Studies have demonstrated repeatedly that air pollution in Athens is associated with a small but statistically significant increase in mortality. Extremely high air temperatures can also cause excess mortality. This study investigated whether air pollution and air temperature have synergistic effects on excess mortality in Athens. Data concerning the increased number of deaths in July 1987 (when a major "heat wave" hit Greece) were compared to the deaths in July of the 6 previous years. This comparison revealed a greater increase in the number of deaths in Athens (97%), compared to all other urban areas (33%) and to all non-urban areas (27%). Data on the daily levels of smoke, sulfur dioxide, and ozone; the number of deaths that occurred daily; and meteorological variables were collected for a 5-y period. The daily value of Thom's discomfort index was calculated. Multiple linear regression models were used to investigate main and interactive effects of air temperature and Thom's discomfort index and air pollution indices. The daily number of deaths increased by more than 40 when the mean 24-h air temperature exceeded 30 degrees C. The main effects of an air pollution index are not statistically significant, but the interaction between high levels of air pollution and high temperature (> or = 30 degrees C) are statistically significant (p < .05) for sulfur dioxide and are suggestive (p < .20) for ozone and smoke. Similar results were obtained when the discomfort index was used, instead of temperature in the models.

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.

Artificial Neural Network (ANN) models were developed and applied in order to predict the total weekly number of Childhood Asthma Admission (CAA) at the greater Athens area (GAA) in Greece. Hourly meteorological data from the National Observatory of Athens and ambient air pollution data from seven different areas within the GAA for the period 2001-2004 were used. Asthma admissions for the same period were obtained from hospital registries of the three main Children's Hospitals of Athens. Three different ANN models were developed and trained in order to forecast the CAA for the subgroups of 0-4, 5-14-year olds, and for the whole study population. The results of this work have shown that ANNs could give an adequate forecast of the total weekly number of CAA in relation to the bioclimatic and air pollution conditions. The forecasted numbers are in very good agreement with the observed real total weekly numbers of CAA.

In recent years, significant changes in precipitation regimes have been observed and these manifest in socio economic and ecological problems especially in regions with increased vulnerability such as the Mediterranean region. For this reason, it is necessary to estimate the future projected precipitation on short and long-term basis by analyzing long time series of observed station data. In this study, an effort was made in order to forecast the monthly maximum, minimum, mean and cumulative precipitation totals within a period of the next four consecutive months, using Artificial Neural Networks (ANNs). The precipitation datasets concern monthly totals recorded at four meteorological stations (Alexandroupolis, Thessaloniki, Athens, and Patras), in Greece. For the evaluation of the results and the ability of the developed prognostic models, appropriate statistical indexes such as the coefficient of determination (R 2 ), the index of agreement (IA) and and P. Ralli Str., 122 44 Athens, Greece 1980 K.P. Moustris et al.the root mean square error (RMSE) were used. The findings from this analysis showed that the ANN's methodology provides satisfactory precipitation totals in four consecutive months and these results are better results, than those obtained using classical statistical methods. A fairly good consistency between the observed and the predicted precipitation totals at a statistical significance level of p < 0.01 for the most of the examined cases has been revealed. More specifically, the Index of Agreement (IA) ranges between 0.523 and 0.867 and the coefficient of determination (R 2 ) ranges between 0.141 and 0.603. The most accurate forecasts concern the mean monthly and the cumulative precipitation for the next four consecutive months.

This work examines if chaos and long memory exist in PM 10 concentrations recorded in Athens, Greece. The algorithms of Katz, Higuchi, and Sevcik were employed for the calculation of fractal dimensions and Rescaled Range (R/S) analysis for the calculation of the Hurst exponent. Windows of approximately two months' duration were employed, sliding one sample forward until the end of each utilized signal. Analysis was applied to three long PM 10 time series recorded by three different stations located around Athens. Analysis identified numerous dynamical complex fractal time-series segments with patterns of long memory. All these windows exhibited Hurst exponents above 0.8 and fractal dimensions below 1.5 for the Katz and Higuchi algorithms, and 1.2 for the Sevcik algorithm. The paper discusses the importance of threshold values for the postanalysis of the discrimination of fractal and long-memory windows. After setting thresholds, computational calculations were performed on all possible combinations of two or more techniques for the data of all or two stations under study. When all techniques were combined, several common dates were found for the data of the two combinations of two stations. When the three techniques were combined, more common dates were found if the Katz algorithm was not included in the meta-analysis. Excluding Katz's algorithm, 12 common dates were found for the data from all stations. This is the first time that the results from sliding-window chaos and long-memory techniques in PM 10 time series were combined in this manner.

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