Short-term prediction of means of artificial neural urban NO2 pollution by networks Online publication date: Mon, 19-Jul-2004
by C. Cappa, D. Anfossi, M.M. Grosa, P. Natale
International Journal of Environment and Pollution (IJEP), Vol. 15, No. 5, 2001
Abstract: A neural network model for the short-term prediction of concentrations of urban pollutants was developed and applied to the Turin (Northern Italy) air quality network. In particular, the study was focused on NO2 concentrations measured at five stations; t + 3 and t + 24 hour NO2 concentration forecasting based on hourly meteorological and concentration data gave good agreement with observed concentrations. This is particularly true for the mean concentration values and concentration distribution. The time of occurrence of peak values was correctly forecast but the amounts were generally underestimated. To reduce this underestimation, an empirical step function was applied in the t + 24 case. This allowed an accurate estimate to be obtained of the few cases in which 50% of the air quality monitoring stations exceeded the attention level (200 µg m-3) during the following day for at least one hour.
Online publication date: Mon, 19-Jul-2004
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Environment and Pollution (IJEP):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com