In this study we assess capabilities of NASA and NOAA satellite data for improved monitoring and prediction of air quality associated with fine particulates in California's San Joaquin Valley (SJV). SJV currently experiences some of the worst air quality associated with fine particulates in the United States, significantly affecting the public, environmental, and economic health of the area. Currently, point source measurements from ground-based monitoring networks are used to validate air quality model results, however these measurements are limited spatially. Satellite observations from NASA and NOAA sensors currently provide measurements of total vertical column aerosol amounts, or Aerosol Optical Depth (AOD), over the entire region at daily (NASA MODIS) to half-hourly (NOAA GOES) frequencies. However, the application of this satellite data for air quality purposes depends critically on characterizing the vertical distribution of the aerosol. Also, the southwestern US is a challenging environment for conducting accurate AOD retrievals due to regions of high surface reflectivity. Here we show results of an intensive measurement campaign conducted during February 2007 in which airborne lidar measurements were obtained to provide detailed aerosol vertical and size information to interpret and link the satellite and surface observations. The NASA Langley High Spectral Resolution Lidar (HSRL) instrument was flown through SJV on 9 occasions during a 10-day period that included the development of a significant air quality event and the clean periods preceding and following the event. We use the lidar observations to derive relationships between AOD and surface particulate concentrations. We also show how targeted improvements to the MODIS aerosol retrieval algorithm improve the measurement capability over the SJV region.

This project is conducted under the US EPA Advanced Monitoring Initiative (AMI) with support from the NASA Applied Sciences Program. The AMI program supports the goals of the Global Earth Observation System of Systems (GEOSS).