The US EPA's AIRNow program (airnow.gov) delivers real-time air quality conditions and forecasts to the public for more than 300 locations throughout the United States. These air quality data are available in all major US cities as well as several other smaller cities. AIRNow focuses much of its efforts in urban areas where there is an increased number of man-made pollution sources relative to less populated locales. As populations in urban areas increases, so does the concern over the air the people are breathing and the need for effective communication of air quality information.

Air quality agencies across the U.S., Canada, and Mexico deliver hourly real-time air quality data from over 2000 monitors to EPA's AIRNow program. These data are then transformed into air quality contour maps that can be easily understood by the public. However, in portions of the country, even in some urban areas, the network of air quality monitors can be sparse, which makes communicating pollutant concentrations and air quality mapping difficult. This work explores geo-spatial techniques for incorporating model-observation air quality maps in order to estimate pollutant concentrations where sparse data creates too much uncertainty to rely on observations alone. Output from the NOAA Ozone Model and the BlueSky Gateway PM2.5 Modeling Network was used to provide concentrations for these regions. An interpolated surface was then generated using both observations and model output for mapping.

A similar technique was applied to air quality forecast maps. The NOAA Ozone Model maximum 8-hour average ozone forecasts were used to fill in gaps between official city forecasts issued by state and local air quality agencies. Where these forecasts were available, model output was adjusted to match them. The resulting product provided spatially complete ozone forecasts while remaining consistent with agencies' forecasts. It also provided an hourly animation of ozone evolution for a particular region.

In this talk, we will present the progress made towards this more advanced mapping system for the AIRNow program. We will also explain the implications this new mapping system has for communicating air quality internationally throughout major world cities and developing nations. An explanation of the new AIRNow mapping system's immediate role in Shanghai, China as well as long term involvement in the Global Earth Observation System of Systems (GEOSS) will also be discussed.