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The present paper investigates the impact of canyon geometry on the temperature regime and nocturnal heat island development in the very dense urban area of Athens, Greece. Detailed measurements of air temperature have been carried out within three deep urban canyons of different aspect ratios ( H/ W = 3, 2.1 and 1.7) during the night period of the summer and autumn of 2007. An analysis was carried out to investigate the relative impact of the canyon geometry, the undisturbed wind velocity, ambient temperature, and cloud cover on the development of a nocturnal heat island. A clear increase of the median, maximum and minimum values of the cooling rates has been observed for decreasing aspect ratios. Under low ambient temperatures, high wind speeds correspond to a substantial rise of the cooling rate in the urban canyons mainly because of the increased convective losses. On the contrary, cooling rates decrease substantially under high-undisturbed wind speeds and ambient temperatures because of the important convective gains. The impact of cloud cover was found to be important as cloudy skies cause a substantial decrease of the cooling rates in the urban canyons. Comparisons were performed between the temperature data collected in the three studied urban canyons and temperatures recorded in an urban as well as a suburban open space station. [ABSTRACT FROM AUTHOR]

The role of urban green areas in the microclimatic conditions of cities, during summer, is investigated in this paper through monitoring campaigns carried out at the National garden, at the city centre of Athens. Two types of investigations were carried out: i) a microscopic one that investigated the thermal conditions inside the Garden and the immediate surrounding urban area and ii) a macroscopic one that compared the temperature profile of the Garden with that of the greater city centre area. It was concluded that in microscopic level, the temperature profile inside the National Garden and the immediate surrounding urban area did not showed a clear evidence of the influence of the Garden and it was dependent on the characteristics of each location. In a macroscopic scale, the Garden was found cooler than the other monitored urban locations and temperature differences were mainly greater during the night, especially in streets with high building height to street width (H/W) ratio and low traffic, while in streets with high anthropogenic heat during the day, the biggest temperature differences were recorded during the day. [ABSTRACT FROM AUTHOR]

The influence of temperature on tropospheric ozone (O3) concentrations in urban and photochemically polluted areas in the greater Athens region are investigated in the present study. Hourly values of the ambient air temperature used for studying the urban heat island effect in Athens were recorded at twenty-three experimental stations while ozone concentration values were measured at three of the above-mentioned stations and for a period of two years (1996-1997). The linear correlation between ozone concentration and air temperature values as well as the temporal variation of temperature and ozone concentration, for the above-mentioned experimental stations, were calculated and analysed. Moreover, a neural network approach was used for investigating the impact of temperature on the ozone concentration values over the greater Athens area. The neural network model used ambient air temperature as one of its input parameters and it was found that temperature is a predominant parameter, affecting considerably the ozone concentration values. [ABSTRACT FROM AUTHOR]

Heat island is a very well documented climatic phenomenon that has an important energy and environmental impact in the urban environment. The main energy problems are related to the important increase of the energy consumption for cooling purposes as well as to the important increase of the peak electricity load. Heat island in Athens, Greece, is measured during the last decade and its energy impact is calculated in details. The aim of the present paper is to estimate the direct and indirect environmental impact of the heat island effect in Athens. This is achieved through the estimation of the additional ecological footprint caused by the urban heat island phenomenon over the city. The ecological footprint estimation is performed at a first step by calculating the increase of the cooling demand caused by the heat island over the whole city and then by translating the energy to environmental cost. Two years annual experimental data from many urban stations have been used. The results show that the ecological footprint because of the heat island ranges 1.5-2 times the city's political area that have to be reserved every year to compensate the additional CO2 emissions caused by the presence of the heat island effect while the maximum potential ecological footprint, provided that all buildings are air conditioned, is almost 110,000 hectares. [ABSTRACT FROM AUTHOR]

An intelligent “data-driven” method is used in the present study for investigating, analyzing and quantifying the urban heat island phenomenon in the major Athens region where hourly ambient air-temperature data are recorded at twenty-three stations. The heat island phenomenon has a serious impact on the energy consumption of buildings, increases smog production, while contributing to an increasing emission of pollutants from power plants, including sulfur dioxide, carbon monoxide, nitrous oxides and suspended particulates. The intelligent method is an artificial neural network approach in which the urban heat island intensity at day and nighttime are estimated using as inputs several climatic parameters. Various neural network architectures are designed and trained for the output estimation, which is the daytime and nighttime urban heat island intensity at each station for a two-year time period. The results are tested with extensive sets of non-training measurements and it is found that they correspond well with the actual values. Furthermore, the influence of several input climatic parameters measured at each station, such as solar radiation, daytime and nighttime air temperature, and maximum daily air temperature, on the urban heat island intensity fluctuations is investigated and analyzed separately for the day and nighttime period. From this investigation it is shown that heat island intensity is mainly influenced by urbanization factors. A sensitivity investigation has been performed, based on neural network techniques, in order to adequately quantify the impact of the above input parameters on the urban heat island phenomenon. [ABSTRACT FROM AUTHOR]

Studies the existence of the heat island effect in urban places in Athens, Greece. Thermal structures between the urban and rural environments; Measurement of climatic parameters; District variations of the heat island intensity.

The present study describes a neural network approach for modeling and making short-term predictions on the total solar radiation time series. The future hourly values of total solar radiation for several years are predicted, by extracting knowledge from their past values, using feed-forward back-propagation neural networks. The results are tested using various sets of non training measurements, the findings are very encouraging and the model is found able to simulate the future values of total solar radiation time series based on their past values. 'Multi-lag' output predictions are performed using the predictedvalues to the input database in order to model future total solar radiation values with sufficient accuracy. Furthermore, an autoregressive model is developed for analysing and representing the total solar radiation time series. The predicted values of solar radiation are compared with the observed data series and it was found that the neuralnetwork approach leads to better predictions than the AR model. [ABSTRACT FROM AUTHOR]