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Characterizing groundwater quality for a safe supply of water using WQI and
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Article in Water Practice & Technology · April 2023

DOI: 10.2166/wpt.2023.046

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© 2023 The Authors Water Practice & Technology Vol 18 No 4, 859 doi: 10.2166/wpt.2023.046

Characterizing groundwater quality for a safe supply of water using WQI and GIS
in Bahir Dar city, northwest Ethiopia

Menen Asmamaw and Ermias Debie *

Department of Geography and Environmental Studies, Bahir Dar University, Bahir Dar, Ethiopia
*Corresponding author. E-mail: ermi272004@gmail.com

ED, 0000-0002-9367-4762

ABSTRACT

Although both urban and rural residents benefit from drinking enough high-quality water in the right amounts, the degree of
contamination from artificial sources has been increasing. The study aims to assess the quality and availability of groundwater
potential in Bahir Dar City using geographic information systems (GIS)-based ordinary kriging (OK) and analytical hierarchy pro-
cess methods, respectively. The concentrations of pH, alkalinity, Escherichia coli, nitrite manganese, and iron in the
groundwater of built-up areas were found to exceed the limits set by the World Health Organization. The groundwater quality
distribution contained 69.6% of good water, 19.6% of the excellent class, and 10.8% of the poor class. The high potential of
groundwater, particularly in the Lake Tana shoreline sedimentation areas, revealed the poor quality class. The results suggest
that improving groundwater quality should be prioritized in areas with high potential groundwater availability.

Key words: geoinformation science, groundwater potential, groundwater quality, water quality index

HIGHLIGHTS

• Variation in groundwater quality across the city’s major land-use classes.

• Relationship between groundwater’s potential and its spatial distribution of quality.
• Residential groundwater extraction must follow a more stringent set of regulatory requirements to safeguard the health and
safety of consumers contributing to the localization of both point and nonpoint sources of groundwater pollution.

GRAPHICAL ABSTRACT

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying,
adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
 Water Practice & Technology Vol 18 No 4, 860

INTRODUCTION
Water is among the most crucial natural resources that guarantee the survival of all life forms. It is advantageous
to one’s health to consume enough high-quality water in sufficient amounts. Access to clean water is crucial for
reducing sickness and enhancing the quality of life. Due to a rise in population and human activity, groundwater
use has increased. The only source of drinking water for at least two billion people worldwide is groundwater,
making it one of the most important and frequently used renewable resources in the world (Mahmud et al.
2020; Aniteneh 2021).
Anthropogenic activities, such as urbanization, industry, and agricultural intensification, damage groundwater
quality worldwide (Kawo & Karuppannan 2018). Due to inadequate water sources, poor sanitation, and poor
hygiene, 3.4 million people die each year from diseases associated with water (UNICEF 2008). The physical,
chemical, and biological characteristics of water can be deemed to be the components of groundwater purity
(Zeabraha et al. 2020). Many commonplace activities, including the use of pesticides and fertilizers as well as
the removal of human, animal, and agricultural refuse, can contaminate groundwater. Many academic studies
have focused on the state of the world’s groundwater and the factors that contribute to contamination from
both human activity and natural processes (Al-Sudani 2019).
Groundwater quality has declined and becomes contaminated as a result of population growth, changes in land
use and land cover (LULC), human activities, and worldwide climate change (Tefera et al. 2021). One problem in
the twenty-first century is the worsening of water quality in emerging nations because of unregulated incidents of
industrial, farming, and residential pollution (Oki & Akana 2016). A basic human right and a necessity for health
and growth are access to clean drinking water. However, it is inaccessible to millions of individuals in developing
nations (UNICEF 2008).
Groundwater is an important supply of domestic water in many African cities. It is an essential water supply for
Ethiopia’s industrial and drinking needs (Karuppannan & Kawo 2019). Numerous studies stated the problems with
the quality of groundwater (Gorelick & Zheng 2015; Vetrimurugan et al. 2017; Wu et al. 2019; Elumalai et al. 2020).
Analysis of groundwater quality for safe and beneficial use requires the use of geographic information systems (GIS).
It is an efficient tool for understanding and managing all water resources, and it can be used to create geographic
decision support systems by combining spatial data with models for assessing groundwater quality (Singha et al.
2015). Groundwater susceptibility to contamination, potential from nonpoint sources of pollution, and other factors
can be assessed through the integration of the groundwater quality index (GWQI) and GIS (Machiwal et al. 2018;
Gonçalves et al. 2022; Jenifer & Jha 2022). The water quality index (WQI) assigns a single value to each water
sample, specifies its overall quality category, and compares the quality of various selections based on the values
acquired to assess and analyze groundwater quality for human consumption (Boudibi et al. 2019).
Numerous studies observed that Ethiopia’s groundwater has been contaminated due to ineffective refuse man-
agement, inadequate sanitation, insufficient management, and fertilizer usage. For instance, groundwater in the
rift valley is contaminated by liquid waste flows from the towns (Rango et al. 2010; Karuppannan & Kawo 2019).
The Dire Dawa groundwater area was contaminated due to ineffective liquid and solid waste management
(Tilahun & Merkel 2010). Research by Tamiru et al. (2004) indicates that untreated waste dumped into waterways
has the potential to contaminate the groundwater in Addis Ababa. Dinka (2017) also mentioned the human-
caused groundwater pollution in the Matahara area. Urban garbage and industrial and agricultural expansion
are to blame for the surface and groundwater quality degradation in the main Ethiopian rift, particularly in
the Modjo river basin (Kawo & Karuppannan 2018). Conducting the study in the Upper Blue Nile Basin has
revealed that the majority of shallow wells near Lake Tana are unsafe to drink (Tefera et al. 2021). Total dissolved
solids (TDS), total hardness (TH), nitrate (NO
3 ), and electrical conductivity (EC) levels that are all higher than
the permitted maximum limit are the main contributors to it. The groundwater quality in the Chilanchil Abay
watershed is generally in poor condition at the sampling locations (Haile & Gabbiye 2021).
Similar to other cities in Ethiopia and other countries, the quality of the surface and groundwater continues to
decline in Bahir Dar city where there is insufficient waste management and environmental protection process
(Alemu et al. 2022). In and around Bahir Dar city, the growth of urbanization, population, and human activity
could be a factor in the increased waste discharged into open areas, which will ultimately result in a decline
in groundwater quality. Due to numerous pollution-related factors, the bacteriological quality of the surface
and groundwater in Bahir Dar City is continuously declining. About 60% of city dwellers use pit latrines,
which are ill-built, badly maintained, and frequently overflow (Tabor et al. 2021). Liquid refuse from most
 Water Practice & Technology Vol 18 No 4, 861

