Advances in multiangle satellite remote sensing of speciated airborne particulate matter and association with adverse health effects: from MISR to MAIA

作者:Diner, David J.*; Boland, Stacey W.; Brauer, Michael; Bruegge, Carol; Burke, Kevin A.; Chipman, Russell; Di Girolamo, Larry; Garay, Michael J.; Hasheminassab, Sina; Hyer, Edward; Jerrett, Michael; Jovanovic, Veljko; Kalashnikova, Olga, V; Liu, Yang; Lyapustin, Alexei, I; Martin, Randall, V; Nastan, Abigail; Ostro, Bart D.; Ritz, Beate; Schwartz, Joel; Wang, Jun; Xu, Feng
来源:Journal of Applied Remote Sensing, 2018, 12(4): 042603.
DOI:10.1117/1.JRS.12.042603

摘要

Inhalation of airborne particulate matter (PM) is associated with a variety of adverse health outcomes. However, the relative toxicity of specific PM types-mixtures of particles of varying sizes, shapes, and chemical compositions-is not well understood. A major impediment has been the sparse distribution of surface sensors, especially those measuring speciated PM. Aerosol remote sensing from Earth orbit offers the opportunity to improve our understanding of the health risks associated with different particle types and sources. The Multi-angle Imaging SpectroRadiometer (MISR) instrument aboard NASA's Terra satellite has demonstrated the value of near-simultaneous observations of backscattered sunlight from multiple view angles for remote sensing of aerosol abundances and particle properties over land. The Multi-Angle Imager for Aerosols (MAIA) instrument, currently in development, improves on MISR's sensitivity to airborne particle composition by incorporating polarimetry and expanded spectral range. Spatiotemporal regression relationships generated using collocated surface monitor and chemical transport model data will be used to convert fractional aerosol optical depths retrieved from MAIA observations to near-surface PM10, PM2.5, and speciated PM2.5. Health scientists on the MAIA team will use the resulting exposure estimates over globally distributed target areas to investigate the association of particle species with population health effects. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

  • 出版日期2018-7-28