Techniques for improving air quality maps with data fusion

The U.S. Environmental Protection Agency's (EPA's) AIRNow Program provides maps of near real-time hourly Air Quality Index (AQI) conditions and daily AQI forecasts nationwide. The public uses these maps to make decisions concerning their respiratory health. The usefulness of the AIRNow air quality maps depends on the accuracy and spatial coverage of air quality measurements. Currently, the maps use only ground-based measurements, which have significant gaps in coverage. As a result, estimated AQI levels have high uncertainty in regions far from monitors.

To improve the usefulness of air quality maps, scientists at EPA and Sonoma Technology, Inc. (STI) are working in collaboration with the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and university researchers on a project to incorporate additional measurements into the maps via the AIRNow Satellite Data Processor (ASDP). These measurements include estimated surface PM2.5 concentrations from NASA/NOAA satellite aerosol optical depth (AOD) retrievals and surface PM2.5 concentration predictions from the Community Multi-scale Air Quality (CMAQ) model. The development of the ASDP will allow for the fusion of multiple PM2.5 concentration data sets to generate AQI maps with improved spatial coverage. The goal of ASDP is to provide better AQI information in monitor-sparse locations.

This work tests several data fusion techniques to produce the most accurate surface of ground-level PM2.5 concentrations. Among the techniques tested are a weighted average technique, hierarchical Bayesian modeling, and co-kriging. This presentation will show results of these data fusion techniques, focusing on each method's accuracy and suitability to produce air quality maps in real time.