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http://elartu.tntu.edu.ua/handle/lib/47609| Title: | Application of machine learning methods to the prediction of NO2 concentration in the air environment |
| Authors: | Iryna, Didych Andrii, Mykytyshyn Andrii, Stanko Mykola, Mytnyk |
| Affiliation: | Ternopil Ivan Puluj National Technical University, Ruska 56, 46001 Ternopil, Ukraine |
| Bibliographic description (Ukraine): | Didych, I., Mykytyshyn, A., Stanko, A., Mytnyk, M. (2024). Application of machine learning methods to the prediction of NO2 concentration in the air environment. CEUR Workshop Proceedings, 3896, 569-577. |
| Bibliographic citation (APA): | Didych, I., Mykytyshyn, A., Stanko, A., Mytnyk, M. (2024). Application of machine learning methods to the prediction of NO2 concentration in the air environment. CEUR Workshop Proceedings, 3896, 569-577. |
| Conference/Event: | CEUR Workshop Proceedings |
| Issue Date: | 23-Okt-2024 |
| Date of entry: | 23-Jan-2025 |
| Publisher: | CEUR-WS |
| Country (code): | UA |
| Place of the edition/event: | Ternopil, Ukraine, Opole, Poland, October 23-25, 2024. |
| Keywords: | Air quality prediction nitrogen dioxide machine learning |
| Page range: | 569-577 |
| Abstract: | Air quality significantly impacts public health, with nitrogen dioxide (NO2) being a key pollutant linked to respiratory and cardiovascular diseases. In this study, we developed a machine learning model to accurately predict hourly NO2 concentrations in Ternopil, Ukraine, using readily available meteorological and temporal data. The model was trained on a large dataset and tested using data from the Ecocity monitoring station, known for recording NO2 levels exceeding legal limits. By employing neural networks, the model demonstrated high accuracy in predicting NO2 concentrations, with the error of 3.9% and 1.4%, respectively, in the test samples. Our findings underscore the potential of machine learning techniques to enhance air quality monitoring and forecasting, particularly in urban areas with limited resources. This approach offers a valuable tool for real-time pollution management and public health protection. |
| URI: | http://elartu.tntu.edu.ua/handle/lib/47609 |
| Copyright owner: | Iryna Didych, Andrii Mykytyshyn, Andrii Stanko, Mykola Mytnyk |
| References (Ukraine): | [1] González Ortiz, A., Guerreiro, C., & Soares, J. (2020). Air Quality in Europe: 2020 Report. In European Environment Agency. EU Publications: Luxembourg. [2] Latza, U., Gerdes, S., & Baur, X. (2009). Effects of nitrogen dioxide on human health: Systematic review of experimental and epidemiological studies conducted between 2002 and 2006. In International Journal of HEH, 212, 271–287. [3] Okudo, C.C., Ekere, N.R., & Okoye, C.O.B. (2022). Evaluation of Particulate Matter (PM2.5 and PM10) Concentrations in the Dry and Wet Seasons As Indices of Air Quality in Enugu Urban, Enugu State, Nigeria. In Journal of CSN, 47(5), 998-1015. [4] Impacts of air pollution and acid rain on wildlife. In Air Pollution. http://www.air- quality.org.uk [5] U.S. Environmental Protection Agency (USEPA).. Particulate Matter (PM) Basics. In EPA. http://www.epa.gov.gov/pm-pollution/particulate-matter-pm-basics. [6] Stanko, A., Wieczorek, W., Mykytyshyn, A., Holotenko, O., & Lechachenko, T. (2024). Real- time air quality management: Integrating IoT and Fog computing for effective urban monitoring. CITI’2024: 2nd International Workshop on Computer Information Technologies in Industry 4.0, June 12–14, 2024, Ternopil, Ukraine. [7] Duda, O., Mykytyshyn, A., Mytnyk, M., & Stanko, A. (2020). The network platform cyber- physical systems application for smart buildings air pollution indicators monitoring," Časopis Manažérska Informatika, Univerzita Komenského v Bratislave, Slovakia, vol. 1, no. 1, 2023, ISSN 2729-8310. [8] Environmental Protection Agency. (2023). Nitrogen dioxide (NO2) pollution: Basic information about NO2. In EPA. www.epa.gov [9] Baklanov, A., Molina, L.T., & Gauss, M. (2016). Megacities, air quality and climate. In Atmospheric Environment, 126, 235–249. [10] Canepa, E., & Builtjes, P.J.H. (2017). Thoughts on Earth System Modeling: From global to regional scale. In Earth-Science Reviews, 171, 456–462. [11] Arhami, M., Kamali, N., & Rajabi, M.M. (2013). Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations. In Environmental Science and Pollution Research, 20, 4777–4789. [12] Cabaneros, S.M., Calautit, J.K., & Hughes, B.R. (2019). A review of artificial neural network models for ambient air pollution prediction. In Env. Modelling & Software, 119, 285–304. [13] Wu, Y., & Zhang, Y. (2020). Artificial neural network approaches for modeling air pollutants concentrations: A case study in Jinan, China. In Atm. Env., 224, 117333. [14] Gardner, M., & Dorling, S. (1999). Neural network modelling and prediction of hourly NOx and NO2 concentrations in urban air in London. In Atm. Env., 33, 709–719. [15] Kolehmainen, M., Martikainen, H., & Ruuskanen, J. (2001). Neural networks and periodic components used in air quality forecasting. In Atm. Env., 35, 815–825. [16] Perez, P., & Trier, A. (2001). Prediction of NO and NO2 concentrations near a street with heavy traffic in Santiago, Chile. In Atmospheric Environment, 35, 1783–1789. |
| Content type: | Article |
| Appears in Collections: | Навчальна література кафедри КТ |
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|---|---|---|---|---|
| short15.pdf | 680,06 kB | Adobe PDF | View/Open |
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