Characterization of MQ-2, MQ-7 and MQ-135 Gas Sensors for Arduino-Based Smoke Detection

Agustina Rachmawardani; Annas Dhamar Galuh; Endang Dwi Wiwin Arinio

doi:10.33394/j-ps.v14i3.21385

Authors

Agustina Rachmawardani State College of Meteorology, Climatology and Geophysics
Annas Dhamar Galuh State College of Meteorology, Climatology and Geophysics
Endang Dwi Wiwin Arinio Meteorology, Climatology and Geophysical Agency

DOI:

https://doi.org/10.33394/j-ps.v14i3.21385

Keywords:

Gas sensor, MQ-series, Smoke detection, Arduino, Sensor characterization

Abstract

Smoke detection in enclosed spaces is important for early fire detection and air-quality monitoring. This paper characterizes and compares the performance of three MQ-series gas sensors (MQ-2, MQ-7 and MQ-135) under an Arduino UNO R3-based system. Three smoke sources (paper combustion, cigarette smoke, and motorcycle exhaust) were evaluated against a commercial CO detector. Parameters assessed include linearity, sensitivity, response time, error, measurement uncertainty (ISO GUM), and output-voltage correction. MQ-2 and MQ-7 exhibit logarithmic voltage–concentration relationships consistent with the power-law model of SnO₂-based metal-oxide-semiconductor sensors, while MQ-135 exhibits a quadratic pattern; all R² values exceed 0.98. MQ-7 produced the lowest errors among the three sensors for paper combustion and cigarette smoke, indicating suitability for indicative early-warning detection; however, its best-case relative error of 38% confirms it is unsuitable for precise quantitative CO monitoring without additional calibration. The three sensors exhibited errors exceeding 85% for motorcycle exhaust due to cross-sensitivity resulting from the complex chemical composition of engine exhaust. A voltage-correction analysis showed the errors in Arduino ADC readings were less than 6%, confirming the reliability of the electronic system; this does not mean the accuracy of the gas concentration will be the same, since it depends on the selectivity of the sensors, calibration and environmental conditions.

References

Adinara, D. B., Rachmawardani, A., Wastumirad, A. W., Rusanto, B. H., Prasetyo, D. I., Kurniawan, H., & Hani, A. M. (2025). Design and development of IoT-enabled portable device for multiparameter air quality monitoring. ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika, 13(4), 339. https://doi.org/10.26760/elkomika.v13i4.339

Ajiboye, A., Opadiji, J., Yusuf, A., & Popoola, J. (2021). Analytical determination of load resistance value for MQ-series gas sensors: MQ-6 as case study. TELKOMNIKA (Telecommunication Computing Electronics and Control), 19(2), 575. https://doi.org/10.12928/telkomnika.v19i2.17427

Al Ahasan, M. A., Roy, S., Saim, A. H. M., Akter, R., & Hossain, M. Z. (2018). Arduino-based real time air quality and pollution monitoring system. International Journal of Innovative Research in Computer Science & Technology, 6(4), 81–86. https://doi.org/10.21276/ijircst.2018.6.4.8

Al-Chalabi, S. M., Al-Chalabi, A. M., & Al-Khafaji, R. A. (2024). Innovative pre-fire alert smart detection system-based embedded system. AIP Advances, 14(5). https://doi.org/10.1063/5.0195026

Carrillo-Amado, Y. R., Califa-Urquiza, M. A., & Ramón-Valencia, J. A. (2020). Calibration and standardization of air quality measurements using MQ sensors. Respuestas, 25(1), 70–77. https://doi.org/10.22463/0122820X.2408

Easterline, L. M., Putri, A. A.-Z. R., Atmaja, P. S., Dewi, A. L., & Prasetyo, A. (2024). Smart air monitoring with IoT-based MQ-2, MQ-7, MQ-8, and MQ-135 sensors using NodeMCU ESP32. Procedia Computer Science, 245, 815–824. https://doi.org/10.1016/j.procs.2024.10.308

Furuta, D., Sayahi, T., Li, J., Wilson, B., Presto, A. A., & Li, J. (2022). Characterization of inexpensive metal oxide sensor performance for trace methane detection. Atmospheric Measurement Techniques, 15(17), 5117–5128. https://doi.org/10.5194/amt-15-5117-2022

Goel, N., Kunal, K., Kushwaha, A., & Kumar, M. (2023). Metal oxide semiconductors for gas sensing. Engineering Reports, 5(6). https://doi.org/10.1002/eng2.12604

