A Statistical Model For 1-hour- To 24-hour-ahead
Prediction Of Hourly Ozone Concentrations At
Ground Level In Singapore
Author(s)
X. Liu, Y. Hwang, K. Yeo, J. Hosking, A. Barut,
J. Singh & Y. Amemiya
Abstract
A spatio-temporal statistical model is proposed for 1- to 24-hour-ahead prediction
of hourly ozone concentrations. This is a joint work with the National
Environmental Agency Singapore, and is Singapore’s first predictive model for
ozone concentrations. Unlike many existing models which focus on either daily
maximum or 8h average daylight ozone concentrations, the present work is
concerned with the prediction of hourly ozone concentrations which are usually
associated with higher variability. A recently proposed framework for spatiotemporal
prediction is used to model ozone concentration data. The macroscale
spatio-temporal variation of ozone concentrations is modeled by a linear
function of five carefully constructed predictors, while the micro-scale variation is captured by a mean-zero spatio-temporally correlated random process. We show
that this model also provides useful insights about the effects of some complex
environmental processes on ozone concentration; this is indeed an attractive
feature for any data-driven air quality model.
Keywords
ozone, spatio-temporal statistics, random process.
Related Book
Other papers in this volume
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