Groundwater is the most important natural resource, and many people throughout the world rely on it for drinking, particularly in rural areas. The present study was carried out to assess the status of groundwater quality and to check its suitability for domestic purposes in urban centres of Bahir Dar City, Ethiopia. Twelve shallow wells were selected for sampling. The sampled water was taken during the dry and summer seasons of the year 2019/2020. pH, turbidity, EC, TDS, chloride (Cl), nitrate (NO3), phosphate (PO4−3), total hardness, and Escherichia coli were measured for the suitability analysis. Comparison of measured results with those of WHO and Ethiopian drinking water quality standards was done. Moreover, Geographic Information System (GIS) and Water Quality Index (WQI) data analysis techniques were applied in order to investigate the groundwater quality. The spatial distribution map showed that the city's core area had the poorest groundwater quality status. The WQI result obtained from the analysis showed that 41.67, 33.33, and 25% of the sampled groundwater has low, extremely poor, and unsafe quality for drinking purposes, respectively. The present study revealed that anthropogenic activities have a great impact on the quality of groundwater in the area, necessitating immediate mitigating actions.

  • Anthropogenic activities influence the quality of groundwater.

  • The groundwater quality is fully contaminated with fecal coliforms.

  • The WQI and GIS techniques show a majority of the city groundwater is unfit for drinking purposes.

  • The highest concentration occurred in the central and mid-southern regions of the surveyed field.

Groundwater is a major and crucial natural resource in both rural and urban areas, which is not only used for drinking purposes but also plays a major role in many segments of the country's economy such as hydropower, irrigation, fisheries, industrial production, and livestock production (Amadi et al. 2012; Tyagi et al. 2013). Recently, groundwater contamination has become a serious problem worldwide (Yolcubal et al. 2016) because of the rapid rise in population growth, urbanization, the quick speed of industrialization, and climate change (Tyagi et al. 2013; Tiwari et al. 2014).

Human activities that introduce contaminants into the environment have a significant impact on groundwater quality. Groundwater availability and quality have been impacted by rapid urbanization, particularly in developing nations like Ethiopia, due to over-exploitation, inappropriate waste disposal, and mismanagement of underground water resources. Thus, groundwater quality is under a serious threat particularly in metropolitan areas (Ramakrishnaiah et al. 2009; Prasanth et al. 2012; Yousefi et al. 2018; Saleh et al. 2019). Groundwater is the ecosystem's most essential natural resource, renewable but subject to natural and anthropogenic influences. According to Khatri & Tyagi (2015) and Babiker et al. (2007), groundwater is one of the most vulnerable natural resources, as it is susceptible to contamination from industrial effluents as well as sewage created by residential and commercial activities.

Groundwater in Ethiopia has been shown to be contaminated as a result of inadequate sanitation provision, poor waste management practices, and due to poor agricultural fertilizer management practices (Alemayehu 2001; Goshu & Akoma 2011). Bahir Dar and its surroundings have become a focal point for industrial, urban, institutional, economic, and tourism development center. Human activities in and around Bahir Dar increase the amount of garbage dumped directlyinto open land fields. The bulk of pit latrines and septic tanks used by local people in the area are often poorly constructedand not maintained properly, thus resulting in regular overflow of septic tanks, which poses a larger risk of infiltration into groundwater.

In this area, there is relatively little research on groundwater quality status done on a regular basis, and its impact on community health and specified purposes such as drinking, domestic, irrigation, and other industrial activities. As a result, this research was carried out in order to provide the necessary data for determining the water quality status of groundwater as well as the key contrasting element that restricts its suitability for the intended (drinking) purpose. As it is very difficult to repair groundwater quality once it has been polluted by limiting pollutants at the source, it is critical to check the groundwater on a frequent basis to protect its quality (Hasan et al. 2014; Puri et al. 2015; Akale et al. 2017). As a result, modeling and monitoring the state of groundwater quality is critical for water management decision-making.

The Water Quality Index (WQI) is the most effective method for communicating information about water quality to concerned stakeholders and policy-makers, allowing them to make corrective and integrated measurements for the assessment of groundwater appropriateness for most residential applications (Kumar et al. 2007; Avvannavar & Shrihari 2008; Rajankar et al. 2011; Puri et al. 2015; Saleem et al. 2016; Khosravi et al. 2017; Abbasnia et al. 2019; Khangembam & Kshetrimayum 2019). It assesses groundwater quality using a simple mathematical equation that converts large water quality parameter data into a single integer and gives a score that indicates groundwater quality status. Similarly, Boateng et al. (2016) and Akter et al. (2016) studied the WQI as a method for converting several water quality parameter data into a single number and assessing the geographical and temporal quality of the tested water.

The most extensively used program for the assessment and management of natural and environmental resources, including groundwater is the Geographical Information System (GIS). It is the most widely used software for site suitability study, calculating water availability, predicting groundwater sensitivity to pollution potential from nonpoint sources, and assisting in the management of water resources (Singh & Khan 2011).

A spatial groundwater quality distribution map is required to monitor and evaluate the area of groundwater pollution and to show the level of contamination. Thus, a spatial analysis extension of ArcGIS v10.1 aids in the interpolation of groundwater quality parameter spatial distribution maps at unknown locations from known values by producing continuous surfaces, which aids in the understanding of water quality parameter geographic distribution (Sumathi et al. 2008; Selvam et al. 2014; Shabbir & Ahmad 2015). The most extensively utilized interpolation techniques available in ArcGIS v10.1 software for the creation of water quality distribution maps are Kriging and Inverse Distance weight (IDW) (Johnston et al. 2001).

