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Author:   Abhishek Kumar (Chandigarh University) ,  Pooja Dixit (Sophia Girls' College (Autonomous), Ajmer, India) ,  Pramod Singh Rathore (Manipal University Jaipur, Rajasthan, India) ,  Arun Lal Srivastav (Chitkara University, India)
Publisher:   John Wiley & Sons Inc
Imprint:   Wiley-Scrivener
ISBN:  

9781394450084

ISBN 10:   1394450087
Pages:   384
Publication Date:   29 April 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Preface xv 1 Introduction to Microplastic and the Role of AI 1 Pooja Dixit, Shaloo Dadheech, Priya Batta and Neeraj Bhargava 1.1 Introduction 2 1.2 Microplastic Distribution and Pathways 5 1.3 Current Methods of Microplastic Detection 8 1.4 Role of Artificial Intelligence (AI) in Microplastic Research 12 1.5 Case Studies and Applications 16 1.6 Challenges and Limitations 18 1.7 Future Directions 20 1.8 Conclusion 21 2 A CNN-ViT Hybrid Deep Learning Architecture for Accurate Microplastic Detection 23 B. Dhanalaxmi, B. Saritha, P. Punitha, G. Jagan Naik and B. Anupama 2.1 Introduction 24 2.2 Literature Review 26 2.3 Proposed Mythology 29 2.4 Result and Discussion 31 2.5 Concluding Remarks and Future Scope 33 3 XAI for Decision Support in Microplastic Pollution Management 37 Srinibas Pattanaik, Sachin Ahuja, Sartajvir Singh Dhillon, Jasneet Chawla, Deeksha Sonal and Alessandro Vinciarelli 3.1 Introduction 38 3.2 Causes and Consequences and Effects of Microplastic Pollution 40 3.3 The Application of AI in Management of the Environment 42 3.4 XAI Frameworks are Flexible and for the Micro Plastic Environmental Management and the Summary to Explainable Artificial Intelligence 43 3.5 Application and Case Studies of XAI Microplastic Pollution Management 45 3.6 The Utilization of Machine Learning with Explainable AI (XAI) Regarding Decision Support Systems 48 3.7 Futures Directions and Challenges of Explainable AI with Microplastic Pollution 49 3.8 Conclusion 51 4 AI-Driven Technologies in Mitigation of Microplastic Pollution 55 Lata Rani, Hurmat, Deepa Singh, Babu Bharman, Arun Lal Srivastav, Jyotsna Kaushal, Komal Thapa and Neha Kanojia 4.1 Introduction 56 4.2 AI Assisted Detection Techniques for the Microplastic 60 4.3 Application of AI in Microplastic Pollution Control 71 4.4 Conclusion 74 5 AI Driven Optical Imaging and Spectroscopic Techniques 83 Muchukota Sushma, Mekkanti Manasa Rekha, Ramya C. V. and Zaid Khan List of Abbreviations 84 5.1 Introduction 84 5.2 Fundamentals of Optical Imaging and Spectroscopic Techniques 90 5.3 AI Innovations in Microplastic Detection 92 5.4 Applications in Real-Time Monitoring 94 5.5 Case Studies in AI-Driven Microplastic Detection 95 5.6 Challenges in AI-Driven Microplastic Monitoring 97 5.7 Future Directions 99 5.8 Conclusion 101 6 Integrating AI with Advanced Sensor Technologies for Real-Time Monitoring 109 Avnish Chauhan, Shivam Attri, Aanchal Saklani, Prabhat K. Chauhan, Man Vir Singh, Vishal Rajput, Muneesh Sethi and Samuele Barrili 6.1 Introduction 110 6.2 Bibliographic Study 111 6.3 AI-Enabled Sensor Technologies for Microplastic Detection 113 6.4 Challenges and Future Prospects 120 6.5 Conclusion 122 7 Machine Learning for Microplastic Source and Pathway Prediction 127 Vanshika and Neetu Rani 7.1 Introduction 128 7.2 Microplastic Sources and Pathways: An Overview 130 7.3 Data Acquisition and Preprocessing 132 7.4 Machine Learning Approaches for Microplastic Modeling 134 7.5 Model Development and Validation 137 7.6 Case Studies and Real-World Implementations 138 7.7 Visualization and Decision Support 138 7.8 Challenges and Ethical Considerations 142 7.9 Conclusion and Future Scope 143 8 Big Data Analytics in Mapping the Global Microplastic Distribution 147 Prasann Kumar 8.1 Introduction 148 8.2 Data Sources for Microplastic Mapping 152 8.3 Big Data Techniques in Microplastic Analytics 155 8.4 Challenges in Big Data for Microplastic Studies 159 8.5 Case Studies 163 8.6 Applications and Implications 166 8.7 Future Directions 170 8.8 Conclusion 173 8.9 Acknowledgement 174 9 Automation in Sampling and Processing, Robotics, and AI Synergy 179 Prasann Kumar 9.1 Introduction 180 9.2 Robotics in Sampling and Processing 185 9.3 AI-Driven Processing Workflows 189 9.4 Challenges and Limitations 193 9.5 Case Studies and Applications 195 9.6 Innovations and Emerging Trends 198 9.7 Future Directions 202 9.8 Conclusion 205 10 Cross-Disciplinary Case Studies: AI in Action for Microplastic Research 209 B. Dhanalaxmi, V. Prema Tulasi, Mittapalli Anusha, G. Sreeram and Komati Sathish 10.1 Introduction 210 10.2 Literature Review 212 10.3 Proposed Methodology 216 10.4 Result and Discussion 218 10.5 Concluding Remarks and Future Scope 222 11 Ethical and Social Implications of AI in Environmental Science: Balancing Innovation and Responsibility 225 Priyanka 12 Regulatory and Policy Challenges for AI-Enhanced Microplastic Monitoring 239 Gurjeet Kour, Mansi Rana, Pratibha Singh and Ajay Sharma 12.1 Introduction 240 12.2 Microplastic Monitoring through AI 243 12.3 The Current State of Microplastic Monitoring Regulations 245 12.4 Regulatory Obstacles in AI-Powered Microplastic Identification 250 12.5 Privacy and Ethical Issues with AI-Powered Environmental Monitoring 252 12.6 Policy Ideas for Including AI in Microplastic Monitoring 253 12.7 Multidisciplinary Cooperation's Function in Policy Development 257 12.8 Conclusion 259 13 Future Trends: AI Driven Innovation in Environmental Science 267 Priyanka Sharma, Ankita Sharma and Prashant Ahluwalia 13.1 Introduction to AI in Environmental Science 268 13.2 AI and Climate Change Mitigation 270 13.3 AI in Water Resource Management 272 13.4 AI in Biodiversity Conservation 274 13.5 AI for Sustainable Agriculture and Forestry 276 13.6 AI in Air Pollution Control 279 13.7 AI and Renewable Energy Optimization 280 13.8 AI for Smart Disaster Resilience 281 13.9 Environmental Sustainability 283 13.10 Future Scope 285 14 XAI for Decision Support in Microplastic Pollution Management 293 Yeligeti Raju, N. Venkatesh, S. Adilakshmi, Namita Parati and A. Kalaivani 14.1 Introduction 294 14.2 Literature Review 297 14.3 Proposed Methodology 299 14.4 Result and Discussion 301 14.5 Concluding Remarks and Future Scope 305 15 The Road Ahead: AI's Role in Tackling Global Microplastic Pollution 309 Yeligeti Raju, K. Damodhar Rao, M. Lavanya, Mursubai Sandhya Rani and Sendhil Kumar B.B. 15.1 Introduction 310 15.2 Literature Review 312 15.3 Proposed Methodology 317 15.4 Result and Discussion 319 15.5 Concluding Remarks and Future Scope 322 References 323 16 Intelligent Environmental Surveillance: Integrating AI Systems for Comprehensive Microplastic Monitoring and Analysis 325 Mamta 16.1 Introduction 326 16.2 Understanding Microplastic Pollution 328 16.3 AI-Based Monitoring Systems 331 16.4 Implementation and Case Studies 333 16.5 Future Scope 336 16.6 Conclusion 340 Bibliography 342 Index 347

