Despina Deligiorgi

Observations of sea-breeze fronts near the shoreline

Results from an observational study of sea-breeze fronts as they cross a shoreline are presented. Two kinds of fronts are analyzed, one with an offshore regional wind and one without. Their structure is found to be substantially different, the former being steeper and having stronger gradients. Measurements of the profiles of the vertical component of the wind speed, its standard deviation and the structure parameter for temperature are presented along with time series of the structure parameters for water vapor pressure and wind speed. The vertical wind component, w, is found to be of the order of 1.0–1.5 ms−1 in the front zone of the sharp front but only 5 as large in the weaker front. The usual height variation laws under convective conditions are found to apply for both the vertical velocity variance and the temperature structure parameter, which in conjunction with the appropriate spectra indicate that local equilibrium is re-established fairly quickly after the passage of the front. Substantial differences have also been noted in the values of the structure parameters before and after the front, especially in the water vapor pressure and wind speed, differences which are of dissimilar magnitude and sign for the two kinds of fronts.

On the limits of the air pollution predictability: the case of the surface ozone at Athens, Greece

PurposeThe aim of this study is to investigate the potential effects of increased urbanization in the Athens city, Greece on the intrinsic features of the temporal fluctuations of the surface ozone concentration (SOC).MethodsThe detrended fluctuation analysis was applied to the mean monthly values of SOC derived from ground-based observations collected at the centre of Athens basin during 1901–1940 and 1987–2007.ResultsDespite the present-day SOC doubling in respect to SOC historic levels, its fluctuations exhibit long-range power-law persistence, with similar features in both time periods. This contributes to an improved understanding of our predictive powers and enables better environmental management and more efficient decision-making processes.ConclusionsThe extensive photochemistry enhancement observed in the Athens basin from the beginning of the twentieth century until the beginning of the twenty-first century seems not to have affected the long memory of SOC correlations. The strength of this memory stems from its temporal evolution and provides the limits of the air pollution predictability at various time scales.

An experimental study of nightime air-pollutant transport over complex terrain in Athens

The Thriassion plain is a heavily industrialized area to the west of the Athens basin, separated from it by Mount Aegaleo, a 468-m high ridge about 15 km long. Three field experiments were performed to determine the possibility of air pollutant transport into the Athens basin. Sulphur hexafluoride (SF6) was released from one of the stacks of the Hellenic Oil Refineries, situated in the eastern part of the Thriassion plain, together with several releases of tetroons. These experiments revealed two mechanisms of air mass transport from the Thriassion plain, a daytime mechanism, when the air mass is transported along the Mount Aegaleo ridge and through the passage between Mount Aegaleo and Mount Parnitha to the north, and a nighttime mechanism, when transport occurs over Mount Aegaleo. SF6 was released only during the night and although in all three occassions it reached the western suburbs of Athens, it did so by different simultaneous mechanisms. Furthermore its advection-diffusion once in the basin showed large variability, whose cause was the details of the flow field as it developed under the influence of the thermal stratification. The experiments demonstrate the complexity of the diffusion of air pollutants in complex terrain and the influence of both the details of the flow field and the stratification in determining the local ground-level concentrations. They also point out the need for simultaneous modelling of both factors, for the correct computation of pollution levels.

Wind Speed Distribution Modeling in the Greater Area of Chania, Greece

The current study presents a wind speed regional assessment for the greater area of a Mediterranean coastal valley in the island of Crete, Greece. Wind speed and direction experimental data are employed from six sites, appropriately located to incorporate the effect of the main topographical features. The mean wind speed and direction assessment is performed for the overall and seasonal periods and distinct wind speed patterns are identified. The wind power density is estimated for each site and regarding the development of wind energy applications, two areas with different characteristics are established. The Weibull, Rayleigh, Lognormal, Gamma, and Inverse Gaussian distributions are assessed for their ability to model the experimental wind speed frequency distributions for the monthly and overall period. Their goodness-of-fit is assessed using the coefficient of determination and the χ2 hypothesis test. Additionally, the visual inspection of their fits to the corresponding histograms is done and the error on the estimated mean wind speed and its variance is examined. The Gamma probability distribution function is proposed as an alternative to the Weibull distribution for the area under study.

Spatial Interpolation Methodologies in Urban Air Pollution Modeling: Application for the Greater Area of Metropolitan Athens, Greece

Air pollution in urban environments has serious health and quality of life implications. A wide variety of anthropogenic air pollution sources increase the levels of background air pollutant concentrations, leading to the deterioration of the ambient air quality. Principal sources of urban air pollution are vehicular traffic, industrial activity and in general fossil fuel combustion, introducing a mixture of chemical components, particulate matter and biological material into the atmosphere. The deterioration of urban air quality is considered worldwide one of the primary environmental issues and current scientific evidence associate the exposure to ambient air pollution with a wide spectrum of health effects like cardiopulmonary diseases, respiratory related hospital admissions and premature mortality (Analitis et al. 2006; Ito et al., 2005; Samet et al., 2000). Direct measurements of sensitive population groups’ exposure to air pollution are scarce and therefore methods of accurate point and areal air quality estimations are prerequisite. This fact highlights the importance of generating accurate fields of air pollution for quantifying present and future health related risks. In the field of air pollution modeling, two different approaches have been adopted by the scientific community, differentiated by their applied fundamental principles. The first approach involves the numerical simulation of atmospheric dispersion based on the current understanding of physics and chemistry that govern the transport, dispersion and transformation of pollutants in the atmosphere. The modeling process typically requires a set of parameters such as meteorological fields, terrain information along with a comprehensive description of pollution sources. An alternative approach is based on statistical analysis of pollutant concentrations collected from air quality monitoring networks commonly deployed in urban areas. The reasoning of the statistical approach is that physical processes are likely to induce correlations in air quality data collected over space and time. Statistical models generate predictions by exploiting these spatio-temporal patterns, enabling the estimation of pollutant concentrations in unmonitored locations. The chapter’s main objective is to present and review the statistical spatial interpolation methodologies which are commonly employed in the field of air pollution modeling. An additional scope of the chapter is to compare and evaluate the accuracy of the interpolation methods for point estimations, using data from a real urban air quality monitoring network located at the greater area of metropolitan Athens in Greece.