The primary goal of this research is to provide an overview of current groundwater quality and its suitability for drinking purposes and present the results in the form of WQI- and GIS-plotted maps.

Description of the study area

The research is being carried out at Bahir Dar City, Ethiopia, which is located in the Amhara National Regional State and is bordered to the north by Lake Tana, which represents the source of the Blue Nile River as seen in Figure 1. The area is roughly bounded by latitudes 11°32′0″ N and 11°38′0″ N, and longitudes 37°20′0″ E and 37°27′40″ E. The terrain is mostly flat, with a few minor hills to the east and west. The city is believed to be 62.22 km2 in size, with an average height of 1,795 meters above sea level.
Figure 1

Location map of the study area.

Figure 1

Location map of the study area.

Close modal

The temperature in the area is generally nice, with warm days and mild evenings. The average annual precipitation is 1,037 mm, with 54% falling in July and August and only 3% falling during the 4 dry months. The average yearly temperature is 16 °C. Maximum temperatures are most common from March through May, and the average monthly maximum temperature reaches 26 °C. Bahir Dar's soil type is represented by residual fine soils, such as silt clay or clay soil. The prevailing soil color in the research area is red, and no coarser soils can be detected in outcrops or deep-water well logs. As a result, permeability is estimated to be between 1 × 10−4 and 1 × 10−5 m day−1. The underlying rocks, on the other hand, are worn and broken and hence have considerable permeability (Goshu & Akoma 2011).

Bahir Dar city is situated on the Southern shore of Lake Tana. Important landscapes include natural (church) forests with indigenous tree species (including shade-growing coffee), wildlife like hippopotamus, papyrus bed wetlands, important bird areas of key global species (nesting, feeding, and roosting sites), and agricultural landscapes. Bahir Dar's main attraction is the selection of Ethiopian Christian monasteries which are found on some of Lake Tana's 37 islands. Most of the Bahir Dar monasteries date from the 16th and 17th centuries and have changed little since their founding.

Sampling site and sampling design

A total of twelve (12) sampling stations from groundwater sources were chosen to examine the state of groundwater quality depending on the location and magnitude of human influence. Ten (10) of the sampled water sources are shallow wells, while two are moderately deep wells. From January to March 2019, groundwater samples were collected once a month and then again from June to August 2019. Table 1 and Figure 1 show the sampling station co-ordinates and location of sampling points in Bahir Dar city, respectively, which were collected using a Geographic Positioning System (GPS) with the designated model Aquaprobe-7000.

Table 1

GPS reading of sampling sites with brief descriptions

StationXYWell locationWell typeWell depth (m)
W1 37°24.89′ E 11°36.37′N Kebele 11 Moderately deep 25 
W2 37°23.89′E 11°35.93′N Poly Campus shallow hand dug 1.5 
W3 37°23.22′ E 11°35.29′N kebele 4 shallow hand dug 
W4 37°23.00′E 11°35.30′N kebele 15 shallow hand dug 
W5 37°23.02E 11°35.66′N kebele 4 shallow hand dug 
W6 37°22.92′E 11°35.50′N kebele 15 shallow hand dug 
W7 37°22.18′E 11°35.12′N kebele 14 Moderately deep 25 
W8 37°22.31′E 11°35.34′N kebele 16 shallow hand dug 
W9 37°22.18′E 11°36.34′N kebele 13 shallow hand dug 
W10 37°21.75′E 11°36.47′N kebele 13 shallow hand dug 
W11 37°23.38′E 11°34.44′N Kebele 7 shallow hand dug 
W12 37°23.59′E 11°32.47′N Waste dump site Spring 0.3 
StationXYWell locationWell typeWell depth (m)
W1 37°24.89′ E 11°36.37′N Kebele 11 Moderately deep 25 
W2 37°23.89′E 11°35.93′N Poly Campus shallow hand dug 1.5 
W3 37°23.22′ E 11°35.29′N kebele 4 shallow hand dug 
W4 37°23.00′E 11°35.30′N kebele 15 shallow hand dug 
W5 37°23.02E 11°35.66′N kebele 4 shallow hand dug 
W6 37°22.92′E 11°35.50′N kebele 15 shallow hand dug 
W7 37°22.18′E 11°35.12′N kebele 14 Moderately deep 25 
W8 37°22.31′E 11°35.34′N kebele 16 shallow hand dug 
W9 37°22.18′E 11°36.34′N kebele 13 shallow hand dug 
W10 37°21.75′E 11°36.47′N kebele 13 shallow hand dug 
W11 37°23.38′E 11°34.44′N Kebele 7 shallow hand dug 
W12 37°23.59′E 11°32.47′N Waste dump site Spring 0.3 

Measurement of groundwater samples

Water samples were taken from shallow groundwater at several sampling locations in 1-L pre-cleaned tightly closed polyethylene bottles, and the sampling container was washed (renised) twice or three times with distilled water or the water to be sampled.