Hilary Kelechi, A., Alsharif, M. H., Agbaetuo, C., Ubadike, O., Aligbe, A., Uthansakul, P., Kannadasan, R., & Aly, A. A. (2022). Design of a low-cost air quality monitoring system using Arduino and ThingSpeak. Computers, Materials & Continua, 70(1), 151–169. https://doi.org/10.32604/cmc.2022.019431

Hossaini, H., Pirsaheb, M., Barzegar, A., Abdi, K., & Derakhshan, A. A. (2022). Performance evaluation of surgical, N95 and FFP2 respiratory masks by manikin-based sampling system for assessing the protection against airborne dust. Atmospheric Pollution Research, 13(12), 101619. https://doi.org/10.1016/j.apr.2022.101619

Jain, J. (2026, March 11). MQ-135 vs MQ-7 vs MQ-4: Choosing gas sensors for air quality. Zbotic. https://zbotic.in/mq-135-vs-mq-7-vs-mq-4-choosing-gas-sensors-for-air-quality/

Kanaparthi, S., & Singh, S. G. (2022). Reduction of the measurement time of a chemiresistive gas sensor using transient analysis and the Cantor pairing function. ACS Measurement Science Au, 2(2), 113–119. https://doi.org/10.1021/acsmeasuresciau.1c00043

Kobbekaduwa, N., de Mel, W. R., & Oruthota, P. (2021). Calibration and implementation of heat cycle requirement of MQ-7 semiconductor sensor for detection of carbon monoxide concentrations. Advances in Technology, 1(2), 377–392. https://doi.org/10.31357/ait.v1i2.5068

Lee, J.-H. (2009). Gas sensors using hierarchical and hollow oxide nanostructures: Overview. Sensors and Actuators B: Chemical, 140(1), 319–336. https://doi.org/10.1016/j.snb.2009.04.026

Kumar, M., Mishra, G., Sharma, A., Shaini, A., & Saxena, S. (2025). Air quality monitoring using MQ135 gas sensor and Arduino Uno. International Journal of Latest Technology in Engineering Management & Applied Science, 14(5), 1097–1101. https://doi.org/10.51583/IJLTEMAS.2025.140500119

Nagpal, R., Ababii, N., & Lupan, O. (2026). Comprehensive advances in gas sensing: Mechanisms, material innovations, and applications in environmental and health monitoring. Materials Today Electronics, 15, 100192. https://doi.org/10.1016/j.mtelec.2025.100192

Pering, T. D., Wilkes, T. C., Layana, S., Aguilera, F., & Aguilera, M. (2024). The PiGas: A low-cost approach to volcanic gas sampling. Journal of Volcanology and Geothermal Research, 449, 108063. https://doi.org/10.1016/j.jvolgeores.2024.108063

Rachmawardani, A., Prabowo, D., Nardi, Toruan, K. L., Abdurachman, S., & Adinara, D. B. (2024). Designing a cost-effective IoT air quality sensor for real-time data monitoring. In 2024 IEEE International Conference on Computing (ICOCO) (pp. 219–224). IEEE. https://doi.org/10.1109/ICOCO62848.2024.10928212

Reimringer, W., & Bur, C. (2023). Promoting quality in low-cost gas sensor devices for real-world applications. Frontiers in Sensors, 4. https://doi.org/10.3389/fsens.2023.1317533

Technical data MQ-135 gas sensor. (2019). Olimex. https://www.olimex.com/Products/Components/Sensors/Gas/SNS-MQ135/Resources/SNS-MQ135.pdf

Yamazoe, N., & Shimanoe, K. (2008). Theory of power laws for semiconductor gas sensors. Sensors and Actuators B: Chemical, 128(2), 566–573. https://doi.org/10.1016/j.snb.2007.07.036

Yasin, A., Delaney, J., Cheng, C.-T., & Pang, T. Y. (2022). The design and implementation of an IoT sensor-based indoor air quality monitoring system using off-the-shelf devices. Applied Sciences, 12(19), 9450. https://doi.org/10.3390/app12199450

Zimmerman, N., Presto, A. A., Kumar, S. P. N., Gu, J., Hauryliuk, A., Robinson, E. S., & Robinson, A. L. (2018). A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring. Atmospheric Measurement Techniques, 11(1), 291–313. https://doi.org/10.5194/amt-11-291-2018