Assessing the Spatial Distribution of Groundwater Quality of Addis Ababa City by Using Geographical Information Systems.

Assessing the Spatial Distribution of Groundwater Quality of Addis Ababa City by Using Geographical Information Systems.
IJGRP
Assessing the Spatial Distribution of Groundwater
Quality of Addis Ababa City by Using Geographical
Information Systems.
Aderaw Tsegaye
Climate, Geospatial and Biometrics research Directorate. Debre Zeit Agricultural Research Center, Ethiopia
Email Address: aderaw.tsegaye@eair.gov.et/aderawtsegaye@yahoo.com
This study has been undertaken to analyze the spatial variability of groundwater quality for
Addis Ababa city. Groundwater is one of the most important natural and necessary resources
over the past years due to an increase in its usage for drinking, irrigation, and other purposes.
The aim of this research is to provide an overview of groundwater quality of spatial distribution
over the city of Addis Ababa. Geographical information systems were used to determine the
spatial distribution of groundwater quality parameters in the study area. Geo-statistical
techniques specifically Ordinary kriging interpolation method was applied to generate water
quality maps. The major water quality parameters such as Total Dissolved Solids, Total
hardness, Chloride, Nitrate, Sulphates, Magnesium and Calcium have been analyzed. The
spatial variation maps of these groundwater quality parameters show that mostly in the central
part of the city there is high concentration of nitrate, total dissolved solids and total hardness.
But chloride, Magnesium, calcium and Sulphate have low concentration. From the water quality
index assessment 78.18 % of the groundwater of the city were found to be in the excellent water
class, 20.86 % good, 0.9 % poor and the remaining 0.06 % was classified under very poor water
class.
Keywords: Geo-statistics, GIS, Groundwater, Interpolation, water quality
INTRODUCTION
Most of the Earth’s liquid freshwater is found, not in lakes
and rivers, but stored underground in aquifers (Morris et
al., 2003). Where surface water is absent, the supply of
good quality groundwater is essential for the health and
development of nations. Until recently the issues of
groundwater use and quality have less consideration than
surface water, and data on groundwater stocks and flows
are less reliable. The world health organization has
repeatedly insisted that the single major factor adversely
influencing the general health and life expectancy of a
population in many countries of the developing world is the
access of clean drinking water.
The groundwater quality is equally important as that of
quantity. Water quality degradation is one of the major
environmental problems these days. Safe water is a
precondition for health and development and a basic
human right, yet it is still denied to hundreds of millions of
people throughout the developing world (UNICEF, 2008).
Ethiopia is also facing the problem of water quality
degradation. However, the extent and degree of severity
of water pollution are more prominent in major cities, like
Addis Ababa where the problem is at its peak in this
century. Rapid urbanization in most of these cities has led
to unprecedented population growth, resulting in the
development of large areas of unplanned and sub-
standard housing. The lack of services in such informal
settlements poses serious threats to groundwater through
sewerage and effluent leakages, the dumping of domestic
waste, and uncontrolled industrial and commercial
activities. As many of these settlements rely on
groundwater as their main source of potable water, such
pollution poses major health risks to a large proportion of
their population (Morris et al., 2003). Water-related
Research Article
Vol. 7(1), pp. 182-199, January, 2021. © www.premierpublishers.org. ISSN: 2021-6009
International Journal of Geography and Regional Planning

Aderaw T. 183
diseases caused by insufficient safe water supplies
coupled with poor sanitation and hygiene cause 3.4 million
deaths a year, mostly among children (UNICEF, 2008).
Despite continuing efforts by governments, civil society
and the international community, over a billion people still
do not have access to improved water sources (UNICEF,
2008). In developing countries sources of pollution from
domestic, agricultural, industrial activities are unregulated.
Likewise in Addis Ababa, where there is no as such
environmental protection practice, there are a number of
pollutant sources that continuously deteriorate the quality
of surface and groundwater (Legesse et al., 2006). The
issue of groundwater pollution has not been considered as
a major problem in Ethiopia until recent times (Tilahun et
al., 2010). However, currently, there is an ever increasing
demand for application of fertilizers and pesticides to
enhance food production. At the same time there is an
expansion of settlements and industries in most cases
happens without the proper installation of sewer and
drainage systems and poor practices of waste disposal
management. So these practices polluted the groundwater
of Addis Ababa (Tilahun et al., 2010).
In groundwater studies, GIS is commonly used for site
suitability analyses, managing site inventory data,
estimation of groundwater vulnerability to contamination,
and integrating groundwater quality assessment models
with spatial data to create spatial decision support systems
(Balakrishnan et al., 2011). Therefore it is suggested that
the use of GIS techniques is vital in testing and improving
the groundwater contamination risk assessment methods.
For any city, groundwater quality map is important to
evaluate the water safeness for drinking and irrigation
purposes and also as a preventive indication of potential
environmental health problems. Mapping of spatial
variability of groundwater quality is vital importance and it
is particularly significant where groundwater is a primary
source of potable water. Considering the above aspects of
groundwater contamination and use of GIS in groundwater
quality mapping, this study demonstrates to map the
groundwater quality in Addis Ababa city, Ethiopia.
Study area
Addis Ababa city is located in the central highlands of
Ethiopia, which covers an area of about 540 km2. Its
geographic location is between 38.638o and 38.906o east
and 8.832o and 9.09o north. The administration of the city
is divided into ten sub-cities. Addis Ababa has a
subtropical highland climate. The city has a complex mix
of highland climate zones, with temperature differences of
up to 10 °C (18 °F), depending on elevation and prevailing
wind patterns. Its elevation ranges generally between
1780 m and 3380 -meter a.m.s.l.
Figure 1: Map of the study area

