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Modelling Groundwater Monitoring for Rural Areas using Cloud

Computing

by

Mulunghisi Preference Mahwayi

A dissertation submitted in partial fulfilment of the degree:

MTech Electrical Engineering

in the

Department of Electrical and Electronic Engineering Technology

at the

Faculty of Engineering and Built Environment

University of Johannesburg

Supervisor: Prof. M.K. Joseph

29 September 2016

Johannesburg

South Africa

1

Declaration

I Mulunghisi Preference Mahwayi hereby declare that the dissertation titled “Modelling

Groundwater Monitoring for Rural Areas using Cloud Computing” submitted for MTech Electrical

Engineering at the University of Johannesburg is my original work. This dissertation has not been

previously submitted to another University or higher educational institution.

Signed ……………………………… Date……2016-09-29…...

2

List of publications

Mahwayi, M. P. & Joseph , M. K., 2016. Technologies for Groundwater Quality Monitoring in

Rural Areas, ICIDA'16, STH, UJ.

Acknowledgements

I am grateful to the Faculty committee for the opportunity given to enroll and do the research. We

are thankful to the Department of Water Affairs and Sanitation staff members for being available

and for their assistance with the data required for the study and also for the participation in

answering the questionnaires quickly and accurately. A special thank you to my supervisor Prof.

M.K. Joseph and for the guidance, advice and support throughout the study. I would also like to

thank the previous co-supervisor Prof. B. Twala for the support and guidance. I would like to thank

for the financial support provided by the National Research Foundation (NRF) for my MTech

studies.

3

Abstract

The study makes use of cloud computing for monitoring the quality of groundwater and electronic

sensors to detect physical and chemical characteristics of domestic water used in the rural areas,

Mpumalanga province. The data is made available via the database and then to the cloud. The

monitoring of water quality is important to humans because access to clean and potable water is

the greatest challenge experienced by rural residents in South Africa. The study determines the

physical and chemical constituents and highlights the key relevant substances to domestic water

quality and detects the level of contamination in the groundwater from the boreholes in

Mpumalanga province. By setting up an experiment based on secondary and primary data from the

boreholes used by the rural residents of Mpumalanga province, a cost-effective, scalable and

flexible model using cloud computing was developed. There were three phases of this study, first

to determine the constituents that highly contaminates the boreholes and it was carried out by

analysing the existing groundwater boreholes data (secondary data) provided by the Department of

Water and Sanitation (DWS) or by manually checking the properties using sensors (primary data)

and making this available in the database. The study used the cloud to monitor the quality of

groundwater. The second phase was to develop a cloud computing model for groundwater quality

monitoring in the rural areas. The third phase qualitative interviews were conducted to get more

insight on contamination of groundwater and this assisted in enhancing the developed model. The

contribution of the dissertation was towards providing a centralised solution for groundwater

monitoring to assist water consumers, water monitoring organisation (E.g.: Department of Water

Affairs and Sanitation) to be pro-actively informed concerning issues in the water supplied to the

rural residents. This will be a major contribution for the country in achieving the Sustainable

Development Goals and Millennium Development Goals by ensuring that safe drinking water is

supplied to the residents in rural areas. Our study showed that all the parameters from the actively

monitored boreholes located in the 18 Mpumalanga (South Africa) municipalities were within the

4

prescribed limits for no risk, as per the recommendation by the national guidelines for domestic

water use. The Emalahleni local municipality was the only municipality in terms of pH that was

below the prescribed limits. This research also indicates that in some municipalities, high

concentrations of Arsenic, Potassium, Manganese, Iron and Cadmium were within the prescribed

limits for the samples taken, despite the fact that the Fluoride, Nitrate concentrations, Chloride and

Magnesium in certain boreholes exceeded the prescribed limits. Due to the water shortages because

of the drought and contamination in the province, the reality is people will opt to use borehole water

due to the increase in contamination in groundwater hence the study monitored the groundwater

quality using the cloud and electronic sensors

5

Abbreviations and Acronyms

Abbreviation Meaning

DWS Department of Water and Sanitation

WHO World Health Organisation

WRC Water Research Commission

SANS South African National Standards

FEBE Faculty of Engineering and Built Environment

HSRC Human Sciences Research (South Africa)

SAAS Software as a Service

PAAS Platform as a Service

IAAS Infrastructure as a Service

ICOMMS Information for Community Oriented Municipal

Services

SMS Short Message Service

GSM Global System for Mobile

ADC Analogue to Digital Converter

WIFI Wireless Fidelity

DBLC Database Life Cycle

ERD Entity Relationship Diagram

RDBMS Relational Database Management System

6

Figures

Figure 2.1: Groundwater flow (Adapted from Lyle and Raymond 1988) ……………………….20

Figure 2.2: The conceptual reference model (Adapted from Mell & Grance 2011)………………23

Figure 2.3: Cloud Computing Usage (Adapted from Gartner 2009) …………………………….23

Figure 3.1: Schematic diagram for electronic sensors ……………………………………...........28

Figure 3.2: Methodology for Developing the Model …………………………………………….30

Figure 3.3: Cloud computing groundwater quality model ……………………………………….31

Figure 3.4: DBLC Database Life Cycle phases (Adapted from Rob et al. 2008)…………...........35

Figure 3.5: MVC software design pattern (Adapted from Bhatt et al. 2014) …………………….37

Figure 4.1: Water samples collection points for domestic water quality (Adapted from The

Department of Water Affairs and Forestry et al. 2003).……………………………………..........45

Figure 4.2: Count of active boreholes in Mpumalanga …………………………………………..48

Figure 4.3: Entity relationship diagram groundwater monitoring system.……………………..…52

Figure 4.4: Initial model for groundwater quality monitoring using cloud computing

……………………………………………………………………………………….…………...55

Figure 4.5: Final model for groundwater quality monitoring using cloud computing…………...56

Figure 4.6: Login screen……...………………………………………………………………......58

Figure 4.7: Search page……...…………………………………………………………………...58

Figure 4.8: Result view……………………………………………………………………..........59

7

Tables

Table 3.1: Substances of key relevance for domestic water quality use (Adapted from The

Department of Water Affairs et al. 2003) …………………………………………………........32

Table 3.2: South African standards for domestic water quality target water quality (Adapted from

DWS 1996) …………………………………………………………………………………......32

Table 3.3: Technology used to design the system ……………………………………………...37

Table 4.1: Groundwater data boreholes and locations.……………………………………........40

Table 4.2: Groundwater data monitored for domestic use

(Ca,Cl,Ec,F,K,Mg,NO3+NO2,Na,Temp,pH,As)…………………………………………………………..40

Table 4.3: Groundwater data monitored for domestic use (Cd, Fe, Mn, Zn) ….…………………..40

Table 4.4: South Africa standards for domestic water quality target water quality (Adapted from

Department of Water and Sanitation 1996) ………………………………………………….…41

Table 4.5: Details of electronic mail interviews ………………………………………………..42

Table 4.6: Substances of key relevance for domestic water quality use (Adapted from The

Department of Water Affairs and Forestry et al. 2003) ………………………………………...43

Table 4.7: Sampling period frequencies (Adapted from (The Department of Water Affairs and

Forestry et al. 2003) …………………………………………………………………………….45

Table 4.8: List and locations active boreholes in Mpumalanga ……………………………….47

Table 4.9: List of offline and online key substances monitored by the model ………………...49

Table 4.10: Hardness of water classified by Kunin (Adapted from DWS 1996) ………………49

Table 4.11: Physical and Chemical water quality results…………………………………….....50

Table 4.12: Pre-collected versus primary samples ……………………………………………..51

Table 4.13: The boreholes table.……………………………………………………………......52

Table 4.14: The municipality table.……………………………………………………………53

Table 4.15: The province table….……………………………………………………………...53

Table 4.16: The elements table.…………………………………………………………….......53

Table 4.17: The monitoring table.………………………………………………………….......54

8

Table of Contents

1 CHAPTER 1: INTRODUCTION ............................................................................... 12

1.1 Background review ......................................................................................................... 12

1.2 Aim of the research ......................................................................................................... 14

1.3 Problem statement .......................................................................................................... 15

1.4 Research questions.......................................................................................................... 15

1.5 Research objectives ........................................................................................................ 15

1.6 Issues and challenges ...................................................................................................... 15

1.6.1 Network and service availability ............................................................................. 16

1.6.2 Data migration between non-standard environments .............................................. 16

1.6.3 Endless resource ...................................................................................................... 16

1.6.4 Data security and privacy ........................................................................................ 16

1.7 Limitations of the study .................................................................................................. 17

1.8 Research methodology and design ................................................................................. 17

1.9 Ethical considerations ..................................................................................................... 17

1.10 Significance and Unique contributions ........................................................................... 18

1.11 Chapter summary ............................................................................................................ 18

2 CHAPTER 2: LITERATURE REVIEW ................................................................... 19

2.1 Introduction .................................................................................................................... 19

2.2 Groundwater ................................................................................................................... 19

9

2.3 Groundwater Flow .......................................................................................................... 19

2.4 Groundwater contamination sources .............................................................................. 21

2.5 Monitoring groundwater contamination ......................................................................... 21

2.6 Contaminated groundwater effects ................................................................................. 21

2.7 Treatment of contaminated groundwater ........................................................................ 22

2.8 Cloud computing ............................................................................................................ 22

2.9 Cloud computing usage .................................................................................................. 23

2.10 Related work ................................................................................................................... 24

2.11 Chapter summary ............................................................................................................ 24

3 CHAPTER 3: RESEARCH METHODOLOGY AND DESIGN ............................. 25

3.1 Introduction .................................................................................................................... 25

