Air monitoring network design using Fisher's information measures—A case study

Abstract Statistical technique based on Fisher's Information Measures is applied to design an optimal air monitoring network for the Eastern Province of Saudi Arabia. The methodology presented in this study deals with both randomly distributed as well as autocorrelated data series. The study area, which has boundaries 23–30° latitude and 44–53.39° longitude, is divided into 15 × 10 equal grids. The major pollution-causing sources are identified and their emission inventories are compiled. Taking six-hourly meteorological records, collected at the meteorological stations as input to a long-range transport model, time series data on SO 2 concentration are simulated. The information content, which is the reciprocal of the variance of the parameters being estimated, is calculated for station pairs. Using an optimization algorithm, an optimum set of stations transmitting the maximum amount of information are selected for monitoring purposes.