Int. J. Geogr. Reg. Plan. 184
MATERIALS AND METHODS
Preparation Of Well Location Point Feature
Water quality data for a number of 450 wells were collected
from Addis Ababa water and sewerage Authority
(AAWSA). Based on the location data obtained, the point
feature showing the position of wells was prepared. Data
stored in excel format and linked with the spatial data by
join option in Arc Map and then changed in to shapefile.
The spatial and the non-spatial database were formed and
integrated for the generation of spatial distribution of water
quality Maps.
Kriging Interpolation Techniques
The dataset of water quality parameters was imported into
Arc Map software. The ESRI Geographic information
system (GIS) was used for the construction of the
interpolation surfaces through applying the ‘Geostatistical
Analyst’ extensions of ArcGIS 10 software package.
Examining The Distribution Of The Data
In the Arc GIS Geostatistical analyst, the histogram and
normal QQ Plots (quantile quantile) were used to see the
distribution of data. If the data is not normally distributed,
it should be transformed by using log transform
application. The histograms and normal QQ plots were
plotted to check the normality of the observed data.
Histogram and QQ Plot analyses were carried out for each
water quality parameter and it was found that all the
analyzed parameters Nitrate, TDS, Chloride, Ca, Mg,
Hardness and Sulphate showed skewed distribution.
Log Transformation
Water quality parameters such as Hardness, TDS, Ca, Mg,
NO3, Cl and Sulphate are highly skewed in their
distribution. So in order to minimize the error for those
parameters, a log transformation has been applied to
make the distribution closer to normal.
Semivariogram Model
Semivariogram models (Spherical, Stable, Exponential
and Gaussian) were tested for each parameter data set.
Prediction performances were assessed by cross
validation. Cross validation allows determination of which
model provides the best predictions. According to (Berktay
et al., 2008) for a model that provides accurate predictions,
the standardized mean error should be close to 0, the root
mean square error and average standard error should be
as small as possible (this is useful when comparing
models), and the root mean square standardized error
should be close to 1.
Cross Validation Result
According to the cross-validation results presented in
Table 1 below all models proved robust and can thus be
used to predict values and create surfaces.
Table 1: Cross validation results
Parameters Best fitted
Models
Number of
samples
Prediction errors
Mean Root-mean
square
Average
standard
error
Mean
standardized
Root-mean-
square
standardized
Ca2+
Mg2+
Cl-
No-
3
TDS
So4
—
Hardness
Gaussian
Gaussian
Exponential
Stable
Spherical
Gaussian
Stable
156
156
171
140
247
116
134
-0.30097
0.002169748
0.1501
0.09289
1.318391
-0.24710
-1.4692
27.74338
11.82397
24.8545
15.3728
229.35520
14.12892
101.4403
31.0446
11.8782
31.7748
11.8127
314.5023
14.004
115.8076
-0.0052258
-0.0001535
0.00308
0.010297
0.00202
-0.0163
-0.00477
0.9198172
1.018596
0.8064344
1.27816
0.77038446
0.9782
0.8895
Procedures to Generate Water Quality Index
To generate the groundwater quality index (WQI) map,
seven parameters such as TDS, hardness, nitrate,
Chloride, Sulphate, Magnesium and calcium were
selected from the data set. Those seven parameters
fall under the category of chemically derived
contaminants that could alter the water taste, odour or
appearance and affect its acceptability by consumers
(WHO, 2011).
First Step
Each of the seven parameters has been assigned a
weight (Wi) according to its relative importance in the
overall quality of water for drinking purposes (Table 2).
The maximum weight of 5 has been assigned to

