A comprehensive guide to the development and application of smart sensing technologies for water and food quality monitoring With contributions from a panel of experts on the topic, Sensing Technologies for Real Time Monitoring of Water Quality offers an authoritative resource that explores a complete set of sensing technologies designed to monitor, in real time, water and food (aquaculture) quality. The contributing authors explore the fundamentals of sensing technologies and review the most recent advances of various materials and sensors for water quality monitoring. This comprehensive resource includes information on a range of designs of smart electronics, communication systems, packaging, and innovative implementation approaches used for remote monitoring of water quality in various atmospheres. The book explores a variety of techniques for data analysis of the sensors as well as contains artificial intelligence, big data technologies, and machine learning approaches used for monitoring and evaluation. In addition, this indispensible resource highlights sustainable environmental and policy issues, including ways for food and water managers to can help to reduce their carbon footprint. This important book: * Puts the spotlight on the potential capabilities and the limitations of various sensing technologies and wireless systems * Offers an evaluation of a variety of sensing materials, substrates, and designs of sensors * Includes information on the common characteristics, ideas, and approaches of water quality and quantity management * Presents techniques for manager for reducing their carbon footprint Written for students and practitioners/researchers in food and water quality management, Sensing Technologies for Real Time Monitoring of Water Quality offers, in one volume, a guide to the real time sensing techniques that can improve water and food quality.
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Table of Contents
About the Editors
List of Contributors
Section I: Materials and Sensors Development Including Case Study
1 Smart Sensors for Monitoring pH, Dissolved Oxygen, Electrical Conductivity, and Temperature in Water
1.2 Water Quality Parameters and Their Importance
1.3 Water Quality Sensors
1.4 Smart Sensors
2 Dissolved Heavy Metal Ions Monitoring Sensors for Water Quality Analysis
2.2 Sources and Effects of Heavy Metals
2.3 Detection Techniques
2.4 Future Direction
3 Ammonia, Nitrate, and Urea Sensors in Aquatic Environments
3.2 Detection Techniques for Ammonia, Nitrate, and Urea in Water
3.6 Conclusion and Future Perspectives
4 Monitoring of Pesticides Presence in Aqueous Environment
4.1 Introduction: Background on Pesticides
4.2 Current Pesticides Detection Methods
5 Waterborne Bacteria Detection Based on Electrochemical Transducer
5.2 Typical Waterborne Pathogens
5.3 Traditional Diagnostic Tools
5.4 Biosensors for Bacteria Detection in Water
5.5 Various Electrochemical Biosensors Available for Pathogenic Bacteria Detection in Water
5.6 Conclusion and Future Prospective
6 Zinc Oxide‐Based Miniature Sensor Networks for Continuous Monitoring of Aqueous pH in Smart Agriculture
6.2 Metal Oxide‐Based Sensors and Detection Methods
6.3 pH Sensor Fabrication
Section II: Readout Electronic and Packaging
7 Integration and Packaging for Water Monitoring Systems
7.2 Advanced Water Quality Monitoring Systems
7.3 Basics of Packaging
7.4 Hybrid Flexible Packaging
8 A Survey on Transmit and Receive Circuits in Underwater Communication for Sensor Nodes
8.2 Sensor Networks in an Underwater Environment
Section III: Sensing Data Assessment and Deployment Including Extreme Environment and Advanced Pollutants
9 An Introduction to Microplastics, and Its Sampling Processes and Assessment Techniques
9.2 Microplastic Sampling Tools
9.3 Microplastics Separation
9.4 Microplastic Sample Digestion Process
9.5 Microplastic Identification and Classification
10 Advancements in Drone Applications for Water Quality Monitoring and the Need for Multispectral and Multi‐Sensor Approaches
10.2 Airborne Drones for Environmental Remote Sensing
10.3 Drone Multispectral Remote Sensing
10.4 Integrating Multiple Complementary Sensor Strategies with a Single Drone
11 Sensors for Water Quality Assessment in Extreme Environmental Conditions
11.2 Physical Parameters
11.3 Chemical Parameters
11.4 Biological Parameters
11.5 Sensing in Extreme Water Environments
11.6 Discussion and Outlook
Section IV: Sensing Data Analysis and Internet of Things with a Case Study
12 Toward Real‐Time Water Quality Monitoring Using Wireless Sensor Networks
12.2 Water Quality Monitoring Systems
12.3 The Use of Industry 4.0 Technologies for Real‐Time WQM
13 An Internet of Things‐Enabled System for Monitoring Multiple Water Quality Parameters
13.2 Water Quality Parameters and Related Sensors
13.3 Design and Fabrication of the Proposed Sensor
13.4 Experimental Process
13.5 Autonomous System Development
13.6 Experimental Results
IEEE Press Series on Sensors
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Table 1.1 Fixed points defined by temperature scales.
Table 2.1 Permissible limit as recommended by WHO and EU of some toxic heav...
Table 2.2 Different nanomaterial modifications of electrochemical sensor fo...
Table 2.3 Biosensor modification for heavy metal detection.
Table 2.4 Comparison of different modification methods in electrochemical s...
Table 3.1 Overview of common and upcoming detection techniques for ammonia,...
Table 3.2 Percentage of unionized ammonia in aqueous solution for pH 6–9 an...
Table 3.3 A summary of different sensors used for the detection of NH
Table 3.4 A summary of different sensors used for the detection of NO
Table 3.5 A summary of different sensors used for the detection of urea.
Table 4.1 Examples of pesticides detection by electrochemical technique.
Table 4.2 Examples of pesticides detection on fruit surface by SERS.
Table 4.3 Examples of pesticides detection in food matrix and soil by SERS....
Table 5.1 Microbial bacteria associated with waterborne diseases.
Table 5.2 Summary of the reported electrochemical‐based biosensors for wate...
Table 6.1 Metal oxide (MOx)‐based electrochemical pH sensors.
Table 6.2 ZnO nanostructured‐based pH sensors.
Table 6.3 Dakshina Kannada District laterite soil samples‐based elemental c...
Table 9.1 Techniques used for microplastic identification and/or classifica...
Table 11.1 Top 10 parameters monitored online by drinking water companies i...
Table 12.1 Suggested water quality parameters for monitoring [2, 16].
Table 12.2 Summary of measured water quality parameters in different WQM sy...
