Linking NASA Data with Environmental Exposures and Health Outcomes in Theater of War

Abstract

The hypothesis of this study is that a suite of remote sensing data products on atmospheric aerosols used in their meteorological context and processed by machine learning can provide a daily estimate of the global PM 2.5 abundance. This information is of considerable value to Global Health Surveillance (GHS), providing a capability to routinely estimate troop deployment exposure to elevated levels of particulate matter (PM) globally, significantly contributing to DoD-wide force health protection initiatives. We have exceeded our promised goal and have provided a daily global estimate of the PM2.5 distribution from February 2000 up through the present (we promised from 2006-present).

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2012
Accession Number
AD1035835

Entities

People

  • David Lary

Organizations

  • University of Texas at Dallas

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Atmospheric Chemistry
  • Atmospheric Sciences
  • Department Of Defense
  • Environment
  • Environmental Exposure
  • Environmental Protection
  • Geography
  • Health
  • Machine Learning
  • Measurement
  • Optical Properties
  • Particles
  • Particulate Matter
  • Remote Sensing
  • Three Dimensional
  • United States

Fields of Study

  • Environmental science

Readers

  • Aerosol Science/Aerosol Physics
  • Aerospace logistics and air mobility.
  • Marine Mammal Biology

Technology Areas

  • AI & ML
  • AI & ML - DoD AI Strategy