A hybrid AI-IoT framework for real-time monitoring and prediction of urban air pollution

Authors

  • Natalja Osintsev * Fraunhofer Institute for Wood Research Wilhelm-Klauditz Institute WKI; Bienroder Weg 54 E, 38108 Brunswick, Germany.

https://doi.org/10.22105/sci.v2i4.47

Abstract

Urban air pollution is a growing concern due to its adverse effects on public health and the environment. While effective, traditional air quality monitoring systems are limited in coverage, data granularity, and timeliness. To address these limitations, AI-IoT-based solutions offer a transformative approach by integrating the Internet of Things (IoT) with Artificial Intelligence (AI) to enable real-time, large-scale monitoring and predictive air-quality analysis in urban areas. This paper explores the implementation of AI-IoT systems in which IoT sensors collect real-time data on key pollutants (e.g., PM2.5, NOx, CO2) from multiple locations across a city. The data is transmitted via wireless networks to cloud platforms, where AI algorithms analyze it to detect pollution patterns, forecast air quality trends, and identify pollution sources. Machine learning techniques are used to predict future air quality levels, while anomaly detection models alert authorities to sudden pollution spikes. Additionally, edge computing is integrated to process data locally, reducing latency and bandwidth consumption. The significant results show that AI-IoT systems provide more precise, timely, and actionable insights than conventional methods. Cities that have implemented these solutions report improved air quality management, enabling them to take preventive actions such as traffic regulation or public advisories. These results highlight the scalability and flexibility of AI-IoT systems in urban settings. The implications for the field are substantial, as this technology can transform how cities monitor air quality, making urban areas healthier and more sustainable through smarter environmental management.

 

Keywords:

Urban air pollution, Air quality monitoring, Predictive analysis, Machine learning, Smart cities

