K. P. Moustris

Application of Multiple Linear Regression Models and Artificial Neural Networks on the Surface Ozone Forecast in the Greater Athens Area, Greece

An attempt is made to forecast the daily maximum surface ozone concentration for the next 24 hours, within the greater Athens area (GAA). For this purpose, we applied Multiple Linear Regression (MLR) models against a forecasting model based on Artificial Neural Network (ANN) approach. The availability of basic meteorological parameters is of great importance in order to forecast the ozone’s concentration levels. Modelling was based on recorded meteorological and air pollution data from thirteen monitoring sites within the GAA (network of the Hellenic Ministry of the Environment, Energy and Climate Change) over five years from 2001 to 2005. The evaluation of the performance of the constructed models, using appropriate statistical indices, shows clearly that in every aspect, the prognostic model by far is the ANN model. This suggests that the ANN model can be used to issue warnings for the general population and mainly sensitive groups.

Artificial neural network models as a useful tool to forecast human thermal comfort using microclimatic and bioclimatic data in the great Athens area (Greece)

The present study deals with the development and application of Artificial Neural Network (ANN) models as a tool for the evaluation of human thermal comfort conditions in the urban environment. ANNs are applied to forecast for three consecutive days during the hot period of the year (May-September) the human thermal comfort conditions as well as the daily number of consecutive hours with high levels of thermal discomfort in the great area of Athens (Greece). Modeling was based on bioclimatic data calculated by two widely used biometereorogical indices (the Discomfort Index and the Cooling Power Index) and microclimatic data (air temperature, relative humidity and wind speed) from 7 different meteorological stations for the period 2001–2005. Model performance showed that the risk of human discomfort conditions exceeding certain thresholds can be successfully forecasted by the ANN models. In addition, despite the limitations of the models, the results of the study demonstrated that ANNs, when adequately trained, could have a high applicability in the area of prevention human thermal discomfort levels in urban areas, based on a series of relatively limited number of bioclimatic data values calculated prior to the period of interest.

Forecasting the discomfort levels within the greater Athens area, Greece using artificial neural networks and multiple criteria analysis

In this work, artificial neural networks (ANNs) were developed and applied in order to forecast the discomfort levels due to the combination of high temperature and air humidity, during the hot season of the year, in eight different regions within the Greater Athens area (GAA), Greece. For the selection of the best type and architecture of ANNs-forecasting models, the multiple criteria analysis (MCA) technique was applied. Three different types of ANNs were developed and tested with the MCA method. Concretely, the multilayer perceptron, the generalized feed forward networks (GFFN), and the time-lag recurrent networks were developed and tested. Results showed that the best ANNs type performance was achieved by using the GFFN model for the prediction of discomfort levels due to high temperature and air humidity within GAA. For the evaluation of the constructed ANNs, appropriate statistical indices were used. The analysis proved that the forecasting ability of the developed ANNs models is very satisfactory at a significant statistical level of p < 0.01.

Seven-days-ahead forecasting of childhood asthma admissions using artificial neural networks in Athens, Greece

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 Childrens 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.

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 μm (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.

Assessment of the Thermal Comfort Conditions in a University Campus Using a 3D Microscale Climate Model, Utilizing Mobile Measurements

The objective of the study is to assess the human thermal comfort at a University Campus in the metropolitan area of Athens. The equipment setup consists of all the necessary sensors for human thermal comfort estimation along with a high resolution GPS, mounted on a cargo bicycle. The experiment was carried out for midday and night on July 29, 2015. Besides, long term 5 min measurements from a meteorological station, established on the roof of a building within the University Campus, were also used to quantify the mean thermal environment. The densely carried out bicycle measurements every 5 s was the input data utilized by the ENVI-met model; a three-dimensional microclimate model designed to simulate the surface-plant-air interactions in urban environment. The in situ measurements along with the model’s output results reveal the thermal comfort regime of the selected area and the ability of the model to estimate accurately the micrometeorological conditions.

Weekend—Weekday Effect Assessment of PM 10 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 μm (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.

Temporal Variability of Surface Ozone Concentrations in Volos, Greece

The surface ozone is a pollutant of major concern due to its impact on receptors, at currently occurring ambient levels in many regions of the world. The aim of this work is to present the results derived from an analysis of hourly surface ozone concentrations, measured at the urban station of Volos, a coastal medium-sized city at the eastern seaboard of Central Greece, during the 10-year period 2001–2010. The regional climate, which is characterized by hot and dry summers with intense sunshine, plays an important role in the observed exceedances of the air quality ozone limits. The analysis showed that, ozone diurnal patterns depict daytime photochemical ozone built up, during the sunlight hours of the day. It is remarkable that the maximum daily 8-h averages often exceeded the standard value that is assigned by the EU Directive for human health protection, during almost the warm period of the year, mainly at noon and afternoon hours.

24-Hours Ahead Forecasting of PM10 Concentrations Using Artificial Neural Networks in the Greater Athens Area, Greece

The study of atmospheric concentration levels at a local scale is one of the most important topics in environmental sciences. Multivariate analysis, fuzzy logic and neural networks have been introduced in forecasting procedures in order to elaborate operational techniques for level characterization of specific atmospheric pollutants at different spatial and temporal scales. Particularly, procedures based on artificial neural networks (ANNs) have been applied with success to forecast concentration levels of PM10, CO and O3. The present study deals with the development and application of ANN models as a tool to forecast daily concentration levels of PM10 in five different regions within the greater Athens area (GAA). Modeling was based on mean daily PM10 concentration, the maximum hourly NO2 concentration, air temperature, relative humidity, wind speed and the mode daily value of wind direction from five different monitoring stations for the period 2001–2005. Model performance showed that the ANN models could successfully forecast the risk of daily PM10 concentration levels exceeding certain thresholds. In addition, despite the limitations of the models, the results of the study demonstrated that ANN models, when adequately trained, could have a high applicability to predict the PM10 daily concentration 1 day ahead within the GAA.