The quantification of factors leading to harmfully high levels of particulate matter (PM) remains challenging. This study presents a novel approach using a statistical model that is trained to predict hourly concentrations of particles smaller than 10 μm (PM_(10)) by combining satellite-borne aerosol optical depth (AOD) with meteorological and land-use parameters. The model is shown to accurately predict PM_(10) (overall R2 =0.77, RMSE=7.44 μg/m~3) for measurement sites in Germany. The capability of satellite observations to map and monitor surface air pollution is assessed by investigating the relationship between AOD and PM_(10) in the same modeling setup. Sensitivity analyses show that important drivers of modeled PM_(10) include multiday mean wind flow, boundary layer height (BLH), day of year (DOY), and temperature. Different mechanisms associated with elevated PM_(10) concentrations are identified in winter and summer. In winter, mean predictions of PM_(10) concentrations >35 μg/m3 occur when BLH is below ~500 m. Paired with multiday easterly wind flow, mean model predictions surpass 40 μg/m3 of PM_(10). In summer, PM_(10) concentrations seemingly are less driven by meteorology, but by emission or chemical particle formation processes, which are not included in the model. The relationship between AOD and predicted PM_(10) concentrations depends to a large extent on ambient meteorological conditions. Results suggest that AOD can be used to assess air quality at ground level in a machine learning approach linking it with meteorological conditions.
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