Preview

Nature Management

Advanced search

Modeling road transport emissions of pollutants with high spatial resolution

Abstract

Road transport is a significant contributor to atmospheric air pollution, and accurate estimates of emissions are crucial for understanding and managing air pollution. This paper presents a bottom-up approach for estimating annual emissions based on vehicle mileage modeling with a spatial resolution of 1 km × 1 km for the entire territory of Belarus. The study highlights the high spatial heterogeneity and differences in the territorial structure of emissions of various pollutants. It also identifies the contribution of roads of different levels and categories of vehicles that form them. The emission densities of four major pollutants, namely carbon monoxide, nitrogen oxides, non-methane volatile organic compounds, and fine particulate matter in urban and other areas were calculated.

About the Authors

O. Yu. Krukowskaya
Institute of Nature Management of the National Academy of Sciences of Belarus
Belarus

Olga Yu. Krukowskaya – Ph. D. (Georgaphy),  Senior Researcher

10, F. Skoriny Str., 220076, Minsk



S. V. Kakareka
Institute of Nature Management of the National Academy of Sciences of Belarus
Belarus

Sergey V. Kakareka – D. Sc. (Technical), Professor, Head of Lab of Transboundary Pollution

10, F. Skoriny Str., 220076, Minsk



References

1. Maes A. de S., Hoinaski L., Meirelles T. B. and Carlson R. C. A methodology for high resolution vehicular emissions inventories in metropolitan areas: automotive technologies improvement. Transportation Research Part D: Transport and Environment. 2019, no. 77, pp. 303–19. doi:10.1016/j.trd.2019.10.007

2. Ferreira J. [et al.]. A comparative analysis of two highly spatially resolved European atmospheric emission inventories. Atmospheric Environment, 2013, no. 75, pp. 43–57. doi:10.1016/j.atmosenv.2013.03.052

3. Trombetti M. [et al.]. Spatial inter-comparison of Top-down emission inventories in European urban areas. Atmospheric Environment, 2018, no. 173, pp. 142–56. doi:10.1016/j.atmosenv.2017.10.032

4. Borge R. [et al.]. Comparison of road traffic emission models in Madrid (Spain). Atmospheric Environment, 2012, no. 62, pp. 461–71. doi:10.1016/j.atmosenv.2012.08.073

5. Saide P., Zah R., Osses M., Eicker M.O. De Spatial disaggregation of traffic emission inventories in large cities using simplified top-down methods. Atmospheric Environment, 2009, no. 43, pp. 4914–23. doi:10.1016/j.atmosenv.2009.07.013

6. Tuia D. [et al.]. Evaluation of a simplified top-down model for the spatial assessment of hot traffic emissions in mid-sized cities. Atmospheric Environment, 2007, no. 41, pp. 3658–71. doi:10.1016/j.atmosenv.2006.12.045

7. Instruktsiya o poryadke ucheta vybrosov zagryaznyayushchikh veshchestv v atmosfernyy vozdukh ot mobil'nykh istochnikov vybrosov : postanovleniye Ministerstva prirodnykh resursov i okhrany okruzhayushchey sredy Respubliki Belarus' № 6 ot 15.02.2010 [Instructions on the procedure for recording emissions of pollutants into the atmospheric air from mobile emission sources. Resolution of the Ministry of Natural Resources and Environmental Protection of the Republic of Belarus, 15 February 2010, no. 6]. Available at: https://pravo.by/document/?guid=3961&p0=W21022110 (accessed 4 January 2023). (in Russian)

8. Ob utverzhdenii ekologicheskikh norm i pravil: postanovleniye Ministerstva prirodnykh resursov i okhrany okruzhayushchey sredy Respubliki Belarus' ot 29 dekabrya 2022 g. № 32-T [On the approval of environmental norms and rules. Resolution of the Ministry of Natural Resources and Environmental Protection of the Republic of Belarus, 29 December 2022, no. 32-T]. Available at: https://pravo.by/document/?guid=12551&p0=W22339600p&p1=1 (accessed 18 June 2023). (in Russian)

9. Interaktivnaya informatsionno-analiticheskaya sistema rasprostraneniya ofitsial'noy statisticheskoy informatsii: Natsional'nyy statisticheskiy komitet Respubliki Belarus' [Interactive information and analytical system for disseminating official statistical information]. National Statistical Committee of the Republic of Belarus. Available at: http://dataportal.belstat.gov.by/ (accessed 22 January 2022). (in Russian)

10. Mangaraj P., Sahu S. K., Beig G., Yadav R. A comprehensive high-resolution gridded emission inventory of anthropogenic sources of air pollutants in Indian megacity Kolkata. SN Applied Sciences, 2022, no. 4. doi:10.1007/s42452-022-05001-3

11. Alam M. S., Duffy P., Hyde B., McNabola A. Downscaling national road transport emission to street level: A case study in Dublin, Ireland. Journal of Cleaner Production, 2018, no. 183, pp. 797–809. doi:10.1016/J.JCLEPRO.2018.02.206

12. Fameli K. M., Assimakopoulos V. D. Development of a road transport emission inventory for Greece and the Greater Athens Area: Effects of important parameters. Science of the Total Environment, 2015, no. 505, pp. 770–86. doi:10.1016/j.scitotenv.2014.10.015

13. Romero Y. [et al.]. Quantifying and spatial disaggregation of air pollution emissions from ground transportation in a developing country context: Case study for the Lima Metropolitan Area in Peru. The Science of the Total Environment, 2020, no. 698.

