correlation matrix shows that salinity as a measure of the amount of dissolved particles and ion in well-water sample can account for TDS (measure of all dissolve substances) and conductivity (measure of charged ion or ionic particles) of the water sample. The negative correlation obtained between well distance from septic tank and soluble particles represented by salinity, TDS, conductivity show a possible contamination through leaching from proximate septic tank. This was justified by SWRC [25] findings that discharges from septic systems increased the salinity of the receiving water body. Most salt resulting from these soluble particles do not naturally degrade, and can remain in groundwater for decades [25]. The negative correlation that was found to exist between well depth and the following variables: phosphate; nitrate; ammonia; chloride; sodium; magnesium; calcium; conductivity; TDS; salinity and hardness further establishes the fact that areas with high aquifer levels are most predisposed to particulate contamination [40]. This may account for the reason why 73.9% of well samples at Agbowo study area, with well depth range (0.25 to 5.5 m), were unsuitable for drinking; when compared to Akobo which recorded a well depth range of 4 m to 15 m with a lower percentage (54.55%) of unsuitable drinking water (Table 9). The weak negative correlation that existed between well proximity to septic tank and Nitrate; Table 4a. Correlation matrix showing relationship between variables evaluated Temp (°C) PO43- (mg/L) NO3 (mg/L) + NH4 (mg/L) Cl (mg/L) pH + Na (mg/L) 2+ Mg (mg/L) 3PO4 (mg/L) 2+ Ca (mg/L) Conductivity (µs/cm) TDS (mg/L) Salinity (mg/L) Hardness (mg/L) DO (mg/L) %O2 Log coliform Well Depth (m) Distance [From soak away] (M) -0.26 -0.31 -0.33 -0.35 -0.81 0.23 -0.29 -0.30 0.13 -.013 -0.35 Temp 0 ( C) PO43(mg/L) NO3(mg/L) NH4+ (mg/L) CL(mg/L) 1 -0.09 0.04 0.00 -0.10 -0.05 -0.03 0.14 0.09 -0.23 -0.02 1 0.96** 0.92** 0.90** -0.16 0.94** 0.90** 0.15 0.80** 0.93** 1 0.95** 0.94** -0.17 0.95** 0.94** 0.247 0.78** 0.95** 1 0.91** -0.20 0.94** 0.90** 0.236 0.78** 0.90** 1 -0.18 0.92 0.90** 0.242 0.83** 0.95** 1 -0.17 -0.22 -0.14 -0.16 -0.17 1 0.94** 0.16 0.80** 0.94** 1 0.28 0.73** 0.93** -0.36 -0.35 -0.32 -0.0 -0.02 -0.00 0.92** 0.92** 0.96** 0.95** 0.96** 0.95** 0.91** 0.90** 0.95** 0.94** 0.95** 0.95** -0.17 -0.20 -0.19 0.94** 0.93** 0.97** 0.93** 0.92** 0.96** -0.21 -0.22 -0.61** 0.51** 0.05 0.04 0.08 0.17 0.00 0.01 0.43* -0.57** 0.02 0.02 0.43* 0.50** -0.04 -0.05 0.47** -0.51** -0.03 0.37* -0.45* -0.20 -0.29 -0.30 -0.05 0.08 0.08 0.44* -0.43* 0.08 0.08 0.35 -0.38* pH Na+ Mg2+ (mg/L) (mg/L) *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2-tailed). DO= Dissolve Oxygen, %O2= Percentage Oxygen, TDS= Total Dissolve Oxygen 9 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 Table 4b. Correlation matrix showing relationship between variables evaluated 3+ 3+ PO4 (mg/L) 2+ Ca (mg/L) Conductivity (µs/cm) TDS (mg/L) Salinity (mg/L) Hardness (mg/L) DO (mg/L) %O2 Log coliform count Well Depth (m) 2+ PO4 (mg/L) Ca (mg/L) Conductivity (µS/CM) TDS (mg/L) Salinity (mg/L) 1 0.18 0.25 1 0.78** 1 0.25 0.24 0.78** 0.78** 0.19 Hardness DO (mg/L) (mg/L) 1.00** 0.99** 1 0.99** 1 0.81** 0.97** 0.97** 0.98** 1 -0.13 -0.13 -0.25 0.02 0.01 0.39* 0.09 0.09 0.46** 0.1 0.10 0.46** 0.12 0.12 0.46** 0.06 0.06 0.45* 1 0.96** 0.27 0.34 -0.40* -0.48** -0.48** -0.47** -0.51** -0.00 %O2 1 0.21 0.04 Log coliform count 1 -0.51** *. Correlation is significant at the 0.05 level (2-tailed). **. Correlation is significant at the 0.01 level (2 tailed). DO= Dissolve Oxygen, %O2= Percentage