GEOSPATIAL TECHNOLOGY FOR GROUND WATER QUALITY …€¦ · Azawi,A.,AL-Shammaa,A.M., 2016)Geospatial Technology for ground Water quality parameters assessment in Dhi-Qar governorate-Iraq

http://www.iaeme.com/IJCIET/index.asp 358 [email protected]

International Journal of Civil Engineering and Technology (IJCIET)

Volume 9, Issue 1, January 2018, pp. 358–370, Article ID: IJCIET_09_01_035

Available online at http://http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=1

ISSN Print: 0976-6308 and ISSN Online: 0976-6316

© IAEME Publication Scopus Indexed

GEOSPATIAL TECHNOLOGY FOR GROUND

WATER QUALITY PARAMETERS

ASSESSMENT IN DHI-QAR GOVERNORATE-

IRAQ BY USING (GIS)

Kadhim Naief Kadhim

Assistant Professor, College of Engineering,

University of Babylon, IRAQ

ABSTRACT

Groundwater serves as the main sources of water in the urban environment, which

is used for drinking, industrial and domestic purposes and often, it is over exploited.

Now a days, the groundwater is facing threats due to anthropogenic activities. In this

study, groundwater samples were collected in four different seasons from 25 wells

drilling in Dhi-Qar district. The water samples were analyzed for physico-chemical

parameters like TDS, Chloride, sulfates, PH and EC using standard techniques in the

laboratory. Also, geographic information system-based groundwater quality mapping

in the form of visually communicating contour maps was developed using ArcGIS-

version 10.2 software to delineate spatial distribution in physicochemical

characteristics of groundwater samples. the result showed that all samples

concentration for TSS and Sulfates are well over the Standard recommended by WHO

. and Cl is below WHO Besides thematic maps of these mentioned quality parameters

showed a strong prediction and water suitable for using in concrete mixture. From

ground water properties we can note the type and quantity of cement for each one

cubic meter use in foundation of new structures.

Key words: Ground water, Geospatial Technology, Gis, Semivariogram.

Cite this Article: Kadhim Naief Kadhim, Geospatial Technology for Ground Water

Quality Parameters Assessment in Dhi-Qar Governorate-Iraq by using (GIS).

International Journal of Civil Engineering and Technology, 9(1), 2018, pp. 358-370.

http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=1

1. INTRODUCTION

In southern Iraq, semiarid and arid regions with low precipitation and high potential of

evapotranspiration are abundant. Rapid investment growth through last years, increased

irrigation, and industrial development during the past decades have caused an increasing

demand on water resources in semiarid and arid regions. The increased knowledge of

geochemical processes that control groundwater chemical composition could lead to improve

the understanding of hydrochemical systems in such areas. Such understandings like rock-

water interactions, aquifer lithology, and dissolution and residence time of groundwater may

Geospatial Technology for Ground Water Quality Parameters Assessment in Dhi-Qar Governorate

-Iraq by using (GIS)


be helpful to improve the groundwater quality. The hydrochemical of groundwater is an

important factor in determining its use for different patterns such as agriculture, industry,

livestock ranches and household activities. Water quality has an individual pattern of physical

and chemical characteristics, which are determined largely by the climatic, geomorphological

and geochemical conditions prevailing in the drainage basin and the underlying aquifer . (AL-

Azawi,A.,AL-Shammaa,A.M., 2016)Geospatial Technology for ground Water quality

parameters assessment in Dhi-Qar governorate-Iraq using (GIS).

2. STUDY AREA

2.1. Location

The studied area shown in figure(1) is located in Dhi Governorate at the south of Iraq away

from the capital Baghdad at about 350 km south. It has an area of 12900 Km2. It constitutes

approximately 3.1% of Iraq's total area. Its geographical area extends between longitudes (46º

- 47º) Eastwards and latitudes )31

º - 32

º) Northwards .population in 2014 was 2040126.

It is bordered by Smawa Governorate from North, and Basrah Governorate from

South.(https://ar.wikipedia.org/wiki/%D8%B0%D9%8A_%D9%82%D8%A7%D8%B1_(%D

9%85%D8%AD%D8%A7%D9%81%D8%B8%D8%A9)

2.2. Climate

The climate in the forefront with its various natural factors affecting the agricultural

production process is decided by an appropriate area for the cultivation of certain crops or not

. Because the climate conditions of each crop to grow if the available gives the best yield and

vice versa, the impact of climate does not stop at this point, but affects all other factors

surrounding the process of agricultural production such as soil and water resources and factors

of life and also includes the activity of works in agriculture and vitality

Dhi -Qar has a desert climate during the year, there is no actual rainfall - Geiger The

average annual temperature is 23.6 ° C in Dhi -Qar The average precipitation here is 104 ml.