Table 13.1 Comparison among existing pH sensors.
Table 13.2 Comparison among the existing research on nitrate detection.
Table 13.3 Comparison among phosphate sensors for water bodies.
Table 13.4 Necessary electronics components for the autonomous system.
Table 13.5 Dataset matrix to train the system.
Table 13.6 Testing reproducibility of the MWCNTs/PDMS sensor.
Table 13.7 Current drawn of the proposed system in one cycle (one hour).
Figure 1.1 Principles and methods of measuring dissolved oxygen.
Figure 1.2 Schematic representation of as Clarke cell‐type electrochemical D...
Figure 1.3 Schematic representations of (a) two‐electrode and (b) four‐elect...
Figure 1.4 Different types of temperature sensors.
Figure 2.1 Working range of different analytical techniques to quantify heav...
Figure 2.2 Schematic showing the various electrochemical methods for the det...
Figure 3.1 (a) Major processes of the nitrogen cycle, including nitrogen fix...
Figure 3.2 Schematic representation of an UV–Vis absorption measurement of a...
Figure 3.3 Schematic representation of a spectrofluorometer.
Figure 3.4 (a) Electrochemical sensors device scheme.(b) Transduction cl...
Figure 3.5 Common sensor designs used for the different electrochemical tech...
Figure 3.6 (a) Device for colorimetric sensing of dissolved NH
Figure 3.7 (a) The Griess assay method.(b) Electrochemical detection of ...
Figure 3.8 (a) Ionovoltaic urea sensors.(b) Enzymatic biosensor using ur...
Figure 4.1 Five categories of pesticides: organochlorines, organophosphates,...
Figure 4.2 Different pesticides detection methods: (a) chromatography‐based ...
Figure 4.3 Fluorescence band diagram.
Figure 4.5 Raman scattering band diagram.
Figure 4.6 SERS phenomenon between two metallic nanoparticles.
Figure 4.7 SERS substrate fabrication by electrochemical deposition.
Figure 5.1 Schematic illustration of (a) Fe
preparation, (b) rGO...
Figure 5.2 Representation of functionalization and detection process of impe...
Figure 5.3 Illustration of the process of the MAS@CS method, (I) the aptamer...
Figure 5.4 Illustration of the potentiometric sensor by employing electrospu...
Figure 5.5 Illustration of the measurement setup of the light‐addressable po...
Figure 6.1 Schematic representation of the pH sensor electrochemical mechani...
Figure 6.2 (a) Schematic illustration of pH sensor fabrication process, (b) ...
Figure 6.3 Surface morphology of the various ZnO nanostructured active layer...
Figure 6.4 Electrochemical response of the pH sensors: (a) and capacitance v...
Figure 6.5 Block diagram of IoT‐enabled embedded system for real‐time sensor...
Figure 6.6 Impedance characteristic measurement electronic circuit with 1 kH...
Figure 7.1 Hydrolab HL7 – Multiparameter Sonde.
Figure 7.2 Overall principle of a multiparameter sensor system (top) and wat...
Figure 7.3 (a) Water quality monitoring sensor package, (b) copper face plat...
Figure 7.4 (a) WaterWiSe@SG communication structure and (b) WaterWiSe@SG sen...
Figure 7.5 Schematic diagram of a state‐of‐the‐art LOC chip (a) and the fabr...
Figure 7.6 Photograph of the dual ion‐selective lab chip and the ESEM images...
Figure 7.7 (a) Schematic demonstration of a system in package (SiP) concept ...
Figure 7.8 Concept design and a fabricated package of MoboSens. (a) Assembly...
Figure 7.9 Different levels of packaging.
Figure 7.10 (a) Thermosonic flip chip bonding.(b) ACA illustration.
Figure 7.11 Flip chip package.
Figure 7.12 The results of the cyclic bending test; the effects of die thick...
Figure 7.13 Stacked thin chip on Flex.
Figure 7.14 Overview of thin die integration in foil.
Figure 7.15 System in foil via die embedding (top) and PVD routings (bottom)...
Figure 7.16 Cross‐section of material stacking.
Figure 7.17 Roll‐to‐roll manufacturing process flow.
Figure 8.1 Underwater Acoustic Sensor Network.
Figure 8.2 Main modules of an underwater acoustic sensor node.
Figure 8.3 A typical class B stage.
Figure 8.4 Simplified block diagram of a class G amplifier.
Figure 8.5 A typical class D amplifier.
Figure 8.6 Concept of voltage‐boosting output stage.
Figure 8.7 The concept of the Boost inverter circuit.
Figure 8.8 Charge redistribution DAC architecture.
Figure 8.9 Split capacitor array method in a 6‐bits SAR‐ADC.
Figure 8.10 Modification of the split capacitor array.
Figure 8.11 Low kickback dynamic comparator.
Figure 8.12 Dipole antenna.
Figure 8.13 Circular loop antenna.
Figure 8.14 Multipath propagation of EM waves in seawater.
Figure 9.1 Global plastic production percentage based on regions.
Figure 9.2 (a) Plastic consumption in different sectors. (b) Global plastic ...
Figure 9.3 Process of microplastic collection and identification process.
Figure 9.4 Schematics of net used for water sample collection.
Figure 9.5 Pump underway system.
Figure 9.6 Vacuum filtration system schematic.
Figure 9.7 (a) Sieving device.(b) Schematic of sieving container.(c)...
Figure 9.8 Density separation schematic.
Figure 9.9 Elutriation.
Figure 9.10 Froth flotation.
Figure 10.1 Photograph of algal bloom on southern UK water reservoir, July 2...
Figure 10.2 Spatial and temporal scale characteristics of climate systems an...
Figure 10.3 Quadcopter drone with a hyperspectral imaging system mounted und...
Figure 11.1 Schematic representation of (a) four electrode‐based and (b) tor...
Figure 11.2 A fully inkjet printed sensor with magnified images displaying t...
Figure 11.3 Schematic representation of (a) the design of the laser‐induced ...
Figure 11.4 (a) Schematic illustration of fluorescent pH probe using boron−d...
Figure 11.5 Schematic illustration of (a) DO, pH, temperature, and conductiv...
Figure 11.6 Schematic illustration of (a) underwater vehicle CWolf from Frau...
Figure 11.7 (a) Photograph of multi‐sensor chip, chip size 7.16 mm × 7.16 mm...
