Evolutionary lagrangian inverse modeling for PM 10 pollutant dispersion

Alejandro Peña; Jesús A. Hernández; María Victoria Toro

doi:10.4018/978-1-61520-893-7.ch018

Evolutionary lagrangian inverse modeling for PM ₁₀ pollutant dispersion

Alejandro Peña, Jesús A. Hernández, María Victoria Toro

Research output: Chapter in Book/Report/Conference proceeding › Book chapter as a result of research › peer-review

2 Scopus citations

Abstract

One of the main concerns when it comes to mitigating the effects of the concentration of the particulate matter PM_x in an area of study is the fact to determine its behavior over time, overcoming both physical and mathematical limitations in terms of a phenomenon of dispersion. Therefore, this chapter develops and analyzes a model based on the principles of evolutionary computation (EC) in order to determine the space-time behavior of the concentration of the particulate matter PM_x in a study area. The proposed model has three submodels within an integrated solution, which constitute the individual to evolve. The transformation of the possible solutions or generational population is made by using an asynchronous evolutionary model, due to genetic dependency between substructures. The proposed model was validated for configurations of n sources of emissions and m monitoring stations that measure the quality of the air in a study area.

Original language	English
Title of host publication	Soft Computing Methods for Practical Environment Solutions
Subtitle of host publication	Techniques and Studies
Publisher	IGI Global
Pages	293-312
Number of pages	20
ISBN (Print)	9781615208937
DOIs	https://doi.org/10.4018/978-1-61520-893-7.ch018
State	Published - 2010
Externally published	Yes

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10.4018/978-1-61520-893-7.ch018

Cite this

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Evolutionary lagrangian inverse modeling for PM ₁₀ pollutant dispersion. / Peña, Alejandro; Hernández, Jesús A.; Toro, María Victoria.
Soft Computing Methods for Practical Environment Solutions: Techniques and Studies. IGI Global, 2010. p. 293-312.

Research output: Chapter in Book/Report/Conference proceeding › Book chapter as a result of research › peer-review

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T1 - Evolutionary lagrangian inverse modeling for PM 10 pollutant dispersion

AU - Peña, Alejandro

AU - Hernández, Jesús A.

AU - Toro, María Victoria

PY - 2010

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N2 - One of the main concerns when it comes to mitigating the effects of the concentration of the particulate matter PMx in an area of study is the fact to determine its behavior over time, overcoming both physical and mathematical limitations in terms of a phenomenon of dispersion. Therefore, this chapter develops and analyzes a model based on the principles of evolutionary computation (EC) in order to determine the space-time behavior of the concentration of the particulate matter PMx in a study area. The proposed model has three submodels within an integrated solution, which constitute the individual to evolve. The transformation of the possible solutions or generational population is made by using an asynchronous evolutionary model, due to genetic dependency between substructures. The proposed model was validated for configurations of n sources of emissions and m monitoring stations that measure the quality of the air in a study area.

AB - One of the main concerns when it comes to mitigating the effects of the concentration of the particulate matter PMx in an area of study is the fact to determine its behavior over time, overcoming both physical and mathematical limitations in terms of a phenomenon of dispersion. Therefore, this chapter develops and analyzes a model based on the principles of evolutionary computation (EC) in order to determine the space-time behavior of the concentration of the particulate matter PMx in a study area. The proposed model has three submodels within an integrated solution, which constitute the individual to evolve. The transformation of the possible solutions or generational population is made by using an asynchronous evolutionary model, due to genetic dependency between substructures. The proposed model was validated for configurations of n sources of emissions and m monitoring stations that measure the quality of the air in a study area.

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Evolutionary lagrangian inverse modeling for PM 10 pollutant dispersion

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Evolutionary lagrangian inverse modeling for PM ₁₀ pollutant dispersion