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Aerosol optical depth (AOD) acquired from satellite measurements demonstrates good correlation with particulate matter with diameters less than 2.5 μm (PM2.5) in some regions of the United States and has been used for monitoring and nowcasting air quality over the United States. This work investigates the relation between Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 over the 10 U.S. Environmental Protection Agency (EPA)-defined geographic regions in the United States on the basis of a 2-yr (2005-2006) match-up dataset of MODIS AOD and hourly PM2.5 measurements. The AOD retrievals demonstrate a geographical and seasonal variation in their relation with PM2.5. Good correlations are mostly observed over the eastern United States in summer and fall. The southeastern United States has the highest correlation coefficients at more than 0.6. The southwestern United States has the lowest correlation coefficient of approximately 0.2. The seasonal regression relations derived for each region are used to estimate the PM2.5 from AOD retrievals, and it is shown that the estimation using this method is more accurate than that using a fixed ratio between PM2.5 and AOD. Two versions of AOD from Terra (v4.0.1 and v5.2.6) are also compared in terms of the inversion methods and screening algorithms. The v5.2.6 AOD retrievals demonstrate better correlation with PM2.5 than v4.0.1 retrievals, but they have much less coverage because of the differences in the cloud-screening algorithm. [ABSTRACT FROM AUTHOR]

A system has been developed to combine remote sensing and ground-based measurements of aerosol concentration and aerosol light scattering parameters into a threedimensional view of the atmosphere over the United States. Utilizing passive and active remote sensors from space and the ground, the system provides tools to visualize particulate air pollution in near real time and archive the results for retrospective analyses. The main components of the system (Infusing satellite Data into Environmental Applications [IDEA], the U.S. Air Quality Weblog [Smog Blog], Smog Stories, U.S. Environmental Protection Agency's AIRQuest decision support system, and the Remote Sensing Information Gateway [RSIG]) are described, and the relationship of how data move from one system to another is outlined. To provide examples of how the results can be used to analyze specific pollution episodes, three events (two fires and one wintertime low planetary boundary layer haze) are discussed. Not all tools are useful at all times, and the limitations, including the sparsity of some data, the interference caused by overlying clouds, etc., are shown. Nevertheless, multiple sources of data help a state, local, or regional air quality analyst construct a more thorough picture of a daily air pollution situation than what one would obtain with only surface-based sensors. [ABSTRACT FROM AUTHOR]