Figure 11.8 Real‐time WQM, sampling, and visualization platform using unmann...
Figure 11.9 Schematic illustration depicting various human activities contri...
Figure 12.1 Water quality parameters.
Figure 12.2 Laboratory‐based WQM system [2, 15].
Figure 12.3 Structure of energy harvesting WSN node.
Figure 12.4 Water monitoring using WSNs [2, 15].
Figure 12.5 General architecture of smart river monitoring system.
Figure 12.6 Unmanned surface vehicles‐based WQM system.
Figure 12.7 A typical IoT‐based WQM system.
Figure 12.8 Big data analytics framework for WQM system.
Figure 13.1 A typical diagram of smart fisheries.
Figure 13.2 Impact of PDMS and MWCNTs ratio on the conductivity of the nanoc...
Figure 13.3 Schematic representation of the sensor fabrication process.
Figure 13.4 Scanning electron microscopic (SEM) images showing the (a) top v...
Figure 13.5 Sensor's working principle.
Figure 13.6 Schematic of experimental process using MWCNTs/PDMS sensor.
Figure 13.7 Connection diagram of the proposed system.
Figure 13.8 (a) Inset of the proposed system and (b) the final system instal...
Figure 13.9 Workflow of the proposed system.
Figure 13.10 (a) Change in sensor's resistances with temperature variations ...
Figure 13.11 (a) Change in sensor's resistances with pH variations (1.3–12.4...
Figure 13.12 (a) Change in sensor's resistances with nitrate concentration v...
Figure 13.13 (a) Change in sensor's resistances with phosphate concentration...
Figure 13.14 (a) Change in sensor's resistances with calcium concentration v...
Figure 13.15 (a) Change in sensor's resistances with magnesium concentration...
Figure 13.16 Repeatability test of sensor for various solutions of (a) pH (1...
Figure 13.17 Validation of sensor's measurement with standard method for (a)...
Figure 13.18 Sensor data collected into ThingSpeak server: (a) temperature, ...
About the Editors
List of Contributors
Table of Contents
IEEE Press Series on Sensors
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Library of Congress Cataloging‐in‐Publication Data:Names: Manjakkal, Libu, author. | Lorenzelli, Leandro, author. | Willander, Magnus, author.Title: Sensing technologies for real time monitoring of water quality / Libu Manjakkal, Leandro Lorenzelli, Magnus Willander.Description: Hoboken, New Jersey : Wiley‐IEEE Press,  | Includes index.Identifiers: LCCN 2023012128 (print) | LCCN 2023012129 (ebook) | ISBN 9781119775812 (cloth) | ISBN 9781119775829 (adobe pdf) | ISBN 9781119775836 (epub)Subjects: LCSH: Water quality–Measurement. | Water quality–Remote sensing. | Intelligent sensors.Classification: LCC TD367 .M3175 2023 (print) | LCC TD367 (ebook) | DDC 628.1/61–dc23/eng/20230422LC record available at https://lccn.loc.gov/2023012128LC ebook record available at https://lccn.loc.gov/2023012129
Cover Design: WileyCover Image: © webphotographeer/Getty Images
Libu Manjakkal (PhD, MRSC) received B.Sc. and M.Sc. degrees in physics from Calicut University and Mahatma Gandhi University, India, in 2006 and 2008, respectively. From 2009 to 2012, he was with CMET, Thrissur, India, and in 2012, for a short period, he worked at CENIMAT, FCT‐NOVA, Portugal. He completed his PhD in electronic engineering from the Institute of Electron Technology, Poland (2012–2015) (Marie Curie ITN Program), and after this one year, he continues as a post‐doctoral fellow in the same institute. From 2016 to 2022, he has been a post‐doctoral fellow at the University of Glasgow (UoG) and also worked in the role of Scientific Project Manager in a Marie Curie ITN project (AQUASENSE) at UoG. Currently, he is working as a Lecturer at Edinburgh Napier University. He has authored/co‐authored more than 65 peer‐reviewed papers (47 journals) and 1 patent. His research interests include material synthesis, wearable energy storage, electrochemical sensors, pH sensors, water quality monitoring, health monitoring, supercapacitors, and energy‐autonomous sensing systems.
Leandro Lorenzelli, Head of the Microsystems Technology Research Unit at FBK‐Center for Materials and Microsystems (Trento, Italy), received the Laurea degree in Electronic Engineering from the University of Genova in 1994 and a PhD in 1998 in Electronics Materials and Technologies from the University of Trento. His objective has been to strengthen the reliability of the microfabrication technologies in the sectors of MEMS and BioMEMS (lab on a chip and microfluidics), microsensors, flexible electronics, and to set up initiatives for technological transfer. His current main research interests are in the fields of technologies for organs on a chip and bendable electromagnetic metasurfaces. He is the author or co‐author of about 90 scientific publications and four patents. He has been a coordinator of European projects in the areas of innovative technologies for flexible electronics, microsystems for food analysis, quality control, and water monitoring, and sensors in prosthetic applications. He has been a program chair and speaker in many international conferences.
Magnus Willander has been a chair professor in Physics at Göteborg University and Linköping University in Sweden. He has also been a visiting scientist and visiting professor in different parts of the world. His research was concentrated on material synthesis, characterization, and devices, and he used both experimental and theoretical approaches. For the last 15 years, Professor Willander has concentrated his work on chemical and mechanical problems for sensing and energy conversions. In these fields, Professor Willander has published around 1000 scientific/technical papers and 13 international scientific books, which are cited around 29 000 times. In addition, Willander has supervised 55 PhD students and worked as a specialist in electronics in the industry. He has been a PhD examiner for numerous PhD theses around the world.
