Regarding the data inconsistency from different data sources on emissions and ground-level pollutants’ concentrations in the atmospheric air over Ukraine

Mykhailo Savenets
Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Sciences of Ukraine, Kyiv, Ukraine
https://orcid.org/0000-0001-9429-6209

Liudmyla Nadtochii
Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Sciences of Ukraine, Kyiv, Ukraine
https://orcid.org/0000-0003-3038-5960

Tetiana Kozlenko
Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Sciences of Ukraine, Kyiv, Ukraine
https://orcid.org/

Kateryna Komisar
Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Sciences of Ukraine, Kyiv, Ukraine
https://orcid.org/

Antonina Umanets
Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Sciences of Ukraine, Kyiv, Ukraine
https://orcid.org/0009-0008-4867-4430

Natalia Zhemera
Ukrainian Hydrometeorological Institute of the State Emergency Service of Ukraine and the National Academy of Sciences of Ukraine, Kyiv, Ukraine
https://orcid.org/

DOI: http://doi.org/10.15407/Meteorology2024.06.017

Keywords: atmospheric air, pollution, emissions, concentrations, reanalysis, observations

Abstract

The development of action plans and strategies to reduce atmospheric air pollution requires the use of emissions and concentration data over extended periods. At such scales, the role of uncertainties increases, potentially leading to the development of ineffective measures. This article presents a study of the consistency of data from various sources, including official emissions inventories, modeled emissions data from the Copernicus Atmosphere Monitoring Service (CAMS), pollutant concentration data measured at stationary monitoring stations of hydrometeorological organizations, and ground-level content data from CAMS reanalysis for carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2). The research revealed significant inconsistencies among different datasets, often reflecting entirely different interannual variability and trends. While emissions predominantly show a declining trend, concentrations in most cities continue to rise based on observational data and often show no significant changes according to reanalysis data. Notably, agreement between emissions data from different sources was found in only 12 cases across citypollutant pairs. Consistency in pollutant concentration data was identified in only 3 cities for CO and 4 cities for SO2. The differences in emission volumes, even where high correlations exist, can vary by an order of magnitude for certain cities. The article provides a list of cities for each of the studied pollutants where consistency between different data sources is observed, identifying cases where the data can be complementary or interchangeable. The study emphasized that this inconsistency has negative implications for the ability to assess interannual changes, the quality of modeling and data interchangeability, the verification of evaluation results regarding the effectiveness of air quality management measures, and a wide range of other consequences.

References

1. Babak, V., Zaporozhets, A., Isaienko, V., & Babikova, K. (2020). Analysis of the Air Pollution Monitoring System in Ukraine. Systems, Decision and Control in Energy I. Studies in Systems, Decision and Control, 298. Springer, Cham. https://doi.org/10.1007/978-3-030-48583-2_6

2. Bashtannik, M.P., Zhemera, N.S., Kiptenko, E.M., & Kozlenko, T.V. (2014). Stan zabrudnennia atmosfernogo povitria nad terytorieyu Ukrainy [The state of atmospheric air pollution over Ukraine]. Naukovi pratsi UkrNDGMI, 266, 70-93

3. Brasseur, G.P. (1997). Formulation of a Chemical Transport Model. In: Brasseur, G.P. (eds) The Stratosphere and Its Role in the Climate System. Nato ASI Series, vol 54. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-03327-2_18

4. Chastko K, & Adams M. (2019). Improving long-term air pollution estimates with incomplete data: A method-fusion approach. MethodsX, 6:1489-1495. https://doi.org/10.1016/j.mex.2019.06.005

5. Chugai, A.V., & Safranov, T.A. (2020). Features of air pollution the cities of the North-Western Black Sea region. Visnyk of V. N. Karazin Kharkiv National University, Series Geology. Geography. Ecology, 52, 251-260. https://doi.org/10.26565/2410-7360-2020-52-18

6. Galán-Madruga D. (2021) A methodological framework for improving air quality monitoring network layout. Applications to environment management. J Environ Sci (China), 102:138-147. https://doi.org/10.1016/j.jes.2020.09.009

7. Granier, C., Bessagnet, B., Bond, T., D’Angiola, A., Denier van der Gon, H., Frost. G.J., Heil, A., Kaiser, J.W., Kinne, S., Klimont, Z., Kloster, S., Lamarque, J.-F., Liousse, C., Masui, T., Meleux, F., Mieville, A., Ohara, T., Raut, J.-C., Riahi, K., Schultz, M.G., Smith, S.J., Thompson, A., van Aardenne, J., van der Werf, G.R. & van Vuuren, D.P. (2011) Evolution of anthropogenic and biomass burning emissions of air pollutants at global and regional scales during the 1980–2010 period. Climatic Change, 109, 163. https://doi.org/10.1007/s10584-011-0154-1

8. Inness, A., Ades, M., Agustí-Panareda, A., Barré, J., Benedictow, A., Blechschmidt, A.-M., Dominguez, J. J., Engelen, R., Eskes, H., Flemming, J., Huijnen, V., Jones, L., Kipling, Z., Massart, S., Parrington, M., Peuch, V.-H., Razinger, M., Remy, S., Schulz, M., & Suttie, M. (2019). The CAMS reanalysis of atmospheric composition, Atmos. Chem. Phys., 19, 3515–3556, https://doi.org/10.5194/acp-19-3515-2019

