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Internet of Things for Water Quality Monitoring and Assessment: A Comprehensive Review


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The implementation of urbanisation and industrialisation plans lead to the proliferation of contaminants in water resources which is a severe public challenge. These have led to calls for innovative means of water quality monitoring and mitigation, as highlighted in the sustainable development goals. Environmental engineering researchers are now seeking more intricate techniques conducting real-time monitoring and assessing of the quality of surface and groundwater that is assessable to the human population across various locations. Numerous recent technologies now utilise the Internet of Things (IoT) as a platform in water quality monitoring and assessment. Wireless sensor network and IoT environments are currently being used more frequently in contemporary times. In this paper, the recent technologies harnessing the potentials and possibilities in the IoT for water quality monitoring and assessment is comprehensively discussed. The main contribution of this paper is to present the research progress, highlight recent innovations and identify interesting and challenging areas that can be explored in future studies.
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Internet of Things for Water Quality
Monitoring and Assessment:
A Comprehensive Review
Joshua O. Ighalo, Adewale George Adeniyi, and Goncalo Marques
Abstract The implementation of urbanisation and industrialisation plans lead to
the proliferation of contaminants in water resources which is a severe public chal-
lenge. These have led to calls for innovative means of water quality monitoring and
mitigation, as highlighted in the sustainable development goals. Environmental engi-
neering researchers are now seeking more intricate techniques conducting real-time
monitoring and assessing of the quality of surface and groundwater that is assessable
to the human population across various locations. Numerous recent technologies
now utilise the Internet of Things (IoT) as a platform in water quality monitoring
and assessment. Wireless sensor network and IoT environments are currently being
used more frequently in contemporary times. In this paper, the recent technologies
harnessing the potentials and possibilities in the IoT for water quality monitoring
and assessment is comprehensively discussed. The main contribution of this paper is
to present the research progress, highlight recent innovations and identify interesting
and challenging areas that can be explored in future studies.
Keywords Actuators ·Environment ·Internet of things ·Sensors ·Sustainable
development ·Water quality
J. O. Ighalo ·A. G. Adeniyi
Department of Chemical Engineering, University of Ilorin, P. M. B. 1515, Ilorin, Nigeria
A. G. Adeniyi
G. Marques (B)
Instituto de Telecomunicações, Universidade da Beira Interior, 6200-001 Covilhã, Portugal
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer
Nature Switzerland AG 2021
A. E. Hassanien et al. (eds.), Artificial Intelligence for Sustainable Development: Theory,
Practice and Future Applications, Studies in Computational Intelligence 912,
246 J. O. Ighalo et al.
1 Introduction
Water is one of the most abundant natural resources in the biosphere and one that
is important for the sustenance of life on earth [1]. The implementation of urbani-
sation and industrialisation plans lead to the proliferation of contaminants in water
resources which is a severe public challenge [24]. About 250 million cases of
diseases infections are annually reported world-wide due to water pollution-related
causes [5]. Therefore, innovative means of monitoring and mitigation water pollution
are required [68] so that environmental sustainability can be achieved as highlighted
in the sustainable development goals (SDGs). Environmental engineering researchers
are now developing more intricate techniques for conducting real-time monitoring
and assessing of the quality of surface and groundwater that is assessable to the human
population across various locations [9,10]. The internet has powered a lot of tech-
nologies and applications which make possible in our time. The Internet of Things
(IoT) is an integration of many newly developed digital/information technologies
The IoT now has applications in diverse anthropogenic activities both in the
domestic and industrial domain [13]. These include transportation and logistics,
healthcare, smart homes and offices [2], water quality assessment [14], tourism,
sports, climatology [15], aquaculture [16] and a host of others [17]. More discussion
on the IoT can be found elsewhere [18,19]. Numerous recent technologies now
utilise the IoT as a platform in water quality monitoring and assessment [19]. Wire-
less sensor network and IoT environments are currently being used more frequently
in contemporary times. The intricacies of the system require that aspects such as
software programming, hardware configuration, data communication and automated
data storage be catered for [20].
IoT-enabled AI for Water Quality Monitoring is quite relevant for sustainable
development purposes. The presence of clean water to humans is a fundamental
part of the sixth (6th) sustainable development goal. It would be difficult to assess
which water body and sources is actually clean enough to drink without water quality
monitoring. Furthermore, the utilisation of IoT-enabled AI means that any potential
water pollution arising from a point or non-point source is quickly identified and
mitigated. For 14th sustainable development which emphasises the need to protect
life below water, IoT-enabled AI for Water Quality Monitoring would ensure that
the quality of water do not go below threshold detrimental to the survival of aquatic
flora and fauna.
Within the scope of the authors’ exhaustive search, the last detailed review on the
subject was published over 15 years ago by Glasgow et al. [21]. In that time frame,
a lot has changed in the technology as much advancements and breakthroughs have
been made. It would not be out of place to revisit the topic and evaluate recent
In this chapter, the recent technologies harnessing the potentials and possibil-
ities in the IoT for water quality monitoring and assessment is comprehensively
discussed. The main contribution of this paper is to present the research progress,
Internet of Things for Water Quality Monitoring … 247
highlight recent innovations and identify interesting and challenging areas that can
be explored in future studies. After the introduction, the first section discusses the
fundamental reasons behind water quality assessment and defines the fundamental
indices involved. The next section discusses the importance of IoT in water quality
monitoring and assessment. The hardware and software designs for IoT enabled water
quality monitoring and assessment for a smart city was discussed in the foregoing
section. This is succeeded by an empirical evaluation on the subject matter based on
published literature in the past decade and concluded by discussions on knowledge
gap and future perspectives.
2 Water Quality Assessment in Environmental Technology
Water quality refers to the physical, chemical and biological characteristics of water
[22]. Assessment and monitoring of water quality are essential because it helps in
timely identification of potential environmental problems due to the proliferation of
pollutants (from anthropogenic activities) [11]. These are usually done both in the
short and long term [23]. Monitoring and assessment are also fundamental so that
potential regulation offenders can be identified and punished [24]. Technical details
as regards the methods for environmental monitoring is discussed by McDonald [25].
