Enhancement of Air Quality in Urban Traffic Signals Using AI-Based Analysis

Author(s)	Ms. Shraddha Prashant Mhaske, Ms. Kejal Anand Kasliwal, Ms. Sejal Sandeep Nandre, Ms. Rucha Rajesh Malpure
Country	India
Abstract	This paper presents an AI-driven approach to monitor and improve air quality at urban traffic signals. The Urbanbreathe system integrates low cost gas sensors cloud connectivity and machine learning to detect pollution hotspots and automatically trigger corrective actions the system uses the esp32 microcontroller with integrated wi-fi for seamless cloud connectivity for the communication between the hardware and the software modules sensor data is streamed to firebase then analyzed by a decision tree classifier and visualized via a web dashboard experimental evaluation with combined government datasets and local sensor readings shows that the system attains reliable classification performance and fast automated response offering a scalable low-cost solution for smart-city air.
Keywords	Air Quality, Artificial Intelligence, IoT, ESP32, Smart City, Pollution Control, Automation, Urban Environment.
Field	Engineering
Published In	Volume 7, Issue 6, November-December 2025
Published On	2025-11-13
DOI	https://doi.org/10.36948/ijfmr.2025.v07i06.60534

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