Air Pulse – An Air Quality Status Analysis Model Authors Prabhjot Kaur Sidhu Maharaja Surajmal Institute of Technology, New Delhi, India https://orcid.org/0000-0003-0496-2744 Kavya Jain Maharaja Surajmal Institute of Technology, New Delhi, India Inika Maharaja Surajmal Institute of Technology, New Delhi, India DOI: https://doi.org/10.64383/irjss.DEC2401201 Keywords: Air Quality Prediction, Air Quality Index (AQI), Machine Learning Algorithms, Environmental Pollution, Central Pollution Control Board Abstract A global concern is scaling due to the rapid growth of air pollution, particularly the intensified concentration of impurities and pollutants exposing the risk to the health of innumerable species worldwide. Air Pollution is a combination of various gases in the air with the presence of countless solid particles. To protect human life and the deteriorating situation of air quality that is severely affecting the flora and fauna, we aim to design and train a system to predict the quality of the air that surrounds us with the utmost accuracy and reliability. The Air Quality Index (AQI) of India is a standard computation used to specify the extent of impurities (so2, no2, rspm, spm, etc) over an interval. India is impacted severely as it hosts 22 of the world’s 30 extremely populated cities. We have used and contrasted the machine learning algorithms to achieve the prime results. Interjection, feature analysis, and prediction are three major factors in inner-city air computing. Considering this research, multiple processing methods are used to process the data. Our model will have the capacity to predict the status of the air quality in different parts of the Delhi. The model provides almost 85% accuracy by outlining the correct status of the quality of air in different regions of Delhi. This system can be referred to by the Central Pollution Control Board to understand whether the air is safe or unsafe and record the air quality along with the congregation of defiled particles. A user interface will support this model and display the necessary status to the user at their convenience. The user interface will act as a one-stop platform to track and get updates about the air quality of different regions of the national capital, Delhi. Author Biographies Prabhjot Kaur Sidhu, Maharaja Surajmal Institute of Technology, New Delhi, India Department of Information Technology Kavya Jain, Maharaja Surajmal Institute of Technology, New Delhi, India Department of Information Technology Inika, Maharaja Surajmal Institute of Technology, New Delhi, India Department of Information Technology References [1] Maltare, N.N. and Vahora, S., 2023. Air Quality Index prediction using machine learning for Ahmedabad city. Digital Chemical Engineering, 7, p.100093. 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Artificial Intelligence Review, pp.1-36. [7] Murukonda, V.S.N.M. and Gogineni, A.C., 2022. Prediction of Air Quality Index Using Supervised Machine Learning. [8] Shankar, L. and Arasu, K., 2023. Deep Learning Techniques for Air Quality Prediction: A Focus on PM2. 5 and Periodicity. Migration Letters, 20(S13), pp.468-484 Downloads PDF Published 2024-12-11 How to Cite Sidhu, P. K., Jain, K., & Inika. (2024). Air Pulse – An Air Quality Status Analysis Model. International Research Journal of Scientific Studies, 1(3), 1–7. https://doi.org/10.64383/irjss.DEC2401201 More Citation Formats ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver Download Citation Endnote/Zotero/Mendeley (RIS) BibTeX Issue Vol. 1 No. 3 (2024): December Section Research Papers Categories Information Technology License Copyright (c) 2024 International Research Journal of Scientific Studies This work is licensed under a Creative Commons Attribution 4.0 International License. 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