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Tytuł artykułu

UAV-based statistical analysis of seasonal traffic pollutant diffusion: a case study of Poland’s A4 highway

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Road transport is a significant source of pollution, accounting for up to 25% in the EU. Traffic pollution resulting from the movement of vehicles harms human health. Therefore, it is important to understand the process of spreading harmful substances from the source of their formation. This article presents an original method for measuring pollution in the roadside area for a key road in the Silesian Voivodeship. The focus was on determining the mathematical dependencies related to the diffusion of pollutants from the selected road section. The determined correlations may contribute to the implementation of traffic route projects, which can reduce air pollution. A key innovation of this study is the use of an unmanned aerial vehicle equipped with air quality sensors to perform spatial pollution measurements near a highway. This method enables a high-resolution, three-dimensional assessment of pollutant dispersion, which is then compared to conventional point-based data from the Chief Inspectorate of Environmental Protection (GIOŚ). This novel approach not only improves measurement coverage but also provides new insights for air quality modeling and environmental planning.
Czasopismo
Rocznik
Strony
223--235
Opis fizyczny
Bibliogr. 30 poz.
Twórcy
autor
  • Military University of Technology; gen. Kaliskiego 2, 00-902 Warsaw, Poland
autor
  • Military University of Technology; gen. Kaliskiego 2, 00-902 Warsaw, Poland
  • Military University of Technology; gen. Kaliskiego 2, 00-902 Warsaw, Poland
Bibliografia
  • 1. Andrych-Zalewska, M. & Chłopek, Z. & Merkisz, J. & Pielecha, J. Research on the results of the WLTP procedure for a passenger vehicle. Eksploatacja i Niezawodność - Maintenance and Reliability. 2024. Vol. 26(1). No. 176112.
  • 2. Žvirblis, T. & Hunicz, J. & Matijošius, J. et al. Improving diesel engine reliability using an optimal prognostic model to predict diesel engine emissions and performance using pure diesel and hydrogenated vegetable oil. Eksploatacja i Niezawodność - Maintenance and Reliability. 2023. Vol. 25(4). No. 174358.
  • 3. Krajowy bilans emisji SO2, NOx, CO, NH3, NMLZO, pyłów, metali ciężkich i TZO za lata 1990 2021. Available at: https://www.kobize.pl/uploads/materialy/Inwentaryzacje_krajowe/Krajowy_bilans_emisji.pdf. [In Polish: National emission balance of SO2, NOx, CO, NH3, NMVOCs, dust, heavy metals and POPs for the years 1990–2021].
  • 4. European Environment Agency (EEA). Available at: https://www.eea.europa.eu/data-andmaps/indicators/transport-emissions-of-air-pollutants-8/transport-emissions-of-air-pollutants-4.
  • 5. Jaroń, A. & Borucka, A. & Parczewski, R. Analysis of the impact of the COVID-19 pandemic on the value of CO2 emissions from electricity generation. Energies. 2022. Vol. 15(13). No. 4541.
  • 6. Sawczuk, W. & Merkisz-Guranowska, A. & Rilo Cañás, A. et al. New approach to brake pad wear modelling based on test stand friction-mechanical investigations. Eksploatacja i Niezawodność -Maintenance and Reliability. 2022. Vol. 24(3). No. 3. P. 419-426.
  • 7. Zheng, T. & LI, B. & Bing, X. et al. Vertical and horizontal distributions of traffic-related pollutants beside an urban arterial road based on unmanned aerial vehicle observations. Building and Environment. 2021. Vol. 187. No. 107401.
  • 8. Kozłowski, E. & Borucka, A. & Oleszczuk, P. & Leszczyński, N. Evaluation of readiness of the technical system using the semi-Markov model with selected sojourn time distributions. Eksploatacja i Niezawodność – Maintenance and Reliability. 2024. Vol. 26(4). DOI: 10.17531/ein/191545.
  • 9. Andrych-Zalewska, M. & Chlopek, Z. & Pielecha, J. et al. Investigation of exhaust emissions from the gasoline engine of a light duty vehicle in the real driving emissions test. Eksploatacja i Niezawodność - Maintenance and Reliability. 2023. Vol. 25(2). No. 165880.
  • 10. Fuller, C. & Brugge, D. & Williams, P.L. et al. Spengler estimation of ultrafine particle concentrations at near-highway residences using data from local and central monitors. Atmos. Environ. 2012. Vol. 57. No. 004. P. 257-265.
  • 11. Sun, Y. & Du, W. & Wan, Q. et al. Real-time characterization of aerosol particle composition above the urban canopy in Beijing: insights into the interactions between the atmospheric boundary layer and aerosol chemistry. Environ. Sci. Technol. 2015. Vol. 49(19). No. 02373. P. 11340-11347.