The physico-chemical and bacteriological properties of sampled water quality were assessed in monthly samples collected from January to March 2019 and then again from June to August 2019. The container was then entirely filled with the sampled water, leaving no air space. All the methods used for sample analysis (Table 2) were according to the standard methods for water and wastewater examination as specified (APHA 1998, 2012).

Table 2

Instruments used for onsite field measurements and laboratory test equipment

ParameterInstrument/methodInstrument modelStandard method number
Turbidity Turbiditymeter Hanna HI 98708 APHA 2130B 
EC Multi-parameter water quality probe Aquaprobe-7000 SUK: AP-7000 
pH Multi-parameter water quality probe Aquaprobe-7000 SUK: AP-7000 
TDS Multi-parameter water quality probe Aquaprobe-7000 SUK: AP-7000 
Chloride (ClPalin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
Nitrate  Palin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
Phosphate  Palin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
T.Hardness Palin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
Faecal coliform Membrane filtration method N/A APHA 9222 
ParameterInstrument/methodInstrument modelStandard method number
Turbidity Turbiditymeter Hanna HI 98708 APHA 2130B 
EC Multi-parameter water quality probe Aquaprobe-7000 SUK: AP-7000 
pH Multi-parameter water quality probe Aquaprobe-7000 SUK: AP-7000 
TDS Multi-parameter water quality probe Aquaprobe-7000 SUK: AP-7000 
Chloride (ClPalin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
Nitrate  Palin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
Phosphate  Palin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
T.Hardness Palin test (low range spectrophotometer) Palintest 8000 Palintest photometer 8000 
Faecal coliform Membrane filtration method N/A APHA 9222 

The groundwater source collection method was used on a regular basis; otherwise, the source should be cleaned continously before the sample. To remove all sediment, slime, and turbidity from hand pump sources, the water was pumped and washed for 3–5 min. The collected samples from each source were tagged and transported to the laboratory in an ice-box at a low temperature before being analyzed.

pH, turbidity, electrical conductivity (EC), total dissolved solids (TDS), chloride (Cl), nitrate , phosphate , total hardness, and bacteriological analysis, including Escherichia coli, were all measured in the samples. The pH, EC, and TDS were measured in situ with an Aquaprobe-7000, whereas the ionic species (Cl), dissolved ion species ( and ), and total hardness were determined in the laboratory with a Palin test 8000 spectrophotometer. According to the standard water and wastewater experimental method as specified by American Public Health Association standard methods (APHA 1998) during fecal coliform (E. coli) level testing, 100 ml sample of water was sucked through a filter using an electrically driven pump.

Quality assurance and control procedure

During sample collection and analysis, quality assurance and control procedure were followed so as to eliminate and or minimize errors. Hence, errors in the water quality data collection case may lead to mistakes and bring a wrong conclusion for the entire research work. Therefore, for quality assurance and control purposes, all water monitoring equipments are calibrated and any equipment failing calibration was not used for the entire data collection and record system.

The Statistical Package for Social Sciences (SPSS v21) was employed to analyze the basic descriptive statistics such as minimum, maximum, average, and standard deviation. The data which were collected from the selected groundwater parameters were analyzed using the descriptive statistics method. The results of the analyzed groundwater parameters (Table 5) were compared to WHO (2011) and the Federal Democratic Republic of Ethiopian Ministry of Health (MoH, F.D.R.E 2011) drinking water quality guidelines.

Determination of WQI

To assess the water quality rate of the measured groundwater parameter and to provide insight into the degree to which water quality is affected by anthropogenic activity, a desirable level or defined water quality parameters aim was utilized. As a result, eight (8) water quality parameters were examined in the current study to determine the WQI rate: pH, turbidity, EC, TDS, chloride, nitrate, phosphate, and total hardness. Three steps were followed to calculate the WQI of the measured groundwater, as formulated by others (Abdul Hameed et al. 2010). The first stage was to assign a weight (Wi) to each of the water quality metrics, such as pH, turbidity, EC, TDS, chloride, nitrate, phosphate, and total hardness, based on their relative importance in the overall quality of groundwater for drinking purposes, which ranged from 3 to 5 (Khan & Jhariya 2017; Khosravi et al. 2017; Solangi et al. 2019; El Mountassir et al. 2020) as shown in Table 3. The highest weight of 5 is given to the water quality parameter if it has a significant impact on the water quality, while the lowest weight of 3 is given if it has no significant impact on the water quality.

Table 3

The recommended standard value, ideal value, and weight factor of the selected parameter

Physico-chemical ParameterStandard valueIdeal valueWeight (Wi)
pH 8.5 
Turbidity (NTU) 
Electrical conductivity (EC) (μS/cm) 1,000 
TDS (mg/l) 500 
Chloride (Cl) (mg/l) 250 
Nitrate (mg/l) 45 
Phosphate (mg/l) 0.02 
Total hardness (mg/l) 300 
Physico-chemical ParameterStandard valueIdeal valueWeight (Wi)
pH 8.5 
Turbidity (NTU) 
Electrical conductivity (EC) (μS/cm) 1,000 
TDS (mg/l) 500 
Chloride (Cl) (mg/l) 250 
Nitrate (mg/l) 45 
Phosphate (mg/l) 0.02 
Total hardness (mg/l) 300 

