Combination of spaceborne sensor(s) and 3-D aerosol models to assess global daily near-surface air quality

Aerosol Particulate Matter (PM), measured by ground-based monitoring stations, is used as a standard by the EPA (Environmental Protection Agency) to evaluate daily air quality. PM monitoring is particularly important for human health protection because the exposure to suspended particles can contribute, among others, to lung and respiratory diseases and even premature death. However, most of the PM monitoring stations are located close to cities, leaving large areas without any operational data. Satellite remote sensing is well suited for a global coverage of the aerosol load and can provide an independent and supplemental data source to in situ monitoring. Nevertheless, PM at the ground cannot easily be determined from satellite AOD (Aerosol Optical Depth) without additional information on the optical/microphysical properties and vertical distribution of the aerosols. The objective of this study is to explore the efficacy and accuracy of combining a 3-D aerosol transport model and satellite remote sensing as a cost-effective approach for estimating ground-level PM on a global and daily basis. The estimation of the near-surface PM will use the vertical distribution (and, if possible, the physicochemical properties) of the aerosols inferred from a transport model and the measured total load of particles in the atmospheric column retrieved by satellite sensor(s). The first step is to select a chemical transport model (CTM) that provides “good” simulated aerosol vertical profiles. A few global (e.g., WRF-Chem-GOCART) or regional (e.g., MM5-CMAQ, PM-CAMx) CTM will be compared during selected airborne campaigns like ARCTAS-CARB (Arctic Research of the Composition of the Troposphere from Aircraft and Satellites- California Air Resources Board). The next step will be to devise an algorithm that combines the satellite and model data to infer PM mass estimates at the ground, after evaluating different spaceborne instruments and possible multi-sensor combinations.