Sensing Technologies for Real Time Monitoring of Water Quality

LIBU MANJAKKAL, PhD, is a Lecturer at Edinburgh Napier University, UK, and was a Research Associate at James Watt School of Engineering, University of Glasgow, UK. LEANDRO LORENZELLI, PhD, is Head of the Microsystems Technology Research Unit at Fondazione Bruno Kessler — Center for Sensors and Devices (FBK-SD - Italy). MAGNUS WILLANDER, PhD, is Former Chair Professor in Gothenburg University and Linköping University and Visiting Professor and Scientist in various countries.

• About the Editors xiiiList of Contributors xvPreface xixSection I Materials and Sensors Development Including Case Study 11 Smart Sensors for Monitoring pH, Dissolved Oxygen, Electrical Conductivity, and Temperature in Water 3Kiranmai Uppuluri1.1 Introduction 31.2 Water Quality Parameters and Their Importance 41.2.1 Impact of pH on Water Quality 41.2.2 Impact of Dissolved Oxygen on Water Quality 51.2.3 Impact of Electrical Conductivity on Water Quality 51.2.4 Impact of Temperature on Water Quality 51.3 Water Quality Sensors 61.3.1 pH 71.3.1.1 pH Sensors: Principles, Materials, and Designs 71.3.1.2 Glass Electrode 71.3.1.3 Solid- State Ion- Selective Electrodes 81.3.1.4 Metal Oxide pH Sensors 81.3.2 Dissolved Oxygen 101.3.2.1 DO Sensors: Principles, Materials, and Designs 101.3.2.2 Chemical Sensors 101.3.2.3 Electrochemical Sensors 111.3.2.4 Optical or Photochemical Sensors 121.3.3 Electrical Conductivity 131.3.3.1 Conductivity Sensors: Principles, Materials, and Designs 131.3.4 Temperature 151.3.4.1 Temperature Sensors: Principles, Materials, and Designs 161.3.4.2 Thermocouples 171.3.4.3 Resistance Temperature Detector 171.3.4.4 Thermistor 171.3.4.5 Integrated Circuit 181.4 Smart Sensors 181.5 Conclusion 18Acknowledgment 19References 192 Dissolved Heavy Metal Ions Monitoring Sensors for Water Quality Analysis 25Tarun Narayan, Pierre Lovera, and Alan O’Riordan2.1 Introduction 252.2 Sources and Effects of Heavy Metals 262.3 Detection Techniques 262.3.1 Analytical Detection: Conventional Detection Techniques of Heavy Metals 262.3.2 Electrochemical Detection Techniques of Heavy Metals 262.3.2.1 Nanomaterial- Modified Electrodes 292.3.2.2 Metal Nanoparticle- Based Modification 292.3.2.3 Metal Oxide Nanoparticle- Based Modification 332.3.2.4 Carbon Nanomaterials- Based Modification 342.3.3 Biomolecules Modification for Heavy Metal Detection 352.3.3.1 Antibody- Based Detection 352.3.3.2 Nucleic Acid- Based Detection 372.3.3.3 Cell- Based Sensor 382.4 Future Direction 402.5 Conclusions 40Acknowledgment 41References 423 Ammonia, Nitrate, and Urea Sensors in Aquatic Environments 51Fabiane Fantinelli Franco3.1 Introduction 513.2 Detection Techniques for Ammonia, Nitrate, and Urea in Water 533.2.1 Spectrophotometry 533.2.2 Fluorometry 543.2.3 Electrochemical Sensors 543.3 Ammonia 593.3.1 Ammonia in Aquatic Environments 593.3.2 Ammonia Detection Techniques 623.4 Nitrate 653.4.1 Nitrate in Aquatic Environments 653.4.2 Nitrate Detection Techniques 653.5 Urea 673.5.1 Urea in Aquatic Environment 673.5.2 Urea Detection Techniques 693.6 Conclusion and Future Perspectives 71Acknowledgment 71References 714 Monitoring of Pesticides Presence in Aqueous Environment 77Yuqing Yang, Pierre Lovera, and Alan O’Riordan4.1 Introduction: Background on Pesticides 774.1.1 Types and Properties 774.1.2 Risks 784.1.3 Regulation and Legislation 794.1.4 Occurrence of Pesticide Exceedance 804.2 Current Pesticides Detection Methods 804.2.1 Detection of Pesticides Based on