Advances in air quality modeling and forecasting

Details

• Journal Title:
  Global Transitions
• Personal Author:
  Baklanov, Alexander ; Zhang, Yang
• NOAA Program & Office:
  OAR (Oceanic and Atmospheric Research)
• Description:
  The importance of and interest to research and investigations of atmospheric composition and its modeling for different applications are substantially increased. Air quality forecast (AQF) and assessment systems help decision makers to improve air quality and public health, mitigate the occurrence of acute air pollution episodes, particularly in urban areas, and reduce the associated impacts on agriculture, ecosystems and climate. Advanced approaches in AQF combine an ensemble of state-of-the-art models, high-resolution emission inventories, satellite observations, and surface measurements of most relevant chemical species to provide hindcasts, analyses, and forecasts from global to regional air pollution and downscaling for selected countries, regions, and urban areas. Based on published reviews and recent analyses, the article discusses main gaps, challenges, applications and advances, main trends and research needs in further advancements of atmospheric composition and air quality modeling and forecasting.
• Source:
  Global Transitions, 2, 261-270
• DOI:
  https://doi.org/10.1016/j.glt.2020.11.001
• ISSN:
  2589-7918
• Format:
  PDF
• Publisher:
  Elsevier BV
• Document Type:
  Journal Article
• Funding:
  Grant no. NA20OAR4590259
• License:
  https://creativecommons.org/licenses/by/4.0/
• Rights Information:
  CC BY
• Compliance:
  Library
• Main Document Checksum:
  urn:sha256:accee2741f2f8c39ac1e826d4d3572f6b85b581ad460af8cd7480fff59c234dd
• Download URL:
  https://repository.library.noaa.gov/view/noaa/59030/noaa_59030_DS1.pdf
• File Type:
  [PDF - 658.27 KB ]