3.2 Study area ....................................................................................................................... 25

3.3 Research methodology and design ................................................................................. 25

3.4 Phase 1 ............................................................................................................................ 26

3.4.1 Data collection (secondary data) ............................................................................. 26

3.4.2 Advantages of using secondary data for this study ................................................. 26

3.4.3 Disadvantages of using pre-collected data for this study ........................................ 27

3.4.4 Sample and sampling techniques ............................................................................ 27

3.4.5 Data collection (primary data) ................................................................................ 28

3.5 Phase 2 ............................................................................................................................ 29

3.5.1 Identifying the research problem ............................................................................ 30

3.6 Procedure to develop the model ..................................................................................... 31

10

3.7 Phase 3 ............................................................................................................................ 33

3.7.1 Qualitative method .................................................................................................. 34

3.8 Database Life Cycle ........................................................................................................ 35

3.8.1 Database initial study .............................................................................................. 36

3.8.2 Database Design ...................................................................................................... 36

3.8.3 Implementation and loading .................................................................................... 36

3.8.4 Testing and evaluation ............................................................................................ 38

3.9 Study limitations ............................................................................................................. 38

3.10 Chapter summary ............................................................................................................ 38

4 CHAPTER 4: DATA ANALYSIS AND RESULTS .................................................. 39

4.1 Introduction .................................................................................................................... 39

4.2 Data analysis ................................................................................................................... 39

4.3 Qualitative interviews ..................................................................................................... 41

4.3.1 Primary Analysis: Electronic mail questionnaires .................................................. 41

4.3.2 Electronic mail questionnaires and responses ......................................................... 42

4.4 Quantitative Data ............................................................................................................ 47

4.4.1 Secondary data analysis .......................................................................................... 47

4.4.2 Analysis of water samples ....................................................................................... 48

4.4.3 Water hardness determination ................................................................................. 49

4.5 Results ............................................................................................................................ 50

4.5.1 Primary versus secondary data processing .............................................................. 51

4.6 Database life cycle phases output ................................................................................... 52

11

4.6.1 Initial study ............................................................................................................. 52

4.6.2 Database Design ...................................................................................................... 52

4.6.3 Implementation and loading .................................................................................... 52

4.6.4 Testing and evaluation ............................................................................................ 57

4.7 System outputs ................................................................................................................ 57

5 CHAPTER 5: FINDINGS AND DISCUSSION ......................................................... 60

5.1 Introduction .................................................................................................................... 60

5.2 Motivation use of cloud computing ................................................................................ 60

5.3 Conclusion ...................................................................................................................... 61

5.4 Future work ..................................................................................................................... 62

Annexure A: (PIC16F877A Source code) ................................................................................. 70

Annexure B: Procedure for traditional sampling ....................................................................... 73

Annexure C: Information required for data sheets ..................................................................... 77

Annexure E: System for groundwater quality monitoring ......................................................... 79

12

1 CHAPTER 1: INTRODUCTION

1.1 Background review

Domestic water quality is the greatest contributing factor to human health, an assurance of the water

quality safety is the foundation to the deterrence and control of diseases caused by contaminated

water. Studies have indicated that many people around the world are deprived of quality water.

Fifty percent of the world population were reportedly said to rely on groundwater for daily water

needs and forty-three percent of the remaining water is utilized for irrigation (UN-Water 2015). In

South Africa, there are approximately 51.77 million people (Statistics South Africa 2012) and from

this population, 52% live in the rural areas. It has been estimated that around 6 million do not have

access to safe sources of drinking water. The implication is the fact that a number of rural

communities rely on unprocessed groundwater and surface water for domestic and irrigation use.

In South Africa, groundwater contributes in the middle of 13 to 15 percent of the total available

water. The study determines constituents that highly contaminate the sources of groundwater for

domestic use in Mpumalanga province. The study uses cloud computing technology to develop a

model for analysing and monitoring the physical and chemical groundwater quality (primary data)

using electronic sensors. Present quality valuation methods are laboratory based, necessitating fresh

supplies of chemicals, trained staff and longer testing periods. By setting up the experiment, the

study developed a cost-effective, scalable and flexible model for groundwater quality assessment

monitoring using cloud computing and sensors to monitor the elements in groundwater in real-

time. Cloud computing is said to be a new business and evolving paradigm in the world of

computing (Rosenthal et al. 2010). Cloud computing well known as the “cloud”, is the technology

with the use of Internet and centralised remote servers hosted at the service provider premises. The

cloud computing enables individuals and companies to utilise software applications without the

worry of building their own hosting infrastructure and also have the ability to access information

hosted in the cloud by using any Internet enabled device which then allows efficient computing by

13

centralising processing power, bandwidth and data storage. Some well-known cloud computing

services are the social networking websites (E.g. LinkedIn, Instagram, Facebook, Twitter,

Myspace, etc.), Web-based email Gmail and documents or hosting services that are shared on the

Internet Dropbox and Google drive. A number of studies in the past 10 years have attempted to

define cloud computing based on different research conducted. There still is no standard definition

of cloud computing due to its complexity and broader coverage in terms of infrastructure and the

hosting of the software application. Mell & Grance (2011) defines “cloud computing as a

convenient model for on-demand shared pool of configurable networking resources for example

storage, software application and networks”. Boss G et al (2007) said “It is a pool of virtualized

computer resources”. Some of the benefits of cloud computing are to reduce the cost by allowing

customers to rent IT infrastructure instead of purchasing it, which therefore reduces the cost of

obtaining, supplying and supporting required computing power. Cloud computing bills customers

based on their usage of the service on a daily or monthly basis which further reduces costs. The

cloud is centralised, it enables users to access their cloud services using any web-enabled device

with Internet access irrespective of the location. The cloud from the consumer perspective is

scalable and flexible. This means that an organization can change and scale the level of cloud

service it is receiving and cloud softwares offered as building blocks. Consumers have the option

to select the software blocks they need and organizations can customize enterprise applications by

just selecting the software and services which are suitable for their business needs. Due to cloud

computing cost effectiveness, mobility, scalability and flexibility. The cloud computing model

framework was used (Chen et al. 2002) to model educational application taking the benefit of the

available cloud services which enables them to communicate with students and perform their day

to day operational duties over the Internet. One of the monitoring methods as an example is the

traditional monitoring borehole sampling method (Mpenyana-Montasti et al. 2012). A remote

water quality monitoring system using wireless sensors for prawn farming pond was developed

14

(Haron et al. 2009). In Bangladesh, Kamal et al (2000) developed a geographic information system

to detect areas with Arsenic contamination.

1.2 Aim of the research

The research aimed at investigating the use of cloud computing to develop a model to assist in

monitoring groundwater contamination, checking both physical and chemical characteristics in

water for domestic use in the rural communities of Mpumalanga province. Traditional monitoring

boreholes sampling methods were used (Mpenyana-Monyatsi et al. 2012) to collect groundwater

samples and this was done between September and November 2008 from a number of villages in

the rural communities of Mpumalanga province. Mpenyana-Monyatsi et al (2012) reported that the

water for domestic use in some rural areas is not fit for human consumption. The study showed that

the water had a high concentration of Magnesium, Calcium, Fluoride, Nitrate and high Turbidity

levels. The poor bacteriological characteristics can cause harm to the health of the consumers. The

distribution of groundwater directly to residents without any form of purification or testing may

lead to contaminated water being used by rural residents. The assurance of the delivery of good

quality water is required, with real-time capability to monitor levels of contamination. The cloud

computing technology is an excellent alternative for monitoring the quality of groundwater in

Mpumalanga province. The cloud is a cost-effective, mobile, scalable and flexible way of collecting

the water samples and delivering the water quality results as a service to consumers. The research

provides substantial benefits for public health, the economy, the environment and a major

contribution to the country in achieving the Sustainable Development Goals and Millennium

Development Goals by ensuring that safe drinking water is supplied to the residents in rural areas.

Monitoring groundwater quality is important for minimizing negative impacts to the environment

and it improves public health protection.

15

1.3 Problem statement

The Mpumalanga rural communities depend mostly on groundwater from the boreholes for

domestic and irrigation use. The Olifants River that runs from the Witbank passing the Kruger

National Park to the Mozambique Ocean, is said to be contaminated from mining effluent and raw

sewage. This results in the majority of the rural communities to depend on groundwater for

domestic and irrigation use. The study conducted (Mpenyana-Monyatsi et al. 2012) showed and

also revealed that the water is non-potable for a number of rural communities. Higher concentration

of Magnesium, Calcium, Fluoride, Nitrate, Turbidity levels was reported. The distribution of

groundwater directly to residents without any form of purification or testing has led to contaminated

water being used by rural residents. There is no groundwater quality monitoring model developed

using cloud computing in South Africa.

1.4 Research questions

How do we develop a model for groundwater quality monitoring in rural areas?

1.5 Research objectives

The main objective is to develop a cloud computing groundwater quality monitoring model for

rural areas in South Africa. The sub-objectives are:

To determine which physical and chemical constituents highly contaminate groundwater

in Mpumalanga province.

To monitor the quality of water in rural areas of Mpumalanga.

To develop a model for groundwater quality monitoring using cloud computing.

1.6 Issues and challenges

The decision to adapt cloud computing is tackled by several issues and challenges. Some of the

challenges can be resolved with careful planning and design. The following are some of the known

challenges with cloud computing implementations (Kuo 2011).

16

1.6.1 Network and service availability

The benefit of cloud computing can only be achieved with the availability of the cloud provider

service and adequate network bandwidth. Any service or network absence will disconnect the

consumers off their valuable data and applications.