Aderaw T. 185
parameters such as nitrate due to their major importance
in water quality assessment (Vasanthavigar et al., 2010).
Other parameters like magnesium, chloride, calcium,
hardness, sulphate and TDS were assigned a weight
between 1 up to 4 depending on their importance in the
overall quality of water for drinking purposes.
Table 2: WHO standards weight (wi) and calculated relative weight (Wi) for each parameter (source,
Vasanthavigar et al., 2010)
Drinking Water
Quality Parameter
WHO Standard( mg/l)
(mg/L)
Weight (WI) Relative Weight (Wi)
Weight (WI)
Relative Weight (%)
Weight (%)
Nitrate 50 5 0.17 17
Magnesium 50 2 0.07 7
Chloride 250 3 0.10 10
Calcium 75 2 0.07 7
Hardness 300 2 0.07 7
Sulphate 250 4 0.13 13
TDS 1000 4 0.13 13
Total 30 1 100
Second Step
The relative weight (WI) is computed using a weighted
arithmetic index method given below (Panwar et al., 2012)
in the following steps.
WI= 𝑊𝑖
∑ 𝑊𝑖
𝑛
𝑖=1
Where:
WI = relative weight;
wi = weight of each parameter and,
n = number of parameter.
Third Step
A quality rating scale (Qi) for each parameter is assigned
by dividing its concentration in each water sample by its
respective standard according to the guidelines of WHO
(2011) and then multiplied by 100.
Qi = (Ci / Si) × 100
Where:
Qi = quality rating,
Ci = concentration of each chemical parameter in each
water sample in mg/l, and
Si = WHO drinking water standard for each chemical
parameter in mg/l.
Fourth Step
The SIi is first determined for each chemical parameter,
which is then used to determine
the WQI as per the following equation:
SIi = Wi × Qi
Where:
SIi = sub index of ith parameter and
Qi = rating based on concentration of ith parameter. The
overall water quality index (WQI) was calculated by adding
together each sub index values of each groundwater
samples as follows:
WQI = ∑SIi
Water quality types, were determined on the basis of WQI.
The last result of WQI of the study area is ranged between
17 and 233.

Figure 2: Flow chart of the method (followed by the researcher).
Non-Spatial Data
Spatial Data
Data collection
Obtain Well location
Digitizing the study
area map using Arc
GIS software
Collecting Ground water
sample from each well
location
Generating Final
study area map
Import to Arc GIS using UTM
Projection & zone 37 &
convert to point feature
&assign ID for each well.
Physico chemical analysis of
Ground water sample
Generating of water quality data for
each well & entering in to excel
sheet and assign ID for each cell
Spatial and non spatial
data Join
Generating of thematic maps for
individual quality parameters using spatial
interpolation technique (kriging)
Thematic map Interpretation
TH
No3
Calculate WQI and interpolate the result
Spatial distribution of Ground water
quality for drinking purpose: potable and
Non Potable
SO4
Log transformation of Skewed to
Normal
TD Ca
TH
Mg
TH
Cl

Aderaw T. 187
RESULTS AND DISCUSSION
Spatial Distribution of Groundwater
Groundwater quality maps are useful in assessing the
usability of the water for different purposes. The spatial
and attribute data are integrated for the generation of
spatial variation maps of major water quality parameters
like Nitrate, Total Dissolved Solids (TDS), Total hardness,
Sulphates, Calcium, and magnesium. Based on these
spatial variation maps of major water quality parameters,
an Integrated Groundwater quality map of the study area
was prepared. This integrated groundwater quality map
helps us to know the existing groundwater condition of
Addis Ababa city.
Nitrate
Nitrate is very mobile in water, and groundwater typically
contains higher levels than surface water. In the well
samples of study area, Nitrate concentrations showed high
spatial variations and ranged from 0.02 to 176 mg/l with a
mean and standard deviation as 11.58 and 20.54
respectively.

