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Atmospheric aerosol particles have a significant impact on both the climatic conditions and human health, especially in densely populated urban areas, where the particle concentrations in several cases can be extremely threatening (increased anthropogenic emissions). Most large cities located in high-income countries have stations responsible for measuring particulate matter and various other parameters, collectively forming an operating monitoring network, which is essential for the purposes of environmental control. In the city of Athens, which is characterized by high population density and accumulates a large number of economic activities, the currently operating monitoring network is responsible, among others, for PM10 and PM2.5 measurements. The need for satisfactory data availability though can be supported by using machine learning methods, such as artificial neural networks. The methodology presented in this study uses a neural network model to provide spatiotemporal estimations of PM10 and PM2.5 concentrations by utilizing the existing PM data in combination with other climatic parameters that affect them. The overall performance of the predictive neural network models' scheme is enhanced when meteorological parameters (wind speed and temperature) are included in the training process, lowering the error values of the predicted versus the observed time series' concentrations. Furthermore, this work includes the calculation of the contribution of each predictor, in order to provide a clearer understanding of the relationship between the model's output and input. The results of this procedure showcase that all PM input stations' concentrations have an important impact on the estimations. Considering the meteorological variables, the results for PM2.5 seem to be affected more than those for PM10, although when examining PM10 and PM2.5 individually, the wind speed and temperature contribution is on a similar level with the corresponding contribution of the available PM concentrations of the neighbouring stations. [ABSTRACT FROM AUTHOR]

We investigate the temporal evolution of the planetary boundary-layer (PBL) height over the basin of Athens, Greece, during a 6-year period (2011–2016), using data from a Raman lidar system. The range-corrected lidar signals are selected around local noon (1200 UTC) and midnight (0000 UTC), for a total of 332 cases: 165 days and 167 nights. In this dataset, the extended-Kalman filtering technique is applied and tested for the determination of the PBL height. Several well-established techniques for the PBL height estimation based on lidar data are also tested for a total of 35 cases. The lidar-derived PBL heights are compared to those derived from radiosonde data. The mean PBL height over Athens is found to be 1617 ± 324 m at 1200 UTC and 892 ± 130 m at 0000 UTC for the period examined, while the mean PBL-height growth rate is found to be 170 ± 64 m h−1 and 90 ± 17 m h−1 during daytime and night-time, respectively. [ABSTRACT FROM AUTHOR]

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air-quality-monitoring networks that operate in urban areas provide data on the most important pollutants, which, via environmental statistics, can be used for the development of continuous surfaces of pollutants' concentrations. Generating ambient air-quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air-quality distributions. This study used point-monitoring data from the network of stations that operates in Athens, Greece. A machine-learning scheme was applied as a method to spatially estimate pollutants' concentrations, and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes. [ABSTRACT FROM AUTHOR]

Temperature and humidity play a key role in climate change assessment. This study focuses on the analysis of surface and upper-air temperature and humidity trends for a domain centered over the Greek region in the Eastern Mediterranean. The main purpose of this work is to investigate whether possible changes in the climate of the greater Greek region exist over the recent years. ERA-Interim reanalysis, surface and radiosonde observational data of air temperature, specific and relative humidity are used for a 39-year period (1979–2017). Through the spatial distribution of their trends, at different atmospheric pressure levels and seasons, it is possible to detect statistically significant spatial and temporal changes in the troposphere and stratosphere above the examined domain. Statistically significant warming trends at the 99% level are found at the surface over land and at the 95% level over sea during spring and summer for Greece; less pronounced and more sparsely distributed trends are identified during autumn and winter. Additionally, positive trends prevail throughout the entire extent of the troposphere, more pronounced during summer, whereas the stratosphere is characterized by negative trends. Trends derived from the reanalysis and observational data exhibit high degree of correlation both at surface and at upper atmospheric levels. In accordance, specific humidity has generally increased in the lower troposphere. Relative humidity time series demonstrate trends, which vary seasonally and geographically, due to its relationship with specific humidity and temperature. • Statistically significant warming trends at surface during spring and summer. • Strong warming trends at 1000 and 250 hPa during summer. • Reanalysis and radiosonde temperature profiles over Athens correlate well. • Statistically significant cooling trends in the stratosphere during summer. • Statistically significant specific humidity trends in the lower troposphere. [ABSTRACT FROM AUTHOR]