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Submit a ProposalMicroplastic Monitoring Using Artificial Intelligence
 | Innovations in Detection, Monitoring and Mitigation Edited by Abhishek Kumar, Pooja Dixit, Pramod Singh Rathore, Arun Lal Srivastav, and Ashutosh Kumar Dubey Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394233977 | Hardcover | 368 pages Price: $225 USD  |
One Line 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.
Audience
Researchers, academics, engineers, scientists, technologists, and policymakers in the fields of renewable energy, materials science, computer science (specifically AI and machine learning), and environmental science.
Researchers, academics, engineers, scientists, technologists, and policymakers in the fields of renewable energy, materials science, computer science (specifically AI and machine learning), and environmental science.
Description
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.
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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.
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Author / Editor Details
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.
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.
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Table of Contents
Preface
1. Introduction to Microplastic and the Role of AI
Pooja Dixit, Shaloo Dadheech, Priya Batta and Neeraj Bhargava
1.1 Introduction
1.1.1 Background and Importance of the Study
1.1.2 Definition of Microplastics
1.1.3 Sources and Types of Microplastics
1.1.4 Environmental and Health Impacts
1.2 Microplastic Distribution and Pathways
1.2.1 Marine and Freshwater Systems
1.2.2 Soil and Agricultural Environments
1.2.3 Airborne Microplastics
1.2.4 Bioaccumulation in the Food Chain
1.3 Current Methods of Microplastic Detection
1.3.1 Sampling and Collection Techniques
1.3.2 Conventional Analytical Methods (Microscopy, FTIR, Raman Spectroscopy)
1.3.3 Limitations of Traditional Approaches
1.4 Role of Artificial Intelligence (AI) in Microplastic Research
1.4.1 Introduction to AI and Machine Learning Concepts
1.4.2 AI for Image-Based Microplastic Identification
1.4.3 AI for Predictive Modeling of Microplastic Pollution
1.4.4 AI in Real-Time Monitoring and Sensing
1.4.5 Integration of AI with IoT and Remote Sensing
1.5 Case Studies and Applications
1.5.1 AI-Driven Microplastic Detection in Marine Systems
1.5.2 AI for Wastewater Treatment Monitoring
1.5.3 Predictive Analytics for Microplastic Pollution Hotspots
1.6 Challenges and Limitations
1.6.1 Data Availability and Quality Issues
1.6.2 Technical and Computational Challenges
1.6.3 Ethical and Policy Considerations
1.7 Future Directions
1.7.1 Advancements in AI Models for Environmental Applications
1.7.2 Cross-Disciplinary Research Opportunities
1.7.3 AI for Policy Support and Decision-Making
1.7.4 Towards Sustainable Microplastic Management
1.8 Conclusion
References
2. A CNN-ViT Hybrid Deep Learning Architecture for Accurate Microplastic Detection
B. Dhanalaxmi, B. Saritha, P. Punitha, G. Jagan Naik and B. Anupama
2.1 Introduction
2.2 Literature Review
2.3 Proposed Mythology
2.4 Result and Discussion
2.5 Concluding Remarks and Future Scope
References
3. XAI for Decision Support in Microplastic Pollution Management
