Advancements in Monitoring Water Quality Based on Various Sensing Methods: A Systematic Review

Abstract

Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness.

Full text

Citation: Zainurin, S.N.; Wan Ismail,
W.Z.; Mahamud, S.N.I.; Ismail, I.;
Jamaludin, J.; Ariffin, K.N.Z.; Wan
Ahmad Kamil, W.M. Advancements
in Monitoring Water Quality Based
on Various Sensing Methods: A
Systematic Review. Int. J. Environ.
Res. Public Health 2022,19, 14080.
https://doi.org/10.3390/
ijerph192114080
Academic Editor: Yung-Tse Hung
Received: 12 May 2022
Accepted: 20 September 2022
Published: 28 October 2022
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4.0/).
International Journal of
Environmental Research
and Public Health
Review
Advancements in Monitoring Water Quality Based on Various
Sensing Methods: A Systematic Review
Siti Nadhirah Zainurin 1, Wan Zakiah Wan Ismail 1,* , Siti Nurul Iman Mahamud 2, Irneza Ismail 1,
Juliza Jamaludin 1, Khairul Nabilah Zainul Ariffin 1and Wan Maryam Wan Ahmad Kamil 3
1Advanced Devices and System, Faculty of Engineering and Built Environment, Universiti Sains Islam
Malaysia, Nilai 71800, Negeri Sembilan, Malaysia
2TF AMD Microelectronics Sdn Bhd, Kawasan Perindustrian Bayan Lepas,
Bayan Lepas 11900, Pulau Pinang, Malaysia
3School of Physics, Universiti Sains Malaysia, Gelugor 11800, Pulau Pinang, Malaysia
*Correspondence: drwanzakiah@usim.edu.my
Abstract:
Nowadays, water pollution has become a global issue affecting most countries in the
world. Water quality should be monitored to alert authorities on water pollution, so that action
can be taken quickly. The objective of the review is to study various conventional and modern
methods of monitoring water quality to identify the strengths and weaknesses of the methods. The
methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical
techniques. In this review, water quality monitoring systems and process control in several countries,
such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional
and modern methods are compared in terms of parameters, complexity, and reliability. Recent
methods of water quality monitoring techniques are also reviewed to study any loopholes in modern
methods. We found that CPS is suitable for monitoring water quality due to a good combination
of physical and computational algorithms. Its embedded sensors, processors, and actuators can be
designed to detect and interact with environments. We believe that conventional methods are costly
and complex, whereas modern methods are also expensive but simpler with real-time detection.
Traditional approaches are more time-consuming and expensive due to the high maintenance of
laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications.
Apart from that, previous monitoring methods have issues in achieving a reliable measurement
of water quality parameters in real time. There are still limitations in instruments for detecting
pollutants and producing valuable information on water quality. Thus, the review is important in
order to compare previous methods and to improve current water quality assessments in terms of
reliability and cost-effectiveness.
Keywords:
embedded sensors; water quality monitoring system; water pollution and sensing methods
1. Introduction
Water pollution is a detrimental issue that should be taken seriously by the govern-
ment, private sectors, non-private sectors, and the public. It is because 70% of the earth is
made up of water, and the human body is made up of more than 60% of water [
1
]. Apart
from that, the main water supply in Malaysia is originated from 99% of surface water and
1% of groundwater [
2
]. World Health Organization (WHO) states that clean and safe water
is important for drinking, household use, industries, and health, where polluted water and
poor sanitation can cause transmission diseases such as cholera, diarrhea, hepatitis, skin
infection, typhoid, and other health risks [
3
]. For instance, 2300 people were affected by
drinking contaminated water, and as a result, an outbreak of a waterborne disease epidemic
happened in Walkerton, Ontario, Canada, in 2000, which was sourced from cattle manure
from a nearby farm [
4
,
5
]. Water pollution in Lake Toba in North Sumatra was also caused
Int. J. Environ. Res. Public Health 2022,19, 14080. https://doi.org/10.3390/ijerph192114080 https://www.mdpi.com/journal/ijerph

Int. J. Environ. Res. Public Health 2022,19, 14080 2 of 21
by household, industrial, agricultural, and public transportation wastes [
6
]. Thus, it has
affected the tourism industry and local residents’ health [6].
Water is used for drinking, agriculture work, industry operations, and cleaning. Water
needs to be clean and safe because contaminated water can endanger human and aqua-
culture ecosystems. Malaysia has abundant water resources, but the accelerating pace of
industrial developments, urbanization, and population growth has impacted water quality.
States with large numbers of industrial areas and factories, such as Selangor, Johor, Penang,
and Perak, have high levels of river pollution compared to other states in Malaysia. Move-
ment Control Order (MCO) was enforced from 18 March to 4 May 2020, when Malaysia
was badly affected by COVID-19. Malaysia enforced ‘Ops Air Raya’, where two factories,
a glove factory in Ipoh, Perak and an oil palm mill in Johor, had their operations sus-
pended [
7
]. Thus, restricted business activities during MCO, which affect human mobility
restrictions, and anthropogenic activities have directly shown a positive impact as they
improved the quality index of water [
8
]. The three most common anthropogenic activities
are industrial areas, sewage, agricultural activities, and animal husbandry activities. Kim
Kim River in Johor also faces a problem of illicit untreated sewage wastewater discharge
due to premise maintenance works [
9
]. In 2019, Kim Kim River had the worst river pollu-
tion due to illegal chemical waste dumping, and 6000 affected people were hospitalized.
Apart from that, natural disasters such as floods can also pollute the water. In 2014, a huge
flood occurred in Kelantan, Malaysia leading to murky and contaminated river. Due to the
shortage of water supply during the flood, the victims had to use the polluted water for
everyday usages, such as drinking, cooking, and bathing [
10
]. This situation caused the
spreading of waterborne disease outbreaks such as diarrhea, malaria, and cholera. In other
words, water pollution can cause deaths, unhealthy life, destruction of ecosystems, and
contagious diseases.
Thus, a water quality monitoring system is considered the best solution to provide
early assessments of contaminants in water. In Malaysia, monitoring water quality is
normally performed by the government, assisted by private sectors, including weekly
checks on groundwater, rivers, and lakes. Without adequate awareness of the importance
of quality water management, Malaysia will face a water crisis by 2025 [
2
]. Groundwater is
one of the critical sources of drinking water for households because 79% of the total popu-
lation across 10 southeast Asia and pacific countries use groundwater which has common
concerns related to pollution from unsafe sanitation, leading to dry season shortages and
others [
11
]. Moreover, contaminants that lead to water pollution can be detected through
physical, chemical, and biological properties, as shown in Table 1[
12
]. These water quality
properties are important to determine water suitability for human consumption and ecosys-
tem health. River water quality status from 2008 until 2020 is depicted in Figure 1[
13
].
A total of 672 rivers have been monitored regularly. In 2020, 443 (66%) rivers show good
quality of water, 195 (29%) rivers are slightly polluted, and the remaining
34 (5%) rivers
are
polluted. Water samples were collected from designated stations for in situ and laboratory
analysis to determine physical, chemical, and biological characteristics. The Water Quality
Index (WQI) is used to determine the quality of water and to indicate the pollution level
based on National Water Quality Standards for Malaysia (NWQS) (ANNEX). The six water
quality parameters used to determine the water quality index are dissolved oxygen (DO),
biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids
(TSS), ammonia, and pH value [
14
,
15
]. Figure 1shows the water quality for rivers in
Malaysia from 2008–2020. The quality of river water fluctuates across three types of rivers;
unpolluted rivers, slightly polluted rivers, and polluted rivers. The highest percentage of
polluted rivers is 13% (2010), and the lowest percentage of polluted rivers is 5% (2020).

