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Article | Green Intelligent Systems and Applications | Volume 6 - Issue 1 - 2026 | 34−50 | https://doi.org/10.53623/gisa.v6i1.1012
© 2026 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

Design and Implementation of a Multi-Node Gas Sensor-Based Indoor Air Quality Monitoring and Control System

Author(s): Siti Milda Alkan Dawasoka , Eka Puji Widiyanto
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Faculty of Computer Science and Engineering, Multi Data Palembang University, Palembang, Indonesia

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Green Intelligent Systems and Applications

Volume 6 - Issue 1 - 2026

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:  Air quality monitoring was a crucial aspect of maintaining occupational health and safety, particularly in industrial environments. This study proposed the design and implementation of an Internet of Things (IoT)-based indoor air quality monitoring system capable of measuring environmental parameters in real time. The system integrated an ENS160 gas sensor and an AHT21 temperature–humidity sensor with a Wemos D1 Mini microcontroller. Sensor data were transmitted via the MQTT protocol to an Orange Pi 4A server and visualized using a Node-RED dashboard. The monitored parameters included Total Volatile Organic Compounds (TVOC), equivalent CO₂ (eCO₂), temperature, and humidity. Experimental evaluation demonstrated that the system responded proportionally to different pollutant exposure levels. Under high NH₃ exposure (100%), TVOC values reached a maximum of 12,697 ppb with an average of 5,037 ppb, clearly exceeding the hazardous threshold (>200 ppb). At moderate exposure (50%), the average TVOC decreased to 2,106 ppb, while at low exposure (10%), the average value remained within the safe range at 84 ppb. For eCO₂ testing, cigarette smoke exposure produced a peak value of 11,524 ppm with an average of 1,663 ppm, indicating hazardous conditions (>1000 ppm). Statistical analysis using mean and standard deviation confirmed that sensor stability improved at lower pollutant concentrations. The proposed system successfully provided stable real-time monitoring, threshold-based classification, and automatic mitigation control, demonstrating its feasibility for intelligent indoor air quality management in industrial workspaces.

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WHO Global Air Quality Guidelines: Particulate Matter (PM2.5 and PM10), Ozone, Nitrogen Dioxide, Sulfur Dioxide and Carbon Monoxide. (accessed on 1 December 2025) Available online: https://www.who.int/publications/i/item/9789240034228.

Status Lingkungan Hidup Indonesia (SLHI) 2023, Jakarta, Indonesia, 2023. (accessed on 1 December 2025) Available online: https://www.menlhk.go.id.

Nashira, A.; Nugrahani, A.P.; Rahmah, A.U.; Cahyono, T. (2025). Quantitative Risk Assessment of Ammonia Release from Storage Tanks Using RISKCURVES Software. Engineering Proceedings, 84, 80. https://doi.org/10.3390/engproc2025084080.

Nie, T.; Zhang, G.; Sun, Y.; Wang, W.; Wang, T.; Duan, H. (2025). Effects of Indoor Air Quality on Human Physiological Impact: A Review. Buildings, 15, 1296. https://doi.org/10.3390/buildings15081296.

Gorgoglione, A.; Bombardelli, F.A.; Pitton, B.J.L.; Oki, L.R.; Haver, D.L.; Young, T.M. (2018). Role of Sediments in Insecticide Runoff from Urban Surfaces: Analysis and Modeling. International Journal of Environmental Research and Public Health, 15, 1464. https://doi.org/10.3390/ijerph15071464.

DFRobot. ENS160 Digital Multi-Gas Sensor Datasheet, Shenzhen, China, 2023. [Online]. Available: https://wiki.dfrobot.com/ENS160_Air_Quality_Sensor_SKU_SEN0544.

da Silva, A.H.M.C.; da Silva, M.K.; Santos, A.; Gómez-Malagón, L.A. (2025). A Systematic Review for Ammonia Monitoring Systems Based on the Internet of Things. IoT, 6, 66. https://doi.org/10.3390/iot6040066.

SparkFun ENS160 Air Quality Sensor Hookup Guide. (accessed on 1 December 2025) Available online: : https://learn.sparkfun.com/tutorials/ens160-air-quality-sensor-hookup-guide.