1.6.2 Data migration between non-standard environments

Most cloud service providers use proprietary cloud-based applications. Those proprietary

applications are not interoperable which makes it very difficult for consumers to move their data

to another provider’s infrastructure or back to their in-house servers.

1.6.3 Endless resource

Meeting consumer resource requirement by providing them the ability of scaling resources up or

down is one of the most desired cloud computing advantages. This feature has to be carefully

implemented to prevent service failures due to insufficient resources. To prevent service failures

and control the continuous increase in the allocation of resources, scaling must be limited by agreed

on threshold or scaling rate.

1.6.4 Data security and privacy

Two important issues challenging cloud computing are associated with storing and securing

information. In cloud computing data has to travel between user’s devices and the cloud computing

service provider data centres, which will make it an easy target for hackers. Data security and

privacy must be guaranteed whether it is traveling or still kept in the cloud. Encryption algorithms

can be used ensuring data security in the cloud-based storage, but it is not very useful with cloud

applications because data has to be decrypted inside the cloud at some point. Cloud applications

can work on encrypted data without decrypting them but it would require more time and consume

more resources.

17

1.7 Limitations of the study

Any cloud computing design or implementation depends on network and service availability in the

areas of operation. Poor or limited broadband connectivity may pose serious problems in data

transportation from and to the receiving devices. HSRC (2011) reported that most of the South

African rural areas have limited broadband connectivity. This may be a limiting factor for the

research.

1.8 Research methodology and design

The study determines the physical and chemical constituents of key relevant to domestic water

quality and detects the level of contamination in groundwater in boreholes. The study proposes a

cost-effective, scalable and flexible model to monitor the water quality from the boreholes using

cloud computing. The research demonstrates the model by conducting three phases: First, the

determination of the constituents that highly contaminates the boreholes and it was carried out by

analysing the existing groundwater boreholes data (secondary data) provided by the Department of

Water and Sanitation (DWS) or by manually checking the characteristics using sensors (primary

data) and making this available in the database. The study used the cloud to monitor the quality of

groundwater. In the second phase, the study developed a cloud computing model that will assist to

analyse and monitor the physical and chemical characteristics of water supplied by the boreholes

for domestic water use. The third phase qualitative interviews were conducted to get more insight

on contamination of groundwater and this assisted in enhancing the developed model. The research

methodology chapter discusses the three phases in more detail.

1.9 Ethical considerations

All information regarding documents to be analysed and data collection, consents were distributed

seeking for permission to use the data. An ethical clearance was also submitted to the University

of Johannesburg FEBE ethics committee with the questionnaires used to interview the research

participants.

18

1.10 Significance and Unique contributions

Sufficient supply of clean and fresh water is a basic need for rural and urban areas. The research

has significance in constructing a more scalable, flexible model to monitor groundwater quality in

rural areas using cloud computing and electronic sensors, ensuring that communities are supplied

with good water quality for their daily needs. Other studies have used electronic sensors to monitor

physical and chemical characteristics in water, the literature review chapter 2 section 2.12 related

work discusses the studies in more detail. In this study electronic sensors were used detect

contamination constituents in groundwater from the boreholes, both physical and chemical

characteristics and the data is made available via the database and then to the cloud. The electronic

sensors enabled the physical and chemical characteristics to be monitored in real-time from the

cloud, giving authorised users access from any type of device with Internet access to view the

groundwater quality results of a particular borehole and its location in Mpumalanga. The study

developed a model for groundwater quality monitoring with the use of cloud computing and

electronic sensors technology.

1.11 Chapter summary

Domestic water quality remains the greatest contributing factor to human health. This chapter

discussed the research aim, problem statement, limitation of the study and research questions and

research objectives. Section 1.8 research method described the methods to answer the research

question and chapter 3 will discuss the methods in more detail. The following chapter discusses the

literature of the study.

19

2 CHAPTER 2: LITERATURE REVIEW

2.1 Introduction

The chapter reviews the study literature. The chapter begins by defining groundwater in section

2.2 and section 2.3 to 2.7 discuss the groundwater flow, the effects of contamination in

groundwater, monitoring methods of groundwater and methods available to treat contaminated

water. Section 2.8 provides the definition of cloud computing and section 2.9 shows the cloud

computing usage in different sectors. Section 2.10 concludes the chapter by reviewing related work

conducted by other experts.

2.2 Groundwater

Groundwater is precipitation of rain from the clouds that seeps into the recharge areas of the earth

surface. The earth recharge areas are regions of the earth surface where water can penetrate into

the ground. The volume of water entering the ground is determined by the ability of the rock or soil

type to absorb the water. The ability of rocks or soil to hold water from the earth surface is called

Porosity. Different soil types can absorb a certain percentage of surface water into the ground. Clay

soil is said to absorb up to 48 percent of water, gravel can absorb up to 25 percent, permeable rocks

can absorb water less than 1 percent and saturated sand can hold up to 20 percent of surface water.

Clay soil is well known to have particles creating friction and can excellently stop water

movements, regardless of the larger absorption ability (Lyle & Raymond 1988).

2.3 Groundwater Flow

The water from the rainfall entering the ground flows downwards until a point where the water

seals the penetration openings in the rocks or the soil. The water table is the highest level of the

penetration zone. The water table changes in pattern and the levels are controlled by the yearly

seasons and the volume of rainfall received. In spring it is normally higher and in summer it drops

due to less rainfall. The unsaturated zone is the region between the earth surface and the water

20

table. The zone cannot provide water for the boreholes, but can provide the roots of the plants with

moisture while the water passes through the zone. The underground level of permeable rock or

soil that harvests water is called the Aquifer (Lyle & Raymond 1988). The Aquifer harvests large

amounts of water when there are huge penetrations openings and harvests small amounts when

there are tiny opening in the rocks or the earth surface. The region that provides the boreholes with

water is called the contribution zone and some refer to it as the catchment areas. The area consists

of a large part of the recharge area of the aquifer. The boreholes pumping is influenced by the area

that superimposes cone of depression, this might extend further than the contribution zone. The

boreholes pumping induced recharge forces the groundwater to move towards the borehole that

will not regularly contribute groundwater to the borehole (Lyle & Raymond 1988). Figure 2.1

illustrates the entire process that occurs from the formation of the clouds and the resulting moisture

condenses into water droplets (rain).

Figure 2.1: Groundwater flow (Adapted from Lyle and Raymond 1988)

21

2.4 Groundwater contamination sources

Groundwater can become contaminated in many ways. Sources of groundwater contamination can

be humanitarian disasters, the disposal of mining polluted effluents, household products

(chemicals, oil, gasoline, road salts etc.) that may be mixed and carried with the water from the

earth surface into the ground. Animal faeces and fertilizers, overtime may come in contact with

sources of groundwater (boreholes) or be carried with the water that seeps through penetration areas

on the earth surface.

2.5 Monitoring groundwater contamination

The groundwater could be contaminated from a range of improper disposal as discussed in section

2.4. It can be challenging to identify the sources of contamination without some sort of monitoring

methods in place. Monitoring methods or systems to assist in identifying the sources of

contamination and also the monitoring of microbiological, chemical and physical properties are

required to facilitate the monitoring process. One of the monitoring method as an example is the

traditional monitoring borehole sampling method (Mpenyana-Monyatsi et al. 2012). Water samples

were collected directly from the boreholes using a sampling device or equipment and taken to the

laboratory for testing. Arsenic contamination was monitored using a GIS system with the aim to

assist water operators on deciding proper usage of the groundwater and ensuring residents are

supplied with good water quality (Kamal et al. 2000). Section 2.12 highlight related work

conducted by other studies to facilitate the monitoring of the groundwater.

2.6 Contaminated groundwater effects

Fifty percent of the world population rely on groundwater for domestic and irrigation use. Polluted

water has short and long term effects on human and animal’s health. Polluted water may also have

an effect on crop farming. In South Africa, the common water diseases resulting from polluted

water are, Billarzia, Malaria and Cholera.

22

2.7 Treatment of contaminated groundwater

The process called groundwater remediation is regarded as the only approach used to treat

groundwater. The method is said to include contaminated soil, contaminates transfer and the move

of groundwater to a place for the final treatment. The remediation process involves the removal of

contaminates or transforming the contaminants into harmless harvests. The groundwater is at times

pumped and treated by removing contaminants using techniques which include air stripping,

thermal desorption, extraction, containment, precipitation, soil washing and then pumped back to

the boreholes, storage tanks or into the ground.

2.8 Cloud computing

The cloud computing is a technology that enables individuals to host, operate and do business via

the Internet from anywhere in the world from their Internet web-enabled devices which include

smartphones, tablets, computers and other devices available in the market today that can connect

to the Internet. A number of studies in the past 10 years have attempted to define cloud computing

and there still is no accepted definition of cloud computing today due to its complexity and broader

coverage in terms of its offering. Mell & Grance (2011) defines “cloud computing as a convenient

model for on-demand shared pool of configurable networking resources for example storage,

software application and networks”. Boss G et al (2007) said “It is a pool of virtualized computer

resources”. Cloud computing is said to be a new business and evolving paradigm in the world of

computing (Rosenthal et al. 2010). Figure 2.2 adopted from (Mell & Grance 2011), shows three

cloud computing actors that are applicable to any cloud computing model which are cloud

consumer, cloud provider and cloud broker. The cloud consumer procures cloud services from the

cloud provider via a cloud broker that act as an intermediary between the consumer and the

provider. Cloud servicers available today are as follows, software as a service, Platform as a service

and Infrastructure as a service. The consumer also has deployment options to select from and they

23

are public cloud, private cloud, community cloud and hybrid cloud to address the target audience

and security requirements.

