A Methodology Based on Advanced Modeling Techniques for Groundwater Monitoring and Management-Part A

1. [INTERNATIONAL JOURNAL FOR RESEARCH &
DEVELOPMENT IN TECHNOLOGY]
Volume-3,Issue-1, Jan 2015
ISSN (O) :- 2349-3585
www.ijrdt.org | copyright © 2014, All Rights Reserved. 6
A Methodology Based on Advanced Modeling
Techniques for Groundwater Monitoring and
Management-Part A
Sameh S. Ahmed1
, Yousef H. Okour2
and Eyad Haj. Said3
1
Mining and Metallurgical Engineering Department, Assiut University, Assiut, Egypt,
In secondment: Civil and Environmental Engineering Department, Majmaah University, KSA
2
Civil and Environmental Engineering Department, Majmaah University, KSA
3
Information Technology Department; Majmaah University, KSA
Abstract— Groundwater is considered as one of the most
important water resources in the Kingdom of Saudi Arabia.
(In order to monitor, manage and protect groundwater
resources, this research suggests a methodology that
implements new advanced modeling techniques in terms of
monitoring, analysis, prediction and treatment. The current
monitoring process and data sampling in most of the
groundwater wells are arbitrary. Therefore, this imposed a
challenge of a frequently monitoring on a periodic time-base
to obtain the optimum sampling frequency for groundwater
quality parameters. Then, analyzing those parameters was
proposed using Geostatistics techniques to generate a
reliable 3D prediction model developed based on measured
field groundwater parameters with the assistance of Global
Positioning System and Geographic Information System -
techniques. The model was used to build a dynamic system
that capable to reveal the changes in any parameter in the
form of contour maps and related attributes. After that, to
predict the probable changes in the groundwater properties,
the leakage of toxic-trace elements and heavy metals due to
the expected industrial and human activities in Sudiar
Industrial and Business City, data fusion techniques were
employed to develop virtual instrument to predict and
monitor groundwater variations. Finally, a novel
nanotechnology treatment with a photocatalyst of Titania
nanoparticles was suggested to treat the groundwater from
the toxic-trace elements and heavy metals.
Index Terms— Groundwater, Monitoring Program,
Geostatistics Techniques, Data Fusion, Nanotechnology
Treatment.
INTRODUCTION
In the Kingdom of Saudi Arabia (KSA), the demand of
using Groundwater (GW) resources is significantly increases
due to the highly usage in domestic, industry and agricultural
fields as a result of the incredible progress achieved in all
aspects of life as well as the population growth [1].
In order to protect the national fortune of GW, this research
suggests advanced methods to overcome the hurdles that face
the management process of GW properties in terms of
monitoring, analysis, prediction and treatment. The region of
the research interest is the northern Riyadh region which
contains main cities such as Majmaah, Al-zulfi, Al-Ghat, and
Sudair Industrial and Business City.
GW properties are supposed to be monitored on regular
intervals to understand the variations in their properties due to
GW well depletion or intrusion of any source of contamination.
The current monitoring process and data sampling in most of
the GW wells are carried out without scientific bases of
frequency procedure. Therefore, the GW wells in the
mentioned locations are proposed to be frequently monitored
on periodic time-bases (using high frequency rate) and
compared with the standard water quality parameters. This is
imposed a challenge of proposing a reliable and accurate
procedure to find out the optimum sampling frequency of GW
properties to save time spending and labor expenditure [2].
On the other hand, Multivariate statistics methods;
Principal Components Analysis (PCA) and Factor Analysis
(FA) have an advantage of using a sufficient numbers of
measured GW parameters in the field and correlate them with
the measured GW parameters in the laboratory. So, a reliable
prediction model could be created to determine the most
significant GW parameters and interpret the physically
interrelation between them within the same geographical area
[3].
It should be mention that, the drawback of a point sampling
is that the data are collected from a certain observed GW wells,
so the data do not provide detailed information on the same
geographical region. Thus, in this research, 3D
characterizations of GW parameters using Geostatistics
techniques are proposed to provide a detailed data of GW
parameters within the same geographical region to reveal the
characterization of the GW parameters in terms of contour
maps and related attributes [4 and 5].

