2A.2 From Aerosol Optical Depth from Space to Near-Surface Particulate Matter

Monday, 7 January 2019: 10:45 AM
North 124A (Phoenix Convention Center - West and North Buildings)
Michael J. Garay, JPL, Pasadena, CA; and O. V. Kalashnikova, F. Xu, D. J. Diner, Y. Liu, H. Chang, M. Franklin, and J. Wang

The Multi-Angle Imager for Aerosols (MAIA) investigation was selected in 2016 by NASA’s Earth Venture Instrument program to improve the understanding of the connections between airborne particulate matter (PM) and human health through satellite observations. The MAIA instrument builds upon and extends the capabilities of the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments by making observations in the ultraviolet through the shortwave infrared, including three polarimetric bands. Data from MISR and MODIS have already been used to create global maps of fine particulate matter (PM2.5) and this information has, in turn, been used in global health studies. In contrast to the global surveys made possible by MISR and MODIS, the MAIA investigation will rely on targeted observations of a number of Primary Target Areas (PTAs) that represent major cities experiencing different levels and types of pollution.

A critical piece of the MAIA investigation is transforming the total column aerosol information retrieved from space to estimates of near-surface PM mass. In spite of years of work, there is at present skepticism in the community whether this is possible with sufficient fidelity to be useful for health studies. To address such concerns, we will describe the statistical approach that will be used by the MAIA team to establish the spatio-temporal relationships between MAIA-retrieved aerosols and PM measured by ground-based monitors to develop predictions of PM at km-scale resolution within the MAIA PTAs. This effort is not without its challenges, and these will be discussed, along with the results of some initial studies based on ground-based and airborne observations demonstrating and validating the MAIA strategy.

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