Secondly, the relative weight (Wi) of each water quality parameter is calculated as shown in Equation (1).
formula
(1)
where Wi denotes relative weight, wi denotes the weight assigned to each water quality parameter, and n denotes the number of parameters. Finally, the quality rating scale (qi) for each water quality parameter is calculated by dividing the concentration of each water quality parameter by its appropriate water quality standard (MoH, F.D.R.E 2011; WHO 2011) and then multiplying by 100 as stated in Equation (2).
formula
(2)
where qi is the ith water quality parameter's quality rating, Vi is the ith water quality parameter's measured value at a specific sample location, and Si is the ith water quality parameter's standard value. In pure water, Vid is the optimal value of ith parameter. Except for pH, which is 7, the optimum value for other parameters is zero.
Finally, using Equation (3), the SIi is obtained for each physico-chemical water quality parameter before computing the WQI.
formula
(3)
where SIi is the sub-index of ith parameter whereas, qi is the rating based on the concentration of ith parameter.
Then, WQI can be calculated using Equation (4).
formula
(4)

Table 3 shows the recommended standard value, ideal value, weight and relative weight of the selected physico-chemical water quality parameter.

As shown in Table 4, the WQI rate classification standard was used to calculate the groundwater quality status classification at each station (Sahu & Sikdar 2008; Ravikumar et al. 2013; Puri et al. 2015; Rabeiy 2018).

Table 4

Water quality status classification based on the Water Quality Index

Water Quality Index rangeWater quality status
<50 Excellent water quality 
50–100 Good water quality 
100.1–200 Poor water quality 
200.1–300 Very poor water quality 
>300 Unfit for drinking 
Water Quality Index rangeWater quality status
<50 Excellent water quality 
50–100 Good water quality 
100.1–200 Poor water quality 
200.1–300 Very poor water quality 
>300 Unfit for drinking 
Table 5

Statistical summary of various physico-chemical parameters of sampled groundwater

Parameter (mg/l)MinimumMaximumMeanStandard deviationGuide line
WHO (2011) MoH, F.D.R.E (2011) 
pH 7.21 7.31 7.27 0.03 6.5–8.5 6.5–8.5 
Turbidity (NTU) 1.96 8.87 4.63 1.89 <5 <5 
E.Conductivity (μS/cm) 285 1313.39 652.11 274.55 1000  
TDS (mg/l) 185.33 853.22 412.27 175.63 500 1000 
Chloride (Cl) (mg/l) 16.33 108.67 56.12 30.31 250 250 
Nitrate (mg/l) 6.24 36.20 22.28 9.75 45 50 
Phosphate (mg/l) 0.24 0.53 0.36 0.09 0.02  
Total Hardness (mg/l) 83.94 294.67 157.7 54.63 300 300 
E. coli (cfu/100 ml) 101 61 36 
Parameter (mg/l)MinimumMaximumMeanStandard deviationGuide line
WHO (2011) MoH, F.D.R.E (2011) 
pH 7.21 7.31 7.27 0.03 6.5–8.5 6.5–8.5 
Turbidity (NTU) 1.96 8.87 4.63 1.89 <5 <5 
E.Conductivity (μS/cm) 285 1313.39 652.11 274.55 1000  
TDS (mg/l) 185.33 853.22 412.27 175.63 500 1000 
Chloride (Cl) (mg/l) 16.33 108.67 56.12 30.31 250 250 
Nitrate (mg/l) 6.24 36.20 22.28 9.75 45 50 
Phosphate (mg/l) 0.24 0.53 0.36 0.09 0.02  
Total Hardness (mg/l) 83.94 294.67 157.7 54.63 300 300 
E. coli (cfu/100 ml) 101 61 36 

Spatial analysis and GIS mapping

For the particular examination of physico-chemical and bacteriological groundwater quality parameters, the study used topographic sheets and the spatial analyst module in ArcGIS 10.1 software. For the creation of the water quality parameter spatial distribution and water quality index map of the study region, the well locations were collected using a GPS. For geographical modeling, IDW interpolation techniques were utilized, and the values of water quality parameters were categorized according to (WHO 2011) drinking water standards. The WQI map was created by computing the point data at each station using GIS and IDW interpolation techniques. The general methodologies adopted for this study are presented in Figure 2.
Figure 2

General flowchart of methodology adopted for the study.

Figure 2

General flowchart of methodology adopted for the study.

Close modal

The statistical summary of observed concentration of various physico-chemical and bacteriological parameters in the sampled groundwater is presented in Table 5.

The pH of all groundwater samples ranged between 7.21 and 7.31, with an average value of 7.27 as shown in Table 5. This implies that the groundwater remained somewhat alkaline in all test stations (confirms surplus hydroxyl ions with a pH value greater than 7). The presence of bi-carbonate content in the water, which is created by the free interaction of carbon di oxide (CO2) with water to form carbonic acid, was largely connected with the modest high pH value in the research location (Azeez et al. 2000; Prasanth et al. 2012). The spatial distribution of pH (Figure 3(a)) depicted that all of the sampled stations had a pH value that was within a specific maximum acceptable limit as defined by WHO (2011) and MoH, F.D.R.E (2011)) guidelines. The minor variation in pH, on the other hand, may not have a negative impact on human health.
Figure 3

Spatial distribution map of (a) pH, (b) turbidity, (c) EC, (d) TDS, (e) chloride, (f) nitrate, (g) phosphate, (h) total hardness, and (i) E. coli.