There are specific indices used in water quality. A water quality index (WQI) is
a dimensionless number used in expressing the overall quality of a water sample
based on measurable parameters [26]. Many indices have been developed (as much
as 30), but only about seven (7) are quite popular in contemporary times [26]. In all
these, the foundational information about the water is gotten from the measurable
parameters [27]. The important measurable parameters of water quality are defined
below [28].
1. Chemical oxygen demand (COD): This is the equivalent amount of oxygen
consumed (measured in mg/l) in the chemical oxidation of all organic and
oxidisable inorganic matter contained in a water sample.
2. Biochemical oxygen demand (BOD): This is the oxygen requirement of all the
organic content in water during the stabilisation of organic matter usually over
a 3 or 5 day.
3. pH: This is the measure of the acidity or alkalinity of water. It is neutral (at 7)
for clean water and ranges from 1 to 14.
4. Dissolved oxygen (DO): This is the amount of oxygen dissolved in a water
sample (measured in mg/l).
5. Turbidity: This is the scattering of light in water caused by the presence of
suspended solids. It can also be referred to as the extent of cloudiness in water
measured in nephelometric turbidity units (NTU).
6. Electrical conductivity (EC): This is the amount of electricity that can flow
through water (measured in Siemens), and it is used to determine the extent of
soluble salts in the water.
248 J. O. Ighalo et al.
7. Temperature: The is the degree of hotness or coldness of the water and usually
measured in degrees Celsius (°C) or Kelvin (K).
8. Oxidation-reduction potential (ORP): This is the potential required to transfer
electrons from the oxidant to the reductant, and it is used as a qualitative measure
of the state of oxidation in water.
9. Salinity: This is the salt content of the water (measured in parts per million).
10. Total Nitrogen (TN): This is the total amount of nitrogen in the water (in mg/l)
and is a measure of its potential to sustain and eutrophication or algal bloom.
11. Total phosphorus (TP): This is the total amount of phosphorus in the water (in
mg/l) and is a measure of its potential to sustain and eutrophication or algal
3 Internet of Things in Water Quality Assessment
Environmental engineering researchers are now seeking more intricate techniques
for conducting real-time monitoring and assessing of the quality of surface and
groundwater that is assessable to the human population across various locations.
Digital communication technologies are now the bedrock of modern society [29]
and IoT enabled water quality monitoring and assessment is a vital aspect of that.
The traditional method of water quality monitoring requires human personnel taking
the readings by instruments and logging the data [30] is considered inefficient, slow
and expensive [20]. In this section, the importance of IoT in water quality monitoring
and assessment is itemised in light of its advantages over the traditional methods of
water sampling and analysis utilised by environmental engineers and scientists when
conducting water quality monitoring.
1. The most significant advantage of IoT in water quality monitoring and assess-
ment is the possibility of real-time monitoring. Here, the status of the water
quality (based on the different indices) can be obtained at any given time. This
is facilitated by the speed of internet communications where data can be trans-
mitted from the sensors in fractions of a second. These incredible speeds are not
achievable in traditional water quality monitoring.
2. IoT in water quality monitoring and assessment can be automated. This means
that it does not require the presence of human personnel to take readings and
log data [31]. Moreover, these IoT systems would require less human resources
and eliminate human errors in data logging and computations. Automation is the
foundational concept of smart cities and its associated technologies.
3. Alongside the advantage of automation, IoT has led to the use of adaptive and
responsive systems in water quality monitoring. These smart-systems can alert
authorities or personnel regarding impending danger (such as high water level of
an impending flood) or non-optimal conditions (such as in aquaponic systems)
Internet of Things for Water Quality Monitoring … 249
4. IoT in water quality monitoring and assessment is cheaper than hands-on
personnel conducting the monitoring and assessment. The cost of human
resources is minimised, and an IoT based system would not require.
4 Water Quality Monitoring Systems
IoT aims to provide a continuous presence of distinct cyber-physical systems which
incorporate and intelligence capabilities [33,34]. On the one hand, IoT has changed
people daily routine and is today included in most social activities and in particular
regarding smart city concept [35]. On the other hand, IoT is a relevant architecture
for the design and development of intelligent monitoring systems for water quality
IoT is currently applied in different kinds of monitoring activities such as thermal
comfort [3640], acoustic comfort [41] and air quality [42,43]. Moreover, IoT is
also applied in agricultural environments such as aquaponics and hydroponics [44
47]. Water quality is crucial in agricultural activities and significantly affect the
productivity and efficiency of agricultural ecosystems. IoT systems for enhanced
water quality allow to store and compare the water quality data to support the decision
making of agricultural plant managers.
The smart city concept associated with multiple strategies which aim to address the
most relevant cities challenges using computer science technologies [48]. Currently,
cities face crucial challenges regarding their socio-economic goals and the best
approaches to meet them and at the same time, improve public health [49]. Water
resources are an integral element of cities and are also a crucial challenge regarding
their management and quality assessment [50]. Water contamination significantly
affects the health and well-being of citizens, and real-time supervisor systems can
be used to detect possible contamination scenarios for enhanced public health early.
IoT systems can be located in multiple places and provide a continuous stream
of real-time water quality data to various municipal authorities to improve water
resources management. The data collected can also be used to plan interventions for
enhanced public safety [51].
The technologies used in the design and development of IoT systems in the water
management domain are presented in Sect. 4.1.
4.1 Hardware and Software Design
Currently, multiple technologies are available for the design and development of IoT
systems. On the one hand, numerous open-source platforms for IoT development such
as Arduino, Raspberry Pi, ESP8266 and BeagleBone [52]. These platforms support
various short-range communication technologies such as Bluetooth and Wi-Fi but
also long-range such as GPRS, UMTS, 3G/4G and LoRA that are efficient methods
250 J. O. Ighalo et al.
Fig. 1 IoT architecture
for data transmission. Moreover, IoT platforms also support multiple identification
technologies, such as NFC and RFID identification technologies [53].
