Improvement of air quality forecasts with satellite and ground based particulate matter observations

Daily regional scale forecasts of particulate air pollution are simulated for public information and warning. An increasing amount of air pollution measurements is available in real-time from ground stations as well as from satellite observations. In this paper, the Support Vector Regression technique is applied to derive highly-resolved PM10 initial fields for air quality modeling from satellite measurements of the Aerosol Optical Thickness. onally, PM10-ground measurements are assimilated using optimum interpolation. The performance of both approaches is shown for a selected PM10 episode.