Project Details

Remote sensing images and data, such as aerosol optical depth (AOD) in conjunction with light detection and ranging (lidar) aerosol extinction profiles, can provide a 3D-profile of pollutant concentrations and transport, as shown in Figures 1-4. This information can greatly enhance air quality forecasts and regulatory analysis, which rely primarily on ground-based pollutant monitors. NASA satellite data are available for use by air quality forecasters and analysts, but the data can be difficult for non-experts to manipulate and interpret.

To make satellite data more accessible to the air quality community, the 3D-AQS project is combining NASA satellite data and imagery with lidar data to provide a three-dimensional profile of pollutant concentrations, with an emphasis on aerosol and particulates.

3D-AQS project members also provide interpretation of air quality in near real time on the U.S. Air Quality Blog, the "Smog Blog." With nearly 5 million visits to the site over the last three years, the Smog Blog has become a tool to help air quality forecasters augment their analyses with a "big picture" satellite view of national air quality.

The 3D-AQS project has three key initiatives. First, the project is improving existing data and visualization methods by developing finer resolution products, quantitative analysis of the satellite data with ground-based data, and creating new visualizations of 3D air quality information. Second, the project is integrating satellite and lidar data into EPA's AirQuest system, a database that merges AQS and AIRNow data with other monitor, model, and socioeconomic data, making the NASA data easily accessible to users. Third, the Integrating satellite Data from Environmental Applications (IDEA) product, which unites a range of satellite data in near real time, is being migrated to an operational environment within NOAA's National Environmental Satellite, Data, and Information Service (NESDIS). 3DAQS is also enhancing IDEA to include data from GASP and the ground-based lidar sensors.

An End User Advisory Committee comprised of air quality forecasters and analysts is providing input and advice at each stage of the project.

For more information about the 3D-AQS project, please download the initial project Benchmark Report. For a quick summary of the 3D-AQS project, please download the project summary.



Figure 1: MODIS Aqua true color image of smoke plume from wildfires in Montana on July 30, 2007, taken at approximately 2 PM local time. Because smoke has a very high concentration of particulates, it can be a public health hazard.

Figure 2: July 30 AIRNow 24-hr air quality index (AQI) values for the region affected by the Montana wildfires. Colored dots correspond to ground-based monitors; green and yellow indicate good and moderate air quality, respectively. There is a large gap in the monitor network in the vicinity of the wildfire, so an alternate means of tracking the impact of the plume, such as satellite data, is required.


Figure 3: MODIS aerosol optical depth (AOD) of smoke plume from wildfires in Montana on July 30, 2007. Note the swath of high AOD (colored red) in eastern Montana that corresponds to the visible smoke plume in Figure 1. AOD correlates to PM2.5 concentrations, so analysts and forecasters can use the satellite data to predict the transport and distribution of particulates from the fire in areas where there are no ground-based monitors in Montana and Wyoming.


Figure 4: 532 nm backscatter return signal from the CALIPSO lidar, taken at 3:20 AM local time on July 31, 2007. The satellite data show the vertical distribution of fine particles over eastern Montana from the wildfires on July 30, 2007. The red and yellow areas indicate elevated particulate concentrations. The CALIPSO data show that smoke from the wildfires rose high into the troposphere; smoke was in contact with the ground only at high altitude locations.