2. Volume-3,Issue-1, Jan 2015
ISSN (O) :- 2349-3585
Paper Title:- A Methodology Based on Advanced Modeling Techniques
for Groundwater Monitoring and Management-Part A
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Furthermore, in the northern Riyadh region, a prospective
Sudiar Industrial and Business City, established in 2008 with
total area of 265 km2
, will contain a variety of light, heavy
industrial and business activities (6). Therefore, the leakage of
contaminants, heavy metals and polluted discharged water to
the GW wells is highly expected and will be a major concern
that needs to be treated. Therefore, algorithms based on data
fusion techniques can be developed to support GW monitoring
by estimating the levels of sensitive trace elements in the GW
and predict any future variations in the GW parameters [7 and
8]. As a result of the expected leaking of traces of toxic
contaminants and heavy metals in the GW wells, a new
nanotechnology treatment technique with Titania
photocatalysis is proposed to be applied to degrade most of the
contaminants found in the GW. Titania photo-catalysis
nanoparticles was found to be tremendously effective in
removing contaminants in air, water and soil using UV-light or
natural solar light [9].
Consequently , the main objectives of this research are to
develop a monitoring program that implements advanced
methods in the management process of GW properties in terms
of a frequently monitoring of GW wells to obtain the optimum
sampling frequency for different GW parameters; then,
observing and analyzing the changes in the GW parameters in
the form of contour maps and related attributes; later on,
prediction and monitoring of the probable changes in the GW
properties and the possible leaking of toxic-trace elements
using a virtual instrument; finally, treating GW wells from all
the contaminants using a novel nanotechnology treatment.
LITERATURE REVIEW
The importance of integrated GW resources management is
essential to balance human water needs with environmental
preservation [10]. The GW resources are exposed to a variety
of contaminants, arising from the utilization and disposal of
inorganic and organic compounds. The availability of GW is
on the decline and water demand is on the rise for industrial,
agricultural and human consumption [11]. The complexity of
GW quality as a subject is reflected to the GW parameters
measurements. The most accurate measurements of GW
parameters are made on-site, because water exists in
equilibrium with its surroundings. Measurements commonly
made on-site and in direct contact with the water source
include temperature, pH, dissolved oxygen, conductivity and
turbidity [12].
While unreliable sampling and analyzing of GW
parameters may lead to problems of pollution, qualified
technicians and certified laboratories are needed to perform the
process of sampling and analysis. In addition, qualitative and
quantitative measurements should periodically be performed
for monitoring the quality of the GW sources. Depending on
the actual state of the region infrastructure and environmental
conditions, monitoring should be carried out according to a
specific program for each source of GW supply [13].
The Kingdom of Saudi Arabia is known as one of the most
water scarce countries in the world. The main water resources
in Kingdom of Saudi Arabia are: renewable water resources,
non-renewable groundwater resources and desalinated
seawater (Table (1)). While a summary of water use in KSA
is shown in Table (2).
Table (1): Available water resources in Saudi Arabia in 2003-
2004 (MCM) [14]
Surface Water 5000-8000 (2,239 available for use)
Groundwater
Resources
2,269,000 (84,400 renewable
groundwater in shallow aquifers)
Groundwater
Recharge
3,985 (1,96 to shallow aquifers in the
Arabian Shelf)
Desalination 1,050
Treated Wastewater 240
Table (2): Water use in Saudi Arabia, MCM/yr. (Source:
Country Report 1) [15]
Total% of
total
Agriculture% of
Total
Domestic &
Industry
Year
235278.7185021.35021980
2723993.94255896.0616501990
1846988.831640611.1720631997
2074089391854010.6122001999
2027086.481753013.5227402004
1826082.641509017.3631702009
Sampling frequency mainly depends on the purpose of the
sampling and the depth of the aquifer of the GW wells. It has
been reported in literature review that in large production wells
that pump water from more than 100 meter deep aquifer,
sampling every year are sufficient due to changes of GW
quality are gradual, while, shallow wells with low pumping
rates should be sampled twice a year because they are more
affected to short-term variation of the GW quality and
contamination. If, for example, iron content was identified,
frequent sampling may be necessary regardless of well depth.