urban dwellings either drains into the septic tanks and dry pits that are typically located near most shelters or
ends up in the open ditches and marshes of the city. The open site in Bahir Dar is one of the unregulated
open dumps that are near many residential areas. The communities located upstream and downstream of the dis-
posal site use tainted groundwater and surface water for everyday needs. The shallow groundwater quality in
cities can be influenced by the season, related agricultural management techniques, and close-by refuse manage-
ment sanitation facilities.
There may be substantial groundwater pollution in the study area because of poor sanitation and a lack of effi-
cient waste disposal and management practices. The most obvious pollution effects were seen in shallow wells,
which are a reflection of all anthropogenic effects on groundwater sources (Alemu et al. 2022). Given the serious
challenge to the quality of the groundwater in the municipality in question (Haile & Gabbiye 2021), it is necess-
ary to investigate the issue’s spatial distribution for the long-term management of a safe water supply. The
groundwater quality should be periodically evaluated and tracked to safeguard its numerous applications. In
areas where groundwater is the main source of potable water, it is essential to evaluate the geographical variabil-
ity of groundwater quality to ensure the security of the water supply and the welfare of the population (Li et al.
2017). The GIS-based study is the most effective way to track the constantly changing evolution of water quality.
For environmental executives and decision-makers, this cutting-edge tactic significantly simplifies environmental
monitoring and encourages action. With the aforementioned characteristics of groundwater contamination and
its application in groundwater quality evaluation, this work employs GIS to evaluate groundwater quality in
Bahir Dar, Ethiopia. There is a need for a case study report that focuses on mapping the groundwater quality
of Bahir Dar city particularly and uses geostatistical techniques along with a WQI. Therefore, the study aimed
to evaluate how the main LULCs in the city differed in terms of groundwater quality. The study also examined
the potential of groundwater and the spatial differences in its quality.

MATERIALS AND METHODS

Location
Bahir Dar City is situated in the northwest of Ethiopia. Lake Tana, from which the Blue Nile River rises, borders it
in the north. The city is found between 11° 300 0″ and 11° 400 0″ North latitudes and 37° 180 0″ and 37° 280 0″ longi-
tudes (Figure 1). The city has a total area of roughly 399.95 km2. It consists of 9 rural kebeles, 3 small satellite

Figure 1 | Map of the study area.

 Water Practice & Technology Vol 18 No 4, 862

kebeles (Tis Abay, Zenzelma, and Zegie), and 11 urban sub-cities (Meshenti, Sebatamit, Dagmawi minilik, Belay
zeleke, Gish Abay, Sefene Selam, Tana, Fasilo, Shumabo, Aste Tewodros and Shimbit). Out of them, the study
included 10 major centers, 1 satellite kebele (Zenzelema), and 3 rural kebeles (Woramit, Adis Alem, and Woreb).

Climate
Bahir Dar typically experiences mild to warm weather. The highest relative humidity (84.57%) of any month is in
August. The lowest relative humidity (43.81%) of any month is in March. July is the month with the rainiest days
(28.97 days). A few days in January experience precipitation with a monthly average of 4 mm. It rarely rains in the
winter, but it usually does in the summer. About 102.1 mm of precipitation fall on the city annually. The wettest
month is August, with an average rainfall of 528 mm (Merkel 2020). The average annual temperature in Bahir
Dar is 20.1 °C.

Topography and land use/covers

The elevation of the city varies from 1,716 to 2,037 masl (Figure 2(a)). The majority of the area is plain, with some
sections having hills and undulating ground. A promising chance to carry out various urban development oper-
ations in the centers and their surroundings is provided by the plain topography. Three land-use/cover classes,
including farmland, plantations, settled regions, and bodies of water, constitute Bahir Dar City (Figure 2(b)).

Figure 2 | (a) Topography map and (b) land use/covers the map of Bahir Dar city (January 2022).

Land-use activities directly affect water quality, even though water quality has a significant effect on the chance
for land-use activities. Inappropriate land use, in particular poor land management, is the cause of groundwater
pollution worries. Inappropriate land use frequently causes acute problems with groundwater quality (Omer
2019). The built-up area grew mainly due to horizontal growth from 80 ha in 1957 to 848 ha in 1994 and predict
that the current urban region will have doubled by 2024 at the cost of agricultural lands. This will have extensive
ecological, socioeconomic, and environmental impacts.

Hydrology and water supply

Lake Tana, which drains more than 3,000 km2 and is the source of the Blue Nile River, was accessible to Bahir
Dar city. The drinking water of the city is supplied from 19 boreholes (not deemed private boreholes) and three
springs, accounting for about 86.6 and 13.4%, respectively. Water supply from these holes has not adequately
addressed the consumption rate of the rapidly growing population of the city. The rate of consumption of the
rapidly expanding population of the city has not been properly met by the water supply from these holes. In
response to expansion and its associated commercial, residential, institutional, and industrial activities of the
city, the rate of groundwater consumption greatly increased. Every year, Bahir Dar’s population has increased
significantly. The urban population has grown over the past 10 years, going from 180,174 in 2007 to 313,997
in 2017 (Wubie et al. 2020).

Sampling site selection

Issues with acute groundwater quality are common and are brought on by improper land use and management,
especially point sources of hazardous chemicals (Alemu et al. 2022). From the main land uses and land areas
 Water Practice & Technology Vol 18 No 4, 863

(built-up, plantation, and agricultural) of the city, about 33 groundwater samples were purposefully collected
(Figure 1; Table 1).

Table 1 | Sampling sites of the study area

SI.No Well name Latitude Longitude Land-use type Well type Owner

1 W1 319,384 1,280,438 Built-up Borehole Governmental

2 W2 320,051 1,280,804 Built-up Borehole Governmental
3 W3 318,454 1,280,018 Agricultural Borehole Governmental
4 W4 327,086 1,284,729 Plantation Borehole Governmental
5 W5 327,205 1,284,966 Plantation Borehole Governmental
6 W6 327,294 1,285,067 Plantation Borehole Governmental
7 W7 327,269 1,285,353 Plantation Borehole Governmental
8 W8 327,074 1,284,531 Plantation Borehole Governmental
9 W9 328,575 1,281,643 Plantation Borehole Governmental
10 W10 328,720 1,281,602 Plantation Borehole Governmental
11 W11 320,517 1,280,560 Built-up Borehole Governmental
12 W12 320,337 1,280,284 Built-up Borehole Governmental
13 W13 320,118 1,279,990 Built-up Borehole Governmental
14 W14 319,524 1,279,104 Built-up Borehole Governmental
15 W15 319,566 1,279,295 Built-up Borehole Governmental
16 W16 323,864 1,278,457 Plantation Borehole Governmental
17 W17 322,155 1,281,099 Built-up Borehole Governmental
18 W18 321,953.9 1,283,926 Built-up Hand dug Private
19 W19 323,968.7 1,277,549 Plantation Borehole Bono Private
20 W20 326,834.4 1,283,421 Built-up Borehole Private
21 W21 327,235.8 1,283,232 built-up Borehole Private
22 W22 323,616.2 1,283,439 Built-up Borehole Private
23 W23 319,547.8 1,283,243 Agricultural Borehole Governmental
24 W24 322,063.4 1,282,330 Built-up Borehole Private
25 W25 321,637.3 1,283,908 Built-up Borehole Private
26 W26 321,777.1 1,283,956 Built-up Hand dug Private
27 W27 321,852.1 1,283,962 Agriculture Hand dug Private
28 W28 322,063.4 1,274,468 Agriculture Hand dug Private
29 W29 327,176.3 1,280,490 Agriculture Hand dug Private
30 W30 316,077.5 1,281,167 Agriculture Hand dug Private
31 W31 322,498.1 1,274,349 Agriculture Hand dug Private
32 W32 330,889.5 1,279,033 Plantation Hand dug Private
33 W33 315,314.1 1,285,818 Plantation Hand dug Private

Five private and eight public boreholes, as well as two private hand-dug holes, were taken from the built-up
area. Eight public and one private borehole, as well as two private hand-dug holes, were used from the plantation.
Moreover, two government boreholes and five private hands dug were sampled from agricultural land. Using
location data obtained with a handheld Global Position System(GPS), a point feature showing the location of
the wells were produced (Figure 1). The sampling locations are listed in Table 1.