Figure 3: Prediction Map of Nitrate.
.
As seen from Figure 3 above, the nitrate concentrations in
the western part is higher than the guideline or standard
value of 50 mg/l set by WHO standard for drinking water
specifications. The ministry of water and energy (2013)
report shows that at present, the high amount of nitrate
observed at Merkato area corresponds to the maximum
population density within Addis Ababa. The same is true in
this study the central part of the city specifically in the
borderline of Lideta, Arada and Addis sub-city.
Total Dissolved Solids (TDS)
TDS is a measure of the amount of material dissolved in
water. This material can include carbonate, bicarbonate,
chloride, Sulphate, phosphate, nitrate, calcium,
magnesium, sodium, organic ions, and other ions
(UNICEF, 2008). The total concentration of dissolved
minerals in water is a general indication of the overall
suitability of water for many types of uses (Karthikeyan et
al., 2013). Different researches Karthikeyan et al., (2013)
classified the TDS value in to different ranges. Water
contains less than 500 mg/L of dissolved Solids; it is
generally satisfactory for domestic use and for many
industrial purposes. If the Water with more than 1000 mg/l
of dissolved solids usually gives disagreeable taste or
makes the water unsuitable. Water with high TDS often
has a bad taste and high water hardness, and could result
in a laxative effect. High concentrations of TDS may also
reduce water clarity.

Aderaw T. 189
Figure 4: Prediction Map of TDS distribution.
TDS concentration in the ground water of study area is
ranged from 38 to 2292 mg/l with mean and standard
deviation as 335.4 and 295.31 respectively. The spatial
variation map for TDS was prepared in to nine class
ranges and presented in Figure 4. From the spatial
variation map, it is observed that, the larger portion of the
study area has under the good range (0-500 mg/l). But the
small portion of central part of the city there is high
concentration of TDS (1000 up to 2292 mg/l).
Figure 5: Prediction Map of hardness.
Total Hardness
Hardness in water is caused primarily by the presence of
carbonates and bicarbonates of calcium and magnesium,
Sulphates, chlorides and nitrates. The total hardness of
water classified in to three ranges (0-300 mg/l, 300-600
mg/l and >600 mg/l) low, medium and high respectively
(Karthikeyan et al., 2013). To evaluate hardness
distribution based on these ranges the spatial variation
map for total hardness of Addis Ababa city has been
presented in Figure 5 below.
Hardness concentration in the ground water of study area
is ranged from 11 to 866 mg/l with mean and standard
deviation as 170.85 and 110.78 respectively. From the
map it observed that for most part of the city areas, the

hardness value is less than 300 mg/l is observed, except
the central part of city, which has 308 up to 866 mg/l. The
most common problem associated with groundwater may
be hardness, caused by an abundance of calcium or
magnesium. Hard water causes no health problems, but
can be a nuisance as it may cause soap curds to form on
pipes and other plumbing fixtures (UNICEF, 2008). The
total hardness of water may be divided in to two types,
carbonate or temporary and bi-carbonate or permanent
hardness. The hardness produced by the bi-carbonates of
calcium and magnesium can be virtually removed by
boiling the water and is called temporary hardness. The
hardness caused mainly by Sulphates and chlorates of
calcium and magnesium cannot be removed by boiling and
is called permanent hardness (Karthikeyan et al., 2013).
Chloride
Chloride is present in all natural waters, mostly at low
concentrations. It is highly soluble in water and moves
freely with water through soil and rock. High
concentrations of Chloride can make water unpalatable
and, therefore, unfit for drinking or livestock watering
(UNICEF, 2008). According to CGWB (2010) in ground
water the chloride content is mostly below 250 mg/l except
in cases where inland salinity is prevalent and in coastal
areas. The same is true in Addis Ababa, the ground water
chloride ion concentration is below 250 mg/l. It varies
between 0.1 to 196 mg/l with mean and standard deviation
as 19.8 and 25.15 respectively. The spatial distribution of
chloride concentration is illustrated in Figure 6 below. Even
though the amount of chloride distribution in the whole
study area is less than World health organization standard
(250 mg/l) but relatively higher concentration of chloride is
observed in the northwestern and south western part of
Addis Ababa.
Figure 6: Prediction Map of Chloride.
Calcium
Calcium occurs in water mainly due to the presence of
limestone, gypsum and dolomite minerals. Industrial, as
well as water and wastewater treatment, processes also
contribute calcium to surface waters and ground water.
Acidic rainwater can increase the leaching of calcium from
soils. Calcium concentrations in natural waters are
typically less than 15 mg/l but for water associated with
carbonate rich rocks, concentrations may reach 30 up to
100 mg/l. Salt water have concentrations of several
hundred milligrams per liter or more (UNICEF, 2008).

