AIR SHPERE: AI Powered Real-Time Air Quality Monitoring and Health Intelligence System

Abstract

AIR SPHERE is an intelligent web-based system designed to monitor, analyze, and predict air quality and climate conditions in real time. The project aims to address growing environmental concerns by providing users with accurate Air Quality Index (AQI) data along with future predictions using machine learning techniques. By integrating environmental datasets such as temperature, humidity, and pollutant concentrations (PM2.5, PM10, CO, NO₂), the system delivers meaningful insights into air quality trends.

The platform utilizes advanced algorithms to forecast AQI levels, helping users take preventive measures for health and safety. A user-friendly and visually appealing interface ensures easy understanding of complex environmental data through graphs, indicators, and alerts. Additionally, the system can provide recommendations based on AQI levels, such as whether it is safe to go outdoors.

AIR SPHERE serves as a practical solution for individuals, researchers, and urban planners by promoting awareness and enabling data-driven decisions toward a healthier and more sustainable environment.

Country : India

1 Shakti Gotpagar2 Ravindra Waghmare3 Samiksha Khandagale4 Swaroop Jadhav5 Prof. Sanket Sontakke6 Prof. Mayuri Narudkar

  1. Student, Artificial Intelligence & Machine Learning Engineering Diploma, Ajeenkya D.Y Patil School of Engineering, Charholi, Pune, India
  2. Student, Artificial Intelligence & Machine Learning Engineering Diploma, Ajeenkya D.Y Patil School of Engineering, Charholi, Pune, India
  3. Student, Artificial Intelligence & Machine Learning Engineering Diploma, Ajeenkya D.Y Patil School of Engineering, Charholi, Pune, India
  4. Student, Artificial Intelligence & Machine Learning Engineering Diploma, Ajeenkya D.Y Patil School of Engineering, Charholi, Pune, India
  5. Guide, Professor, Artificial Intelligence & Machine Learning Engineering Diploma, Ajeenkya D.Y Patil School of Engineering, Charholi, Pune, India
  6. HoD, Professor, Artificial Intelligence & Machine Learning Engineering Diploma, Ajeenkya D.Y Patil School of Engineering, Charholi, Pune, India

IRJIET, Volume 10, Issue 2, February 2026 pp. 105-109

doi.org/10.47001/IRJIET/2026.102018

References

  1. U.S. Environmental Protection Agency (EPA), Technical Assistance Document for the Reporting of Daily Air Quality – the Air Quality Index (AQI), EPA-454/B-18-007, 2018.
  2. World Health Organization (WHO), Air Pollution and Health, Geneva, Switzerland, 2021. [Online]. Available: https://www.who.int
  3. L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001.
  4. G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, New York, NY, USA: Springer, 2013.
  5. A.Géron, Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow, 2nd ed. Sebastopol, CA, USA: O’Reilly Media, 2019.
  6. S. Russell and P. Norvig, Artificial Intelligence: A Modern Approach, 4th ed. Pearson, 2020.
  7. R. J. Hyndman and G. Athanasopoulos, Forecasting: Principles and Practice, 3rd ed. Melbourne, Australia: OTexts, 2018.
  8. S. Ramachandran et al., “Air Pollution Prediction Using Machine Learning Algorithms,” IEEE Access, vol. 7, pp. 128325–128337, 2019.
  9. S. K. Sharma and B. K. Sharma, “Air Quality Prediction Using Machine Learning Techniques: A Review,” International Journal of Environmental Science and Technology, vol.17, no. 2, pp. 1015–1034, 2020.
  10. T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System,” in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16), San Francisco, CA, USA, 2016, pp. 785–794.
  11. Python Software Foundation, “Scikit-learn: Machine Learning in Python,” 2023. [Online]. Available: https://scikit-learn.org
  12. S. Ramírez, “FastAPI: Modern, Fast (High-Performance) Web Framework for Building APIs with Python,” 2023. [Online]. Available: https://fastapi.tiangolo.com