Jo, J.H.; Jo, B.; Kim, J.H.; Choi, I. (2020). Implementation of IoT-Based Air Quality Monitoring System for Investigating Particulate Matter (PM10) in Subway Tunnels. International Journal of Environmental Research and Public Healt, 17, 5429. https://doi.org/10.3390/ijerph17155429.

Mota, A.; Serôdio, C.; Briga-Sá, A.; Valente, A. (2025). Implementation of an Internet of Things Architecture to Monitor Indoor Air Quality: A Case Study During Sleep Periods. Sensors, 25, 1683. https://doi.org/10.3390/s25061683.

Flowerday, C.E.; Lundrigan, P.; Kitras, C.; Nguyen, T.; Hansen, J.C. (2023). Utilizing Low-Cost Sensors to Monitor Indoor Air Quality in Mongolian Gers. Sensors, 23, 7721. https://doi.org/10.3390/s23187721.

Spinelle, L.; Gerboles, M.; Kok, G.; Persijn, S.; Sauerwald, T. (2017). Review of Portable and Low-Cost Sensors for the Ambient Air Monitoring of Benzene and Other Volatile Organic Compounds. Sensors, 17, 1520. https://doi.org/10.3390/s17071520.

Yasin, M.; Nugroho, A.A.; Prasetyo, H. (2022). An IoT Framework for Indoor Air Quality Monitoring Using Commercial Sensors and Cloud-Based Visualization. Applied Sciences, 12, 1–17. https://doi.org/10.3390/app12199450.

Taştan, M.; Gökozan, H. (2019). Real-Time Monitoring of Indoor Air Quality Using Low-Cost IoT-Based Electronic Nose. Applied Sciences, 9, 1–15. http://doi.org/10.3390/app9163435.

Mota, L.; Silva, J.P.; Oliveira, R. (2025). An IoT-Based Indoor Air Quality Monitoring System Using ESP32-C6 and MQTT Protocol. Sensors, 25, 1–18. http://doi.org/10.3390/s25061683.

Shahid, S.; Brown, D.J.; Wright, P.; Khasawneh, A.M.; Taylor, B.; Kaiwartya, O. (2025). Innovations in Air Quality Monitoring: Sensors, IoT and Future Research. Sensors, 25, 2070. http://doi.org/10.3390/s25072070.

Garcia, A.; Saez, Y.; Harris, I.; Huang, X.; Collado, E. (2025). Advancements in Air Quality Monitoring: A Systematic Review of IoT-Based Air Quality Monitoring and AI Technologies. Artificial Intelligence Review, 58, 275. http://doi.org/10.1007/s10462-025-11277-9.

Zidni, H.; Putra, K.T.; Santosa, F.G.; Nugroho, A.; Pranoto, S.; Rahaman, M.; Chu, H.-T. (2024). Developing a Scalable IoT-Based Platform for Enhancing Air Quality Monitoring in Public Transport Using Node-RED. Journal of Electrical Technology UMY, 8, 60–66. http://doi.org/10.18196/jet.v8i2.24701.

Azizah, D.N.; Heranurweni, S.; Idris, L.O.M. (2025). Internet of Things Based Air Quality Monitoring System with Automatic Notification. MALCOM: Indonesian Journal of Machine Learning and Computer Science, 5, 776–787. http://doi.org/10.57152/malcom.v5i3.1945.

Kim, K.; Chun, D.; Yang, W. (2025). IoT-Based Filter Management System Using Reinforced ANFIS for VOCs Reduction in Urban Industrial Facilities. Scientific Reports, 15, 17455. http://doi.org/10.1038/s41598-025-02435-8.

Montgomery, D.C.; Runger, G.C. (2018). Applied Statistics and Probability for Engineers, 7th ed.; Wiley: Hoboken, NJ, USA.

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SUBMITTED: 25 January 2026
ACCEPTED: 19 February 2026
PUBLISHED: 24 February 2026
SUBMITTED to ACCEPTED: 25 days
DOI: https://doi.org/10.53623/gisa.v6i1.1012

Cite this article
Alkan Dawasoka, S. M., & Puji Widiyanto, E. . (2026). Design and Implementation of a Multi-Node Gas Sensor-Based Indoor Air Quality Monitoring and Control System. Green Intelligent Systems and Applications, 6(1), 34−50. https://doi.org/10.53623/gisa.v6i1.1012
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