The MODIS on NASA's Earth Observation System (EOS) Terra and Aqua provide almost daily observation of aerosols over global land and ocean surfaces with moderate spatial resolution. Recent studies have demonstrated the potential of using satellite derived aerosol properties as a surrogate for fine particulate matter (PM2.5) air quality over local, regional and global scales. While satellite data sets provides the aerosol loading integrated over a column from surface to top of the atmosphere, the PM2.5 mass is measured from surface stations. These two measures of pollution correlate well with each other but their relationship is highly dependent on local meteorological conditions as well as vertical distribution of aerosols. The current study will explore the possibility of combining satellite data with model derived meteorological field into a neural network system to better estimate and monitor particulate matter air quality in south-eastern United States.