Int. J. Environ. Res. Public Health 2022,19, 14080 3 of 21
Table 1. Water Quality Properties.
Chemical Properties Biological Properties Physical Properties
Dissolved oxygen(DO), chemical
oxygen demand (COD), biological
oxygen demand(BOD)
Bacteria Turbidity
pH level
Ammonia
Salinity
Harness
Organic Compounds
Metals
Algae
Viruses
Temperature
Color
Taste and Odor
Suspended Solids
Metals
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 3 of 23
Table 1. Water Quality Properties.
Chemical Properties Biological Properties Physical Properties
Dissolved oxygen(DO), chemical oxy-
gen demand (COD), biological oxygen
demand(BOD)
Bacteria Turbidity
pH level$$$Ammonia$$$Salin-
ity$$$Harness$$$Organic Com-
pounds$$$Metals
Algae$$$Viruses Temperature$$$Color$$$Taste and
Odor$$$Suspended Solids$$$Metals
Figure 1. River Water Quality Trends from 2008 until 2020 year versus percentage number of rivers
(%) in Malaysia [13]. Blue bar chart refers to unpolluted rivers; yellow bar chart refers to slightly
polluted rivers; red bar chart refers to polluted rivers; green line shows total number of rivers.
Here, we review recent methods of monitoring water quality in terms of features and
parameters used. Section 2 discusses various water quality monitoring methods where
traditional methods are compared with modern methods. Various water quality monitor-
ing methods in many countries are discussed in this section. Some methods have the po-
tential to be repeated and enhanced to produce a better water quality monitoring system.
The review also compares previous water quality monitoring systems from 2015 until
2022 to detect many types of contamination with different approaches. Water quality
monitoring methods which are based on the Internet of Things (IoT), real-time monitor-
ing, wireless sensor network (WSN), filtration including traditional methods, and optical
techniques, are discussed in Section 3. The existing techniques of water quality monitoring
systems show reliable data with efficient processes. Therefore, the main objective of this
review is to study existing methods of monitoring water quality, such as real-time with
IoT, virtual sensing, and cyber physical systems (CPS) based on time, instrumentation,
types of water quality parameters, or contaminants. Then, the strengths and weaknesses
of the methods can be identified. CPS can support real-time monitoring, and performance
can be guaranteed in safety-critical applications [16]. We discuss essential components of
CPS in water quality monitoring, such as its history, benefits, and working principles.
Thus, we believe that CPS is a reliable technique for water quality monitoring systems.
2. Comparison of Various Water Quality Monitoring Methods
Figure 1.
River Water Quality Trends from 2008 until 2020 year versus percentage number of rivers
(%) in Malaysia [
13
]. Blue bar chart refers to unpolluted rivers; yellow bar chart refers to slightly
polluted rivers; red bar chart refers to polluted rivers; green line shows total number of rivers.
Here, we review recent methods of monitoring water quality in terms of features and
parameters used. Section 2discusses various water quality monitoring methods where
traditional methods are compared with modern methods. Various water quality monitoring
methods in many countries are discussed in this section. Some methods have the potential
to be repeated and enhanced to produce a better water quality monitoring system. The
review also compares previous water quality monitoring systems from 2015 until 2022 to
detect many types of contamination with different approaches. Water quality monitoring
methods which are based on the Internet of Things (IoT), real-time monitoring, wireless
sensor network (WSN), filtration including traditional methods, and optical techniques, are
discussed in Section 3. The existing techniques of water quality monitoring systems show
reliable data with efficient processes. Therefore, the main objective of this review is to study
existing methods of monitoring water quality, such as real-time with IoT, virtual sensing,
and cyber physical systems (CPS) based on time, instrumentation, types of water quality
parameters, or contaminants. Then, the strengths and weaknesses of the methods can be
identified. CPS can support real-time monitoring, and performance can be guaranteed in
safety-critical applications [
16
]. We discuss essential components of CPS in water quality
monitoring, such as its history, benefits, and working principles. Thus, we believe that CPS
is a reliable technique for water quality monitoring systems.

Int. J. Environ. Res. Public Health 2022,19, 14080 4 of 21
2. Comparison of Various Water Quality Monitoring Methods
Water treatment plant and water distribution system have their specific water quality
monitoring tools to detect contaminants and check the suitability of the water for drinking
purposes. In order to develop robust and efficient techniques with minimum operating
cost and energy, numerous sensing and monitoring analyses research have been conducted
over the past decades. There are still some tool limitations for detecting pollutants. Thus,
current water quality assessments need to be improved. Khatri et al. [
17
] proposed a water
monitoring system using a Raspberry Pi-based hardware platform. The system used a
python framework for the development of a graphical user interface (GUI) and fuzzy logic
for decision making. Apart from that, another system [
18
] used wireless sensor networks to
continuously monitor water quality in remote places. The wireless sensor network (WSN)
system consists of three parts: data monitoring nodes, a base station of data, and a remote
monitoring station. The software design used MATLAB to interact with the hardware at
the remote monitoring station. Development of a water monitoring system used Arduino
that interfaced with LabVIEW that controlled pH level, turbidity, and temperature. The
data are displayed in a graphical user interface (GUI) [
1
]. Table 2summarizes the existing
water quality monitoring systems from 2015 until 2022.
Table 2. Previous works on water quality monitoring system.
No. Water Quality Monitoring Method Types of Contaminant/Water
Quality Parameters
Real-Time
Monitoring
References
1.
IoT environment such as Intel Galileo Gen 2 which
acted as an interface to obtain data from multiple
electronics sensors.
pH, dissolved oxygen
concentration, turbidity,
and temperature.
Yes [19]
2. Supervisory Control and Data Acquisition
(SCADA) system was integrated with IoT
technology to determine water contaminations,
leakage in pipeline and automatic measure of
several water parameters with Global System for
Mobile Communication (GSM) module.
Temperature, color, turbidiy. Yes [20]
3. Real-time bacteria sensor to detect four different
types of specified water pollution locations. The
result was compared with laboratory analysis. It
demonstrates the benefit of the bacteria sensor over
turbidity sensors in monitoring bacteria in drinking
water as early warning of
microbiological pollutions.
Bacteria or abiotic particles. Yes [21]
4. Several technologies for water quality monitoring
such as discontinuous sample-based methods for
biological and non-biological contaminants. Then,
for in-line sensor monitoring, there are sensor
placement approach, microfluidic sensors, and
spectroscopic techniques.
Biological: Escherichia coli (E.
coli), Intestinal enterococci.
Chemical: aldicarb, glyphosate,
colchicines and nicotine.
Physical: temperature,
conductivity, pH, ORP and
turbidity simultaneously.
No [22]
5. Near Infrared (NIR) reflectance spectroscopy to
predict water quality in Aceh River based on
salinity and total dissolved solids.
Total dissolved solids No [23]
6. Toxicity tests to detect chemical contaminants
specifically toxic and adenosine triphosphate (ATP)
level method to indicate contaminants
by microorganisms.
Toxicity level and
microorganisms.
No [24]
7. IoT technology development such as ThingSpeak
to monitor the water quality. Data from multiple
sensors which are connected to Arduino is sent to
cloud using ThingSpeak.
pH value, turbidity, level of
water in the tank, temperature,
and humidity.
Yes [25]

Int. J. Environ. Res. Public Health 2022,19, 14080 5 of 21
Table 2. Cont.
No. Water Quality Monitoring Method Types of Contaminant/Water
Quality Parameters
Real-Time
Monitoring
References
8. A rapid Ultraviolet (UV)/Visible (UV-Vis)
spectroscopy method for water quality monitoring
and the water is sourced from on-farm root
vegetable washing processes. The measurement is
based on UV-VIS absorbance and used statistical
methods such as principal component analysis
(PCA) and partial least squares (PLS) regression.
Physical: Suspended solids, pH,
BOD, COD, color dilution.
Chemical: Organic substances,
nitrogen, phosphorus
Biological: E.coli, coliform
bacteria pathogenic
microorganisms
No [26]
9. Biological and chemical contaminants in water
where polymerase chain reaction (PCR) is a
suitable method for bacteria detection in water
samples, based on extraction and replication of
deoxyribonucleic acid (DNA) fragment samples.
Microorganisms and viruses. Yes [24]
10. IoT based system in water monitoring by adding
LEDs. The LEDs lighted up depending on the
range of water quality that was detected by several
sensors. The system was connected to Raspberry Pi,
programmed with Java.
pH, turbidity, chlorine, nitrate,
and electrical conductivity.
Yes [27]
11.
Surface-enhanced Raman Scattering (SERS) as new
modern bacteria detection method, based on
ultrasensitive vibrational spectroscopy in surface
and water waters.
Bacteria No [28]
12. An efficient bacterial rapid detection using
laser-induced fluorescence (LIF) spectroscopy
technology based on the fluorescence intensity ratio
(FIR) and fluorescence intensity to retrieve the
bacteria concentrations.
Bacteria: E.coli,K.pneumonia,
S.aureus
Yes [29]
13. A wireless multi-sensor system by integrating the
temperature, pH, DO, and EC sensors with an
ESP32 Wi-Fi module platform to monitor water
quality of freshwater aquaculture. The estimated
salinity level is by EC level sensing data.
Temperature, pH, DO, electrical
conductivity (EC), salinity level
Yes [30]
14. A multi-source transfer learning (MSTL) for water
quality prediction and effectively used water
quality information of multiple nearby monitoring
points to improve the water prediction accuracy
and reduce bias.
Water quality information such
as DO, phosphate, water
temperature, nitrite
Yes [31]
15. Virtual sensing system feasibility from physical
sensor methods for water quality assessment and
focused on the water use for
agricultural purposes
.
pH, turbidity, temperature,
conductivity, DO, total
phosphorus
Yes [32]
16. Various effective water quality monitoring system
(WQSN) for fishponds using IoT and underwater
sensors to record the parameter values
continuously in the regular time interval using
Arduino/Raspberry Pi board.
pH, DO, nitrogen, ammonia,
temperature
Yes [33]
17. An online UV-Vis Spectrophotometer for drinking
water quality monitoring and process control. The
approach is reagent-free, does not require sample
pre-treatments and can provide continuous and
reliable water parameter measurement with
quicker response compared to
conventional techniques.
Color, dissolved organic carbon
(DOC), total organic carbon
(TOC), turbidity, nitrate
Yes [34]