Oxygen, TDS= Total Dissolve Oxygen 3.5 Discriminant Analysis (DA) classification of the study areas. It may be that geographical location with respect to groundwater hydrological properties did not have an influence on the variation in the physical and microbial properties of the groundwater between the two study sites. The result of a one-way ANOVA discriminant analysis for the variables in Table 5 shows that temperature DO, pH, K, %O2 and coliform count failed the tolerance test at 0.001 and did not contribute to the discriminant model classifying the two study area. The Eigenvalue value (16.151) for the first discriminant function accounts for 100% of the variance that exists among the variables obtained from the two study 2 areas and the Chi square test (X = 62.526, df=30, P<0.01) shows that the difference in the two groups was not just due to chance. The strong canonical correlation of 0.970, low Wilk’s Lambda value of 0.058 shows a strong relationship between the discriminant score and the group classification. Predicted group membership (Table 6) shows 100% perfect classification of sampling site based on discrimination between variables observed. The result of the discriminant analysis (Tables 5 and 6) shows that differences which exist among variables in the study areas were not only limited to the proximity to septic tank or water level nor due to chance, therefore the significant difference in the hydrologic properties of the two locations might be a contributory factor [41-42]. 3.6 Regression Analysis Dependent Variables Linear Model (GLM) for by Multiple General The result of the GLM Multivariate procedure presented in Table 7 provides regression analysis for multiple dependent variables by factor variables (location of the study area) and the covariates (well closeness to septic tank and well depth) based on the model, in which location, distance and well depth were assumed to have linear relationships to the physicochemical parameters and coliform count of the well water. The Pillar’s trace for location presented the highest value of 0.74 showing location as the effect that contributed most to the model followed by distance. The closeness of Hotelling’s trace (0.159) and Pillai’s Trace (0.137) for the average well depth showed that well depth does not contribute much to the model. Roy’s Largest Root showed the degree of The low Wilk’s Lambda value (0.058) reveals the strength of the variable at discriminating between the two study areas in decreasing order: phosphate > nitrate > salinity > conductivity >hardness>TDS > chloride > ammonia > magnesium > sodium > calcium. The standardized coefficients comparing variables measured on different scales downgraded the discriminative ability of phosphate to 5th position while promoting discriminant ability of conductivity, sodium, TDS and hardness above phosphate (Table 5). Physical parameters like temperature, DO, and %O2 and coliform count did not contribute to the 10 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 contribution to the model as Location > Distance > Well depth. The equality of Roy's largest root and Hotelling's trace statistics revealed that the effect is predominantly associated with the strong correlation between the dependent variables. The GLM Partial eta squared presented the practical significance of effect based upon the ratio of the variation accounted for by the effect to the sum of the variation accounted for by the effect and the variation left to error. Partial Eta Squared value of 0.740, 0.382 and 0.137 were reported for location, Distance and well depth