At the hardware level, IoT cyber-physical system can be divided into three
elements: microcontroller, sensor and communication (Fig. 1). Commonly, an IoT
system is composed by the processing unit, the sensing unit and the communica-
tion unit. The processing unit is the microcontroller which is responsible for the
interface with the sensor part and can have integrated communication unit or be
connected to the communication module for data transmission. The sensor unit is
responsible for the physical data collection and is connected to the microcontroller
using several interfaces such as analogue input, digital input and I2C. The communi-
cation unit is related to the communication technologies used for data transmission.
These technologies can be wireless such as Wi-Fi or cabled such as Ethernet.
The data collected using the sensor unit is processed and transmitted to the Internet.
These activities are handled using the microcontroller. The analysis, visualization and
mineralization of the collected data are conducted using online services and carried
by backend services which include more powerful processing units. Multiple low-
cost sensors are available with different interface communication and support for
numerous microcontrollers which can be applied in the water management domain
4.2 Smart Water Quality Monitoring Solutions
Water quality assessment also plays a significant role in multiple agricultural domains
such as hydroponics, aquaponics and aquaculture. In these environments water
quality must be monitored; however, the main applications involve high priced solu-
tions which cannot be incorporated in the developing countries. Therefore, the cost
of water quality monitoring system is a relevant factor for their implementation.
Internet of Things for Water Quality Monitoring … 251
On the one hand, hydroponic applications the nutrients in the water are a crucial
factor to be monitored in real-time to provide high-quality products and avoid prob-
lems related to contaminations [57]. Therefore, water quality monitoring systems
must be incorporated as long with advanced techniques of energy consumption
monitoring since hydroponics is associated with high energy consumptions [58,59].
Moreover, real-time monitoring is essential also in aquaponics since this approach
combines the conventional aquaculture methods in the symbiotic environment of
plants and depends on nutrient-generators. In aquaponic environments, the excre-
ment produced by animals is used as nitrates that are used nutrient by plants [60].
On the other hand, smart cities require efficient and effective management of water
resources [61].
Currently, the availability of low-cost sensors promotes the development of contin-
uous monitoring systems for water monitoring [62]. Furthermore, numerous connec-
tivity methods are available for data transmission of the collected data using wireless
technologies [63]. Bluetooth and Zigbee communication technologies can be used
to interface multiple IoT units to create short-range networks and be combined with
Wi-Fi and mobile networks for Internet connection [64,65].
Furthermore, smartphones currently have high computational capabilities and
support NFC and Bluetooth, which can be used to interface external components
such as IoT [66]. In particular, Bluetooth technologies can be used to configure and
parametrize IoT water quality monitoring systems and retrieve the collected data in
locations where Internet access are not available. On the one hand, mobile devices
enable numerous daily activities and provide a high number of solutions associated
with data visualization and analytics [67]. On the other hand, people commonly
prefer to use their smartphones when compared with personal computers [68,69].
The current water quality monitoring systems are high cost and do not support data
consulting features in real-time. The data collected by these systems are limited since
it is not related to the date of data collection and location. The professional solutions
available in the literature can be compact and portable. However, that equipment
does not provide a continuous data collection and sharing in real-time. Most of these
systems only provide a display for data consulting or provide a memory card for data
storage. Therefore, the user must extract the information and analyses the results
using third-party software.
TDS and conductivity pens are quickly found in the market and are also widely
used for water assessment. However, these portable devices do not incorporate data
storage or data-sharing features. The user can only check the results using an LCD
existent on the equipment. Moreover, this equipment commonly does not have any
data storage method.
The development of smart water quality solutions using up to date technologies
which provide real-time data access is crucial for the management of water resources
(Fig. 2). It is necessary to design architectures which are portable, modular, scalable,
and which can be easily installed by the user. The real-time notifications are also a
relevant part of this kind of solutions. The real-time notification feature can enable
intervention in useful time and consequently address the contamination scenarios in
an early phase of development.
252 J. O. Ighalo et al.
Fig. 2 Smart water monitoring system
5 An Empirical Evaluation of IoT Applications in Water
Quality Assessment
In this section, a brief chronological evaluation is made of some of the interesting
empirical investigations where IoT enabled technology was utilised in water quality
monitoring and assessment. The focus will be not just on studies where an IoT-
enabled system was designed for water quality monitoring and assessment but for
studies where this technology was applied to specific water bodies within the past
Wang et al. [70] monitored the water quality in the scenic river, Xinglin Bay
in Xiamen, China. Their system was divided into three subsystems. There was
the data acquisition subsystem, the digital data transmission subsystem and data
processing subsystem. The indices monitored were pH, dissolved oxygen (DO),
turbidity, conductivity, oxidation-reduction potential (ORP), chlorophyll, temper-
ature, salinity, chemical oxygen demand (COD), NH4+, total phosphorus (TP) and
total nitrogen (TN). The results of the study were positive as the design was adequate
in achieving the set objectives. Furthermore, the water quality was of a good standard
as the water had a powerful self-purification ability.
Shafi et al. [71] investigated the pH, turbidity and temperature of surface water
across 11 locations in Pakistan, using an IoT enabled system that in-cooperated
machine learning algorithms. The four algorithms considered were Support Vector
Machine (SVM), k Nearest Neighbour (kNN), single-layer neural network and deep
neural network. It was observed from the learning process on the 667 lines of data
that deep neural network had the highest accuracy (at about 93%). The model could
accurately predict water quality in the future six months.
Saravanan et al. [72] monitored the turbidity, temperature and colour at water
pumping in Tamilnadu, India using a Supervisory Control and Data Acquisition
Internet of Things for Water Quality Monitoring … 253
(SCADA) system that is enabled by IoT. The technology was usable in real-time and
employed a GSM module for wireless data transfer.
