Computational Approach for Air Pollution

Akhilesh Kumar Yadav graduated Bachelor of Technology in Electronics and Communication Engineering from Chaudhary Charan Singh University in Meerut, India. He then obtained a Master of Technology in Environmental Engineering from Madan Mohan Malaviya Engineering College in Gorakhpur, India (affiliated with Dr. A. P. J. Abdul Kalam Technical University, Lucknow). His doctoral degree, with a research focus on air pollution, was awarded by the Indian Institute of Technology (Banaras Hindu University), Varanasi, India. Dr. Yadav has served at various institutions, including Indian Institute of Technology (Banaras Hindu University), Varanasi, India; Indian Institute of Technology Bombay, Mumbai, India; Bhabha Atomic Research Centre, Mumbai, India (as a research collab-orator); Manipal University Jaipur, Jaipur, India and Chaoyang University of Technology in Taichung, Taiwan. He has authored/co-authored several research and review articles, book chapters, and edited/authored books, and holds a patent. Dr. Yadav received the Young Engineer Award from the Institution of Engineers (India) and the Young Scientist Award from VDGOOD Technology Factory, Kolkata. His research interests include air, water, and soil pollution; climate change; vulnerability; human health risk assessments; and GIS applications in environmental pollution and management. He is an active member of several reputed international professional bodies, including the IEI (Kolkata), ECI (New Delhi), AEACI (Mumbai), MSI (New Delhi), and IASTA (Mumbai).

• Chapter 1. Introduction to computational approaches in air pollution.- Chapter 2. Understanding the health and environmental impacts of air pollutants.- Chapter 3. Assessing the health and environmental consequences of air pollution.- Chapter 4. Assessment on health and environmental impacts due to air pollution.- Chapter 5. Air pollution and its impact on health and environment in India.- Chapter 6. Analyzing air pollution sources effect and mitigation strategies.- Chapter 7. Towards a data-driven air pollution control strategy.- Chapter 8. Air pollution modeling using computational approaches.- Chapter 9. Machine learning in air quality prediction and control.- Chapter 10. Transforming air quality forecasting with machine learning techniques.- Chapter 11. Machine learning for air pollution prediction: A case study of Bhilwara, Rajasthan.- Chapter 12. Emission inventories to AI-based prediction for air pollutants.- Chapter 13. Harnessing biodiversity for air pollution mitigation through nature-based strategies and bioresource utilization.- Chapter 14. Digital pathways to cleaner air: Reducing construction-related CO₂ through BIM, digital twins, and artificial intelligence.- Chapter 15. Investigating the relationship between urbanization and air pollution: New insights from developed countries.- Chapter 16. Air pollution and recent trends in control strategy.- Chapter 17. COVID-19 emission inventory and air quality simulation in Madrin, Spain.- Chapter 18. Intuitionistic Fuzzy MCDM Decision Making Technique for the Selection of Property.- Chapter 19. Probabilistic and computational health risk modelling for assessment of air pollution exposure.- Chapter 20. Advancing sustainable and inclusive evaluation frameworks for health and environmental impact assessment.- Chapter 21. Environmental and Public Health Concerns in a Sustainable Future.