1st Edition

Artificial Intelligence for Air Quality Monitoring and Prediction

    328 Pages 18 Color & 42 B/W Illustrations
    by CRC Press

    This book is a comprehensive overview of advancements in artificial intelligence (AI) and how it can be applied in the field of air quality management. It explains the linkage between conventional approaches used in air quality monitoring with AI techniques such as data collection and preprocessing, deep learning, machine vision, natural language processing, and ensemble methods. The integration of climate models and AI enables readers to understand the relationship between air quality and climate change. Different case studies demonstrate the application of various air monitoring and prediction methodologies and their effectiveness in addressing real-world air quality challenges.


    • Provides a thorough coverage of air quality monitoring and prediction techniques.
    • Includes in-depth evaluation of cutting-edge AI techniques such as machine learning and deep learning.
    • Explores diverse global perspectives and approaches in air quality monitoring and prediction.
    • Discusses practical insights and real-world case studies from different monitoring and prediction techniques.
    • Offers future directions and emerging trends in AI-driven air quality monitoring.

     This is a great resource for professionals, researchers, and students interested in air quality management and control in the fields of environmental science and engineering, atmospheric science and meteorology, data science and AI.

    1. Air Quality Monitoring (AQM) and Prediction: Transitioning from Conventional to AI Techniques

    Amit Awasthi, Kanhu Charan Pattnayak, PushpRaj Tiwari, et al.

    2. Temporal Variations of Sulphur Dioxide Levels across India: A Biennial Assessment (2020-2021)

    Bisma Nadeem, Navjot Hothi, Rahul Malik, et al.

     3. The Effectiveness of Machine Learning Techniques in Enhancing Air Quality Prediction

    Sushant Das and P. R. Tiwari

     4. Enhancing Environmental Resilience: Precision in Air Quality Monitoring through AI-Driven Real-Time Systems

    Ankit Mahule, Kaushik Roy, Ankush D. Sawarkar, et al. 

    5. Forecasting Air Pollution with Artificial Intelligence: Recent Advancements at Global Scale and Future Perspectives

    Prem Rajak, Satadal Adhikary, Suchandra Bhattacharya, et al. 

    6. Integrating AI into Air Quality Monitoring: Precision and Progress

    Rakesh Kumar, Ayushi Sharma, and Siddharth Swami

     7. Application of AI-based Tools in Air Pollution Study

    Sashi Yadav, Abhilasha Yadav, Asha Singh, et al.

     8. Study of Extreme Weather Events in the Central Himalayan Region through Machine Learning and Artificial Intelligence: A Case Study

    Alok Sagar Gautam, Aman Deep Vishwkarma, Yasti Panchbhaiya, et al.

    9. Machine Learning Applications in Air Quality Management and Policies

    Abhishek Upadhyay, Puneet Sharma, and Sourangsu Chowdhury

     10. A Glimpse into Tomorrow's Air: Leveraging PM 2.5 with FP Prophet as a Forecasting Model

    Kush Singla, Love Singla, and Mamta Bansal

     11. Air Quality Forecast using Machine Learning Algorithms

    Saurabh Kumar

     12. Deep Learning Approaches in Air Quality Prediction

    Prabhjot Kaur, Soni Chaurasia, Mamta Bansal, et al.

    13. Incorporation of AI with Conventional Monitoring Systems

    Tania Ghatak (Chakraborty), Rashi Jain, and Abhijit Sarkar

     14. A Comparative Evaluation of AI-Based Methods and Traditional Approaches for Air Quality Monitoring: Analyzing Pros and Cons

    NZEYIMANA Bahati Shabani

     15. ML Driven Hydrogen Yield Prediction for Sustainable Environment

    Kumargaurao D. Punasea, Mukul Kumar Gupta, and Abhinav Sharmac


    Dr. Amit Awasthi is an Assistant Professor at the University of Petroleum and Energy Studies, Dehradun, India, teaching and doing research in the areas of Atmospheric and Environment Sciences, Aerosol Technology and Measurements, Air Monitoring, and Climate Change. He has published 4 books and 40 research papers with a total Impact factor of ~100, an h-index of 16, and a total of 1050 citations. He received his Ph. D from Thapar University, Patiala in 2011.

    Dr. Kanhu Charan Pattnayak is a Senior Climate Impact Scientist at the National Environmental Agency, Singapore with over 17 years of experience in climate impact research and climate modeling. He has a Ph.D. in Climate Science from the Indian Institute of Technology Delhi and has held research positions at the University of Leeds, the National Environment Agency of Singapore, and the NCMRWF. He has published over 25 research papers in top tier journals and has served as a reviewer for the Sixth Assessment Report of the United Nations Intergovernmental Panel on Climate Change.

    Dr. Gaurav Dhiman is an Assistant Professor in the School of Sciences and Emerging Technologies, Jagat Guru Nanak Dev Punjab State Open University, Patiala, Punjab, India. He holds a Ph.D. in Computer Engineering from Thapar Institute of Engineering & Technology, Patiala. He is recognized as one of the world's top researchers by Stanford University's list of the world's top 2% scientists prepared by Elsevier and the top 1% as a highly cited researcher by Clarivate Analytics. He is a Senior Member of IEEE. He has authored over 300 peer-reviewed research papers and 10 books. He is currently serving as a guest editor for more than forty special issues in various peer-reviewed journals. He is an Editor-in-Chief of the International Journal of Modern Research (IJMORE). He is an Associate Editor of IEEE Transactions on Industrial Informatics, IET Software (Wiley), Expert Systems (Wiley), IEEE Systems, Man, and Cybernetics Magazine, IEEE Transactions on Consumer Electronics, and more.

    Dr. Pushp Raj Tiwari is a climate scientist and Fellow of UK’s Higher Education Academy (FHEA), specializes in climate change, big data and earth system modelling. A former RCUK-ECR Fellow, he now leads research group on Climate Change Modelling and Applications. His work focuses on aerosol-cloud–climate interaction and reducing the associated uncertainties related to them in climate models.