Predictive Modeling of Particulate Matter levels: A Machine Learning Approach for Air Quality Monitoring

DOI

10.2991/978-94-6463-754-0_22How to use a DOI?

Keywords

Air Pollution; Particulate matter; PM2.5 Prediction; Machine Learning Algorithms

Abstract

Worldwide, air pollution is a significant issue due to its detrimental effects on human health and the environment. Particulate matter (PM) is a major cause of air pollution, with exposure linked to respiratory and cardiovascular diseases. Current air quality monitoring systems primarily rely on manual methods, which are costly and require extensive laboratory analysis. Consequently, there is a growing need for efficient, real-time air quality monitoring and predicting strategies. Machine learning (ML), which makes use of extensive datasets and complex algorithms, provides a potent substitute to enhance predictive accuracy and uncover insights into PM behavior under varying conditions. In this paper, we explore three machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Random Forest—for predicting PM_2.5 levels using air quality data from the Central Pollution Control Board of India. Model performance is evaluated using the statistical metrics Mean Squared Error (MSE), and the Coefficient of Determination (R²). Because of its ensemble nature, which enables it to capture complex trends by averaging over numerous decision trees, the Random Forest algorithm produced the best results.

Copyright

© 2025 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  - Navitha K. Krishnan
AU  - J. Roopa Jayasingh
PY  - 2025
DA  - 2025/06/30
TI  - Predictive Modeling of Particulate Matter levels: A Machine Learning Approach for Air Quality Monitoring
BT  - Proceedings of the 2025 International Conference on Advanced Research in Electronics and Communication Systems (ICARECS-2025)
PB  - Atlantis Press
SP  - 238
EP  - 248
SN  - 2589-4943
UR  - https://doi.org/10.2991/978-94-6463-754-0_22
DO  - 10.2991/978-94-6463-754-0_22
ID  - Krishnan2025
ER  -