Aderaw T. 191
Figure 7: Prediction Map of Calcium.
According to Karthikeyan et al (2013) the amount of
Calcium in water was classified in to three ranges (0-75
mg/l, 75-200 mg/l and >200 mg/l) low, moderate and poor
respectively. Based on these ranges the spatial variation
of Calcium in Addis Ababa, except the little parts of the city
(central and southern) almost all area has low
concentration. From the Figure 7 is evident that the
distribution of calcium is ranged from 0.8 to 228 mg/l with
mean and standard deviation as 46.95 and 29.01
respectively.
Magnesium
Magnesium occurs typically in dark colored minerals
present in igneous rocks such as plagioclase, pyroxenes,
amphiboles, and the dark colored micas. It also occurs in
metamorphous rocks, as a constituent of chlorite and
serpentine. Magnesium is common in natural waters as
Mg2+, and along with calcium, is a main contributor to water
hardness. Natural concentrations of magnesium in fresh
waters may range from 1 to 100 mg/l (UNICEF, 2008).
Magnesium is usually less abundant in waters than
calcium, because magnesium is found in the earth’s crust
in much lower amounts as compared with calcium
(Kozisek, 2003). Similarly, as it shown in the Figure 8 in
the ground water of Addis Ababa the distribution of
magnesium (which is 1 to 92 mg/l) is less than calcium
(which is 0.8 to 228 mg/l).
Figure 8: Prediction Map of Magnesium.

From the map shown above, Magnesium concentration in
the ground water of study area is ranged from 1 to 92 mg/l
with mean and standard deviation as 16.51 and 12.74
respectively.
Sulphate
Sulphate (SO42-) is a combination of sulphur (S) and
oxygen (O). It occurs naturally in many soil and rock
formations. In groundwater, most sulphates are generated
from the dissolution of minerals, such as gypsum and
anhydrite. Saltwater intrusion and acid rock drainage are
also sources of Sulphates in drinking water. Manmade
sources include industrial discharge and deposition from
burning of fossil fuels (WHO, 2011). Sulphate
concentrations in natural waters are usually between 2 and
80 mg/l. High concentrations greater than 400 mg/l may
make water unpleasant to drink (UNICEF, 2008).
Figure 9: Prediction Map of Sulphate.
Sulphate concentration in the ground water of study area
is ranged from 0.5 to 78 mg/l with mean and standard
deviation as 16.35 and 15.59 respectively. The
concentrations of all the groundwater samples analyzed
were found to be below within the desirable limit (250
mg/l). But as shown in the above figure the concentration
of Sulphate is not evenly distributed. In central part of the
city there is more concentration of Sulphate (30 up to 78
mg/l) than other areas. Sulphate present above 500 mg/l
in water may affect the taste of water. At levels above
1000 mg/l, Sulphate in drinking water can have a
laxative effect, although these levels are not normally
found in drinking water (WHO, 2011).
Water Quality Index (WQI)
Water quality assessment of Addis Ababa city was done
by calculated Water Quality Index (WQI) using water
quality parameters, drinking water standard of WHO
(2011). Nine parameters such as: pH, TDS, Total
Hardness, Calcium, Magnesium, Sulphates, Chlorides,
Fluorides and Nitrates have been used to produce water
quality index. The final result shows that the water quality
index value is ranged from 17.32 to 233.09. Water quality
index (WQI) value has been classified in to five classes.
WQI is less than 50, 50 to 100, 101 to 200, 201 to 300
and greater than 300 have the value Excellent, good,
poor, very poor and unsuitable for drinking respectively.
Table 3: The Water quality index (source Vasanthavigar et al., 2010)
Range Type of water
<50 Excellent water
50_100 Good water
101_200 Poor water
201_300 Very poor water
>300 Water unsuitable for drinking
purposes
Based on this range shown by above table water quality of study area is categorized excellent to water unsuitable for
drinking.

Aderaw T. 193
Figure 10. Groundwater quality index of the study area.
From the above map of ground water quality index, shows
that most part of the study area is ranged the value
between 17 up to 50, whereas very smallest portion of the
city is ranged between 201 and 233. Water quality index
was reclassified into four classes. These four classes are:
Excellent, good, poor and very poor.
Figure 11:Reclass Map of groundwater quality index