respectively (Table 7), showing their degrees of contribution to the model in that order. examined, than septic tank proximity to well site and well depth. However, proximity to septic tank has a greater impact on the coliform count. Most of the well water quality in Akobo and Agbowo were below acceptable limit of NESREA [43] except for coliform count and chloride concentration. Pillai's trace is a positive-valued statistic. Increasing values of the statistic indicate effects that contribute more to the model. Wilks' Lambda is a positive-valued statistic that ranges from 0 to 1. Decreasing values of the statistic indicate effects that contribute more to the model. Hotelling's trace is the sum of the eigenvalues of the test matrix. It is a positive-valued statistic for which increasing values indicate effects that contribute more to the model. Roy's largest root on the other hand, is the largest eigenvalue of the test matrix. Thus, it is a positive-valued statistic for which increasing values indicate effects that contribute more to the model. Table 5. The standardized coefficients comparing variables measured in the different study sites. ANOVA tests of equality of group means for discriminant analysis Temperature Phosphate Nitrate Ammonia Chloride Ph Sodium Magnesium Potassium Calcium Conductivity TDS Salinity Hardness DO %O2 Coliform Wilks' Lambda 0.98 0.23 0.30 0.42 0.34 0.94 0.45 0.45 0.99 0.52 0.30 0.30 0.30 0.30 1.00 1.00 0.93 F 0.51 95.25* 68.46* 40.11* 55.40* 1.77 35.17* 36.16* 0.35 26.72* 67.35* 66.84* 67.79* 67.10* 0.12 0.09 2.04 Standardized coefficient 0.20 2.75 -2.17 0.27 -0.12 -0.40 -4.96 -1.04 -0.07 -0.01 6.23 -4.25 -0.48 4.25 3.7 Water Quality Index (WQI) All the physico-chemical and Microbial parameters analysed were used to calculate the WQI in accordance with the procedure explained in the methodology section. The results of the WQI were presented in Tables 8 and 9. Only 16.13% of the sampled well water fall within the excellent water quality range and 9.68% were within the very poor water range; while 74.19% of the sampled well waters were unsuitable for drinking. The quality of the well water collected from the two different study areas have shown that in Agbowo, 73.9% of the water samples collected were unsuitable for drinking, while 54.55% of that collected from Akobo were unsuitable for drinking. High coliform count was the predominant variable responsible for the poor quality of the water sample in Akobo, while high chlorine and coliform count were responsible for the poor well water quality in Agbowo. *Values do not have a significant contribution to the discriminant model. The higher the Wilk’s Labda value to stronger the contributory strength of the variable. The multivariate GLM presented in Table 7 conclusively reveals that location contributes more to the variation that exist among physical and chemical parameters of the well water Table 6. Predicted group membership Grouping Original Count % Cross-validated Count % Agbowo Akobo Agbowo Akobo Agbowo Akobo Agbowo Akobo 11 Predicted group membership Agbowo Akobo 20 0 0 11 100.0 0 0 100.0 20 0 0 11 100.0 0 0 100.0 Total 20 11 100.0 100.0 20 11 100.0 100.0 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 Table 7. Regression analysis for multiple dependent variables by General Linear Model (GLM) Effect Intercept Pillai's trace Wilks' lambda Hotelling's trace Roy's Largest root Pillai's trace Wilks' lambda Hotelling's trace Roy's largest root Pillai's trace Wilks' lambda Hotelling's trace Roy's largest root Pillai's trace Wilks' lambda Hotelling's trace Roy's largest root Distance Average well depth Location Value F 0.93 0.07 13.51 13.51 0.38 0.62 0.62 0.62 0.14 0.86 0.16 0.16 0.740 0.260 2.852 2.852 74.28 74.28 74.28 74.28 3.39 3.39 3.39 3.39 0.87 0.87 0.87 0.87 15.70 15.70 15.70 15.70 Hypothesis df 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 Error df Sig. 