Electrochemical Methods 824.2.1.1 Brief Overview of Electrochemical Methods 824.2.1.2 Detection of Pesticides by Electrochemistry 824.2.2 Detection of Pesticides Based on Optical Methods 834.2.2.1 Detection of Pesticides Based on Fluorescence 874.2.3 Detection of Pesticides Based on Raman Spectroscopy 894.2.3.1 Introduction to SERS 894.2.3.2 Fabrication of SERS Substrates 914.2.3.3 Detection of Pesticide by SERS 924.2.3.4 Challenges and Future Perspectives 954.3 Conclusion 96Acknowledgment 96References 965 Waterborne Bacteria Detection Based on Electrochemical Transducer 107Nasrin Razmi, Magnus Willander, and Omer Nur5.1 Introduction 1075.2 Typical Waterborne Pathogens 1085.3 Traditional Diagnostic Tools 1085.4 Biosensors for Bacteria Detection in Water 1105.4.1 Common Bioreceptors for Electrochemical Sensing of Foodborne and Waterborne Pathogenic Bacteria 1105.4.1.1 Antibodies 1115.4.1.2 Enzymes 1115.4.1.3 DNA and Aptamers 1115.4.1.4 Phages 1125.4.1.5 Cell and Molecularly Imprinted Polymers 1125.4.2 Nanomaterials for Electrochemical Sensing of Waterborne Pathogenic Bacteria 1125.4.2.1 Metal and Metal Oxide Nanoparticles 1135.4.2.2 Conducting Polymeric Nanoparticles 1145.4.2.3 Carbon Nanomaterials 1145.4.2.4 Silica Nanoparticles 1145.5 Various Electrochemical Biosensors Available for Pathogenic Bacteria Detection in Water 1155.5.1 Amperometric Detection 1155.5.2 Impedimetric Detection 1215.5.3 Conductometric Detection 1235.5.4 Potentiometric Detection 1245.6 Conclusion and Future Prospective 126Acknowledgment 127References 1276 Zinc Oxide- Based Miniature Sensor Networks for Continuous Monitoring of Aqueous pH in Smart Agriculture 139Akshaya Kumar Aliyana, Aiswarya Baburaj, Naveen Kumar S. K., and Renny Edwin Fernandez6.1 Introduction 1396.2 Metal Oxide- Based Sensors and Detection Methods 1406.3 pH Sensor Fabrication 1416.3.1 Detection of pH: Materials and Method 1416.3.2 Detection of pH: Surface Morphology of the Nanostructured ZnO and IDEs 1446.3.3 Detection of pH: Electrochemical Sensing Performance 1456.3.4 Detection of Real- Time pH Level in Smart Agriculture: Wireless Sensor Networks and Embedded System 1496.4 Conclusion 151Acknowledgment 152References 152Section II Readout Electronic and Packaging 1617 Integration and Packaging for Water Monitoring Systems 163Muhammad Hassan Malik and Ali Roshanghias7.1 Introduction 1637.2 Advanced Water Quality Monitoring Systems 1677.2.1 Multi- sensing on a Single Chip 1677.2.2 Heterogeneous Integration 1697.2.3 Case Study: MoboSens 1697.3 Basics of Packaging 1717.4 Hybrid Flexible Packaging 1737.4.1 Interconnects 1747.4.2 Thin Die Embedding 1767.4.3 Encapsulation and Hermeticity 1787.4.4 Roll to Roll Assembly 1807.5 Conclusion 181References 1818 A Survey on Transmit and Receive Circuits in Underwater Communication for Sensor Nodes 185Noushin Ghaderi and Leandro Lorenzelli8.1 Introduction 1858.2 Sensor Networks in an Underwater Environment 1868.2.1 Acoustic Sensor Network 1868.2.1.1 Energy Sink- Hole Problem 1878.2.1.2 Acoustic Sensor Design Problems 1888.2.1.3 The Underwater Transducer 1898.2.1.4 Amplifier Design 1908.2.1.5 Analog- to- Digital Converter 1948.2.2 Electromagnetic (EM) Waves Underwater Sensors 1978.2.2.1 Antenna Design 1988.2.2.2 Multipath Propagation 1988.3 Conclusion 199Acknowledgment 199References 200Section III Sensing Data Assessment and Deployment Including Extreme Environment and Advanced Pollutants 2039 An Introduction to Microplastics, and Its Sampling Processes and Assessment Techniques 205Bappa Mitra, Andrea