Reprint

Atmospheric Dispersion and Chemistry Models: Advances and Applications

Edited by
September 2023
222 pages
  • ISBN978-3-0365-8755-4 (Hardback)
  • ISBN978-3-0365-8754-7 (PDF)

This book is a reprint of the Special Issue Atmospheric Dispersion and Chemistry Models: Advances and Applications that was published in

Chemistry & Materials Science
Environmental & Earth Sciences
Summary

Atmospheric dispersion and chemical transport models (CTMs) are a key tool in both atmospheric chemistry and environmental sciences. From urban air pollution modeling to ozone depletion, these models give us a picture, at different scales, of the distribution of species concentrations and pollutant deposition rates, among other relevant quantities. These models help us to interpret observational data which, in some cases, are sparse and incomplete. The papers included in this Special Issue, "Atmospheric Dispersion and Chemistry Models: Advances and Applications", published between 2022 and 2023, cover several developments and applications related to atmospheric dispersion and chemistry models. These studies highlight the potential benefits of using such models for many scientific and technical applications in atmospheric chemistry and environmental sciences, involving, for example, the characterization of aerosols and chemical species in the atmosphere, and risk analyses and decision making for potential pollutant releases, nuclear accidents, and climate change.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
air pollution; air quality modelling; CMAQ; WRF; particulate matter; PM2.5; West Midlands; oil and gas; methane; atmospheric dispersion modeling; sensor network; crisis management; safety; emergency event; civil protection; ALOHA software; scenario; case study; simulation; risk; sandstorm; coupling effect; chemical components; pollutants source; health risk; cattle; intensive livestock farming; particulate matter; Escherichia coli; source characterization; atmospheric dispersion modelling; inverse modelling; Bayesian inference; ensemble forecast; below-cloud scavenging; raindrop; contamination; aerosols; pollutants; Cuenca; parameterization; modeling performance; land surface model; lsm; carbon dioxide; carbon monoxide; chemical production; modeling; GEOS-Chem; carbon cycle; atmospheric dispersion; nuclear accident; radionuclides; radiological impact; Lagrangian particle model; urban modelling; CBRN events; emergency response; Joint Urban 2003 Experiment; n/a