Figure 3

Spatial distribution map of (a) pH, (b) turbidity, (c) EC, (d) TDS, (e) chloride, (f) nitrate, (g) phosphate, (h) total hardness, and (i) E. coli.

Close modal

The turbidity value of all sampled groundwater ranged from 1.96 to 8.87 NTU, with an average value of 4.63 NTU (Table 5). The turbidity values at stations W2, W11, and W12 exceeded the acceptable limit set by WHO (2011) and MoH, F.D.R.E (2011) as shown in Table 6. Hence, it is an indication of light interference caused by the presence of suspended particles, which is mostly caused by a wide range of suspended particles and is a sign of the presence of waste discharge in the studied water body. Furthermore, the spatial distribution map of turbidity depicted that the highest concentrations of turbidity were reported in the city's central and mid-southern regions, as shown in Figure 3(b).

Table 6

The physico-chemical and bacteriological properties of sampled groundwater

StationpHTurbidityECTDSClT. HardnessE.coli
NTUμS/cmmg/lmg/lmg/lmg/lmg/lcfu/100 ml
W1 7.29 1.96 285.00 185.33 16.33 6.24 0.30 83.94 5.00 
W2 7.21 8.87 777.61 503.83 39.83 28.23 0.38 294.67 101.00 
W3 7.29 4.97 816.31 398.41 52.14 32.36 0.53 161.63 102.00 
W4 7.25 4.22 538.78 348.44 44.28 15.87 0.38 164.89 113.00 
W5 7.30 3.38 465.61 303.72 87.22 17.32 0.26 110.72 92.00 
W6 7.25 3.73 623.33 404.78 30.28 14.89 0.30 148.67 24.00 
W7 7.26 3.78 393.06 254.94 23.44 16.91 0.27 125.39 52.00 
W8 7.29 4.87 917.11 598.89 60.00 31.00 0.46 160.11 63.00 
W9 7.25 3.53 620.78 404.61 45.72 17.30 0.32 121.50 42.00 
W10 7.26 3.43 512.89 333.11 59.44 15.83 0.24 144.78 37.00 
W11 7.29 5.55 1313.39 853.22 108.67 35.18 0.46 218.89 52.00 
W12 7.31 7.32 561.50 357.89 106.13 36.20 0.41 157.22 44.00 
StationpHTurbidityECTDSClT. HardnessE.coli
NTUμS/cmmg/lmg/lmg/lmg/lmg/lcfu/100 ml
W1 7.29 1.96 285.00 185.33 16.33 6.24 0.30 83.94 5.00 
W2 7.21 8.87 777.61 503.83 39.83 28.23 0.38 294.67 101.00 
W3 7.29 4.97 816.31 398.41 52.14 32.36 0.53 161.63 102.00 
W4 7.25 4.22 538.78 348.44 44.28 15.87 0.38 164.89 113.00 
W5 7.30 3.38 465.61 303.72 87.22 17.32 0.26 110.72 92.00 
W6 7.25 3.73 623.33 404.78 30.28 14.89 0.30 148.67 24.00 
W7 7.26 3.78 393.06 254.94 23.44 16.91 0.27 125.39 52.00 
W8 7.29 4.87 917.11 598.89 60.00 31.00 0.46 160.11 63.00 
W9 7.25 3.53 620.78 404.61 45.72 17.30 0.32 121.50 42.00 
W10 7.26 3.43 512.89 333.11 59.44 15.83 0.24 144.78 37.00 
W11 7.29 5.55 1313.39 853.22 108.67 35.18 0.46 218.89 52.00 
W12 7.31 7.32 561.50 357.89 106.13 36.20 0.41 157.22 44.00 

The EC of the sampled groundwater varies greatly, ranging from 285 μS/cm to 1,313.39 μS/cm with a mean value of 652.11 μS/cm (Table 5). As stated in Table 6, the EC value in W11 exceeds the drinking water quality limit. The highest value was observed in the mid-southern section of the city, namely in W11 of the sample station, according to the EC distribution shown in Figure 3(c). This study shows that inorganic dissolved solids like nitrate and phosphate, as well as the geology of the area through which the water flows, affect the EC of the water, indicating the presence of TDS. Similarly, Vincy et al. (2015) and Wondie (2009) illustrated that the greater EC in groundwater is related to increased dissolved solids, and an increased ion concentration in the water body can increase the EC of the water (Meride & Ayenew 2016).

The TDS concentration ranges from 185.33 to 853.22 mg/l at all measured stations, with an average value of 412.27 mg/l (Table 5). The greatest concentration of TDS was detected in W2, W8, and W11 (Figure 3(d), Table 6). The greater TDS concentration in the area is mostly attributable to anthropogenic activities such as the leaching of domestic and institutional sewage. Similarly, Prasanth et al. (2012) explored whether water mixed with residential sewage might percolate into the groundwater, resulting in an increase in TDS concentration.

Chloride concentrations range from 16.33 to 108.67 mg/l at all measured sites, with an average of 56.12 mg/l (Table 5). People with heart and kidney illnesses may be harmed by the high content of chloride in their drinking water (Saleem et al. 2016). However, according to the analysis results obtained from this study, the chloride concentration was within the acceptable level (250 mg/l) for drinking purposes in all sampled stations, as shown in Table 6. The largest concentrations were found in W11 and W12 (Figure 3(e), Table 6) which is related to the leaching of domestic organic waste and detergents, as well as the leaching of animal manure near the sampling stations, fertilizers, and septic tanks. Similarly, Prasanth et al. (2012), Graham & Polizzotto (2013), Saleem et al. (2016), and Rabeiy (2018) looked into the possibility of chloride in groundwater being caused by domestic or municipal sewage.

