Evaluation of the water quality status of two surface water reservoirs in a Mediterranean island.

A 1-year monitoring study is presented on the chemical status of two surface water reservoirs in a decentralized Mediterranean island. Water samples were collected at varying depths in the two surface water reservoirs and different seasons between November 2015 and September 2016, covering both wet and dry periods of the year. Samples were analyzed in order to determine major chemical parameters and priority substances based on the EU Water Framework Directive (2000/60/EC) and the latest revision of the Priority Substances Policy Directive (2013/39/EU).

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

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.

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 Children's Hospitals of Athens.

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.