Air-pollution modelling in an urban area: Correlating turbulent diffusion coefficients by means of an artificial neural network approach

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dc.contributor.authorPerez Roa, R
dc.contributor.authorCastro, J
dc.contributor.authorJorquera, H
dc.contributor.authorPerez Correa, JR
dc.contributor.authorVesovic, V
dc.date.accessioned2024-01-10T13:10:58Z
dc.date.available2024-01-10T13:10:58Z
dc.date.issued2006
dc.description.abstractThe vertical pollutant dispersion is quite sensitive to the eddy diffusivity, K-V. Therefore, good estimations of K-V are essential for improving the predictive performance of Eulerian dispersion models; especially in urban areas where literature based K-V correlations are not always accurate. Here, we present a methodology to obtain a more accurate, but site-specific, Kv correlation. It is based on using artificial neural networks (ANN) to find the best Kv function for a particular urban area by minimizing, in a least-squares sense, the difference between ambient measurements of carbon monoxide and dispersion simulations of this tracer species. The resulting ANN-K-V correlation is a function of three parameters namely, the stability parameter (z/L), the height within the mixing layer (z/h), and the scaled height (zf(C)/u(*))-hence the Monin-Obukhov (L), mixing (h) and Ekman (u(*)/f(C)) lengths are used to predict Kv across the atmospheric boundary layer.
dc.description.abstractWe then assess how such an ANN-K-V model improves the capability of a dispersion model (CAMx) to predict peak concentrations of ambient carbon monoxide in a large city. The evaluation has been performed with a set of eight air-quality meteorological stations evenly spread across the city of Santiago, Chile, during springtime. Results show that with the ANN-K-V model, CAMx achieved better predictions of peak CO concentration levels than has been hitherto possible. Typically root-mean-square errors are reduced to half their original values. The resulting ANN-K-V model-without any additional training-was then used to predict CO ambient concentrations at another period (summertime) and also to predict ambient concentrations of total carbon (PM2.5) at both periods. A much-improved agreement was observed. Furthermore, the ANN formulation allowed for the quality of the urban emission inventory to be critically assessed indicating that the weekend emissions in Santiago are most likely underestimated. (c) 2005 Elsevier Ltd. All rights reserved.
dc.fechaingreso.objetodigital20-03-2024
dc.format.extent17 páginas
dc.fuente.origenWOS
dc.identifier.doi10.1016/j.atmosenv.2005.09.032
dc.identifier.eissn1873-2844
dc.identifier.issn1352-2310
dc.identifier.urihttps://doi.org/10.1016/j.atmosenv.2005.09.032
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/77969
dc.identifier.wosidWOS:000234726200009
dc.information.autorucIngeniería;Jorquera H;S/I;100302
dc.information.autorucIngeniería;Pérez J;S/I;100130
dc.issue.numero1
dc.language.isoen
dc.nota.accesocontenido parcial
dc.pagina.final125
dc.pagina.inicio109
dc.publisherPERGAMON-ELSEVIER SCIENCE LTD
dc.revistaATMOSPHERIC ENVIRONMENT
dc.rightsacceso restringido
dc.subjectartificial neural network
dc.subjectvertical eddy diffusivity
dc.subjectturbulent transport
dc.subjecturban air pollution
dc.subjectmodel performance
dc.subject2ND-GENERATION MATHEMATICAL-MODEL
dc.subjectOXIDATIVE CAPACITY
dc.subjectBIOGENIC EMISSIONS
dc.subjectOZONE FORMATION
dc.subjectQUALITY MODEL
dc.subjectSAO-PAULO
dc.subjectCALIFORNIA
dc.subjectPERFORMANCE
dc.subjectSANTIAGO
dc.subjectPARAMETERIZATION
dc.subject.ods13 Climate Action
dc.subject.ods11 Sustainable Cities and Communities
dc.subject.odspa13 Acción por el clima
dc.subject.odspa11 Ciudades y comunidades sostenibles
dc.titleAir-pollution modelling in an urban area: Correlating turbulent diffusion coefficients by means of an artificial neural network approach
dc.typeartículo
dc.volumen40
sipa.codpersvinculados100302
sipa.codpersvinculados100130
sipa.indexWOS
sipa.indexScopus
sipa.trazabilidadCarga SIPA;09-01-2024
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