Shallow monitoring wells that are installed to monitor a
potential pollution source may be sampled monthly, quarterly
or semi-annually. Some monitored GW parameters may
require more frequent monitoring samples [16].
Multivariate statistics methods using principle component
method (PCA) and factor analysis (FA) can develop a reliable
prediction model to determine the most significant GW

3. Volume-3,Issue-1, Jan 2015
ISSN (O) :- 2349-3585
Paper Title:- A Methodology Based on Advanced Modeling Techniques
for Groundwater Monitoring and Management-Part A
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parameters and interpret the physically interrelation between
them within the same geographical area. The disadvantage of a
point sampling is that the data are collected from known GW
wells, so the data do not cover the entire geographical region.
Therefore, some researchers suggest using GPS and GIS
techniques to determine the location of the GW wells based on
3D dimensions and identify and map the GW properties in
terms of contour maps and related attributes [3, 4].
A virtual instrument can be developed to determine the
levels of sensitive GW parameters, particularly those with low
range values such as Antimony, Beryllium, Cadmium, and
Mercury, from major quality parameters by employing sensor
fusion algorithms. An approach was presented based on
Bayesian networks to implement target virtual sensor from
multiple source sensors [17]. The concept of sensor fusion is
to integrate and fuse data from multiple sensors or sources to
overcome the problems of imprecision, uncertainty, and noise
in order to get reliable results. The proposed approach will be
based on the neural networks and Bayesian networks to
compute and estimate the target parameters [18, and 19].
Some researchers used innovative smart sensor device to
predict some sensitive elements in GW wells and monitored
GW qualities The proposed system employed spectroscopic
techniques in combination with the measurements of physio-
chemical parameters . Chemical Oxygen Demand (COD) in
GW sample was estimated and compared with the traditional
methods [20]. Therefore, adequate and suitable treatment must
be applied to the wells having elevated concentrations of the
metals and supplying drinking water to the consumers.
Trace elements were reported in drinking water of different
GW sources and notified that concentration levels of trace
elements represented a potential health risks to man and require
a great attention [21, 22, 23, 24, 25 and 26]. Trace
concentrations of cadmium and zinc [26] and lead and
chromium [22] were detected in potable water of the Eastern
Province of the KSA.
Detailed study was performed for the assessment of trace
metals in GW resources used for drinking purposes in Riyadh
region, KSA [25]. Samples were collected from 200 GW wells
to the citizens. All GW samples were analyzed for 17 traces
using Inductively Coupled Plasma (ICP) spectrophotometer.
The results pointed out the presence of heavy metals of Fe, Mn,
Al, Se, and Hg in all sampled GW wells. Fe concentrations
exceeded the maximum contaminant level up to 46.5%, and
Mn, Al, Ba and Hg concentrations were also above maximum
contaminant level up to 0.5-19.5%.
To solve the problem of trace toxic elements and heavy
metals that found in the GW resources, Titania photoctalysis
nanoparticles were suggested as a new nanotechnology
treatment [26]. Titania nanoparticles are the most widely used
metal oxide for environmental applications, cosmetics, paints,
electronic paper and solar [27]. As a photocatalyst, Titania
nanoparticles have been used for environmental remediation
purposes such as in the purification of water, air and soil.
Titania nanoparticles used solar light or UV light to completely
degrade al the toxic contaminants without any toxic by-
products [28].
RESEARCH METHODOLOGY
 Sufficient GW samples (historical data) were collected
from different GW wells in the northern Riyadh region
which contain main cities such as Majmaah, Al-Zulfi, Al-
Ghat, and Sudair Industrial and Business City.
 GW sampling with high frequency rate will be employed
for GW data and determination of the optimum sampling
frequency was followed based on Power Spectrum Density
Theory.