14. Sun S., Jiang W., Gao W. Vehicle emission trends and spatial distribution in Shandong province, China, from 2000 to 2014. Atmospheric Environment, 2016, no. 147, pp. 190–9. doi:10.1016/j.atmosenv.2016.09.065

15. Gioli B. [et al.]. Improving high resolution emission inventories with local proxies and urban eddy covariance flux measurements. Atmospheric Environment, 2015, no. 115, pp. 246–56. doi:10.1016/j.atmosenv.2015.05.068

16. Guevara M. [et al.]. An improved system for modelling Spanish emissions: HERMESv2.0. Atmospheric Environment, 2013, no. 81, pp. 209–21. doi:10.1016/j.atmosenv.2013.08.053

17. Gately C. K., Hutyra L. R., Wing I. S., Brondfield M. N. A bottom up approach to on-road CO2 emissions estimates: Improved spatial accuracy and applications for regional planning. Environmental Science and Technology, 2013, no. 47, pp. 2423–30. doi:10.1021/es304238v

18. Breuer J. L., Samsun R. C., Peters R., Stolten D. The impact of diesel vehicles on NOx and PM10 emissions from road transport in urban morphological zones: A case study in North Rhine-Westphalia, Germany. Science of the Total Environment, 2020, no. 727, pp. 138583. doi:10.1016/j.scitotenv.2020.138583

19. Pallavidino L. [et al.]. Compilation of a road transport emission inventory for the Province of Turin: Advantages and key factors of a bottom-up approach. Atmospheric Pollution Research, 2014, no. 5, pp. 648–55. doi:10.5094/APR.2014.074

20. Yang D. [et al.]. High-resolution mapping of vehicle emissions of atmospheric pollutants based on large-scale, real-world traffic datasets. Atmospheric Chemistry and Physics, 2019, no. 19, pp. 8831–43. doi:10.5194/acp-19-8831-2019

21. López-Aparicio S. [et al.]. Assessment of discrepancies between bottom-up and regional emission inventories in Norwegian urban areas. Atmospheric Environment, 2017, no. 154, pp. 285–96. doi:10.1016/j.atmosenv.2017.02.004

22. European Environment Agency European Union emission inventory report 1990–2021 under the UNECE Convention on Long-range Transboundary Air Pollution (LRTAP). EEA Tech. Rep. 2023. Available at: https://www.eea.europa.eu/publications/european-union-emissions-inventory-report-1990-2021/download (accessed 18 May 2023).

23. Belarusian emission inventory data informative inventory report to CLRTAP/EMEP 2020. Available at: https://web-dab01.umweltbundesamt.at/download/submissions2020/BY_IIR2020.zip?cgiproxy_skip=1 (accessed 14 January 2023).

24. Kousoulidou M. [et al.]. Validation of the COPERT road emission inventory model with real-use data. Emissions Inventories-Informing Emerging Issues, 2010, p. 40.

25. Kakareka S. V., Krukovskaya O. Yu. Otsenka vybrosov zagryaznyayushchikh veshchestv ot dorozhnykh peredvizhnykh istochnikov s ispol'zovaniyem modeli COPERT IV [Estimation of pollutant emissions from road mobile sources using the COPERT IV model]. Sbornik nauch. trudov "Okhrana atmosfernogo vozdukha. Atmosfera" [Proc. of sci. works "Atmospheric air protection"]. Atmosphere, 2013, pp. 35–41. (in Russian)

26. RUP "BELDORTSENTR" – Respublikanskoye unitarnoye predpriyatiye "Belorusskiy dorozhnyy inzhenerno tekhnicheskiy tsentr [RUE "BELDORTSENTR – Republican Unitary Enterprise "Belarusian Road Engineering and Technical Center"] [Electronic resource]. Available at: https://beldor.centr.by/ (accessed 12 January 2021). (in Russian)

27. OpenStreetMap (2023). Available at: https://www.openstreetmap.org/#map=10 (accessed 12 April 2023).

28. Climate Data Online: Web Services Documentation. National Center Environmental Information (2023). Available at: https://www.ncei.noaa.gov/cdo-web/ (accessed 15 April 2023).

29. Venter Z. S. [et al.]. Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover. Remote Sensing, 2022, no. 14, pp. 4101. doi:10.3390/rs14164101.

30. Markakis K., Poupkou A., Melas D., Zerefos C. A GIS based anthropogenic PM10 emission inventory for Greece. Atmospheric Pollution Research, 2010, no. 1, pp. 71–81. doi:10.5094/APR.2010.010.

31. Puliafito S. E. [et al.]. High-resolution seasonal and decadal inventory of anthropogenic gas-phase and particle emissions for Argentina. Earth System Science Data, 2021, no. 13, pp. 5027–69. doi:10.5194/essd-13-5027-2021.


Review

For citations:


Krukowskaya O.Yu., Kakareka S.V. Modeling road transport emissions of pollutants with high spatial resolution. Nature Management. 2023;(2):24-38. (In Russ.)

Views: 79

JATS XML


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 2079-3928 (Print)