Int. J. Environ. Res. Public Health 2022,19, 14080 6 of 21
Hu et al. [
7
] applied water quality monitoring sensors which were placed in a water
distribution system (WDS). WDS is a system where water sources, tanks, and connections
are represented by nodes and borders to indicate pipes between the nodes. The system
could be well implemented in many applications such as the health monitoring industry,
smart buildings, localization, estimation and prediction, and diagnosis of fault [
7
]. However,
the system can create some problems and difficulties, such as data obtained can be very
complex in nature, leading to uncertainty and high cost. Several types of water quality
monitoring sensors were used to detect water quality parameters, and Arduino was also
used to integrate with the sensors to display the data efficiently [
8
]. The method used
Arduino to receive reading values from every sensor, and then the data were sent to the
Raspberry Pi through the internet [8].
Another enhancement was made by Y. K. Taru et al. [
1
], where the system interfaced
the Arduino with the LabVIEW, which increased the performance of data acquisition. The
system was flexible and easy to operate and install. The application of fuzzy logic to make
a decision making was also developed by Khatri et al. [
17
], where the fuzzy approach was
developed in MATLAB, and a Python framework was used to calculate the water quality
index. Apart from that, optical techniques based on light propagation theory are important
to track down the location sources and timing of sewage contaminations in real-time field
settings with minimal cost, easy-to-handle process, and high accuracy results. Statistical
relations of optical properties of water samples, such as reflection, refraction, fluorescence,
and absorbance spectra can be used to calibrate and discover sewage contamination by
using optical spectroscopy. For instance, optical technique that has been used recently is a
vibrational spectroscopy. The instruments used for vibrational spectroscopy are Infrared
(IR) and Raman spectrometer [
22
]. IR and Raman Spectroscopy are two commonly used
vibration spectroscopy techniques for chemical and biological analysis that allow rapid
and simple non-destructive measurement of several parameters simultaneously [
35
]. The
approach is widely utilized in the study of the liquid and gas phases of water as it is highly
dependent on the sample’s physical state.
Furthermore, a fluorescence spectroscopy method is required for the rapid detection
of three common pathogenic bacteria such as E.coli,K. pneumonia, and S. aureus, with high
sensitivity and efficiency to maintain water quality [
29
]. LIF (laser-induced fluorescence)
was conducted using a UV laser as an excitation light source to excite dilutions that contain
bacteria, and a spectrometer was used to receive fluorescence emission spectra concurrently.
This study also analyzed various bacteria concentration gradients and proved that a good
linear relationship exists between the height of fluorescence peak and bacteria concentration.
Inactive E.coli does not influence the fluorescence peak position compared to active E.coli.
The peak height of fluorescence differs greatly because inactive bacteria cannot grow
continuously. Five critical factors that need attention are water temperature, pH, DO, EC,
and salinity levels. Then, a wireless multi-sensor system was proposed, where an ESP 32
Wi-Fi module and Wi-Fi access point (AP) were integrated and displayed in the ThingSpeak
IoT platform to monitor water quality parameters of freshwater aquaculture [
30
]. The
authors mentioned that in order to estimate the level of salinity, the EC level information
was acquired from the EC sensor. High-sensitivity sensors were used to provide good data
accuracy and reliability. Based on the viewpoint of smart sensor aquaculture, the technique
provides a simple feature for set up and maintenance, more cost-effective, simultaneous
on-site monitoring, and thus, the overall system is highly reliable to use.
2.1. Traditional Methods versus Modern Methods in Monitoring Water Quality
Traditional methods can be used to monitor water quality. It is based on sample
collection on site, chemical, physical and microbiological analysis performed in a labora-
tory. This technique involves labor and is cost-intensive [
17
]. Outcomes from traditional
methods normally can be accessed after a few days, whereas modern methods can produce
output in real time. An example of the traditional methods is performed by Central Water
Commission. Water samples are collected from specific locations within the processing and

Int. J. Environ. Res. Public Health 2022,19, 14080 7 of 21
distribution system, and the samples are tested at well-equipped laboratories. Samples of
raw water, filtered water, and treated water were analyzed, and water quality parameters
such as pH, turbidity, and dissolved oxygen were estimated by using lab-based equip-
ment [
36
]. Results can be questionable due to errors from field sampling and equipment
miscalibration. Apart from that, the sampling method can be very time-consuming due to
the complicated process. The disadvantages of the traditional method are that the system
is not continuous and not reliable as human energy is used to handle work, and the testing
frequency can be very low [
36
]. The analysis works are normally carried out by a skilled
person with high accuracy parameter detection results. Apart from that, laboratory facilities
and maintenance are expensive [
32
]. The traditional laboratory methods consume more
time, are costly, use chemical materials, and cannot give real-time readings [
22
]. Hence, the
analysis lacks the continuous monitoring of systems.
Amrita et al. [
37
] performed a survey on water quality analysis between traditional
and modern methods. Modern methods have more benefits than traditional methods since
modern methods can produce output results and analyze the water quality parameters
in real time. After a quick identification of poor water quality, faster action can be taken
to handle undesired substances in the water. The traditional methods can cause delay
and manual errors, which possibly occurs during the processes [
27
]. The traditional
methods are basically based on sampling and monitoring water samples [
37
], and the
analysis is performed in a laboratory. Errors can occur while doing sample preparation
in the laboratory. Amrita et al. [
37
] used the titration method to determine water quality
parameters in traditional ways. The titration method is time-consuming as it cannot be
carried out within a day. The titration method is used to determine the carbon dioxide level
in a solution using sodium hydroxide. By using potentiometric, pH can be determined
when there is an exchange of ions between the swollen layer and the H+ ions in the emf.
The swollen layer was formed when the outer layer of the glass bulb was hydrated as
the glass electrode was dipped in water [
37
]. The method was developed using wireless
sensor nodes to monitor water quality. The system consisted of ten parameters which were
monitored inside node boxes, and it was connected through Wi-Fi with the wireless sensor
node. There was an access point to send data to the farmers. If any problem occurred,
the alarming pattern was used [
37
]. In contrast to the modern method, where sensors
can monitor water quality parameters more quickly and deliver better results, the manual
method of assessing water quality in aquaculture cannot produce consistent results, and it
requires more time and more manpower. Due to the time-consuming and complex setup,
the contents of the water sample may change and, therefore, produce less valuable data
for monitoring water quality [
25
]. Thus, implementing more sensors can enhance the
functionality of water quality monitoring system that indirectly can help authorities to
implement quick measures to improve water quality.
2.2. Methods of Monitoring Water Quality in Various Countries
Many water quality monitoring methods have been established in many countries
recently. For example, a fully automated nitrate monitoring station was created using
the optical sensor [
38
], where the applications of UV optical nitrate sensors on surfaces
and groundwater were introduced. The results showed that most of the nitrate variation
had been observed at or near the water edge, with annual maxima occurring in late
winter/early spring between the months of August and November due to leaching from
New Zealand agricultural land [
38
]. Apart from that, Li et al. [
39
] established a new
multistage decision support system with a complex multi-criteria decision making (MCDM)
for regional water quality assessment to overcome issues regarding regional water quality
assessment. The system consisted of three stages. The first stage involved 21 multiple
water quality indicators excluded the temperature indicators, and it used the probabilistic
linguistic term set (PLTS) technique to process massive monitoring data. For the second
and third stages, the proposed methods, such as regression-based decision-making trial
and evaluation laboratory (DEMATEL), generated relative weight that considered the