Adami, Ravinder Dahiya, and Leandro Lorenzelli9.1 Introduction 2059.1.1 Properties of Microplastics 2089.1.2 Microplastics in Food Chain 2099.1.3 Human Consumption of Microplastics and Possible Health Effects 2099.1.4 Overview 2109.2 Microplastic Sampling Tools 2129.2.1 Non- Discrete Sampling Devices 2129.2.1.1 Nets 2129.2.1.2 Pump Tools 2139.2.2 Discrete Sampling Devices 2159.2.3 Surface Microlayer Sampling Devices 2159.3 Microplastics Separation 2159.3.1 Separating Microplastics from Liquid Samples 2159.3.1.1 Filtration 2159.3.1.2 Sieving 2169.3.2 Separating Microplastics from Sediments 2189.3.2.1 Density Separation 2189.3.2.2 Elutriation 2189.3.2.3 Froth Floatation 2199.4 Microplastic Sample Digestion Process 2209.4.1 Acidic Digestion 2219.4.2 Alkaline Digestion 2219.4.3 Oxidizing Digestion 2219.4.4 Enzymatic Degradation 2229.5 Microplastic Identification and Classification 2229.5.1 Visual Counting 2229.5.2 Fluorescence 2239.5.3 Destructive Analysis 2239.5.3.1 Thermoanalytical Methods 2249.5.3.2 High- Performance Liquid Chromatography 2259.5.4 Nondestructive Analysis 2259.5.4.1 Fourier Transform Infrared Spectroscopy 2259.5.4.2 Raman Spectroscopy 2269.6 Conclusions 228Acknowledgment 229References 22910 Advancements in Drone Applications for Water Quality Monitoring and the Need for Multispectral and Multi- Sensor Approaches 235Joao L. E. Simon, Robert J. W. Brewin, Peter E. Land, and Jamie D. Shutler10.1 Introduction 23510.2 Airborne Drones for Environmental Remote Sensing 23710.3 Drone Multispectral Remote Sensing 23910.4 Integrating Multiple Complementary Sensor Strategies with a Single Drone 24110.5 Conclusion 242Acknowledgment 243References 24311 Sensors for Water Quality Assessment in Extreme Environmental Conditions 253Priyanka Ganguly11.1 Introduction 25311.2 Physical Parameters 25511.2.1 Electrical Conductivity 25511.2.2 Temperature 25811.2.3 Pressure 26011.3 Chemical Parameters 26211.3.1 pH 26211.3.2 Dissolved Oxygen and Chemical Oxygen Demand 26511.3.3 Inorganic Content 26811.4 Biological Parameters 27111.5 Sensing in Extreme Water Environments 27311.6 Discussion and Outlook 27611.7 Conclusion 278References 278Section IV Sensing Data Analysis and Internet of Things with a Case Study 28312 Toward Real- Time Water Quality Monitoring Using Wireless Sensor Networks 285Sohail Sarang, Goran M. Stojanović, and Stevan Stankovski12.1 Introduction 28512.2 Water Quality Monitoring Systems 28612.2.1 Laboratory- Based WQM (LB- WQM) 28612.2.2 Wireless Sensor Networks- Based WQM (WSNs- WQM) 28712.2.2.1 Solar- Powered Water Quality Monitoring 28912.2.2.2 Battery- Powered Water Quality Monitoring 29112.3 The Use of Industry 4.0 Technologies for Real- Time WQM 29612.4 Conclusion 297References 29813 An Internet of Things- Enabled System for Monitoring Multiple Water Quality Parameters 305Fowzia Akhter, H. R. Siddiquei, Md. E. E. Alahi, and S. C. Mukhopadhyay13.1 Introduction 30513.2 Water Quality Parameters and Related Sensors 30613.3 Design and Fabrication of the Proposed Sensor 31013.3.1 Sensor’s Working Principle 31213.4 Experimental Process 31213.5 Autonomous System Development 31313.5.1 Algorithm for Data Classification 31513.6 Experimental Results 31813.6.1 Sensor Characterization for Temperature, pH, Nitrate, Phosphate, Calcium, and Magnesium Measurement 31913.6.2 Repeatability 32313.6.3 Reproducibility 32513.6.4 Real Sample Measurement and Validation 32713.6.5 Data Collection 33013.6.6 Power Consumption 33013.7 Conclusion 333Acknowledgment 333References 333Index 339