In a quite interesting study, Esakki et al. [73] designed an unmanned amphibious
vehicle for pH, DO, EC, temperature, and turbidity of water bodies. The device could
function both in air and in water. Part of the mechanical design considerations was
in its power requirements, propulsion, hull and skirt material, hovercraft design and
overall weight. It was designed for military and civil applications with a mission of
time of 25 min, a maximum payload of 7 kg and utilised an IoT based technology.
Liu et al. [74] monitored the drinking water quality at a water pumping station
along the Yangtze river in Yangzhou, China. The technology was IoT enabled but
incorporated a Long Short-Term Memory (LSTM) deep learning neural network.
The parameters assessed were Temperature, pH, DO, Conductivity, Turbidity, COD
and NH3.
Zin et al. [75] utilised wireless sensor network enabled by IoT for the monitoring
of water quality in real-time. The system they utilised consisted of Zigbee wireless
communication, protocol, Field Programmable Gate Array (FPGA) and a personal
computer. They utilised the technology to monitor the pH, turbidity, temperature,
water level and carbon dioxide on the surface of the water at Curtin Lake, northern
Sarawak in the Borneo island. The system was able to minimise cost and had lesser
power requirements. Empirical investigations of IoT applications in water quality
monitoring and assessment is summarised in Table 1.
Due to the nature of the sensors, parameters like TDS, turbidity, electrical conduc-
tivity, pH and water level are the more popularly studied indices. This was quite
apparent from Table 1. It would require a major breakthrough in sensor technology
to have portable and cheap sensors that can detect other parameters like heavy metals
and other ions. The future of research in this area is likely to be investigations on
alternative sensor technologies to determine the wide range of parameters that can
adequately describe the quality of water. If this is achievable, then water quality
monitoring and assessment would be able to apply correlations of Water Quality
Index (WQI) to get quick-WQI values. This would enable rapid determination of the
suitability of water sources for drinking.
The current water quality monitoring systems are relatively expensive and do not
support data consulting features in real-time. It is predicted that researchers will
gradually shift focus from portability in design to affordability. Furthermore, the
development of smart water quality solutions using up to date technologies which
provide real-time data access is crucial for the management of water resources. It is
necessary to design architectures which are portable, modular, scalable, and which
can be easily installed by the user. Researchers in the future will likely delve into
better real-time monitoring technologies that would incorporate notifications and
social media alerts.
254 J. O. Ighalo et al.
Tabl e 1 Summary of IoT applications in water quality monitoring and assessment
Year Location Parameters monitored Ref
2019 Curtin Lake, Borneo island pH, turbidity, temperature, water level and
2019 Pumping station, Yangtze river,
Yangzhou, China
Temperature, pH, DO, EC, turbidity, COD
and NH3
2019 Unspecified location in
pH, turbidity, ORP and temperature [76]
2018 Pumping station, Tamilnadu,
Turbidity, temperature and colour [72]
2018 Unspecified location pH, DO, EC, temperature, and turbidity [73]
2018 11 locations in Pakistan pH, turbidity and temperature [71]
2018 Unspecified location in India pH, water level, temperature and CO2[13]
2017 Lab setup, India pH and EC [1]
2017 Aquaponics system, Manchay,
near Lima, Peru
pH, DO and temperature [77]
2017 Aquaponic system, Chennai,
pH, water level, temperature and ammonia [78]
2017 Unspecified location in India pH, turbidity and EC [55]
2017 Unspecified location in India pH, turbidity and water level [15]
2017 Nibong Tebal, Malaysia pH and temperature [79]
2015 Unspecified location in Malaysia Wat e r level [12]
2013 Scenic river, Xiamen, China pH, DO, turbidity, EC, ORP, chlorophyll,
temperature, salinity, COD, NH4+,TPand
2006 7 locations in South Africa Unspecified [80]
2002 Tagus estuary, near Lisbon,
pH, turbidity and temperature [81]
6 Conclusions
Urbanisation and industrialisation plans have led to the proliferation of contaminants
in water resources which is now a severe environmental challenge. These have led to
calls for innovative means of water quality monitoring and mitigation, as highlighted
in the SDGs. The recent technologies harnessing the potentials and possibilities in
the IoT for water quality monitoring and assessment is comprehensively discussed
in this paper. Advantages of IoT in water quality monitoring and assessment are in
the possibility of real-time monitoring, automation for smart solutions, adaptive and
responsive systems and in a reduction of water quality monitoring costs. A brief
chronological evaluation is made of some of the interesting empirical investigations
where IoT enabled technology was utilised in water quality monitoring and assess-
ment in the last decade. It was observed that IoT in water quality monitoring and
assessment had not been applied to some more sophisticated parameters like heavy
Internet of Things for Water Quality Monitoring … 255
metals and other ions. The future of research in this area is likely to be investigations
on alternative sensor technologies to determine the wide range of parameters that
can adequately describe the quality of water. Cost considerations in the design and
real-time data management are also areas of future research interest on the subject
matter. The paper was successfully able to present the research progress, highlight
recent innovations and identify interesting and challenging areas that can be explored
in future studies.
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... In water distribution systems through pipes, water could trap unwanted substances like rust and metals from the wall of old distribution pipes, silt and mud from damaged pipes, and sediments during the pipe repairing process [5]. Therefore, innovative means of monitoring and mitigating water pollution are required [6]. According to the United States Geological Survey, water quality is "a measure of the acceptability of water for a particular purpose based on specified physical, chemical, and biological parameters" (USGS). ...
... In [6], the creation of intelligent water quality solutions utilizing cutting-edge technology that provide real-time data access is essential for the management of water resources. Designing structures that are adaptable, modular, scalable, and simple for the user to install is essential. ...