The reclassed map showed that 78.18 % (422.17 km2) of
the groundwater of the city were found to be in the
excellent water class, 20.86 % (112.62 km2) good, 0.9 %
(4.87 km2) poor and the remaining 0.06 % (0.34 km2) was
classified under very poor water class based on the
computed WQI classification results.
CONCLUSION
To map and evaluate the groundwater quality in Addis
Ababa City Spatial distribution of groundwater quality
parameters was carried out through GIS and geo-
statistical techniques. These techniques have successfully
demonstrated its capability in groundwater quality
mapping of Addis Ababa City. This study has been
undertaken to analyze the spatial variation of major
groundwater quality parameters such as Total Dissolved
Solids, Total hardness, Sulphate, Nitrate, Chloride,
Magnesium and Calcium using GIS approach. The spatial
variation maps of these groundwater quality parameters
show that there is uneven water quality distribution. Nitrate
is highly concentrated in the central part of the city which
has over the maximum recommended value. Chloride
distribution in the whole study area is less than WHO
standard, but relatively higher concentration of chloride is
observed in the northwestern and south western part of the
Addis Ababa. The final map, which is groundwater quality
Index map, shows that majority parts of the city has
optimum groundwater quality (Excellent), and, very small
area of the central part has poor and very poor water
quality. In general, the groundwater quality decreases from
marginal part towards the central part of the city.
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Aderaw T. 195
Accepted 30 December 2020
Citation: Aderaw T (2021). Assessing the Spatial Distribution of Groundwater Quality of Addis Ababa City by Using
Geographical Information Systems. International Journal of Geography and Regional Planning 7(1): 182-199.
Copyright: © 2021: Aderaw T. This is an open-access article distributed under the terms of the Creative Commons
Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original
author and source are cited.
APPENDIX
X Y TDS mg/l Cl mg/l Ca mg/l Mg mg/l NO3 mg/l Hardness mg/l SO4 mg/l
444000 977700 375 7.1 20 46 0.1 69 37
445000 978200 426 14.2 24.1 3.9 9.3 76 2.6
455300 985250 139 7.1 35.3 2.9 8.69 100 1.8
455350 985100 144 5 36 9 1.1 77 0.3
455500 985200 117 3.5 28.8 4.84 14 92 15
455525 984000 140 3.88 14 4 3 50 1
457030 984617 597 7.1 22 4 0.23 31 9.2
457300 1001008 121 7.5 60.1 15.8 6.5 215 3.2
457700 1001008 127 13 30.4 6.32 0.5 102 1.6
459800 998250 127 10.6 21.6 4 0.3 160 0.7
460500 986500 136 4.4 9.6 3.4 1.5 70.2 0.9
460850 985850 159 1.5 40.8 15.5 11.58 68 5
461300 1002250 92 9.5 36.83 11.65 22 38 18
461500 1001023 111 1.99 43.2 13.4 0.02 102 14.3
462200 986250 551 7.5 26.4 6.3 7.9 185 3
462550 986950 256 10 35.2 10.1 0.1 164 20
462558 1002487 131 1.5 56.1 9.7 16.2 92 19
462743 1002521 203 70 20.8 6.8 3.08 130 3.7
463200 1002650 105 7.1 16 3.9 0.3 18 13.5
463675 987975 217 2 56.8 12.1 2.83 22.5 45
463700 988500 336 7.1 28.8 1 15.1 200 16.5
464538 991302 168 7.1 25.6 12.18 7.4 76 42
464600 1003075 340 7.1 8 2 3.96 106 16
465161 1003103 130 15 42 14 14.96 28 14.5
465243 1003393 85 11.3 40.1 6.8 2.8 148 1
465410 1002944 89 0.1 16 5 40.5 36 45
465550 1003250 78 14.2 10.4 2.9 50.68 235 31
465600 1001855 127 5 53.71 16 3.1 44 23.3
465651 1001575 250 11.3 12.8 3.48 3.3 300 10
465900 1002875 126 6 80 24 2.45 294 15.7
466050 993650 160 2.5 68.9 48.9 12 66 1.5
466175 1001800 257 35.5 18.4 4.8 75.3 182 4.5
466200 1001008 517 28.5 39.239 10.2 0.9 201.5 0.5
466200 993000 181.17 103 58.4 8.6 5.7 180 7.4
466200 988800 279 35.4 53.7 16.4 176 312 7
466250 993050 180 4 53 9 97.5 280 7.4
466400 987600 351 1.5 86.8 23.4 11.961 38 8.85
466440 1001760 96 16.5 64.1 29.2 74.4 866 6.58
466550 1000150 162 10.6 12.8 1.74 25.1 372 9.5
467100 1000550 170 23 228 71.1 75 374 26.75