Climatological evaluation of sodar recordings over complex terrain

Abstract In this study, an analysis of the thermal structure of the lower atmosphere, using a conventional Acoustic Sounder is attempted. This analysis is applied on acoustic sounder records taken over three topographically different locations for a period of one year each. The frequency distribution of sodar derived categories for thermal structure are examined and discussed. The detected differences are related with the microclimatic conditions for each site. These results, supplement previously published work and indicate the usefulness of Acoustic Sounder records in meteorological and air pollution studies

The impact of sea breeze under different synoptic patterns on air pollution within Athens basin

Air quality in densely populated urban coastal areas is directly related to the coupling of the synoptic and the local scale flows. The dispersion conditions within Athens basin, under the influence of different meteorological forcings, lead to distinct spatio-temporal air pollution patterns. The aim of the current observational research is to identify and examine the effect of sea breeze under different atmospheric circulation patterns on air pollution levels for a one-year study period (2007). The study employs surface pressure maps, routine meteorological observations at two coastal sites and nitrogen monoxide (NO), nitrogen dioxide (NO(2)) and ozone (O(3)) concentrations from a network of four air quality stations within the Athens basin. A three-step methodology is applied that incorporates a set of criteria for classifying atmospheric circulation and identifying sea breeze events under each circulation pattern. Two types of sea breeze development are identified (pure sea breeze-PSB and modified sea breeze-MSB) with distinct characteristics. Sea breeze is found to develop more frequently under offshore compared to onshore and parallel to the shoreline background flows. Poor dispersion conditions (high nitrogen oxides-NO(x) and O(3) concentrations) are connected to the pure sea breeze cases and to those cases where sea breeze interacts with a moderate northerly flow during the warm period. The levels of NO(x) and O(3) for the northern Athens basin area are found to be significantly higher during the sea breeze days compared to the Etesian days. Regarding the diurnal variation of ozone for the sea breeze days, peak concentrations and higher intra-daily ranges are observed. Day-to-day pollution accumulation (build-up effect) is measured for O(3) at the northern stations in the Athens basin.

Artificial Neural Network Based Methodologies for the Estimation of Wind Speed

Recent advances in artificial neural networks (ANN) propose an alternative promising methodological approach to the problem of time series assessment as well as point spatial interpolation of irregularly and gridded data. In the field of wind power sustainable energy systems ANNs can be used as function approximators to estimate both the time and spatial wind speed distributions based on observational data. The first part of this work reviews the theoretical background, the mathematical formulation, the relative advantages, and limitations of ANN methodologies applicable to the field of wind speed time series and spatial modeling. The second part focuses on implementation issues and on evaluating the accuracy of the aforementioned methodologies using a set of metrics in the case of a specific region with complex terrain. A number of alternative feedforward ANN topologies have been applied in order to assess the spatial and time series wind speed prediction capabilities in different time scales. For the temporal forecasting of wind speed ANNs were trained using the Levenberg–Marquardt backpropagation algorithm with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. For the spatial estimation of wind speed the nonlinear Radial basis function Artificial Neural Networks are compared versus the linear Multiple Linear Regression scheme.

Artificial Neural Network Modeling of Relative Humidity and Air Temperature Spatial and Temporal Distributions Over Complex Terrains

In this work we present a methodological approach of applying Artificial Neural Networks (ANN) for modeling of both the air temperature (AT) and relative humidity (RH) spatial and temporal distributions over complex terrains. A number of implementation issues are discussed, along with their relative advantages and limitations. Moreover, after the introduction of a set of metrics, the accuracy of the evaluation of ANN based spatial and time series AT and RH modeling in the case of a specific region is examined, by applying a number of alternative feed forward ANN topologies. The Levenberg-Marquardt back propagation algorithm was used for the ANNs training in the temporal forecasting of AT and RH, with the optimum architecture being the one that minimizes the Mean Absolute Error on the validation set. The Radial Basis Function and the Multilayer Perceptrons non-linear Feed Forward ANNs schemes are compared for the spatial estimation of AT and RH. We found that the spatial and temporal AT and RH variability over complex terrains can be modeled efficiently by ANNs.

A self-organizing maps multivariate spatio-temporal approach for the classification of atmospheric conditions

This work demonstrates the potential of Self-Organizing Maps (SOM) as a multivariate clustering approach of spatio-temporal datasets in atmospheric physics. A comprehensive framework is proposed and the method is applied and assessed for its performance in the field of synoptic climatology within a specific region at southeastern Mediterranean. The results indicate that the SOM can be a powerful tool for the identification and classification of atmospheric conditions, allowing an analytical description of the principal atmospheric states. The coupling of sea level pressure (SLP) and 500hPa geopotential (Φ500) in a synoptic-scale domain with the wind, the specific humidity and the air and dew point temperature in the chosen mesoscale subdomain, allows the SOM algorithm to define the relevant atmospheric circulation patterns. The corresponding patterns are well documented and the method accounts for their seasonality. Furthermore, in the resulting two-dimensional lattice the similar patterns are mapped closer to each other, compared to more dissimilar ones.