A Review of MQ135 Sensor-Based IoT Solutions for Real-Time Air Pollution Tracking

Abstract

Abstract Air pollution has in fact become a daily issue of. Many in developing areas which see great urban. Growing, heavy traffic, and industry are present. Also. High quality air monitoring stations exist and put out reliable data. They are expensive, hard to maintain, and usually installed in only that which. A few places out of many. Thus people rarely get a full picture of. What they are living in their own streets, homes, or. workplaces. With the growth of cheap sensors and the Internet of Things. In IoT we have seen the design of small affordable devices which. That measures air quality in real time and shares data online. Of the common sensors which include the MQ135 gas sensor. Appears in many academic projects and prototypes which include it. This paper reports on a in depth review of what is out there in terms of IoT solutions which are based on the MQ135 sensor for real time air pollution tracking. We go over the MQ135 sensor’s operation principle and we also look at its issues. Also covered are common IoT architecture models, communication protocols, cloud platforms, data processing techniques, and we also look at which are the typical system designs. We discuss a wide range of related research works which we then put under the microscope and do a comparison of which does what better. Also we look at which are the main issues of calibration, power use, data reliability and security which these systems have and then we look at what the future holds which is the combination of IoT with AI, edge computing and large scale distributed sensing. Our aim is to present a very practical and useful overview for students, hobbyists and researchers of how you may use MQ135 based IoT systems for air quality monitoring.

Introduction

Air pollution has become a serious global health issue caused by emissions from vehicles, industries, construction activities, biomass burning, and domestic fuel use. Major pollutants include carbon monoxide (CO), carbon dioxide (CO?), nitrogen oxides (NOx), ammonia (NH?), volatile organic compounds (VOCs), and particulate matter (especially PM2.5), which can severely affect human lungs, heart, and overall health.

Traditional air quality monitoring systems are highly accurate but expensive, require skilled operators, regular calibration, and are limited to fixed locations. As a result, they do not provide localized or real-time pollution data for most citizens. To address this limitation, Internet of Things (IoT)-based low-cost sensor networks have been developed for widespread and real-time monitoring.

The MQ135 gas sensor is widely used in IoT-based air quality systems due to its low cost, easy integration with microcontrollers (such as NodeMCU ESP8266), and ability to detect multiple gases including NH?, CO?, benzene, smoke, and VOCs. The sensor works using a tin dioxide (SnO?) semiconductor whose resistance changes based on gas concentration. However, it requires proper preheating, calibration, and is affected by temperature, humidity, cross-sensitivity, and long-term drift.

The review discusses various system architectures for MQ135-based monitoring, including:

• Three-layer IoT architecture (sensing, network, application)

• Cloud-centric systems

• Edge/Fog computing systems

• Hybrid architectures

Different communication technologies such as Wi-Fi, LoRa/LoRaWAN, cellular networks (NB-IoT, LTE-M), Zigbee, and Bluetooth are used depending on power, range, and deployment needs.

Various cloud platforms are commonly used for data storage and visualization, including ThingSpeak, Blynk, Firebase, and enterprise platforms like AWS IoT, Azure IoT Hub, and Google Cloud IoT.

Conclusion

MQ135 sensors which we see in the IoT systems are a great entry point for real time air quality analysis. They are very much a part of what we see in the field in terms of educational platforms, small scale research and as prototypes in both indoor and outdoor settings. Although the sensor does come with issues related to accuracy and selectivity, it still is a very good base from which to study air quality, sensing, and the Internet of Things. This review covers what the MQ135 sensor does in terms of its operation, we also look at typical IoT structures, commu- nication protocols, cloud platforms, what is out there in terms of signal processing methods and a large range of research that applies the above. Also we looked at what are the issues at hand like sensor drift, calibration, power issues, network constraints, and security which also included what the future holds for this field in terms of multi sensor fusion, machine learning, energy efficiency and advanced architectural designs. For students and new researchers MQ135 based IoT systems are a good base platform. For community and hobbyist groups they put out info on local air quality. With care in design and what is realistic to expect in terms of accuracy these systems may play a role in environmental monitoring and education.

References

[1] World Health Organization, “Ambient air pollution: Health impacts,” Online resource. [2] Hanwei Electronics, “MQ135 Gas Sensor Technical Data,” Sensor datasheet. [3] Espressif Systems, “ESP8266EX Datasheet,” Official documentation. [4] MathWorks, “ThingSpeak IoT Platform Documentation,” Online plat- form guide. [5] U. Raza, P. Kulkarni and M. Sooriyabandara, “Low Power Wide Area Networks: A Survey,” in IEEE Communications Surveys and Tutorials, 2017. [6] S. Kumar and A. Singh, “A Review on Low Cost Air Quality Moni- toring Using IoT Based Systems,” International Journal of Engineering Research, 2020. [7] M. Z. A. Bhuiyan et al. report in the Proceedings of an international conference on smart environments which took place in 2021. [8] R. V. Merugu and R. K. Ghosh present at International Conference on Internet of Things in 2019 about LoRa Based Air Quality Monitoring System for Smart Cities. [9] P. K. Sharma and R. Jain in 2022 had their work published in Journal of Environmental Informatics which is about Drone Assisted Air Quality Monitoring in Urban Areas. [10] J. Lee et al. in 2019 published in Sensors Journal which is related to Machine Learning Based Calibration of Low Cost Gas Sensors for Air Quality Monitoring. [11] A. Castell et al., “Can Low Cost Particulate Matter Sensors Contribute to Air Quality Monitoring and Exposure Estimates?,” Environment International, 2017. [12] F. Bonomi et al., “Fog Computing: A Platform for Internet of Things and Analytics,” Studies in Computational Intelligence, 2014. [13] S. Sicari et al., “Security, Privacy and Trust in Internet of Things: The Road Ahead,” Computer Networks, 2015. [14] W. Shi and S. Dustdar, “The Promise of Edge Computing,” Computer, 2016. [15] Bosch Sensortec, “BME680 Environmental Sensor,” Product datasheet.

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.