References

  1. [1] Srivastava, H., Mishra, S., Das, S. K., & Sarkar, S. (2020). An IoT-based pollution monitoring system using data analytics approach. Electronic systems and intelligent computing: proceedings of esic 2020 (pp. 187–198). Springer. https://doi.org/10.30574/wjarr.2021.12.1.0411
  2. [2] 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
  3. [3] Alsamrai, O., Redel-Macias, M. D., & Dorado, M. P. (2025). Real-time intelligent monitoring of outdoor air quality in an urban environment using IoT and machine learning algorithms. Applied sciences, 15(16), 9088. https://doi.org/10.3390/app15169088
  4. [4] Gryech, I., Asaad, C., Ghogho, M., & Kobbane, A. (2024). Applications of machine learning & Internet of Things for outdoor air pollution monitoring and prediction: A systematic literature review. Engineering applications of artificial intelligence, 137, 109182. https://doi.org/10.1016/j.engappai.2024.109182
  5. [5] 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(7), 2070. https://doi.org/10.3390/s25072070
  6. [6] Alsamrai, O., Redel-Macias, M. D., Pinzi, S., & Dorado, M. P. (2024). A systematic review for indoor and outdoor air pollution monitoring systems based on internet of things. Sustainability, 16(11), 4353. https://doi.org/10.3390/su16114353
  7. [7] Gololo, M. G. D., Nyathi, C. W., Boateng, L., Nkadimeng, E. K., Mckenzie, R. P., Atif, I., … & Mellado, B. (2024). Review of IoT systems for air quality measurements based on LTE/4G and lora communications. IoT, 5(4), 711–729. https://doi.org/10.3390/iot5040032
  8. [8] Bonilla, V., Campoverde, B., & Yoo, S. G. (2023). A systematic literature review of LoRaWAN: Sensors and applications. Sensors, 23(20), 8440. https://doi.org/10.3390/s23208440
  9. [9] Idrees, Z., Zou, Z., & Zheng, L. (2018). Edge computing based IoT architecture for low cost air pollution monitoring systems: A comprehensive system analysis, design considerations & development. Sensors, 18(9), 3021. https://doi.org/10.3390/s18093021
  10. [10] Bagkis, E., Hassani, A., Schneider, P., DeSouza, P., Shetty, S., Kassandros, T., ... & 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://www.nature.com/articles/s41612-025-01216-4
  11. [11] Karagulian, F., Barbiere, M., Kotsev, A., Spinelle, L., Gerboles, M., Lagler, F., …& Borowiak, A. (2019). Review of the performance of low-cost sensors for air quality monitoring. Atmosphere, 10(9), 506. https://doi.org/10.3390/atmos10090506
  12. [12] Popescu, S. M., Mansoor, S., Wani, O. A., Kumar, S. S., Sharma, V., Sharma, A., …& Chung, Y. S. (2024). Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management. Frontiers in environmental science, 12, 1336088. https://doi.org/10.3389/fenvs.2024.1336088
  13. [13] Karmoude, M., Munhungewarwa, B., Chiraira, I., Mckenzie, R., Kong, J., Smith, B., ... & 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/10.1016/j.scitotenv.2025.180593
  14. [14] Rollo, F., Bachechi, C., & Po, L. (2023). Anomaly detection and repairing for improving air quality monitoring. Sensors, 23(2), 640. https://doi.org/10.3390/s23020640
  15. [15] Choi, Y., Kang, B., & Kim, D. (2024). Utilizing machine learning-based classification models for tracking air pollution sources: A case study in Korea. Aerosol and air quality research, 24(7), 230222. https://doi.org/10.4209/aaqr.230222
  16. [16] Olawade, D. B., Wada, O. Z., Ige, A. O., Egbewole, B. I., Olojo, A., & Oladapo, B. I. (2024). Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions. Hygiene and environmental health advances, 12, 100114. https://doi.org/10.1016/j.heha.2024.100114
  17. [17] Abimannan, S., El-Alfy, E.-S. M., Hussain, S., Chang, Y.-S., Shukla, S., Satheesh, D., & Breslin, J. G. (2023). Towards federated learning and multi-access edge computing for air quality monitoring: Literature review and assessment. Sustainability, 15(18), 13951. https://doi.org/10.3390/su151813951
  18. [18] McCarron, A., Semple, S., Braban, C. F., Swanson, V., Gillespie, C., & Price, H. D. (2023). Public engagement with air quality data: Using health behaviour change theory to support exposure-minimising behaviours. Journal of exposure science & environmental epidemiology, 33(3), 321–331. https://www.nature.com/articles/s41370-022-00449-2
  19. [19] Das, S., Singh, K., & Kaur, K. (2024). Air quality prediction in beijing: machine and deep learning analysis. ITM web of conferences (Vol. 68, p. 1012). EDP Sciences. https://doi.org/10.1051/e3sconf/202340603020
  20. [20] Perelló, J., Cigarini, A., Vicens, J., Bonhoure, I., Rojas-Rueda, D., Nieuwenhuijsen, M. J., …& Ripoll, A. (2021). Large-scale citizen science provides high-resolution nitrogen dioxide values and health impact while enhancing community knowledge and collective action. Science of the total environment, 789, 147750. https://doi.org/10.1016/j.scitotenv.2021.147750
  21. [21] Kaginalkar, A., Kumar, S., Gargava, P., & Niyogi, D. (2021). Review of urban computing in air quality management as smart city service: An integrated IoT, AI, and cloud technology perspective. Urban climate, 39, 100972. https://doi.org/10.1016/j.uclim.2021.100972
  22. [22] Huang, T., Fang, T., Feng, L., Leong, C. Y., & Yim, S. H. L. (2024). Investigating the interaction between transboundary haze and planetary boundary layer in Singapore. Geophysical research letters, 51(8), e2023GL107667. https://doi.org/10.1029/2023GL107667
  23. [23] Méndez, M., Merayo, M. G., & Núñez, M. (2023). Machine learning algorithms to forecast air quality: A survey. Artificial intelligence review, 56(9), 10031–10066. https://doi.org/10.1007/s10462-023-10424-4
  24. [24] Connolly, R. E., Yu, Q., Wang, Z., Chen, Y. H., Liu, J. Z., Collier-Oxandale, A., …& Zhu, Y. (2022). Long-term evaluation of a low-cost air sensor network for monitoring indoor and outdoor air quality at the community scale. Science of the total environment, 807, 150797. https://doi.org/10.1016/j.scitotenv.2021.150797
  25. [25] Lu, Y., Giuliano, G., & Habre, R. (2021). Estimating hourly PM2. 5 concentrations at the neighborhood scale using a low-cost air sensor network: A Los Angeles case study. Environmental research, 195, 110653. https://doi.org/10.1016/j.envres.2020.110653
  26. [26] Vu, T. V, Shi, Z., Cheng, J., Zhang, Q., He, K., Wang, S., & Harrison, R. M. (2019). Assessing the impact of clean air action on air quality trends in Beijing using a machine learning technique. Atmospheric chemistry and physics, 19(17), 11303–11314. https://doi.org/10.5194/acp-19-11303-2019%0A
  27. [27] Zhang, C., Niu, X., Wu, H., Ding, Z., Chan, K. L., Kim, J., …& Liu, C. (2025). Unleashing the potential of geostationary satellite observations in air quality forecasting through artificial intelligence techniques. Atmospheric chemistry and physics, 25(2), 759–770. https://doi.org/10.5194/acp-25-759-2025
  28. [28] Kumar, K., & Pande, B. P. (2023). Air pollution prediction with machine learning: a case study of Indian cities. International journal of environmental science and technology, 20(5), 5333–5348. https://doi.org/10.1007/s13762-022-04241-5
  29. [29] Zeng, F., Pang, C., & Tang, H. (2024). Sensors on internet of things systems for the sustainable development of smart cities: A systematic literature review. Sensors, 24(7), 2074. https://doi.org/10.3390/s24072074
  30. [30] Carotenuto, F., Bisignano, A., Brilli, L., Gualtieri, G., & Giovannini, L. (2023). Low-cost air quality monitoring networks for long-term field campaigns: A review. Meteorological applications, 30(6), e2161. https://doi.org/10.1002/met.2161
  31. [31] Kumar, V., Senarathna, D., Gurajala, S., Olsen, W., Sur, S., Mondal, S., & Dhaniyala, S. (2023). Spectral analysis approach for assessing the accuracy of low-cost air quality sensor network data. Atmospheric measurement techniques, 16(21), 5415–5427. https://doi.org/10.5194/amt-16-5415-2023
  32. [32] Bellini, P., Nesi, P., & Pantaleo, G. (2022). IoT-enabled smart cities: A review of concepts, frameworks and key technologies. Applied sciences, 12(3), 1607. https://doi.org/10.3390/app12031607
  33. [33] Sun, Y., Mousavi, A., Masri, S., & Wu, J. (2022). Socioeconomic disparities of low-cost air quality sensors in California, 2017--2020. American journal of public health, 112(3), 434–442. https://ajph.aphapublications.org/doi/full/10.2105/AJPH.2021.306603

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Published

2025-12-16

Issue

Vol. 2 No. 4 (2025): Smart City Insights

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Articles

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How to Cite

Osintsev, N. (2025). A hybrid AI-IoT framework for real-time monitoring and prediction of urban air pollution. Smart City Insights, 2(4), 177-186. https://doi.org/10.22105/sci.v2i4.47

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