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

doi:10.5772/intechopen.97503

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

Mathematical Physics

Research output: Chapter in Book/Conference proceedings/Edited volume › Chapter › Scientific

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Abstract

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 language	English
Title of host publication	Environmental Sustainability
Subtitle of host publication	Preparing for Tomorrow
Editors	Syed Abdul Rehman Khan
Publisher	IntechOpen
Number of pages	19
ISBN (Electronic)	978-1-83968-787-7
ISBN (Print)	978-1-83968-786-0, 978-1-83968-785-3
DOIs	https://doi.org/10.5772/intechopen.97503
Publication status	Published - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

chemical transport model
air quality
data assimilation
LOTOS-EUROS
low-cost networks

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10.5772/intechopen.97503

76649Final published version, 4.74 MBLicence: CC BY

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Lopez Restrepo, S., Yarce Botero, A., Quintero Montoya, O. L., Pinel Pelaez, N., Hinestroza Ramirez, J. E., Nino-Ruiz, E. D., Anderson Flórez, J., Rendón, A. M., Alvarez-Laínez, M. L., Heemink, A. W., & More Authors (2021). Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries. In S. A. R. Khan (Ed.), Environmental Sustainability : Preparing for Tomorrow IntechOpen. https://doi.org/10.5772/intechopen.97503

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abstract = "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 Aburr{\'a} 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.",

keywords = "chemical transport model, air quality, data assimilation, LOTOS-EUROS, low-cost networks",

author = "\{Lopez Restrepo\}, S. and \{Yarce Botero\}, A. and \{Quintero Montoya\}, O.L. and \{Pinel Pelaez\}, N. and \{Hinestroza Ramirez\}, J.E. and Nino-Ruiz, \{Elias David\} and \{Anderson Fl{\'o}rez\}, Jimmy and Rend{\'o}n, \{Angela Ma{\'i}ra\} and Alvarez-La{\'i}nez, \{Monica Lucia\} and A.W. Heemink and \{More Authors\}",

year = "2021",

doi = "10.5772/intechopen.97503",

language = "English",

isbn = "978-1-83968-786-0",

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Lopez Restrepo, S, Yarce Botero, A, Quintero Montoya, OL, Pinel Pelaez, N, Hinestroza Ramirez, JE, Nino-Ruiz, ED, Anderson Flórez, J, Rendón, AM, Alvarez-Laínez, ML, Heemink, AW & More Authors 2021, Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries. in SAR Khan (ed.), Environmental Sustainability : Preparing for Tomorrow. IntechOpen. https://doi.org/10.5772/intechopen.97503

Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries. / Lopez Restrepo, S.; Yarce Botero, A.; Quintero Montoya, O.L. et al.
Environmental Sustainability : Preparing for Tomorrow. ed. / Syed Abdul Rehman Khan. IntechOpen, 2021.

Research output: Chapter in Book/Conference proceedings/Edited volume › Chapter › Scientific

TY - CHAP

T1 - Data Assimilation as a Tool to Improve Chemical Transport Models Performance in Developing Countries

AU - Lopez Restrepo, S.

AU - Yarce Botero, A.

AU - Quintero Montoya, O.L.

AU - Pinel Pelaez, N.

AU - Hinestroza Ramirez, J.E.

AU - Nino-Ruiz, Elias David

AU - Anderson Flórez, Jimmy

AU - Rendón, Angela Maíra

AU - Alvarez-Laínez, Monica Lucia

AU - Heemink, A.W.

AU - More Authors, null

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AB - 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.

KW - chemical transport model

KW - air quality

KW - data assimilation

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KW - low-cost networks

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BT - Environmental Sustainability

A2 - Khan, Syed Abdul Rehman

PB - IntechOpen

ER -

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

Abstract

UN SDGs

Keywords

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