Research on Precise Monitoring and Prevention of Environmental Pollution Based on Environmental Big Data Analysis
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Abstract
Environmental big data analysis has become an important approach for achieving intelligent environmental governance, while reliable wireless communication and electromagnetic signal transmission provide the essential infrastructure for distributed sensing and real-time data acquisition in modern monitoring systems. This study established a VOC-based online monitoring cloud platform to analyze gas distribution characteristics within a chemical park. Five monitoring stations were deployed to continuously collect real-time VOC concentration data, enabling the evaluation of localized pollution patterns and emission characteristics. Based on the acquired VOC datasets, environmental information was uploaded to the cloud platform through wireless communication networks and analyzed using the USEPA Positive Matrix Factorization (PMF) model to identify and localize pollution sources with high precision. The results demonstrate that the proposed framework effectively distinguishes multiple VOC emission sources, including alkane leakage, preparation process emissions, olefin synthesis, urban transmission, and storage tank emissions, thereby providing accurate source apportionment and spatial localization for environmental management. By integrating environmental big data analytics with cloud-based monitoring architecture and electromagnetic information transmission technologies, the proposed approach enhances the efficiency and reliability of real-time pollution surveillance and offers a practical reference for intelligent environmental monitoring and industrial emission prevention.
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References
K. Soni, “Environmental pollution and waste management,” Journal of Science Innovations and Nature of Earth, pp. 43–47, 2023, doi: 10.59436/jsiane.com/archives3/4/120.
K. Sankar, D. Rohatgi, and S. B. Reddy, “Bivariate Gibbs based deep prediction for uncertain spatial environmental big data analytics,” AIP Conference Proceedings, vol. 2755, 020015, 2023, doi: 10.1063/5.0148348.
D. Zhu, Y. He, and K. Moore, “Novel environmental big data grid integration and interoperability model,” 2025, doi: 10.5194/egusphere-egu24-6798.
Y. H. Fei, “Design and development of environmental pollution source monitoring management information system,” Proceedings of SPIE, vol. 12707, p. 6, 2023, doi: 10.1117/12.2681348.
E. Bainomugisha, P. A. Warigo, F. B. Daka, et al., “AI-driven environmental sensor networks and digital platforms for urban air pollution monitoring and modelling,” Societal Impacts, vol. 3, 100044, 2024, doi: 10.1016/j.socimp.2024.100044.
M. Luty-Bocho and A. Podborska, “The diversity of MOF structures and their impact on photoelectrochemical sensors for monitoring environmental pollution,” Crystals, vol. 14, no. 7, 2024, doi: 10.3390/cryst14070626.
S. M. Popescu, S. Mansoor, O. A. Wani, et al., “Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management,” Frontiers in Environmental Science, vol. 12, 2024, doi: 10.3389/fenvs.2024.1336088.
N. Mishra and S. Choubey, “Artificial intelligence-driven energy platforms: Applications and challenges,” E3S Web of Conferences, vol. 540, no. 000, p. 12, 2024, doi: 10.1051/e3sconf/202454008012.
A. K. Pati, A. R. Tripathy, and S. Subudhi, “Intelligence frameworks for environmental pollution assessment: A review on air and water quality monitoring systems,” Measurement, vol. 258, Part B, 2025, doi: 10.1016/j.measurement.2025.119122.
Y. He, “Development of a trend forecasting model for environmental pollution monitoring,” Management of Development of Complex Systems, vol. 57, pp. 62–66, 2024, doi: 10.32347/2412-9933.2024.57.62-66.
C. N. Tadros, N. Shehata, and B. Mokhtar, “Unsupervised learning-based WSN clustering for efficient environmental pollution monitoring,” Sensors, vol. 23, no. 12, 2023, doi: 10.3390/s23125733.
M. S. Gobade, G. B. Sawarkar, and M. R. Bhandekar, “Wireless sensor networks for green cities: A comprehensive review of environmental pollution monitoring,” International Journal of Advanced Research in Science, Communication and Technology, pp. 422–432, 2024, doi: 10.48175/ijarsct-15063.
A. Dwivedi, K. Manivannan, S. Kumar, et al., “A real-time environmental pollution monitoring framework using IoT and remote sensing technologies,” International Journal of Environmental Sciences, vol. 11, 2025, doi: 10.64252/repndy27.
I. U. Ajakwe, S. O. Ajakwe, J. M. Lee, et al., “Internet-of-Things-blockchain integration in environmental pollution monitoring data management: Trends and techniques,” International Journal of Environmental Science & Technology, vol. 22, no. 15, 2025, doi: 10.1007/s13762-025-06615-x.
R. A. Abdulrazzq, N. M. Sajid, and M. S. Hasan, “Artificial intelligence-driven predictive maintenance in IoT systems,” South Florida Journal of Development, vol. 5, no. 12, e4781, 2024, doi: 10.46932/sfjdv5n12-030.
Y. Wang, “IoT-enabled smart sensors for real-time environmental justice monitoring in industrial zones,” GeoJournal, vol. 90, 2025, doi: 10.1007/s10708-025-11410-9.
C. Purcarea, R. Ruginescu, R. M. Banciu, et al., “Extremozyme-based biosensors for environmental pollution monitoring: Recent developments,” Biosensors, vol. 14, no. 3, 2024, doi: 10.3390/bios14030143.
L. Sharipova and M. Khusenov, “Creation of environmental pollution monitoring systems using digital educational technologies,” AIP Conference Proceedings, vol. 3268, 070013, 2025, doi: 10.1063/5.0257086.
S. F. Sulthana, U. M. Iqbal, R. Anbazhagan, et al., “Investigations of materials and technologies behind environmental pollution monitoring sensors for global health,” ACS Omega, vol. 10, no. 27, pp. 28499–28514, 2025, doi: 10.1021/acsomega.4c09790.
B. Devender, C. Srinivas, K. Shivaprasad, et al., “A machine learning-driven framework for real-time environmental pollution monitoring and prediction using IoT and remote sensing data,” International Journal of Environmental Sciences, vol. 11, 2025, doi: 10.64252/n7ears56.
G. Wang, “Environmental pollution monitoring system based on gas water quality sensors and visual recognition,” Results in Engineering, vol. 26, 2025, doi: 10.1016/j.rineng.2025.105524.
M. Shalaby and F. S. Alorifi, “Environmental pollution detection: A novel chirped spectral modulation algorithm for a more accurate monitoring of gas pollutants in the atmosphere,” Journal of the European Optical Society, vol. 19, no. 1, p. 9, 2023, doi: 10.1051/jeos/2023005.