Srinibas Pattanaik, Sachin Ahuja, Sartajvir Singh Dhillon, Jasneet Chawla, Deeksha Sonal and Alessandro Vinciarelli
3.1 Introduction
3.2 Causes and Consequences and Effects of Microplastic Pollution
3.3 The Application of AI in Management of the Environment
3.4 XAI Frameworks are Flexible and for the Micro Plastic Environmental Management and the Summary to Explainable Artificial Intelligence
3.5 Application and Case Studies of XAI Microplastic Pollution Management
3.6 The Utilization of Machine Learning with Explainable AI (XAI) Regarding Decision Support Systems
3.7 Futures Directions and Challenges of Explainable AI with Microplastic Pollution
3.8 Conclusion
References
4. AI-Driven Technologies in Mitigation of Microplastic Pollution
Lata Rani, Hurmat, Deepa Singh, Babu Bharman, Arun Lal Srivastav, Jyotsna Kaushal, Komal Thapa and Neha Kanojia
4.1 Introduction
4.2 AI Assisted Detection Techniques for the Microplastic
4.2.1 AI-Assisted Image Processing Technology
4.2.2 AI-Assisted FTIR
4.2.3 AI-Assisted Raman Spectroscopy
4.2.4 AI-Assisted HSI
4.3 Application of AI in Microplastic Pollution Control
4.4 Conclusion
References
5. AI Driven Optical Imaging and Spectroscopic Techniques
Muchukota Sushma, Mekkanti Manasa Rekha, Ramya C. V. and Zaid Khan
List of Abbreviations
5.1 Introduction
5.1.1 Origins of Microplastics: Sources, Types, and Impact
5.1.2 Traditional Detection Methods
5.1.3 Potential of AI in Transforming Microplastic Monitoring
5.2 Fundamentals of Optical Imaging and Spectroscopic Techniques
5.2.1 Optical Imaging: Principles and Applications
5.2.2 Spectroscopic Techniques: Raman and FTIR Spectroscopy
5.2.3 Integration of AI into Optical and Spectroscopic Tools
5.3 AI Innovations in Microplastic Detection
5.3.1 Machine Learning for Image Analysis and Classification
5.3.2 Neural Networks in Spectral Data Processing
5.3.3 Data Fusion for Enhanced Detection Accuracy
5.4 Applications in Real-Time Monitoring
5.4.1 Aquatic Ecosystem Analysis
5.4.2 Airborne Microplastic Detection
5.4.3 Industrial and Urban Monitoring Systems
5.5 Case Studies in AI-Driven Microplastic Detection
5.5.1 AI-Enhanced Raman Spectroscopy in Marine Monitoring
5.5.2 Automated Optical Imaging Systems for Waste Management
5.5.3 Community-Based Monitoring Initiatives
5.6 Challenges in AI-Driven Microplastic Monitoring
5.6.1 Technical Barriers: Data Volume and Processing Power
5.6.2 Scalability and Cost Constraints
5.6.3 Ethical and Privacy Concerns in Data Use
5.7 Future Directions
5.7.1 Innovations in AI Algorithms for Detection
5.7.2 Advancements in Sensor Technologies
5.7.3 Policy and Regulatory Frameworks Supporting Adoption
5.7.4 Pathways for Addressing Microplastic Pollution with AI
5.8 Conclusion
5.8.1 Summary of Key Developments
5.8.2 Future Perspectives
Acknowledgement
References
6. Integrating AI with Advanced Sensor Technologies for Real-Time Monitoring
Avnish Chauhan, Shivam Attri, Aanchal Saklani, Prabhat K. Chauhan, Man Vir Singh, Vishal Rajput, Muneesh Sethi and Samuele Barrili
6.1 Introduction
6.2 Bibliographic Study
6.3 AI-Enabled Sensor Technologies for Microplastic Detection
6.4 Challenges and Future Prospects
6.5 Conclusion
References
7. Machine Learning for Microplastic Source and Pathway Prediction
Vanshika and Neetu Rani
