Abstract
Budapest, the capital of Hungary, has been facing serious air pollution episodes in the heating season similar to other metropolises. In the city a dense urban air quality monitoring network is available; however, air quality prediction is still challenging. For this purpose, 24-h PM2.5 forecasts obtained from seven individual models of the Copernicus Atmosphere Monitoring Service (CAMS) were downscaled by using hourly measurements at six urban monitoring sites in Budapest for the heating season of 2018–2019. A 10-day long training period was applied to fit spatially consistent model weights in a linear combination of CAMS models for each day, and the 10-day additive bias was also corrected. Results were compared to the CAMS ensemble median, the 10-day bias-corrected CAMS ensemble median, and the 24-h persistence. Downscaling reduced the root mean square error (RMSE) by 1.4 µg/m3 for the heating season and by 4.3 µg/m3 for episodes compared to the CAMS ensemble, mainly by eliminating the general underestimation of PM2.5 peaks. As a side-effect, an overestimation was introduced in rapidly clearing conditions. Although the bias-corrected ensemble and model fusion had similar overall performance, the latter was more efficient in episodes. Downscaling of the CAMS models was found to be capable and necessary to capture high wintertime PM2.5 concentrations for the short-range air quality prediction in Budapest.
Highlights
Hungary’s capital, Budapest, the ninth largest city of the European Union, has been facing high concentrations of particulate matter (PM), and the guidelines defined by the European EnvironmentalAgency have been exceeded at urban monitoring sites, especially in the winter [1]
A 10-day additive bias was corrected for each model and the Copernicus Atmosphere Monitoring Service (CAMS) ensemble median
Both the bias-corrected ensemble and the model fusion-improved model predictions compared to the original CAMS ensemble
Summary
Hungary’s capital, Budapest, the ninth largest city of the European Union, has been facing high concentrations of particulate matter (PM), and the guidelines defined by the European EnvironmentalAgency have been exceeded at urban monitoring sites, especially in the winter [1]. Hungary’s capital, Budapest, the ninth largest city of the European Union, has been facing high concentrations of particulate matter (PM), and the guidelines defined by the European Environmental. Air Pollution and Health in Europe) project found that excess pollution caused by particulate matter smaller than 2.5 μm diameter (PM2.5 ) reduced life expectancy by 19 months in Budapest in the period of 2004–2006, which was the second highest value among the 25 investigated European cities [3]. Winter stagnation episodes with persistent inversions and high PM2.5 concentrations pose a major environmental risk in Budapest [7,8] and are expected to amplify the seasonal flu epidemic [9]. Atmosphere 2020, 11, 669 policies rely on the monitoring sites operated by the Hungarian Air Quality Network. The dense urban monitoring network consists of 12 stations, 6 of which provide hourly PM2.5 measurements
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