Long-term Characterization of Urban PM10 in Hungary

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 g...

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Bibliographic Details
Published in:Aerosol and air quality research 2021-10, Vol.21 (10), p.210048
Main Authors: Ferenczi, Zita, Imre, Kornélia, Lakatos, Mónika, Molnár, Ágnes, Bozó, László, Homolya, Emese, Gelencsér, András
Format: Article
Language:English
Subjects:
Aerosols
Air pollution
Boundary layers
Emission analysis
Emissions
Local government
Meteorological data
Meteorological parameters
Mitigation
Optical properties
Outdoor air quality
Particle size
Particulate matter
Pollutants
Pollution forecasting
Radiation
Regression analysis
Temporal variations
Traffic
Trends
Urban areas
Weather
Winter
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Summary: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.
ISSN:1680-8584
2071-1409
DOI:10.4209/aaqr.210048