ESTIMATION AND DISCRIMINATION OF ALTERNATIVE AIR-POLLUTION MODELS

This paper provides essential background to and illustration of the procedures available within the PROFIT computer package. The package aids the practitioner in selecting an appropriate probability distribution from a set of alternatives to represent the frequency of air pollutant concentrations; it also estimates the parameters of the distribution, predicts any desired percentile values, and calculates the minimum errors associated with that prediction. As an illustration, two- and three-parameter gamma, Weibull and lognormal distributions are applied to annual sets of air pollutant concentrations recorded at several monitoring sites in Melbourne. Australia. Six types of pollutants, namely carbon monoxide, nitrogen monoxide, nitrogen dioxide, nitrogen oxides, sulphur dioxide and beta-scattering, from up to five monitoring stations are analysed using a comprehensive model selection procedure. This procedure incorporates recently developed estimation and discrimination methods, together with analysis of the effects of misspecifying the distribution and of errors in estimation of observed upper percentile values. The emphasis is placed on how to select an appropriate distribution for a given pollutant over different years and whenever possible, over different sites. An important issue addressed here is the need for compromise between using discrimination criteria and analysing relative root mean square values in fitting percentiles. The results are useful for general purposes, such as summarizing or smoothing data, particularly the upper percentiles, as well as providing statistical information to construct hybrid models when the data contain trends.