Andrea AdamiFondazione Bruno Kessler Center for Sensors and Devices (FBK‐SD) Trento, Italy
Fowzia AkhterFaculty of Science and Engineering Macquarie University, Sydney NSW Australia
Md. E. E. AlahiShenzhen Institute of Advanced Technology, Chinese Academy of Science, Shenzhen, China
Akshaya Kumar AliyanaDepartment of Electronics, Mangalore University, Konaje, India
Aiswarya BaburajDepartment of Electronics, Mangalore University, Konaje, India
Robert J. W. BrewinCentre for Geography and Environmental Science, University of Exeter, Penryn, UK
Ravinder DahiyaBendable Electronics and Sensing Technologies Group, Department of Electrical and Computer Engineering Northeastern University, Boston, USA
Fabiane Fantinelli FrancoInfrastructure and Environment Research Division, James Watt School of Engineering, University of Glasgow Glasgow, UK
Renny Edwin FernandezDepartment of Engineering, Norfolk State University, Norfolk, VA, USA
Priyanka GangulyChemical and Pharmaceutical Sciences, School of Human Sciences, London Metropolitan University London, UK
Noushin GhaderiFondazione Bruno Kessler Center for Sensors and Devices (FBK‐SD) Trento, Italy
Naveen Kumar S. K.Department of Electronics, Mangalore University, Konaje, India
Peter E. LandPlymouth Marine Laboratory Plymouth, UK
Leandro LorenzelliFondazione Bruno Kessler Center for Sensors and Devices (FBK‐SD) Trento, Italy
Pierre LoveraNanotechnology Group, Tyndall National Institute, University College Cork, Cork, Ireland
Muhammad Hassan MalikSilicon Austria Labs GmbH Heterogeneous Integration Technologies, Villach, Austria
Bappa MitraFondazione Bruno Kessler Center for Sensors and Devices (FBK‐SD) Trento, Italy
S. C. MukhopadhyayFaculty of Science and Engineering Macquarie University, Sydney NSW Australia
Tarun NarayanNanotechnology Group, Tyndall National Institute, University College Cork, Cork, Ireland
Omer NurDepartment of Sciences and Technology, Physics and Electronics, Linköping University Norrköping, Sweden
Alan O'RiordanNanotechnology Group, Tyndall National Institute, University College Cork, Cork, Ireland
Nasrin RazmiDepartment of Sciences and Technology, Physics and Electronics, Linköping University Norrköping, Sweden
Ali RoshanghiasSilicon Austria Labs GmbH Heterogeneous Integration Technologies, Villach, Austria
Sohail SarangFaculty of Technical Sciences University of Novi Sad Novi Sad, Serbia
H. R. SiddiqueiFaculty of Science and Engineering Macquarie University Sydney, NSW, Australia
Joao L. E. SimonCentre for Geography and Environmental Science, University of Exeter, Penryn, UK
Jamie D. ShutlerCentre for Geography and Environmental Science University of Exeter, Penryn, UK
Stevan StankovskiFaculty of Technical Sciences University of Novi Sad, Novi Sad, Serbia
Goran M. StojanovićFaculty of Technical Sciences University of Novi Sad Novi Sad, Serbia
Kiranmai UppuluriLukasiewicz Research Network – Institute of Microelectronics and Photonics Department of LTCC Technology and Printed Electronics, Krakow, Poland
Magnus WillanderDepartment of Sciences and Technology Physics and Electronics, Linköping University, Norrköping, Sweden
Yuqing YangNanotechnology Group, Tyndall National Institute, University College Cork, Cork, Ireland
Water quality (WQ) degradation is caused due to multiple reasons that directly impact public health and the economy. Sensor technology and various programs are implemented for monitoring and assessing the status and the causes of WQ degradation. This is reflected through programs such as National Rural Drinking Water Quality Monitoring and Surveillance in India, the Water Framework Directive in the European Union, and part of the Water Quality Framework of Environmental Protection Agencies (EPA) in the United States. Over the past decade, WQ observing technology has risen to the challenge of scientists to identify and mitigate poor WQ by providing them with tools that can take measurements of essential biogeochemical variables autonomously. Commercial sensors for in situ monitoring using buoys and boats are being deployed to broaden data coverage in space and time. Yet, despite these options becoming more readily available, there is a gap between the technology and the end user and a disconnect between data quality, data gathering by autonomous sensors, and data analysis. Further, real‐time monitoring of various physical‐chemical‐biological (PCB) parameters remains a challenge, and methods that allow holistic water management approach (also considering the catchment management) need greater attention and innovation. The need for real‐time water quality monitoring (WQM) is highlighted in recent white papers from the European Innovation Partnerships on Water (EIP water), an initiative of the European Commission (EC). Given these, a fundamental rethinking of monitoring approaches could yield substantial savings and increased benefits of monitoring.
This book will cover a complete set of sensing technologies for WQM, particularly in relation with real‐time monitoring. A few review articles and books reported in this field have reviewed some of the sensing technologies only and that too is not directly related to real‐time monitoring. This book will cover smart sensing technologies for WQM, and it will highlight the current progress in this area. As mentioned above, the major obstacle in WQM is related to (i) data quality, (ii) data gathering, and (iii) data analysis. To address these challenges, this book will present a detailed overview of these topics through several chapters related to each. The above topics also define the three broad sections of this book. In fact, this will be one of the distinguishing features of this book as compared to previously reported review articles or books. Potential capabilities and critical limitations of each sensing technology and wireless system will be highlighted and possible solutions or alternatives will be explored. In terms of sensors, the book will evaluate various sensing materials, substrates, and designs of sensors including flexible or non‐flexible and multi‐sensory patches. Overall, this book will be the first choice for researchers to explore the potential of different sensing technologies, electronics/communication designs, and algorithms for data analysis and select the best one closely matching to their resources for desired application including water and food quality in harsh environments.
This book will be organized into four major sections. Section I (Materials and Sensors Development Including Case Study) will be devoted to the introduction and development of various materials and sensors for WQM. To describe all these features categorically, Section I is divided further into six chapters as described later in more detail. Section II (Readout Electronic and Packaging) will describe in full detail the various design of electronics, communication systems, and packaging. Section II is divided into two chapters as described later in more detail. Section III (Sensing Data Assessment and Deployment Including Extreme Environment and Advanced Pollutants) will present innovative deployment strategies used for remote monitoring of WQ in various atmospheres. This includes chapters related to microplastics, the deployment of sensors, and sensors for extreme environmental conditions. Section III is divided into three chapters as described later in more detail. Section IV (Sensing Data Analysis and Internet of Things with a Case Study) discusses real‐time WQM using IoT and wireless sensor networks. The section will be divided into two chapters. The unique combination as proposed here has not been provided so far by any other book on WQM. Editors acknowledged the support provided by the European Commission through the AQUASENSE (H2020‐MSCA‐ITN‐2018‐813680) project.
