Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/40821

TitlePredicting SO2 pollution incidents by means of additive models with optimum variable selection
Author(s)Sestelo, Marta
Roca-Pardiñas, Javier
Ordóñez, Celestino
KeywordsVariable selection
Bootstrap
Additive models
Nonparametric regression
Pollution incident
Issue date2014
PublisherElsevier
JournalAtmospheric Environment
Abstract(s)The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.
TypeArticle
URIhttp://hdl.handle.net/1822/40821
DOI10.1016/j.atmosenv.2014.06.025
ISSN1352-2310
Peer-Reviewedyes
AccessRestricted access (UMinho)
Appears in Collections:CMAT - Artigos em revistas com arbitragem / Papers in peer review journals

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