7.1 Introduction
7.1.1 Overview of Microplastic Pollution and Its Global Impact
7.1.2 Limitations of Conventional Methods in Identifying Microplastic Sources and Tracking Their Dispersion
7.1.3 The Case for Using Machine Learning in Environmental Studies
7.2 Microplastic Sources and Pathways: An Overview
7.2.1 Classifying Microplastic Sources Into Primary and Secondary
7.2.2 Main Pathways of Microplastic Movement: Rivers, Runoff, Currents, and Air
7.2.3 Impact of Location and Climate on Microplastic Spread
7.3 Data Acquisition and Preprocessing
7.3.1 Types of Data Required
7.3.2 Data Sources
7.3.3 Challenges in Data Collection, Quality Control, and Labelling for Machine Learning
7.4 Machine Learning Approaches for Microplastic Modeling
7.4.1 Supervised Learning
7.4.2 Unsupervised Learning
7.4.3 Deep Learning
7.5 Model Development and Validation
7.6 Case Studies and Real-World Implementations
7.7 Visualization and Decision Support
7.7.1 Role of Visualization in Microplastic Prediction
7.7.2 Role of GIS in Data Integration and Monitoring
7.7.3 Decision Support Systems and Their Role in Policy
7.7.4 Multi-Stakeholder Impact and Use Cases
7.8 Challenges and Ethical Considerations
7.9 Conclusion and Future Scope
References
8. Big Data Analytics in Mapping the Global Microplastic Distribution
Prasann Kumar
8.1 Introduction
8.2 Data Sources for Microplastic Mapping
8.3 Big Data Techniques in Microplastic Analytics
8.4 Challenges in Big Data for Microplastic Studies
8.5 Case Studies
8.6 Applications and Implications
8.7 Future Directions
8.8 Conclusion
8.9 Acknowledgement
References
9. Automation in Sampling and Processing, Robotics, and AI Synergy
Prasann Kumar
9.1 Introduction
9.2 Robotics in Sampling and Processing
9.2.1 Types of Robotic Systems Used in Sampling and Processing
9.2.2 Automation in Environmental Sampling
9.2.3 Role of Robotics in Industrial and Biomedical Processing
9.3 AI-Driven Processing Workflows
9.4 Challenges and Limitations
9.5 Case Studies and Applications
9.6 Innovations and Emerging Trends
9.7 Future Directions
9.8 Conclusion
References
10. Cross-Disciplinary Case Studies: AI in Action for Microplastic Research
B. Dhanalaxmi, V. Prema Tulasi, Mittapalli Anusha, G. Sreeram and Komati Sathish
10.1 Introduction
10.2 Literature Review
10.3 Proposed Methodology
10.4 Result and Discussion
10.5 Concluding Remarks and Future Scope
References
11. Ethical and Social Implications of AI in Environmental Science: Balancing Innovation and Responsibility
Priyanka
Introduction
Methodology
Result and Evaluation
Challenges and Limitations
Governance and Regulatory Frameworks
Strategies for Responsible Integration
Future Outcomes
Conclusion
References
12. Regulatory and Policy Challenges for AI-Enhanced Microplastic Monitoring
Gurjeet Kour, Mansi Rana, Pratibha Singh and Ajay Sharma
12.1 Introduction
12.2 Microplastic Monitoring through AI
12.2.1 Microplastic Detection
12.2.2 Classification and Quantification
12.2.3 Real-Time Monitoring and High-Resolution
12.3 The Current State of Microplastic Monitoring Regulations
12.3.1 Current Environmental Regulations and Microplastic Surveillance Guidelines
12.3.2 National and International Guidelines
12.3.3 Complications in Implementing and Complying with Policies
