Microplastic Monitoring Using Artificial Intelligence

• Autore: Kumar Abhishek
• Pubblicazione: Newark : , : John Wiley & Sons, Incorporated, , 2026; ©2026
• Edizione: 1st ed.
• Descrizione fisica: 1 online resource (374 pages)
• Disciplina: 628.5
• Soggetto topico: Microplastics; Artificial intelligence
• Nota di contenuto: Cover -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1 Introduction to Microplastic and the Role of AI -- 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 -- Chapter 2 A CNN-ViT Hybrid Deep Learning Architecture for Accurate Microplastic Detection -- 2.1 Introduction. ; 2.2 Literature Review -- 2.3 Proposed Mythology -- 2.4 Result and Discussion -- 2.5 Concluding Remarks and Future Scope -- References -- Chapter 3 XAI for Decision Support in Microplastic Pollution Management -- 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 -- Chapter 4 AI-Driven Technologies in Mitigation of Microplastic Pollution -- 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 -- Chapter 5 AI Driven Optical Imaging and Spectroscopic Techniques -- 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 -- Chapter 6 Integrating AI with Advanced Sensor Technologies for Real-Time Monitoring -- 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 -- Chapter 7 Machine Learning for Microplastic Source and Pathway Prediction -- 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 -- Chapter 8 Big Data Analytics in Mapping the Global Microplastic Distribution -- 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 -- Chapter 9 Automation in Sampling and Processing, Robotics, and AI Synergy -- 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 -- Chapter 10 Cross-Disciplinary Case Studies: AI in Action for Microplastic Research -- 10.1 Introduction. ; 10.2 Literature Review -- 10.3 Proposed Methodology -- 10.4 Result and Discussion -- 10.5 Concluding Remarks and Future Scope -- References -- Chapter 11 Ethical and Social Implications of AI in Environmental Science: Balancing Innovation and Responsibility -- Introduction -- Methodology -- Result and Evaluation -- Challenges and Limitations -- Governance and Regulatory Frameworks -- Strategies for Responsible Integration -- Future Outcomes -- Conclusion -- References -- Chapter 12 Regulatory and Policy Challenges for AI-Enhanced Microplastic Monitoring -- 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.
• Sommario/riassunto: 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.
• Titolo autorizzato: Microplastic Monitoring Using Artificial Intelligence
• ISBN: 1-394-45011-7; 1-394-45010-9; 9781394450107
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9911073922003321
• Lo trovi qui: Univ. Federico II