Design and Implementation of an IoT-Based Dust Exposure Monitoring System for Marble Cutting Activities in Campus Environment
DOI:
https://doi.org/10.62671/jataed.v3i2.104Kata Kunci:
Dust Exposure Monitoring System, Marble Cutting, IoT, Campus EnvironmentAbstrak
Marble cutting activities in campus workshop environments generate substantial concentrations of airborne particulate matter, particularly PM2.5 and PM10, which pose serious risks to occupational health and ambient air quality. This study presents the design, implementation, and experimental evaluation of a real-time IoT-based dust exposure monitoring system with emphasis on sensing performance, data reliability, and environmental analysis. The system employs a laser scattering dust sensor (PMS7003) integrated with an ESP8266 microcontroller for data acquisition and edge preprocessing, and utilizes Wi-Fi communication with the MQTT protocol for low-latency data transmission to a cloud-based monitoring platform. Sensor calibration was conducted using linear regression against a reference air quality monitor, resulting in improved measurement accuracy with a coefficient of determination (R²) of 0.96 for PM2.5 and 0.94 for PM10. The system operates with a 5-second sampling interval and applies a moving average filter (window size = 5) to reduce signal noise. Experimental deployment was carried out in a campus marble workshop over a 5-day observation period. Results indicate that during active cutting sessions, PM2.5 concentrations ranged from 85 to 210 µg/m³ and PM10 from 120 to 350 µg/m³, significantly exceeding WHO air quality guidelines (PM2.5: 15 µg/m³, PM10: 45 µg/m³, 24-hour mean). Peak concentrations were observed within the first 10 minutes of operation, followed by gradual dispersion depending on ventilation conditions. Network performance evaluation shows an average transmission latency of 1.8 seconds, packet delivery ratio of 97.2%, and system uptime of 99% over the testing period. Power consumption analysis indicates an average current draw of 82 mA, enabling efficient long-term deployment. The results confirm that the proposed system provides accurate, stable, and high-resolution monitoring of particulate pollution, supporting real-time decision-making for exposure mitigation and smart environmental management in campus settings.
Referensi
Agbehadji, I. E., & Obagbuwa, I. C. (2025). Explainable Artificial Intelligence and Machine Learning for Air Pollution Risk Assessment and Respiratory Health Outcomes: A Systematic Review. In Atmosphere (Vol. 16, Issue 10, p. 1154). https://doi.org/10.3390/atmos16101154
Alabdulkreem, E., Allafi, R., Arasi, M. A., Geetha, P., Nafie, F. M., Begum, A. S., Nallasivan, G., & Vivek, S. (2025). Innovative IoT and blockchain integration for real-time urban air quality monitoring and autonomous response system. Sustainable Computing: Informatics and Systems, 48, 101250. https://doi.org/https://doi.org/10.1016/j.suscom.2025.101250
Armengol, J., Masip, V., Barrantes, A., Beltrami, G., Albiach, S., Rodriguez Rey, D., Guevara, M., Soret, A., Quiñones, E., & Kartsakli, E. (2025). IoT- and AI-informed urban air quality models for vehicle pollution monitoring. https://doi.org/10.48550/arXiv.2511.00187
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(3 SE-), 776–787. https://doi.org/10.57152/malcom.v5i3.1945
Bagkis, E., Hassani, A., Schneider, P., DeSouza, P., Shetty, S., Kassandros, T., Salamalikis, V., Castell, N., Karatzas, K., Ahlawat, A., & Khan, J. (2025). Evolving trends in application of low-cost air quality sensor networks: challenges and future directions. Npj Climate and Atmospheric Science, 8(1), 335. https://doi.org/10.1038/s41612-025-01216-4
Chasapi, M.-A., Moustris, K., Fameli, K.-M., & Spyropoulos, G. (2025). The Application of an Empirical Method for the Estimation of Vehicles’ Contribution to Air Pollution in an Urban Environment: A Case Study in Athens, Greece. In Air (Vol. 3, Issue 2, p. 14). https://doi.org/10.3390/air3020014
Chen, J., González, Ó., O’Connor, D., Tallon, L., & Pilla, F. (2025). Assessment of IoT low-cost sensor networks for long-term outdoor and indoor air quality monitoring: a case study in Dublin. Atmospheric Pollution Research, 16, 102651. https://doi.org/10.1016/j.apr.2025.102651
Gabriel, M. F., Marques, G., Filipe, D., Felgueiras, F., Cardoso, J. P., Azeredo, J., Kazdaridis, G., Symeonidis, P., Keranidis, S., Conradie, P., Azevedo, I., & Anagnostopoulos, F. (2024). Implementation of an IoT architecture for promoting healthy air quality in 84 homes of families with children. Building and Environment, 266, 112040. https://doi.org/https://doi.org/10.1016/j.buildenv.2024.112040