12.3.3.1 Lack of Techniques Installed for Detection and Measurement
12.3.3.2 Variations in Legal Definitions
12.3.3.3 Inconsistent Methods of Enforcement
12.3.3.4 Inadequate Stakeholder Partnership
12.3.3.5 New Potential Risks and Limitations in Technology
12.4 Regulatory Obstacles in AI-Powered Microplastic Identification
12.4.1 Inadequate Worldwide Standards
12.4.2 Problems with Data Difference, Accuracy, and Reproducibility
12.4.3 Accountability and Transparency of Algorithms
12.5 Privacy and Ethical Issues with AI-Powered Environmental Monitoring
12.5.1 The Ethical Consequences of AI in Science Research
12.5.2 Privacy Concerns: Acquiring Geographical and Sensitive Data
12.5.3 Ownership, Security, and Accessibility of Data
12.6 Policy Ideas for Including AI in Microplastic Monitoring
12.6.1 Need for Standardized Protocols, Especially for AI
12.6.2 Install the Default for Transparency and Algorithm Verification
12.6.3 Encouraging International Regulatory Coordination
12.7 Multidisciplinary Cooperation’s Function in Policy Development
12.7.1 Connecting AI Developers, Scientists, and Policymakers
12.7.2 Promoting Interoperability and Open Data Sharing
12.7.3 International Collaborations for Successful AI-Based Environmental Regulations
12.8 Conclusion
References
13. Future Trends: AI Driven Innovation in Environmental Science
Priyanka Sharma, Ankita Sharma and Prashant Ahluwalia
13.1 Introduction to AI in Environmental Science
13.1.1 Definition and Scope of AI in Environmental Research
13.1.2 Historical Evolution and Current Applications
13.1.3 Importance of AI in Addressing Environmental Challenges
13.2 AI and Climate Change Mitigation
13.2.1 AI-Driven Climate Modeling and Prediction
13.2.2 Machine Learning for Greenhouse Gas Monitoring
13.2.3 AI-Based Carbon Capture and Sequestration Techniques
13.3 AI in Water Resource Management
13.3.1 Smart Sensors for Water Quality Monitoring
13.3.2 AI for Efficient Irrigation and Water Conservation
13.3.3 Predictive Analytics for Flood and Drought Forecasting
13.3.4 Additional Applications
13.4 AI in Biodiversity Conservation
13.4.1 AI for Species Identification and Monitoring
13.4.2 Deep Learning for Habitat Mapping
13.4.3 AI-Powered Drones for Wildlife Protection
13.5 AI for Sustainable Agriculture and Forestry
13.5.1 AI Driven Precision Farming and Crop Yield Prediction
13.5.2 Smart Forestry Management Using AI
13.5.3 AI-Enabled Pest and Disease Detection
13.6 AI in Air Pollution Control
13.6.1 AI for Real-Time Air Quality Monitoring and Forecasting
13.6.2 AI-Driven Emission Reduction Strategies
13.6.3 Autonomous Systems for Pollution Control
13.7 AI and Renewable Energy Optimization
13.7.1 AI for Solar and Wind Energy Forecasting
13.7.2 Smart Grids and AI-Powered Energy Distribution
13.8 AI for Smart Disaster Resilience
13.9 Environmental Sustainability
13.10 Future Scope
References
14. XAI for Decision Support in Microplastic Pollution Management
Yeligeti Raju, N. Venkatesh, S. Adilakshmi, Namita Parati and A. Kalaivani
14.1 Introduction
14.2 Literature Review
14.3 Proposed Methodology
14.4 Result and Discussion
14.5 Concluding Remarks and Future Scope
References
15. The Road Ahead: AI’s Role in Tackling Global Microplastic Pollution
Yeligeti Raju, K. Damodhar Rao, M. Lavanya, Mursubai Sandhya Rani and Sendhil Kumar B.B.
