Traditional surface-based particle pollution measurements are the basis for compliance assessment, but they have limited spatial coverage. Satellites can provide much broader coverage, but they currently do not offer extensive particle vertical distribution information, and cannot distinguish among spherical aerosol compositions without calibration by ground truth. The proposal team has developed a spatial modeling approach that derives near-surface SO4 regional maps by combining satellite-derived spherical aerosol optical depth (AOD) fraction information with model-simulated aerosol vertical profiles. The results compare favorably with surface measurements [Liu, et al., 2007a; Liu, et al., 2007b; Liu, et al., 2004]. This proposal offers to take the next steps needed to bridge the gap between the recently published work and the operational use of this capability, by analyzing multiple years of predicted SO4 concentrations and comparing our model p redictions with other methods of estimating ground-level SO4 concentrations. EPA's Clean Air Interstate Rule (CAIR) proposes to dramatically reduce SO2 emissions in eastern United States in the next 10 - 15 years. Particle sulfate (SO4) measurements covering areas more remote from major pollution sources and population centers must be used to assess CAIR effectiveness in reducing long-range transport of SO4 precursors such as SO2. The proposed two-component project will apply our satellite-driven modeling approach, testing its feasibility for assessing SO4 concentrations in the context of CAIR. An advanced spatial-statistical model will be developed using MISR and OMI satellites and GEOS-Chem model data in Component A, to produce multi-year temporal and spatial distributions of SO4 concentrations for eastern US. In Component B, baseline results from Component A in 2004 will be compared with alternative SO4 concentration estimates from CMAQ simulations and from models combining satellite data and CMAQ simulations. We will also demonstrate a source apportionment technique to link pred icted SO4 concentrations with SO2 sources, and will explore possible computationally less-demanding indicators of CAIR progress. Our study addresses the priority topic of Air Quality (1.3.2). The goal of this project is a robust and cost-effective modeling framework to support EPA evaluation of the important CAIR regulation progress. This project will also help NASA improve predictive capability for atmospheric-composition-driven changes in air quality, expanding and accelerating the realization of societal benefits from Earth system science.