Methods and procedures of data analysis

The bottles are properly cleaned with water before being filled with the sample water. Samples are collected,
stored, and then transported to the laboratory for physiochemical and bacteriological examination. The chemical
laboratory study is carried out by Amhara Design and Supervision Work Enterprise Soil Chemistry and Water
 Water Practice & Technology Vol 18 No 4, 864

Quality Section, and the physical–biological inspection is done by Choice Water Bottling Company. Information
on water quality was prepared to create laboratory results. The laboratory results of each water quality parameter
were then linked with the geographic data and saved in excel format before being converted into shape files using
Arc Map’s joining feature. The formation and combination of the spatial and nonspatial files allowed for the cre-
ation of maps showing the distribution of the water quality parameters.
On-site water temperature was measured with a mercury thermometer, and a digital pH meter (Model
Metrohm, Zofingen, Switzerland) was used to measure pH levels (Alramthi et al. 2022). The turbidity, TDS,
total alkalinity, TH, chloride, sulfate, nitrite, nitrate, iron, and manganese measurements were all made by
APHA/AWWA/WEF (2017) guidelines. EC was measured using a conducting meter. The atomic absorption
spectrophotometric method was used to analyze the amount of phosphate. The most probable number (MPN)
method was employed to quantify Escherichia coli to highlight the microbiological quality of the water
(APHA/AWWA/WEF 2017).

OK interpolation technique
One of the popular univariate geostatistical techniques is kriging interpolation, which offers a minimal mean
error (ME) to produce an excellent linear unbiased estimate (Dimri et al. 2023). Given that geostatistical
approaches have various benefits over deterministic techniques, the kriging method was used in this study
(Boudibi et al. 2019). Kriging has the advantage of offering impartial forecasts with a little variance while also
accounting for the geographical correlation between data collected at various sites. In addition to interpolation,
kriging provides information about interpolation errors. The OK approach was chosen out of the several kriging
methods because it predicts outcomes more accurately than other kriging techniques (Kumar et al. 2022). The
dataset of water quality parameters was imported into ArcMap software. The ‘Geostatistical analyst’ extensions
of the ArcGIS 10.7 software were used to generate the interpolation surfaces using GIS. An ArcMap is an effec-
tive tool for user-input data visualization and analysis. The best linear unbiased estimate (BLUE), ordinal kriging,
seeks to reduce the error variance (Isaaks & Srivastava 1989).

X
n X
n
The OK is determined as follows:Z( xo ) ¼ li  Z(xi ) with li ¼ 1 (1)
i¼1 i¼1

where Z( xo ) is the estimated value at the location xo , Z (xi) is the measured value at location xi, li is the weighting
factor assigned to Z (xi), and n is the number of observations (Brands et al. 2016). The weight li is determined in
such a way as to satisfy the optimizing conditions of unbiasedness and minimum variance (Boudibi et al. 2019).

Examining the distribution of the data

The histogram and regular quantile–quantile (QQ) plots were used in the ArcGIS version 10.7 geostatistical study
to visualize the distribution of data. The distribution of the data is compared to a typical normal distribution using
the QQ plot. Normal QQ plots show asymmetric (i.e., far from normal) distribution of water quality metrics and
univariate normality. The points are no longer in a straight line. All 12 of the other characteristics had a skewed
distribution, except temperature and overall hardness, which were both somewhat regularly distributed.
One of the frequently employed techniques for data normalization is log transformation. It is optimal for kri-
ging approaches if the data are roughly regularly distributed. Any interpolation method that is used to interpolate
data spatially presupposes a normal distribution. Before using skewed data in any geostatistical analysis, it must
first be transformed into a normal distribution. The normalization results in Table 2 disclose that except tempera-
ture and TH, all other parameters, including nitrate (NO



3 ), nitrite (NO2 ), TDS, chloride (Cl ), manganese (Mn),
2

3
E. coli, alkalinity (CaCO3), sulfate (SO4 ), Phosphate (PO4 ), EC, iron (Fe), and pH, had a skewed distribution as
the result all are transformed using log transformation.

Semivariogram models
Each parameter dataset has been tested using an appropriate semivariogram model. Cross-validation was used to
evaluate the performance of predictions. Which model makes the best predictions can be found through cross-
validation. Elubid et al. (2019) assert that the root-mean-square standardized error should be near one and
that a model’s average standard error (ASE) should be as small as possible (this is helpful when comparing
models). The spatial correlation of the data is depicted by a semivariogram. A discrete function called the exper-
imental semivariogram is computed using a measure of variability between pairs of sites separated by different
 Water Practice & Technology Vol 18 No 4, 865

Table 2 | Summary of the analyzed water quality parameters

Drinking water quality parameters Min. Max. Mean Standard deviation Skewness Kurtosis

Temperature 26 29.3 27.5 0.7 0.02 0.5

pH 6.2 8.9 6.9 0.6 1.5 3.7
pHa 3.3 3.4 3.3 0.03
0.1 3.2
EC 49 799 358.2 218.7
0.1
1.1
a
EC 3.9 6.7 5.6 0.9
0.8 1.9
TDS 20 319 139.4 84.1
0.1
0.9
a
TDS 3 5.8 4.6 0.9
0.8 1.8
CaCO3 100 200 137.8 28.4 0.5
0.9
CaCOa3 4.6 5.3 4.9 0.2 0.3 1.8
T. Hardness 54 198 98.4 24.5 2 7.9
Turbidity 0.001 4 0.7 1.1 1.5 1.5
Turbiditya
6.9 1.4
4.2 3.6 0.6 1.4
E. coli 0.001 272 26.4 49.6 3.9 19.5
E. coli a
6.9 5.6 0.4 4.4
0.9 2.2
NO

2 0.01 0.6 0.1 0.1 4.7 26.5
NO

2
a

4.3
0.5
2.7 0.6 0.8 7.3
Mn 0.001 1.4 0.4 0.5 0.9
0.2
Mna
6.9 0.4
3.7 3.4 0.1 1.0
Fe 0.001 0.5 0.04 0.1 4.6 26.1
Fea
6.9
0.7
5.02 2.0 0.3 1.5
NO
3 0.001 16.3 2.4 4.8 1.9 2.6
NO
3
a

6.9 2.8
4.4 3.9 0.9 1.9
PO
3
4 0.001 0.6 0.1 0.2 1.7 1.9
PO
3
4
a

6.9
0.4
5.2 2.7 0.9 1.9


Cl 0.001 35.8 5.4 10.9 1.8 2.0
Cl
a
6.9 3.6
4.1 4.4 0.9 1.9
SO2

4 0.001 32.5 1.1 5.5 5.5 32.8
a
SO2

4
6.9 3.5
4.9 3.2 1.1 2.6
a
Transformed using log transformation.

distances. Depending on the type of semivariogram chosen, a specific measurement may be employed. However,
the semivariogram is typically calculated using the following formula (Said & Yurtal 2019).

