Microplastic Monitoring Using Artificial Intelligence
1st Edition
Will be published approx. on 29. April 2026
Book
Hardback
384 pages
978-1-394-45008-4 (ISBN)
Description
Revolutionize your approach to environmental protection with this groundbreaking resource, which details how to replace labor-intensive manual analysis with deep learning and explainable AI (XAI) to achieve precise, real-time identification and scalable monitoring of microplastic pollution.
AI-driven microplastic monitoring sits at the intersection of environmental science, artificial intelligence, and data analytics, representing a rapidly developing frontier in both research and industry. Microplastic pollution, which has become a critical environmental and public health concern, is challenging to monitor using traditional techniques due to the vast scale, complexity, and minute size of microplastics. Conventional methods, such as manual filtration, microscopic examination, and chemical analysis, are often labor-intensive, time-consuming, and limited in their ability to provide real-time, large-scale data. This book is a groundbreaking exploration of how artificial intelligence, particularly deep learning and explainable AI (XAI), is revolutionizing microplastic research. It highlights innovative applications of deep learning for precise identification and classification of microplastics, while emphasizing the role of XAI in providing transparency and interpretability to AI-driven methods. By integrating these approaches with advanced sensing technologies and predictive models, the book addresses key limitations of traditional methods, offering robust solutions for scalable and accurate monitoring. Additionally, the book considers the ethical, regulatory, and policy implications of deploying AI in environmental science, providing a balanced perspective on the potential benefits and challenges. With contributions from leading researchers and practitioners, this book is an essential resource for environmental scientists, data scientists, policymakers, and technologists committed to sustainable solutions for combating microplastic pollution.
AI-driven microplastic monitoring sits at the intersection of environmental science, artificial intelligence, and data analytics, representing a rapidly developing frontier in both research and industry. Microplastic pollution, which has become a critical environmental and public health concern, is challenging to monitor using traditional techniques due to the vast scale, complexity, and minute size of microplastics. Conventional methods, such as manual filtration, microscopic examination, and chemical analysis, are often labor-intensive, time-consuming, and limited in their ability to provide real-time, large-scale data. This book is a groundbreaking exploration of how artificial intelligence, particularly deep learning and explainable AI (XAI), is revolutionizing microplastic research. It highlights innovative applications of deep learning for precise identification and classification of microplastics, while emphasizing the role of XAI in providing transparency and interpretability to AI-driven methods. By integrating these approaches with advanced sensing technologies and predictive models, the book addresses key limitations of traditional methods, offering robust solutions for scalable and accurate monitoring. Additionally, the book considers the ethical, regulatory, and policy implications of deploying AI in environmental science, providing a balanced perspective on the potential benefits and challenges. With contributions from leading researchers and practitioners, this book is an essential resource for environmental scientists, data scientists, policymakers, and technologists committed to sustainable solutions for combating microplastic pollution.
More details
Language
English
Place of publication
United States
Publishing group
John Wiley & Sons Inc
Target group
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
ISBN-13
978-1-394-45008-4 (9781394450084)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Abhishek Kumar | Pooja Dixit | Pramod Singh Rathore
Microplastic Monitoring Using Artificial Intelligence
E-Book
04/2026
1st Edition
Wiley
€171.99
Available for download
Abhishek Kumar | Pooja Dixit | Pramod Singh Rathore
Microplastic Monitoring Using Artificial Intelligence
E-Book
03/2026
1st Edition
Wiley
€171.99
Available for download
Persons
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 Sophia Girls' College and is pursuing her Ph.D. in Computer Science from Manipal University. 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 an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with more than 11 years of academic experience. He has published more than 55 papers in reputable, peer-reviewed national and international journals, books, and conferences. His research interests include NS2, computer networks, mining, 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.
Pooja Dixit is an Assistant Professor in the Department of Computer Science at Sophia Girls' College and is pursuing her Ph.D. in Computer Science from Manipal University. 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 an Assistant Professor in the Department of Computer and Communication Engineering at Manipal University with more than 11 years of academic experience. He has published more than 55 papers in reputable, peer-reviewed national and international journals, books, and conferences. His research interests include NS2, computer networks, mining, 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.
Content
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
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