15.1 Introduction
15.2 Literature Review
15.3 Proposed Methodology
15.4 Result and Discussion
15.5 Concluding Remarks and Future Scope
References
16. Intelligent Environmental Surveillance: Integrating AI Systems for Comprehensive Microplastic Monitoring and Analysis
Mamta
16.1 Introduction
16.1.1 Scope of Global Microplastic Crisis
16.1.2 Emergence of AI in Environmental Monitoring
16.1.3 Objectives and Organization
16.2 Understanding Microplastic Pollution
16.2.1 Definition and Sources
16.2.2 Environmental Impact
16.2.2.1 Marine Ecosystem Effects
16.2.2.2 Human Health Implications
16.2.3 Current Monitoring Challenges
16.3 AI-Based Monitoring Systems
16.3.1 Machine Learning Approaches
16.3.2 Computer Vision Technologies
16.3.3 Real-Time Detection Capabilities
16.3.4 Data Processing and Analysis
16.4 Implementation and Case Studies
16.4.1 Existing AI-Powered Systems
16.4.2 Field Implementation Examples
16.4.3 Performance Analysis
16.4.4 Best Practices
16.5 Future Scope
16.5.1 Emerging Technologies
16.5.2 Integration with Global Networks
16.5.3 Scalability Considerations
16.5.4 Research Directions
16.6 Conclusion
16.6.1 Summary of Key Findings
16.6.2 Implementation Recommendations
16.6.3 Future Research Needs
Bibliography
Index
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Preface
1. Introduction to Microplastic and the Role of AI
Pooja Dixit, Shaloo Dadheech, Priya Batta and Neeraj Bhargava
1.1 Introduction
1.1.1 Background and Importance of the Study
1.1.2 Definition of Microplastics
1.1.3 Sources and Types of Microplastics
1.1.4 Environmental and Health Impacts
1.2 Microplastic Distribution and Pathways
1.2.1 Marine and Freshwater Systems
1.2.2 Soil and Agricultural Environments
1.2.3 Airborne Microplastics
1.2.4 Bioaccumulation in the Food Chain
1.3 Current Methods of Microplastic Detection
1.3.1 Sampling and Collection Techniques
1.3.2 Conventional Analytical Methods (Microscopy, FTIR, Raman Spectroscopy)
1.3.3 Limitations of Traditional Approaches
1.4 Role of Artificial Intelligence (AI) in Microplastic Research
1.4.1 Introduction to AI and Machine Learning Concepts
1.4.2 AI for Image-Based Microplastic Identification
1.4.3 AI for Predictive Modeling of Microplastic Pollution
1.4.4 AI in Real-Time Monitoring and Sensing
1.4.5 Integration of AI with IoT and Remote Sensing
1.5 Case Studies and Applications
1.5.1 AI-Driven Microplastic Detection in Marine Systems
1.5.2 AI for Wastewater Treatment Monitoring
1.5.3 Predictive Analytics for Microplastic Pollution Hotspots
1.6 Challenges and Limitations
1.6.1 Data Availability and Quality Issues
1.6.2 Technical and Computational Challenges
1.6.3 Ethical and Policy Considerations
1.7 Future Directions
1.7.1 Advancements in AI Models for Environmental Applications
1.7.2 Cross-Disciplinary Research Opportunities
1.7.3 AI for Policy Support and Decision-Making
1.7.4 Towards Sustainable Microplastic Management
1.8 Conclusion
References
2. A CNN-ViT Hybrid Deep Learning Architecture for Accurate Microplastic Detection
B. Dhanalaxmi, B. Saritha, P. Punitha, G. Jagan Naik and B. Anupama
2.1 Introduction
2.2 Literature Review
2.3 Proposed Mythology
2.4 Result and Discussion
2.5 Concluding Remarks and Future Scope
References
3. XAI for Decision Support in Microplastic Pollution Management
Srinibas Pattanaik, Sachin Ahuja, Sartajvir Singh Dhillon, Jasneet Chawla, Deeksha Sonal and Alessandro Vinciarelli
3.1 Introduction
3.2 Causes and Consequences and Effects of Microplastic Pollution
3.3 The Application of AI in Management of the Environment