Int. J. Environ. Res. Public Health 2022,19, 14080 8 of 21
interrelationship of indicators and then further formed combined weight by balancing
single-factor weight. For the final stage, a new LTS measure was demonstrated, and the
fuzzy technique was extended to provide assessment findings. The proposed method
was then used to investigate the water quality status of sixteen administrative districts in
Shanghai, China [39].
Furthermore, Horvat et al. [
40
] developed an in-depth analysis of water quality in
Lake Palic, Serbia. The analysis was performed by taking water quality measurements
for 9 years, from 2011 to 2019. A principal component analysis (PCA) and machine
learning classification methods were used to identify a seasonal feature of the water quality,
and a fitted model was created via multivariate regression to determine water quality
parameters [
40
]. Hasan et al. also used the multivariate analysis method to determine
the quality of groundwater in the northeast part of Bangladesh [
41
]. Multivariate analysis
was used in the system to interpret the water quality of selected pumps and to produce
important results that could not be obtained from a cursory examination of the data.
N. Khatri et al. [
42
] determined the pollution levels in the River Sabarmati, Gujarat,
India, and assessed the levels of multiple parameters with respect to drinking water
standards. The system used water quality parameters such as pH, turbidity, total dissolved
solids (TDS), total alkalinity, total hardness, chloride, ammoniacal nitrogen, biochemical
oxygen demand (BOD), dissolved oxygen, and conductivity. The correlation analysis matrix
showed that the basic ionic chemistry was influenced by these water quality parameters,
especially pH, EC, TDS, K
+
, Na
+
, Mg
2+
, and SO
42−
[
41
]. In order to give an overall
result of contamination in River Sabarmati, the Weighted Arithmetic Water Quality Index
(WAWQI) and the Canadian Council of Ministers of the Environment Water Quality Index
(CCMEWQI) were chosen. The results depicted differences between two indices in which
the WAWQI showed the River Sabarmati was severely contaminated, not suitable to drink,
and the condition of the river was even worse during the post-monsoon season, whereas
the water quality ranged from ‘fair to marginal’ according to CCMEWQI [
42
]. Furthermore,
the water quality of another river in India, River Netravati, was studied to determine heavy
metal contaminations [
43
]. The technique used was similar to Horvat et al. and Hasan
et al., where multivariate analysis was applied. Water and sediment samples were collected
from ten locations along the Netravati River basin during the pre-monsoon season of 2019,
and then hydrogeochemical features were investigated. Hydrogeochemical properties of
water are important to determine the types of water used for domestic, industrial, and
irrigation purposes. Metal contaminations were analyzed using multivariate techniques
and environmental indices. Environmental indices are applied to indicate the status of
water quality. In order to evaluate the water quality of the river, a comprehensive WQI
method was adopted. Based on twelve measured water quality parameters, WQI was
calculated for ten sampling stations. The analysis of total heavy metal concentrations and
distributions proved that sediments were slightly contaminated with heavy metals which
were due to increase in urbanization and agricultural practices, which changed the river
hydrological regimes. The persistent exposure to pollutants, even at low concentrations,
can cause changes in metabolic processes and changes in river community structure and
thus, pose a serious threat of aquatic life [43].
2.3. Water Quality Monitoring Methods in Malaysia
Water quality has become a major concern in Malaysia. Aquaculture activities can
impact the changes in water quality. Hettige et al. [
44
] performed research on water
quality in the aquaculture sites at the Rawang sub-basin of the Selangor River. They
quantified water quality parameters such as pH, dissolved oxygen (DO), ammoniacal
nitrogen, turbidity, total suspended solids (TSS), chemical oxygen demand (COD), and
biochemical oxygen demand (BOD) based on WQI. By using GIS (ArcGIS 10.2.1 software),
the Inverse Distance Weighted (IDW) can be developed to determine the status of the
water quality. The result indicates that the IDW can bolster identifying the potential
aquaculture-impacted sites along the river.

Int. J. Environ. Res. Public Health 2022,19, 14080 9 of 21
A study on the quality of river water in Penang and Klang during (MCO) has been
carried out by A. Najah et al. [
45
]. The impact of the MCO on the water quality index (WQI)
in Putrajaya Lake was also examined by using four machine learning algorithms [
45
]. The
water quality was improved during MCO as COD, BOD, and total suspended solid (TSS)
were reduced. WQI Class I increased significantly from 24% in February 2020 to 94% in
March 2020. Before MCO, the lake had only achieved WQI Class II for 94% in January and
76% in February 2020, respectively. During MCO in March 2020, the water quality of the
lake experienced a rapid shift from WQI Class II to I. This condition is the best recorded
WQI of Putrajaya Lake over the past 10 years. For WQI prediction, Multi-layer Perceptron
(MLP) outperformed other models in predicting the changes in the index with a high
level of accuracy. The sensitivity analysis results show that nitrogen content of ammonia
(NH3-N) and COD plays a vital role and contribute significantly to predicting the class of
WQI, followed by BOD, whereas the remaining three parameters; pH, DO, and TSS, do not
contribute significantly towards WQI [45].
Abdul Maulud et al. [
46
] studied water quality in Kelantan River, Kelantan, Malaysia,
during dry and rainy seasons by calculating the WQI based on the National Water Quality
Standards for Malaysia (NWQS). The variables measured in the system are temperature,
pH, TSS, DO, BOD, COD, ammonia nitrogen (AN), nitrate (NO
3
), phosphorus (P), and
manganese (Mn). Othman et al. implemented a water quality monitoring system to main-
tain the aquaculture industry (Tilapia) in Malaysia and added the features of LABVIEW.
The proposed system was in real-time, in which the data could be monitored continuously
with the capability of recording and analyzing each reading in a more efficient way [
47
]. An
alarm system was also available in the system to notify the users if any of the parameters
deviated. The outcome of the developed system showed that the water quality for the
tilapia industry could be measured by taking two parameters such as pH and tempera-
ture. The experiment shows that the percentage error between manual and automated
measurements is less than 7% for the temperature parameter [47].
3. Common Methods of Water Quality Monitoring System
3.1. Virtual Sensing System
A virtual sensing system is basically enhanced from a fully physical system [
32
]. In
contrast to a physical sensor, a virtual or soft sensor processes the accessible secondary
data through models and enables the prediction of target parameters [
32
]. It converts
several inputs from cheaper sensors and combines them to execute the outputs of more
complex and expensive sensors. This model is constructed with three main approaches,
which are knowledge-based, mechanism-based, and data-derived or machine learning
methods. The soft sensing technique can be implemented as an alternative method to
measure online water quality parameters, which are COD, BOD, chlorine, and total phos-
phorus [
48
]. Meanwhile, machine learning has the ability to extract informative data from
an accessible database. Thus, it proves that this method is an ideal framework for virtual
sensor applications. For example, the IBK algorithm machine learning (ML) based soft
sensor model is an alternative method for estimating BOD level. It shows that the BOD
soft sensors are efficient, reasonably accurate, and economical [
49
]. The system had been
tested, validated, and verified with the sewage data from the water treatment plant and
from the Ganges River. K-Nearest Neighbor (KNN) technique is another data-driven ML
algorithm that proved to be an efficient method for COD prediction and evaluation in terms
of response time and other performance matrices [
48
]. In the wastewater treatment plant,
a few indicators, such as BOD and COD, are difficult to perform timely with hardware
tools and hard to obtain accurate measurements [
50
]. The author proposed a soft measure-
ment model construction by the lion-swarm-optimizer-based extreme learning machine
(LSO-ELM). It can improve soft quality measurement in the wastewater treatment process
because the method is able to achieve satisfied prediction accuracy [
50
]. Therefore, virtual
sensing approach entirely promotes some benefits in cost and quality. However, the overall