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Garfan, S.; Talal, M.; Alamoodi, A.H.; Alamleh, A.; Ahmaro, I.Y.Y.; Sulaiman, S.; Ibrahim, A.B.; Zaidan, B.B.; Ismail, A.R.b.; et al. IoT-Based Water Monitoring Systems: A Systematic Review. Water 2022, 14, 3621.
... Recently, under the influence of the fourth industrial revolution, numerous studies have been conducted to develop water quality monitoring systems (WQMS) by using various microcontrollers and Internet of Things (IoT) technologies to monitor and predict water quality [29]. As summarized in Table S1, many researchers have designed WQMSs for various water bodies such as artificial water tanks [30,31], lakes [32,33], rivers [34,35], and wastewater treatment facilities [36]. ...
... These values confirmed that the filter showed a similar RGB ratio before the test, but the RGB ratios converged to different values for each turbidity-causing compound after the test. In particular, in the case of FeO(OH), the ratio of red and blue changed significantly to 29 a strong red color, the ratio of red increased significantly to 17 % compared to those of green and blue colors. This result indicated that the turbidity-causing compounds can be distinguished by monitoring the color ratio with the direct filter observation system, and that the system would help one track the source of pollution when a water quality accident occurs. ...
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Globally, the red water crisis continues to occur because of aging water pipelines, excessive water system conversion, and inadequate water tank management. Therefore, in this study, we have developed a monitoring system for water tank filters in apartment buildings by using Arduino and Internet of Things (IoT) technology to realize efficient water tank management. The proposed system uses grayscale, RGB color, and pressure sensors, and these sensors are connected to an Arduino. In the performance evaluation of the direct filter monitoring system using a color sensor, the system could effectively detect the turbid water inflow by the signal-to-noise ratio of derivative of color change. Moreover, the representative turbidity-causing minerals such as iron (III) oxide-hydroxide (FeO(OH)), iron (III) oxide (Fe2O3), manganese oxide (MnO2) could be distinguished by a different color. A correlation analysis between pressure change and turbidity of filtered water yielded an R-square value of 0.9 or higher under most conditions. Based on the results obtained in the lab-scale test, a four-stage notification algorithm for safe filter operation was proposed. Finally, the developed direct filter monitoring system was installed in an apartment complex as a field demonstration, and it verified that the direct filter monitoring system can provide reliable notifications with high accuracy. Therefore, the direct filter monitoring system developed in this study can be widely used to manage water filter to ensure safe water supply in apartment complexes.
... The data collection of water quality with wireless sensor networks and internet of things (IoT) technologies is rapidly increasing and providing very-high-frequency WQ data (sub-hourly) [165,166]. There is evidence that the high-frequency data better represent the dynamics variation of river discharge and sediment and solute fluxes [167]. ...
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This paper reviews the current GeoAI and machine learning applications in hydrological and hydraulic modeling, hydrological optimization problems, water quality modeling, and fluvial geomorphic and morphodynamic mapping. GeoAI effectively harnesses the vast amount of spatial and non-spatial data collected with the new automatic technologies. The fast development of GeoAI provides multiple methods and techniques, although it also makes comparisons between different methods challenging. Overall, selecting a particular GeoAI method depends on the application’s objective, data availability, and user expertise. GeoAI has shown advantages in non-linear modeling, computational efficiency, integration of multiple data sources, high accurate prediction capability, and the unraveling of new hydrological patterns and processes. A major drawback in most GeoAI models is the adequate model setting and low physical interpretability, explainability, and model generalization. The most recent research on hydrological GeoAI has focused on integrating the physical-based models’ principles with the GeoAI methods and on the progress towards autonomous prediction and forecasting systems.
... Sensor technology use can be particularly relevant in assessing chronically contaminated sites with dynamic contaminant profiles or at sites situated in locations where hardwiring deployment is impractical (Sohraby et al., 2006). Despite the potential advantages of autonomous sensor use, limited information on sensor application for water quality monitoring is available in the primary literature (Ighalo et al., 2021). Most cases reported relate to the development of the technology itself (e.g., Parameswari & Moses, 2017;Qin et al., 2018;Zakaria & Michael, 2017) and, to a lesser extent, the interpretation and validation of data previously gathered in the field (e.g., Adu-Manu et al., 2020). ...
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There is an increasing trend in the use of real‐time sensor technology to remotely monitor aquatic ecosystems. Commercially available probes, however, are currently not able to measure aqueous selenium (Se) concentrations. Because of the well‐described bioaccumulation potential and associated toxicity of Se in oviparous vertebrates, it is crucial to monitor Se concentrations at sites receiving continuous effluent Se input. This study aimed to estimate Se concentrations in a boreal lake (McClean Lake) downstream from a Saskatchewan uranium mill using real‐time electrical conductivity (EC) data measured by autonomous sensors. Additionally, this study aimed to derive a site‐specific total aqueous Se (TSe) threshold based on Se concentrations in periphyton and benthic macroinvertebrates sampled from the same lake. To characterize effluent distribution within the lake, 8 Smart Water (Libelium) sensor units were programmed to report EC and temperature for 5 and 7 consecutive weeks in 2018 and 2019, respectively. In parallel, periphyton and benthic macroinvertebrates were sampled with Hester‐Dendy's artificial substrate samplers (n=4) at the same sites and subsequently analysed for Se concentrations. Electrical conductivity was measured with a handheld field meter for sensor data validation and adjusted to the median lake water temperature (13 ºC) registered for the deployment periods. Results demonstrated good accuracy of sensor readings relative to handheld field meter readings and the successful use of real‐time EC in estimating TSe exposure (r= 0.87; r2=0.84). Linear regression equations derived for Se in detritivores vs. Se in periphyton and Se in periphyton vs. sensor estimated TSe were used to estimate a site‐specific TSe threshold of 0.7 µg/L (±0.2). Moreover, mean Se concentrations in periphyton (16.7 ± 4.4 µg/g d.w.) and benthic detritivores (6.0 ± 0.4 µg/g d.w.) from one of the exposure sites helped identify an area with potential for high Se bioaccumulation and toxicity in aquatic organisms in McClean Lake. This article is protected by copyright. All rights reserved.© 2022 Society of Environmental Toxicology & Chemistry (SETAC).
