Using Internet of Things technologies for atmospheric air pollution monitoring

Serhii Bohdan, Andrii Zelenkov

Abstract


Subject of Study: The article focuses on methods and tools for developing flexible distributed environmental monitoring systems for ambient air in urbanized areas and zones of high technogenic load. Objective: To enhance the reliability, timeliness, and spatial resolution of information regarding pollutant concentrations by developing architectural solutions based on modern Internet of Things (IoT) technologies, incorporating mobile measurement platforms and machine learning methods. Tasks: To analyze the limitations of existing stationary monitoring stations; to define the architecture of a hybrid monitoring system combining ground-based sensor networks and mobile segments based on Unmanned Aerial Vehicles (UAVs) to expand coverage; to perform a comparative analysis of sensor technologies, energy-efficient communication protocols (LoRaWAN, NB-IoT), and data processing platforms; to investigate the effectiveness of applying artificial intelligence algorithms to improve the measurement quality of low-cost sensors. Results: The study investigates the shortcomings of traditional monitoring systems, particularly their high cost and low coverage density. A comprehensive approach to deploying IoT systems is proposed, involving the use of distributed sensor nodes for real-time data collection. Special attention is paid to the use of Unmanned Aerial Vehicles as carriers of measurement equipment, which enables the collection of data on the vertical distribution of pollutants and monitoring in hard-to-reach places or directly in the emission plumes of pollution sources. Key challenges in implementing such systems have been identified: the accuracy of low-cost sensors, the energy efficiency of autonomous devices, and data transmission reliability in complex conditions. Scientific Novelty: The scientific novelty lies in the systematization of architectural approaches to building heterogeneous IoT environmental monitoring systems, which, unlike existing solutions, dynamically combine stationary and mobile measurement tools. The effectiveness of applying machine learning methods for automatic sensor calibration and cross-sensitivity compensation has been proven. Conclusions: Implementing the proposed IoT solutions enables a significant increase in the density of monitoring points and reduces network deployment costs. The use of UAVs ensures monitoring flexibility, while the integration of artificial intelligence facilitates the necessary measurement accuracy for decision-making.

Keywords


Internet of Things; air pollution; air quality monitoring; low-cost sensors; unmanned aerial vehicle (UAV); machine learning; edge computing; data quality

References


Garcia, A., Saez, Y., Harris, I., Huang, X., & Collado, E. Advancements in air quality monitoring: a systematic review of IoT-based air quality monitoring and AI technologies. Artificial Intelligence Review, 2025, vol. 58, art. no. 275. DOI: 10.1007/s10462-025-11277-9.

Pavelko, O., Kulykovska, N., & Timenko, A. Avtomatyzovana systema monitorynhu yakosti povitria [Automated air quality monitoring system]. Modeling, Control and Information Technologies: Proceedings of International Scientific and Practical Conference, 2023, no. 6, pp. 238-241. DOI: 10.31713/MCIT.2023.074. (In Ukrainian).

Ministry of Environmental Protection and Natural Resources of Ukraine. Ekolohichnyi monitorynh dovkillia [Ecological monitoring of the environment]. Available at: https://mepr.gov.ua/diyalnist/napryamky/ekologichnyj-monitoryng/ekologichnyj-monitoryng-dovkillya/ (accessed 12 October 2025). (In Ukrainian).

Bobulski, J., Szymoniak, S., & Pasternak, K. An IoT System for Air Pollution Monitoring with Safe Data Transmission. Sensors, 2024, vol. 24, no. 2, art. no. 445. DOI: 10.3390/s24020445.

Kobrina, N. Monitorynh dovkillia z vykorystanniam bezpilotnykh litalnykh aparativ [Environmental monitoring using unmanned aerial vehicles]. International Science Journal of Engineering & Agriculture, 2024, vol. 3, no. 6, pp. 101-106. DOI: 10.46299/j.isjea.20240306.10. (In Ukrainian).

Manfreda, S., McCabe, M. F., Miller, P. E., Lucas, R., Pajuelo Madrigal, V., Mallinis, G., Ben Dor, E., Helman, D., Estes, L., Ciraolo, G., & et al. On the Use of Unmanned Aerial Systems for Environmental Monitoring. Remote Sensing, 2018, vol. 10, no. 4, art. 641. DOI: 10.3390/rs10040641.

Yang, X., Chen, J., Lu, X., Liu, H., Liu, Y., Bai, X., Qian, L., & Zhang, Z. Advances in UAV Remote Sensing for Monitoring Crop Water and Nutrient Status: Modeling Methods, Influencing Factors, and Challenges. Plants, 2025, vol. 14, no. 16, art. no. 2544. DOI: 10.3390/plants14162544.