Int. J. Environ. Res. Public Health 2022,19, 14080 10 of 21
system is quite complex with the expensive sensor replacement possibility from cheaper
sensors such as pH, temperature, DO, conductivity, and others.
Basically, there are three virtual-sensor (VS) constellations, including VS based entirely
on a physical sensor, VS based only on another VS, and VS based on both virtual and
physical sensors, as depicted in Figure 2. Virtual sensing intercorrelates with data captured
by physical sensors, which are embedded into software applications to implement the
algorithmic analytics from all the data sets given. VS is cheap as no equipment is needed to
buy and maintain. It is ideal for high-frequency monitoring because it does not require a
long chemical reaction process and can be easily scaled in many locations without extra
investment. For virtual sensor development in water quality monitoring, there are four
steps which are data acquisition, data pre-processing, model design, and model mainte-
nance (Figure 3). Data collection is the first step to developing the data-derived virtual
sensing and achieving the associated water quality targets [
51
]. Low-quality data lead to
low-quality models. Data inspection is used to investigate the prominent data structure
from data outliers, missing values, and others. The second step, data pre-processing, refers
to data processing that includes typical data cleaning, transformation, and reduction. It
can reduce the data size by redundant and non-relevant input reductions. Next, model
design is very important in virtual sensing development as the model structure selec-
tion is task-dependent, and currently, there is no standard approach to perform this task.
Normally, this step will start with a simple model type, performance verification, and
model system improvement. The model complexity will gradually increase to obtain the
expected outcome [
52
]. The last step is model maintenance. The model design that has
been constructed and evaluated needs to be maintained and updated regularly as the data
will change timely.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 10 of 23
[48]. Meanwhile, machine learning has the ability to extract informative data from an ac-
cessible database. Thus, it proves that this method is an ideal framework for virtual sensor
applications. For example, the IBK algorithm machine learning (ML) based soft sensor
model is an alternative method for estimating BOD level. It shows that the BOD soft sen-
sors are efficient, reasonably accurate, and economical [49]. The system had been tested,
validated, and verified with the sewage data from the water treatment plant and from the
Ganges River. K-Nearest Neighbor (KNN) technique is another data-driven ML algorithm
that proved to be an efficient method for COD prediction and evaluation in terms of re-
sponse time and other performance matrices [48]. In the wastewater treatment plant, a
few indicators, such as BOD and COD, are difficult to perform timely with hardware tools
and hard to obtain accurate measurements [50]. The author proposed a soft measurement
model construction by the lion-swarm-optimizer-based extreme learning machine (LSO-
ELM). It can improve soft quality measurement in the wastewater treatment process be-
cause the method is able to achieve satisfied prediction accuracy [50]. Therefore, virtual
sensing approach entirely promotes some benefits in cost and quality. However, the over-
all system is quite complex with the expensive sensor replacement possibility from
cheaper sensors such as pH, temperature, DO, conductivity, and others.
Basically, there are three virtual-sensor (VS) constellations, including VS based en-
tirely on a physical sensor, VS based only on another VS, and VS based on both virtual
and physical sensors, as depicted in Figure 2. Virtual sensing intercorrelates with data
captured by physical sensors, which are embedded into software applications to imple-
ment the algorithmic analytics from all the data sets given. VS is cheap as no equipment
is needed to buy and maintain. It is ideal for high-frequency monitoring because it does
not require a long chemical reaction process and can be easily scaled in many locations
without extra investment. For virtual sensor development in water quality monitoring,
there are four steps which are data acquisition, data pre-processing, model design, and
model maintenance (Figure 3). Data collection is the first step to developing the data-de-
rived virtual sensing and achieving the associated water quality targets [51]. Low-quality
data lead to low-quality models. Data inspection is used to investigate the prominent data
structure from data outliers, missing values, and others. The second step, data pre-pro-
cessing, refers to data processing that includes typical data cleaning, transformation, and
reduction. It can reduce the data size by redundant and non-relevant input reductions.
Next, model design is very important in virtual sensing development as the model struc-
ture selection is task-dependent, and currently, there is no standard approach to perform
this task. Normally, this step will start with a simple model type, performance verification,
and model system improvement. The model complexity will gradually increase to obtain
the expected outcome [52]. The last step is model maintenance. The model design that has
been constructed and evaluated needs to be maintained and updated regularly as the data
will change timely.
Figure 2.
Three virtual sensor (VS) constellations: (
a
) VS based entirely on physical sensor (PS),
(
b
) VS based only another VS and (
c
) VS based on both virtual and physical sensors [
32
]. Virtual
sensor intercorrelates with data captured by physical sensors which are embedded into software
applications to implement the algorithmic analytics from all the data sets given.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 11 of 23
Figure 2. Three virtual sensor (VS) constellations: (a) VS based entirely on physical sensor (PS), (b)
VS based only another VS and (c)VS based on both virtual and physical sensors [32]. Virtual sensor
intercorrelates with data captured by physical sensors which are embedded into software applica-
tions to implement the algorithmic analytics from all the data sets given.
Figure 3. An overview of virtual sensing development steps [32].
Moreover, there have been several machine learning algorithms used for water qual-
ity monitoring systems in the past three years, from 2019 to 2021. According to the author,
artificial neural network (ANN) modeling is the most-used ML modeling approach ap-
plied for water quality monitoring, as demonstrated in Figure 4 [53]. ANN techniques
provide more accessible calibration and robustness capable of processing nonlinear and
complex datasets and can provide satisfactory prediction results with a small data amount
[53]. ANN modeling configurations can be used to predict BOD and identify the
wastewater treatment plant performance processes [54]. The prediction models that are
used to estimate BOD can save time and allow online control systems. Other ML tech-
niques such as random forest (RF) and multiple linear regression (MLR) are normally used
as the algorithm as it is simpler compared to other ML algorithms. The new generation of
ML is hybrid models, which are developed from different conventional ML models and
integrated with optimization methods. It is applied in order to achieve better performance
and empower computation, functionality, and accuracy from the single model [55].
Figure 4. Machine learning (ML) Techniques used the most from 2019–2021 [32]. ANN refers to
artificial neural network; RF refers to random forest; MLR refers to multiple linear regression; SVM
Figure 3. An overview of virtual sensing development steps [32].

Int. J. Environ. Res. Public Health 2022,19, 14080 11 of 21
Moreover, there have been several machine learning algorithms used for water quality
monitoring systems in the past three years, from 2019 to 2021. According to the author,
artificial neural network (ANN) modeling is the most-used ML modeling approach applied
for water quality monitoring, as demonstrated in Figure 4[
53
]. ANN techniques provide
more accessible calibration and robustness capable of processing nonlinear and complex
datasets and can provide satisfactory prediction results with a small data amount [
53
].
ANN modeling configurations can be used to predict BOD and identify the wastewater
treatment plant performance processes [
54
]. The prediction models that are used to estimate
BOD can save time and allow online control systems. Other ML techniques such as random
forest (RF) and multiple linear regression (MLR) are normally used as the algorithm as
it is simpler compared to other ML algorithms. The new generation of ML is hybrid
models, which are developed from different conventional ML models and integrated with
optimization methods. It is applied in order to achieve better performance and empower
computation, functionality, and accuracy from the single model [55].
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 11 of 23
Figure 2. Three virtual sensor (VS) constellations: (a) VS based entirely on physical sensor (PS), (b)
VS based only another VS and (c)VS based on both virtual and physical sensors [32]. Virtual sensor
intercorrelates with data captured by physical sensors which are embedded into software applica-
tions to implement the algorithmic analytics from all the data sets given.
Figure 3. An overview of virtual sensing development steps [32].
Moreover, there have been several machine learning algorithms used for water qual-
ity monitoring systems in the past three years, from 2019 to 2021. According to the author,
artificial neural network (ANN) modeling is the most-used ML modeling approach ap-
plied for water quality monitoring, as demonstrated in Figure 4 [53]. ANN techniques
provide more accessible calibration and robustness capable of processing nonlinear and
complex datasets and can provide satisfactory prediction results with a small data amount
[53]. ANN modeling configurations can be used to predict BOD and identify the
wastewater treatment plant performance processes [54]. The prediction models that are
used to estimate BOD can save time and allow online control systems. Other ML tech-
niques such as random forest (RF) and multiple linear regression (MLR) are normally used
as the algorithm as it is simpler compared to other ML algorithms. The new generation of
ML is hybrid models, which are developed from different conventional ML models and
integrated with optimization methods. It is applied in order to achieve better performance
and empower computation, functionality, and accuracy from the single model [55].
Figure 4. Machine learning (ML) Techniques used the most from 2019–2021 [32]. ANN refers to
artificial neural network; RF refers to random forest; MLR refers to multiple linear regression; SVM
Figure 4.
Machine learning (ML) Techniques used the most from 2019–2021 [
32
]. ANN refers to
artificial neural network; RF refers to random forest; MLR refers to multiple linear regression; SVM
refers to support vector machine; Adaboost refers to adaptive boosting; kNN refers to k-nearest
neighbor and NM refers to numerical models.
Data-driven virtual sensing technique needs to have several inputs and easily measur-
able parameters to construct prediction models. Input parameters such as pH, electrical
conductivity (EC), temperature, turbidity, and DO can be used to measure output param-
eters such as total phosphorus (TP), sodium absorption ratio (SAR), total nitrogen (TN),
magnesium absorption ratio (MAR) and residual sodium carbonate (RSC). For example, to
predict the TP and TN, we need COD as one of the inputs. The sensing module placed in
the water can convert the water quality parameters into an equivalent measurable electrical
quantity that is transmitted to the coordinator module. Accurate and reliable sensors used
are important as it affects the efficiency. In other words, the predictive accuracy of virtual
sensing degraded gradually because of inappropriate input parameter sensor selection,
insufficient sample numbers, process nonlinearity, and others.
3.2. IoT and Real-Time Implementation of Water Quality Monitoring
Internet of Things (IoT) can be applied in water quality monitoring systems in order
to send data through the internet. For example, Pasika et al. [
25
] used IoT to transmit,
gather and analyze information in real-time. The proposed system used multiple sensors