Lukasiewicz Research Network – Institute of Microelectronics and Photonics, Department of LTCC Technology and Printed Electronics, Krakow, Poland
All life on planet Earth is supported by water and it has not been found in liquid form anywhere else in the universe. However, this precious resource suffers from issues such as pollution. To solve these problems and to avoid them in the future, it is critical to continuously monitor the quality of water. Water quality monitoring is defined as the collection of data at regular intervals from set locations across and along the water body to establish accurate values of various parameters such that trends, variations, and conditions can be observed. Water can be contaminated by pollutants due to human activities, especially industrial and agricultural practices . Polluted water is unfit for use and poses a threat to the health of humans and other organisms including plants and animals that depend on water for the sustenance of life. Use of harmful chemical and the disposal of untreated waste into the environment pollute the groundwater as well as the surface water. A few examples of such activities are mining of materials, manufacturing of products, addition of chemical fertilizers to the soil, production of energy, improper treatment and disposal of sewage, and transportation via waterways.
The traditional laboratory‐based methods of water quality testing are time‐consuming and expensive with low value for money because they require specific infrastructure and equipment, skilled personnel, sample collection, transportation, and storage. When water samples are collected from the site and transported to the laboratory, the final result of the quality test may be biased because different errors are introduced during collection and transportation, such as contamination and changes in data . Additionally, some oxidation–reduction processes and parameters such as temperature must be measured on‐site. Laboratory‐based testing also does not provide real‐time data. This is dangerous because the faster we know about a problem in the water, the faster we can act on it. Real‐time water quality data is the paramount requirement for early warning systems (EWS) and contamination warning systems (CWS) which help to immediately notify the responsible authorities of undesirable changes in the water. Sensors are crucial in this process because for engineers and operators handling such systems, accurate sensors aid in tracking changes in water quality, predicting the generation of regulated compounds, and ensuring their elimination after a treatment or remediation process. Online water quality monitoring is designed for faster data collection and communication using smart sensor systems that eliminate the need for sample collection and transportation using wireless communication and Internet of Things (IoT). Consequently, this reduces the overall time, expense, space, workforce, and energy required for water quality monitoring.
Water quality testing indicates the health of water and informs us whether it is fit or unfit for a particular application such as drinking, agriculture, recreation, or disposal to open waters and thereby acts as the guiding factor in decisions related to national and international environmental regulations for water quality. Additionally, the variation in a particular parameter may indicate the presence or absence of a threat such as a microbial community or ecosystem warming. Therefore, real‐time water quality monitoring simultaneously acts as an early warning system and thus the importance of low cost, miniaturized, and effective wireless sensors is well acknowledged by researchers, industries, and environmental authorities worldwide.
Compared to microbial parameters, physicochemical parameters such as electrical conductivity, temperature, pH, and chlorine are cheaper, simpler to detect, and have the ability to be measured using online instrumentation . Moreover, variation in a parameter can also act as an indicator of changes in another parameter.
There have been many studies showing evidence of the impact of pH on water quality and consequently aquatic life [4–6]. Marine ecological systems are so fragile yet complex that changes in one water quality parameter can lead to a domino effect causing changes in the hydrologic system and put an entire species at risk, eventually affecting other species including humans, their health, and livelihood. For example, Bradley and Sprague  found that zinc toxicity to rainbow trout (Salmo gairdneri) was directly related to pH and water hardness levels. Zinc toxicity rose by factors of two to five when pH changed from 5.5 to 7.0 and at pH 9.0 it was 10 times more toxic. However, at higher pH, zinc precipitated and has much lesser lethality to fish. Another study by Schubaur‐Berigan et al.  observed that total ammonia was more toxic at higher pH to a species of nonbiting midge larvae (Chironomus tentans) and blackwork (Lumbriculus variegatus). At high pH, more unionized ammonia prevails which is important for determination of toxicity of ammonia to the two species.
Dissolved oxygen (DO) is a very crucial parameter while monitoring water quality, especially to ensure the well‐being of aquatic creatures . In wastewater treatment, if DO level is too low, the important bacteria that decompose the solids will die whereas if DO content is too high, aeration consumes much more energy. In aquaculture, DO acts as the lifeline of fish and if it is too low, the fish will suffocate and perish.
In water quality monitoring, electrical conductivity is nonselective and does not consider the individual concentrations of various ions but instead their collective concentration. Regardless of that, changes in conductivity act as a warning signal of contamination and environmental changes. High levels of electrical conductivity can be due to industrial or urban runoff, low‐flow conditions, or long periods of dry weather whereas low levels might occur due to organic compounds such as oils . The measurement of electrical conductivity of water is therefore a good method to investigate the dissolved substances, chemicals, and minerals in it.
Temperature has garnered more attention from researchers than any other environmental variable for aquatic organisms, possibly because of the simplicity in measuring it for both field and laboratory experiments . Temperature alone may be lethal. Most aquatic organisms are ectothermic which means that they have a body temperature that is similar to their environment. The worrisome phenomenon of coral bleaching has very often been attributed to elevated temperatures . A study by Gock et al. in 2003 highlighted that temperature with respect to water activity strongly impacted the germination of xerophilic fungi, which is responsible for spoilage of bakery and very important in food quality testing .
Therefore, in order to safeguard delicate natural ecosystems, it is vital for environmental authorities to have continuous real‐time data of water quality parameters on a high spatial resolution at various depths. For example, rapid detection of hydrologic variability is crucial for EWS and subsequent swift response .
A sensor is a machine or a device that detects a change or an event in its environment and sends a signal to another electronic device to display the message. It is usually used in combination with another electronic device that translates its signal into a readable output. A sensor may be quantitative or qualitative. Quantitative sensors give numerical output in respective units whereas qualitative sensors suggest the descriptive value or a specific feature of a sample. A sensor may furthermore measure chemical, physical, or biological properties of water and examples of their quantitative counterparts are pH, temperature, and bacterial density, respectively. On the other hand, odor and taste are qualitative physical properties of water.