The lowest amount of rainfall is in June. The average for this month is 0 mm in December,

the rainfall reaches its peak with an average of 23 mm. Temperatures are at the highest level

in July at about 34.5 ° C at 10.2 ° C on average, January is the coldest month of the year .The

variation in rainfall between the most drier months and the most rainy months is 23 mm The

annual temperature ranges at about 24.3 ° C (https://ar.climate-data.org/location/936019/,

n.d.)

2.3. Geology of the study Area

Most of the area specified for the province covers sediment dating back to the four-day period

and represents modern deposits classified as follows:

1. Flood plain sediments.

2. Marshlands and depressions (Shallow depression and marsh sediment).

3 sand dunes and continuous Sand Sheet.

4 Alluvial fan deposits

In general, sediment sedimentary sediments are suitable for the manufacture of bricks, as

geological investigations estimated reserves of dust suitable for the manufacture of bricks and

the most important sites of these deposits are:

https://ar.wikipedia.org/wiki/%D8%B0%D9%8A_%D9%82%D8%A7%D8%B1_(%D9%85%D8%AD%D8%A7%D9%81%D8%B8%D8%A9)

https://ar.wikipedia.org/wiki/%D8%B0%D9%8A_%D9%82%D8%A7%D8%B1_(%D9%85%D8%AD%D8%A7%D9%81%D8%B8%D8%A9)

Kadhim Naief Kadhim


1 - the site of Atiyas Nasiriyah (Dhi Qar):

It is located 13 km north of Nasiriyah. The thickness of the mud deposits (2.76) m. The

reserves of the tiles for the brick industry amounted to about (6.39) million m3 and within the

category C1. Note table (1).

2 - Site of the Senate (Dhi Qar): Sediments are located 25 km south-east of Nasiriyah and the

thickness of sediments is 2 m. The calculated reserve in this area is 12.89 million m 3 in C1

(http://www.thiqarinvest.gov.iq/this-dhi-qar.html)

Figure 1 Study Area and Location of Sampling Points

3. MATERIALS AND METHODS

3.1. Field work

Borehole (twenty five) have been by using mechanical machine type Flight Augers drill

method . the method of drilling was carried out according to the standard of the American

society for testing and materials (ASTM D-1452 –D5783) which are used for taking the

samples. The depth of boring were selected by the client to extend to underneath the zone of

influence of significant foundation pressure to materials that were relatively incompressible.

Three types of sample were taken i.e. the first sample were disturbed samples its symbol is

(DS) its were obtained , according to(ASTM D-1586), and as required to determine the

classification of the soil layers .all disturbed samples were sent to the laboratory for further

examination and testing . the second sample take from standard penetration Test (S.P.T); its

symbol is (SS) take from split spoon of standard penetration test carried out in site, were also

used as undisturbed samples and the third samples were undisturbed, its symbol is (US), its

were obtained according to(ASTM D-1587) Disturbed sample take and covered with

polyethylene sacks, whereas samples of SS take from split spoon of standard penetration,

undisturbed samples waxed from top and bottom and transported to laboratory. Standard

penetration test carried out in site .water table measured after 24 hours of boring. (ASTM D-

4750).the some bore log of the soil type of study area are shown in figure(2).




Figure 2 Sub-Soil profile

3.2. Lab work

The ground Water Samples were collected from the bore hole which were distributed all over

20 location of Dhi-Qar. Plastics bottles were used for the collection of water samples and

analyses were carried for the water quality parameters i.e. pH, Sulfate, TDS, Swage

conductivity and Chloride. These tests are very important to know the validity of groundwater

as shown in plate(1).

Kadhim Naief Kadhim


PH Sulfates

TDS EC

Plate 1 Devices test

3.3. Geo-statistical Analysis

GIS is designed to support a range of different kinds of analysis of geographic information:

techniques to examine and explore data from a geographic perspective, to develop and test

models, and to present data in ways that lead to greater insight and understanding .A linkage

between GIS and spatial data analysis is considered to be an important aspect in the

development of GIS into a research tool to explore and analyze spatial relationships (Salah,

2009)

Krigin is an interpolation method used in geostatistics that uses semivariograms to

generate a trend surface map using sampled data. There are seven different types of kriging:

Simple, Ordinary, Universal, Block, Punctual, Indicator, and Cokriging methods. (GIS and

Geocomputation for Water Resource Science and Engineering, p. 490)Kriging involve the

following steps: First step: To check data consistency, removing outliers, statistical

distribution, exploratory data analysis needs to be performed. Kriging methods work best for

normally distributed data . If the data are not normally distributed, they need to be

transformed into normally distributed data using the transformation methods. The most

common transformation type is logarithmic because of its simplicity. The log transformation

is as follows

Y(s) = ln (Z(s)) (1)

For Z(s) > 0Where Z(s) is observed data, Y(s) is transform normal data and in is the

natural logarithm. Second step: Semivariogram is estimated to determine the spatial

correlation or dependence from the observed data. semivariogram is estimated from half the

expected squared difference between paired data values z(x) and z(x + h) to the distance lag h,

by which locations are separated.

