Environmental Science/Climate Change & Mitigation

AAP Advances in Green Technologies for Sustainable Energy Solutions

Advances in Air Quality Monitoring
Machine Learning Models and Low-Cost Sensor Applications

Editors: Dr. Sakshi Sarathe
Dr. Gaurav Dwivedi
Dr. Prashant Baredar
Dr. Ashwani Kumar

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Advances in Air Quality Monitoring

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Series: AAP Advances in Green Technologies for Sustainable Energy Solutions

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Short description about the volume:

The book, Advances in Air Quality Monitoring: Machine Learning Models and Low-Cost Sensor Applications, is addressed to future and experienced professionals working with environmental science, air quality monitoring, data analysis, and smart urban planning. The book plans to present a multidisciplinary framework for understanding how the emerging sensor technologies and artificial intelligence, in the form of machine learning, are revolutionizing air quality measurement and management. Through this sequence of chapters, readers will explore theoretical foundations, field deployments, real case studies, and directions for the future to build intelligent, low-cost, and scalable air pollution monitoring systems.

Air pollution is among the most pressing environmental and public health challenges facing our world. Traditional air quality monitoring systems, while precise, are costly, stationary, and unsuitable for capturing the dynamic character of pollution in highly dynamic urban and industrial atmospheres. As an alternative to these shortcomings, this book—Advances in Air Quality Monitoring: Machine Learning Models and Low-Cost Sensor Applications—will offer an end-to-end, application-oriented solution that leverages the power of artificial intelligence and the low cost of sensing technologies.

This book is intended for environmental scientists, engineers, AI practitioners, urban planners, policy analysts, and graduate students. For the purpose of developing a real-time air quality network, training AI models, or policy-making based on environmental data, readers will appreciate both depth and breadth in this handbook. Our intention is to provide stakeholders with tools and knowledge to implement effective, ethical, and future-proof air quality monitoring systems.

Coverage

To create an authoritative and comprehensive volume, we invite scholars, researchers, and industry professionals to contribute original research and perspectives. While the following topics are the primary focus, we encourage submissions that explore additional areas within this evolving field.

1. Foundations of Air Quality Monitoring: Scientific Principles, Technologies, and Global Standards
• Sources, dispersion, and chemical transformations
• Health and environmental impacts of air pollutants
• Traditional air quality monitoring techniques and limitations
• Global air quality regulations and compliance frameworks

2. The Rise of Low-Cost Sensors: A Disruptive Innovation in Air Quality Assessment
• The evolution of air pollution monitoring
• Sensor technologies
• Case studies: Real-world deployment and performance evaluation

3. Machine Learning for Air Quality Monitoring: Theoretical Foundations and Practical Applications
• Role of AI in environmental monitoring
• AI in air pollution management

4. Data Acquisition, Preprocessing, and Sensor Calibration: Ensuring Accuracy and Reliability
• Data collection methodologies: Fixed stations, mobile monitoring, and satellite integration, handling missing data, outliers, and noise reduction strategies, sensor drift correction, calibration techniques, cross-validation approaches, data fusion and assimilation

5. Predictive Analytics for Air Pollution Forecasting: Supervised Machine Learning Models
• Regression-based models
• Time-series forecasting models
• Air quality classification techniques using machine learning
• Model evaluation metrics and optimization strategies

6. Advanced AI Techniques for Air Quality Monitoring: Deep Learning, Unsupervised Learning, and Hybrid Models
• Neural network architectures for air pollution forecasting
• Clustering and anomaly detection in air quality data using unsupervised learning
• Transfer learning and federated learning in distributed sensor networks
• Hybrid AI models for enhancing air pollution prediction accuracy

7. Large-Scale Sensor Network Deployment: Strategies, Data Integration, and Cloud-Based Analytics
• The novel approaches of sensor placement optimization: urban vs. rural deployment challenges
• Cloud computing
• Edge AI for real-time air quality monitoring

8. Real-World Applications of AI and Low-Cost Sensors in Air Quality Management
• Case study: AI-powered urban air pollution monitoring and forecasting
• Industrial applications
• Smart transportation and air quality
• Indoor air quality monitoring and building automation systems