Figure 2.2: The conceptual reference model (Adapted from Mell & Grance 2011)

2.9 Cloud computing usage

Cloud computing is being used in different sectors e.g. Financial services, Telecommunications,

Manufacturing, Education and other sectors. A survey conducted in 2009 on the usage of cloud

computing revealed that cloud computing is being used more in the Financial and Business sectors

in comparison with other sectors. Figure 2.3 adapted from Gartner (2009), shows the results of the

survey.

Figure 2.3: Cloud computing usage (Adapted from Gartner 2009)

24

2.10 Related work

In Bangladesh, Kamal et al (2000) developed a geographic information system (GIS) to detect

areas with Arsenic contamination. A microcontroller based system was proposed (Jain et al.

2014) to detect chemical properties in groundwater using electronic sensors. Rivett et al (2013)

a research team (ICOMMS) at the University of Cape Town the Civil Engineering department,

developed a cell-phone application to improve the groundwater quality data collection in the

rural communities, South Africa. Haron et al (2009) developed a remote water quality monitoring

system using wireless sensors for prawn farming pond. The developed prototype system was

leveraging on wireless sensors in detecting the water quality and short message service (SMS)

technology in delivering an alert to the farmers upon detection of degradation of the water

quality. Three critical water quality parameters to prawn health were monitored which are pH,

temperature and dissolved oxygen. A cost-effective microcontroller based system was developed

(Fisher & Kebede 2010) to detect temperature and water status for crops. Investigation of the

use of satellite technology to predict aquifer vulnerability, locating and tracking groundwater

resources and depletion was conducted (Becker 2005). A River water pollution detection and

an alert system was developed (Markovic et al. 2009) for the prevention of water contamination.

A web-based water level detection system was developed (Reza & Tariq, 2010) to enable the

monitoring of water level via the Internet. Mahwayi and Joseph (2016) proposed technologies

for groundwater quality monitoring in rural areas. Above studies are all related to what this study

intends to answer.

2.11 Chapter summary

The study literature was discussed in this chapter. Groundwater and cloud computing was defined

and also highlights related work conducted by other studies in section 2.10. The next chapter,

chapter 3 discuss the study research methodology.

25

3 CHAPTER 3: RESEARCH METHODOLOGY AND DESIGN

3.1 Introduction

In this chapter the study overall research approach will be discussed. The chapter begins by

discussing the study research area in section 3.2. Section 3.3 provides the study research

methodology and design in more detail. Section 3.4 to 3.7 covers the procedure to develop the

model. Section 3.8 concludes the chapter by discussing the study limitations.

3.2 Study area

The study was conducted in Mpumalanga province. The province is located in the eastern part of

South Africa, bordering Swaziland and Mozambique. The province is a home to 4.04 million South

Africans. The rural communities in the province depend mostly on groundwater from the boreholes

for domestic and irrigation use. The alternative to the source of water in the province is the Olifants

river that runs from the Witbank passing through the Kruger National Park to the Mozambique

Ocean and the river is reportedly (Veelen and Dhemba 2011) said to be contaminated from mining

effluent and raw sewage. This leaves the groundwater from the boreholes as the primary source of

water in the province.

3.3 Research methodology and design

This study aims to answer the research question “How do we develop a model for groundwater

quality monitoring in rural areas?” and its sub-objectives which are, to determine which physical

and chemical characteristics highly contaminate groundwater in Mpumalanga province, to monitor

the quality of water in the rural areas of Mpumalanga and to develop a model for water quality

monitoring using cloud computing. The research was broken down into three phases and these are

discussed in 3.4, 3.5 and 3.6.

26

3.4 Phase 1

This phase determines the characteristics of constituents that highly contaminate the boreholes and

this task was carried out by analysing the existing groundwater boreholes data (secondary data)

provided by the Department of Water and Sanitation (DWS) and also by manually checking its

properties using electronic sensors (primary data) and making this available in the database and

then to the cloud. Section 3.4.5 will explain how the primary data was taken to the cloud.

3.4.1 Data collection (secondary data)

Secondary data is data that are collected by someone else for another purpose (Johnston 2014). In

South Africa, the Department of Water and Sanitation (DWS) is responsible for managing the "raw"

water quality data from the Resource Quality Information Services national monitoring

programmes for specific rivers, boreholes and dams. The secondary data collection for the study

was requested by email (Georequests@dwa.gov.za) from the DWS. The data provided was then

formatted and grouped based on the Entity-Relationship Diagram (ERD) illustrated in figure 4.3 to

better organise the data in a Relational Database Management System (RDBMS). MYSQL

database was used for the system model developed. The use of pre-collected data shortened the

data collection process and also the determination of which physical and chemical characteristics

highly contaminate groundwater in Mpumalanga province. Section 4.5 discuss the analysed results.

The quantitative research strategy was used and quantitatively to enhance the model.

3.4.2 Advantages of using secondary data for this study

Time and money was saved by using pre-collected groundwater samples.

Historical and present data made the study even better, giving more insight to the change

of measurable elements overtime and assisted in enhancing the model.

Formed a baseline variable to compare how accurate the groundwater model is.

27

Data verification and validation was not required as it was from a reliable source

(Department of Water and Sanitation).

3.4.3 Disadvantages of using pre-collected data for this study

The secondary data did not give the present reflection of the state of the groundwater

elements.

3.4.4 Sample and sampling techniques

The study used convenience sampling due to limitations in the use of pre-collected borehole

samples. Convenience sampling is a non-probability sampling technique where subjects are

selected because of their convenient accessibility and proximity to the researcher (Zikmund

1984). The samples were taken from the groundwater data supplied by the Department of Water

and Sanitation. The data was analysed and showed that the Mpumalanga province had 1117

boreholes. Out of the 1117 boreholes, 35 of the boreholes were actively monitored. The study

used the 35 boreholes sample size that is actively monitored, as these were the boreholes that the

rural residents from various municipalities in the province rely on as the main source of drinking

water. The sample remains viable since the 35 boreholes have been actively monitored since

1995 up until 2015 have given accurate results to address the objective of this study and to assist

in developing the model in phase 2. Further criteria for selecting the actively monitored sample

size was based on the following:

Regular water samples taken since 1995.

Data consistency over sampling periods.

Physical and chemical substances monitored.

28

3.4.5 Data collection (primary data)

The primary data was collected manually by checking the physical and chemical properties using

sensors and making this available in the database and then to the cloud. Primary data is original

data gathered for a specific research problem intended to be solved (Johnston 2014). In order to

accomplish this, the study adapted the proposed system (Jain et al. 2014) to monitor groundwater

quality with a microcontroller and electronic sensors. A microcontroller based system to sense the

water quality substances of key relevant to domestic water use obtained from the boreholes was

developed. Due to the limitation in availability of water sensors, the system that was developed

used a pH sensor (DFR analogue ph meter kit), Electrical conductivity (CS547A EC sensor) sensor

and a Temperature sensor (18B20 Digital Temp Sensor) to demonstrate the collection of primary

data from the borehole that was scheduled to execute every 5 minutes daily for 31 days. Figure 3.1

shows the schematic diagram of the groundwater quality monitoring system using electronic

sensors. The circuitry for the system was designed using Proteus software, a tool for virtual system

modelling and circuit simulation application.

Figure 3.1: Schematic diagram for electronic sensors

29

An 8bit high-performance microchip processor (PIC16F877A) with built-in support for analog to

digital (ADC) capability was used for the system. An LCD interface was added for displaying the

water quality measured values from the sensors and also the Global System for Mobile (GSM),

wireless fidelity (WIFI) modules and RS232 serial port interfaces. The various channels allowed

for the data to be transferred to external devices in different ways depending on what the destination

device supports e.g. Personal computer can receive data via RS232 cable, USB cable or IEEE

802.11 wireless standard. This implied that the source device must be capable of communicating

using any of the channels. The C program was written which was then loaded to the microcontroller

(PIC16F877A) for measuring pH, Electrical conductivity and Temperature in groundwater.

Annexure C illustrate the C program. The data measured by the sensors was transmitted to MYSQL

database using an RS232 cable which was connecting the system to the computer where MYSQL

database was installed. The local instance of MYSQL database synced data every 5 minutes with

the cloud MYSQL database instance to make the primary data available in the cloud. This allowed

the Java web application to query the data in the cloud and display it in form of tabular form and

graphs. In chapter 4 figure 4.4 and figure 4.5 illustrate the initial and final model for the study.

Section 4.7 discuss and shows the system outputs.

3.5 Phase 2

The study reviewed several models and systems developed discussed in literature (Kamal et al.

2000; Jain et al. 2014; Rivett et al. 2013; Haron et al. 2009; Fisher & Kebede 2010; Becker 2005;

Markovic et al. 2009; Reza & Tariq 2010; Mahwayi & Joseph 2016) to develop a cloud computing

model for groundwater quality monitoring in rural areas. The data taken to the cloud in phase 1

both secondary and primary, was monitored using the developed cloud computing model from the

cloud. In design science (Hevner 2004) “the research output is to produce a viable artifact in the

form of a construct, a model, a method, or an instantiation”. The artifact for the research was

produced in this phase and further enhanced in phase 3. The aim of the study included the design

of a groundwater monitoring model using the cloud computing technology. Figure 3.2 provides the

30

methodology that was followed to develop the cloud computing model. In chapter 4, section 4.6

the model implementation is illustrated.