467200 1001017 114.68 62.4 81.8 40.9 0.88 244 7.6
467250 999800 246 70.9 62.4624 11.1734 19.8 390 5.35
467350 992610 1037 35 92.8 34.6 25.1 230 1
467500 992800 43 78 97.6 36 13.2 100 6.6
468100 993100 279 1.5 96 18 0.2 192 7
468100 1001625 112 60 15.2 27.6 11.6 226 17.5
468200 1001600 107 80 38.8 10.15 3.08 46 4.65
468300 1001003 164 67.5 106 1.16 5.72 256 9
468400 1001016 107.36 18 16.8 11 8 40 51
468425 996350 246 14 84 3 5.5 30 7.9
468650 999800 141 22 11.2 16.5 9.5 188 1
468800 1001007 123.22 33 56.1 2.61 8.8 204 16.05
468800 996600 253 7 8.4 8.6 9.7 228 4.7
468875 993750 163 12.5 60.8 19 12.3 220 21.7
468900 1001007 132 28.4 30 7.3 13.7 224 42.4
468900 991900 189 48.2 60 18 9.2 216 20
469050 994450 221 6.5 96.8 23.35 9.4 220 30
469700 994500 200.69 4.5 49.7 23.35 13.24 222 8
469750 993850 350 13 49.7 26.8 25.7 232 28
469800 996300 104 16 45.7 28.2 13.8 240 24.7
469900 996000 388 8 40 23.4 13.7 236 10.6
470000 996400 485.56 7.2 49.7 26.3 3.9 240 20
470000 996400 703.94 7 48.1 19.6 12.3 224 20.47
470100 993850 180 12 57.36 29.2 9.68 200 36.2
470200 991300 208 14.2 44.9 24.3 18.6 196 27.55
470250 991500 94 14.2 52.9 27.7 16.7 260 129.5
470275 990353 198 14.2 44.1 24.4 21.2 240 15
470400 991300 207 9.2 56.1 19.5 14.42 232 155
470450 990125 179 10.91 40.1 20.4 12 128 8
470465 991100 247 17 60.8 27.2 14.2 57 3
470500 996000 236 14.2 45.7 11.2 16.4 140 1.3
470550 991350 275 8.5 77.6 27.7 8.8 420 13.2
470800 982900 332 14.2 48.5 26.3 13.13 240 16.5
470900 994800 394.06 14.2 46.5 27.21 5.3 248 15
470950 993300 38 7.2 43.29 26 2.7 228 26.2
470950 993400 84 9 39 23.3 45.7 204 6.7
471200 993700 131 3.6 41.7 9 1.6 238 2
471300 995800 542.29 8.7 37 1.5 18.9 232 4
471300 995400 478.85 11.3 9.6 23.6 19 282 20
471300 995050 341.95 6.11 39.7 20.4 11.7 272 16.5
471300 994800 114 9 70.4 25.3 2.7 236 4
471400 997400 1059 14.2 54.5 19.95 3 240 3.8
471400 988250 237 13 60.1 11.7 11.73 240 19.5
471500 995800 470.92 196.6 32.1 8 0.1 152 12
471600 995800 509 9.2 9.6 39.9 9.6 232 3
471700 995100 393 4.5 102.6 20.5 0.7 230 28
471700 996300 522 14 62.5 21.4 9.68 112 55
471704 973385 416 26.8 64.1 22.4 9.25 244 40
471800 995200 194 14.2 54.5 31.15 9.6 62 55
471925 995150 360 28.4 46.6 15.6 11.3 136 54.7
472000 995100 278 5 67.3 25 0.8 152 37
472500 996300 516 60.3 71.2 23.3 8.9 136 20
472600 997400 473.36 21.3 71 92 0.1 131 7
472700 996300 348 14.2 142 2.9 1.8 126 6.79
472700 996500 106 12.5 91.4 26.3 0.1 100 1.3
472870 980925 365 14.2 52.9 15.6 1.8 196 15
472900 995900 50 14.2 35.3 31.15 6.4 190 39.6
472900 992500 264 25 40.9 20.4 5.6 236 8
481162 958481 311 28.4 56.1 28 0.7 200 50.12