γ (ℎ)=

E[ Z(X)-Z(X+h)]

2 (2)

Where Z (Xi) is the value of the variable Z at location of Xi, h is the lag distance, and N

(h) is the number of pairs of sample points separated by h.




γ (ℎ)=

ℎ ∑ [ Z(Xί)-Z(Xί+h)]

2 (3)

For irregular sampling, it is rare for the distance between the sample pairs to be exactly

equal to h. After estimating the semivariogram, the values are fitted through theoretical

models: circular, Gaussian, spherical, exponential. The best fitted model will be used for

further prediction. Ordinary kriging (OK) has been used in the present study for its simplicity

and accuracy. OK uses a probability model where the bias and the error variance can both be

calculated ensuring the average error for the model is close to zero and at the same time

minimize the error variance. Third step: Four theoretical models (circular, Gaussian,

spherical, exponential) were checked for every water quality parameters on the basis of cross

validation test to select the best one. Cross-validation uses all the data to estimate the trend

and autocorrelation models by removing each data location one at a time and predicts the

associated data value .This validation compares the predictive values to the observed values

and obtains useful information about the quality of OK model. Cross validation is performed

automatically in the last step of Arc GIS geostatistical wizard. The values of mean standardize

error (MSE), root mean error (RMSE), average standard error (ASE) and root mean square

standardized error (RMSSE) were the determining factors of selecting best model. Each

kriging techniques provide the kriging variance that estimate the variability of prediction for

known values. The kriging variance must be calculated for each model to avoid the conflict

among errors. MSE is 0 for an accurate model. To assess the prediction errors correctly

RMSE must close to the ASE. RMSSE should close to one. Underestimated predictions have

RMSSE greater than, one; likewise overestimated predictions have RMSSE less that one. The

various errors are defined by the equations (4)-(7):

MSE =

∑ [ Z(Xί)-Z(Xί)]/Ỡ

2 )Xί( (4)

RMSE = √

∑

(5)

ASE = =√

∑

(6)

RMSSE = √

∑

(7)

Where Ỡ2 )Xί) is the Kriging Variance for location Xi.Finally the thematic maps of each

groundwater quality parameters were generated using ordinary kriging. (Munna, 2015)

4. RESULT AND DISCUSSION

4.1. PH

PH was classified in to three ranges (8-8.025), (8.025-8.05) and (8.05- 8.075) . The spatial

variation map for ph was prepared based on these ranges and presented in Fig.(3).From the

spatial variation. The high range of ph value (8.025-8.05 was found to be widely distributed in

the middle of study area the t, small range of ph value (8-8-025) was scattered in east of the

study area . PH fluctuated between(8-8.075) as shown table(2) that made it acceptable for

drinking and(Guidelines for drinking-water quality, 2008) and many uses

4.2. TDS

TDS fluctuated between(728-1091) as shown table(2).The total concentration of dissolved

minerals in water is a general indication of the overall suitability of water for many types of

Kadhim Naief Kadhim


uses. The Total Dissolved Solids (TDS) was classified in to four ranges (1075-1295)mg/l,

(1295-1385) mg/l, (1385- 1606)mg/l and >2000 mg/l). The spatial variation map for TDS was

prepared based on these ranges and presented in Fig.(4).From the spatial variation. The high

range of TDS value (1385-1606)mg/l was found to be widely distributed throughout the

district, small range of TDS value (1075-1295 mg/l) was scattered throughout the district

covering the study area . If the Water with more than 1000mg/L of dissolved solids usually

gives disagreeable taste or makes the water unsuitable in other respects.

