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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]

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]