부산대학교 도서관

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.atmosenv.2005.01.050 Byline: Jef Hooyberghs (a), Clemens Mensink (a), Gerwin Dumont (b), Frans Fierens (b), Olivier Brasseur (c) Keywords: Particulate matter; Prediction; Neural networks; Boundary layer height; Air pollution Abstract: 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 PM.sub.10 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[mu]gm.sup.-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 PM.sub.10 concentrations in Belgian urban areas are to a large extent driven by meteorological conditions and to a lesser extend by changes in anthropogenic sources. Author Affiliation: (a) Flemish Institute for Technological Research (VITO), Boeretang 200, B-2400 Mol, Belgium (b) Interregional Cell for the Environment (IRCEL), Kunstlaan 10-11, B-1210 Brussels, Belgium (c) Royal Meteorological Institute (RMI), Ringlaan 3, B-1180 Brussels, Belgium Article History: Received 13 October 2004; Revised 14 January 2005; Accepted 26 January 2005