Statistical modelling and prediction of atmospheric pollution by particulate material: two nonparametric approaches

Abstract

Atmospheric particles are one of the main factors of air pollution in Santiago, Chile. Inhalation of particulate material is known to lead to serious health problems, including respiratory illness and complications related thereto. Vehicular traffic, industrial activity and street dust are important sources of atmospheric particles. The public authorities in Santiago have been monitoring air pollution by means of a network of semi-automatic sampling stations. At one of these stations, located near the city centre close to Government House, both PM2.5 and PM10 particulate material concentrations have been measured continuously for several years. Here PM2.5 refers to particles having a diameter smaller than 2.5 microns and PM10 corresponds to particles smaller than 10 microns. Hourly averages of the concentrations are available. For the present work, hourly data recorded at intervals of 12 hours have been used. The aim is to describe and forecast these variables with satisfactory precision, including critical pollution episodes, both as a function of previous behaviour and of a set of meteorological variables, comprising wind speed and direction, ambient temperature and relative air humidity. Both non-parametric discriminant analysis and multivariate adaptive regression splines procedures have been applied. Highly satisfactory classification as well as forecasting results were achieved with these approaches, respectively. Copyright © 2001 John Wiley & Sons, Ltd.