Abstract
Based on the Weather Research and Forecasting model with Chemistry (WRF-Chem) model and Gridpoint Statistical Interpolation (GSI) assimilation tool, a forecasting system was used for two selected episodes (winter and summer) over Eastern Europe. During the winter episode, very high particular matter (PM2.5, diameter less than 2.5 µm) concentrations, related to low air temperatures and increased emission from residential heating, were measured at many stations in Poland. During the summer episode, elevated aerosol optical depth (AOD), likely related to the transport of pollution from biomass fires, was observed in Southern Poland. Our aim is to verify if there is a relevant positive impact of surface and satellite data assimilation (DA) on modeled PM2.5 concentrations, and to assess whether there are significant differences in the DA’s impact on concentrations between the two seasons. The results show a significant difference in the impact of surface and satellite DA on the model results between the summer and winter episode, which to a large degree is related to the availability of the satellite data. For example, the application of satellite DA raises the factor of two statistic from 0.18 to 0.78 for the summer episode, whereas this statistic remains unchanged (0.71) for the winter. The study suggests that severe winter air pollution episodes in Poland and Eastern Europe in general, often related to the dense cover of low clouds, will benefit from the assimilation of surface observations rather than satellite data, which can be very sparse in such meteorological situations. In contrast, the assimilation of satellite data can have a greater positive impact on the model results during summer than the assimilation of surface data for the same period.
Highlights
Air pollution forecasts are used by policy makers and society at large to assess air quality for the forthcoming hours and days and to make decisions on future behavior
The assimilation of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) observations has a smaller influence on the mean model results than surface data assimilation (DA)
Our results show that the impact of surface PM2.5 and satellite AOD data assimilation on modeled PM2.5 concentrations can vary considerably for the same region depending on the season, the meteorological conditions, and availability of satellite data
Summary
Air pollution forecasts are used by policy makers and society at large to assess air quality for the forthcoming hours and days and to make decisions on future behavior. This is especially important for correctly predicting high pollution concentrations, as it allows prevention of the harmful effects of pollutants on human health [1]. The combined exposure to air pollutants and allergens has a synergistic or additive effect on asthma and allergies [9] This might be especially relevant in Europe, as it is estimated that the overall prevalence of hay fever among Europeans is approximately 15%–20% [10]. More than 20% of Poles suffer from hazel and alder allergies [11]
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