 First set of the collected samples were analyzed in terms
physical and chemical properties using the standard
methods in Majmaah Water Treatment Plant Laboratory,
and leasing or sending to other expertise laboratories for
double check.
 An electrical photo-sensor is being built on or nearby some
selected GW wells to measure the GW properties in the
field.
 Multivariate statistics methods are proposed to correlate
between the GW properties measured in the field and the
laboratory.
 GW well locations (X, Y, and Z coordinates) have been
identified using Global Positioning System (GPS) and
integrated with the Geographic Information System (GIS)
and the measured GW properties for estimation and
mapping, taking into consideration that GPS was used
whenever the sample location is not defined.
 Then, virtual instrument will be developed on the basis of
data fusion techniques in order to estimate sensitive GW
parameters (target parameters) in the field given some of
the main parameters (source parameters) and accurately
expecting any future variation in the GW parameters.
 In the last stage, a new nanotechnology treatment using
Titania photo-catalysis will be used to eliminate any traces
of toxic contaminants and heavy metals found in GW
resulting from any expected leaking of contaminants and
heavy metals.
Fig. (1) Illustrates the research methodology.

4. Volume-3,Issue-1, Jan 2015
ISSN (O) :- 2349-3585
Paper Title:- A Methodology Based on Advanced Modeling Techniques
for Groundwater Monitoring and Management-Part A
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Figure (1): illustrates the adopted procedure to perform
the research methodology.
EXPERIMENTAL ANALYSIS
GW samples were both analyzed on-site and in the laboratory
as follows:
 GW samples on-site were analyzed using portable
instruments to measure pH, turbidity, color, taste and
TSS (Total suspended solids).
 While, the laboratory analysis was performed to
measure turbidity, pH, conductivity, heavy metals and
mineral salts of GW using turbid meter (2100 N HACH,
USA), pH meter (HACH, USA) thermo-orian EC meter
(Model 150A, USA), inductively coupled plasma-mass
spectrometry (ICP-MS, USA) and inductively coupled
plasma-optical emission spectrometry, (ICP-OES,
USA), respectively. Total dissolved salts of GW were
calculated by multiplying the conductivity value by a
factor of 680. Orthophosphate and alkalinity were
analyzed according to APHA 4500 and APHA 2510
methods, respectively.
 Then, SPSS software is being used to extract PCA,
and/or FA of the collected data at each region to analyze
and characterize the GW parameters. GPS and GIS
techniques were used to determine the location of the
GW wells based on 3D dimensions and introduce the
GW parameters in the form of contour maps.
 Neural and Bayesian networks techniques will be
employed to develop virtual instruments based on the
collected samples to compute and predict GW
parameters.
 Titania nanoparticles photocatalysis experiments will be
performed under solar light to degrade the measured
toxic and heavy metals found in GW to non-toxic
simple components.
PRELIMINARY RESULTS
Management of GW monitoring is a complicated process
that needs to control several tasks at the same time including
defining a fast, accurate and reliable data collection from
different GW resources.
The most important and critical part is the data collection
with expected obstacles from local and private GW wells that
located in farms and rural areas. As well as investigators should
have a well-designed plan to work simultaneously and in
parallel at different stages of the project life.
Results implementation is subjected to the final
achievements, however, and the implementation mechanism
depends on:
 Accurate data collection and success in building the GIS
system and defining the optimum sampling frequency for
different GW parameters.
 While the principles of implementation remain constant, it
is expected that the final results will be differed from one
district to another.
As the project is in its first stage, this paper highlights the
preliminary results, while final results will be published in
part B.
Data Collection:
The samples of GW at the study areas were gathered for
laboratory analyses from one site, while others sampling points
are in process. It is proposed to collect groundwater samples
from over than 120 wells from Majmaah city, Al-zulfi, Al-
Ghat, and Sudair Industrial and Business City.
In the first part of the study areas, samples were collected
from 15 groundwater wells at a farms area near Majmaah city.
Part of the raw measured data is summarized in Table (3).
