A neural network forecast for daily average PM10 concentrations in Belgium

Over the past years, the health impact of airborne particulate matter (PM) has become a very topical subject. In the environmental sciences a lot of research effort goes towards the understanding of the PM phenomenon and the ability to forecast ambient PM concentrations. In this paper, we describe the development of a neural network tool to forecast the daily average PM10 concentrations in Belgium one day ahead. This research is based upon measurements from ten monitoring sites during the period 1997–2001 and upon ECMWF simulations of meteorological parameters. The most important input variable found was the boundary layer height. A model based on this parameter currently operational online serves to monitor the daily average threshold of 100 μg m−3. By extending the model with other input parameters we were able to increase the performance only slightly. This brings us to the conclusion that day-to-day fluctuations of PM10 concentrations in Belgian urban areas are to a large extent driven by meteorological conditions and to a lesser extend by changes in anthropogenic sources.