In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM10 concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOTePM10 correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions. Meteorological factors, particularly relative humidity, were found to influence the AOTePM10 relationship. Their inclusion in regression models leads to more accurate assessment of PM10 from spaceborne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution. In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM10 level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM10 hindcasts for 2008e2009, using observations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing. Further studies will be necessary before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.