The progress in online environmental monitoring goes hand in hand with the technological development of solid‐state sensors. With the possibility of miniaturization and controlled by a signal processing unit, they have no moving parts and require a single parameter measurement such as impedance, current, or voltage alongside a linear calculation of the signal. Such features additionally allow for increased portability of sensors, on‐site testing, remote sensing, applicability on flexible substrates, and integration of multiple sensors within a single platform. The interest in thin and thick film solid‐state sensors over traditionally used water quality sensors also arises from the development of new materials such as metal oxides (RuO2, IrO2, TiO2, etc.) and the variety of technologies that can be used to manufacture them, such as printing (screen, ink‐jet, 3D, etc.) and sputter deposition (magnetron, radio frequency, reactive, etc.). Additionally, they have a longer shelf life as they are less prone to breakability and can tolerate extreme environmental conditions.
In a water quality monitoring setup, it is advisable not to interfere with the environment in which the parameter needs to be tested. Therefore, it significantly depends on the sensor to work well with its environment and deliver an accurate reading. This is why the most important component of the solid‐state sensor which decides the fate of its performance is the material used to fabricate it and the design that is most well aligned with the principle in operation. The relationship between the material and the electrolyte is defined by the electrochemistry that takes place upon their contact. When there is a change in the morphological and structural properties of the sensing electrode of the sensor, there is a significant impact on key sensor characteristics such as response time, sensitivity, and selectivity . Therefore, manipulation of sensor material and design is a popular strategy among researchers to improve the quality of solid‐state sensors.
Described here are the principles, designs, and materials used for making sensors that detect four different types of water quality parameters: pH, DO, temperature, and conductivity.
Introduced as the hydrogen ion exponent with notation pH , the Danish chemist S. P. L. Sørensen was the first to present the concept of pH as a measure of acidity and alkalinity in 1909 at Carlsberg Laboratories in Copenhagen . In 1922, W.S. Hughes invented the famous pH glass electrode, recorded as the first chemical sensor . According to the International Union of Pure and Applied Chemistry (IUPAC), the base‐10 logarithm of the inverse, of the hydrogen ion activity in a solution is defined as the pH of that solution . The equation to represent this relation is:
Instead of hydrogen ion concentration, it is more advantageous to define pH with respect to hydrogen ion activity because ionic activity can be measured directly using potentiometry .
pH measurement can be classified in various groups such as optical sensing, acoustic, electrochemical, and nuclear magnetic resonance (NMR) method. Most common among these groups are electrochemical methods and they are the most developed class of sensors fabricated to detect pH due to their faster response, simplicity, and low cost. pH sensors may depend on one of the principles of potentiometric, chemiresistive, chemi‐transistor, conductimetric, ion‐sensitive field effect transistor (ISFET), extended gate field effect transistor (EGFET), etc., and the primary materials to fabricate them are glass, metal oxides, mixed metal oxides, polymers, and carbon .
The typical glass electrode has established itself as a comfortable favorite over the past few decades not just as a laboratory pH measurement device but also a household tool for measuring pH in aquarium ponds, culinary arts, soil for gardening, etc. The glass electrode relies on the principle of an electrochemical cell. The materials comprising a glass electrode are usually an external body and sensing bulb made from specific glass, an internal electrode (calomel or silver chloride electrode), and an internal solution which is usually a buffer solution of pH 7. However, the glass electrode is very dependent on position when used or stored . They are challenging to minimize, mechanically delicate, instable in aggressive electrolytes , and require wet storage with the bulb immersed in electrolyte . A critical quality of solid‐state sensors that sets them apart from the glass electrode is the absence of an internal electrolyte solution. This is the motivation for development of all‐solid‐state electrodes with large pH range and low sensitivity to redox agents.
Solid‐state ion‐selective electrodes (ISE) are associated with smaller size, easier fabrication, and simpler operation in comparison to conventional ISE such as the glass pH electrode which has an internal filling solution. The first solid‐state ISE design to be invented was a coated‐wire electrode but the blocked interface between the ionically conducting ion‐selective membrane and the electronic conductor resulted in an unstable potential. Hydrogel‐based electrolytes, instead of the liquid internal electrolyte, help to overcome the issue of blocked interface by providing a distinct and clear pathway for the ion‐to‐electron transduction. Another modification in solid‐state ISEs is the application of an intermediate layer of redox active compounds between the ion‐selective membrane and the electronic conductor. Due to this requirement of high redox capacitance in the intermediate layer, conducting polymers are applicable as ion‐to‐electron transducers. Conducting polymers are suitable for use in solid‐state ISEs because they are electroactive, soluble, electronically conducting, and can be easily deposited on the conductor by the electro‐polymerization of big and diverse monomers .
pH measurement techniques that have gained popularity in recent times using oxides as pH‐sensitive layers are conductimetric/capacitive, potentiometric, and ISFETs, based on standard metal oxide field effect transistor (MOSFET). Among these methods, the potentiometric was the most commonly found technique in commercial devices due to its simplicity in fabrication and advancement in technology over the years .
Several possible mechanisms of pH sensitivity in metal oxides were suggested by Fog and Buck . They include oxygen or hydrogen intercalation, ion exchange in the surface layer, corrosion of the material, and a possible equilibrium between the two oxidizing forms of the metal. They strongly supported the possibility of oxygen intercalation because there is nonstoichiometric oxygen content in the oxides. This implies that the activity of oxygen in the solid phase should also be considered while calculating the electrode potential.
Among the many metal oxides that have been researched, ruthenium dioxide (RuO2) and iridium dioxide have been most promising  with fast response due to their resistance to space charge accumulation which is credited to high conductivity and chemical stability. RuO2 is especially a favorite among researchers as it shows close to Nernstian response even in the presence of strong oxidizing agents , organic sediments , and contaminants .
Different methods of fabricating RuO2 electrodes are screen printing , radio frequency magnetron sputtering (RFMS) , molecular beam epitaxy , physical or chemical vapor deposition , thermal decomposition , sol–gel [29, 30], and ultrasonic spray pyrolysis . The most preferred method to fabricate RuO2 electrodes is screen printing because it has been reported to deliver the best sensitivity and response time . Additionally, it gives the flexibility to use various sizes and compositions along with being fast and cost effective .
Binary metal oxides have been used for pH sensing most commonly as dimensionally stable anodes (DSA) in various applications . In these systems, chemically inert oxides are mixed with an active transition oxide which helps to enhance electrochemical properties, stability, and longevity . For example, doping has been found to improve antifouling properties in RuO2 electrodes. Both un‐doped nanostructured RuO2 and doped RuO2 or mixed with other metal oxide have been recommended for pH sensing [16, 33, 34]. Some mixtures have been described below.
