An integrated approach for quality assessment of drinking water using GIS: A case study of Lower Dir

Abstract

The present study was carried out to assess the quality of drinking water quality in district Lower Dir located in Khyber Pakhtunkhwa, northern Pakistan. A total of 33 water samples were collected from different locations of the study area. The parameters studied were pH, electrical conductivity, alkalinity, total hardness, calcium hardness, magnesium hardness, total solids, total dissolved solids, total suspended solids, sodium, chlorides, nitrites, sulphate and potassium. Thematic maps were prepared for all the studied parameters using ArcGIS 9.3 platform. The results were compared against the standard permissible limits suggested by World Health Organization (WHO) for drinking water. The results showed that the water parameters of the areas distant from the Panjkora river lie within the WHO suggested permissible limits, whereas the water quality of the riverine areas is alarming for some parameters. It is suggested to monitor water on regular basis to avoid its further deterioration and minimize its devastating impacts on the local society. The domestic wastes and agricultural run offs should be treated properly before disposing off. The wastes should be dumped to proper disposal sites instead of river banks.

Full text

Journal of Himalayan Earth Sciences Volume 47, No. 2, 2014, pp. 163-174
An integrated approach for quality assessment of drinking water using GIS:
A case study of Lower Dir
Sana Ullah, M. Waqas Javed, Muhammad Shafique and Shah Faisal Khan
National Center of Excellence in Geology, University of Peshawar
Abstract
The present study was carried out to assess the quality of drinking water quality in district Lower Dir
located in Khyber Pakhtunkhwa, northern Pakistan. A total of 33 water samples were collected from
different locations of the study area. The parameters studied were pH, electrical conductivity, alkalinity,
total hardness, calcium hardness, magnesium hardness, total solids, total dissolved solids, total suspended
solids, sodium, chlorides, nitrites, sulphate and potassium. Thematic maps were prepared for all the
studied parameters using ArcGIS 9.3 platform. The results were compared against the standard
permissible limits suggested by World Health Organization (WHO) for drinking water. The results
showed that the water parameters of the areas distant from the Panjkora river lie within the WHO
suggested permissible limits, whereas the water quality of the riverine areas is alarming for some
parameters. It is suggested to monitor water on regular basis to avoid its further deterioration and
minimize its devastating impacts on the local society. The domestic wastes and agricultural run offs
should be treated properly before disposing off. The wastes should be dumped to proper disposal sites
instead of river banks.
Keywords: GIS; Water Quality; River Panjkora; Inverse Distance Weightage (IDW); Permissible limits.
1. Introduction
Water greatly influences all aspects of our
ecosystem and is the primary requirement for
human life sustenance on planet earth (Randhir,
2012). Clean drinking water is the basic need of
human beings. Deprivation of water can kill an
object much earlier than deprivation of food
(Chattarjee, 1983). It has been estimated that
without food, human beings can survive for
approximately 20 days but start struggling for life
in absence of water just after a day (Srivastava,
1995). It is estimated that the water quantity on
earth is approximately 1.4 trillion cubic meters
(Farid et al., 2012). Of the total quantity of water
less than 1% is available for human use (Qadeer,
2004). More than 80% people utilize clean
drinking water in Khyber Pakhtunkhwa (Yousaf et
al., 2013).
Water is always been a valuable natural
resource for different human activities but its
pollution is a major concern (Prasad and
Narayana, 2004). Being the most drinking fluid,
water is a major source of transmitting diseases.
Obtaining safe drinking water is the primary
concern in most of the developing countries
because many of the water sources are non-
potable directly without treatment (Joyce et al.,
1996). About 50% of all reported cases of illness
and 40% of deaths in Pakistan are due to drinking
of poor water quality (Chhatwal, 1990).
According to a survey conducted by World Health
Organization in 1975, 80% of all diseases in
developing countries are water born (Tebbutt,
2002).
Drinking water quality has been debated
throughout the world, firstly because of increasing
utilization for human needs and secondly because
of the ill effects of the increased industrial
activities (Thurman et al., 1998; Leoni et al.,
2005). Generally direct discharge of domestic
wastes, industrial effluent, leakage from septic
tanks and poor management of farm wastes are
the major sources of water pollution (Huttly,
1999; Jain et al., 2005).
The fresh water sources in Pakistan are
glaciers, rivers and lakes. Pakistan is suffering
from water shortage due to reduced rains, melting
of glacier, poor water storage and management
163

