BreatheSafe – Predictive Analysis of Air Pollution Levels

Anushka Jadhav; Indrajit Joshi; Sampada Gupta; Siddharth Joisar; Shweta S. Ashtekar

doi:10.2991/978-94-6239-650-0_13

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BreatheSafe – Predictive Analysis of Air Pollution Levels

Authors

Anushka Jadhav¹, Indrajit Joshi¹, Sampada Gupta¹, Siddharth Joisar¹, Shweta S. Ashtekar¹^{, *}

¹Dept. of Computer Engineering, Ramrao Adik Institute of Technology, Navi Mumbai, India

^*Corresponding author. Email: shweta.ashtekar@dypatil.edu

Corresponding Author

Shweta S. Ashtekar

Available Online 20 April 2026.

DOI: 10.2991/978-94-6239-650-0_13 How to use a DOI?
Keywords: Pollution levels; Linear Regression; Random Forest Regressor; Neural Network; Forecast AQI
Abstract: The Air Quality Index is a good indication tool for the monitoring of air quality in smart cities and the assessment of the cleanliness or pollution level of air. Predictions of AQI values can facilitate people and authorities in taking precautionary steps like avoiding exposure to the outdoors on days when pollution levels are high. This study will look into the analysis of data and machine learning to forecast AQI by using previous pollution data, weather patterns, and environmental factors. A number of models have been trained and compared, which include a neural network to capture intricate non-linear correlations, XGBoost for efficient gradient boosting, Random Forest Regressor for robustness, and Linear Regression as the baseline. Results prove the potential of advanced models in real-time environmental monitoring and public health awareness, where models from an ensemble and deep learning yield better accuracy and reliability in predicting trends in air quality. Future iterations may consider regional or seasonal differences in air quality.
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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Volume Title: Proceedings of the Conference on Technologies for Future Cities (CTFC 2025)
Series: Atlantis Highlights in Sustainable Development
Publication Date: 20 April 2026
ISBN: 978-94-6239-650-0
ISSN: 3005-155X
DOI: 10.2991/978-94-6239-650-0_13 How to use a DOI?
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.

Cite this article

ris enw bib

TY  - CONF
AU  - Anushka Jadhav
AU  - Indrajit Joshi
AU  - Sampada Gupta
AU  - Siddharth Joisar
AU  - Shweta S. Ashtekar
PY  - 2026
DA  - 2026/04/20
TI  - BreatheSafe – Predictive Analysis of Air Pollution Levels
BT  - Proceedings of the Conference on Technologies for Future Cities (CTFC 2025)
PB  - Atlantis Press
SP  - 192
EP  - 203
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6239-650-0_13
DO  - 10.2991/978-94-6239-650-0_13
ID  - Jadhav2026
ER  -

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