3.4 XAI Frameworks are Flexible and for the Micro Plastic Environmental Management and the Summary to Explainable Artificial Intelligence
3.5 Application and Case Studies of XAI Microplastic Pollution Management
3.6 The Utilization of Machine Learning with Explainable AI (XAI) Regarding Decision Support Systems
3.7 Futures Directions and Challenges of Explainable AI with Microplastic Pollution
3.8 Conclusion
References
4. AI-Driven Technologies in Mitigation of Microplastic Pollution
Lata Rani, Hurmat, Deepa Singh, Babu Bharman, Arun Lal Srivastav, Jyotsna Kaushal, Komal Thapa and Neha Kanojia
4.1 Introduction
4.2 AI Assisted Detection Techniques for the Microplastic
4.2.1 AI-Assisted Image Processing Technology
4.2.2 AI-Assisted FTIR
4.2.3 AI-Assisted Raman Spectroscopy
4.2.4 AI-Assisted HSI
4.3 Application of AI in Microplastic Pollution Control
4.4 Conclusion
References
5. AI Driven Optical Imaging and Spectroscopic Techniques
Muchukota Sushma, Mekkanti Manasa Rekha, Ramya C. V. and Zaid Khan
List of Abbreviations
5.1 Introduction
5.1.1 Origins of Microplastics: Sources, Types, and Impact
5.1.2 Traditional Detection Methods
5.1.3 Potential of AI in Transforming Microplastic Monitoring
5.2 Fundamentals of Optical Imaging and Spectroscopic Techniques
5.2.1 Optical Imaging: Principles and Applications
5.2.2 Spectroscopic Techniques: Raman and FTIR Spectroscopy
5.2.3 Integration of AI into Optical and Spectroscopic Tools
5.3 AI Innovations in Microplastic Detection
5.3.1 Machine Learning for Image Analysis and Classification
5.3.2 Neural Networks in Spectral Data Processing
5.3.3 Data Fusion for Enhanced Detection Accuracy
5.4 Applications in Real-Time Monitoring
5.4.1 Aquatic Ecosystem Analysis
5.4.2 Airborne Microplastic Detection
5.4.3 Industrial and Urban Monitoring Systems
5.5 Case Studies in AI-Driven Microplastic Detection
5.5.1 AI-Enhanced Raman Spectroscopy in Marine Monitoring
5.5.2 Automated Optical Imaging Systems for Waste Management
5.5.3 Community-Based Monitoring Initiatives
5.6 Challenges in AI-Driven Microplastic Monitoring
5.6.1 Technical Barriers: Data Volume and Processing Power
5.6.2 Scalability and Cost Constraints
5.6.3 Ethical and Privacy Concerns in Data Use
5.7 Future Directions
5.7.1 Innovations in AI Algorithms for Detection
5.7.2 Advancements in Sensor Technologies
5.7.3 Policy and Regulatory Frameworks Supporting Adoption
5.7.4 Pathways for Addressing Microplastic Pollution with AI
5.8 Conclusion
5.8.1 Summary of Key Developments
5.8.2 Future Perspectives
Acknowledgement
References
6. Integrating AI with Advanced Sensor Technologies for Real-Time Monitoring
Avnish Chauhan, Shivam Attri, Aanchal Saklani, Prabhat K. Chauhan, Man Vir Singh, Vishal Rajput, Muneesh Sethi and Samuele Barrili
6.1 Introduction
6.2 Bibliographic Study
6.3 AI-Enabled Sensor Technologies for Microplastic Detection
6.4 Challenges and Future Prospects
6.5 Conclusion
References
7. Machine Learning for Microplastic Source and Pathway Prediction
Vanshika and Neetu Rani
7.1 Introduction
7.1.1 Overview of Microplastic Pollution and Its Global Impact
7.1.2 Limitations of Conventional Methods in Identifying Microplastic Sources and Tracking Their Dispersion
7.1.3 The Case for Using Machine Learning in Environmental Studies
7.2 Microplastic Sources and Pathways: An Overview
7.2.1 Classifying Microplastic Sources Into Primary and Secondary
7.2.2 Main Pathways of Microplastic Movement: Rivers, Runoff, Currents, and Air
7.2.3 Impact of Location and Climate on Microplastic Spread
7.3 Data Acquisition and Preprocessing
7.3.1 Types of Data Required
7.3.2 Data Sources
7.3.3 Challenges in Data Collection, Quality Control, and Labelling for Machine Learning
