Microplastic Monitoring Using Artificial Intelligence.

Format:

Book

Author/Creator:

Kumar, Abhishek.

Language:

English

Subjects (All):

Artificial intelligence.

Microplastics.

Physical Description:

1 online resource (374 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

Contents:

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

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

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

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

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

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

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

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

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

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

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.

Notes:

Description based on publisher supplied metadata and other sources.

Part of the metadata in this record was created by AI, based on the text of the resource.

ISBN:

1-394-45011-7

1-394-45010-9

9781394450107

OCLC:

1582117490