IoT-Based Real-Time Air Quality Monitoring System Using ESP32, MQ2, DHT11 and SH1106 OLED Display with Cloud Integration

Abstract

Air pollution has become a key issue in today’s urban settings as a result of industrial growth, car emissions, and population increase. We see that it is very important to have continuous reportage of environmental elements which in turn will improve awareness. We present in this paper our work which is the design and implementation of an IoT based real time air quality monitoring system which we have put together with ESP32. The system we have put together uses a DHT11 sensor for temperature and humidity measurement, an MQ2 gas sensor for identification of harmful gases, and an SH1006 OLED display for local data visualization. Also we see that we are able to transmit the collected data via WiFi to the ThingSpeak cloud platform for remote monitoring and analysis. Our put forth system is a low cost, efficient and real time solution for environmental monitoring. We report that in our experiments the system does very well in the capture and display of environmental parameters both locally and on the cloud.

Introduction

Air pollution is a growing environmental and public health issue in urban and semi-urban areas, caused by industrialization, vehicle emissions, construction, and fossil fuel use. Traditional air quality monitoring relies on large, expensive, stationary government equipment, which limits spatial coverage, real-time data availability, and accessibility for the general public.

The study proposes a low-cost, IoT-based air quality monitoring system using an ESP32 microcontroller, an MQ2 gas sensor for harmful gases, a DHT11 sensor for temperature and humidity, and an SH1106 OLED display for local visualization. Data is transmitted to the ThingSpeak cloud platform for remote monitoring and analysis, providing real-time, distributed, and scalable environmental monitoring.

The system addresses issues of high cost, limited coverage, lack of real-time data, and low accessibility seen in conventional and existing solutions (GSM, Zigbee, Arduino, Raspberry Pi). Its design enables local display, cloud connectivity, continuous unattended tracking, and potential scalability, making it a practical, user-friendly solution for real-time air quality monitoring.

Conclusion

This paper reports on the development and roll out of a low cost IoT enabled air quality system that uses ESP32 as the microcontroller. We present MQ2 and DHT11 sensors which we used to monitor gas concentration, temperature, and humidity in real time. Also we used SH1106 OLED display for immediate local results and ThingSpeak for cloud based data storage and remote access. The study reports that we were able to see the system’s performance in terms of it’s ability to detect environmental changes which it does very well and report back in near real time. Also the sensor data is relative which we had put that into play, thus we present a reliable and practical solution for air quality trends. We also looked at the large scale picture which is that the system is low cost and easy to implement which makes it a great fit for use in schools, residential areas, and for small scale monitoring. Also the system is designed with future growth in mind which makes it a great base to develop more complex environmental monitoring tools.

References

[1] World Health Organization (WHO), “Ambient (outdoor) air quality and health,” [Online]. Available: https://www.who.int/news-room/ fact-sheets/detail/ambient-(outdoor)-air-quality-and-health [2] Hanwei Electronics, “MQ-2 Gas Sensor Datasheet,” [Online]. Available: https://www.handsontec.com/dataspecs/MQ-2.pdf [3] ThingSpeak, “IoT Analytics Platform,” [Online]. Available: https:// thingspeak.com/ [4] Espressif Systems, “ESP32 Datasheet,” [Online]. Available: https://www.espressif.com/sites/default/files/documentation/esp32 datasheet en.pdf [5] Aosong Electronics, “DHT11 Temperature and Humidity Sensor Datasheet,” [Online]. Available. [6] Adafruit Industries, “SH1106 OLED Display Documentation,” [Online]. Available. [7] C. Perera, A. Zaslavsky, P. Christen and D. Georgakopoulos, “Context Aware Computing for The Internet of Things: A Survey,” IEEE Communications Surveys and Tutorials, vol. 16, no. 1, pp. 414–454, 2014. [8] K. A. Kumar and P. Singh, “Low Cost Air Pollution Monitoring System using Arduino and Gas Sensors,” International Journal of Engineering Research and Technology, vol. 7, no. 11, 2018. [9] O. Postolache, J. Pereira and P. Girao, “Smart Sensors Network for Air˜ Quality Monitoring Applications,” IEEE Transactions on Instrumentation and Measurement, vol. 58, no. 9, 2009. [10] S. Devarakonda et al., “Real-time Air Quality Monitoring Through Mobile Sensing in Metropolitan Areas,” in Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing, 2013.

Copyright

Copyright © 2026 Priyam Ashish Rolston, Raghav Gupta, Tushar Kumar, Yash Rastogi, Mrs. Meetu Mann. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.