7.4 Machine Learning Approaches for Microplastic Modeling
7.4.1 Supervised Learning
7.4.2 Unsupervised Learning
7.4.3 Deep Learning
7.5 Model Development and Validation
7.6 Case Studies and Real-World Implementations
7.7 Visualization and Decision Support
7.7.1 Role of Visualization in Microplastic Prediction
7.7.2 Role of GIS in Data Integration and Monitoring
7.7.3 Decision Support Systems and Their Role in Policy
7.7.4 Multi-Stakeholder Impact and Use Cases
7.8 Challenges and Ethical Considerations
7.9 Conclusion and Future Scope
References
8. Big Data Analytics in Mapping the Global Microplastic Distribution
Prasann Kumar
8.1 Introduction
8.2 Data Sources for Microplastic Mapping
8.3 Big Data Techniques in Microplastic Analytics
8.4 Challenges in Big Data for Microplastic Studies
8.5 Case Studies
8.6 Applications and Implications
8.7 Future Directions
8.8 Conclusion
8.9 Acknowledgement
References
9. Automation in Sampling and Processing, Robotics, and AI Synergy
Prasann Kumar
9.1 Introduction
9.2 Robotics in Sampling and Processing
9.2.1 Types of Robotic Systems Used in Sampling and Processing
9.2.2 Automation in Environmental Sampling
9.2.3 Role of Robotics in Industrial and Biomedical Processing
9.3 AI-Driven Processing Workflows
9.4 Challenges and Limitations
9.5 Case Studies and Applications
9.6 Innovations and Emerging Trends
9.7 Future Directions
9.8 Conclusion
References
10. Cross-Disciplinary Case Studies: AI in Action for Microplastic Research
B. Dhanalaxmi, V. Prema Tulasi, Mittapalli Anusha, G. Sreeram and Komati Sathish
10.1 Introduction
10.2 Literature Review
10.3 Proposed Methodology
10.4 Result and Discussion
10.5 Concluding Remarks and Future Scope
References
11. Ethical and Social Implications of AI in Environmental Science: Balancing Innovation and Responsibility
Priyanka
Introduction
Methodology
Result and Evaluation
Challenges and Limitations
Governance and Regulatory Frameworks
Strategies for Responsible Integration
Future Outcomes
Conclusion
References
12. Regulatory and Policy Challenges for AI-Enhanced Microplastic Monitoring
Gurjeet Kour, Mansi Rana, Pratibha Singh and Ajay Sharma
12.1 Introduction
12.2 Microplastic Monitoring through AI
12.2.1 Microplastic Detection
12.2.2 Classification and Quantification
12.2.3 Real-Time Monitoring and High-Resolution
12.3 The Current State of Microplastic Monitoring Regulations
12.3.1 Current Environmental Regulations and Microplastic Surveillance Guidelines
12.3.2 National and International Guidelines
12.3.3 Complications in Implementing and Complying with Policies
12.3.3.1 Lack of Techniques Installed for Detection and Measurement
12.3.3.2 Variations in Legal Definitions
12.3.3.3 Inconsistent Methods of Enforcement
12.3.3.4 Inadequate Stakeholder Partnership
12.3.3.5 New Potential Risks and Limitations in Technology
12.4 Regulatory Obstacles in AI-Powered Microplastic Identification
12.4.1 Inadequate Worldwide Standards
12.4.2 Problems with Data Difference, Accuracy, and Reproducibility
12.4.3 Accountability and Transparency of Algorithms
12.5 Privacy and Ethical Issues with AI-Powered Environmental Monitoring
12.5.1 The Ethical Consequences of AI in Science Research
12.5.2 Privacy Concerns: Acquiring Geographical and Sensitive Data
12.5.3 Ownership, Security, and Accessibility of Data
12.6 Policy Ideas for Including AI in Microplastic Monitoring
12.6.1 Need for Standardized Protocols, Especially for AI
12.6.2 Install the Default for Transparency and Algorithm Verification
12.6.3 Encouraging International Regulatory Coordination