The concentration of nitrate in groundwater samples ranged from 6.24 to 36.2 mg/l, with an average value of 22.28 mg/l (Table 5). Excess concentration of nitrate in drinking water can induce methaemoglobinemia or blue infants in babies, as well as gastric cancer and affect central neurological and cardiovascular systems. As indicated in Figure 3(f) and Table 6, the highest concentrations of nitrate were found in W11 and W12, which is mostly attributable to the poor sanitary conditions and indiscriminate use of higher fertilizers, primarily animal dung, in the sampled area. Similarly, sewage, agricultural fertilizers, and animal dung are the main sources of nitrate in the water body, according to Graham & Polizzotto (2013) and Lawrence et al. (2001). However, the concentration of dissolved nutrients of in all sampled station was within the prescribed limit (MOH, F.D.R.E 2011; WHO 2011).

The phosphate concentrations in all groundwater stations ranged from 0.24 to 0.53 mg/l, with an average of 0.36 mg/l (Table 5). The concentrations of dissolved nutrients of in all measured stations were widely spread across the entire area, as indicated in Figure 3(g), and were all above the drinking water quality level. The existence of anthropogenic pollution activity in the area is primarily responsible for the higher phosphate concentration in the area. Similarly, higher phosphate concentrations are linked to high sediment accumulation from fertilized agricultural land, grazing cattle feces, and a high water table near the soil surface, all of which contribute to reduced soil conditions (Akale et al. 2017).

Water hardness is primarily induced by the presence of cations such as calcium and magnesium, as well as anions such as chloride, bi-carbonate, and carbonates in the water (Ravikumar et al. 2011). Total hardness concentrations in drinking water are allowed to be between 150 and 300 mg/l, but anything higher might cause kidney and cardiac problems. However, overall hardness concentrations in the research region ranged from 83.94 to 294.67 mg/l as CaCO3, with an average value of 157.70 mg/l as CaCO3 (Table 5). When compared to the standard 300 mg/l, the concentration of total hardness in all sampled groundwater sites was clearly within the allowed limit for drinking water quality (Table 6).

The total hardness spatial distribution map (Figure 3(h)) demonstrates that the bulk of the groundwater samples (50%) fall into the hard water category. Groundwater with total hardness concentrations of 75, 75–150, 150–300, and >300 mg/l are classified as soft, moderately hard, hard, and extremely hard, respectively, based on total hardness concentrations (Prasanth et al. 2012).

E.coli is most commonly found in wastewater from poorly built sanitation facilities such as pit latrines and septic tanks (Lawrence et al. 2001; Graham & Polizzotto 2013; Meride & Ayenew 2016). During the rainy season, severe contamination was seen due to wastewater seeping into shallow groundwater via preferential flow routes that short-circuit the surface with the groundwater. The highest level of E.coli in the area was detected in the central part of the city, which is likely caused by wastewater generated from institutional and commercial centers (Figure 3(i)). The bacteriological study in the area revealed that all twelve (12) sampling stations were quantitatively positive for fecal coliform count. The number of fecal coliforms in each sample station ranged from 5 to 113 colony/100 ml (Table 5). The E.coli levels found in all of the investigated sites were compared to WHO (2011) and MoH, F.D.R.E (2011) drinking water quality standards. In all of the sampled seasons, the results showed that 100% of the wells were infected with E. coli. This means the groundwater is contaminated and detrimental to people's health.

Water quality index (WQI) analysis and mapping

A Water Quality Index (WQI) is a tool that is used to summarize a significant quantity of data in a simple format for the purposes of consistent water quality and public health management (Puri et al. 2015). To assess the current state of Bahir Dar city groundwater quality, MoH, F.D.R.E (2011) and WHO (2011) drinking water quality standards were considered first, followed by assigning a weight (Wi) to each physico-chemical water quality parameter based on their perceived effect on public health (Saleem et al. 2016; Rabeiy 2018) as shown in Table 3. Because of their relevance in assessing groundwater quality, TDS, chloride, and nitrate have been given a maximum weight of five (5) (Srinivasamoorthy et al. 2008). Other water quality parameters, such as pH, turbidity, EC, phosphate, and total hardness, were given a weight of three (3) based on their importance in determining water quality for drinking.

Second, as indicated in the methodology section, the relative weight of each groundwater quality parameter is evaluated using Equation (1) (Shabbir & Ahmad 2015). Table 7 shows the computed relative weights of each physico-chemical water quality parameter.

Table 7

The computed relative weight of each physico-chemical water quality parameter

Physico-chemical parameterRelative weight (Wi)
pH 0.100 
Turbidity 0.100 
EC 0.100 
TDS 0.167 
Chloride 0.167 
Nitrate 0.167 
phosphate 0.100 
Total hardness 0.100 
 ΣWi = 1 
Physico-chemical parameterRelative weight (Wi)
pH 0.100 
Turbidity 0.100 
EC 0.100 
TDS 0.167 
Chloride 0.167 
Nitrate 0.167 
phosphate 0.100 
Total hardness 0.100 
 ΣWi = 1 

Thirdly, the quality rating scale (qi) for each groundwater quality parameter is calculated by dividing the concentration of each water quality parameter by its respective water quality standard (WHO 2011) and multiplying by 100, as described in the methodology section. Table 8 shows the calculated quality rating scale (qi) of each physico-chemical water quality parameter at each station.