1 X
N
h

g(h) ¼ (gi
gi þ 1)2 (2)

2Nh i¼1

where g(h) is the intended value of the semivariogram for h, Nℎ is the number of pairs location separated by h; gi
and gi þ 1 are values of the variable ‘g’ at the point xi and a point of distance h from the point xi þ 1.3.
Prediction: Each water quality parameter was tested using semivariogram models (circular, spherical, exponen-
tial, Gaussian, tetraspherical, pentaspherical, rational quadratic, hole effect K-Bessel, J-Bessel, and stable). Cross-
validation tests were used to verify the predictive performance of the fitted models. The values of ME, mean
square error (MSE), root-mean error (RME), ASE, and RMSSE were estimated to determine the performance
of the developed models (Kumar et al. 2015).

1X n
ME ¼ [Z(xi )
Z(xi )] (3)
n i¼1
 Water Practice & Technology Vol 18 No 4, 866

1X n
MSE ¼ [Z(xi )
Z(xi )]=s(xi ) (4)
n i¼1
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u n
u1 X
RMSE ¼ t [Z(xi )
Z(xi )]2 (5)
n i¼1
vffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
u
u 1X n
RMSSE ¼ t [Z(xi )
Z(xi )]= s(xi )2 (6)
n i¼1

where Z(xi ) is the estimated value, Z (xi) is the measured value, and s(xi ) is the estimation variance.

Procedures to generate GWQI

Fourteen factors from the dataset, including TDS, TH, nitrate, chloride, sulfate, manganese, phosphate, iron, alka-
linity, nitrite, temperature, EC, and pH were chosen to create the GWQI map. In this study, the weighted
arithmetic index approach was used to calculate WQI (Kizar 2018; Akhtar et al. 2021; Dimri et al. 2021). The
parameters of water quality are multiplied by a weighting factor and then combined using a simple arithmetic
mean. Three steps were used to determine the GWQI. First, a weight (w) has been assigned to each of the 14
factors based on how important it is to the total quality of water that can be consumed (Bawoke & Anteneh
2020). Due to the importance of nitrate in determining the quality of water, the criterion has been assigned a
maximum weight of 5 (Konkey et al. 2014). Other elements were ranked from 1 to 4 in terms of how important
they were to the total drinkability of the water (Table 3). Second, the following equation is used to calculate the
proportional weight (Wi):

Table 3 | WHO and Ethiopian standards weight (wi) and calculated relative weight (Wi) for each parameter (Roy et al. 2021)

Drinking water quality parameters WHO standard Ethiopian standard Weight (wi) Relative weight (Wi) Relative Weight (Wi)%

pH 8.5 8.5 4 0.12 12

EC 1,000 3 0.09 9
TDS 500 1,000 3 0.09 9
Alkalinity 150 1 0.03 3
T.Hardness 300 300 2 0.06 6
Turbidity ,5 ,5 2 0.06 6
Nitrite/NO

2 0.2 2 0.06 6
Manganese/Mn 0.4 2 0.06 6
Iron/Fe 0.3 0.3 2 0.06 6
Nitrate/NO

3 50 50 5 0.15 15
Phosphate/PO
3
4 1 3 0.09 9
Chloride/Cl
250 250 2 0.06 6
Sulfate/SO24
250 250 3 0.09 9
Total 34 1 100

wi
Wi ¼ (7)
P
n
wi
i¼1

where Wi is the relative weight, wi is the weight of each parameter, and n is the number of parameters.
Third, a quality rating scale (qi) for each parameter is assigned by dividing its concentration in each ground-
water sample by its respective standard according to the guidelines of the World Health Organization (WHO)
 Water Practice & Technology Vol 18 No 4, 867

and the result is multiplied by 100:

 
Ci
qi ¼  100 (8)
Si

where qi is the quality rating, Ci is the concentration of each parameter in each water sample, and Si is the WHO
drinking water standard for each parameter. For computing the GWQI, the SI is first determined for each par-
ameter, which is then used to determine the GWQI as indicated by the following equation:

SIi ¼ Wi  qi (9)

where SIi is the sub-index of the ith parameter; qi is the rating based on the concentration of the ith parameter,
and n is the number of parameters. The overall GWQI was calculated by adding together each sub-index value of
each groundwater sample as follows:
X
GWQI ¼ SIi (10)

The last result of GWQI off the study area ranged between 15.6 and 66.9. The GWQI values between 0 and 24
indicate excellent performance, 25 to 50 indicate good performance, and 50 to 70 indicate poor performance
(Elubid et al. 2019).
Note: Groundwater quality values ranging from 0 to 24 are excellent, GWQI values 25–50 represent good, and
GWQI values ranging from 50 to 70 denote poor (Elubid et al. 2019).

Groundwater potential zone mapping

The groundwater potential zone map was prepared using seven different types of thematic maps: rainfall, linea-
ment, slope, land use/cover, drainage density, soil, and geology. The data were acquired from satellites and
meteorological stations in the study area. ASTER (Advanced Space-borne Thermal Emission and Reflection
Radiometer) Digital Elevation Model (DEM) at 30-m spatial resolution was used to create lineament density,
slope, and drainage density maps, which were downloaded from the United States Geological Survey Earth
Explorer (earthexplorer.usgs.gov). The geology and soil data were acquired from the FAO Digital Soil Map of
the World. A land-use/land-cover map was generated from Sentinel 2 satellite imagery. To develop the ground-
water potential maps in the study area, all parameters are categorized into five classes, such as very low, low,
medium, high, and very high. The results of the consistency ratio and the pair-wise comparison matrix categor-
ization of factors influencing groundwater potential zones (GWPZ) were prepared using free web-based
analytical hierarchy process (AHP) software available at http://bpmsg.com.
Literature was used to determine the importance and ranking of each conditioning factor (Arulbalaji et al.
2019; Berhanu & Hatiye 2020; Popoola et al. 2020; Yihunie & Halefom 2020; Melese & Belay 2022). Each
theme layer was given a ranking and weight depending on its ability to contain water (Arulbalaji et al. 2019).
After ranking each layer, the weights of all the layers were summed, and the resulting total was divided into poten-
tial groundwater zones. A groundwater potential map has been produced in GIS by combining thematic layers,
such as slope, drainage density, rainfall, soil, geology, land use/land cover, and lineament density that support the
occurrence of groundwater. All of the layers were rasterized and then combined.

The analytical framework of the study

In the study, both spatial and nonspatial data were used for generating spatial information and physiochemical
analysis of groundwater samples, respectively (Figure 3). Figure 3 displays the analytical procedures of the study.

RESULTS AND DISCUSSION

Best-fitted semivariogram models
Table 4 demonstrates the best-fitted models for the analyzed parameters. The cross-validation results are evalu-
ated and shown in Table 4 based on the semivariogram model assumptions provided (Equations (3)–(6)). In
terms of pH, alkalinity, turbidity, nitrite, and iron, the model stable fits the data the best. The spherical model
 Water Practice & Technology Vol 18 No 4, 868

Figure 3 | Flowchart of the analytical framework.

