Design of a Real-Time Monitoring and Pollution Early Warning System for Industrial Flue Gas Emissions Based on Embedded Edge Computing
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Abstract
Real-time environmental monitoring in industrial plants requires rapid signal processing, reliable data transmission, and accurate anomaly detection under complex operating conditions. To address the limitations of conventional centralized monitoring systems, this study proposes an industrial flue-gas monitoring and early-warning framework based on embedded edge computing. A distributed sensing architecture integrating multi-sensor arrays, edge computing nodes, and cloud-assisted management is developed to enable local real-time processing and low-latency response. Multi-sensor fusion algorithms are employed to improve measurement reliability for pollutant concentration, flow rate, and environmental parameters, while lightweight intelligent models deployed at edge nodes perform anomaly detection and trend prediction. A cloud–edge collaborative mechanism is further introduced to support continuous model optimization and distributed network management. The modular architecture incorporates wireless Mesh networking and fault-tolerant data synchronization, enhancing system robustness in environments affected by electromagnetic interference and unstable communication conditions. Compared with conventional centralized systems, the proposed framework significantly improves response speed, monitoring accuracy, and early-warning capability while reducing network dependence and deployment complexity. The proposed architecture provides an effective engineering solution for distributed sensing, intelligent monitoring, and real-time information processing in large-scale industrial environments.
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References
T. Chai, H. C. Kim, A. Stein, D. Tong, Y. Li, and S. Kondragunta, “A case study of the 2018 Camp Fire event using HYSPLIT-based emission inverse modeling system with GOES Advanced Baseline Imager (ABI) observations and other measurements for wildfire smoke forecasts,” EGU General Assembly 2020. Vienna, Austria: European Geosciences Union; 2020. doi: 10.5194/egusphere-egu2020-12525.
Q. Wu and Q. Xu, “Numerical Study of the impact of Meteorological and Emission control on the decreasing of PM2.5 concentration in Beijing by WRF-SMOKE-CMAQ model system,” EGU General Assembly 2020. Vienna, Austria: European Geosciences Union; 2020. doi: 10.5194/egusphere-egu2020-19694.
V. Vu, D. Tran-Anh, and C. Tran, “A Low-Resource System for Rapid and Accurate Forest Fire Smoke Detection Using Video-Based Multiple Object Kinetic Emission Detection,” 2024. doi: 10.2139/ssrn.5005161.
K. Yang, L. Zhang, Y. Han, K. Zhang, J. Li, W. Wei, et al., “Traffic environmental protection edge computing: A monitoring algorithm and system of truck black smoke emission in complex scene,” Journal of Supercomputing. 2025;81(6). doi: 10.1007/s11227-025-07276-w.
F. M. Oliveira, F. V. Arruda, A. A. C. Alencar, D. C. Morton, S. F. Mardegan, and E. Q. Marques, “Expand air quality monitoring of smoke emissions and exposure in Amazonia to safeguard human health,” Discover Environment. 2025;3(1). doi: 10.1007/s44274-025-00340-5.
S. K. Logesh, S. Maheswari, T. C. Dharshini, C. Bujasri, S. Kabibala, and M. Mugesh Kumar, “IoT-Based CO2 Emissions Monitoring and Control System for Industries and Transportation,” International EdTech Conference. Springer: Singapore; 2025. doi: 10.1007/978-981-96-1587-2_33.
V. M. Panarin, A. Maslova, and S. A. Savinkova, “Automated System for Monitoring Pollutant Emissions at the Sources of Industrial Enterprises,” Ecology and Industry of Russia. 2021. doi: 10.18412/1816-0395-2021-6-44-48.
Y. Gao, J. Wang, Y. Zhu, W. Zhang, F. Teng, and Y. Li, “Real time estimation of carbon emissions for industrial users based on load monitoring in advanced metering infrastructure,” Journal of Cleaner Production. 2024;483:144226. doi: 10.1016/j.jclepro.2024.144226.
A. Pandey, O. Momeni, and P. Pandey, “Design and Implementation of IoT-Enabled Device for Real-Time Monitoring of Greenhouse Gas Emissions, and Pressure in Anaerobic Reactors,” Access, IEEE. 2024;12(000):133848-133862. doi: 10.1109/ACCESS.2024.3449217.
A. Panzitta, C. Bax, B. J. Lotesoriere, C. Ratti, and L. Capelli, “Realisation of a Multi-Sensor System for Real-Time Monitoring of Odour Emissions at a Waste Treatment Plant,” CET Journal - Chemical Engineering Transactions. 2022;95. doi: 10.3303/CET2295024.
S. M. Elsakov, D. A. Drozin, A. V. Gerenshtein, Olenchikova TYu, A. P. Basmanov, S. G. Nishkaya, et al., “Monitoring and Forecasting the Spread of Industrial Emissions in the Atmosphere in Real Time SUSUPLUME,” Ecology and Industry of Russia. 2024;28(5):60-66. doi: 10.18412/1816-0395-2024-5-60-66.
L. F. Ilustre, Y. C. Mortos, J. Enriquez, J. Mindoro, and M. A. Malbog, “See Carbon: Real-Time IoT Monitoring System for Motorcycle Vehicle Carbon Emissions,” E3S Web of Conferences. 2024;488:03014. doi: 10.1051/e3sconf/202448803014.
X. Li, “Embedded Smart Sensor Network Architecture Based on Edge Computing,” Applied and Computational Engineering. 2025;127(1):134-140. doi: 10.54254/2755-2721/2025.20262.
Y. Wang, X. Zhao, T. Chong, and X. Liu, “Embedded Microprocessor Extension Design and Optimization for Real-Time Edge Computing,” Wireless Communications & Mobile Computing. 2022:5705184. doi: 10.1155/2022/5705184.
W. Li, G. Shen, J. Zhang, D. Liu, and C. Cho, “A LoRaWAN monitoring system for large buildings based on embedded edge computing in indoor environment,” Concurrency and Computation Practice and Experience. 2021(5);e6306. doi: 10.1002/cpe.6306.