Ramadan, M. N. A., Ali, M. A. H., Khoo, S. Y., Alkhedher, M., & Alherbawi, M. Real-time IoT-powered AI system for monitoring and forecasting of air pollution in industrial environment. Ecotoxicology and Environmental Safety, 2024, vol. 283, art. no. 116856. DOI: 10.1016/j.ecoenv.2024.116856.

Gololo, M. G. D., Nyathi, C. W., Boateng, L., Nkadimeng, E. K., Mckenzie, R. P., Atif, I., Kong, J., Mahboob, M. A., Cheng, L., & Mellado, B. Review of IoT Systems for Air Quality Measurements Based on LTE/4G and LoRa Communications. IoT, 2024, vol. 5, no. 4, pp. 711-729. DOI: 10.3390/iot5040032.

Mokin, V., Honcharenko, D., & Protsenko, D. Pobudova informatsiinoi systemy monitorynhu fizychnykh pokaznykiv na osnovi tekhnolohii «Internet rechei» [Construction of an information system for monitoring physical indicators based on Internet of Things technology]. Informatsiini tekhnolohii ta kompiuterna inzheneriia – Information technologies and computer engineering, 2023, vol. 57, no. 2, pp. 99-108. DOI: 10.31649/1999-9941-2023-57-2-99-108. (In Ukrainian).

Buelvas, J., Múnera, D., Tobón, V. D. P., Aguirre, J., & Gaviria, N. Data Quality in IoT-Based Air Quality Monitoring Systems: a Systematic Mapping Study. Water, Air, & Soil Pollution, 2023, vol. 234, art. no. 248. DOI: 10.1007/s11270-023-06127-9.

Sladojevic, S., Arsenovic, M., Nikolic, D., Anderla, A., & Stefanovic, D. Advancements in Mobile-Based Air Pollution Detection: From Literature Review to Practical Implementation. Journal of Sensors, 2024, vol. 2024, art. no. 4895068. DOI: 10.1155/2024/4895068.

Collado, E., Calderón, S., Cedeño, B., De León, O., Centella, M., García, A., & Sáez, Y. Open-source Internet of Things (IoT)-based air pollution monitoring system with protective case for tropical environments. HardwareX, 2024, vol. 19, art. no. e00560. DOI: 10.1016/j.ohx.2024.e00560.

Turkin, I., Leznovskyi, V., Zelenkov, A., Nabizade, A., Volobuieva, L., & Turkina, V. The Use of IoT for Determination of Time and Frequency Vibration Characteristics of Industrial Equipment for Condition-Based Maintenance. Computation, 2023, vol. 11, no. 9, art. no. 177. DOI: 10.3390/computation11090177.

Dai, X., Shang, W., Liu, J., Xue, M., & Wang, C. Achieving better indoor air quality with IoT systems for future buildings: Opportunities and challenges. Science of The Total Environment, 2023, vol. 895, art. no. 164858. DOI: 10.1016/j.scitotenv.2023.164858.

Saini, J., Dutta, M., & Marques, G. Indoor Air Quality Monitoring Systems Based on Internet of Things: A Systematic Review. International Journal of Environmental Research and Public Health, 2020, vol. 17, no. 14, art. no. 4942. DOI: 10.3390/ijerph17144942.

Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 2013, vol. 29, no. 7, pp. 1645-1660. DOI: 10.1016/j.future.2013.01.010.

Shahid, I., Shahzad, M. I., Tutsak, E., Mahfouz, M. M. K., Al Adba, M. S., Abbasi, S. A., Rathore, H. A., Asif, Z., & Chen, Z. Carbon based sensors for air quality monitoring networks; middle east perspective. Frontiers in Chemistry, 2024, vol. 12, art. no. 1391409. DOI: 10.3389/fchem.2024.1391409.

Jabbar, W. A., Subramaniam, T., Ong, A. E., Shu'Ib, M. I., Wu, W., & de Oliveira, M. A. LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring. Internet of Things, 2022, vol. 18, art. no. 100540. DOI: 10.1016/j.iot.2022.100540.

Felici-Castell, S., Segura-Garcia, J., Perez-Solano, J. J., Fayos-Jordan, R., Soriano-Asensi, A., & Alcaraz-Calero, J. M. AI-IoT Low-Cost Pollution-Monitoring Sensor Network to Assist Citizens with Respiratory Problems. Sensors, 2023, vol. 23, no. 23, art. no. 9585. DOI: 10.3390/s23239585.

Gangwar, A., Singh, S., Mishra, R., & Prakash, S. The State-of-the-Art in Air Pollution Monitoring and Forecasting Systems Using IOT, Big Data, and Machine Learning. Wireless Personal Communications, 2023, vol. 130, pp. 1699-1729. DOI: 10.1007/s11277-023-10351-1.




DOI: https://doi.org/10.32620/aktt.2025.6.08