Table 4 | Best-fitted models for each groundwater quality parameter

Prediction errors

Drinking water quality Best-fit Root-mean Average standard Mean Root-mean-square

parameters model Mean square error standardized standardized

pH Stable
0.06 0.45 0.45
0.06 0.99

Temperature Spherical
0.04 0.76 0.59
0.07 1.18
EC Spherical
0.83 139.06 371.19
0.03 1.01
TDS Circular 3.88 53.09 152.63 0.001 0.94
CaCO3 Stable 0.14 26.82 25.76
0.003 1.04
T. Hardness Exponential
1.41 24.29 25.08
0.06 0.97
Turbidity Stable
6.29 64.19 48.67
0.06 1.42
E. coli Spherical 1.11 49.63 55.65 0.02 0.92
NO

2 Stable 0.01 0.11 0.18
0.06 0.94
Mn Circular
0.06 0.39 0.35
0.08 1.09
Fe Stable
0.0009 0.08 1.17 0.04 0.86
NO

3 Circular 0.04 4.13 4.78 0.01 0.86
PO
3
4 Exponential
0.01 0.18 0.11
0.02 1.32
Cl
Circular 0.59 8.15 10.23 0.04 0.86
SO2

4 Spherical
0.09 6.14 6.00
0.006 0.84

fits temperature, EC, E. coli, and sulfates the best. The most accurate forecast for TDS, manganese, and nitrate
came from a circular model. For phosphate and overall hardness, exponential was optimum. As a result, each
model proved reliable and could be used to generate surfaces and predict values.
 Water Practice & Technology Vol 18 No 4, 869

Spatial distribution of physiological properties of the groundwater

Turbidity
The turbidity concentrations are generally between 0 and 5 NTU. In the research region, the value of turbidity
ranges from 0 to 4 NTU, with a mean of 0.7 and a standard deviation of +1.1, respectively (Figure 4(a)). Planta-
tion areas have the lowest (0 NTU) concentration, while both plantation and agricultural regions have the highest
(4 NTU) values (Table 5). It is brought on by sedimentary particles, particularly clay and silt, fine organic and
inorganic materials, soluble colored organic compounds, algae, and other tiny creatures that scatter light to
give the appearance of foggy or murky water (Batabyal & Chakraborty 2015).

Figure 4 | Prediction map of turbidity, EC, temperature, and TDS.

Table 5 | Physiological characteristics of drinking water based on LULC for the sampling stations

LULU Drinking water parameters Min. Max. Mean Standard deviation

Built-up Temperature 26 28 27.3 +0.7

Electrical conductivity 49 799 315.7 +281.8
TDS 20 319 126.1 +112.7
Turbidity 0.001 4 0.5 +1.2
Agriculture Temperature 27 28.5 27.9 +5.3
Electrical conductivity 77 530 393.6 +250.9
TDS 31 220 159.9 +100.6
Turbidity 0.001 4 1.6 +1.4
Plantation Temperature 26.5 29.3 27.5 +5.4
Electrical conductivity 69 630 397.2 +221.0
TDS 27 200 148.9 +86.2
Turbidity 0.001 3 0.5 +1.3
 Water Practice & Technology Vol 18 No 4, 870

EC is a measurement of dissolved materials in water and is a function of ionic concentrations. Salinity, which
has a substantial impact on taste and, in turn, the user’s approval of water as potable, is the key component of EC.
The value ranges from 49 to 799 μS/cm in the research region, with a mean and standard deviation of 358.3 and
+218.7, respectively (Figure 4(b)). In the city’s built-up areas, concentrations are reported at both their maximum
(799 μS/cm) and their lowest (49 μS/cm) (Table 5). The findings show that the groundwater was not significantly
ionized, but greater values are noted in built-up portions of the city. The result agrees with the results of Meride &
Ayenew’s (2016) study on the drinking water of the Wondo genet campus in Ethiopia. The concentration of ions
affects how well water conducts electricity. This is because inorganic dissolved solids like nitrate and phosphate,
as well as the geology of the region through which the water travels, have an impact on EC in the sampled built-up
areas (Alemu et al. 2022).

Temperature
With a mean and standard deviation of 27.48 and 0.71, respectively, the temperature values in all of the stations
that were sampled ranged from 26 to 29.3 °C (Figure 4(c) and Table 5). The plantation region recorded the highest
temperature (29.3 °C), while built-up areas recorded the lowest temperature (26 °C). To stop the growth of the
organism, the water temperature should be kept below the range of 25–50 °C (WHO & UNICEF 2013). Although
there are differences in land-use and -cover classes, the groundwater temperature that was sampled is ambient,
which is ideal for customers who prefer cool to warm water and for the specific needs of the water’s quality
for various uses (Meride & Ayenew 2016).

Total dissolved solids

Natural water typically has less than 500 mg/L of dissolved solids, and water with more than 500 mg/L is not
suitable for drinking or many industrial purposes (Jain et al. 2010). Brackish water is subsurface water with a
TDS value of higher than 1,000 mg/L (Pande & Moharir 2018). TDS levels range from 20 to 319 mg/L in the
study area, with a mean of 358.3 and a standard deviation of +84.1, respectively (Figure 4(d)). Built-up locations
have TDS concentrations that are both the highest and lowest. Built-up areas have high TDS values compared to
other land-use groups (Table 5). Akhtar & Tang (2013) reported the higher TDS level in the groundwater was
alarming for the consumers of the second biggest city in Pakistan. High TDS concentrations in groundwater
could affect kidney and cardiac health (Gobalarajah et al. 2020). High-solid water may be laxative or cause diar-
rhea (Yin et al. 2020).

Spatial distribution of chemical properties of the groundwater

pH
One of the most important operational water quality characteristics is pH, with the ideal pH necessarily falling
between 6.5 and 8.5 (WHO 2017). With a mean and standard deviation of 6.939 and +0.569, respectively, the pH
value in the groundwater data gathered ranges groundwater ranges from 6.2 to 8.95 (Figure 5(a) and Table 6).
About 28 of the 33 groundwater samples that were examined for concentration levels fell within the ideal
range (6.5–8.5), whereas 5 sample locations fell outside of the desirable range (Table 6). Built-up regions have
the highest concentration (8.95), and agricultural and plantation areas have the lowest concentrations (6.2)
(Table 6). Some test stations in plantation regions have pH values that are below the ideal range, which could
lead to tuberculosis in water supply systems (Devatha et al. 2016). This demonstrates that the local groundwater
is acidic. If the pH of a sampling station is higher than 8.5 and it is located in a built-up region, the water may taste
harsher. This increased pH can also cause calcium and magnesium carbonate to accumulate in the pipes and can
irritate and dry up the skin (Zhang et al. 2021). Five sampling wells, as indicated in Table 6, had neutral water
with a pH of 7, as can be seen. A pH of less than 7 indicates acidity in around 19 sampling wells (the majority
of which were in built-up regions), while a pH of more than 7 indicates alkalinity in 9 sampling wells (again the
majority of which were in built-up areas). The substances that are present in the water might have an impact on
pH. As a result, it serves as a crucial sign of chemically altering water. Slightly acidic water requires less chlorine
to kill pathogens or disease organisms than water with a pH of 7–8.5 (Omer 2019). Water with a pH level below 6
is corrosive to faucets and piping, whereas water with a pH level above 8.5 may taste sour or like soda (Zhang
et al. 2008). The pH of the water may rise due to the solubility of numerous hazardous and nutritive compounds,
which may have an impact on aquatic microorganisms. In nature, most metals become increasingly water-soluble
and poisonous as the water’s acidity rises (Lawson 2011).
 Water Practice & Technology Vol 18 No 4, 871

Figure 5 | Prediction map of pH, AK, TH, NO

2 , Mn, and Fe.