Figure 3.2: Methodology for developing the model

3.5.1 Identifying the research problem

Mpenyana-Monyatsi et al (2012) revealed that groundwater for domestic use in some rural areas

of Mpumalanga province is not fit for human consumption. The study found that there is high

concentration of Magnesium, Calcium, Fluoride and Nitrate, Turbidity levels and especially poor

bacteriological characteristic of water source pose a serious threat to consumers. Serious

intervention procedures need to be put in place to monitor the quality of drinking water from the

boreholes used by the rural resident for domestic activities. The traditional monitoring sampling

techniques in place (Annexure B for details) may not reflect the current status of the groundwater.

Suggestively the use of cloud computing technology together with electronic sensors to collect

primary data can be used.

Identifying the problem: Research

question to be answered

Literature review

Identify the key elements to be

monitored derived from phase 1 and

phase 3

Phase 2: developing the model based

on findings from phase 1 and based

on similar models

Revising and developing final model

based on feedback from experts and

participants of the research

31

3.6 Procedure to develop the model

Weinhardt et al (2009) designed a cloud computing business model that will allow business to host

their application and data in the cloud. The study adapted the model to come up with a model for

groundwater quality monitoring in rural areas. Figure 3.3 depicts the framework of the cloud

computing groundwater quality model designed for the study. The model is classified under

Software as a Service cloud computing software distribution model. This makes the groundwater

monitoring data to be available to customers over the internet. Table 3.1 illustrate the key

substances monitored by the model for domestic water use. In table 3.2 is the list of substances and

the standard acceptable water target ranges for each substance is shown.

Figure 3.3: Cloud computing groundwater quality model

32

Table 3.1: Substances of key relevance for domestic water quality use (Adapted from The

Department of Water Affairs et al. 2003)

Table 3.2: South African standards for domestic water quality target water quality (Adapted from

DWS 1996)

Key substances Units Target water quality range

Microbiological quality

Faecal coliforms counts/100

mR 0

Total coliforms counts/100

mR 0 – 5

Physical quality

Electrical conductivity total dissolved mS/m 0 – 70

Ph pH units 6.0 - 9.0

Turbidity (NTU) 0 – 1

Chemical quality

Arsenic Fg/R 0 – 10

Cadmium µg/R 0 – 5

Calcium mg/R as Ca 0 – 32

Sodium mg/R 0 – 100

Chloride mg/R 0 – 100

Fluoride mg/R 0 - 1.0

Iron mg/R 0 - 0.1

Manganese mg/R 0 - 0.05

33

3.7 Phase 3

In this phase the developed model was enhanced by conducting qualitative interviews to get more

insight on contamination of groundwater. An online electronic mail questionnaires method was

used to understand the secondary data collection process used by the Department of Water and

Sanitation which include the frequency of data collection and trigger for the data collection as well

as the people who were involved during the data collection process. Section 3.7.1 highlights some

of the studies that used electronic mail questionnaires method to conduct qualitative interviews.

Questionnaires below were asked and ethical clearance was also submitted to the University of

Johannesburg FEBE ethics committee. Some of the questions used were adapted from (The

Department of Water Affairs and Forestry et al. 2003). Chapter 4 in section 4.3.2 presents the

interview responses.

1) Why do we need to collect a water sample?

2) What is meant by water quality?

3) What substances must be analysed to determine the water quality?

4) Why is it important to know how to collect water samples?

5) Where must water samples be collected?

6) How often is the chemistry data collected from different boreholes in Mpumalanga?

7) What is the difference between ground and surface water sources affect sampling?

8) What methods are used to collect the chemistry data and tools?

9) Who collects the chemistry data from the boreholes?

Total hardness mg/R 50 – 100

Magnesium mg/R as Mg 0 – 30

Nitrate/Nitrite mg/R N 0 – 6

Potassium mg/R 0 – 50

Zinc mg/R 0 – 3

34

10) Who validates the chemistry data before the data is published on the WMS database?

11) List of chemical characteristics of groundwater that water affair staff normally checks?

12) Which physical quality of groundwater is most important when it is for domestic use?

13) Is there Internet services/cloud services used for groundwater monitoring?

3.7.1 Qualitative method

An increase in the usage of qualitative research method in various studies have been seen in the

last 20 years. The usage has resulted in an observable shift in relation to studies that depend on in-

depth interviewing and observation in comparison with the usage of structured or questionnaires

interviewing. Challenges related to in-depth interviews and observation have been identified by

studies over the past decades which include time, cost and limited research sample size says (Taylor

& Bogdan 1998; Miles & Huberman 1994; Strauss & Corbin 1998; Patton 2002; Denzin & Lincoln

2005; Gubrium & Holstein 2002; Kvale 1996; Miles & Huberman 1994; Patton 2002; Strauss &

Corbin 1998; Taylor & Bogdan 1998). There are three recognised types of Internet based methods

to be used when one undergoes qualitative data collection and namely they are 1 virtual focus

groups, 2 online asynchronous and 3 synchronous interviews. The online interview method was

used for data collection of qualitative data collection and data analyses. Phase 3 of the study discuss

how this was carried out. The use of computer mediated case tools email was used as form of

communication between the interviewer and the interviewee. In contrast to the studies that used

asynchronous are summarised below with their findings. The electronic mail computer mediated

case tool was used (Foster 1994) with intentions to observe ways University lecturers conducted

curriculum planning and exploring benefits of the use of electronic mail interviewing. Potentials of

the use of electronic mail was investigated where (Murray 1996; Murray 1995) interviewed about

5 nurses to find out how and why they use computer mediated case tools such as electronic mail.

Electronic mail interviewing method was also used (Young et al. 1998) to interview 6 professionals

in the educational domain to find out how technology is used at the workplace, assisting in

35

formulating guidelines for researchers to effectively conduct electronic mall interviews. Viability

of electronic mail as a method for qualitative in-depth interviewing was investigated with prosthesis

users from number of countries (Sixsmith & Murray 1998). The 3rd and 4th year college scholars

were tasked (Curasi 2001) to compare the use of electronic mail versus face to face interviewing

methods. Sample size of 48 customers were interviewed using both methods, allowing them to

weigh the two methods. Internet user experience was examined interviewing a sample size of 17

women designers using the electronic mail method (Kennedy 2000). Electronic mail interviewing

method was used to examine influenced Internet literacy in lecture rooms (Karchmer 2001). Sixty

scholars were interviewed seeking information behaviour of social science faculty through the use

of electronic mail interviewing. Benefits of electronic mail method compared to the use of face to

face interviewing were examined to unpack challenges, benefits to better conduct effective

electronic mail interviews. In chapter 4 the results of the qualitative interviews are discussed for

this research.

3.8 Database Life Cycle

The database life cycle (DBLC) was used as a guideline to produce the artifact for the study. The

database life cycle (DBLC) has six phases and the phases are illustrated in figure 3.4. This section

discusses what was carried out in each phase of the DBLC.

Figure 3.4: DBLC Database Life Cycle phases (Adapted from Rob et al. 2008)

36

3.8.1 Database initial study

The work done in phase 1 of the research was the input to this phase. The objectives and actions

below were carried out.

Analyse the secondary and primary data

Define the data format, entities, structure, tables and the conversion methods to be used.

This was the input to phase 3 implementation and loading

Define data transportation mechanism to the cloud

3.8.2 Database Design

In this phase three stages of database design which are conceptual, logical and physical database

design were carried out. The RDBMS software used was MySQL database as discussed in phase

2. In the conceptual database design stage the actions carried out were data analysis and

requirements, entity relationship modelling and normalization, data verification and distributed

database. The logical design stage actioned the creation of the logical data model, validation of the

logical data using normalization, assigned and validated integrity constraints and reviewed the

logical data model. The physical database design stage focused on translating each relation

identified in the logical data model into tables and estimated the data storage that was required in

the cloud and also database security to be imposed after deployment.

3.8.3 Implementation and loading

In this phase the design specifications were translated into computer code and hardware. MYSQL

database was installed locally on a computer and the cloud server. After the installation of MYSQL

the database and tables were created. Secondary data was then loaded into the database tables. The

primary data from the microcontroller based system using electronic sensors developed in phase 1

was integrated to the database for samples to be loaded. The study also developed a Java web

37

application using the MVC design pattern. Table 3.3 summaries the technology that was used to

develop the Java web application. Chapter 4 section 4.7 discuss and shows the system outputs.

Table 3.3: Technology used to design the system

The Model View Controller (MVC) is a framework that separates an application into three main

components which are:

View: - all the output representation of information for the application which include

user’s, controls, charts or diagrams.

Controller: - accepts inputs and converts it into commands for the model or view

Model: - manages data, logic and roles of the application

Figure 3.5 illustrate the MVC software design pattern used to develop the Java web application for

users to view the monitoring results from the cloud.

Figure 3.5: MVC software design pattern (Adapted from Bhatt et al. 2014)

Development tool Eclipse IDE Kepler

Application server Tomcat 7

Programming language JavaEE

Database MySQL

38

The system deployment package for the Java web application was build using eclipse IDE which

produced a .war file type. The war file was deployed on tomcat server in the cloud. In the literature

section 2.8, discusses deployment options that a consumer can select from when deploying their

solution in the cloud. The system was deployed on a public cloud.

3.8.4 Testing and evaluation

In this phase unit, functional and end to end testing was conducted to determine if the application

meet the requirements. The unit testing was conducted to verify and validate the source code

correctness and the entity relationships amongst tables. The functional testing was performed to

verify the functional requirement of the system. The end to end testing verified the system

integration, to ensure that the data from the electronic sensors flows correctly to the cloud. The

public cloud was used to test the system.

3.9 Study limitations

Any cloud computing design or implementation depend on network and service availability in the

areas of operation. Poor or limited broadband connectivity may pose serious problems in data

transportation from and to the receiving devices. HSRC (2011) reported that most of the South

Africa rural areas have limited broadband connectivity. This may limit the accessibility of the

model from the areas that does not have broadband connectivity.