However, samples were analyzed for other GW parameters
including: Pb, Fe, F, Cn, Cu, Color, Cr, Cl, Cd, Ba, Al, DO,
Ca, Mg, etc.
Table (3): Raw data of water quality parameters collected
from one study area
Wel
l
p
H
EC
(µs/cm
)
TDS
(mg/l)
S
(mg/l
)
Ag
(mg/l
)
NO3
(mg/l
)
Hg
(mg/l
)
1 8.
4
383.10 245.18
4
3 0.03 0.01
9
0.4
2 8.
1
341.80 218.75
2
2 0.03 0.01
7
0.4
3 8.
0
334.20 213.88
8
3 0.03 0.01
9
0.4
4 7.
9
333.60 213.50
4
3 0.03 0.01
9
0.4

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5 7.
9
336.80 215.55
2
2 0.02 0.01
6
0.3
6 8.
2
383.30 245.31
2
2 0.02 0.01
5
0.3
7 8.
3
383.80 245.63
2
2 0.02 0.01
5
0.3
8 8.
2
385.10 246.46
4
2 0.03 0.01
8
0.4
9 8.
3
383.50 245.44
0
3 0.03 0.02
3
0.4
10 8.
3
383.30 245.31
2
3 0.03 0.01
9
0.4
11 8.
3
385.70 246.84
8
3 0.04 0.02
5
0.5
12 8.
1
354.00 226.56
0
3 0.03 0.01
9
0.4
13 8.
1
349.10 223.42
4
3 0.03 0.02 0.4
14 8.
1
351.60 225.02
4
2 0.02 0.01
4
0.3
15 8.
1
356.30 228.03
2
2 0.02 0.01
3
0.3
The measured parameters were compared with the local
and international standards. Table (4) shows part of this
comparison.
A portable GPS instrument was used to determine the X
and Y coordinates of the tested wells. Table (5) summarizes
some of these data.
Table (4): Examples of obtained results in comparison with
standards
Variable
s
Average
Values
SAS
Standard
s
EPA
Standards
WHO
Standards
pH 8.153 6.5-8.5 6.5 – 8.5 6.5- 8.5
Zn
0.072
mg/l
5000
µg/l
5 mg/l 3 mg/l
TDS
232.3
mg/l
1500
mg/l
500 mg/l
1000
mg/l
S 2.53 mg/l 400 mg/l 250 mg/l 400 mg/l
Ag
0.027
mg/l
- 0.1 mg/l 0.1 mg/l
NO3
0.018
mg/l
- 1.0 mg/l
Hg
0.373
µg/l
1 µg/l
0.002
mg/l
0.001
mg/l
Pb
10.67 µg
/l
10 µg/l
0.015
mg/l
0.01 mg/l
Fe
0.130
mg/l
1.0 mg/l 0.3 mg/l 0.3 mg/l
Cn
0.008
mg/l
70 µg/l 0.2 mg/l 0.1 mg/l
Cu
0.200
mg/l
1000
µg/l
1-1.13
mg/l
2.0 mg/l
Table (5): X, Y Coordinates of the tested wells at the first
area.
Well N E X, m Y, m
1 21.375' 464314 2863571
2 459099 2855056
3 459132 2855018
4 459722 2854439
5 456916 2855370
6 457292 2854867
7 48.836' 456647 2855150
8 456649 2855999
9 456917 2855612
10 456455 2855791
11 455781 2854758
12 455607 2854649
13 455606 2854295
14 455031 2854162
15 454377 2853485
The block diagram of the virtual instrument model is shown in
Fig (2). The proposed model consists of two stages, off line
stage and online stage. The database of the collected data will
be divided into training data set and testing data set. In the off
line stage, Bayesian networks and neural network we be
employed to build virtual instrument model by training it using
the training data set. However, several steps of data
preprocessing, normalization, and feature extraction should be
used in this stage. In the online stage, the performance of this
model will be tested using testing data set.
Modified Titania nanoparticles for conducting the treatment
are being prepared, as some samples were subjected for
preparation as shown in Fig. (3).
