Potentialities of quantile regression to predict ozone concentrations

• Tipo: Artigo em Revista Científica Internacional
• Data: 2009

Autores

Sousa, SIV (Autor) FEUP; Pires, JCM (Autor) FEUP; Martins, FG (Autor) FEUP; Pereira, MC (Autor) FEUP; Alvim Ferraz, MCM (Autor) Outra

Revista

Título: Environmetrics

Vol. 20

Páginas: 147-158

ISSN: 1180-4009

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 30 Citações

ISI Web of Science

Scopus - 31 Citações

INSPEC

Classificação Científica

FOS: Ciências exactas e naturais > Matemática

CORDIS: Ciências Tecnológicas > Engenharia

Outras Informações

ID Authenticus: P-003-MR3

DOI: 10.1002/env.916

Abstract (EN): This paper aims: (i) to analyse the influence of ozone precursors (both meteorological variables and pollutant concentrations) on ozone concentrations at different ozone levels; and (ii) to predict next day hourly ozone concentrations using a new approach based on quantile regression (QR). The performance of this model was compared with multiple linear regressions (MLR) for the three following periods: daylight, night time and all day. QR as proven to be an useful mathematical tool to evidence the heterogeneity of ozone predictor influences at different ozone levels. Such heterogeneity is generally hidden when an ordinary least square regression model is applied. The influence of previous concentrations of ozone and nitrogen monoxide on next day ozone concentrations was higher for lower quantiles. When QR was applied, the wind direction (WD) was found to be significant in the medium quantiles and the relative humidity (RH) in the higher quantiles. On the contrary, using the MLR models, both variables were not statistically significant. Moreover, QR allowed more efficient previsions of extreme values which are very useful once the forecasting of higher concentrations is fundamental to develop strategies for protecting the public health. Copyright (C) 2008 John Wiley & Sons, Ltd.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 12