Alkalinity
The mean alkalinity of 137.8 and a standard deviation of +28.4, with alkalinity levels ranging from 100 to
200 mg/L (Figure 5(b)). Alkalinity levels are highest and lowest in populated regions and plantations, respectively
(Table 6). The concentration levels of 26 out of 33 groundwater samples were found to be under the WHO’s
desired standard (150 mg/L), while the levels in the remaining 7 samples were found to be beyond the desirable
limit (Table 6). The majority of values found in populated regions are above the ideal alkalinity range. Salman
et al. (2018) found a high level of alkalinity in the populated area of Bangladesh. This is because the water con-
tains compounds like bicarbonates, carbonates, and hydroxides, which eventually diminish the water body’s
capacity to neutralize acids and bases and so maintain a comparatively steady pH level (Arslan & Demir 2013).

Total hardness
With a mean and standard deviation of 98.4 and +24.5, respectively, the concentration of TH ranged from 54 to
198 mg/L. In the study area, the value of overall hardness is noted below the WHO-recommended level. In the
 Water Practice & Technology Vol 18 No 4, 872

Table 6 | Results of chemical and bacteriological analysis for sampling station based on LULC

LULU Drinking water parameters Min. Max. Mean Standard deviation No. of samples exceeding the permissible limit

Built-up pH 6.4 8.9 7.0 +0.6 4

Alkalinity 102 200 141.9 +32.6 5
T.Hardness 76 125 95.3 +14.6 –
E. coli 0 272 32.5 +70.1 14
Manganese 0.001 1.4 0.4 +0.6 5
Iron 0.001 0.5 0.05 +0.1 2
Nitrite 0.03 0.6 0.1 +0.2 1
Nitrate 0.001 15.5 3.5 +5.5 –
Phosphate 0.001 0.7 0.2 +0.2 –
Chloride 0.001 35.8 9.6 +13.6 –
Sulfate 0.001 31.5 2.4 +8.3 –
Agriculture pH 6.2 7.5 6.874 +1.4 1
Alkalinity 116 180 152.4 +38.1 4
T. Hardness 75 198 106.7 +29.8 –
E. coli 16 72 36.7 +71.8 6
Manganese 0.001 1 0.7 +0.5 7
Iron 0.001 0.1 0.1 +0.1 –
Nitrite 0.03 0.1 0.1 +0.2 –
Nitrate 0.001 17 3.5 +5.7 –
Phosphate 0.001 0.2 0.1 +0.2 –
Chloride 0.001 28.5 4.6 +12.9 –
Sulfate 0.001 0.8 0.2 +8.8 –
Plantation pH 6.2 7.9 6.9 +1.5 3
Alkalinity 100 168 123.6 +36.9 2
T.Hardness 54 132 99.4 +32.8 –
E. coli 0 48 11.4 +59.8 5
Manganese 0.001 0.8 0.2 +0.5 2
Iron 0.001 0.1 0.01 +0.1 –
Nitrite 0.01 0.2 0.06 +0.1 –
Nitrate 0.001 0.6 0.05 +5.3 –
Phosphate 0.001 0.43 0.04 +0.195 –
Chloride 0.001 0.9 0.083 +11.62 –
Sulfate 0.001 0.7 0.065 +7.192 –

plantation and agricultural sectors, respectively, the minimum and maximum values are noted (Table 6).
Balakrishnan et al. (2011) argue that water with a hardness of more than 150 mg/L is dangerous. The abundance
of dissolved calcium and magnesium salts has a significant impact on the hardness of groundwater (Rapant et al.
2017). Heat induces the deposition of calcium and magnesium carbonates as a hard scale in kettles, cooking uten-
sils, heating coils, and boiler tubes, resulting in a loss of fuel. Temporary hardness is removed by heat (Sunitha
et al. 2014).

Manganese
The manganese concentration is well within the 0.4 mg/L limits. Manganese levels range from 0.001 to 1.4 mg/L
in the study city, with a mean and standard deviation of 0.4 and +0.5, respectively. The concentration levels of 21
out of 33 groundwater samples were found to be under the acceptable range (0.4 mg/L), while the levels in the
remaining 12 samples were found to be over the desirable limit. Maximum values are seen in built-up regions,
while plantation areas have the lowest concentration (Table 6). In built-up areas, a high concentration of manga-
nese has mostly decreased (Table 6). Similar findings are made by Hasan & Ali (2010), who also find that the
most frequent sources of iron and manganese in groundwater are naturally occurring. However, other sources
of iron and manganese in local groundwater include industrial effluent, acid-mine drainage, sewage, and landfill
leachate. It may result in stains on clothing, scaling on pipes, and bad-tasting, odorous, or smelling water (Sunitha
et al. 2014). This diagram displays the spatial distribution of manganese (Figure 5(e)).

Iron
With a mean and standard deviation of 0.03 and +0.5, respectively, the iron content in the research area ranges
from 0.001 to 0.5 mg/L. Of the 33 groundwater samples that were studied, 31 samples’ concentration levels were
 Water Practice & Technology Vol 18 No 4, 873

found to be under the ideal limit (0.3 mg/L), whereas the desirable limit was surpassed at 2 sampling sites in the
built-up region (Table 6). Within the plantation areas, there is the lowest concentration (Table 6). Sampling
locations within populated areas went beyond the desired cap. The aquifer contains naturally occurring iron,
but the dissolution of the ferrous borehole and hand pump components can raise groundwater levels (Devatha
et al. 2016). Iron’s spatial distribution is depicted in Figure 5(f).

Nitrite
In the study area, the concentration of nitrite in sampled groundwater stations varied from 0.01 to 0.6 mg/L with
a mean and standard deviation of 0.08 and +0.1, respectively (Table 6 and Figure 5(d)). Out of 33 groundwater
samples analyzed, the concentration levels of 32 samples were found to be within the desirable limit (0.2 mg/L),
whereas 1 sample site within a built-up area exceeds the desirable limit (Table 6). The minimum concentrations
are recorded in plantation areas. There is a sampling station that exceeds the standard limits in built-up areas
(Table 6). This is because nitrites come from fertilizers through runoff water, sewage, and mineral deposits. Nitrite
can stimulate the growth of bacteria when introduced at high levels into a body of water (Parvizishad et al. 2017).

Phosphate
The 33 groundwater stations that were sampled in the research area had phosphate concentrations that ranged
from 0.001 to 0.7 mg/L, with a mean and standard deviation of 0.1 and +0.2, respectively. Plantation and agri-
cultural areas have the lowest numbers, whereas built-up areas have the highest amounts (Table 6). Phosphate
levels in the research area are generally within the desired range. Its concentration in built-up areas is greater
than that of the land-use class. This is a result of the region being impacted by anthropogenic activity. Despite
this, phosphate can enter groundwater as a result of leaking septic systems, penetration of wastewater, dissolution
of phosphate-containing minerals in aquifer sediments, agricultural fertilizer, animal waste, and overlying soils
(Funk et al. 2019). Phosphate’s spatial distribution is depicted in Figure 6(a).

Figure 6 | Prediction map of PO
3 2


4 , Cl , SO4 , and NO3 .
 Water Practice & Technology Vol 18 No 4, 874

Chloride
The concentration of chloride in the research area ranges from 0.001 to 35.8 mg/L, with a mean and standard
deviation of 5.4 and +10.9, respectively (Figure 6(b)). This concentration is within the WHO’s allowed limits
(250 mg/L). Built-up areas have the highest concentration, whereas plantation and agricultural areas have the
lowest (Table 6). Built-up regions have higher chloride concentrations than other types of land use. Fertilizers,
septic systems, and animal manure are typical sources of contamination. Alemu et al. (2022) reported that chlor-
ide concentrations at all measured sites vary from 16.33 to 108.67 mg/L, with an average of 56.12 mg/L, which
could harm kidneys. Additionally, groundwater that is degrading as a result of intense agricultural use or exces-
sive effluent application will nearly always have a rising chloride concentration (Singh et al. 2010).