CONCLUSIONS
The proposed project targets full control over GW
monitoring program by:
1- Management the sampling procedure for GW by
defining the optimum sampling frequency and
revealing the correlation between different GW
quality parameters.
2- Developing an accurate system for 3D
characterization of GW parameters at northern
Riyadh.
3- Producing a dynamic system that can be used to
understand and monitor the changes in the GW
parameters.

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Paper Title:- A Methodology Based on Advanced Modeling Techniques
for Groundwater Monitoring and Management-Part A
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Figure (2): Block diagram of proposed virtual instrument model
Figure (3) shows the steps of using flocculation process with
groundwater to obtain treated groundwater and useful
byproduct of Titania.
1- Providing a modern method for contamination
treatment of trace elements using virtual
instrument. Such management system can be
utilized and easily implemented elsewhere.
2- Nanotechnology treatment of the leaking of trace
elements and heavy metals to the GW wells by
using Titania nanoparticles and the sunlight.
It is expected that the integration between the GW data and
GIS will provide a good assist for the decision maker and
the monitoring program authorities in the region for better
management of the environmental studies at the region.
Having the aims achieved, not only a full characterization of
the northern Riyadh region GWs will be established, but
also a dynamic system that can be updated and used for life-
time will be available It won’t be difficult to carry out
similar system at any region in KSA and even other
countries.
Not to mention, the innovative virtual instrument and the
new implementation nanotechnology achievements to
convert the toxic-elements to harmless materials by Titania
nanoparticles can be utilized in a like cases.
ACKNOWLEDGMENT
The authors are deeply acknowledges Eng. Yahya Al
Jahmany and Majmaah's students for GIS work and data
gathering.
REFERENCES
C. M. Cooney, “Drinking-water analysis turns up even more
toxic compounds”, Environmental Science and
Technology 42, 8175, 2008.
S. S. Ahmed, M.Z. Rashad, and M.R. El Tahlawi,
“Monitoring the Changes in the Water Quality Parameters
Using Geostatistics Techniques”, The International
Workshop on Civil Engineering and Urban Planning
(WCEUP 2011), July 26-28, 2011 in Hangzhou, China.
S. S. Ahmed and M. Hashem, ”Groundwater Evaluation in Al-
Gofra Oasis, Middle Libya Using Statistical Methods”,
Journal of Engineering Sciences (JES), Assiut University,
Assiut, Egypt, Vol. 34, No. 5, pp. 1363-1375, September,
2006.
S. S Ahmed and S Durucan, “Determine of the Optimum
Sampling Frequency for Groundwater Quality
Parameters”, Journal of Engineering Sciences (JES),
Assiut University, Assiut, Egypt, Vol. 35, No. 6, pp.
1541-1558, 2007.
T. Harter, “Groundwater sampling and monitoring”,
University of California, Division of agricultural and
natural resources, ANR publication 8085, 2003.
[6].http://www.modon.gov.sa/en/IndustrialCities/IndustrialCiti
esDirectory/IndustrialCities/Pages/Sudair.aspx.
M. Correa, M., Bielza, C., Pamies-T J.eixeira, J.,
“Comparison of Bayesian networks and artificial neural
networks for quality detection in a machining process,
Expert Systems with Applications”, Volume 36, Issue 3,
Part 2, April 2009, Pages 7270–7279.
P. Reed, Beard, D., Day, H., “Development of an Automated
Groundwater Monitoring System Using a Wireless Based
Sensor Array Deployed in a Groundwater Monitoring
Well”, International Conference on Health, Safety and
Environment in Oil and Gas Exploration and Production,
11-13 September, Perth, Australia, 2012.
Y. Okour, El Saliby, I., Shon, H.K., Vigneswaran, S., Kim, J-
H , Cho, J and Kim, I S , “Recovery of sludge produced
from Ti-salt flocculation as pretreatment to seawater
reverse osmosis”, Desalination, 249, 53-63, 2009.
J. M. Balbus, Lang ME , “Is the water safe for my baby?”,
Pediatr Clin North Am. vol: 48(5):1129–52, 2001.
