Zita Ferenczi This email address is being protected from spambots. You need JavaScript enabled to view it.1, Kornélia Imre2,3, Mónika Lakatos1, Ágnes Molnár2,3, László Bozó1, Emese Homolya1, András Gelencsér2,3 

1 Hungarian Meteorological Service, Kitaibel 1, H-1024, Budapest, Hungary
2 ELKH-PE Air Chemistry Research Group, Egyetem 10, H-8200, Veszprém, Hungary
3 Research Centre for Biochemical, Environmental and Chemical Engineering, University of Pannonia, Egyetem 10, H-8200, Veszprém, Hungary

Received: March 5, 2021
Revised: June 16, 2021
Accepted: June 17, 2021

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.4209/aaqr.210048  

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Cite this article:

Ferenczi, Z., Imre, K., Lakatos, M., Molnár, Á., Bozó, L., Homolya, E., Gelencsér, A. (2021). Long-term Characterization of Urban PM10 in Hungary. Aerosol Air Qual. Res. https://doi.org/10.4209/aaqr.210048


  • Marked seasonality in PM10 concentration observed in Hungary.
  • Variations in PM10 levels were decomposed to principal components of pollution.
  • PM10 have association with temperature, wind speed and boundary layer height.
  • Regression model for predicting daily PM10 concentration was developed.


Over urban areas in Hungary, the annual average PM10 concentrations are not frequently higher than 40 μg m-3. Despite the mitigation efforts of the local governments, the annual number of exceedances of the daily limit of 50 μg m−3 is higher than what is outlined in EU Directive No 2008/50/EC. The goal of the present study is to assess the characteristics of the temporal (annual, seasonal, daily) variations in PM10 concentrations in selected Hungarian cities with large populations, where most of the exceedances have been reported. The impacts of meteorological conditions on the measured PM10 concentrations and their temporal variations are also evaluated. An important aspect of studying the trends of air pollution is that the tendencies depend not only on the emissions of certain pollutants but also on the meteorological conditions in the area of interest. To analyse emission-related trends, the meteorological signal must be removed from the data series. In this study, the Kolmogorov-Zurbenko (KZ) filter was used for this type of trend separation. Moreover, multiple nonlinear regression analysis was used to find relationships between the PM10 concentration and several meteorological parameters. The goal of this analysis is to estimate the expected daily mean PM10 concentration values. The results of this analysis demonstrate that the regression equation can provide an adequate method for PM pollution forecasting. In addition to the hourly PM10 concentrations and basic meteorological data, global radiation and boundary layer height were considered in the characterization process.

Keywords: PM10, Cold season episode, Regression analysis, Kolmogorov-Zurbenko (KZ) filter

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