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There is currently an increasing dependence on freshwater sources for various human activities because of population growth, and rising industrialization across the globe. Meanwhile, the safety of available freshwater is threatened by the massive generation of waste from increasing domestic and industrial activities. The need for continuous assessment of the quality of available environmental water has become a crucial research concern, as the conventional techniques often used are not sufficient to meet the expanding demand for real-time, rapid, low-cost, reliable, and sensitive water quality monitoring (WQM). The use of wireless sensor networks (WSN) has been proposed by various researchers as a sustainable substitute for the traditional processes of monitoring water quality. In this work, an array of the literature on the practical applications of the networks in the assessment of vital water quality parameters such as pH, turbidity, temperature, dissolved oxygen (DO), chlorine content, etc., were surveyed and analyzed. Various technologies such as machine learning, blockchain, IoT, deep reconstruction model, etc., were incorporated with WSN for real-time monitoring of water quality, data acquisition, and reporting for a broad range of water bodies. The survey shows that the networks are comparatively affordable and allow remote, real-time, and sensitive measurement of these parameters with minimal human involvement. The use of a low-power wide area network (LPWAN) was also introduced to solve a major problem of power supply often associated with the use of WSN. Recent developments also showed the capacity of WSN to assess simultaneously multiple water quality parameters from several locations using unmanned aerial vehicles (UAV). However, the networks rely on established parameters to indicate a compromise in water quality, but in most cases, failed to identify which pollutant species could be responsible.
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Wireless sensor networks (WSNs) have become ubiquitous, permeating every aspect of human life. In environmental monitoring applications (EMAs), WSNs are essential and provide a holistic view of the deployed environment. Physical sensor devices and actuators are connected across a network in environmental monitoring applications to sense vital environmental factors. EMAs bring together the intelligence and autonomy of autonomous systems to make intelligent decisions and communicate them using communication technologies. This paper discusses the various architectures developed for WSNs in environmental monitoring applications and the support for specific design goals, including machine learning in WSNs and its potential in environmental monitoring applications.
Conference Paper
Hydroponics is a farming method that grows plants in nutrient solution. However, hydroponics requires a lot of manpower to monitor parameters such as pH, temperature and dissolved oxygen. The digitalization and automation have driven the concept of Industrial Revolution 4.0, which enables automatic collection of data. The data acquisition is developed based on this concept is able to monitor the environmental or physical situation remotely and continuously. Thus, this project was carried out to design a smart water quality monitoring system utilizing IoT platform for hydroponics application. A waterproof temperature sensor (DFRobot (DS18B20s), ±0.13 °C), dissolved oxygen sensor (DFRobot (SEN0237-A), ±0.13) and pH sensor (GI Electronic (E-201-C), ±0.04) were selected to collect the real-time data of the nutrient solutions while an Arduino and Raspberry Pi were used for data processing. Node-RED was used to make real- time and historical viewer program in order to monitor and analyse the parameter remotely. By combining the sensors with Arduino and Raspberry Pi, the hydroponics system can be monitored remotely. Lastly, the water monitoring system was implemented in the hydroponics for 28 days. During the cultivation period, it was observed that the dissolved oxygen concentration and water temperature were generally constant at 6 ppm and 23 oC respectively while the pH of the nutrient solution were reduced from pH 6 to pH 3 during the cultivation. Results suggested that a robust water quality monitoring system was successfully designed, and the sensors are stable for continuous data collection at a 5 seconds sampling rate throughout 28 days for hydroponics application.
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Aquaponics is an innovative, smart, and sustainable agricultural technology that integrates aquaculture (farming of fish) with hydroponics in growing vegetable crops symbiotically. The correct implementation of aquaponics helps in providing healthy organic foods with low consumption of water and chemical fertilizers. Numerous research attempts have been directed toward real implementations of this technology feasibly and reliably at large commercial scales and adopting it as a new precision technology. For better management of such technology, there is an urgent need to use the Internet of things (IoT) and smart sensing systems for monitoring and controlling all operations involved in the aquaponic systems. Thence, the objective of this article is to comprehensively highlight research endeavors devoted to the utilization of automated, fully operated aquaponic systems, by discussing all related aquaponic parameters aligned with smart automation scenarios and IoT supported by some examples and research results. Furthermore, an attempt to find potential gaps in the literature and future contributions related to automated aquaponics was highlighted. In the scope of the reviewed research works in this article, it is expected that the aquaponics system supported with smart control units will become more profitable, intelligent, accurate, and effective.
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Organophosphate pesticides (OPPs) are a type of pesticide that is commonly used to eliminate pests that cause agricultural produce shortages. One of the methods that is effective in its mitigation from the aqueous environment is adsorption. This paper is aimed at reviewing the removal of OPPs from aqueous media via adsorption. The goal was to carefully study the trends of research findings by various authors over the past eleven years, analyzing key results, observing patterns and similarities, and identifying interesting areas that future researchers should consider. It was observed that the highest reported adsorption capacity for OPPs is 4460 mg/g for chlorpyrifos using mesoporous MgFe2O4 adsorbent. Adsorption mechanisms were dominated by hydrogen bonding, complexation reactions, electrostatic interactions, and electron donor–acceptor interactions. The Langmuir or Freundlich classical isotherm models were the best-fit in most cases to describe OPPs’ adsorption equilibrium, while the pseudo-second-order model was the best-fit for the modeling of OPPs’ uptake kinetics. Thermodynamic studies revealed that OPPs’ uptake is usually spontaneous (with a few exceptions). Competitive adsorption studies showed that the uptake capacity of OPPs by the various adsorbents decreased in the presence of negatively charged ions and anions. Most OPPs exhibited a removal efficiency > 70% even after five regeneration cycles. The utilization and toxicity of OPPs as well as the implications of OPPs’ adsorption on sustainable water resource management were also discussed in this review. Interesting areas for future research were identified in the economics of the adsorption processes.