Table 8

The water quality rating scale of each physico-chemical water quality parameter at each station

ParameterWater quality rating
W1W2W3W4W5W6W7W8W9W10W11W12
pH 19.48 13.78 19.15 16.59 19.85 16.44 17.52 19.15 16.41 17.48 19.52 20.41 
Turbidity 39.14 177.38 99.32 84.47 67.62 74.66 75.62 97.48 70.54 68.54 110.9 146.5 
EC 28.5 77.76 81.63 53.88 46.56 62.33 39.31 91.71 62.08 51.29 131.3 56.15 
TDS 37.07 100.77 79.68 69.69 60.74 80.96 50.99 119.8 80.92 66.62 170.6 71.58 
Chloride 6.53 15.93 20.86 17.71 34.89 12.11 9.38 24.00 18.29 23.78 43.5 42.45 
Nitrate 13.86 62.74 71.91 35.27 38.48 33.10 37.58 68.89 38.43 35.17 78.2 80.45 
Phosphate 1494.4 1897 2647 1916 1283 1486 1352 2316 1580 1208 2300 2025 
Total hardness 27.98 98.22 53.88 54.96 36.91 49.56 41.80 53.37 40.50 48.26 73.00 52.41 
ParameterWater quality rating
W1W2W3W4W5W6W7W8W9W10W11W12
pH 19.48 13.78 19.15 16.59 19.85 16.44 17.52 19.15 16.41 17.48 19.52 20.41 
Turbidity 39.14 177.38 99.32 84.47 67.62 74.66 75.62 97.48 70.54 68.54 110.9 146.5 
EC 28.5 77.76 81.63 53.88 46.56 62.33 39.31 91.71 62.08 51.29 131.3 56.15 
TDS 37.07 100.77 79.68 69.69 60.74 80.96 50.99 119.8 80.92 66.62 170.6 71.58 
Chloride 6.53 15.93 20.86 17.71 34.89 12.11 9.38 24.00 18.29 23.78 43.5 42.45 
Nitrate 13.86 62.74 71.91 35.27 38.48 33.10 37.58 68.89 38.43 35.17 78.2 80.45 
Phosphate 1494.4 1897 2647 1916 1283 1486 1352 2316 1580 1208 2300 2025 
Total hardness 27.98 98.22 53.88 54.96 36.91 49.56 41.80 53.37 40.50 48.26 73.00 52.41 

Finally, as indicated in the methodology section, the sub-index of ith parameter (SIi) is derived to compute the WQI for each physico-chemical water quality parameter. Table 9 shows the sub-index of the ith parameter that was analyzed in this study.

Table 9

The sub-index of each physico-chemical water quality parameter at each station

ParameterSub-index of each parameter at each station
W1W2W3W4W5W6W7W8W9W10W11W12
pH 1.95 1.38 1.92 1.66 1.99 1.64 1.75 1.91 1.64 1.75 1.95 2.04 
Turbidity 3.91 17.74 9.93 8.45 6.76 7.47 7.56 9.75 7.05 6.85 11.09 14.65 
EC 2.85 7.78 8.16 5.39 4.66 6.23 3.93 9.17 6.21 5.13 13.13 5.62 
TDS 6.18 16.79 13.28 11.61 10.12 13.49 8.50 19.96 13.49 11.10 28.44 11.93 
Chloride 1.09 2.66 3.48 2.95 5.81 2.02 1.56 4.0 3.05 3.96 7.24 7.08 
Nitrate 2.31 10.46 11.9 5.88 6.41 5.52 6.26 11.48 6.41 5.86 13.03 13.41 
Phosphate 149.4 189.7 264.7 191.7 128.3 148.6 135.28 231.7 158.1 120.8 230.0 202.5 
Total hardness 2.8 9.82 5.39 5.5 3.69 4.96 4.18 5.34 4.05 4.83 7.30 5.24 
ParameterSub-index of each parameter at each station
W1W2W3W4W5W6W7W8W9W10W11W12
pH 1.95 1.38 1.92 1.66 1.99 1.64 1.75 1.91 1.64 1.75 1.95 2.04 
Turbidity 3.91 17.74 9.93 8.45 6.76 7.47 7.56 9.75 7.05 6.85 11.09 14.65 
EC 2.85 7.78 8.16 5.39 4.66 6.23 3.93 9.17 6.21 5.13 13.13 5.62 
TDS 6.18 16.79 13.28 11.61 10.12 13.49 8.50 19.96 13.49 11.10 28.44 11.93 
Chloride 1.09 2.66 3.48 2.95 5.81 2.02 1.56 4.0 3.05 3.96 7.24 7.08 
Nitrate 2.31 10.46 11.9 5.88 6.41 5.52 6.26 11.48 6.41 5.86 13.03 13.41 
Phosphate 149.4 189.7 264.7 191.7 128.3 148.6 135.28 231.7 158.1 120.8 230.0 202.5 
Total hardness 2.8 9.82 5.39 5.5 3.69 4.96 4.18 5.34 4.05 4.83 7.30 5.24 

The WQI at each station was determined by summing the sub-index value of each physico-chemical water quality parameter. The classification of water quality status at each station was classified by taking the standard water quality index bases which is presented in Table 10.

