Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries

S. Lopez Restrepo, A. Yarce Botero, O.L. Quintero Montoya, N. Pinel Pelaez, J.E. Hinestroza Ramirez, Elias David Nino-Ruiz, Jimmy Anderson Flórez, Angela Maíra Rendón, Monica Lucia Alvarez-Laínez, A.W. Heemink, More Authors

Research output: Chapter in Book/Conference proceedings/Edited volumeChapterScientific

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Particulate matter (PM) is one of the most problematic pollutants in urban air. The effects of PM on human health, associated especially with PM of ≤2.5μm in diameter, include asthma, lung cancer and cardiovascular disease. Consequently, major urban centers commonly monitor PM2.5 as part of their air quality management strategies. The Chemical Transport models allow for a permanent monitoring and prediction of pollutant behavior for all the regions of interest, different to the sensor network where the concentration is just available in specific points. In this chapter a data assimilation system for the LOTOS-EUROS chemical transport model has been implemented to improve the simulation and forecast of Particulate Matter in a densely populated urban valley of the tropical Andes. The Aburrá Valley in Colombia was used as a case study, given data availability and current environmental issues related to population expansion. Using different experiments and observations sources, we shown how the Data Assimilation can improve the model representation of pollutants.
Original languageEnglish
Title of host publicationEnvironmental Sustainability
Subtitle of host publicationPreparing for Tomorrow
EditorsSyed Abdul Rehman Khan
Number of pages19
ISBN (Electronic)978-1-83968-787-7
ISBN (Print)978-1-83968-786-0, 978-1-83968-785-3
Publication statusPublished - 2021


  • chemical transport model
  • air quality
  • data assimilation
  • low-cost networks


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