Air and noise pollution have become serious environmental problems in modern urban areas due to rapid industrialization, increasing traffic, and population growth. Continuous monitoring of pollution levels is important to protect public health and maintain environmental safety. Traditional pollution monitoring systems are expensive and limited in coverage, which makes real- time monitoring difficult in many locations. To address this issue, this project proposes a low-cost Internet of Things (IoT) based air and noise pollution monitoring system using the ESP32-S3 microcontroller. The proposed system uses the MQ-135 gas sensor to detect harmful gases in the air and a sound sensor to measure environmental noise levels. The ESP32-S3 microcontroller collects sensor data, processes it, and calculates pollution levels. When pollution exceeds predefined safety limits, the system activates a buzzer to provide an immediate alert. The collected data is transmitted to a cloud platform using Wi-Fi connectivity and stored in a Firebase database for real-time monitoring and historical analysis. A mobile application developed using Flutter allows users to view real-time air quality and noise levels remotely. The system provides a cost-effective, scalable, and efficient solution for environmental monitoring. The proposed system can support smart city applications by enabling continuous pollution monitoring and increasing public awareness about environmental conditions.

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1. Pellegrino J, Aziza H, Guerin M, Taranto P, Rahajandraibe W, Ravelo B. Development of a multi-sensor mobile device for urban air quality monitoring at the street corner: The SMILE project. IEEE Access. 2024;13:14857–14871.
2. Manglani M, Paharia N, Purohit S. IoT based air and sound pollution monitoring system. In: Proc IEEE Int Conf Comput Commun Intell Syst (ICCCIS). 2022. p. 1103–1107.
3. Nandanwar H, Chauhan A. IoT based smart environment monitoring systems: A key to smart and clean urban living spaces. In: Proc Asian Conf Innov Technol (ASIANCON). 2021. p. 1–9.
4. Sajjan V, Sharma P. Analysis of air pollution by using Raspberry Pi-IoT. In: Proc 6th Int Conf Inventive Comput Technol (ICICT). 2021. p. 178–183.
5. Prabha S, Raghav RS, Moulya C, Preethi KG, Sankaran KS. Analysis and monitoring air quality system using Raspberry Pi. In: Proc Int Conf Commun Signal Process (ICCSP). 2020. p. 1385–1389.
6. Kim JY, Chu CH, Shin SM. ISSAQ: An integrated sensing systems for real-time indoor air quality monitoring. IEEE Sens J. 2014;14(12):4230–4244.
7. Chen LJ, Ho YH, Lee HC, Wu HC, Liu HM, Hsieh HH, Huang YT, Lung SC. An open framework for participatory PM2. 5 monitoring in smart cities. Ieee Access. 2017 Jul 6;5:14441-54.
8. Zheng K, Zhao S, Yang Z, Xiong X, Xiang W. Design and implementation of LPWA-based air quality monitoring system. IEEE Access. 2016;4:3238–3245.
9. Zhang D, Woo SS. Real-time localized air quality monitoring and prediction through mobile and fixed IoT sensing network. IEEE Access. 2020;8:90298–90311.
10. Segura-Garcia J, Calero JM, Pastor-Aparicio A, Marco-Alaez R, Felici-Castell S, Wang Q. 5G IoT system for real-time psycho-acoustic soundscape monitoring in smart cities with dynamic computational offloading to the edge. IEEE Internet of Things Journal. 2021 Mar 3;8(15):12467-75.

Air and noise pollution have become serious environmental problems in modern urban areas due to rapid industrialization, increasing traffic, and population growth. Continuous monitoring of pollution levels is important to protect public health and maintain environmental safety. Traditional pollution monitoring systems are expensive and limited in coverage, which makes real- time monitoring difficult in many locations. To address this issue, this project proposes a low-cost Internet of Things (IoT) based air and noise pollution monitoring system using the ESP32-S3 microcontroller. The proposed system uses the MQ-135 gas sensor to detect harmful gases in the air and a sound sensor to measure environmental noise levels. The ESP32-S3 microcontroller collects sensor data, processes it, and calculates pollution levels. When pollution exceeds predefined safety limits, the system activates a buzzer to provide an immediate alert. The collected data is transmitted to a cloud platform using Wi-Fi connectivity and stored in a Firebase database for real-time monitoring and historical analysis. A mobile application developed using Flutter allows users to view real-time air quality and noise levels remotely. The system provides a cost-effective, scalable, and efficient solution for environmental monitoring. The proposed system can support smart city applications by enabling continuous pollution monitoring and increasing public awareness about environmental conditions.

🔒 This is a subscription article

Full text is available to subscribers and institutional members. Please choose an option below to access it.

1. Pellegrino J, Aziza H, Guerin M, Taranto P, Rahajandraibe W, Ravelo B. Development of a multi-sensor mobile device for urban air quality monitoring at the street corner: The SMILE project. IEEE Access. 2024;13:14857–14871.
2. Manglani M, Paharia N, Purohit S. IoT based air and sound pollution monitoring system. In: Proc IEEE Int Conf Comput Commun Intell Syst (ICCCIS). 2022. p. 1103–1107.
3. Nandanwar H, Chauhan A. IoT based smart environment monitoring systems: A key to smart and clean urban living spaces. In: Proc Asian Conf Innov Technol (ASIANCON). 2021. p. 1–9.
4. Sajjan V, Sharma P. Analysis of air pollution by using Raspberry Pi-IoT. In: Proc 6th Int Conf Inventive Comput Technol (ICICT). 2021. p. 178–183.
5. Prabha S, Raghav RS, Moulya C, Preethi KG, Sankaran KS. Analysis and monitoring air quality system using Raspberry Pi. In: Proc Int Conf Commun Signal Process (ICCSP). 2020. p. 1385–1389.
6. Kim JY, Chu CH, Shin SM. ISSAQ: An integrated sensing systems for real-time indoor air quality monitoring. IEEE Sens J. 2014;14(12):4230–4244.
7. Chen LJ, Ho YH, Lee HC, Wu HC, Liu HM, Hsieh HH, Huang YT, Lung SC. An open framework for participatory PM2. 5 monitoring in smart cities. Ieee Access. 2017 Jul 6;5:14441-54.
8. Zheng K, Zhao S, Yang Z, Xiong X, Xiang W. Design and implementation of LPWA-based air quality monitoring system. IEEE Access. 2016;4:3238–3245.
9. Zhang D, Woo SS. Real-time localized air quality monitoring and prediction through mobile and fixed IoT sensing network. IEEE Access. 2020;8:90298–90311.
10. Segura-Garcia J, Calero JM, Pastor-Aparicio A, Marco-Alaez R, Felici-Castell S, Wang Q. 5G IoT system for real-time psycho-acoustic soundscape monitoring in smart cities with dynamic computational offloading to the edge. IEEE Internet of Things Journal. 2021 Mar 3;8(15):12467-75.