AAAR 37th Annual Conference October 14 - October 18, 2019 Oregon Convention Center Portland, Oregon, USA
Abstract View
Integration of Ground-Based Particulate Matter Measurements with Satellite Observations in the Multi-Angle Imager for Aerosols (MAIA) Investigation
SINA HASHEMINASSAB, Kristal Verhulst, Michael Garay, Abigail Nastan, Randall V. Martin, Yang Liu, David Diner, Jet Propulsion Laboratory
Abstract Number: 371 Working Group: Urban Aerosols
Abstract NASA’s upcoming Multi-Angle Imager for Aerosols (MAIA) investigation (expected launch in 2022) is designed to study the adverse health effects of exposure to ambient particulate matter (PM) and its major chemical constituents. Observations collected by the MAIA satellite instrument, currently being built at JPL, will be matched with ground-based PM measurements in order to generate geostatistical regression relationships that will enable mapping of PM2.5 and PM10 total mass and speciated PM2.5 (sulfate, nitrate, ammonium, organic carbon, black carbon, and dust) at 1-km spatial resolution. This process will be carried out within a selected set of highly populated metropolitan areas distributed around the world. The resulting data products will be used by epidemiologists on the team and their collaborators to study acute, sub-chronic, and chronic health effects of PM having different proportions of size and compositional components.
Generation of the MAIA data products relies on the availability of sufficient ground-based PM monitors to relate aerosol properties measured from space to the PM levels on the ground. In this presentation, we will provide an overview of the MAIA investigation and discuss our progress in identifying and accessing existing ground-based PM data provided by governments, research groups, and other sources in the selected target areas. We will also discuss our efforts to evaluate and deploy additional monitors for PM total mass and species measurements in order to ensure adequate representation of surface measurements in the MAIA data processing approach.