initiatives. Pakistan is often top rated among the
countries that might face the severe water
shortage in near future. Water pollution in the
flowing water is also a big concern. The resultant
health risks include disease in almost all the body
systems such as diarrheal diseases, respiratory
disease, cancers, neurological disorders and
cardiovascular disease etc. (Keusch et al., 2006;
Chandra et al., 2006; Brown et al., 2006; Speizer
et al., 2006; Simoes et al., 2006; Gaziano et al.,
2006). Ground water is often recommended for
drinking as the underground layers such as clay,
sand and rocks act as natural filters and can
minimize the hazardous pollutants (Kjellstrom et
al., 2006). Presently groundwater is the most
abundantly (>70 %) consumed valuable natural
resource for various human activities (Prasad and
Narayana, 2004).
GIS is a set of tools for collecting, analyzing
and managing spatial information. It derives new
information from existing datasets. The
geoprocessing functions collect spatial
information, apply analytical functions and
generate results in form of interactive maps and
tables (ESRI, 2008). The spatial analyst extension
in ArcGIS provides several interpolation tools.
Interpolation is a procedure used to analyze the
values of the collected samples and estimate
values for unsampled locations and is frequently
used to assess the water quality at unknown
locations (Saeed et al., 2012; Kayalvizhi and
Sarkar, 2012; Somvanshi et al., 2012; Raikar and
Sneha, 2012). It is based on the principle of
spatial autocorrelation or spatial dependence,
which measures degree of relationship or
dependence between near and distant objects.
Different interpolation approaches produce
varying results. Inverse Distance Weightage
(IDW) method of interpolation is based on spatial
distance among the sample points. It is used when
the set of points are dense enough to capture the
extent of local surface variation needed for
analysis. IDW determines cell values using a
linear-weighted combination set of sampled
points. The weight assigned is a function of the
distance of an input point from the output cell
location. The greater the distance, the less
influence the cell has on the output value (Childs,
2004).
The aim of this study is to assess the drinking
water quality of Lower Dir using ArcGIS.
Study area
The study area is comprised of Lower Dir (Fig.
1). The main drinking water sources of Lower Dir
are tube wells, dug wells, streams and hand pumps.
The elevation of Lower Dir ranges from 1200m to
2800m above sea level and experience an annual
rain fall of 1468.8 mm and 253.7 mm during
December and March respectively (Khan et al.,
2010). River Panjkora originates from Kohistan,
Upper Dir (Ullah et al., 2014). It flow southward
dividing Upper Dir and Lower Dir into two halves.
River Panjkora joins river Swat at Bosaq Pull.
2. Materials and methods
Water samples were collected using polythene
bottles at 33 locations (Fig. 1). These bottles were
first washed with tape water and were then rinsed
using double deionized water. Samples were
collected in equal numbers (11 samples) from tube
wells, dug wells and hand pumps in the study area.
Water sampling was carried out from March
through May 2013. The conductivity and pH were
determined on the site, whereas the samples were
transported to laboratory of Department of
Environmental Sciences, University of Peshawar
and Water Testing laboratory of Carp hatchery and
training center Sher Abad, Peshawar for further
analysis.
Portable pH meter was used for measuring the
pH of the water samples (Natner, UK). The
conventional methods referred by American
Public Health Association (APHA/AWWA, 1998)
were followed for determining total alkalinity,
total suspended solids (TSS), total dissolved
solids (TDS), total hardness, magnesium hardness,
calcium hardness, chlorides, and sulphate
contents. Portable conductivity meter (Jenway,
England) was used for finding conductivity.
Sulphonilic method using UV Spectrophotometer
(Hitachi-U-2000) was used for measuring Nitrite
contents. For measuring sodium and potassium
contents Flame Photometer (Jenway-FPF-7) was
used. The colour, odour and taste of the water
samples were detected organolaptically.
164

ArcGIS 9.3 software was used for mapping
spatial distribution of groundwater quality based
on parameters selected in this study. Water quality
data as well as location data from Global
positioning system (GPS) was combined into
Microsoft Excel and was then imported into
ArcGIS. All measured water quality parameters
were imperiled to basic statistical analyses
namely, minimum, maximum and mean.
Superficial interpolation by IDW was applied to
estimate the concentration of various physical and
chemical parameters over the whole district. The
surface of different parameters generated from
IDW was classified from low to high.
Mean and standard deviation of the samples
were calculated with Microsoft Excel 2010.
Accuracy assessment was carried out by skipping
different sampled locations for all the parameters
and standard error was find out to evaluate the
variation between measured and interpolated values.
Standard deviation was calculated for the values of
measured and interpolated values using Microsoft
Excel 2010. Standard error was calculated using the
given equation, Eq. 1 (SEx is showing standard error,
S is showing standard deviation while n is showing
the number of observations of sample).
(Eq. 1)
Fig. 1. Map of Lower Dir showing sampling sites and river Panjkora.
165