The continuous increase in the rate of industrialization in developing countries, in recent times, calls for continuous industrial water quality assessment and prediction. This is to create more awareness and ensure cleaner and sustainable industrial production. Water quality for industrial uses is often described in terms of corrosion and scaling potentials (CSPs). In this paper, optimized artificial intelligence models (e.g. multiple regressions (MR), hierarchical clusters (HCs), and artificial neural networks (ANNs)) for assessing and predicting the CSPs of water resources were developed, for Ojoto suburb (SE Nigeria). Indices used in evaluating the CSPs are chloride–sulphate mass ratio (CSMR), Larson-Skold index (LSI), Langelier index (LI), aggressive index (AI), Ryznar stability index (RSI), Puckorius scaling index (PSI), and Revelle index (RI). This work is the first of its kind to utilize predictive models in simultaneously predicting the industrial water quality indices. Prior to the predictive modeling, R-mode HCs, correlation, principal component, and factor analyses were used to analyze the relationships between the physicochemical variables (pH–T–EC–TDS–TH–Ca–HCO3–Cl–SO4–Fe–Zn–Pb) and the CSP indices. Q-mode HCs effectively identified the spatiotemporal water corrosion/scaling risk classification and distribution in the area. Both MR and ANN models suitably predicted the CSP indices. However, both models better predicted AI, LI, RSI and PSI than LSI, RI and CSMR. The MR models’ performances were analyzed using R, R², adjusted R² and F-ratio values whereas the ANN models were verified using parity plots, R², RMSE, and residual error plots. For the ANN modeling, scaled-conjugate-gradient optimizer outperformed gradient-descent optimizer. Also, ANN models outperformed the MR models. The practical implications of the present research findings were also discussed.
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Plant barks are among the most widely applied low-cost biomass materials in the study of pollutant removal from aqueous media. This paper extensively reviews the experimental findings presented in open literature with much focus on the last 15 years. This study classified plant bark adsorbents into 5 broad groups (based on their preparation technique): unmodified biosorbent, pre-modified biosorbent, chemically modified biosorbent, physically modified biosorbent and bio-based activated carbon. It was observed that eucalyptus, pine, neem, acacia and mango are the most explored source species in tree bark adsorption studies. About two-third of target impurities reported on the subject in open literature have been on heavy metals. The review elucidated the excellent adsorption capacities of plant bark based adsorbents and biosorbents for the uptake of heavy metals, dyes, pesticides and other pollutants. Adsorption was majorly best-fit to either the Langmuir or Freundlich isotherm models and the pseudo-second order kinetic model. The thermodynamics findings revealed that the adsorption is highly spontaneous and is by a physical mechanism in most cases. It was also observed that plant barks have high reusability potential thereby underlying their usefulness for industrial application. Knowledge gaps in the research area were also discussed in line with future perspectives.
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Cocoa pod husk (CPH) has been valorised as adsorbents for the removal of a variety of chemical species from aqueous media. This review was conducted to catalogue the empirical findings, discuss the current state of knowledge, observe the research trend, identify research gaps and predict future perspectives in the research area. CPH has been processed into unmodified biosorbent, chemically modified biosorbent and bio-based-activated carbon. Much of the research interest in CPH adsorption has been majorly focused on heavy metals and dyes. The removal efficiency of the CPH adsorbent for most of the pollutants was above 90% but for a few exceptions. The effect of temperature, solution pH, adsorbent dosage, agitation time and initial concentration of the pollutants were considered in the review. Furthermore, the equilibrium data were always best fit to either Langmuir or Freundlich isotherm models. It was also observed that the pseudo-second order kinetic model was the best fit for the adsorption of pollutants onto CPH adsorbents. Thermodynamic calculations revealed that CPH adsorption was mainly spontaneous and exothermic. Future perspectives were suggested in the domain of desorption studies, reusability studies, continuous flow experiments and adsorbent immobilisation, pilot and semi-pilot scale-up systems and financial and techno-economic investigations.
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This paper presents iAirBot, an assistive robot for indoor air quality monitoring based on Internet of Things. The system can communicate with occupants and triggers alerts automatically using social networks. The information can be accessed by the caregiver to plan interventions for enhanced living environments in a timely manner. The results are promising, as the proposed architecture presents a cost-effective assistive robot for indoor quality monitoring. It connects several technological fields and knowledge areas, such as ambient assisted living, Internet of Things, wireless sensor networks, social networks, and indoor air quality. When compared to other systems, iAirBot stands out for the modularity and scalability of its sensors network, as well as the use of social networks for information sharing. Therefore, iAirBot is a significant system for enhanced living environments, occupational health, and well-being.
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Internet of Things (IoT) is an evolution of the Internet and has been gaining increased attention from researchers in both academic and industrial environments. Successive technological enhancements make the development of intelligent systems with a high capacity for communication and data collection possible, providing several opportunities for numerous IoT applications, particularly healthcare systems. Despite all the advantages, there are still several open issues that represent the main challenges for IoT, e.g., accessibility, portability, interoperability, information security, and privacy. IoT provides important characteristics to healthcare systems, such as availability, mobility, and scalability, that o�er an architectural basis for numerous high technological healthcare applications, such as real-time patient monitoring, environmental and indoor quality monitoring, and ubiquitous and pervasive information access that benefits health professionals and patients. The constant scientific innovations make it possible to develop IoT devices through countless services for sensing, data fusing, and logging capabilities that lead to several advancements for enhanced living environments (ELEs). This paper reviews the current state of the art on IoT architectures for ELEs and healthcare systems, with a focus on the technologies, applications, challenges, opportunities, open-source platforms, and operating systems. Furthermore, this document synthesizes the existing body of knowledge and identifies common threads and gaps that open up new significant and challenging future research directions.