Utilization of tin oxide (IV) for electrode fabrication allows increase of the lifetime of the electrodes and shortens response time to five to nine seconds. RuO2–SnO2 mixed oxide‐based electrodes show the Nernstian response with the sensitivity of −56.5 mV/pH for the RuO2:SnO2 ratio of 70 : 30 wt.% . Since RuO2 is expensive, combination with TiO2 is used to reduce the cost. Furthermore, substituting part of RuO2 with TiO2 allows higher electric conductivity . The sensitivity of RuO2:TiO2 70 : 30 mol% ratio was found to be −56.11 mV/pH when fabricated by the Pechini method  and −56.03 mV/pH when fabricated by screen‐printing . The RuO2‐TiO2 electrode fabricated by Pocrifka et al.  experienced very low interference from anions such as Li+, Na+, and K+ which is a desirable quality in pH sensors. Combination of RuO2 with Ta2O5, which is known for its use as ISFET , allows to lower the cost, minimize potential drift, and time response [38–40]. Doping of RuO2 with up to 20 mol% of Cu2O allowed to produce electrodes with the nonporous surface that is favorable from the point of antifouling and stable performance even after three months of implementation [41, 42]. Sensitivity of 10 mol% Cu2O‐doped RuO2 electrode was −47.4 mV/pH and did not change with the increase of Cu2O concentration .
The amount of oxygen dissolved in a unit volume of water or other liquids is termed as DO and is expressed in the unit milligram/litre. For pure water at 25 °C and 1 atm, the saturation level of DO is known to be 8.11 mg/l. DO means the free, non‐compounded oxygen (O2) molecules in the water which are not bound to any other element. Therefore, when measuring DO levels, the bound oxygen molecule in water (H2O) which is in a compound is not considered. By natural processes, DO may enter water through slow diffusion from air across the water surface or aeration of water by various forms of running water such as waterfalls, waves, groundwater discharge, etc., and by aquatic plants, seaweed, algae, and phytoplankton as a byproduct of photosynthesis.
There are three ways to measure DO based on three different principles: chemical, electrochemical, and photochemical. These principles and the types of methods based on them are shown in Figure 1.1.
The classical titration also known as volumetric analysis or the indicator method is the oldest known method of measuring DO with a chemical test and it is considered the benchmark of DO measurement . The most popular indicator method for DO detection and measurement is Winkler's method or the iodometric method. Its principle is to produce manganese hydroxide (II) by adding manganese sulfate and alkaline potassium iodide to water. Due to unstable nature, manganese hydroxide produces manganic acid by combining with DO. Potassium iodide and concentrated sulfuric acid are added to separate the iodine and react with DO, respectively. Using starch as an indicator, the amount of DO can be calculated with the titration of released iodine with sodium thiosulfate. Another method of DO measurement by titration is called Miller's method in which the oxidation of ferrous ions is measured in an alkaline medium . Titration methods are only prevalent in laboratories and are not used for online measurement of DO because the design is simply too complicated to serve the demands of a continuous water quality monitoring system .
Figure 1.1 Principles and methods of measuring dissolved oxygen.
Even though all methods are good and sensitive, in wireless sensor networks, electrochemical methods are most preferred due to miniaturization, robustness, and high resistance to fouling. The most commonly used type of electrochemical sensor is Clarke cell , shown in Figure 1.2. This sensor is controlled by a flux of oxygen that diffuses through the gas‐permeable membrane (usually a polytetrafluoroethane film) separating the cell and the solution. By Fick's law, this flux which is indicated by the current produced is proportional to the DO concentration. The oxidation occurs at the anode (negative electrode) and the reduction occurs at the cathode (positive electrode) immediately when the oxygen diffuses through the film. Commonly used electrode material for Galvanic DO sensors are lead, iron, or zinc for anode and silver or platinum for cathode.
The two primary electrochemical techniques in practice for DO measurement are galvanic and polarographic. They both are electrode systems but with a slight difference between them. In a polarographic DO sensing system, an external voltage is applied. However, in the galvanic DO sensing system, an external potential is not required because the electrode materials are selected such that there is a −0.5 V or greater potential between the cathode and the anode. Electrodes in the diaphragm method, on the other hand, measure the amount of oxygen passing through the gas‐permeable diaphragm.
Figure 1.2 Schematic representation of as Clarke cell‐type electrochemical DO sensor.
Solid‐state sensors based on semiconductors have helped to overcome the limitations of size and maintenance while providing researchers with the flexibility to explore various designs and materials. For example, a DO sensor was developed using platinum thin film electrodes which were coated with a solid‐state proton conductive matrix (PCM) and the surface of the planar membrane electrode was covered with solid proton conductive material using spin coating process . Therefore, instead of the traditional Clarke sensor which has a built‐in electrolyte, this sensor had a solid polymer electrolyte. DO sensing systems made with RuO2 as sensitive electrode and Ag/AgCl, Cl− as reference electrode exhibited linear response with detection limits of 0.5–8.0 ppm .
Doped semiconductors are a popular choice of electrode material for electrochemical DO sensors. For example, nanostructured RuO2 has been found to exhibit one of the best performances for DO sensing . When doped with ZnO and Cu2O oxides, it shows an increase in DO sensitivity by −48.6 mV/decade for 10 mol% ZnO  and −47.4 mV/decade for 10 mol% Cu2O , respectively. When a semiconductor is doped, there is a formation of donor levels located on the upper band of the semiconductor due to the deviation in electronic structure of the doped mixed oxide. These donor levels contribute to the enhancement of sensor properties. As a charge donor, the dopant increases the conductivity of the electrode by transforming the structure of the nano‐oxide and supplying excess carriers to the conductivity band .
There are different optical principles which can be used to measure DO such as the phosphorus quenching principle, near‐infrared principle, the absorption principle, and the fluorescence principle. Among these principles, currently the most commonly used one is the fluorescence quenching principle and a DO sensor based on it consists of three constituents: excitation light sources, an optoelectronic detection element, and a substrate film attached to fluorescence‐sensitive substances. In fluorescence quenching, the sensitive material absorbs the ultraviolet light of specific wavelength and the electrons gain energy which they release in order to return to ground state by emitting fluorescence. The amount of DO in the water can be estimated by the intensity of fluorescence or the fluorescence lifetime generated at the sensitive interface because the collisions between oxygen molecules and excited fluorescent substances interfere with the excitation process of fluorescent substance.
