Modeling distributions of air pollutant concentrations—I. Identification of statistical models

Abstract

In this paper the process by which a distributional model may be identified from a range of alternative models is examined. An assessment of the methods by which goodness-of-fit may be evaluated is presented. A procedure for selecting amongst the exponential, gamma, lognormal and Weibull distributions has been applied to 24-h average suspended particulates (β-scattering), ozone, carbon monoxide, sulphur dioxide, oxides of nitrogen, nitrogen dioxide and nitrogen oxide observations recorded in Melbourne, Australia. It is shown that 1. (a) lognormal distribution is appropriate for particulate data and the majority of the nitric oxide, oxides of nitrogen and sulphur dioxide data sets 2. (b) gamma distribution is best for both carbon monoxide, nitrogen dioxide and ozone 3. (c) Weibull distribution is appropriate for a significant number of carbon monoxide and ozone data sets.