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In most higher education establishments and universities, laboratories are also used as classrooms. Different conditions throughout laboratory and teaching activities with reliable data quality should be provided and guaranteed. The thermal comfort of the students must be ensured in teaching activities. During the laboratory activities, several parameters must be ensured and monitored, and data collection must be stored to ensure the stability of the environment when the test is conducted and at the data collection moment as they influence the quality of the results. Oftentimes, there is the requirement of tracking object temperatures with non-contact but also to measure the ambient temperature for comparison. Infrared temperature sensors provide a non-contact measurement in a quickly and accurately process. This paper presents an Internet of Things (IoT) solution for real-time temperature supervision named iRT. The solution is composed of a hardware prototype for temperature data collection and Web compatibility for data access. The iRT uses an infrared thermometer sensor module which incorporates an MLX90614 and provides object and ambient temperature supervision in real-time. The Web application can be used to access the collected data but also provides the history of the temperature evolution. The results obtained are promising, representing a significant contribution to infrared temperature monitoring systems based on IoT.
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Current water quality monitoring system is a manual system with a monotonous process and is very time-consuming. This paper proposes a sensor-based water quality monitoring system. The system consists of several sensors which is used to measure physical and chemical parameters of the water. The main components of Wireless Sensor Network (WSN) include a microcontroller for processing the system, communication system for inter and intra node communication and several sensors. Real-time data access can be done by using remote monitoring and Internet of Things (IoT) technology. Data collected at the apart site can be displayed in a visual format on a server PC with the help of Spark streaming analysis through Spark MLlib, Deep learning neural network models, Belief Rule Based (BRB) system and is also compared with standard values. If the acquired value is above the threshold value automated warning SMS alert will be sent to the agent. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered. Therefore, our proposed system will immensely help Bangladeshi populations to become conscious against contaminated water as well as to stop polluting the water.
Diclofenac is an acidic pharmaceutically active compound and one of the most frequently detected ones in wastewater. This review discusses the research progress and innovations made in recent years on the mitigation of diclofenac from aqueous media by adsorption. It was observed that activated carbon by intense physical activation, metal organic frameworks , and chitosan-cellulose polymeric adsorbents were the best adsorbents for diclofenac removal. Diclofenac sorption follows mainly pseudo-second order kinetics and was best fit to either Freundlich or Langmuir isotherms. Recent research trends involved the investigation of column and continuous experiments, competitive adsorption, and adsorp-tion with photo-catalytic degradation. Whilst numerous types of adsorbents have been explored, there is still need for mechanistic studies.
Hydroponic cultivation is an agricultural method where nutrients are efficiently provided as mineral nutrient solutions. This modern agriculture sector provides numerous advantages such as efficient location and space requirements, adequate climate control, water-saving and controlled nutrients usage. The Internet of things (IoT) concept assumes that various “things,” which include not only communication devices but also every other physical object on the planet, are going to be connected and will be controlled across the Internet. Mobile computing technologies in general and mobile applications, in particular, can be assumed as significant methodologies to handle data analytics and data visualisation. Using IoT and mobile computing is possible to develop automatic systems for enhanced hydroponic agriculture environmental monitoring. Therefore, this paper presents an IoT monitoring system for hydroponics named iHydroIoT. The solution is composed of a prototype for data collection and an iOS mobile application for data consulting and real-time analytics. The collected data is stored using Plotly, a data analytics and visualisation library. The proposed system provides not only temporal changes monitoring of light, temperature, humidity, CO2, pH and electroconductivity but also water level for enhanced hydroponic supervision solutions. The iHydroIoT offers real-time notifications to alert the hydroponic farm manager when the conditions are not favourable. Therefore, the system is a valuable tool for hydroponics condition analytics and to support decision making on possible intervention to increase productivity. The results reveal that the system can generate a viable hydroponics appraisal, allowing to anticipate technical interventions that improve agricultural productivity.
In recent years, research focus has been shifting towards the development of environmentally friendly systems for the removal of pollutants from effluents. The use of green adsorbents from tree leaves is a key research domain in this regard. In this empirical review, plant leaves biosorption studies within the last two decades were considered. The numerous source plants, preparation and modification techniques and adsorbent improvement findings were elucidated. The basic steps of preparation were found to be similar for most studies and it involves pre-cleaning, drying, grinding and sieving. In some studies, further chemical modification was conducted. Comprehensive catalogues of empirical findings were presented for removal efficiency, adsorption capacity, factor optimal, equilibrium, kinetics and thermodynamic studies. In these areas, a variety of findings were deduced by researchers with the peculiarity of obtained results being consequent on the type and nature of the pollutant and plant precursor. The desorption methodology and reusability potential was also considered and key knowledge gaps elucidated. Real wastewater treatment, pilot and full-scale applications, column studies, disposal and treatment ways of the exhausted adsorbents are fallow areas needing more extensive exploration. It can be surmised that plant leaves are excellent, eco-friendly, cost-effective and easy-to-prepare precursors for the development of biosorbents effective in removing heavy metals, dyes, cations and other chemical species from aqueous solutions.
Environmental noise has a direct influence on human health and also on the quality of life. The environmental noise effects on health are not only related to annoyance, sleep and cognitive performance but can also be linked with raised blood pressure. Therefore, noise pollution must be seen as severe world public health challenge and should be monitored not only inside buildings, as people spend about 90% of our lives indoors, for enhanced occupational health but also in outside for enhanced living environments in smart cities. Noise real-time monitoring allows the detection of unhealthy situations and to notify the building or the city managers to take interventions to decrease the sound levels quickly. Considering the proliferation of Internet of Things (IoT) devices and technologies, the iSound, a solution for real-time noise monitoring based on IoT has been developed. This solution is composed by a hardware prototype for ambient data collection and web portal for data consulting. The iSound is based on open-source technologies and is a totality Wi-Fi system, with several advantages compared to existing systems, such as its modularity, scalability, low-cost and easy installation.