BARC Report, “Desalination and water purification
technologies”, DAE, Govt. of India, 2010.

7. Volume-3,Issue-1, Jan 2015
ISSN (O) :- 2349-3585
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D. Johnston, “EPA Guidelines: Regulatory monitoring and
testing -Groundwater sampling”, P, 2007.
L. Yuncong and Kati Migliaccio (Editors), “Water Quality,
Concepts, Sampling, and Analyses”, 1st
edition, Taylor &
Francis Group. LLC, 2011.
M. Al-Saud et.al., “Challenges for an Integrated Groundwater
Management in the Kingdom of Saudi Arabia”
International Journal of Water Resources and Arid
Environments 1(1): 65-70, 2011.
FAO, “Groundwater Management in Saudi Arabia”, A Report
by FAO, 14P, 2009.
J. G. Loftis, and Ward, R. C., “Water quality monitoring –
some practical sampling frequency considerations”,
Environmental Management, Springer New York, Vol. 4,
Number 6/ November 1980, pp. 521-526.
E. H. Said, Abdollah Homaifar, and Michael Grossberg,
“Creating Virtual Sensors Using Learning Based Super
Resolution and Data Fusion”, IEEE Aerospace
Conference, Big Sky, Montana. 2009.
J. Walls, Albert Esterline, and Abdollah Homaifar, “Sensor
Fusion Analysis Using Fuzzy Integral, Bayesian Network
and Neural Network Techniques", ROVISP International
Conference- 2007, Penang, Malaysia, 28- 30.
J. Walls, Albert Esterline, and Abdollah Homaifar, “Sensor
Fusion Analysis Using Fuzzy Integral and Bayesian
Network Techniques", 17th IFAC World Conference,
Seoul, Korea, 6-11, 2008.
A. Charef, Antoine Ghauch, Patrick Baussand, and Michel
Martin-Bouyer, “Water quality monitoring using a smart
sensing system”, J Measurement ( ) –224.
Hassan, H M, Mustafa H T, and Rihan T I, “Lead and
chromium concentrations in the potable water of the
eastern province of Saudi Arabia”, Bulletin of
Environmental Contamination and Toxicology 11/1989;
43(4):529-33.
Abdullah, M. A and Saad M. A., “Fluoride content of bottled
drinking waters available in Riyadh, Saudi Arabia”, Saudi
Dent J., 22(4): 189–193, 2010.
Z. A. Tayyeb, Farid S.M and Otaibi K A , “Race Element
Concentration of Commercially Available Drinking Water
in Makkah and Jeddah”, J King Abdul University: Eng
Sci., vol. 15 no. 1, pp. 149-154, 2005.
H. A. Ghrefat, “Classification and Evaluation of Commercial
Bottled Drinking Waters in Saudi Arabia”, Research J of
Environmental and Earth Sciences 5(4): 210-218, 2013.
H. T. Mustafa, Hassan H M, and Rihan T I “Cadmium and
zinc concentrations in the potable water of the eastern
provinces of Saudi Arabia”, Bulletin of Environmental
Contamination and Toxicology, 40:462–467, 1988.
Y. Okour, Shon, H.K., El Saliby, I.J., Naidu, R., Kim, J.B and
Kim, J H “Preparation and characterization of titanium
dioxide (TiO2) and thiourea-doped titanate nanotubes
prepared from wastewater flocculated sludge”,
Bioresource Technology, 101, 1453-1458, 2010.
H. K. Shon, Puntsho, S., Okour, Y., Cho, D.L., Kim, K.S., Li,
H.J., Na, S.H., Kim, J.B. and Kim, J.-H., “Visible light
responsive titanium dioxide (TiO2)”, Korean Journal of
Chemical Engineering, 19, 1-16, 2008.
Y. Okour, Shon, H.K. and El Saliby, I., “Characterization of
titanium tetrachloride and titanium sulfate flocculation in
wastewater treatment”, Water Science Technology, ,
2463-2473, 2009.