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dc.contributor.author
Pineda Rojas, Andrea Laura
dc.contributor.author
Venegas, Laura Esperanza
dc.contributor.author
Mazzeo, Nicolas Antonio
dc.date.available
2017-06-07T21:57:31Z
dc.date.issued
2016-09
dc.identifier.citation
Pineda Rojas, Andrea Laura; Venegas, Laura Esperanza; Mazzeo, Nicolas Antonio; Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis; Elsevier; Atmospheric Environment; 141; 9-2016; 422-429
dc.identifier.issn
1352-2310
dc.identifier.uri
http://hdl.handle.net/11336/17728
dc.description.abstract
A simple urban air quality model [MODelo de Dispersion Atmosf erica Ubana e Generic Reaction Set (DAUMOD-GRS)] was recently developed. One-hour peak O3 concentrations in the Metropolitan Area of Buenos Aires (MABA) during the summer estimated with the DAUMOD-GRS model have shown values lower than 20 ppb (the regional background concentration) in the urban area and levels greater than 40 ppb in its surroundings. Due to the lack of measurements outside the MABA, these relatively high ozone modelled concentrations constitute the only estimate for the area. In this work, a methodology based on the Monte Carlo analysis is implemented to evaluate the uncertainty in these modelled concentrations associated to possible errors of the model input data. Results show that the larger 1-h peak O3 levels in the MABA during the summer present larger uncertainties (up to 47 ppb). On the other hand, multiple linear regression analysis is applied at selected receptors in order to identify the variables explaining most of the obtained variance. Although their relative contributions vary spatially, the uncertainty of the regional background O3 concentration dominates at all the analysed receptors (34.4 e97.6%), indicating that their estimations could be improved to enhance the ability of the model to simulate peak O3 concentrations in the MABA.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Air Quality
dc.subject
Model Uncertainty
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Sensitivity
dc.subject
Ozone
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Monte Carlo Analysis
dc.subject.classification
Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.title
Uncertainty of modelled urban peak O3 concentrations and its sensitivity to input data perturbations based on the Monte Carlo analysis
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2017-06-07T20:47:26Z
dc.journal.volume
141
dc.journal.pagination
422-429
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Pineda Rojas, Andrea Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; Argentina
dc.description.fil
Fil: Venegas, Laura Esperanza. Universidad Tecnológica Nacional. Facultad Regional Avellaneda; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.description.fil
Fil: Mazzeo, Nicolas Antonio. Universidad Tecnológica Nacional. Facultad Regional Avellaneda; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
Atmospheric Environment
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.atmosenv.2016.07.020
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1352231016305398
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