3.10 Chapter summary

In this chapter the methodology and design was discussed. The chapter also covered the three

phases of the research which include the sampling and sampling techniques, methods and the

procedure to develop the model. The next chapter, chapter 4 discuss the data analysis and results

of the research.

39

4 CHAPTER 4: DATA ANALYSIS AND RESULTS

4.1 Introduction

This chapter presents the data analysis and results of the study. The chapter begins by discussing

the data analysis in section 4.2. Section 4.3 discusses the qualitative analysis of interview data.

Section 4.4 discusses quantitative data analysis of the study. The results of the study are discussed

in section 4.5 and section 4.6 discusses the output of each phase of the database life cycle. Section

4.7 concludes the chapter by illustrating the system outputs.

4.2 Data analysis

During the research process challenges were faced when gathering the pre-collected data from the

Department of Water and Sanitations database for the research area Mpumalanga province that

prolonged the secondary data collection. Below are the challenges:

The Department of Water and Sanitation took time to respond to the data request for the

study.

The data provided indicated that only 35 boreholes were actively monitored out of 1117

uniquely identified boreholes, therefore this resulted in back and forward online

asynchronous emails and questionnaires being sent to DWS to understand why the data

that was provided had only 35 boreholes actively monitored out of the 1117 boreholes in

the province.

These challenges prolonged the data collection phase of the study. The delays of the pre-collected

data and also clarification as to why certain boreholes were not being actively monitored and the

inconsistency sampling period for some of them. It was critical to ensure that all the uncertainty

with the data had to be clarified first before the analysis phase could begin. Table 4.1, table 4.2 and

table 4.3 shows the secondary groundwater quality data provided by the Department of Water and

Sanitation. In table 4.8 the boreholes and their locations in Mpumalanga province are shown. Table

4.6 also shows the elements that are key substance to domestic water use that needs to be monitored.

40

Table 4.1: Groundwater data boreholes and locations

Table 4.2: Groundwater data monitored for domestic use (Ca, Cl, Ec, F, K, Mg, NO3+NO2, Na,

Temp, pH,As)

Table 4.3: Groundwater data monitored for domestic use (Cd, Fe, Mn, Zn)

41

Table 4.4 illustrate the South Africa standards for domestic water quality, indicating the parameters,

units and the accepted target water quality range for drinking water. The table assisted in the

analyses and data interpretation of the water quality from the boreholes in Mpumalanga province.

Table 4.4: South Africa standards for domestic water quality target water quality (Adapted from

Department of Water and Sanitation 1996)

Key substances Units Target water quality range

Microbiological quality

Faecal coliforms counts/100 Mr 0

Total coliforms counts/100 Mr 0 – 5

Physical quality

Electrical conductivity total dissolved mS/m 0 – 70

Ph pH units 6.0 - 9.0

Turbidity (NTU) 0 – 1

Chemical quality

Arsenic Fg/R 0 – 10

Cadmium µg/R 0 – 5

Calcium mg/R as Ca 0 – 32

Sodium mg/R 0 – 100

Chloride mg/R 0 – 100

Fluoride mg/R 0 - 1.0

Iron mg/R 0 - 0.1

Manganese mg/R 0 - 0.05

Total hardness mg/R 50 – 100

Magnesium mg/R as Mg 0 – 30

Nitrate/Nitrite mg/R N 0 – 6

Potassium mg/R 0 – 50

Zinc mg/R 0 – 3

4.3 Qualitative interviews

4.3.1 Primary Analysis: Electronic mail questionnaires

The online electronic mail (e-mail) interview was conducted with the Department of Water and

Sanitation. The Department of Water and Sanitation broadcast mailbox GeoRequests@dwa.goz.za

was used to distribute the questionnaires for the study. Table 4.5 summarises the details of the e-

mail interviews.

42

Table 4.5: Details of electronic mail interviews

N

um

ber

of

elec

tro

ni

c m

ail

pa

rtic

ipa

nts

Fo

cus

gro

up

Ele

ctro

ni

c m

ail

met

ho

d

Qu

esti

on

na

ire

Inte

rvie

w

per

iod

Nu

mb

er

of

elec

tro

ni

c m

ail

exch

an

ge

s Pa

rtic

ipa

tio

n

dro

po

ut

rate

s

20 DWS

staff

Invitation

posted to

GeoRequ

ests<Geo

requests

@dwa.go

v.za>

Single

electron

ic mail

follow

up.

1

month

All staff

members

replied

using the

broadcast

e-mail

None

Twenty staff members from the Department of Water and Sanitation participated in the e-mail

interview and out of the twenty email participants, 86 percent of the study participants replied to

1-4 follow-up emails. The questionnaires for the study were all routed to the generic mailbox

GeoRequests@dwa.goz.za and participants replied using their work email address. Section 4.3.2

shows the questionnaires posted and responses from the staff members.

4.3.2 Electronic mail questionnaires and responses

1) Why do we need to collect a water sample?

Most water quality analyses cannot be done on site. Therefore, a representative volume

of water. Note: All participants gave the same answer.

2) What is meant by water quality?

Water quality is used to describe the microbiological, physical and chemical properties

of water that determines the fitness for use of a specific water source.

Microbiological quality: is the presence of organisms that cannot be individually seen

by the naked eye, which include protozoa, bacteria and viruses.

43

Physical quality: is the properties that may be determined by physical methods, such as

conductivity, pH and turbidity. Physical quality mainly affects the aesthetic quality of

water (taste, odour and appearance).

Chemical quality: is the nature and concentration of dissolved substances, such as

organic and inorganic chemicals including metal.

Note: All participants gave the same answer.

3) What substances must be analysed to determine the water quality?

A large number of substances are found in water. However, only a few commonly occur

in concentrations that cause adverse health, aesthetic or other problems of concern to

domestic users.

Note: 18 staff members interviewed referred the researcher to the recommendations put

forth (The Department of Water Affairs and Sanitation et al. 2000). Table 4.6 presents

the key substances relevance for domestic use that the staff refer to daily.

44

Table 4.6: Substances of key relevance for domestic water quality use (Adapted from The

Department of Water Affairs and Forestry et al. 2003)

4) Why is it important to know how to collect water samples?

Incorrect sampling procedures and methods will affect the accuracy and reliability of

analytical results and lead to misleading conclusions on the quality of the water supply.

It is also important to remember that, once a water sample is taken, the substances in

the sample may deteriorate or the sample may become contaminated before it reaches

the laboratory. The sampler can avoid this by knowing all the correct sampling

requirements and preservation methods beforehand.

Note: All participants gave the same answer.

5) Where must water samples be collected?

45

The actual sampling point in the system is determined by the objective of the sampling

programme, Annexure B for further explanation.

The source: - if the objective is to determine whether a water source is suitable for

domestic purpose, or what level of treatment is required

The outflow from the water treatment works: - if the objective is to determine

operational control and product quality.

A distribution system: - if the objective is to determine whether any changes in water

quality occur in the distribution system

A point of use: - to determine if the water is fit for use.

Note: All participants gave the same answer.

Figure 4.1: Water samples collection points for domestic water quality (Adapted from The

Department of Water Affairs and Forestry et al. 2003)

6) How often is the chemistry data collected from different boreholes in Mpumalanga?

As needed or as scheduled.

7) What is the difference between groundwater and surface water sources affect sampling?

46

The quality of surface water is more variable than the quality of groundwater.

Therefore, the surface water should be sampled more frequently. Table 4.7 shows the

differences.

Note: All participants referred me to recommended guideline published (The Department of

Water Affairs and Forestry et al. 2003).

Table 4.7: Sampling period frequencies (Adapted from (The Department of Water Affairs and

Forestry et al. 2003)

8) What methods are used to collect the chemistry data and tools?

Sample bottles with or without preservatives

Note: All participants gave the same answer.

9) Who collects the chemistry data from the boreholes?

The responsible regions scientists/technicians/auxiliary officers/municipalities/private.

Note: All participants gave the same answer.

10) Who validate the chemistry data before the data is published on the WMS database?

Laboratories.

Note: All participants gave the same answer.

11) List of chemical characteristics of groundwater that water affair staff normally checks?

Table 4.6 (The Department of Water Affairs and Forestry et al. 2003) illustrates the list

of chemical characteristics of groundwater that are applicable for domestic use checked

by the laboratories.

47

12) Which physical quality of groundwater is most important when it is for domestic use?

When it comes to domestic use/human consumption there is no less important

determinant. They are all important but should fall within certain prescribed limits. I

attach a copy of the 2011 SANS 241 for reference. There is no distinction between

surface and groundwater (SABS 2011).

Note: All participants referred me back to 2011 SANS 241 for reference.

13) Is there Internet services/cloud services used for groundwater monitoring?

Yes, the department does use Internet for information dissemination. You may refer the

National Integrated water information system (NIWIS) site on the departmental

website. I am not accustomed with cloud services.

Note: All participants gave the same answer.

4.4 Quantitative Data

4.4.1 Secondary data analysis

The data provided by the Department of Water and Sanitation showed that the Mpumalanga

province had 1117 boreholes and about 3.13% of the boreholes were actively monitored. The non-

monitored boreholes were said to have dried out and some are contaminated beyond treatment.

Table 4.8 shows the list and locations per municipality in Mpumalanga of the 34 actively monitored

boreholes and figure 4.2 present the relationship between municipalities and boreholes using a bar

graph. The study only analysed the 34 active boreholes using the developed cloud computing

model.