Int. J. Environ. Res. Public Health 2022,19, 14080 12 of 21
such as pH sensors, turbidity sensors, water level, temperature, and humidity sensors and
interfaced with the microcontroller unit, Arduino Mega. The system used the ThingSpeak
application to send data to online storage, known as the ‘cloud’. The real-time algorithm
to detect water quality is successfully developed in the proposed system, but it can be
enhanced by adding more parameters to detect water quality, such as an oxygen reduction
potential sensor or dissolved oxygen sensor [25].
Meanwhile, Mahajan et al. [
27
] used LEDs to reduce time delay for water quality
detection. The system performed faster than existing systems. The system was able to
inform the users to detect water quality immediately, but it did not analyze the parameters.
To enhance the deficiency method by Mahajan et al. in 2020, Pujar et al. [
56
] also developed
a water quality monitoring system using IoT where it applied statistical analysis in IoT.
River Krishna, located in the Karnataka region, was chosen as a study area to develop the
system. The system used multiple types of water quality sensors, and the statistical analysis
was based on one-way and two-way analysis of variance (ANOVA). The result showed
that one-way analysis was the most suitable analysis to be implemented with IoT [56].
In 2018, a real-time water quality monitoring system was developed using a low-cost
wireless sensor network. In the system, the ammonia concentration in the water and pH and
the temperature of the water were detected and monitored. When sensors were placed in
the water, water quality parameters were detected and sent to the cloud through an ethernet
shield via phone or computer. Data could be analyzed, and the alarm signal was sent to the
users if any parameter values were out of their safety range [
57
]. Wireless Sensor Network
(WSN) technology was used in [
57
] to provide real-time monitoring. The technology gave
important information on water quality management to ensure the fertility of aquatic life
and enhance human health. Meanwhile, Sabari et al. [
58
] designed a real-time water quality
monitoring system with IoT. The system used several water quality parameters such as pH
sensor, temperature sensor, turbidity sensor, and flow sensor. The system was interfaced
with Arduino, and the data could be viewed through a Wi-Fi system [
58
]. The system was
economical, convenient, and fast as it could automatically monitor the water at a low cost
and with less human energy consumption.
In addition, various water quality monitoring systems (WQMS) with IoT integration
have been reviewed by M. Monaj [
33
]. To build a smart freshwater pond for aquacul-
ture with automatic maintenance and water quality monitoring, the authors proposed
underwater sensors to continuously record parameter values in regular intervals with
Arduino/Raspberry Pi module for processing and transferring data. Underwater sensors
consisted of ammonia sensors which used AmmoLyt, nitrogen sensors, DO sensors, LM35
temperature sensors, and pH sensors. Traditional WMQS systems need to adjust the op-
eration manually when there is a data mismatch, whereas the IoT-based WMQS system
can easily maintain the correct values if any mismatch in the data is found [
33
]. Then,
water quality prediction can be constructed using a prediction framework based on multi-
source transfer learning (MSTL). The system effectively uses water quality information
from multiple nearby monitoring points to enhance prediction accuracy [
31
]. The same
types of sensors need to be used at different monitoring points in order to have the same
input parameter. In contrast, traditional transfer learning prediction methods only use
one monitoring point source of water quality information, which ignores any information
near the monitoring points. They performed the experiment in Hong Kong to verify actual
water quality by training several water prediction models using the adjacency effect to
reduce prediction bias and improve prediction accuracy.
Furthermore, W. Hong [
59
] demonstrated water quality monitoring based on Arduino-
based sensor systems. Temperature, pH, turbidity, and total dissolved solids (TDS) sensors
were used and interfaced with Arduino. The results of this proposed system were taken for
four weeks [
59
]. A simple prototype consisting of a microcontroller and multiple attached
sensors was employed to conduct weekly on-site tests at multiple daily intervals. We
found that the system worked reliably, but it relied on human assistance and was prone to
data inaccuracies. However, the system provided a solid foundation for future expansion

Int. J. Environ. Res. Public Health 2022,19, 14080 13 of 21
works of the same category to elevate the system to become Internet of Things (IoT)
friendly. A recent study by Y. He [
60
] used embedded systems such as STM32F103VET6,
serial communication module, and RS485 interface circuit to detect the aquaculture water
parameter in real-time such as temperature, pH value, dissolved oxygen, turbidity, and
other related information. Apart from that, Chang et al. [
61
] developed a system that used
sensors to develop an unmanned surface vehicle (MF-USV) to avoid any obstacles and
monitor the water quality system and water surface cleaning system. The obstacles can be
any animals, plants, or things on the surface. The MF-USV consisted of several components
that acted as autonomous obstacle detection to detect pH water and water surface cleaning.
It could detect and collect the floating garbage on water and perform remote navigation
control and real-time information display [
61
]. In the system, a pH sensor was used to
detect the pH of the water before being analyzed in a laboratory. Although the system is in
real time, the process consumes extra time for data analysis.
3.3. Cyber-Physical System
Cyber-Physical System (CPS) was firstly introduced by Helen Gill in 2006 at Natural
Science Foundation, United States [
62
]. CPS is a system that incorporates physical compo-
nents into a computational algorithm smoothly. CPS is the future of embedded systems.
A full-fledged CPS is usually configured as a network of interacting components with
physical input and output rather than as stand-alone devices, unlike embedded systems.
After all, CPS offers more benefits as it uses a user-friendly decision support system such
as fuzzy logic to overcome the complexity of data points that are generated from several
sensor nodes or known as sensor arrays [
63
]. According to Lee [
62
], Wiener was the one
who developed the CPS during World War II when he invented the technology of aiming
and firing anti-aircraft guns. CPS is widely connected nowadays, such as in IoT, Industrial
4.0, Industrial Internet, and Machine-to-Machine. CPS can be used in many applications,
such as healthcare applications where the system provides healthcare professionals and
services to patients in real-time [
64
]. CPS also can be used in large commercial and resi-
dential buildings to provide efficient working and living conditions [
64
]. Z. Wang [
16
] has
discussed the opportunities and challenges of CPS for water sustainability which include
four factors; sensing and instrumentation; communications and networking; computing;
and control. The CPS for water sustainability was further investigated by Imen et al. [
65
],
where five level architecture in CPS was developed, such as smart connection level, data-to-
information connection level, cyber level, cognition level, and configuration level towards
smart and sustainable drinking water infrastructure management. Bhardwaj et al. [
63
]
developed a water quality monitoring system that used CPS, which consisted of sensing
and computing frameworks for computational modeling.
CPS helps to monitor several parameters such as light, temperature, pH of water, and
others. The first level of CPS architecture is a smart connection level, where, at this level, the
selection of a correct sensor is important [
65
]. When the parameters of each sensor are read,
the data are transferred to the controllers/software through wired/wireless communica-
tion [
66
]. In this state, a microcontroller such as Arduino is required to communicate with it.
Then, a computational framework is needed to deal with the received data from sensors to
make decision making, and finally, the data are sent to actuators through a communication
system. The physical phenomena can change in return to make a feedback loop [
66
]. The
overall system is shown in Figure 5.
On the other hand, CPS consists of heterogeneous, distrusted components such as
computing nodes, sensors, actuators, smart devices, and software [
64
]. In order to connect
these components, wired and wireless connections are needed, as shown in Figure 6. In
order to connect the cyber world with the physical world, sensors and actuators play a
vital role in interfacing them, as sensors can monitor the physical world, whereas actuators
manipulate the physical world [
64
]. It is basically based on configuration level, a feedback
loop from a cyber system to a physical system [
65
]. Figure 7depicts the operation of the

