Multifractal characterization of Hong Kong air quality data

This article develops a theory for characterization of air quality data based on their measure representation. The measures are shown to be random cascades generated by an infinitely divisible distribution. This probability distribution is uniquely determined by the exponent K (q), q≥0, in the multifractal analysis of the cascade. The theory is applied to the SO2, NO and NO2 time series at seven locations of the Hong Kong Electric Co. monitoring network. The Gamma density function is demonstrated to give an excellent fit to the K (q) curve of each time series. This precise characterization therefore provides a needed tool for modelling pollution episodes as well as classification of the monitoring network. Copyright © 2004 John Wiley & Sons, Ltd.