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Author Information

Abhishek Kumar, PhD is an Assistant Director and Professor in the Computer Science and Engineering Department at Chandigarh University with more than 13 years of teaching experience. He has authored seven books, edited 51 books, and published more than 170 peer-reviewed articles. His research spans AI, renewable energy, image processing, and data mining. Pooja Dixit is an Assistant Professor in the Department of Computer Science at Shri Ratanlal Kanwarlal Patni Girls' College, Kishangarh, India. With more than seven years of academic teaching and two years of research experience, she has published more than 25 research papers in reputed journals, books, and conferences. Her research interests include artificial intelligence, machine learning, and data mining. Pramod Singh Rathore, PhD, is in the Department of Computer and Communication Engineering at Manipal University Jaipur, India with more than 13 years of academic experience. He has published more than 85 papers in reputable, peer-reviewed national and international journals, books, and conferences. His research interests include NS2, computer networks, machine learning, and database management systems. Arun Lal Srivastav, PhD is an Associate Professor in the School of Engineering and Technology at Chitkara University. He has published more than 100 research papers in various prestigious journals, conferences, and book chapters and edited many internationally published books. His research interests include water quality surveillance, climate change, water treatment, river ecosystems, soil health maintenance, engineering education, phytoremediation, and waste management. Ashutosh Kumar Dubey, PhD is an Associate Professor in the Department of Computer Science at in the School of Engineering and Technology at Chitkara University with more than 16 years of experience. He has authored and edited 20 books and published more than 80 articles in peer-reviewed international journals and conference proceedings. His research interests encompass machine learning, renewable energy, health informatics, nature-inspired algorithms, cloud computing, and big data.

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