Assessing the reliability of noise simulation models in a built-up area
DOI:
https://doi.org/10.26906/znp.2025.65.4216Keywords:
modeling, noise propagation, correlation analysisAbstract
In modern environments, noise is a dominant factor negatively impacting the urban ecosystem and, consequently, the health of city residents. Noise exposure reduces labor productivity and the efficiency of rest, serves as a primary cause of nervous disorders, and significantly diminishes the quality and safety of life.
The aim of the study is to determine the reliability of developing simulation models for sound propagation in urban built-up areas. Research methods include field instrumental measurements, a cartographic method for predicting noise pollution from sources with the construction of 3D sound field models, and statistical methods for processing and evaluating research results. The results demonstrate a strong relationship between the outcomes of field instrumental measurements and the cartographic prediction of the noise regime in the studied residential area. The calculated correlation coefficient amounted to r = 0.889, indicating a high level of agreement between the compared datasets. In addition, the required number of observations to determine the degree of correlation between the two groups of compared sound level values was established as 12 observations
References
1. European Environment Agency. (2020). Environmental noise in Europe: 2020 (Report No. 22/2019). https://www.eea.europa.eu/
2. Halonen, J. I., Bhattacharya, S., Smith, A., Jones, D., Kummer, B., & Williams, R. (2015). Road traffic noise is associated with increased cardiovascular morbidity and mortality and all-cause mortality in London. European Heart Journal, 36(39), 2653–2661. https://doi.org/10.1093/eurheartj/ehv216
3. Skrzypek, M., Kowalska, M., Czech, E. M., Niewiadomska, E., & Zejda, J. E. (2017). Impact of road traffic noise on sleep disturbances and attention disorders amongst school children living in Upper Silesian Industrial Zone, Poland. International Journal of Occupational Medicine and Environmental Health, 30(3), 511–520. https://doi.org/10.13075/ijomeh.1896.00823
4. Sankov, P. M. (2024). Organization of noise protection at workplaces in EU countries. In International Science Group (Ed.), New ways of improving outdated methods and technologies: Proceedings of the 16th International Scientific and Practical Conference (pp. 163–168). International Science Group. https://doi.org/10.46299/ISG.2024.2.16
5. Singh, D., Kumari, N., & Sharma, P. (2018). A review of adverse effects of road traffic noise on human health. Fluctuations and Noise Letters, 17(1). https://doi.org/10.1142/S021947751830001X
6. Mascolo, A., Rossi, D., Pascale, A., Mancini, S., Coelho, M. C., & Guarnaccia, C. (2023). Noise assessment during motor race events: New approach and innovative indicators. WSEAS Transactions on Environmental Development, 19, 80–88. https://doi.org/10.37394/232015.2023.19.7
7. Petri, D., Licitra, G., Vigotti, M. A., & Fredianelli, L. (2021). Effects of exposure to road, railway, airport and recreational noise on blood pressure and hypertension. International Journal of Environmental Research and Public Health, 18(17). https://doi.org/10.3390/ijerph18179145
8. World Health Organization. (2019). Noise EURO. World Health Organization. https://www.who.int/
9. Costa, L. G., Cole, T. B., Dao, K., Chang, Y. C., Coburn, J., & Garrick, J. M. (2020). Effects of air pollution on the nervous system and its possible role in neurodevelopmental and neurodegenerative disorders. Pharmacology & Therapeutics, 210, 107523. https://doi.org/10.1016/j.pharmthera.2020.107523
10. Fredianelli, L., Bolognese, M., Fidecaro, F., & Licitra, G. (2021). Classification of noise sources for port area noise mapping. Environments, 8(2), 1–16. https://doi.org/10.3390/environments8020012
11. Fallah-Shorshani, M., Yin, X., McConnell, R., Fruin, S., & Franklin, M. (2022). Estimating traffic noise over a large urban area: An evaluation of methods. Environment International, 170, 107583. https://doi.org/10.1016/j.envint.2022.107583
12. W Group. (2026). IMMI—Noise prediction software. Retrieved January 7, 2026, from https://www.immi.eu/
13. Bravo-Moncayo, L., Lucio-Naranjo, J., Chávez, M., Pavón-García, I., & Garzón, C. (2019). A machine learning approach for traffic-noise annoyance assessment. Applied Acoustics, 156, 262–270. https://doi.org/10.1016/j.apacoust.2019.01.003
14. Fernandes, P., et al. (2020). Impacts of roundabouts in suburban areas on congestion-specific vehicle speed profiles, pollutant, and noise emissions: An empirical analysis. Sustainable Cities and Society, 62, 102386. https://doi.org/10.1016/j.scs.2020.102386
15. Benliay, A., Özyavuz, M., Çabuk, S., & Güneş, M. (2019). Use of noise mapping techniques in urban landscape design. Journal of Environmental Protection and Ecology, 20.
16. Sankov, P. M. (2025). Determination of economic losses due to noise pollution. In Integration of new technologies into science to improve research: Proceedings of the XXIV International Scientific and Practical Conference (pp. 163–168). International Science Group. https://doi.org/10.46299/ISG.2025.1.2413
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Petro Sankov, Yuriy Zakharov, Vadim Zakharov, Nataliia Тkach

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.