Int. J. Environ. Res. Public Health 2022,19, 14080 14 of 21
feedback loop based on CPS. It consists of three main functions such as monitoring using
sensors, making decisions using smart software, and applying actions using actuators [
64
].
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 14 of 23
towards smart and sustainable drinking water infrastructure management. Bhardwaj et
al. [63] developed a water quality monitoring system that used CPS, which consisted of
sensing and computing frameworks for computational modeling.
CPS helps to monitor several parameters such as light, temperature, pH of water, and
others. The first level of CPS architecture is a smart connection level, where, at this level,
the selection of a correct sensor is important [65]. When the parameters of each sensor are
read, the data are transferred to the controllers/software through wired/wireless commu-
nication [66]. In this state, a microcontroller such as Arduino is required to communicate
with it. Then, a computational framework is needed to deal with the received data from
sensors to make decision making, and finally, the data are sent to actuators through a
communication system. The physical phenomena can change in return to make a feedback
loop [66]. The overall system is shown in Figure 5.
Figure 5. Overall Architecture of CPS [66]. Reproduced with permission from Mohamed, M.A.;
Kardas, G.; Challenger, M; 2021.
On the other hand, CPS consists of heterogeneous, distrusted components such as
computing nodes, sensors, actuators, smart devices, and software [64]. In order to connect
these components, wired and wireless connections are needed, as shown in Figure 6. In
order to connect the cyber world with the physical world, sensors and actuators play a
vital role in interfacing them, as sensors can monitor the physical world, whereas actua-
tors manipulate the physical world [64]. It is basically based on configuration level, a feed-
back loop from a cyber system to a physical system [65]. Figure 7 depicts the operation of
the feedback loop based on CPS. It consists of three main functions such as monitoring
using sensors, making decisions using smart software, and applying actions using actua-
tors [64].
Figure 5.
Overall Architecture of CPS [
66
]. Reproduced with permission from Mohamed, M.A.;
Kardas, G.; Challenger, M; 2021.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 15 of 23
Figure 6. The heterogeneous components of CPS that connected through wired and wireless com-
munication [64].
Figure 7. Control system of closed loop in CPS [64].
Bhardwaj et al. [63] proposed a water quality monitoring system based on CPS. Their
system consisted of three stages. The first stage involved designing the sensing frame-
work, where five types of water quality sensors were chosen. Then, it used Arduino to
control the sensors, and data from sensors were sent to a computer framework using
C/C++ and Python. Fuzzy logic was applied in this system to make a reliable and efficient
decision making of the system. Three membership functions (MFs), which consist of not
acceptable (NA), adequate (ADE), and highly acceptable (HACC), were assigned with dif-
ferent ranges of water quality parameters from Fuzzy representation, shown in Table 3.
Table 3. The range of water quality parameters for each membership functions (MF): not acceptable
(NA), adequate (ADE) and highly acceptable (HACC) [63]. Reprinted with permission from Bhard-
waj, J.; Gupta, K.K.; Gupta, R.; 2018.
Parameters NA ADE HACC NA
pH <5.7 5.3–6.7 6.5–8.7 >8.5
Dissolved oxygen <3 2.9–5.1 5.1–11.1 >11
Electrical conductivity <300 290–510 500–1050 >1000
Oxygen reduction potential <550 530–670 650–820 >800
Temperature <2 1.9–10 9–36 >35
Figure 6.
The heterogeneous components of CPS that connected through wired and wireless commu-
nication [64].
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 15 of 23
Figure 6. The heterogeneous components of CPS that connected through wired and wireless com-
munication [64].
Figure 7. Control system of closed loop in CPS [64].
Bhardwaj et al. [63] proposed a water quality monitoring system based on CPS. Their
system consisted of three stages. The first stage involved designing the sensing frame-
work, where five types of water quality sensors were chosen. Then, it used Arduino to
control the sensors, and data from sensors were sent to a computer framework using
C/C++ and Python. Fuzzy logic was applied in this system to make a reliable and efficient
decision making of the system. Three membership functions (MFs), which consist of not
acceptable (NA), adequate (ADE), and highly acceptable (HACC), were assigned with dif-
ferent ranges of water quality parameters from Fuzzy representation, shown in Table 3.
Table 3. The range of water quality parameters for each membership functions (MF): not acceptable
(NA), adequate (ADE) and highly acceptable (HACC) [63]. Reprinted with permission from Bhard-
waj, J.; Gupta, K.K.; Gupta, R.; 2018.
Parameters NA ADE HACC NA
pH <5.7 5.3–6.7 6.5–8.7 >8.5
Dissolved oxygen <3 2.9–5.1 5.1–11.1 >11
Electrical conductivity <300 290–510 500–1050 >1000
Oxygen reduction potential <550 530–670 650–820 >800
Temperature <2 1.9–10 9–36 >35
Figure 7. Control system of closed loop in CPS [64].
Bhardwaj et al. [
63
] proposed a water quality monitoring system based on CPS. Their
system consisted of three stages. The first stage involved designing the sensing framework,

Int. J. Environ. Res. Public Health 2022,19, 14080 15 of 21
where five types of water quality sensors were chosen. Then, it used Arduino to control
the sensors, and data from sensors were sent to a computer framework using C/C++ and
Python. Fuzzy logic was applied in this system to make a reliable and efficient decision
making of the system. Three membership functions (MFs), which consist of not acceptable
(NA), adequate (ADE), and highly acceptable (HACC), were assigned with different ranges
of water quality parameters from Fuzzy representation, shown in Table 3.
Table 3.
The range of water quality parameters for each membership functions (MF): not acceptable
(NA), adequate (ADE) and highly acceptable (HACC) [
63
]. Reprinted with permission from Bhardwaj,
J.; Gupta, K.K.; Gupta, R.; 2018.
Parameters NA ADE HACC NA
pH <5.7 5.3–6.7 6.5–8.7 >8.5
Dissolved oxygen <3 2.9–5.1 5.1–11.1 >11
Electrical conductivity <300 290–510 500–1050 >1000
Oxygen reduction potential <550 530–670 650–820 >800
Temperature <2 1.9–10 9–36 >35
Then, the water quality was decided based on rules: (1) if one parameter is NA, the
water quality is NA, (2) If one parameter is ADE (provided that no parameter is NA),
the water quality is ADE and (3) If all parameters are HACC, the water quality is HACC.
Figure 8shows the fuzzy rule applied in the system.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 16 of 23
Then, the water quality was decided based on rules: (1) if one parameter is NA, the
water quality is NA, (2) If one parameter is ADE (provided that no parameter is NA), the
water quality is ADE and (3) If all parameters are HACC, the water quality is HACC.
Figure 8 shows the fuzzy rule applied in the system.
Figure 8. Membership function (MF) plots based on fuzzy rule for water quality parameters (a) pH
and (b) DO [63].
A water quality monitoring system has also been developed using a similar concept
from [13]. The system used Raspberry Pi as a microcontroller that interfaced directly with
python. The system consisted of a graphical user interface (GUI) that was implemented
on the Raspberry Pi board. The board served as an independent system where any com-
puter was used. The system could observe more than three water quality parameters [17].
Figure 9 shows GUI, where the individual parameter to be measured can be selected, and
the water quality can be checked.
Figure 8.
Membership function (MF) plots based on fuzzy rule for water quality parameters (
a
) pH
and (b) DO [63].

Int. J. Environ. Res. Public Health 2022,19, 14080 16 of 21
A water quality monitoring system has also been developed using a similar concept
from [
13
]. The system used Raspberry Pi as a microcontroller that interfaced directly with
python. The system consisted of a graphical user interface (GUI) that was implemented on
the Raspberry Pi board. The board served as an independent system where any computer
was used. The system could observe more than three water quality parameters [
17
]. Figure 9
shows GUI, where the individual parameter to be measured can be selected, and the water
quality can be checked.
Int. J. Environ. Res. Public Health 2022, 19, x FOR PEER REVIEW 17 of 23
Figure 9. Graphical user interface (GUI) developed for water quality monitoring system [17].
Cyber-physical systems (CPS)s can be used to analyze water quality. CPSs are smart
network systems that are operated with embedded sensors, processors, and actuators. It
is considered an emerging technology and can be designed to sense and interact with the
physical world, such as the water environment [16,63]. CPS systems have high autonomy,
fast quality detection due to quick process of decision making, efficient and flexible. CPS
also involves a stable, robust, scalable, and reliable process. The data analysis by CPS is
precise and accurate. It is communicative where the CPS system can connect and share
the data with entire water quality systems [67].
Engineers and scientists should clearly understand the concept of artificial intelli-
gence, machine learning, neural networks, and other modern online technologies to apply
CPS [68]. Mohamed et al. [66] proposed to design the CPS system that consisted of several
complex software and hardware, with a high-level abstraction of the system. He sug-
gested model-driven engineering, which was commonly used in the business domain for
the development of software. CPS needs many developers from various background stud-
ies such as software engineering, electric and electronic engineering, computer science,
and other sectors. It creates a communication challenge between developers as various
tools and abstractions are implemented in each field.
3.4. Optical Techniques
Optical sensors and spectroscopic approaches are other examples of water quality
monitoring techniques. Recently, smart sensing platforms can work together with elec-
tronics and optical sensors to improve and control the monitoring system. Electronics
sensing is portable and simple to handle, whereas optical sensing does not affect water
sample and provide higher accuracy result [69]. Optical sensors can monitor the total sus-
pended solid (TSS) concentration from light transmission through water samples [69].
Light emitting diode (LED) acts as a transmitter to transmit light through suspended par-
ticles in water samples. Physical variables such as particle size, shape, suspended solid
concentration (SSC), composition, and chemical properties affect light transmission
through water samples. Examples of known optical sensors are the charge-coupled device
(CCD) linear sensor, phototransistor, optical biosensor, fluorescence sensor, lasers, and
others [70–75]. Apart from that, another optical fiber sensor was designed based on the
principle of surface plasmon resonance (SPR) to monitor the interaction of biological mol-
ecules in real time without the need for labeling, separation, and purification [73]. The
Figure 9. Graphical user interface (GUI) developed for water quality monitoring system [17].
Cyber-physical systems (CPS)s can be used to analyze water quality. CPSs are smart
network systems that are operated with embedded sensors, processors, and actuators. It
is considered an emerging technology and can be designed to sense and interact with the
physical world, such as the water environment [16,63]. CPS systems have high autonomy,
fast quality detection due to quick process of decision making, efficient and flexible. CPS
also involves a stable, robust, scalable, and reliable process. The data analysis by CPS is
precise and accurate. It is communicative where the CPS system can connect and share the
data with entire water quality systems [67].
Engineers and scientists should clearly understand the concept of artificial intelligence,
machine learning, neural networks, and other modern online technologies to apply CPS [
68
].
Mohamed et al. [
66
] proposed to design the CPS system that consisted of several complex
software and hardware, with a high-level abstraction of the system. He suggested model-
driven engineering, which was commonly used in the business domain for the development
of software. CPS needs many developers from various background studies such as software
engineering, electric and electronic engineering, computer science, and other sectors. It
creates a communication challenge between developers as various tools and abstractions
are implemented in each field.
3.4. Optical Techniques
Optical sensors and spectroscopic approaches are other examples of water quality
monitoring techniques. Recently, smart sensing platforms can work together with electron-
ics and optical sensors to improve and control the monitoring system. Electronics sensing
is portable and simple to handle, whereas optical sensing does not affect water sample and
provide higher accuracy result [
69
]. Optical sensors can monitor the total suspended solid
(TSS) concentration from light transmission through water samples [
69
]. Light emitting
diode (LED) acts as a transmitter to transmit light through suspended particles in water
samples. Physical variables such as particle size, shape, suspended solid concentration

