• Thursday, Apr 2nd, 2026

International Journal of Advanced Research in Education and TechnologY(IJARETY)
International, Double Blind-Peer Reviewed & Refereed Journal, Open Access Journal
|Approved by NSL & NISCAIR |Impact Factor: 8.152 | ESTD: 2014|

|Scholarly Open Access Journals, Peer-Reviewed, and Refereed Journal, Impact Factor-8.152 (Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool), Multidisciplinary, Bi-Monthly, Citation Generator, Digital Object Identifier(DOI)|

Article

TITLE AQI-Insight: Hyperlocal Air Pollution Monitoring and Advisory System
ABSTRACT Rapid urbanization, industrial expansion, and increased vehicular emissions have significantly contributed to the deterioration of air quality, posing serious risks to human health, agriculture, and ecosystems. Continuous monitoring and analysis of air pollution have therefore become essential for environmental management and public awareness. AQI Insight is a web-based environmental monitoring platform designed to provide real-time and location-specific air quality information. The system tracks key pollutants including PM2.5, PM10, NO₂, SO₂, O₃, and CO, and computes the Air Quality Index (AQI) to represent pollution levels and associated health risks. Users can access air quality data through an interactive geospatial interface, visualize trends using charts and color-coded indicators, and analyze historical records. Developed using Python for backend processing, responsive web technologies for the interface, and PostgreSQL for data storage, the platform ensures reliable monitoring, visualization, and analysis of environmental data.
AUTHOR Varshini J Master of Computer Applications, CMR Institute of Technology, Bangalore, India
VOLUME 13
DOI DOI:10.15680/IJARETY.2026.1302020
PDF 20_AQI-Insight Hyperlocal Air Pollution Monitoring and Advisory System.pdf
KEYWORDS
References [1] M. M. Hassan, M. A. T. Rony, M. A. R. Khan, M. M. Hassan, F. Yasmin, A. Nag, T. H. Zarin, A. K. Bairagi, S. Alshathri, and W. El-Shafai, “Machine learning-based rainfall prediction: Unveiling insights and forecasting for improved preparedness,” IEEE Access, vol. 11, pp. 1–14, 2023. doi: 10.1109/ACCESS.2023.3333876.
[2] A. Hussain, A. Aslam, S. Tripura, and V. Dhanawat, “Weather forecasting using machine learning techniques: Rainfall and temperature analysis,” Journal of Advances in Information Technology, vol. 15, no. 12, pp. 1329–1338, 2024. doi: 10.12720/jait.15.12.1329-1338.
[3] V. Kumar, V. K. Yadav, and S. Dubey, “Rainfall prediction using machine learning,” International Journal for Research in Applied Science & Engineering Technology (IJRASET), vol. 10, no. V, pp. 2494–2498, 2022.
[4] S. Saubhagya, C. Tilakaratne, M. Mammadov, and P. Lakraj, “An application of ensemble spatiotemporal data mining techniques for rainfall forecasting,” Engineering Proceedings, vol. 39, no. 6, pp. 1–6, 2023. doi: 10.3390/engproc2023039006.
[5] S. Kaushik, A. Bhardwaj, and L. Sapra, “Predicting annual rainfall for the Indian state of Punjab using machine learning techniques,” in Proc. 2nd Int. Conf. Advances in Computing, Communication Control and Networking (ICACCCN), IEEE, 2020, pp. 151–156. doi: 10.1109/ICACCCN51052.2020.9362742.
[6] C. M. Liyew and H. A. Melese, “Machine learning techniques to predict daily rainfall amount,” Journal of Big Data, vol. 8, no. 153, pp. 1–20, 2021. doi: 10.1186/s40537-021-00545-4.
[7] J. Refonaa, M. Lakshmi, R. Abbas, and M. Raziullha, “Rainfall prediction using regression model,” International Journal of Recent Technology and Engineering (IJRTE), vol. 8, no. 2S3, pp. 1–5, 2019. doi: 10.35940/ijrte.B1098.0782S319.
[8] A. Rahman, S. Abbas, M. Gollapalli, R. Ahmed, S. Aftab, M. Ahmad, M. A. Khan, and A. Mosavi, “Rainfall prediction system using machine learning fusion for smart cities,” Sensors, vol. 22, no. 9, Art. 3504, 2022. doi: 10.3390/s22093504.
[9] R. Praveena, T. R. Ganesh Babu, M. Birunda, G. Sudha, P. Sukumar, and J. Gnanasoundharam, “Prediction of rainfall analysis using logistic regression and support vector machine,” Journal of Physics: Conference Series, vol. 2466, no. 1, Art. 012032, 2023. doi: 10.1088/1742-6596/2466/1/012032.
[10] F. Simanjuntak, I. Jamaluddin, T.-H. Lin, H. A. W. Siahaan, and Y.-N. Chen, “Rainfall forecast using machine learning with high spatiotemporal satellite imagery every 10 minutes,” Remote Sensing, vol. 14, no. 23, Art. 5950, 2022. doi: 10.3390/rs14235950.
[11] D. Fister, J. Perez-Aracil, C. Pelaez-Rodriguez, J. Del Ser, and S. Salcedo-Sanz, “Accurate long-term air temperature prediction with machine learning models and data reduction techniques,” Applied Soft Computing, vol. 132, Art. 110118, 2023. doi: 10.1016/j.asoc.2023.110118.
[12] H. H. Dawoodi and M. P. Patil, “Rainfall prediction for North Maharashtra, India using advanced machine learning models,” Indian Journal of Science and Technology, vol. 16, no. 13, pp. 956–966, 2023. doi: 10.17485/IJST/v16i13.2235.
[13] Y. Liu, P. Wang, Y. Li, L. Wen, and X. Deng, “Air quality prediction models based on meteorological factors and real-time data of industrial waste gas,” Scientific Reports, vol. 12, Art. 9253, 2022. doi: 10.1038/s41598-022-13579-2.
[14] B. Bochenek and Z. Ustrnul, “Machine learning in weather prediction and climate analyses—Applications and perspectives,” Atmosphere, vol. 13, no. 2, Art. 180, 2022. doi: 10.3390/atmos13020180.
[15] N. B. Shardoor, M. V. Rao, Y. Mane, P. Chettiar, V. Kumari, and K. Arya, “Rainfall prediction and forecasting using time series analysis,” Journal of Emerging Technologies and Innovative Research (JETIR), vol. 6, no. 4, 2019.
[16] A. Parmar, K. Mistree, and M. Sompura, “Machine learning techniques for rainfall prediction: A review,” in Proc. 2017 Int. Conf. Innovations in Information Embedded and Communication Systems (ICIIECS), IEEE, 2017.
[17] LightGBM Documentation, “Light Gradient Boosting Machine,” Available: https://lightgbm.readthedocs.io/
[18] Scikit-learn Documentation, “ExtraTreesClassifier,” Available: https://scikitlearn.org/stable/modules/generated/sklearn.ensemble.ExtraTreesClassifier.html
[19] J. Han, M. Kamber, and J. Pei, Data Mining: Concepts and Techniques, 3rd ed. Morgan Kaufmann, 2011.
[20] T. Fawcett, “An introduction to ROC analysis,” Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, 2006.