4.3. Sulfates

Sulfates classified in to four ranges (728-977.44)mg/ l,(977.44-1057.27)mg/l ,(1057.27-

1082.82)mg/l and(1082.82-1091)mg/l. The spatial variation map for sulfates prepared based

on these ranges and presented in Fig.(5).From the spatial variation. The high range of ph

value (1075-1295)mg/l was found to be widely distributed in the south ,the east and the west

of study area the,. sulfates fluctuated between(1075-1295) as shown table(2) that made it un

acceptable for drinking and(Guidelines for drinking-water quality, 2008). The range of

sulfates less than (1000mg/l) was found to be distributed throughout the district, it covered

about 32% of the total study area that mean ground water acceptable as using in concrete

mixture with (32%) and not acceptable with 68%.(The National Center for Construction

Laboratories (NCCL), (IQS1992/1703), 2005)

4.4. Chloride

Chloride is one of the most important parameter in assessing the water quality and higher

concentration of chloride indicates higher degree of organic pollution. Chloride classified in

to four ranges(51-55.167) mg/l,(55.167-57.455) mg/l ,(57.455-58.711) and(58.711-61) The

spatial variation map for chloride prepared based on these ranges and presented in Fig.(6). ,.

chloride fluctuated between(51-61) mg/l as shown table(2) .the ranges within permissible

limit of drinking water i (Guidelines for drinking-water quality, 2008) and Iraqi Stander

specification and it suitable for using in concrete mixture because it less than 500 mg (The

National Center for Construction Laboratories (NCCL), (IQS1992/1703), 2005) and water

irrigation cause weak damages to moderate damages for resistant plants (hell ،2008)

4.5. Electrical Conductivity (EC)

EC classified into four ranges (1680-2025.11) (µs/cm) ,(2025.11-2165.60) (µs/cm),(2165.60-

2610.62) and (2610.62-3395) (µs/cm)The spatial variation map for chloride prepared based on

these ranges and presented in Fig.(7). EC fluctuated between(1680-3395) (µs/cm) as shown

table(2) .the ranges indicates that ground water unsuitable for many uses (Guidelines for

drinking-wate).

From ground water properties with specifications of water use for concrete mixing we can

note the type and quantity of cement for each one cubic meter use in foundation of new

structures.




Table 1 Ground water Parameter

ID TSS(mg/l) PH CL(mg/l) silfates(mg/l)

EC

(µs/cm) X Y

W1 1319 8 55 1085 2061.000 605762 3509373

W2 1319 8 55 1085 2061.000 605759 3509373

W3 1321 8 58 1087 2064.000 605756 3509335

W4 1315 8 51 1081 2055.000 605757 3509355

W5 1324 8.1 59 1082 2069.000 605757 3509391

W6 1317 8 56 1091 2058.000 605756 3509396

W7 1324 8.1 59 1082 2069.000 605759 3509395

W8 1711 8 56 754 2673.000 664615.99 3427824

W9 1738 8.1 60 791 2716.000 664640.85 3427814

W10 1611 8.1 57 819 2517.000 611760.11 3475460

W11 1580 8.1 54 757 2469.000 611653.54 3475255

W12 1527 8 53 728 2386.000 611665.66 3475152

W13 1189 8.1 56 1067 1858.000 629933.59 3441951

W14 1204 8 59 1080 1881.000 630185.11 3441955

W15 1201 8 58 1038 1877.000 617979.16 3433854

W16 1231 8.1 59 1050 1923.000 617988.1 3433850

W17 1265 8.1 58 983 1977.000 601511 3525283

W18 2150 8 57 1007 3359.000 618974 3438274

W19 2138 8.1 58 1012 3341.000 618974 3438274

W20 1091 8.1 58 983 1705.000 629934 3441952

W21 1075 8 55 961 1680.000 629655 3441948

W22 1412 8 56 1027 2206.000 620850 3435281

W23 1537 8 61 1089 2402.000 620856 3435267

W24 1402 8 55 1015 2191.000 620885 3435206

W25 1451 8.1 59 1063 2268.000 620912 3435210

Table 2 Information of dataset with standard

Param

eter N

Mi

n

M

AX

mea

n

Stander

deviation

Skewn

ees

Kurt

osis

1-st

Qurtile

Medi

an

3-rd

Qurtile

W

HO

Ph

2

5 8 8.1

8.04

4 0.051 0.242 1.058 8 8 8.1 6.5-

8.5

ph*

2.08

5 0.006 0.242 1.058 2.08 2.08 2.092

TSS

2

5

10

75

215

0

143

0.1 275.8 1.3127

4.331

6 1256.5 1324 1547.8

100

0

Sulfate

s

2

5

72

8

109

1

992.