Sensitive materials that are fluorescent substances and used for the film are pyrene butyric acid, fluoroanthene, and polycyclic aromatic compounds . Fluorescence indicators used in optical DO sensors to improve DO detection sensitivity include platinum phosphor porphyrins such as platinum octaethyl porphyrins (PtOEP), platinum tetrafluorophenyl porphyrins (PtTFPP), and ruthenium–chromium complexes. The choice of substrate material while fabricating optical DO sensors is critical because the DO sensors are principally based on the oxygen quenching effect of fluorescence of the luminescent body fixed in the matrix .
A measure of the ability of a solution to conduct electrical current indicated by the amount of free‐flowing electrons and/or ions is termed as conductivity or specific conductance since conductivity is directly proportional to conductance (1/R, where R is resistance of the circuit). Expressed in Siemens per meter (S/m) or micro‐Siemens per centimeter (μS/cm), conductivity is the inverse of the electrical parameter of resistance (ohm).
Measurement of conductivity is used for the determination of the following :
Number of free ions in the water influences some physiological processes in living organism.
Sudden changes in waste and natural water systems.
Amount of reagent required or sample sizes in chemical analysis of water.
Concentration of total dissolved solids.
The conductivity of deionized and pure distilled water is about 0.05 μS/cm, drinking water is 200–800 μS/cm, and sea water is 50 mS/cm.
Conductivity sensors are usually based on Ohm's law wherein the resistance of the water sample can be calculated from known values of resistor, voltage, current, and cell constants, i.e. area and length of the sample . Such sensors are called conductive, contacting, or electrode sensors. In a conductive or electrode sensor which may have two, three, or four electrodes , the geometrical design of the sensor is based on the target conductivity range and determines the proportionality coefficient (KC) . In three‐ or four‐electrode system, if there is any damage, fouling, or polarization on the electrodes, the reference voltage allows compensation for it. Therefore, the reference electrode helps in achieving better accuracy over wide ranges unaffected by minor changes in electrode condition. In this system, an electric field is generated within the electrolyte when an electric current is applied on one of the electrodes causing the negatively charged ions to the cathode and the positively charged ions to the anode. Therefore, the current in the electrolyte is carried by the ions, meaning that an increase in their mobility and concentration is expected to increase conductivity .
In traditional dip or flow‐through style two‐electrode conductivity sensors, electrodes are usually made of platinum, graphite, titanium, or gold‐plated nickel. A comparative study  of four‐electrode systems found electrodes which have high resistance to be more sensitive than electrodes made from Ru nanostructures. Metallic Pt/Ag/Pd alloys exhibited ion sensitivity within the range of 5–200 μS/cm with a response time of approximately one to three seconds whereas Ru‐based electrodes system had a lower limit of 500 μS/cm and response time of two minutes . Figure 1.3 presents the schematic representation of two‐electrode and four‐electrode types of conductivity sensors.
Another way to measure electrical conductivity of water is by using the inductive method based on the principle of mutual inductance . Conductivity sensors based on this method are called toroidal or inductive sensors. This type of sensor has two conducting coils enclosed in a casing made of nonconducting material. By Faraday's law of induction, when an electrical current is induced by the first coil in the water, the second coil measures the magnitude of the induced current . This measured value is directly proportional to the conductivity of the water sample.
Between conductive and inductive sensor, there is a vast divide between their prevalence in practice. Even though electrode conductivity sensors run the risk of delivering inaccurate measurements due to corrosion or deposition of fine particles or bacteria on the electrode , their popularity far outweighs that of the inductive conductivity sensor due to simpler maintenance, lower cost , reduced fringe effect, sensitivity to contact resistance, and extendable linearity which can be achieved by varying the excitation voltage of the cell .
Figure 1.3 Schematic representations of (a) two‐electrode and (b) four‐electrode types of conductivity sensors where I is the current source, R is the resistance, and V is the voltage.
However, some researchers are of the opinion that from the perspective of long‐term monitoring, the requirement of the electrodes to be repeatedly cleaned and replaced is somewhat contradictory to the objectives of wireless sensor networks . The reason for their skepticism toward the precision of conductive sensors is that several factors such as volume of water, salinity of water, and size of coils  that influence the magnitude of induced current are overlooked in the conductive methodology but not in inductive methodology. Nevertheless, the lack of exploration in the potential of inductive sensors has left them with a commercially failed fate in industry. Researchers have not been as vocal in their criticism of inductive sensors as they have been in their appreciation of conductive sensors. Hence, it is most probable that the coil was simply too complicated in comparison to the two‐dimensional electrode as suggested by a group of researchers  who prefer conductive sensors over inductive sensors due to simple and practical advantages such as ease in fabrication, directness in operational principle, and miniature size. However, the same researchers admit that the inductive sensor has a circuitry such that the input and output points do not come in contact with the water, thereby reducing the probability of fouling but there can still be signal loss and electrical interference.
Commercially, such a bias does not exist as both inductive and electrode conductivity sensors are easily available on the market. An example of commercial inductive conductivity sensor is AANDERAA Conductivity Sensor 4319 (range: 0–75 mS/cm, material of coil: titanium) and an example of commercial electrode sensor is YOKOGAWA Model SC4A (two‐electrode system, range: 0.1–50 mS/cm, material of electrode: Stainless steel AISI 316L). In solid‐state sensors, conductivity can be measured using either two electrodes or four electrodes.
One of the most common data logging applications, temperature measurement is required across a multitude of industries and in almost every practical application. When two objects are at the same temperature and do not exchange any thermal energy, they are said to be in thermal equilibrium with each other. A scalar unit, temperature can be expressed using various units according to the thermometric scale used. Most popular temperature measurement scales are Kelvin (K), Fahrenheit (°F), and Celsius (°C). Based on the three physical states of matter, fundamental fixed points defining the temperature scales are listed in Table 1.1.
Table 1.1 Fixed points defined by temperature scales.
Material physical state
Ice point of water (1 atm)
Boiling point of water (1 atm)
a Temperature at which adiabatic and isothermic processes occur at the same time.
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