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 22.00 0.000 0.000 0.000 0.000 0.026 0.026 0.03 0.03 0.50 0.50 0.50 0.50 0.00 0.00 0.00 0.00 Partial eta square 0.93 0.93 0.93 0.93 0.38 0.38 0.38 0.38 0.14 0.14 0.14 0.14 0.74 0.74 0.74 0.74 Table 8. Water quality index (WQI) Location Coliform count NO3- Cl- Ca2+ pH AG1 AG2 AG3 AG4 AG5 AG6 AG7 AG8 AG9 AG10 AG11 AG12 290.00 1600.00 535.00 1600.00 1600.00 815.00 950.00 260.00 825.00 625.00 1050.00 1600.00 30.54 40.48 26.27 44.82 35.06 27.37 32.08 27.08 33.82 35.38 42.41 60.54 199.50 245.00 150.00 355.00 235.00 235.00 295.00 215.00 255.00 325.00 295.00 430.00 9.72 14.05 8.04 15.30 11.06 7.57 11.08 8.14 11.39 12.67 14.98 16.09 6.96 7.00 7.23 7.52 7.16 6.78 7.14 7.02 6.89 7.11 7.01 7.27 Distance (m) between well and septic tank 13 4 16 10.5 12.3 29 7.5 7.6 4.4 12 10 5 12 Water depth in dry season (m) 4 4 3 3 3 5 3 3 1.5 4 4.5 6 Water depth in wet season (m) 2 3 2 2.5 2.5 2.2 2 1.5 0.2 2.3 2.3 5 WQI Water quality Ƿ 305.32 1619.31* Ƿ 551.13 1620.18* 1617.02* Ƿ 831.23 966.49 Ƿ 275.97 Ƿ 840.80 Ƿ 641.20 1066.71* 1619.22* Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Very poor water Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 AG13 AG14 AG15 AG16 AG17 AG18 AG19 AG20 AK1 AK2 AK3 AK4 AK5 AK6 AK7 AK8 AK9 AK10 AK11 1600.00 1600.00 1600.00 1600.00 8.60 1050.00 920.00 31.00 7.50 1600.00 1600.00 185.00 360.00 1150.00 900.000 1600.00 2.000 10.000 1.000 48.92 42.39 47.46 42.30 32.92 34.87 37.13 39.16 17.45 18.56 18.38 18.03 10.06 16.38 15.41 15.87 15.82 18.42 15.33 475.00 430.00 465.00 315.00 245.00 220.00 250.00 340.00 75.00 95.00 75.55 75.00 75.00 80.00 100.00 75.00 80.00 95.00 136.00 16.81 37.11 14.61 14.66 9.91 12.45 13.13 13.97 5.01 5.12 5.11 3.83 2.44 3.47 2.98 4.08 2.00 4.00 4.61 7.46 7.61 7.56 6.65 6.72 7.50 7.27 6.63 6.89 6.87 7.05 6.97 6.01 7.03 5.99 7.17 6.77 6.45 6.96 15.5 25 24 24 18 11 11 18 30 14 8.2 22 14.2 13 15 16 30.5 26 30 4.5 6 6 4 2 1 0.5 1.5 13 6 6 4 8 5 5 15 15 4 15 1.5 2 2 2 0 0.25 0 0 12 2.5 2.3 3 8 4 4.4 14 13 3 14 WQI= Water Quality Index, ǷBelow zone average, *Above zone average. AK= Akobo, AG=Agbowo. Akobo Average=710.0179, Agbowo Average=1025.191 13 1620.72* 1618.79* 1617.44* 1616.51* Ƿ 24.07 1066.25* 936.83Ƿ Ƿ 48.66 Ƿ 21.08 1613.83* 1615.15* Ƿ 201.90 Ƿ 374.75 1166.62* 913.29* 1616.83* 15.75Ƿ Ƿ 255.20 Ƿ 15.80 Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Excellent Unsuitable for drinking Unsuitable for drinking Excellent Excellent Unsuitable for drinking Unsuitable for drinking Very poor water Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Unsuitable for drinking Excellent Very poor water Excellent Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 Table 9. Standard water quality classification scheme based on WQI value WQI value <50 Water quality Excellent Water sample 5 (16.13%) 50 – 100 Good water 0 (0%) 100 – 200 Poor water 0 (0%) 200 – 300 Very poor water 3 (9.68%) >300 Unsuitable for drinking 23 (74.19%) Nitrate level exceeding the recommended standard (45 mg/L) as observed in Wells AG4, AG12, AG13 and AG15 sampled and this has been reported to cause methemoglobinemia in infants [35,36,44-45], thereby rendering the water in these wells unsafe for infant consumption. Location Agbowo Akobo Agbowo Akobo Agbowo Akobo Agbowo Akobo Agbowo Akobo Water sample 2 (10%) 3 (27.27%) 0 (0%) 0 (0%) 0 (0%) 0 (0%) 1 (5.00%) 2 (18.18%) 17 (73.9%) 6 (54.55%) migration of coliforms from various sources to the underground water. There is therefore a need for biogeochemistry assessment of hydrologic environment before considering siting a well in any location. The results suggest that the minimum standard distance between septic tank and well water will vary with different hydrologic environments and recommend a restriction on digging wells for domestic use in areas of high aquifer levels; rather, boreholes should be an alternative for the provision of potable water in such areas. High level of Chloride observed in all Agbowo sample site (Average 299 mg/L against 200 mg/L NESREA Standard) has been associated with sewage pollution [22]. Chloride is often chemically bonded with sodium naturally present in soil to form Sodium Chloride which impact salty taste on water. The simultaneous fluctuation in the concentration of some variables may be accounted for by infiltration of rainwater through the porous and permeable unconfined aquifer into shallow water table that enriches the groundwater with dissolved solute taken from surface and subsurface soil. From observation of water conductivity, TDS and salinity decreased immediately after rainfall. ACKNOWLEDGEMENTS The authors wish to thank Dr. Aina O. Adeogun and Prof. E. O. Fagade for their suggestions and allowing us use their laboratory equipment. COMPETING INTERESTS Authors have declared interests exist. Slightly Low pH level in AK5, AK7 and AK10 well water (below recommended standard 6.5 to 8.5)) observed in some sample points in Akobo may be accounted for by the low level of calcium and magnesium concentration in the study area [46]. that no competing REFERENCES 1. 4. CONCLUSION 2. In this study, various statistical techniques were used to evaluate quality and variation in physical, chemical and microbial parameters of well-water (underground water) in Agbowo and Akobo in relation to their proximity to septic tank. 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Mean of raw data Site AG1 AG2 AG3 AG4 AG5 AG6 AG7 AG8 AG9 AG10 AG11 AG12 AG13 AG14 AG15 AG16 AG17 AG18 AG19 AG20 AK1 AK2 AK3 AK4 AK5 AK6 AK7 AK8 AK9 AK10 AK11 Temp 28.900 28.800 29.900 29.300 29.400 29.400 29.360 29.000 29.700 29.600 29.400 31.200 29.200 28.600 28.600 29.100 29.000 29.200 28.900 29.300 29.200 29.800 29.660 29.600 29.600 29.100 29.000 29.700 29.600 29.200 29.200 3- PO4 18.345 21.415 17.405 21.495 19.340 17.305 17.720 16.470 18.215 18.705 21.370 23.990 23.125 21.910 21.815 21.440 18.255 18.635 20.035 21.140 12.715 13.485 13.500 13.065 6.960 12.325 13.365 13.010 10.330 13.285 13.035 - NO3 30.535 40.480 26.265 44.815 35.055 27.370 32.075 27.075 33.820 35.380 42.410 60.535 48.920 42.385 47.460 42.300 32.915 34.865 37.125 39.160 17.445 18.555 18.380 18.030 10.060 16.375 15.410 15.865 15.820 18.415 15.325 + NH4 42.575 54.770 31.820 63.960 32.605 31.365 47.275 41.415 40.835 53.440 62.405 80.945 73.285 63.505 73.140 62.550 40.905 53.450 54.150 61.755 19.670 40.895 40.730 20.015 11.560 19.575 17.635 19.205 19.060 19.955 19.305 - Cl 199.500 245.000 150.000 355.000 235.000 235.000 295.000 215.000 255.000 325.000 295.000 430.000 475.000 430.000 465.000 315.000 245.000 220.000 250.000 340.000 75.000 95.000 75.550 75.000 75.000 80.000 100.000 75.000 80.000 95.000 136.000 pH 6.956 6.996 7.234 7.516 7.160 6.780 7.142 7.018 6.886 7.108 7.014 7.272 7.460 7.610 7.560 6.650 6.724 7.502 7.272 6.630 6.886 6.870 7.046 6.970 6.014 7.034 5.992 7.168 6.766 6.45 6.960 + Na 2.310 2.930 1.910 3.420 2.515 1.910 2.385 2.040 