Int. J. Environ. Res. Public Health 2022,19, 14080 17 of 21
(SSC), composition, and chemical properties affect light transmission through water sam-
ples. Examples of known optical sensors are the charge-coupled device (CCD) linear sensor,
phototransistor, optical biosensor, fluorescence sensor, lasers, and others [
70
–
75
]. Apart
from that, another optical fiber sensor was designed based on the principle of surface
plasmon resonance (SPR) to monitor the interaction of biological molecules in real time
without the need for labeling, separation, and purification [
73
]. The designed system is
capable of measuring oil in wastewater at different concentrations with high accuracy, fast
detection, good stability, easy operation, and allows online monitoring. Next, a low-cost
autonomous optical sensor is devised to be environmentally robust, easily deployable, and
simple to operate [
74
]. It consists of a multi-wavelength light source with two photodiode
detectors that can measure the transmission and side scattering of the light in the detector
head. Thus, the sensors can provide qualitative data on the changes in the optical opacity
of the water. The optical colorimetric sensor (OCS) provides data on bulk water property
changes, particularly opacity and color changes. This sensor also clearly provides valuable
data related to turbidity events [74].
Meanwhile, spectroscopic techniques for detecting contaminants are continuously
upgraded in terms of detection sensitivity, quantitatively and qualitatively. There are
several methods of spectroscopy that have been analyzed for monitoring water quality,
such as vibrational spectroscopy, light emission or luminescence spectroscopy, fluorescence
spectroscopy, near-infrared (NIR) spectroscopy, and others [
23
,
34
,
76
–
80
]. The techniques
are extremely sensitive by producing accurate detection results of matter composition and
determining physical structures through light propagation. Transmission, absorption, and
reflectance spectra of light in water allow determination of turbidity of the water, size
of particles, and concentration of contaminants in the water. It is suitable for detecting
contaminants because each type of molecule in water samples reflects, absorbs, or emits
electromagnetic radiation from light sources and analyzes light intensity characteristics to
quantify the composition of the sample. The spectrometer can be used to determine the
particle composition and size distribution of samples from optical properties [
79
]. Spec-
troscopy normally uses a light source or a laser as an emitter and a detector or spectrometer
for spectral analysis [
81
]. The approaches are simple, non-invasive, rapid detection, and
pollution-free, as no chemical materials are involved [23].
Z. Shi [
34
] reviewed the applications of online UV-Visible spectrophotometers for
drinking water quality monitoring and process control. Compared to conventional methods,
online UV-Vis sensors can capture events and allow quicker responses to water quality
changes. Water quality measurements such as color, dissolved oxygen carbon (DOC),
total organic carbon (TOC), turbidity, and nitrate can be performed directly using built-in
generic algorithms of the online UV-Vis instruments. Online UV-Vis spectrophotometers
are effective and practical for continuously measuring water quality parameters, and they
do not need physical filtration and low maintenance. Future works require early warning
detections and real-time water process control systems for water quality management.
Next, the spectroscopic technique uses the interaction between scattered light and
water samples to gain knowledge of the chemical and biological components in the water.
NIR spectroscopy with 700 nm to 1200 nm wavelength is widely used for some physical
and chemical characteristics identification [
69
]. Reflectance spectrum can be obtained from
lake water and differs in the water quality based on the presence of algae. Furthermore,
H. Zhang [
80
] studied online water quality monitoring simplification with UV-Vis spec-
trometry and an artificial neural network for river confluence near Sherfield-on-Loddon.
Convolutional neural network (CNN) and partial least squares (PLS) methods are im-
plemented to calculate water parameters and obtain accurate results. Two water quality
parameter, total suspended solids (TSS) and total organic carbon (TOC), concentrations
showed precise results using PLS and CNN models based on predicted experimental values
and true values. TOC is used to monitor changes in organic contents as it measures the
amount of carbon in pure water or an aqueous system, whereas TSS is a particle that is
larger than 2
µ
m in the water. Particles smaller than 2
µ
m are known as total dissolved

Int. J. Environ. Res. Public Health 2022,19, 14080 18 of 21
solids (TDS). Overall, the outcome of the study shows that the combination of spectroscopy
with PLS and CNN models produces an accurate performance in estimating water param-
eters online. Apart from that, infrared (IR) spectroscopy uses a higher wavelength than
UV-Vis with lower photon energy. The infrared spectra are classified into three categories
which are near-infrared (NIR) (750–2500 nm), mid-infrared (MIR) (2.5–16
µ
m), and far-
infrared (16–1000
µ
m) [
82
]. NIR spectrum is widely used for water quality analysis. NIR
spectroscopy has been used for various experimental analyses, such as water monitoring
for microalgae and extracellular polymeric substances in wastewater processes. Optical
systems can provide valuable information on the composition and quality of water [
82
].
Various spectroscopic types with different features can determine chemical, biological,
and physical components in the water by manipulating light characteristics, including
transmission, absorption, reflectance, and fluorescence spectra.
4. Conclusions
In conclusion, water pollution is a detrimental issue that should be taken seriously
by the government, non-public sectors, and society. In order to mitigate the issue, it is
essential to have a reliable and continuous real-time water quality monitoring system
that can provide useful output data and help authorities choose appropriate and fast
actions. Thus, the review aims to investigate previous methods of water quality monitoring
systems, compare traditional and modern methods and study different methods from
various countries. Water quality monitoring methods such as the CPS approach, electronics
sensing methods, virtual sensing system, IoT approach, and optical techniques are reviewed
extensively. The review shows that CPS is relevant and acceptable to be used in water
quality monitoring systems. Apart from that, CPS is a smart and reliable system where it
can connect two worlds: (1) the physical world, such as sensors, environments, and humans,
and (2) the cyber world, such as software and data. Indirectly, real-time monitoring of
water quality can be achieved and offers the possibility of providing early warning in the
water quality management system. Thus, water pollution can be detected, and the quality
of water can be analyzed before it can be safely used by consumers. In the future, CPS
technology can be combined with advanced optical techniques to produce high reliability
and sensitivity because current existing monitoring methods have difficulties in obtaining
an accurate measurement of water quality parameters in real-time and cost-effectiveness
with continuous data measuring. Until now, there are some tool limitations to detect
pollutants and require enhancement on existing water quality assessments.
Author Contributions:
Conceptualization, Supervision, project administration, funding acquisition,
W.Z.W.I., Writing—original daft preparation, S.N.I.M., writing-additional information, review and
editing, S.N.Z., Funding acquisition and review—I.I., K.N.Z.A. and J.J. and reviewing and proof
reading—W.M.W.A.K. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by Ministry of Higher Education, Malaysia under Fundamental
of Research Grant Scheme, grant number FRGS/1/2021/WAB02/USIM/02/1 and USIM-MMU
Matching grant (USIM/MG/MMU-PPKMT-ZDA/FKAB/SEPADAN-S/70822). The APC was funded
by Universiti Sains Islam Malaysia (USIM).
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Acknowledgments:
We would like to acknowledge our funder, Ministry of Higher Education,
Malaysia (MOHE), all members of Faculty of Engineering and Built Environment (FKAB), USIM and
our families.
Conflicts of Interest:
We declare no conflict of interest that can influence the representation or
interpretation of reported research results. The funders had no role in the design of the study; in the
collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.

Int. J. Environ. Res. Public Health 2022,19, 14080 19 of 21
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