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The difficulty in forecasting concentration trends with a reasonable error is still an open problem. In this paper, an effort has been made to this purpose. Artificial Neural Networks are used in order to forecast the maximum daily value of the European Regional Pollution Index as well as the number of consecutive hours, during the day, with at least one of the pollutants above a threshold concentration, 24 to 72 h ahead. The prediction concerns seven different places within the Greater Athens Area, Greece. The meteorological and air pollution data used in this study have been recorded by the network of the Greek Ministry of the Environment, Physical Planning, and Public Works over a 5-year period, 2001-2005. The hourly values of air pressure and global solar irradiance for the same period have been recorded by the National Observatory of Athens. The results are in a very good agreement with the real-monitored data at a statistical significance level of p<0.01.

BackgroundParticulate matter with diameter less than 10 micrometers (PM10) that originates from anthropogenic activities and natural sources may settle in the bronchi and cause adverse effects possibly via oxidative stress in susceptible individuals, such as asthmatic children. This study aimed to investigate the effect of outdoor PM10 concentrations on childhood asthma admissions (CAA) in Athens, Greece.MethodsDaily counts of CAA from the three Children's Hospitals within the greater Athens' area were obtained from the hospital records during a four-year period (2001-2004, n = 3602 children). Mean daily PM10 concentrations recorded by the air pollution-monitoring network of the greater Athens area were also collected. The relationship between CAA and PM10 concentrations was investigated using the Generalized Linear Models with Poisson distribution and logistic analysis.ResultsThere was a statistically significant (95% CL) relationship between CAA and mean daily PM10 concentrations on the day of exposure (+3.8% for 10 μg/m3 increase in PM10 concentrations), while a 1-day lag (+3.4% for 10 μg/m3 increase in PM10 concentrations) and a 4-day lag (+4.3% for 10 μg/m3 increase in PM10 concentrations) were observed for older asthmatic children (5-14 year-old). High mean daily PM10 concentration (the highest 10%; >65.69 μg/m3) doubled the risk of asthma exacerbations even in younger asthmatic children (0-4 year-old).ConclusionsOur results provide evidence of the adverse effect of PM10 on the rates of paediatric asthma exacerbations and hospital admissions. A four-day lag effect between PM10 peak exposure and asthma admissions was also observed in the older age group.

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