2.250 2.630 3.140 3.825 4.110 3.340 3.160 3.150 2.215 2.185 2.675 2.815 1.670 1.950 1.890 1.405 0.760 1.550 1.360 1.625 0.925 1.540 1.910 2+ Mg 1.845 2.175 1.670 2.265 1.940 1.635 1.945 1.745 1.895 2.095 2.185 3.510 3.445 2.405 2.225 2.240 1.950 2.080 2.125 2.290 1.145 1.450 1.325 1.335 0.835 1.215 1.115 1.255 1.255 1.085 1.665 + K 2.330 2.850 1.720 3.300 2.655 1.910 2.440 2.075 2.300 2.550 3.200 3.895 4.045 3.505 3.365 3.140 2.200 2.415 2.690 2.985 1.600 1.910 1.870 1.660 0.890 1.460 1.335 1.670 10.540 1.400 2.130 2+ Ca 9.715 14.050 8.035 15.295 11.055 7.565 11.080 8.140 11.390 12.665 14.980 16.085 16.810 37.110 14.610 14.660 9.910 12.450 13.130 13.970 5.010 5.115 5.110 3.825 2.435 3.465 2.975 4.075 1.995 4.000 4.610 17 Conductivity 606.200 882.600 426.400 1014.600 787.600 581.800 708.600 594.800 636.600 802.400 812.000 1170.000 1340.400 939.800 957.400 726.700 547.260 557.040 590.200 653.460 144.880 186.200 343.800 132.780 88.140 184.720 137.340 157.080 186.900 239.640 200.280 TDS 322.600 472.400 226.800 540.000 419.800 309.400 378.000 316.000 338.600 428.200 432.400 624.600 716.000 501.000 493.800 380.400 288.800 296.800 322.800 351.800 73.460 97.160 195.800 73.360 51.140 91.740 74.240 87.480 101.700 125.040 102.300 Salinity 0.280 0.420 0.200 0.500 0.380 0.280 0.360 0.280 0.300 0.360 0.380 0.560 0.620 0.440 0.460 0.340 0.300 0.280 0.300 0.320 0.100 0.100 0.140 0.100 0.100 0.100 0.100 0.100 0.100 0.100 0.100 Hardness 300.000 380.000 240.000 455.000 330.000 245.000 305.000 255.000 315.000 340.000 420.000 600.000 610.000 465.000 465.000 415.000 310.000 335.000 365.000 395.000 125.000 150.000 150.000 120.000 55.000 110.000 90.000 125.000 50.000 117.500 134.000 DO 0.220 0.730 0.344 0.640 0.376 0.430 0.302 0.334 0.264 0.218 0.274 0.378 0.632 0.376 0.128 0.338 0.272 0.180 0.300 0.546 0.152 0.174 0.340 0.656 0.638 0.604 0.352 0.608 0.222 0.190 0.316 %O2 2.870 10.340 4.040 6.780 4.200 4.760 3.240 4.020 3.660 2.440 3.200 4.600 7.300 4.240 1.060 4.204 3.860 2.020 3.420 5.850 3.560 2.020 4.160 7.460 7.340 5.920 3.980 7.060 2.680 2.280 3.460 Coliform 290.000 1600.000 535.000 1600.000 1600.000 815.000 950.000 260.000 825.000 625.000 1050.000 1600.000 1600.000 1600.000 1600.000 1600.000 8.600 1050.000 920.000 31.000 7.500 1600.000 1600.000 185.000 360.000 1150.000 900.000 1600.000 2.000 10.000 1.000 Nwuba and Philips; BJAST, 10(6): 1-18, 2015; Article no.BJAST.12452 Appendix 2. Coliform count of the well sample (MPN/100ml) Site code Distance (m) Water depth in dry season (m) Water depth in wet season (m) AG1 AG2 AG3 AG4 AG5 AG6 AG7 AG8 AG9 AG10 AG11 AG12 AG13 AG14 AG15 AG16 AG17 AG18 AG19 AG20 AK1 AK2 AK3 AK4 AK5 AK6 AK7 AK8 AK9 AK10 AK11 13 4 16 10.5 12.3 29 7.5 7.6 4.4 12 10 5 15.5 25 24 24 18 11 11 18 30 14 8.2 22 14.2 13 15 16 30.5 26 30 4 4 3 3 3 5 3 3 1.5 4 4.5 6 4.5 6 6 4 2 1 0.5 1.5 13 6 6 4 8 5 5 15 15 4 15 2 3 2 2.5 2.5 2.2 2 1.5 0.2 2.3 2.3 5 1.5 2 2 2 0 0.25 0 0 12 2.5 2.3 3 8 4 4.4 14 13 3 14 Ag= Agbowo, AK= Akobo Dry season coliform count (MPN/100ml) 280 1600 170 >1600 >1600 30 300 1600 50 350 500 >1600 >1600 >1600 1600 1600 8 500 240 13 7 1600 >1600 130 220 900 900 1600 <2 7 0 Wet season coliform count (MPN/100ml) 300 1600 900 >1600 >1600 >1600 >1600 240 >1600 900 >1600 >1600 >1600 >1600 >1600 >1600 9.2 >1600 >1600 49 8 >1600 >1600 170 500 1600 900 1600 2 13 2 ________________________________________________________________________________ © 2015 Nwuba and Philips; This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Peer-review history: The peer review history for this paper can be accessed here: http://sciencedomain.org/review-history/10280 18