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FP5

Predicting Peak Photochemical Pollutant Levels with a Combination of Neural Network Models

Funded under: FP5

Abstract

In the present paper an attempt is made for the 24-hours prediction of photochemical pollutant levels using neural network models. For this purpose, two models are developed that relate peak pollutant concentrations to meteorological and emission variables and indexes. The analysis is based on measurements of O3 and NO2 from the city of Athens. The meteorological variables are selected to cover atmospheric processes that determine the fate of the airborne pollutants while special care is taken to ensure the availability of the required input data from routine observations or forecasts. The comparison between model predictions and actual observations shows very good agreement.

Additional information

Authors: KIOUTSIOUKIS I, European Commission, Joint Research Institute, Environment Institute, Air Quality Unit, Ispra (IT);SKOULOUDIS A, European Commission, Joint Research Institute, Environment Institute, Air Quality Unit, Ispra (IT);MELAS D, Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (GR);ZIOMAS I C, Laboratory of Atmospheric Physics, Aristotle University of Thessaloniki (GR)
Bibliographic Reference: Paper presented: 10th Anniversary International Conference on Artificial Neural Networks and Intelligent Systems, Prague (CZ) 9-12 July 2000
Availability: Available from European Commission, Joint Research Centre, Ispra (IT)
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