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Due to scientific interest on the one hand and political and regulatory obligations on the other hand the monitoring of ozone in the troposphere is an important issue. To this end, in Belgium as in many other countries, a fixed network of monitoring stations is operated. In order to estimate the ozone concentrations over the whole territory, a model is needed to spatially complement the sparse measurements. This paper describes the development of an interpolation scheme which is aimed at fast operational use. The model uses the population density as auxiliary data to remove a spatial trend due to titration by nitric oxide. The residuals are interpolated by kriging. As a benchmark the inverse distance weighting interpolation method is used with and without the detrending. The proposed model systematically improves the interpolation and makes a significant difference when estimating human exposure to ozone. It is generic in design, easy to implement and flexible to changes in the monitoring network.

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Every summer, ground level ozone concentrations rise in Belgium and cause episodes of photochemical summer smog. This phenomenon is the cause of well recognised public health distress, especially for people suffering from respiratory diseases. To warn groups of sensitive people against forthcoming smog episodes, VITO (the Flemish Institute for Technological Research) and VMM (the Flemish Environmental Agency) have joined forces to create an ozone pollution forecasting model, called SMOGSTOP (Statistical Model Of Groundlevel Short Term Ozone Pollution). SMOGSTOP is used by Belgian government agencies to issue ozone reports and warnings in the media (TV, radio, internet, etc.). Ozone pollution levels, as well as concentrations of other air pollutants, are monitored in Belgium by the telemetric air quality measuring networks of the three Belgian regions: Flanders, Wallonia and Brussels. The major meteorological variables, such as the windvector, temperature, pressure, humidity and precipitation, are also monitored by the same networks. The historical time series of those variables, generated by the networks, are the source of input data for SMOGSTOP. Photochemical ozone pollution is the result of complex non‐linear interactions between atmospheric pollutants and meteorology. As a consequence, it is extremely difficult to determine relationships between source emissions (ozone precursors) and ambient pollutant concentrations (ozone). To deal with this complexity, SMOGSTOP was constructed as an empirical model, applying a VITO tailor‐made methodology called stratified pattern matching, to link meteorological and precursor information into ozone forecasts. In this paper, an overview of the process of ozone formation in Belgium is given, followed by the definition of the explanatory variables which will be used in the model. Then the methodology behind the model is reported and finally the results of the forecasting efforts in the period of 1.5.1995 till 31.8.1995 are presented. Copyright © 2000 John Wiley & Sons, Ltd.