3. Results and discussion
The results of the observed physico-chemical
parameters are showing variations within a specific
range, some collections sites were having lower
value for the studied parameters while some higher.
The range (min-max), mean and standard deviation
of physico-chemical parameters for tube well, dug
well and hand pump samples along with the WHO
suggested permissible limits are given in Table 1.
The results showed that 87% of collected water
samples were colorless, odourless and tasteless
while 20% water samples were turbid.
The figures given (Fig 2. to Fig. 4.) are
showing the spatial distribution of the studied
parameters over the whole district. Table 3 is
showing Pearson correlation coefficient matrix for
the studied physico-chemical parameters.
The results showed that electrical conductivity
was higher than the permissible limits suggested by
WHO (1997, 2004, 2008 and 2011). The pH of the
study area had no alarming situation and falls
within the permissible limits suggested by world
health organization (WHO, 2004; WHO, 2011).
The mean TDS value fall within the WHO
suggested limits (WHO, 2008). The TSS values for
most of the samples were higher than the
recommended limits may be due to discharge of
untreated wastewater, infiltration and agricultural
runoff. The TSS results showed that tube wells
were having better quality followed by hand
pumps. The TSS mean value for Dug wells was
quite higher than the WHO suggested permissible
limit. It may be due to the low aquifer of these
wells. Maximum samples were falling within
permissible limits for total hardness, calcium and
magnesium hardness, yet some water samples were
having higher magnesium values than WHO
suggested limits. Alkalinity, chloride, sodium and
potassium values for all sampling sites were lying
within permissible limits while Nitrite value was
higher than the WHO suggested limits for sixteen
samples.
Accuracy assessment was carried out by
skipping one point data from interpolation.
Different parameters were then retrieved for that
point from IDW based map. Subsequently the
IDW estimated values is correlated with in situ
measurements, and RMSD/RMSE was derived to
assess the accuracy of the IDW. The results are
given in Table 2. The results for SE showed that
all the parameters are having less variation when
compared for measured and interpolated values
except total hardness (TH = 7.232) and electrical
conductivity (EC=38.528), thus showing accuracy
of the maps for the parameters studied. These two
parameters (TH and EC) are varying because of
their higher values than WHO suggested ones in
most of the riverine areas, as coastal pollution of
sea or river gives rise to certain parameters due to
local contamination of fish or shellfish
(Kjellstrom et al., 2006).
4. Conclusions
Our study showed that the parameters
including pH, total suspended solids, total solids,
electrical conductivity, nitrate, and Magnesium
hardness were deviating from the standard limits.
All the other parameters were within the WHO
suggested permissible limits. The values of the
samples, collected from the areas situated near
river Panjkora, were higher for four parameters
namely electrical conductivity, pH, total solids
and total suspended solids than the suggested
limits across all sampling points. This might be
due to several factors, including 2010 flood,
agricultural runoff, domestic wastes, and
dumping of the waste materials into the river
Panjkora.
Water resources of the areas distant from river
Panjkora were quite safer and there was no
alarming change. On account of more depth,
water of tube wells was safer as compare to hand
pumps and dug wells. While comparing the
sampled sites, no drastic changes were recorded in
the physico-Chemical parameters among the
sampled sites except in electrical conductivity
(Table 3). It is due to the similar prevailing
climatic conditions during the study period (Singh
et al., 2005) and less pollution due to less
industrialization of the study area.
166

Table 1. Description of physico-chemical parameters of water samples of Lower Dir.
Parameters
Tube wells
Dug wells
Hand pumps
WHO *
Min
Max
Mean
S.D
Min
Max
Mean
S.D
Min
Max
Mean
S.D
Electrical conductivity µs/cm
680
2250
1203
655
717
3250
1402
990
710
3170
1176
732
1000*
pH
6.9
7.8
7.3
0.3
7.1
8.5
7.4
0.4
6.7
7.9
7.3
0.4
6.5-9.2
Total solids mg/L
890
975
902.7
37.04
910
1250
1024.1
116.19
880
1040
951.8
52.83
1000
Total dissolved solids mg/L
720
900
830.9
56.78
800
2010
1198.64
425.78
880
960
899.66
23.1
1000
Total suspended solids mg/L
1
15
5.636
4.632
5
35
14.82
11.74
3
20
7.182
5.793
5
Total hardness mg/L
260
425
338.6
63.09
420
615
517.5
77.37
475
590
524.09
44.99
500
Calcium hardness mg/L
150
240
164.5
46.93
210
340
271.4
41.24
250
340
285.91
32.31
250
Magnesium hardness mg/L
75
205
160
47.802
105
280
212.27
54.9
115
220
155.36
37.01
150
Total alkalinity mg/L
190
420
283.2
68.1
205
345
243.2
16.02
200
415
292.73
79.23
500
Chloride mg/L
60
210
151.91
47.13
160
220
182.27
16.02
88
170
114.45
29.15
250
Sulphate mg/L
60
305
194.1
95.5
170
290
234.54
43.44
200
312
246.82
40.82
250
Nitrate mg/L
0.5
1.8
0.827
0.398
0.5
1.8
0.936
0.51
0.5
2.0
0.736
0.524
0.5
Sodium mg/L
120
250
182.73
39.22
100
220
166.54
39.2
180
232
205.73
16.2
200
Potassium mg/L
1.9
21.3
7.718
6.293
3.6
13.7
7.073
3.631
4.6
20.3
10.82
6.493
75
Min = Minimum, Max = Maximum, S.D = Standard deviation, * = * Limits suggested by World Health Organization
167