Sulfate
With a mean and standard deviation of 1.1 and +5.5, respectively, the concentration of sulfate in the study area
ranges from 0.001 to 31.5 mg/L (Figure 6(c)). Built-up areas have the highest concentration, whereas plantation
and agricultural areas have the lowest (Table 6). Sulfate’s average value is within the accepted range. High sulfate
values are seen in built-up regions compared to the plantation and rural areas. Aniteneh (2021) looked into find-
ings similar to those on the evaluation of water quality in Addis Ababa city. Mineral dissolution, atmospheric
deposition, and other anthropogenic sources are all sources of sulfate in groundwater. Sewage treatment facilities
and industrial outputs from tanneries, pulp mills, and textile mills are examples of point sources. Additionally,
fertilized agricultural fields’ runoff feeds water bodies with sulfates (Sharma & Kumar 2020; Han et al. 2021).

Nitrate
With a mean and standard deviation of 2.4 and +4.8, respectively, the nitrate concentration ranged from 0.001 to
17 mg/L (Table 6 and Figure 5(d)). One sample site within a built-up area surpasses the desirable limit out of 33
groundwater samples that were studied, while the concentration levels of 32 samples were found to be under the
desirable limit (0.2 mg/L). Cropland areas followed by built-up regions have the highest concentrations, whereas
plantation areas have the lowest concentration. The research area’s overall mean value for nitrate is within the
desired range. In populated regions, there is a sampling station that exceeds the permitted limits. This is because
runoff water, sewage, and mineral deposits all transport nitrates from fertilizers to the environment. Nitrate,
which is formed from pit latrines and is the most well-studied chemical contaminant, is the primary indicator
of fecal pollution in groundwater sources since it is present in high concentrations in human excreta (Graham
& Polizzotto 2013). When nitrate is added to a body of water in large quantities, it can encourage the growth
of bacteria (Parvizishad et al. 2017). Inadequate good construction, well placement, excessive use of chemical
fertilizers, and inappropriate handling of human and animal waste are all common causes of high amounts of
nitrate in well water (Yu et al. 2020).

Microbiological parameter
E. coli
The mean and standard deviation of the groundwater samples taken from all 33 measuring stations in the study
area, which ranged from 0 to 272 CFU/100 mL, were 26.4 and +49.6, respectively (Figure 7 and Table 6).
Eight of the 33 groundwater samples that were evaluated for E. coli concentration levels were found to be
within the desired limit (zero counts per 100 mL), but the levels in the remaining 25 samples were found to be
beyond the desirable limit (Table 6). The highest amounts were found in the built-up areas. According to the find-
ings, fecal coliforms were identified practically everywhere that was sampled. Alemu et al. (2022) also discovered
comparable findings. Therefore, it is evident that disease-causing organisms are present in the investigated
groundwater stations due to leakage creation from wastewater produced by on-site sanitation systems, primary
pit latrines, and septic tanks. Despite this, Azizullah et al. (2011) discovered that poorly designed on-site sani-
tation facilities, such as pit latrines and septic tanks, are the leading causes of groundwater contamination and
the primary source of fecal coliform.

WQI values in land use/covers

Results in Table 7 reveal the GWQI values range from 15.64 (excellent) to 66.88 (Poor). According to Raheja
et al. (2022), 31.57 and 68.43% of samples fell into the excellent and good drinking water quality categories,
respectively. In agriculture areas, 3.1% of WQI agricultural areas were rated excellent, and 18.2% were rated
good. Agricultural areas showed the highest average WQI value (36.5), with ranges from 16.2 to 47.3. The overall
 Water Practice & Technology Vol 18 No 4, 875

Figure 7 | Prediction map of E. coli in the study area.

mean highest value was found in agricultural areas (Jalali & Kolahchi 2008; He et al. 2021). Animal manure, fer-
tilizers, and pesticides used in agricultural areas all contribute to pollution. In addition, runoff including bacteria
from manure, dissolved nutrients from commercial fertilizers, and manure or pesticides that go through the soil to
the groundwater can pollute groundwater (Malki et al. 2017).

Built-up areas
The GWI in built-up areas was rated as excellent in 21.2% of cases, good in 9.1%, and poor in 15.1% of cases
(Table 7). The Groundwater index(GWI) in built-up areas had a mean value of 34.6 and a maximum and mini-
mum value of 66.9 and 15.6 respectively. These sorts of low water quality fall under this category. The poor
GWQI value covered about 2,430 ha (Figure 6). Most measurements that exceed the WHO standard for pH, alka-
linity, E. coli, nitrate, manganese, and iron are taken in the built-up areas. This is because the majority of the
hotels and recreational facilities in this area act as point sources and produce pollutants. Urban locations have
distinct groundwater recharge methods from rural ones. In the case of urban groundwater recharge, various
new factors must be taken into account in addition to the natural recharge from precipitation (Wakode et al.
2018). Built-up environments provide both nonpoint and point sources of pollutants, according to a study con-
ducted similarly in the USA by Brett et al. (2005). Leaking subterranean storage facilities, as well as different
unintentional discharges of organic or inorganic contaminants, are examples of point sources that have an
impact on groundwater quality.

Plantation areas
Groundwater quality was rated as 9.1% good and 24.2% excellent. The greatest and minimum values of the WQI
in plantation areas were 37.3 and 17.6, respectively, with a mean value of 25.2 (Table 7). The findings show that
among the LULC classifications examined, plantation areas have the highest concentrations of high-quality
 Water Practice & Technology Vol 18 No 4, 876

Table 7 | GWQI results for each sampling site

LULCs Sampling sites GWQI Water classes (%) Mean GWQI

Built-up W1 18.4 Excellent (21.2) 34.6

W17 18.2
W11 15.6
W12 15.8
W13 16.9
W14 16.4
W15 17.6
W2 35.9 Good (9.1)
W25 43.4
W20 42.2
W18 50.4 Poor (15.1)
W21 54.4
W24 66.9
W26 50.7
W22 57.6
Plantation W4 20.8 Excellent (24.2) 25.2
W5 20.9
W6 20.7
W7 19.1
W8 25.2
W9 24.6
W10 22.8
W16 17.6
W19 33.5 Good (9.1)
W32 37.3
W33 35.1
Agriculture W3 16.3 Excellent (3.1) 36.5
W23 47.3 Good (18.2)
W27 44.9
W28 33.4
W29 37.8
W30 38.9
W31 36.9

groundwater. Before entering an aquifer, plants can consume dissolved nutrients like nitrogen and phosphorus
from eroded sediments and some gray water or home wastewater (Mekonnen & Hoekstra 2010). Table 7
shows that 48.5% of groundwater quality is classified as excellent, 36.4% as good, and 15.1% as poor. The
bulk of populated regions, including Shimbit, Tana, Fassilo, Shum Abo, Atse Tewodros, and part of Woramit
(near Lake Tana), is classified as having poor water quality (Figure 8).