  • 12. Wang, D. & Huo, J. & Duan, Y. et al. Vertical distribution and transport of air pollutants during a regional haze event in eastern China: a tethered mega-balloon observation study. Atmos. Environ. 2021. Vol. 246. No. 118039.
  • 13. Samad, A. & Alvarez, D. & Florez, I. et al. Concept of using an unmanned aerial vehicle (UAV) for 3D investigation of air quality in the atmosphere – example of measurements near a roadside. Atmosphere. 2022. Vol. 13. No. 663.
  • 14. Lee, S. & Hwang, H. & Young, J. Vertical measurements of roadside air pollutants using a drone. Atmospheric Pollution Research. 2022. Vol. 13(12). No. 101609.
  • 15. Kuuluvainen, H. & Poikkimäki, M. & Järvinen, A. et al. Vertical profiles of lung deposited surface area concentration of particulate matter measured with a drone in a street canyon. Environ. Pollut. 2018. Vol. 241. No. 100. P. 96-105.
  • 16. Liu, X. & Shi, X. & He, X. et al. Vertical distribution characteristics of particulate matter beside an elevated expressway by unmanned aerial vehicle measurements. Build. Environ. 2021. Vol. 206. No. 108330.
  • 17. Park, Y. Assessing personal exposure to traffic-related air pollution using individual travel-activity diary data and an on-road source air dispersion model. Health & Place. 2020. Vol. 63. No. 102351.
  • 18. Wei, P. & Brimblecombe, P. & Yang, F. et al. Determination of local traffic emission and nonlocal background source contribution to on-road air pollution using fixed-route mobile air sensor network. Environ. Pollut. 2021. Vol. 290. No. 118055.
  • 19. Miller, D. & Actkinson, B. & Padilla, L. et al. Characterizing elevated urban air pollutant spatial patterns with mobile monitoring in Houston. Environ. Sci. Technol. 2020. Vol. 54. No. 4. P. 2133-2142.
  • 20. Lim, C.C. & Kim, H. & Vilcassim, M.J.R. et al. Mapping urban air quality using mobile sampling with low-cost sensors and machine learning in Seoul. South Korea. Environ. 2019. Vol. 131. No. 105022.
  • 21. Belkacem, I. & Khardi, S. & Helali, A. et al. The influence of urban road traffic on nanoparticles: roadside measurements. Atmos. Environ. 2020. Vol. 242. No. 117786.
  • 22. Pateraki, S. & Manousakas, M. & Bairachtari, K. et al. The traffic signature on the vertical PM profile: environmental and health risks within an urban roadside environment. Sci. Total Environ. 2019. Vol. 646. No. 289. P. 448-459.
  • 23. Komnos, D. & Tsiakmakis, S. & Pavlovic, J. et al. Analysing the real-world fuel and energy consumption of conventional and electric cars in Europe. Energy Conversion and Management. 2022. Vol. 270. No. 116161.
  • 24. Jaroń, A. & Borucka, A. & Deliś, P. & Sekrecka, A. An assessment of the possibility of using unmanned aerial vehicles to identify and map air pollution from infrastructure emissions. Energies. 2024. Vol. 17(3). No. 577.
  • 25. Mikhailuta, S.V. & Taseiko, O.V. et al. Seasonal fluctuations in air pollution concentrations in the city of Krasnoyarsk. Environ Monit Assess. 2008. Vol. 149. No. 9. P. 329-341.
  • 26. Li, B. & Cao, R. & He, H.D. et al. Three-dimensional diffusion patterns of traffic-related air pollutants on the roadside based on unmanned aerial vehicles monitoring. Building and Environment. 2022. Vol. 219. No. 109159.
  • 27. Sang, T. & Zhu, K. & Shen, J. & Yang, L. An uncertain programming model for fixed charge transportation problem with item sampling rates. Eksploatacja i Niezawodność – Maintenance and Reliability. 2025. Vol. 27(1). DOI: 10.17531/ein/192165.
  • 28. Furch, J. & Jelínek, J. Design of a tribotechnical diagnostics model for determining the technical condition of an internal combustion engine during its life cycle. Eksploatacja i Niezawodność –Maintenance and Reliability. 2022. Vol. 24(3). No. 5. P. 437-445.
  • 29. Kosucki, A. & Szczepaniak, M. & Skowrońska, J. et al. Data-driven operational pressure estimation for hydraulic actuators fed by fixed displacement pump with variable speed. Eksploatacja i Niezawodność – Maintenance and Reliability. 2025. Vol. 27(1). DOI: https://doi.org/10.17531/ein/192758.
  • 30. Oszczypała, M. & Ziółkowski, J. & Małachowski, J. Semi-Markov approach for reliability modelling of light utility vehicles. Eksploatacja i Niezawodność – Maintenance and Reliability. 2023. Vol. 25(2). No. 161859.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-23dda5de-2c88-4ebd-9bd4-a987c4c36c37
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