Table 2. Accuracy and assessment of IDW based maps created for spatial distribution.
S.No
Location
Parameter
M. V
I. V
S. D
S. E
1
Koto
Electrical conductivity
735
1048
221.32
38.528
2
Timergara
pH
7.8
7.525
0.194
0.0338
3
Chakdara
Total solids
895
932.5
26.516
4.616
4
Samar Bagh
Total dissolved solids
870
910
28.284
4.923
5
Talash
Total suspended solids
4
4.25
0.1768
0.031
6
Khall
Total hardness
600
541.25
41.542
7.232
7
Munjai
Calcium hardness
310
290
14.142
2.462
8
Sadu
Magnesium hardness
190
175
10.606
1.846
9
Maidan
Total alkalinity
220
243.75
16.794
2.923
10
Haji Abad
Chloride
180
137.5
30.052
5.231
11
Munda
Sulphate
200
190
7.071
1.231
12
Balambat
Nitrate
2
1.425
0.406
0.071
13
Rabat
Sodium
232
208
16.971
2.954
14
Haya Serai
Potassium
6.3
4.475
1.290
0.225
M.V = Measured value, I.V= Interpolated Value, S.D= Standard Deviation, S.E= Standard Error
5. Recommendations
To conserve and maintain the water quality,
all those anthropogenic activities that cause water
pollution should be controlled. Regular
assessment of water quality and mass awareness
programs regarding the major issue of water
scarcity, water pollution and preserving water
quality should be organized to educated local
masses to cope with the current scenario.
Furthermore installation of water filters and
replacement of old pipes will greatly help to
control waterborne diseases. Treatment of the
water wastes from houses and agricultural run offs
should also be carried out before entering into the
river and other water bodies.
Acknowledgment
The authors convey their gratitude to
anonymous reviewers for their useful suggestions
to improve the manuscript. We thank Mr. Gul
Hassan (Department of Environmental Sciences,
University of Peshawar) and Mr. Wali Jan (ARO,
Carp hatchery and Training Center, Sher Abad
Peshawar) for their help during water quality tests.
168

Table 3. Correlation coefficient matrix of the studied physico-chemical parameters of the water at Lower Dir.
EC
pH
TS
TDS
TSS
T.H
Ca.H
Mg.H
T.A
Cl
2
SO
4
NO
2
Na
K
EC
1
pH
0.797
1
TS
0.692
0.657
1
TDS
0.577
0.483
0.81
1
TSS
0.797
0.578
0.787
0.829
1
TH
0.644
0.283
0.6
0.541
0.512
1
Ca.H
0.247
0.284
0.596
0.441
0.477
0.825
1
Mg.H
0.31
0.35
0.597
0.608
0.697
0.489
0.346
1
T.A
0.318
-0.09
-0.052
-0.11
0.022
-0.06
-0.07
0.002
1
Cl
2
0.016
0.06
0.159
0.344
0.255
0.044
-0.23
0.586
0.083
1
SO
4
-0.096
0.396
0.458
0.347
0.5
0.444
0.59
0.41
0.038
0.025
1
NO
2
0.341
0.627
0.605
0.546
0.74
0.318
0.249
0.574
0.03
0.178
0.476
1
Na
0.723
0.225
0.196
0.197
0.217
0.267
0.196
0.201
0.208
0.033
0.497
0.385
1
K
0.221
-0.48
-0.16
-0.15
-0.29
0.139
-0.09
0.077
0.444
0.261
-0.225
-0.36
0.097
1
Bold r-Values >0.500 are significant at p < 0.05.
169

Fig. 2. IDW based distribution. (a) electrical conductivity (b) pH (c) Total solids (d) Total dissolved
solid (e) Total suspended solids (f) Total hardness.
170

Fig. 3. IDW based distribution. (g) Calcium (h) Magnesium (i) Total alkalinity (j) Chloride (k) Sulphate
(l) Nitrate
171

Fig. 4. IDW based distribution. (m) Sodium (n) Potassium
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