9. Krupnick, A.J. (2008). Challenges to managing air pollution. J Toxicol Environ Health A., 71(1):13-23. https://doi.org/10.1080/15287390701557404

10. Malik, A., Aggarwal, S.G., Sinha, P.R., Kondo, Y., & Ohata, S. (2024). On the biases of MERRA-2 reanalysis and ground-based measurements of black carbon aerosols over India. Atmospheric Pollution Research in press, 102325. https://doi.org/10.1016/j.apr.2024.102325

11. Malytska, L., Ladstätter-Weißenmayer, A., Galytska, E., & Burrows, J.P. (2024) Assessment of environmental consequences of hostilities: Tropospheric NO2 vertical column amounts in the atmosphere over Ukraine in 2019–2022. Atmospheric Environment, 318, 120281. https://doi.org/10.1016/j.atmosenv.2023.120281

12. Manisalidis, I., Stavropoulou, E., Stavropoulos, A., & Bezirtzoglou, E. (2020). Environmental and Health Impacts of Air Pollution: A Review. Front. Public Health, 8:14. https://doi.org/10.3389/fpubh.2020.00014

13. Melnychenko, S. G., Bohadorova, L. M., & Okhremenko, I. V. (2023). Pollutants emissions dynamics by stationary and mobile sources of pollution within Ukraine. Man and Environment. Issues of Neoecology, (40), 42-52. https://doi.org/10.26565/1992-4224-2023-40-04

14. Puiu, S., Udriștioiu, M.T. & Velea, L. (2022) Air Pollution Management: A Multivariate Analysis of Citizens' Perspectives and Their Willingness to Use Greener Forms of Transportation. Int J Environ Res Public Health., 19(21):14613. https://doi.org/10.3390/ijerph192114613

15. Qiu, M., Zigler, C., & Selin, N. E. (2022). Statistical and machine learning methods for evaluating trends in air quality under changing meteorological conditions. Atmos. Chem. Phys., 22, 10551–10566, https://doi.org/10.5194/acp-22-10551-2022

16. Rentschler, J. & Leonova, N. (2023). Global air pollution exposure and poverty. Nat Commun, 14, 4432. https://doi.org/10.1038/s41467-023-39797-4

17. Ryu, Y-H. & Min, S-K. (2021). Long-term evaluation of atmospheric composition reanalyses from CAMS, TCR-2, and MERRA-2 over South Korea: Insights into applications, implications, and limitations. Atmospheric Environment, 246, 118062. https://doi.org/10.1016/j.atmosenv.2020.118062

18. Savenets M., Dvoretska I., Nadtochii L., & Zhemera N. (2022). Comparison of TROPOMI NO2, CO, HCHO, and SO2 data against ground-level measurements in close proximity to large anthropogenic emission sources in the example of Ukraine. Meteorological Applications, 29(6), e2108. https://doi.org/10.1002/met.2108

19. Shaddick, G., Thomas, M.L., Mudu, P., Ruggeri, G., & Gumy, S. (2020). Half the world’s population are exposed to increasing air pollution. npj Clim Atmos Sci, 3, 23. https://doi.org/10.1038/s41612-020-0124-2

20. Shahgedanova, M., & Burt, T.P. (1994). New data on air pollution in the former Soviet Union. Global Environmental Change, 4(3), 201-227. https://doi.org/10.1016/0959-3780(94)90003-5

21. Smith, W.H. (1992). Air Pollution Effects on Ecosystem Processes. In: Barker, J.R., Tingey, D.T. (eds) Air Pollution Effects on Biodiversity. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-3538-6_11

22. Sofiev, M., Ermakova, T., & Vankevich, R. (2012) Evaluation of the smoke-injection height from wild-land fires using remote-sensing data, Atmos. Chem. Phys., 12, 1995–2006, https://doi.org/10.5194/acp-12-1995-2012

23. Vedrenne, M., Borge, R., Lumbreras, J., Conlan, B., Rodríguez, M.E., de Andrés J. M., de la Paz, D., Pérez, J., & Narros, A. (2015) An integrated assessment of two decades of air pollution policy making in Spain: Impacts, costs and improvements. Science of The Total Environment, 527–528, 351-361, https://doi.org/10.1016/j.scitotenv.2015.05.014

24. Vilcins, D., Christofferson, R.C., Yoon, J.H., Nazli, S.N., Sly, P.D., Cormier, S.A., & Shen, G. (2024). Updates in Air Pollution: Current Research and Future Challenges. Ann Glob Health., 90(1):9. https://doi.org/10.5334/aogh.4363

25. Yatsenko Y., Shevchenko O., & Snizhko S. (2018). Assessment of air pollution level of nitrogen dioxide and trends of it changes in the cities of Ukraine. Visnyk of Taras Shevchenko National University of Kyiv: Geology, 3(82), 87-95. http://doi.org/10.17721/1728-2713.82.11

About ׀ Editorial board ׀ Ethics ׀ For authors ׀ For reviewers ׀ Archive ׀ Contacts