12.7 Multidisciplinary Cooperation’s Function in Policy Development
12.7.1 Connecting AI Developers, Scientists, and Policymakers
12.7.2 Promoting Interoperability and Open Data Sharing
12.7.3 International Collaborations for Successful AI-Based Environmental Regulations
12.8 Conclusion
References
13. Future Trends: AI Driven Innovation in Environmental Science
Priyanka Sharma, Ankita Sharma and Prashant Ahluwalia
13.1 Introduction to AI in Environmental Science
13.1.1 Definition and Scope of AI in Environmental Research
13.1.2 Historical Evolution and Current Applications
13.1.3 Importance of AI in Addressing Environmental Challenges
13.2 AI and Climate Change Mitigation
13.2.1 AI-Driven Climate Modeling and Prediction
13.2.2 Machine Learning for Greenhouse Gas Monitoring
13.2.3 AI-Based Carbon Capture and Sequestration Techniques
13.3 AI in Water Resource Management
13.3.1 Smart Sensors for Water Quality Monitoring
13.3.2 AI for Efficient Irrigation and Water Conservation
13.3.3 Predictive Analytics for Flood and Drought Forecasting
13.3.4 Additional Applications
13.4 AI in Biodiversity Conservation
13.4.1 AI for Species Identification and Monitoring
13.4.2 Deep Learning for Habitat Mapping
13.4.3 AI-Powered Drones for Wildlife Protection
13.5 AI for Sustainable Agriculture and Forestry
13.5.1 AI Driven Precision Farming and Crop Yield Prediction
13.5.2 Smart Forestry Management Using AI
13.5.3 AI-Enabled Pest and Disease Detection
13.6 AI in Air Pollution Control
13.6.1 AI for Real-Time Air Quality Monitoring and Forecasting
13.6.2 AI-Driven Emission Reduction Strategies
13.6.3 Autonomous Systems for Pollution Control
13.7 AI and Renewable Energy Optimization
13.7.1 AI for Solar and Wind Energy Forecasting
13.7.2 Smart Grids and AI-Powered Energy Distribution
13.8 AI for Smart Disaster Resilience
13.9 Environmental Sustainability
13.10 Future Scope
References
14. XAI for Decision Support in Microplastic Pollution Management
Yeligeti Raju, N. Venkatesh, S. Adilakshmi, Namita Parati and A. Kalaivani
14.1 Introduction
14.2 Literature Review
14.3 Proposed Methodology
14.4 Result and Discussion
14.5 Concluding Remarks and Future Scope
References
15. The Road Ahead: AI’s Role in Tackling Global Microplastic Pollution
Yeligeti Raju, K. Damodhar Rao, M. Lavanya, Mursubai Sandhya Rani and Sendhil Kumar B.B.
15.1 Introduction
15.2 Literature Review
15.3 Proposed Methodology
15.4 Result and Discussion
15.5 Concluding Remarks and Future Scope
References
16. Intelligent Environmental Surveillance: Integrating AI Systems for Comprehensive Microplastic Monitoring and Analysis
Mamta
16.1 Introduction
16.1.1 Scope of Global Microplastic Crisis
16.1.2 Emergence of AI in Environmental Monitoring
16.1.3 Objectives and Organization
16.2 Understanding Microplastic Pollution
16.2.1 Definition and Sources
16.2.2 Environmental Impact
16.2.2.1 Marine Ecosystem Effects
16.2.2.2 Human Health Implications
16.2.3 Current Monitoring Challenges
16.3 AI-Based Monitoring Systems
16.3.1 Machine Learning Approaches
16.3.2 Computer Vision Technologies
16.3.3 Real-Time Detection Capabilities
16.3.4 Data Processing and Analysis
16.4 Implementation and Case Studies
16.4.1 Existing AI-Powered Systems
16.4.2 Field Implementation Examples
16.4.3 Performance Analysis
16.4.4 Best Practices
16.5 Future Scope
16.5.1 Emerging Technologies
16.5.2 Integration with Global Networks
16.5.3 Scalability Considerations
16.5.4 Research Directions
16.6 Conclusion
16.6.1 Summary of Key Findings
16.6.2 Implementation Recommendations
16.6.3 Future Research Needs
Bibliography
Index
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