Air Pollution Monitoring and Prediction using Big Data Analytics and Machine Learning

DOI

10.2991/978-94-6239-664-7_84How to use a DOI?

Keywords

Air Quality Index; Machine Learning; Big Data; Air Pollution Prediction

Abstract

Air pollution is a growing threat to the environment in many developing countries that impacts millions of lives. Rapid urbanization and industrial activity have led to a worse impact of air pollution in Bangladesh. Accurate monitoring and forecasting are essential for public safety. This research adopts recent advances in machine learning to predict real-time air quality in a District of Bangladesh named Narsingdi, using hourly pollution and meteorological data from 2020 to 2024. We have tested modern machine learning and deep learning models such as Support Vector Machine, Regression, XGBoost, Artificial Neural Networks, and a powerful stacked ensemble that blends Random Forest, XGBoost and LightGBM. Following the standard Air Quality Index and adding weather characteristics to the analysis, we have made our predictions more accurate and relevant. Our proposed model correctly classifies air quality with healthy accuracy of 99.6%. It can also pinpoint and predict the main pollutants such as PM2.5, PM10, NO2, SO2, CO, and O3 with high reliability. Our research shows that machine learning can help in predicting air quality quickly and affordably for health warnings and clean air. This approach can be adapted in other regions that face similar challenges.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Md.Ferdous Rahman
AU  - Mahmud Bin Farid Hasan
AU  - Tamanna Akter
AU  - Md.Solaiman Mia
PY  - 2026
DA  - 2026/06/08
TI  - Air Pollution Monitoring and Prediction using Big Data Analytics and Machine Learning
BT  - Proceedings of the International Conference on Intelligent Data Analysis and Applications (IDAA 2025)
PB  - Atlantis Press
SP  - 1244
EP  - 1257
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-664-7_84
DO  - 10.2991/978-94-6239-664-7_84
ID  - Rahman2026
ER  -