Groundwater potential zones

Figure 9 depicts each theme map that was used for overlaying the cumulative weight applied to all of the thematic
layers in the spatial analysis tool using weighted overlay techniques. The final product displays the spatial distribution
of GWPZ for Bahir Dar city in four classifications, as shown in Table 9: ‘Very high,’ ‘High,’ ‘Medium,’ and ‘Low’.
Very high and high GWPZ cover 737 ha of the research area and are primarily found in the city’s northwest
and northern areas (Table 8). The southern and central areas of the city contain medium GWPZ that covers an
area of 14,249 ha. Low GWPZs are found in the 407 ha research area’s southern, south-eastern, and northern
regions. Zones with very high and high groundwater potential account for 0.78 and 4% of the total land area,
respectively. Figure 9 discloses that the northern and northwestern regions of the research area have extremely
high and high water storage, respectively, owing to higher rainfall and a lower degree of slope (Melese & Belay
2022).
Gentle slope, low drainage density, high lineament density, and a good hydro-geomorphological form are all
characteristics of favorable GWPZ. When compared to high GWPZ, ‘poor’ GWPZ has a lower recharge capacity
and a steeper slope (Allafta et al. 2021). The study area is dominated by a medium groundwater potential zone,
which accounts for 92% of the total area. The poor groundwater potential zone contains 3% of the research area.
 Water Practice & Technology Vol 18 No 4, 877

Figure 8 | Groundwater quality index map of the study.

The study’s results show how the groundwater potential varies spatially throughout the study area as a result of
variations in the area’s rainfall, geology, drainage density, soil, land use, land cover, slope, and lineament. Yihunie
& Halefom (2020) stated that groundwater potential varied spatially from very low to high levels. The present
study’s groundwater potential zone map offers guidance to decision-makers for effective groundwater manage-
ment and planning for urban and agricultural uses.

Groundwater quality versus potential

To implement sustainable groundwater resource management strategies, it is necessary to understand the quality
and potential of the groundwater in a particular region. Knowing which portion of the region is suitable to pro-
duce a good amount and quantity of water is also crucial. Table 9 and Figure 10 show the results of groundwater
potential versus quality distribution.
Table 9 depicts the southwest and northeastern regions of the study area have the best groundwater quality,
while the northwest and north have the most productive aquifer.
The entire area of the very high groundwater potential zone is classified as poor groundwater quality (GWQ).
This implies that very high GWPZs require immediate action to improve water quality to ensure a safe water
supply. It is observed that the north and northwestern regions of the research area, which have high groundwater
potential, are primarily distinguished by low-quality water. This could be because the area is a sedimentation site
for nutrient-rich eroded soil from the upper and middle parts of the Gilgel Abay watershed, which is the main
tributary of the Lake Tana Sub-basin. The movement of contaminated groundwater from higher hydraulic
heads to lower hydraulic heads is accompanied by the sideways and downward leaching of farming nutrients
and fertilizers into the groundwater (Tinonetsana et al. 2022). In addition, poorly treated industrial effluents (typi-
cally from hostels) and city sewage could contaminate areas with high to very high groundwater potential. Areas
with high groundwater potential are degrading into poor quality and unfit for human consumption as a result of
contamination from improperly treated industrial effluents and urban sewage (Rao & Jugran 2003; Popoola et al.
2020). Water quality in highly productive aquifers can be degraded by salinity, nonpoint-source pollutants such as
nitrate from agriculture, and geogenic pollutants such as arsenic (Al-Abadi et al. 2021).
 Water Practice & Technology Vol 18 No 4, 878

Figure 9 | Geology, rainfall, drainage density, soil, lineament, slope, LULC (January 2022), and groundwater potential zone
maps.

The quality of high groundwater potential consists of 1,116 ha of excellent, 4,699 ha of good, and 1,335 ha of
low. This shows that 65.7 and 15.6% of the high groundwater potential zone are classified as good and excellent
quality for a safe water supply, respectively. The medium groundwater potential zone covers an area of 14,249 ha,
 Water Practice & Technology Vol 18 No 4, 879

Table 8 | Area covered by groundwater potential zone

Groundwater potential classes Area in ha % Relative location

Low 407 3 Southern, Southeastern, and Northeastern parts of the study area
Medium 14,249 92 In the middle and southern parts of the study area
High 71 4 Middle, Northern, and Northwestern parts of the study area
Very high 22 0.78 Northwestern part of the study area(along the shore of Lake Tana)

Table 9 | Area covered by GWPZs under each groundwater quality type

GWPZ under each GWQ Type in ha (%)

GWPZ Excellent Good Poor Total area in ha (%)

Very high – – 22 (100) 22 (100)

High 1,116 (15.6) 4,699(65.7) 1,335 (18.7) 7,154 (100)
Medium 3,151 (22) 10,093 (71) 997 (7) 14,249 (100)
Low 13 (3.2) 393 (96.5) 1 (0.3) 407 (100)

Figure 10 | Groundwater potential and groundwater quality distribution.

with 22% having excellent quality and 71% having good quality. The quality of the groundwater should be a sig-
nificant factor in determining groundwater potential (Al-Abadi et al. 2021). Plans for sustainable groundwater
management are required, especially for groundwater zones with high potential but poor quality (Jasrotia
et al. 2013; Al-Abadi et al. 2021).

CONCLUSION
The study aimed to evaluate and map the quality and potential of groundwater under land use/covers in Bahir
Dar City, northwest Ethiopia, using the WQI and geospatial technology. The findings revealed that 48.5% of
 Water Practice & Technology Vol 18 No 4, 880

the groundwater has the excellent quality, 36.4% has good quality, and 15.1% has poor quality. The land use/
covers had an impact on groundwater quality. The pH, alkalinity, E. coli, nitrate, manganese, and iron measure-
ments in the built-up area exceed the WHO standard, implying a low groundwater quality in the built-up area
because it emits both nonpoint and point pollutants. The excellent groundwater quality is noticeable in the plan-
tation area. The majority of the study area falls into the medium groundwater potential category, with high
groundwater potential in the northern and northeastern areas and low groundwater potential in the eastern
and northeastern areas. The medium to very high groundwater potential in the northern and northwest areas
is characterized by low quality for a safe supply due to contamination from poorly treated industrial effluents
and city sewage.
Based on the findings, it is suggested that the city’s water and sewerage authority should prioritize managing
and controlling point sources of pollution, especially in areas with very high to high groundwater potential.
The appropriate remedial actions are taken, such as imposing limits on hotels and municipalities for the
proper treatment of effluents and wastes. The Bahir Dar water and sewerage authority should monitor and evalu-
ate private borehole owners’ claims that the designated location where water is extracted is safe or not. Water
supply boreholes should be located at a sufficient horizontal distance from dirty areas and inadequately designed
sanitary facilities to reduce the risk of infection. Hand-dug and boreholes should be covered by the plantation to
improve groundwater quality and ensure a safe supply of water. The groundwater potential zone with quality and
the temporal variations of groundwater quality and potential could be determined through an additional quanti-
tative study.

ACKNOWLEDGEMENTS
The authors gratefully acknowledge Choice Water Bottling Factory’s financial assistance.

DATA AVAILABILITY STATEMENT

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

CONFLICT OF INTEREST
The authors declare there is no conflict.

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First received 30 November 2022; accepted in revised form 23 March 2023. Available online 5 April 2023

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