Development and application of artificial neural network models to estimate values of a complex human thermal comfort index associated with urban heat and cool island patterns using air temperature data from a standard meteorological station

The present study deals with the development and application of artificial neural network models (ANNs) to estimate the values of a complex human thermal comfort-discomfort index associated with urban heat and cool island conditions inside various urban clusters using as only inputs air temperature data from a standard meteorological station. The index used in the study is the Physiologically Equivalent Temperature (PET) index which requires as inputs, among others, air temperature, relative humidity, wind speed, and radiation (short- and long-wave components). For the estimation of PET hourly values, ANN models were developed, appropriately trained, and tested. Model results are compared to values calculated by the PET index based on field monitoring data for various urban clusters (street, square, park, courtyard, and gallery) in the city of Athens (Greece) during an extreme hot weather summer period. For the evaluation of the predictive ability of the developed ANN models, several statistical evaluation indices were applied: the mean bias error, the root mean square error, the index of agreement, the coefficient of determination, the true predictive rate, the false alarm rate, and the Success Index. According to the results, it seems that ANNs present a remarkable ability to estimate hourly PET values within various urban clusters using only hourly values of air temperature. This is very important in cases where the human thermal comfort-discomfort conditions have to be analyzed and the only available parameter is air temperature. © 2018, ISB

Human thermal sensation over a mountainous area, revealed by the application of ANNs: the case of Ainos Mt., Kefalonia Island, Greece

Mt. Ainos in Kefalonia Island, Greece, hosts a large variety of plant species, some of them endemic to the region. Because of its rich biodiversity, a large portion of the mountain area is designated as National Park and is protected from human activities such as hunting or logging. Therefore, the area presents a lot of opportunities for ecotourist activities, such as trekking, birdwatching, and mountain climbing. In order to estimate its touristic activities potential, it is essential to assess the mountain’s biometeorological conditions. To achieve that, the human thermal index PET (physiologically equivalent temperature) was used, which is based on a human energy balance model. However, it is difficult to get the specific meteorological data over mountainous areas (air temperature, humidity, wind speed, and global solar radiation), appropriate as input variables for PET modeling. In order to overcome this limitation, artificial neural networks (ANNs) were developed for the estimation of PET index in ten sites within the Ainos National Park. In the process, the spatiotemporal distributions of the PET thermal index were illustrated, taking into consideration the ANN modeling. The findings of the performed analysis shed light that Mt. Ainos offers the greatest touristic opportunities from May to September, when thermal comfort conditions appear. The study also proves that the highest frequency of thermal comfort appears within the aforementioned time period over the highest altitudes, while on the contrary, slightly warm class appears as the altitude decreases on both sides of the mountain. © 2020, ISB

One-Day Prediction of Biometeorological Conditions in a Mediterranean Urban Environment Using Artificial Neural Networks Modeling

The present study, deals with the 24-hour prognosis of the outdoor biometeorological conditions in an urban monitoring site within the Greater Athens area, Greece. For this purpose, artificial neural networks (ANNs) modelling techniques are applied in order to predict the maximum and the minimum value of the physiologically equivalent temperature (PET) one day ahead as well as the persistence of the hours with extreme human biometeorological conditions. The findings of the analysis showed that extreme heat stress appears to be 10.0% of the examined hours within the warm period of the year, against extreme cold stress for 22.8% of the hours during the cold period of the year. Finally, human thermal comfort sensation accounts for 81.8% of the hours during the year. Concerning the PET prognosis, ANNs have a remarkable forecasting ability to predict the extreme daily PET values one day ahead, as well as the persistence of extreme conditions during the day, at a significant statistical level of

Estimation of Hospital Admissions Respiratory Disease Attributed to PM10 Exposure Using the AirQ Model Within the Greater Athens Area

The main objective of this work is the assessment of the annual number of hospital admissions for respiratory disease (HARD) due to the exposure to inhalable particulate matter (PM10), within the greater Athens area (GAA), Greece. Towards this aim, the time series of the particulate matter with aerodynamic diameter less than 10 mu m (PM10) recorded in six monitoring stations located in the GAA, for a 13-year period 2001-2013, is used. In this study AirQ2.2.3 software developed by the WHO, was used to evaluate adverse health effects by PM10 in the GAA during the examined period. The results show that, the mean annual HARD cases per 100,000 inhabitants ranged between 20 (suburban location) and 40 (city centre location). Approximately 70 % of the annual HARD cases are due to city centre residents. In all examined locations, a declining trend in the annual number of HARD cases is appeared. Moreover, a strong relation between the annual number of HARD cases and the annual number of days exceeding the European Union daily PM10 threshold value was found

Weekend-Weekday Effect Assessment of PM10 in Volos, Greece (2010-2014)

Several epidemiological studies have shown an association between particulate air pollution and adverse health effects. The consensus among the scientific community is that suspended particulate matter is one of the most harmful pollutants, particularly the inhalable particulate matter with aerodynamic diameters less than 10 mu m (PM10) causing respiratory health effects and heart diseases. The effects of aerosols on human health are determined by both their size and their chemical composition. Average daily concentrations exceeding the EU daily threshold concentration appear, among other cases, during Sahara dust episodes, a natural phenomenon that degrades the air quality in the urban area of Volos. The city of Volos is a coastal city of medium size in the eastern seaboard of Central Greece. The main objective of this work is the study of the temporal evolution and the assessment of weekend effect in particulate matter concentration levels in the centre of the city of Volos. PM10 data obtained by a fully automated station that was established by the Hellenic Ministry of Environment and Energy, for a 5-year period (2010-2014) are analyzed in order to study the day-of-week variations during the cold and warm period of the year. As these variations are mostly expected to be due to the human working cycle, a strong weekly cycle would be indicative of the dominance of anthropogenic particles

The use of a complex thermohygrometric index in predicting adverse health effects in Athens

Mortality and morbidity indices are known to depend on changes in meteorological conditions. In Athens, severe adverse health effects following extreme heat conditions have been reported. The usefulness has been investigated of the complex thermohygrometric index (THI), a simple index based on maximum daily temperature and relative humidity, in predicting the health effects of specific meteorological conditions. The values of THI were found to correlate well with more complex bioclimatic indices; the THI could successfully replace temperature and humidity in predicting the daily number of deaths through multiple linear regression modelling. Thus the introduction of THI levels more than 28.5° C and between 26.5 and 28.5° C, through dummy variables, in a regression model explained 40% of the variability in the number of deaths during the months of July and August. During days with THI values less than 26.5° C the mean number of deaths was 33.5, compared to 41.8 when THI was between 26.5 and 28.5° C. The daily number of deaths increased to 108.2 when THI exceeded 28.5° C. From this study, the exact level of THI at which public health measures must be taken was not clear and more work is needed to identify it. However, given its simplicity, the use of THI for predicting meteorological conditions which are adverse to health would appear to be promising in preventive medicine and in health services planning. © 1995 Springer-Verlag