Emphasize the Health Benefits of Improved Air Quality with Mini Ozone Generator in Trains

International Journal of Emerging Research in Science, Engineering, and Management
Vol. 2, Issue 3, pp. 297-302, March 2026.

https://doi.org/10.58482/ijersem.v2i3.38

This work is licensed under a Creative Commons Attribution 4.0 International License.

Emphasize the Health Benefits of Improved Air Quality with Mini Ozone Generator in Trains

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Dabbara Jayanayudu

Gudi Manvanth Sai

T S Nandhini

M Lavanya

Goddevelagala Murari

N Mahesh

Department of ECE, Siddartha Institute of Science and Technology, Puttur, India.

Abstract: The Mini Ozone Generator–based Air Quality Improvement System for Trains is designed to enhance passenger health and safety by continuously monitoring and purifying the air inside railway coaches. The system uses an Arduino Mega2560 as the primary controller and integrates multiple sensors, including DHT11 for temperature and humidity measurement, MQ135 for air quality monitoring, MQ7 for gas detection, and a PMS7003 particulate matter sensor for precise pollutant analysis. Environmental data is processed and transmitted via a NodeMCU to an IoT platform, allowing real-time remote monitoring. Additionally, machine learning techniques—specifically the Random Forest algorithm—are applied through Python to predict air quality conditions and generate early alerts when pollution levels rise. Based on these predictions, a buzzer, GSM module, and relays are activated to notify authorities and control ventilation mechanisms. The system features dual Mini Ozone Generators that automatically operate to eliminate harmful pathogens, reduce odor, neutralize toxic gases, and improve the air quality inside the train. By ensuring cleaner air, healthier breathing conditions, and reduced exposure to infectious particles, this prototype contributes significantly to enhancing passenger well-being and creating a safer travel environment.

Keywords: Air Quality Monitoring, Mini Ozone Generator, Internet of Things, Machine Learning Prediction, Smart Railway Coach.

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This article is part of Volume 2, Issue 3 (March 2026)