I. K. Larissi

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.

Energy consumption based on heating/cooling degree days within the urban environment of Athens, Greece

The degree-day method is considered to be a fundamental and a rather simple method to estimate heating and cooling energy demand. This study aims in a detailed and accurate assessment of cooling and heating degree days in different locations within the Greater Athens area (GAA), Greece. To achieve this goal, hourly values of air temperature from eight different locations within the GAA, covering the period 2001–2005, were used. Thus, the monthly and the annual number of cooling and heating degree days for each one of the examined locations could be estimated separately. Furthermore, an effort is made to evaluate the energy consumption for a specific building, based on the degree-day method, to indicate the impact of the canopy layer urban heat island on neighboring regions within the GAA. Results reveal that there is great spatial variability of energy demand and energy consumption along with significant differences in expenses for heating and cooling among neighboring regions within the GAA. Finally, regarding the energy demands of buildings, it is important to take into account intra-urban variability of canopy layer climates against an ensemble mean throughout the city, because the latter can result in inaccurate estimations and conclusions.

Health impacts due to particulate air pollution in Volos City, Greece

ABSTRACT There is great consensus among the scientific community that suspended particulate matter is considered as one of the most harmful pollutants, particularly the inhalable particulate matter with aerodynamic diameter less than 10 μm (PM10) causing respiratory health problems and heart disorders. Average daily concentrations exceeding established standard values appear, among other cases, to be the main cause of such episodes, especially during Saharan dust episodes, a natural phenomenon that degrades air quality in the urban area of Volos. In this study the AirQ2.2.3 model, developed by the World Health Organization (WHO) European Center for Environment and Health, was used to evaluate adverse health effects by PM10 pollution in the city of Volos during a 5-year period (2007–2011). Volos is a coastal medium size city in the Thessaly region. The city is located on the northern side of the Gulf of Pagassitikos, on the east coast of Central Greece. Air pollution data were obtained by a fully automated monitoring station, which was established by the Municipal Water Supply and Sewage Department in the Greater Area of Volos, located in the centre of the city. The results of the current study indicate that when the mean annual PM10 concentration exceeds the corresponding European Union (EU) threshold value, the number of hospital admissions for respiratory disease (HARD) is increased by 25% on average. There is also an estimated increase of about 2.5% in HARD compared to the expected annual HARD cases for Volos. Finally, a strong correlation was found between the number of days exceeding the EU daily threshold concentration ([PM10] ≥ 50 μg m−3) and the annual HARD cases.

Case studies on summertime measurements of O3, NO2, and SO2 with a DOAS system in an urban semi-industrial region in Athens, Greece

The objective of this study is to analyze the concentrations of SO2, NO2, and O3 measured by a Differential Optical Absorption Spectroscopy (DOAS) system that was operating at the campus of Technological Education Institute of Piraeus during 2008 and 2009 warm periods (July to September) in relation to the prevailing meteorological conditions. The DOAS system was operating in a particularly polluted area of the West part of Attica basin on a continuous basis, measuring the concentration levels of the main pollutants (O3, NO2, and SO2) as well as aromatic hydrocarbon substances (benzene, toluene, and xylene). According to the analysis, the SO2 concentration levels at this measuring site are rather high and this may be attributed to the characteristics of this measuring site. Proximity of roadways and local circulation are just some of the factors that can affect the concentration levels of monitoring of pollutant concentrations such as NO2 and surface ozone. The results provide evidence for the occurrence of an atmospheric phenomenon that produces higher ozone concentrations during weekends despite lower concentrations of ozone precursors. This phenomenon is known as the weekend effect.

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.

Air Quality and Bioclimatic Conditions within the Greater Athens Area, Greece - Development and Applications of Artificial Neural Networks

Panagiotis Nastos1, Konstantinos Moustris2, Ioanna Larissi3 and Athanasios Paliatsos4 1Laboratory of Climatology and Atmospheric Environment, Faculty of Geology and Geoenvironment, University of Athens, 2Department of Mechanical Engineering, Technological Educational Institute of Piraeus, 3Department of Electronic-Computer Systems Engineering, Technological Educational Institute of Piraeus, 4General Department of Mathematics, Technological Educational Institute of Piraeus, Greece

Indoor and Outdoor Particulate Matter Variability in Athens, Greece

The objective of this study is to present and analyse the indoor and outdoor temporal variability of the particulate matters of different sizes (namely: PM10, with diameter less than 10 µm; PM7, with diameter less than 7 µm; PM2.5, with diameter less than 2.5 µm and PM1, with diameter less than 1 µm) found in Athens, Greece. Four detectors were used for indoor and outdoor recordings. More specifically, the indoor recordings were performed in seminar rooms, full of students attending lectures, at the University of Athens and the Technological Education Institute of Piraeus. Findings of this research show that on one hand, the indoor particulate matter concentrations remained at lower levels than the thresholds set by the European Council Directives, but on the other hand, the outdoor particulate matter concentrations exceeded the thresholds many times.

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.

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.