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Improving Estimates of Sulfur, Nitrogen, and Ozone Total Deposition through Multi-Model and Measurement-Model Fusion Approaches.

Joshua S FuGregory R CarmichaelFrank DentenerWenche AasCamilla AnderssonLeonard A BarrieAmanda ColeCorinne Galy-LacauxJeffrey GeddesSyuichi ItahashiMaria KanakidouLorenzo LabradorFabien PaulotDonna SchwedeJiani TanRobert Vet
Published in: Environmental science & technology (2022)
Earth system and environmental impact studies need high quality and up-to-date estimates of atmospheric deposition. This study demonstrates the methodological benefits of multimodel ensemble and measurement-model fusion mapping approaches for atmospheric deposition focusing on 2010, a year for which several studies were conducted. Global model-only deposition assessment can be further improved by integrating new model-measurement techniques, including expanded capabilities of satellite observations of atmospheric composition. We identify research and implementation priorities for timely estimates of deposition globally as implemented by the World Meteorological Organization.
Keyphrases
  • particulate matter
  • healthcare
  • primary care
  • machine learning
  • high resolution
  • risk assessment
  • air pollution
  • deep learning
  • quality improvement
  • human health
  • neural network