9. Challenges, Ethical Considerations, and Limitations of AI-Based Air Quality Monitoring
• Technical limitations of low-cost sensor
• Ethical dilemmas in air quality surveillance
• Bridging the gap between AI-driven insights and policy implementation

10. Sensor-to-Cloud Architecture: Enabling Scalable and Real-Time Air Quality Monitoring
• Comparison between Edge computing vs. cloud computing for air quality data processing
• IoT frameworks for air quality sensor networks and secure data transmission and storage solutions

11. Explainable AI (XAI) in Air Quality Prediction: Enhancing Transparency and Trust
• Need for interpretability in air quality models
• Feature importance analysis and model explainability techniques
• Case studies on explainable AI applications in air quality forecasting

12. Remote Sensing and Satellite Data Integration for Large-Scale Air Quality Assessment
• Satellite-based air pollution monitoring
• Combining ground sensor networks with remote sensing data
• AI-driven geospatial analysis for pollution hotspot detection

13. Urban Governance and Energy: Policies and Interventions for Achieving Climate Change Targets
• Impact of climate variability on air pollution levels
• AI models for correlating climate trends with air quality data
• Methodologies for predicting extreme pollution events under future climate scenarios

14. Citizen Science and Participatory Sensing: Engaging Communities in Air Quality Monitoring
• Crowdsourced air quality data collection using low-cost sensors
• Community-driven air pollution initiatives and their impact
• AI-assisted mobile applications for real-time air quality awareness

15. Cross-Border Air Pollution and Transboundary Haze: AI Solutions for Regional Air Quality Management
• Long-range air pollution transport mechanisms
• AI-driven modeling of transboundary pollution movement
• Policy frameworks for international air quality collaboration


Important Dates:
Initial Proposal / Abstract Submission (400 – 600 words)
Deadline: November 15th, 2025
Notification of Acceptance: November 20th, 2025
Full Chapter (4,000 – 7,000 words) Submission Due: On or before February 25th, 2026

Submission procedure:

We invite researchers and practitioners to contribute original chapters to this book. Prospective authors should submit a one-page proposal or abstract outlining the chapter’s content, objectives, and methodology by November 15th, 2025. The proposal must include the chapter title and author details. Notifications of acceptance will be sent by November 20th, 2025. Accepted authors will be required to submit full chapters (15–20 pages) by February 25th, 2026. All submissions will undergo a rigorous peer-review process.

To submit your proposal or full-length chapter, please send a Word document attachment to editors:

Editors: Dr. Sakshi Sarathe (sakshisarathe.96@gmail.com)
Dr. Gaurav Dwivedi (gdiitr2005@gmail.com)
Dr. Prashant Baredar (prashant.baredar@gmail.com)
Dr. Ashwani Kumar (drashwanikumardte@gmail.com)

Authors must refer to the following link for detailed guidelines for chapter preparation: http://www.appleacademicpress.com/publishwithus

Note: Authors submitting manuscripts to this book do not incur any publication fees. To ensure the originality and quality of the content, all submissions must be previously unpublished and not under consideration for publication in any other venue.


About the Authors / Editors:
Editors: Dr. Sakshi Sarathe
Research Associate, Maulana Azad National Institute of Technology (MANIT) Bhopal, Madhya Pradesh 462003, India
Email: sakshisarathe.96@gmail.com


Dr. Gaurav Dwivedi
Assistant Professor, Maulana Azad National Institute of Technology (MANIT) Bhopal, Madhya Pradesh 462003, India
Email: gdiitr2005@gmail.com


Dr. Prashant Baredar
Professor, Maulana Azad National Institute of Technology (MANIT) Bhopal, Madhya Pradesh 462003, India
Email: prashant.baredar@gmail.com


Dr. Ashwani Kumar
Professor & Head Mechanical Engineering, Technical Education Department Uttar Pradesh Kanpur, 208024, India
Email: drashwanikumardte@gmail.com





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