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(9), 275. https://doi.org/10.1007/s10462-025-11277-9
Gueye, A., Drame, M., & Niang, S. A. A. (2024). A low-cost IoT-based real-time pollution monitoring system using ESP8266 NodeMCU. Measurement and Control, 58, 1337–1345. https://doi.org/10.1177/00202940241306690
Hassan, A. K., Saraya, M. S., Ali-Eldin, A. M. T., & Abdelsalam, M. M. (2024). Low-Cost IoT Air Quality Monitoring Station Using Cloud Platform and Blockchain Technology. In Applied Sciences (Vol. 14, Issue 13, p. 5774). https://doi.org/10.3390/app14135774
Hwang, S., Park, J., Chu, Y.-T., & Choi, J. (2026). A GNN-based interpolation method for enhancing air pollution prediction based on Internet of Things (IoT) data. Atmospheric Environment, 371, 121835. https://doi.org/https://doi.org/10.1016/j.atmosenv.2026.121835
Jang, B.-J., Park, N., & Jung, I. (2025). IoT Sensing-Based High-Density Monitoring of Urban Roadside Particulate Matter (PM10 and PM2.5). Applied Sciences, 15, 11608. https://doi.org/10.3390/app152111608
Karmoude, M., Munhungewarwa, B., Chiraira, I., Mckenzie, R., Kong, J., Smith, B., Ayana, G., Njara, N., Mathaha, T., Kumar, M., & Mellado, B. (2025). Machine learning for air quality prediction and data analysis: Review on recent advancements, challenges, and outlooks. Science of The Total Environment, 1002, 180593. https://doi.org/https://doi.org/10.1016/j.scitotenv.2025.180593
Kumaran, G., & Mohan, S. (2026). A Review on Air Pollution Prediction Using Artificial Intelligence. IEEE Access, PP, 1. https://doi.org/10.1109/ACCESS.2026.3664880
Li, Y., Guo, J., Sun, S., Li, J., Wang, S., & Zhang, C. (2022). Air quality forecasting with artificial intelligence techniques: A scientometric and content analysis. Environmental Modelling & Software, 149, 105329. https://doi.org/https://doi.org/10.1016/j.envsoft.2022.105329
Lopes, S. I., Orłowski, C., Branco, P. T. B. S., Karatzas, K., Villena, G., Saffell, J., Marques, G., Sousa, S. I. V, Lenartz, F., Bergmans, B., Bigi, A., Pflanzner, T., & Ródenas García, M. (2025). Low-Cost Sensor Systems and IoT Technologies for Indoor Air Quality Monitoring: Instrumentation, Models, Implementation, and Perspectives for Validation. In Sensors (Vol. 25, Issue 24, p. 7567). https://doi.org/10.3390/s25247567
Luangwilai, T., Kunno, J., Manomaipiboon, B., Ruamtawee, W., & Ong-Artborirak, P. (2025). Risk Perception and Self-Monitoring of Particulate Matter 2.5 (PM 2.5) Associated with Anxiety Among General Population in Urban Thailand. In Urban Science (Vol. 9, Issue 7, p. 256). https://doi.org/10.3390/urbansci9070256
Maulidi, A., Sambodho, K., Dinariyana, A. A. B., & Syafruddin, M. (2025). IoT Based Early Monitoring System for Air Quality Assessment. IOP Conference Series: Earth and Environmental Science, 1473(1), 12010. https://doi.org/10.1088/1755-1315/1473/1/012010
O’Regan, A. C., Grythe, H., Schneider, P., & Nyhan, M. M. (2026). Integrating Low-Cost Sensors with Dispersion Modelling for High-Resolution Insights into Urban Air Quality. Environment International, 209, 110157. https://doi.org/https://doi.org/10.1016/j.envint.2026.110157
Otanasap, N., Chalermsuk, C., & Tadsuan, S. (2024). An AIoT based Air Quality Monitoring System for Real-time PM2.5 Prediction in Urban Environments. ASEAN Journal of Scientific and Technological Reports, 28, e255168. https://doi.org/10.55164/ajstr.v28i1.255168
Peixe, J., & Marques, G. (2024). Low-cost IoT-enabled indoor air quality monitoring systems: A systematic review. Journal of Ambient Intelligence and Smart Environments, 16, 1–14. https://doi.org/10.3233/AIS-220577
Relvas, H., Lopes, D., & Armengol, J. M. (2025). Empowering communities: Advancements in air quality monitoring and citizen engagement. Urban Climate, 60, 102344. https://doi.org/https://doi.org/10.1016/j.uclim.2025.102344
Wu, C., Wang, R., Lu, S., Tian, J., Yin, L., Wang, L., & Zheng, W. (2025). Time-Series Data-Driven PM2.5 Forecasting: From Theoretical Framework to Empirical Analysis. In Atmosphere (Vol. 16, Issue 3, p. 292). https://doi.org/10.3390/atmos16030292
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Hak Cipta (c) 2026 Rudi Arif Candra, Depi Ginting, Dirja Nur Ilham, Arie Budiansyah, Erwinsyah Sipahutar
Artikel ini berlisensi Creative Commons Attribution-NonCommercial 4.0 International License.
