Improving Real-Time AIRNow Maps using Data Fusion

Air quality agencies across the United States, Canada, and Mexico deliver hourly, near real-time air quality data from over 2,000 monitors to the U.S. Environmental Protection Agency's AIRNow Program.  Providing spatially complete air quality maps in real time is integral to protecting public health.  However, portions of the country, including some urban areas, lack a dense network of air quality monitors, which can create difficulties estimating pollutant concentrations and mapping air quality (data?).  In this presentation, we explore techniques for incorporating model-generated air quality concentrations into air quality maps where station observations are lacking.  Model output and station observations were used to create a “fused” interpolated map of real-time air quality.  A similar technique was applied to fill in regions on air quality forecast maps where agency forecasts were not available.  The resulting product provided generally spatially complete ozone forecasts while maintaining consistency between agencies' forecasts and hourly animation of ozone evolution for regions with fewer monitors.