AIR QUALITY PROGNOSIS USING ARTIFICIAL NEURAL NETWORKS MODELING IN THE URBAN ENVIRONMENT OF VOLOS, CENTRAL GREECE

It is well known that natural and anthropogenic emissions of ambient pollutants affect air quality and as a consequence the public health. Various epidemiological studies have identified particulate matter (PM[sub 10]) and surface ozone (O[sub 3]) as key air pollutants triggering adverse health effects on humans. The objective of this study is the prognosis, one day ahead, of air quality in the Volos urban area, a medium sized city at the eastern seaboard of Central Greece, using Artificial Neural Networks (ANNs). Results indicate that ANN modeling is a promising tool at an operational planning level for State bodies in order to forecast air pollution and protect, public health. Furthermore, the forecasting index of agreement was found to be 0.777 for PM[sub 10] and 0.958 for O[sub 3], which indicates that the forecasting values for concentrations are very close to the observed concentrations. Overall, the statistical analysis showed that the predictive ability of the proposed ANN forecasting models is very good at a significant statistical level of p < 0.01.