Potentialities of quantile regression to predict ozone concentrations

• Type: Article in International Scientific Journal
• Date: 2009

Authors

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

Journal

Title: Environmetrics

Vol. 20

Pages: 147-158

ISSN: 1180-4009

Publisher: Wiley-Blackwell

Indexing

ISI Web of Knowledge - 30 Citations

ISI Web of Science

Scopus - 31 Citations

INSPEC

Scientific classification

FOS: Natural sciences > Mathematics

CORDIS: Technological sciences > Engineering

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12