Integration of Satellite, Modeled, and Ground Based Aerosol Data for use in Air Quality and Public Health Applications

The advancements in the remote sensing of scientific datasets over the past decade have been recognized by governments around the world and led to the development of international Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan, which was endorsed by nearly 60 countries at the Earth Observation Summit-III in Brussels, February 2005. The plan for the U.S. contribution to GEOSS has been put forth in the The Strategic Plan for the U.S. Integrated Earth Observation System (IEOS) developed under the Interagency Working Group on Earth Observations (IWGEO, now a subcommittee) of the Committee on Environment and Natural Resources (CENR). The approach for the development of the U.S. IEOS is to focus on specific and achievable societal benefits, one of them being our ability to understand the impact of poor air quality on human health and well being, by integrating the nation's Earth observation capabilities.

Historically, the only source of aerosol air quality data available on an ongoing and systematic basis at national levels was generated by ambient air monitoring networks put in place for the US EPA's Air Quality Programs. However, scientific advances in the remote sensing of aerosols from space have improved dramatically. The emergence and application of these measurements adds a new dimension to air quality by enabling consistent observations of pollutants over large spatial domains. Current instruments aboard NASA and NOAA satellites can provide derived measurements of aerosols and have been demonstrated to correlate with high levels of particulate matter (PM10 and PM2.5) at the surface. In addition, within the next several years NOAA and EPA will begin to issue PM2.5 air quality forecasts over the entire domain of the eastern United States, eventually extending to national coverage. These forecasts will provide continuous estimated values of particulate matter on a daily basis. This modeled data, combined with the ground-based and satellite measures described above will result in the availability of enriched air quality information on an ongoing and systematic basis for use in a multitude of applications.

A multi-agency collaborative project among government and academia is underway to improve the spatial prediction of fine particulate matter through the integration of multi-sensor and multi-platform aerosol observations (MODIS and GOES), numerical model output, and air monitoring data. By giving more weight to monitoring data in monitored areas and relying on adjusted model output and satellite data in non-monitored areas, a statistical space-time model will be used to improve the accuracy of prediction and associated prediction errors. The improved spatial predictions will be used as estimates of exposure for input to modeling relationships between air quality and asthma/other respiratory diseases in the New York City for the time period 2000 - present. We will present the overall project plan and preliminary results with emphasis on how GEOSS framework is facilitating this effort.