481694 965913 308 10.06 51.7 13.6 4.8 88.2 0.5
484325 969692 291 20 32.1 12.6 2.64 228 2
473576 972821 305 14.2 40.1 6.8 0.2 48 0.53
476574 975607 234 28.4 43.3 11.7 0.44 202 7
484475 975622 408 5 33.3 9.2 7.7 210 7
477181 975680 291 19.9 35.3 18.5 6.2 244 9
477651 975923 301 14.2 72.1 5.8 4.4 34 6
478154 975966 266 21.3 30.5 20 13.2 260 7.8
479058 976020 292 32.5 45.7 13 26.4 220 13
477162 976038 301 15.6 57 16.5 2 180 78
478580 976051 291 21.3 67.3 14.6 1.5 196 1.3
482480 976133 375 14.2 56.1 9.8 3.44 376 15
478199 976361 300 10.5 72 20 5.7 140 39.6
479061 976370 283 17.7 58 1.6 0.4 160 3
477856 976402 312 10.6 16 10.4 2.2 122 28
479405 976735 301 7.1 60.8 9.86 23.8 142 55
478347 976752 331 14 70.4 24.8 1.32 156 19
477330 976793 291 7.76 56.9 2.4 2.6 56
478808 976867 311 10.4 9.6 7.54 3.5 24
479696 976936 295 11.4 93.6 9.5 0.04 160
477945 976985 294 17.02 72 12.76 3.5 64
479246 977104 312 13 54.4 15.08 2.2 26
478780 977307 315 14.2 57.6 44.06 41.8 11
479942 977322 310 70 88.8 8 2.64 20
480895 977403 298 78.5 43 14 0.88 50
479526 977468 315 6.2 40 7.2 0.03 484
478462 977506 227 62 50.4 5.8 2.13 48
478462 977721 287 55 12.8 6.4 2.1 194
478019 977900 530 8.5 0.8 14.2 11 104
478998 977937 303 5 36.8 5.22 11.67 152
480517 977974 314 18.5 18.4 1.16 3.4 64
478019 977985 367 100 8.8 1.45 7.5 40
473566 978610 424 13 2.4 2 1.32 84
480900 978800 330 14.2 6 2 6.7 102
477232 978999 351 1 4.8 3.4 0.03 189
477500 979300 618 3.5 14.4 32.6 0.2
477400 979500 368 4 140 2.4 0.44
473108 979851 397 34 15.2 11.31 7.7
473069 979881 346 0.5 62 8 6.2
478450 979950 343 10.5 36 6.89 9.68
481200 980000 396 1 27.81 10.7 18.6
481200 980000 385 1 24.1 25.3
476400 980600 395 0.5 62.4 14.6
476520 980710 305 43 36.9 31
476426 980749 445 49.6 62 3.4
476000 980900 745 82.5 10.4 6.8
476500 981300 525 12.5 22.4 16.5
478425 981350 371 6 56 6.81
479340 981400 748 7 29.6 22.8
479400 981400 390 11.42 50 21.9
479400 981400 362 14.2 54.4 24
476500 981500 342 6.1 43.2 19.8
474225 982650 356 25.5 35 36
481600 982850 720 4.5 44 36
477900 982875 368 12 43.3 26.3
481600 982900 424 131 44.9 29.2
481600 982900 380 14.2 36 36
482400 983000 400 7
474556 983625 288 6.09

475300 983800 301 2
475300 983800 391 8
475300 984000 276 6
474800 984700 354 7
475650 984750 423 12.8
474900 985000 336 28.4
475050 985050 362 50
475300 985080 263 10.6
473900 985100 280 14.2
475300 985325 339 15.9
473433 985658 344 10.6
475000 985800 337 10
473466 987247 206 10
473760 987300 364
473500 987600 421
475000 987800 217
473500 987900 285
473700 988850 253
473900 989000 451
473900 989000 253
474075 989600 411
473300 989630 267
473225 989850 293
473700 989900 301
473848 990072 99
473900 990100 412
473800 990200 315
473100 991800 295
473100 991900 246
473200 992400 404
473200 992400 345
473100 992600 279
473000 992700 564
474300 992700 179
473500 992900 245
473900 993100 375
476963 994106 159
476800 994200 207
477463 994346 180
483750 994550 397
483550 995325 308
473900 995500 529
478450 995600 720.89
473400 995800 1868
474500 996200 249
473000 996300 932
473300 996300 1848
473100 996400 2292
474175 996550 2049
474050 996600 2253
479400 996800 74
481650 997725 237
476450 998100 220.21
476500 998250 220.21
474100 998500 614
484190 998500 141
475200 999200 73
483550 999250 265
473400 999600 249

483250 999600 251
482070 999823 260
481878 1000148 195
474050 1000875 270
474300 1001005 189.1
473900 1001200 138.59
475000 1001300 176.9
473350 1003000 121
485798 968308 344
485925 1000975 320
486000 1001115 333
486075 1000865 293
486200 1001042 369
487800 1002200 240
487900 972421 386
488150 1002300 595
488600 1001027 158
489950 976019 315
490000 968000 454
490193 968059 212
491300 1004800 154
494078 1012755 152
494145 966864 196
494829 967088 349
495447 968068 286
495561 968574 302
495647 968068 365
495765 966397 375
499700 1008450 193
500078 968505 635

Assessing the Spatial Distribution of Groundwater Quality of Addis Ababa City by Using Geographical Information Systems.

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