Table 4.8: List and locations active boreholes in Mpumalanga

MUNICIPALITIES NUMBER OF BOREHOLES

Albert Luthuli Local Municipality 3

Bushbuckridge Local Municipality 5

Emakhazeni Local Municipality 1

48

Emalahleni Local Municipality 5

Govan Mbeki Local Municipality 1

Lekwa Local Municipality 1

Mbombela Local Municipality 2

Mkhondo Local Municipality 2

Nkomazi Local Municipality 5

Steve Tshwete Local Municipality 3

Thaba Chweu Local Municipality 2

Thembisile Hani Local Municipality 2

Umjindi Local Municipality 1

Victor Khanye Local Municipality 1

Figure 4.2: Count of active boreholes in Mpumalanga

4.4.2 Analysis of water samples

This research only focused on the water quality key substances applicable for domestic use. The

term quality describes the water properties, determining its fitness for specific use. The analysis of

microbiological properties is normally collected and analysed at the laboratory, therefore only the

physical and chemical water qualities were analysed using the model developed. Two types of

analysis were conducted, the online analysis and offline analysis. The offline analysis is the analysis

that was conducted against the secondary data provided by the DWS while the online analysis

49

refers to the real-time analysis of data from the electronic sensors. Table 4.9 shows the offline key

substances and online substances. The pH and Electrical conductivity was analysed using the

offline and the online for the new data captured by the sensors. Apart from the online key

substances other key substances which include Chlorine, Fluoride, Nitrate, Turbidity, particles

count or total organic carbon (TOC) as an example can be also monitored online. Due to time and

availability of sensors from the manufactures the model demonstrated the real-time monitoring

with an Electrical conductivity, pH and Temperature sensors.

Table 4.9: List of offline and online key substances monitored by the model

4.4.3 Water hardness determination

The water hardness is a complex property of water and is governed by the concentration of Calcium,

Magnesium and other polyvalent cations. Below is the formula used to calculate this property. The

water hardness is expressed as mgCaCO3/R, the total hardness is calculated from the Calcium and

Magnesium concentrations as follows: Total hardness (mg CaCO3/R) = 2.497 x [mg Ca/R] + 4.118

x [mg Mg/R]. Table 4.10 illustrate the hardness of water classified by Kunin adapted from DWS

1996.

OFFLINE MONITORING ONLINE MONITORING

Zinc Temperature

Potassium Ph

Nitrate/Nitrite Electrical conductivity total dissolved

Magnesium

Total hardness

Manganese

Iron

Fluoride

Chloride

Sodium

Calcium

Cadmium

Arsenic

Turbidity

Ph

50

Table 4.10: Hardness of water classified by Kunin (Adapted from DWS 1996)

4.5 Results

The results of the analysis both the physical and chemical properties of key relevant to domestic

water from the boreholes in the Mpumalanga rural communities are summarised in table 4.11. The

developed cloud computing model was used to determine which physical and chemical constituents

highly contaminate groundwater in Mpumalanga province. The study showed that all the

parameters from the actively monitored boreholes located in the 18 Municipalities were within the

prescribed limits for no risk, as per the recommendation by the national guidelines for domestic

water use. The Emalahleni local municipality was the only municipality in terms of pH that was

below the prescribed limits with an average mean value of 4.63 pH. The average mean value for

other local municipality ranged between 7.3 and 8.4. The Electrical conductivity ranged from 4.92

to 264.92. An indication of a change in water was detected and this affect the taste of water in

Lekwa, Govan Mbeki, Bushbuckridge, Mkhondo, Steve Tshwete and Victor Khanye local

municipalities. This study also indicated that in some municipalities, high concentrations of

Arsenic, Potassium, Manganese, Iron and Cadmium were within the prescribed limits for the

samples taken, despite the fact that the Fluoride, Nitrate concentrations, Chloride and Magnesium

in certain boreholes exceeded the prescribed limit.

HARDNESS RANGE (MG CACO3/L) DESCRIPTION OF HARDNESS

0 – 50 Soft

50 -100 Moderately Soft

100 -150 Slightly hard

150 -200 Moderately Hard

200 -300 Hard

> 300 Very hard

51

Table 4.11: Physical and Chemical water quality results

4.5.1 Primary versus secondary data processing

Demonstration of real-time monitoring using electronic sensors and pre-collected data were

analysed using the cloud computing model developed. For real-time, three elements parameters

were analysed namely pH, Electrical conductivity and Temperature. The Emalahleni local

municipality water was used to do the comparison. The results were taken over month with daily

10 minute sampling intervals and then compared with the pre-collected water samples provided by

the Department of Water and Sanitation. Below table 4.12 shows the results comparison of the

parameters. The real-time analyses compared to the pre-collected samples were also within the

recommended limits for no risk in terms of the recommendations by the national guidelines for

domestic purposes, with the exclusion of the pH level with a mean value of 3.97 difference of 0.66

from the pre-collected data.

Table 4.12: Pre-collected versus primary samples

PRE-COLLECTED SAMPLES REAL-TIME SAMPLES

Municipality pH(mean) EC(mean) pH(mean) EC(mean)

Emalahleni Local

Municipality

4.63 33.75 3.97 33.75

52

4.6 Database life cycle phases output

4.6.1 Initial study

In the research methodology chapter section 3.8.1, the database initial phase is discussed. This

phase is the input to the proceeding phases of the database life cycle.

4.6.2 Database Design

In the conceptual database design, the following entities were defined to design the database entity

relationship diagram (Entities: Monitoring, Elements, Boreholes, Province and Municipalities).

Figure 4.2 illustrate the entity relationship diagram for the system.

Figure 4.3: Entity relationship diagram groundwater monitoring system

4.6.3 Implementation and loading

In this phase the secondary and primary data from phase 1 of the study was loaded to the database

tables, Boreholes (Table 4.13), Municipality (Table 4.14), Province (Table 4.15), Elements (Table

4.16) and Monitoring (Table 4.17). In this phase the developed system from phase 2 was deployed

53

into the public cloud. The output of the system is shown in section 4.7 that was captured during the

testing of the system.

Table 4.13: The boreholes table

Table 4.14: The municipality tables

Table 4.15: The province table

54

Table 4.16: The elements table

Table 4.17: The monitoring table

4.6.3.1 Development of the model

Similar works by experts were adapted to develop the final model.

A microcontroller based system was proposed (Jain et al. 2014) to detect chemical

properties in groundwater using electronic sensors

A web-based water level detection system was developed (Reza & Tariq 2010) to enable

the monitoring of water level via the Internet

Rivett et al (2013) a research team (ICOMMS) at the University of Cape Town the Civil

Engineering department, developed a cell-phone application to improve the groundwater

quality data collection in the rural communities, South Africa.

55

Haron et al (2009) developed a remote water quality monitoring system using wireless

sensors for prawn farming pond

In figure 4.4 and figure 4.5 shows the initial and the final model. The initial model as in figure 4.4

(Mahwayi & Joseph 2016) was revised further after discussions with experts to come up with the

final model for the study in figure 4.5.

4.6.3.2 Initial model

The initial model was presented at the ICIDA’16 conference to get opinions, views of the proposed

model for groundwater quality monitoring in rural areas, as the literature survey was done on

similar works to revise and enhance the model. In the initial model the technologies that can be

used for monitoring quality of groundwater in rural areas were proposed.

Figure 4.4: Initial model for groundwater quality monitoring using cloud computing

56

4.6.3.3 Final model

In the final model the initial model was revised based on the suggestions and inputs from the experts

during the qualitative interviews and based on phase 2. The DWS experts suggested that addition

of doing the monitoring using primary data source will add more weight to the model, as this will

give immediate contamination detection in groundwater as opposed to secondary data that may be

available after an individual has loaded the data into the database. The Initial model sourced the

data for groundwater from a database. If the database is not already in the cloud, the data is copied

to a database hosted in the cloud and this will allow the interpretation of the data from the cloud.

Figure 4.5: Final model for groundwater quality monitoring using cloud computing

4.6.3.4 Functionality of the model

A user with a web-enabled device will access a URL link to retrieve the groundwater quality

monitoring web page using a web browser. A login screen will be presented to the user prompting

them to enter a username and a password. After a successful login, the system home page will be

presented to the user. The user will have a 360 degrees’ view of a borehole or boreholes from the

time the monitoring began to present. Secondary data provide historical events while primary data

57

provide the current events. The array of sensor design was adapted from Jain et al (2014). The

design allowed various sensors to be added to the system. As the water is pumped from the ground,

the sensors start taking the groundwater samples. The measured values from the sensors are loaded

to the database. This can be a database on a local premise or a database in the cloud. If the database

is on a local premise, the data will be transported to a database hosted in the cloud. The measured

results can be viewed using a web-enabled device with internet access from anywhere any the

world.

4.6.4 Testing and evaluation

The testing was conducted in two phases. First phase was to test the secondary data provided by

the Department of Water and Sanitation to ensure that the data is analysed and interpreted correctly

by the system. Unit testing, functionality testing and end to end testing were performed. Phase 2

of the dissertation aimed at testing the real-time data with the secondary data and three parameters

were compared, Temperature, pH and Electrical conductivity. Table 4.11 summarises the results

of the testing. Groundwater from the Emalahleni borehole was collected and stored in a bucket to

be able to do offsite testing of the water using sensors and samples were taken over a month with

daily 10 minute sampling intervals.

4.7 System outputs

Figures below shows the system screenshots. All the user interface screens were designed during

the designed phase of the system. Figure 4.6 shows the system login screen that will appear after

a user calls the system URL from a browser. Authorised users will be able to login to the system

from this page.