Table 10

The estimated WQI value at each station

StationX-co-ordinateY-co-ordinateWQI rateClassification
W1 37.415 11.606 170.53 Poor water quality 
W2 37.398 11.599 256.34 Very poor water quality 
W3 37.387 11.588 318.86 Unfit for drinking 
W4 37.383 11.588 233.10 Very poor water quality 
W5 37.384 11.594 167.78 Poor water quality 
W6 37.382 11.592 189.94 Poor water quality 
W7 37.370 11.585 169.03 Poor water quality 
W8 37.372 11.589 300.28 Unfit for drinking 
W9 37.370 11.606 200.95 Very poor water quality 
W10 37.363 11.608 160.32 Poor water quality 
W11 37.390 11.574 312.19 Unfit for drinking 
W12 37.393 11.541 262.46 Very poor water quality 
StationX-co-ordinateY-co-ordinateWQI rateClassification
W1 37.415 11.606 170.53 Poor water quality 
W2 37.398 11.599 256.34 Very poor water quality 
W3 37.387 11.588 318.86 Unfit for drinking 
W4 37.383 11.588 233.10 Very poor water quality 
W5 37.384 11.594 167.78 Poor water quality 
W6 37.382 11.592 189.94 Poor water quality 
W7 37.370 11.585 169.03 Poor water quality 
W8 37.372 11.589 300.28 Unfit for drinking 
W9 37.370 11.606 200.95 Very poor water quality 
W10 37.363 11.608 160.32 Poor water quality 
W11 37.390 11.574 312.19 Unfit for drinking 
W12 37.393 11.541 262.46 Very poor water quality 

The results of this research (Table 10) revealed that 41.67% of sample stations have a low groundwater quality status, 33.33% have a very bad groundwater quality status, and the remaining 25% have groundwater that is unsafe for consumption. The WQI spatial distribution map (Figure 4) shows that three stations (W3, W8, and W11) which are found in the Central and Mid-Southern areas of the city are deemed to be unfit for drinking purposes, and the rest stations have water quality indexes ranging from bad to extremely poor. In general, the quality of the city's groundwater is deteriorated in the central and Mid-Southern areas of the city.
Figure 4

Spatial distribution map of the Water Quality Index.

Figure 4

Spatial distribution map of the Water Quality Index.

Close modal

In the present study, an attempt was made to assess and examine the groundwater quality status of Bahir Dar city which is found in the northern part of Ethiopia. The study evaluated the physico-chemical and bacteriological groundwater quality characteristics for the designated (drinking) water use. For the analysis of spatial patterns of groundwater quality, SPSS, GIS, and WQI have been applied.

The spatial distribution of each physico-chemical and bacteriological map showed that the highest concentration was found in the Central and Mid-Southern regions of the surveyed field. In particular, turbidity, EC, TDS, phosphate, and E.coli concentrations in the Central and Mid-Southern regions were found beyond the drinking water quality standard. These highly polluted regions were located in the areas where higher groundwater table was available and this is mainly due to seepage of wastewater to the shallow groundwater from improperly constructed and designed sanitation facilities, animal dung from animal breeding areas and due to the seepage of fertilizers from agricultural area. Moreover, the evaluation of groundwater quality status using the WQI showed that 41.67% of sampling stations had poor groundwater quality, 33.33% of the sampling stations had very poor groundwater quality, whereas the remaining 25% of the sampling stations had unfit groundwater quality for drinking purpose.

The overall study indicates that almost more than half (>58%) of the sampled water showed that the groundwater quality status is very poor and is unfit for drinking purposes. From this study, it is suggested that proper treatment of the groundwater is essential before its use for domestic purposes in the study area.

It is recommended that residents of the Bahir Dar city shallow well-users should be conscientized about the status of the water they are using and the cheap effective possible methods of treatment of water such as boiling and use of chlorination tablets so as to prevent possible adverse health effects. In addition, the attention of concerned authorities must be made to take appropriate steps in providing necessary waste management facilities to supply safe drinking water to the residents such as the provision of a standard sanitary landfill system and proper lining of soak-away and pit-latrine should be enforced in the area so as to prevent the groundwater contamination. Also, continuous water quality control monitoring should be done to prevent and control pollution in order to safeguard human health and to facilitate Bahir Dar's attainment of the Sustainable Development Goals (SDGs) for water and sanitation. In general, the findings of this study will be useful for government, policy-makers as well as the public to be aware of the status of groundwater contamination and will be supportive of monitoring and managing the vulnerability of water resources to mitigate its adverse impacts on human health in the district.

All authors contributed to the study's conception and design. Material preparation, data collection, and analysis were performed by C.M.A., Y.F.A., G.S.A., and S.S.E. The first draft of the manuscript was written by C.M.A. and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

This material is the authors' own original work. A preprint of this paper has previously been published in the author name of (Alemu et al. 2022). No one was involved in this research except the prescribed authors and co-authors. No approval required.

The paper reflects the authors' own research and analysis in a truthful and complete manner. All authors express their consent to publish.

All relevant data are included in the paper or its Supplementary Information.

The authors declare there is no conflict.

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