68 120.58

-

1.2267

2.982

7 977.5 1038 1082 250

sulfates

*

6.89

25 0.13253

-

1.3157

3.175

2 6.885

6.945

1 6.9866 250

CL

2

5 51 61

56.8

8 2.3331

-

0.5355

2

2.980

8 55 57 59 250

EC

2

5

16

80

339

5

223

4.6 430.88 1.3126

4.335

8 1963.5 2069 2418.8

100

0

Kadhim Naief Kadhim


Table 3 Characteristics of semivariogram

Figure 3 Spatial distribution map of PH Figure 4 Spatial distribution map of TDS

Figure 5 Spatial distribution map of Sulfates Figure 6 Spatial distribution map of CL

Ground water

parameter

Best fitted

model

Nugge

t (c.)

Sill(C

.+C)

Lag

size

ran

ge

(c./c.+c)*

100%

RM

S

MS

E

RM

SS

AS

E

PH

exponentia

l 0.0025

0.003

3

21.4

89

257

.85 75.758

0.05

95

-

0.06

1

1.0

07

0.0

59

TSS Spherical 757.28

3033.

18

39.6

5

475

.77 24.97

102.

97 0.13

2.3

6

42.

5

Sulfates Gaussian 331.57

41077

.57

237.

863

201

9.8 0.81

36.6

3

-

0.00

08

1.2

4

63.

6

CL Spherical 1.066

13.18

6

4.71

8

46.

281 8.084

2.64

2

-

0.01

6

0.8

83

3.1

71

EC Gaussian 1856.1

7970.

5

38.4

52

308

.19 23.287

158.

329

0.14

86

2.2

884

66.

648




Figure 7 Spatial distribution map of EC

5. SEMIVARIOGRAM PARAMETERS

Semivariogram the following parameters are often used to describe variograms (figure 8):

nugget : The height of the jump of the semivariogram at the discontinuity at the origin.

sill : Limit of the variogram tending to infinity lag distances.

range : The distance in which the difference of the variogram from the sill becomes negligible.

In models with a fixed sill, it is the distance at which this is first reached; for models with an

asymptotic sill, it is conventionally taken to be the distance when the semivariance first

reaches 95% of the sill.

Figure 8 Parameters of semivariance

From figure 9 we conclude in figure 10. blue line indicates the theoretical model that has

been selected for prediction as most of the semivariance values are close to that line as shown

for each quality parameters. Table 3 shows the characteristics parameters i.e. sill, nugget,

range, lag size and prediction error for the reliance of selected models. The best fitted model

for of pH,TSS , Sulfate, Chloride and EC were Exponential, Spherical, Gaussian, spherical

and , Gaussian respectively. Table3 shows TSS , Sulfate, Chloride and EC that have strong

spatial dependence as percentage of the ratio of nugget variance to sill is less than 25%. ph

has weak spatial dependence having percentage of the ratio of nugget variance to sill is 75.6 .

the Mean Standardize Error for pH,TSS , Sulfate, Chloride were,-0.0610, 0.1300, -0.0008, -

0.0160 representing good prediction model. Through the results of the tests we can know the

type of cement used and quantity per cubic meter in each of the study areas

Kadhim Naief Kadhim


Qqplote of ph log transformed Qqplote of ph

Qqplote of Tss Qqplote of EC

QQplote of sulfates log transformed Qqplote of sulfates

QQplote of CL

Figure 9 Qqplote of ground water parameters




Ph-Exponential TSS-Spherical

Sulfates-Gaussian CL- Spherical

EC- Gaussian

Figure 10 Best fitted of ground water parameters

6. CONCLUSIONS

The spatial distribution analysis of groundwater quality was done in Dhi-Qar Corporation area

with GIS geostatistical techniques. As sampling from every possible location is not

economical, the interpolation technique (ordinary Kriging) played a vital role to predict the

values from unmeasured location. Lab analysis of water quality parameters (table 2) showed

that 100% of the samples concentration for TSS and Sulfates are well over the Standard

recommended by WHO . and Cl is below WHO Besides thematic maps of these mentioned

quality parameters showed a strong prediction results. ground water not suitable for drinking

and very suitable for using in concrete mixture . From ground water properties we can note

the type and quantity of cement for each one cubic meter use in foundation of new structures.

Kadhim Naief Kadhim


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[1] D.,Uddameri .V., Dixon ،GIS and Geocomputation for Water Resource Science and

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[3] Guidelines for drinking-water quality. (2008). Geneva.

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[5] http://www.thiqarinvest.gov.iq/this-dhi-qar.html. (n.d.).

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85%D8%AD%D8%A7%D9%81%D8%B8%D8%A9). (n.d.).

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Documents

GEOSPATIAL TECHNOLOGY FOR GROUND WATER QUALITY …€¦ · Azawi,A.,AL-Shammaa,A.M., 2016)Geospatial Technology for ground Water quality parameters assessment in Dhi-Qar governorate-Iraq