58

Figure 4.6: Login screen

Figure 4.7 shows the search page that appears after a user has been successfully authenticated to

the system. A user can do a search based on the filters with drop down combo box and these include

a province, municipality, borehole, elements and a date range. After a search has been performed,

the search results will appear on the result grid as it can be seen in figure 4.8. The system presents

the groundwater information in forms of reports graphs and charts to make it easier for the users to

understand the information.

Figure 4.7: Search page

59

Figure 4.8: Result view

Figure 4.8 illustrate the results after a user has performed a search. In the result widget on the left

displays the monitoring point or name of the boreholes, the status indicating whether the borehole

is active or not, the date that the sample was taken and also the element together with the measured

values. The results are then presented in a form of a bar graph on the right as shown in figure 4.8.

The system can be accessed online using a web-enabled device with internet access using the

following URL: http://196.41.123.37:8081/wms/Controller.jpf

60

5 CHAPTER 5: FINDINGS AND DISCUSSION

5.1 Introduction

Based on the discussion in Chapter 4, the study demonstrated the use of a cloud computing platform

to monitor the groundwater from the boreholes in rural areas. The study also demonstrated the

monitoring of groundwater using secondary data and primary data with the system developed to

test the model proposed. The elements that affect the quality were also analysed using the

developed Java web application that was linked to MySQL database and the microcontroller based

system. This whole system was deployed into the public cloud during the implementation and

loading phase of the database life cycle (DBLC).

5.2 Motivation use of cloud computing

Although monitoring of water quality can be performed using different approaches which include

fixed continuous monitoring systems, sampling with portable devices and post-analysis. Typically,

these approaches to an extent are not scalable and flexible enough to be used for large geographical

areas. The turnaround time with these approaches turn to be longer as well when collecting water

samples from the boreholes. An example is the traditional monitoring borehole sampling method

(Mpenyana-Monyatsi et al. 2012). The method required a lot of human interventions when it comes

to water samples data collection, hence it required someone to visit each and every borehole of the

100 boreholes monitored in the study. This alone took a duration of 3 months to gather the water

samples data from the boreholes. The other cons with existing approaches is the issue of data

management and data centralization for easy access. At any point in time, an entity would require

to know about current status of the drinking water for any borehole in question. Without any

portable devices to check the water quality in real time, it would be impossible for the entity to get

such information. Cloud computing and electronic sensors enable the monitoring of water quality

to be monitored in both real time and non-real time without human interventions. It reduces the

data collection process to be completed in seconds as opposed to the 3 months it took (Mpenyana-

61

Monyatsi et al. 2012). Any entity can access the water quality results from anywhere and from any

web-enabled device with internet access. The cloud also centralizes the water samples data which

enables easy integration with other systems these include GIS, electronic sensors placed in different

locations, business intelligent application for data interpretations and reporting and also mobile

platforms application via API’s. Rivett et al (2013) with the mobile application developed for the

purpose of data collection at the water sites. The data can be taken directly to the database in the

cloud instead of pushing the data to the private network database via GSM. This will also reduce

the internal IT support and setup cost which include hardware, licenses and labour. The use of the

cloud lets the water operators to focus on the what, how and why is the water contaminated instead

of focusing on the what and how to collect the water samples. In terms of the cost-effective of the

cloud, the cloud consumer pays for the usage on a monthly basis, no need to worry about the

management of the infrastructure which includes maintenance, server’s connectivity, hard disk

space and most importantly performance issues. The issues that always arises with cloud computing

implementations is the internet connectivity and data security, just like any other implementation

weather is hosted on consumers premises or in the cloud. Connectivity and data security need to

defined and addressed accordingly depend on the requirements for the solution.

5.3 Conclusion

Domestic water quality remains the greatest contributing factor to human health. Systems that will

pro-actively monitor the source of drinking water are needed, especially in rural areas. In this

study, three phases were conducted to answer the research question “How do we develop a model

for groundwater quality monitoring using cloud computing” and its sub-objectives. Firstly, the

study determined the constituents that highly contaminates the boreholes and this was carried out

by analysing the existing groundwater boreholes data (secondary data) provided by the Department

of Water and Sanitation (DWS) or by manually checking the properties using sensors (primary

data) and making this available in the database. The cloud was used to monitor the quality of

groundwater. In the second phase a cloud computing model was developed for groundwater quality

62

monitoring in the rural areas. The third phase qualitative interviews were conducted to get more

insight on contamination of groundwater. Based on the above mentioned phases the developed

model was revised to propose a suitable final model for groundwater quality monitoring in rural

areas. It is believed that the proposed model will effectively monitor groundwater in rural areas of

Mpumalanga province.

5.4 Future work

New water quality sensors are actively being designed and increasingly appearing on the market.

There is currently a huge gap in the South African market for the effective implementation in water

utilities. The future work will investigate the use of the technology proposed in this study with a

real end user test market to enable the demonstration of the proposed model and also demonstrate

the benefits and then gain production efficiency through scale-up. Apart from the analysis of

ground water for domestic use the future work will also explore the use of this technology in other

areas which include recreational, Industrial water use and agriculture water use (E.g. Irrigation,

livestock watering and Aquaculture).

63

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69

ANNEXURE

70

Annexure A: (PIC16F877A Source code)

/*Project: Groundwater Monitoring Model

Student Name: MP Mahwayi

Student No: 200802748

Course: MTech Electrical Engineering

*/

#include <pic.h>

#include <htc.h>

#include <stdlib.h>

#include <stdio.h>

#include <conio.h>

#include <string.h>

#include "lcdUJ.h"

#include "usart.h"

#define _XTAL_FREQ 4000000

__CONFIG(FOSC_XT & WDTE_OFF & PWRTE_ON & BOREN_OFF & LVP_OFF &

CPD_OFF & WRT_OFF & CP_OFF);

unsigned char Vstr[5];

unsigned int result,result1,Scan1,Scan2,distance;

const unsigned char mes1[]= "parking 1 Open";

const unsigned char mes2[]= "parking 2 Open";

const unsigned char mes3[]= "parking 1 Taken";

const unsigned char mes4[]= "parking 2 Taken";

unsigned char strRES1[5],strRES2[5];

char rxchar, i = 0, flag = 0;

unsigned char rxarray[80];

int adc_read();

void main()

{

TRISD=0;

TRISA=0xff;

TRISC=0B10000000;

PORTD=0;

lcd_init();

lcd_clear();

USARTInit();

while(1)

{

ADCON1=0b10000000;

ADCON0=0b01000001;

__delay_us(20);

result=adc_read();

__delay_ms(100);

Scan1=(result*4.88);

lcd_clear();

if(Scan1<=2200)

{

71

putch('A');

putch(' ');

RD0=1;

lcd_goto(0x00);

lcd_putrs(mes1);

}

if(Scan1>2400)

{

putch('B');

putch(' ');

RD1=1;

lcd_goto(0x00);

lcd_putrs(mes3);

}

ADCON1=0b10000000;

ADCON0=0b01001001;

__delay_us(20);

result1=adc_read2();

__delay_ms(100);

Scan2=(result1*4.88);

__delay_ms(1000);

if(Scan2<=2200)

{

putch('C');

putch(' ');

RD3=1;

lcd_goto(0x28);

lcd_putrs(mes2);

}

if(Scan2>2400)

{

putch('D');

putch(' ');

RD2=1;

lcd_goto(0x28);

lcd_putrs(mes4);

}

}

lcd_clear();

}

int adc_read()

{

unsigned int x=0;

GO=1;

while(GO)continue;

x=(ADRESH<<8)+ADRESL;

return x;

}

72

int adc_read2()

{

unsigned int y=0;

GO=1;

while(GO)continue;

y=(ADRESH<<8)+ADRESL;

return y; }

73

Annexure B: Procedure for traditional sampling

Below table illustrate the type of bottles used by water operators to take the water samples with

minimum size required onsite for domestic water quality. The samples are therefore taken to the

laboratory for checking possible contamination. The table is adapted from the Department of Water

Affairs and Forestry et al.(2003).

Procedure for traditional sampling

74

The figures below illustrate the step by step procedures for collecting water samples. The

procedures are documented to assist water operators when taking water samples in rivers, streams,

lake dam or reservoirs. The procedures and steps the water operator do in preparation before taking

the water samples from the borehole are as follows:

First check for any possible vapours, especially at landfill and fuel-spilled areas before

opening any borehole.

First measurement that must be taken when collecting water samples at the borehole is the

water level followed by the depth of the borehole. If the borehole has been sampled before,

the depth measurement will indicate whether borehole collapsed or silting has occurred.

Microbiological samples examination should be collected first if samples for chemical

and microbiological analyses are collected from the same borehole. This precautionary

measure is to avoid the danger of contamination of the water at the sampling point.

Procedures should be followed when taking water samples

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Preparation procedure before taking the water samples from the borehole

Sampling of borehole without pump

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Sampling of borehole with pump

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Annexure C: Information required for data sheets

The information data sheets that goes together with the collected water samples. The datasheets

provide information to the laboratory to where the samples were taken, type of water source, the

station number, the date when the samples were taken and other related information.

Information required for data sheets (Adapted from the Department of Water Affairs and Forestry

et al. 2003).

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Annexure D: Equipment checklist for domestic water quality

Equipment checklist for domestic water quality (Adapted The Department of Water Affairs et al.

2003). The checklist is used by the water operator to check that all the sampling equipment are

available before going onsite to take water samples for domestic use.

Equipment checklist for domestic water quality (Adapted from the Department of Water Affairs

and Forestry et al. 2003).

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Annexure E: System for groundwater quality monitoring

System for groundwater quality monitoring using cloud computing