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Kalamata’s decentralized waste management and composting scheme is a successful demonstration of the capacity of a municipality to manage its Municipal Solid Wastes (MSW) by adopting methodologies that can be supported by small scale mobile equipment. According to the revised legislation, Join Ministerial Decision (JMD) 51373/4684/2015, each municipality in Greece should organize a regional decentralized waste management plan to comply with the National and EU Legislation. In Kalamata, a mobile Mechanical Biological Treatment (MBT) unit is in operation which consists of a sorting facility in which recyclable materials are recovered from mixed wastes and a composting facility where the Organic Fraction of Municipal Solid Wastes (OFMSW) is treated into closed aerated windrows. This paper attempts to analyze the characteristics of the applied methodology as well as to set a framework for the optimal treatment of the OFMSW in small decentralised units. Several trials have been performed to optimise the effectiveness of different composting practices for OFMSW. Desirable temperature values for degradation (over 50°C), were achieved during the composting process, while oxygen content was kept between 15-20%. Heavy metals were presented in all the samples analyzed, with concentration levels below the limits set by National Legislation. The values of the physicochemical parameters monitored in this study did not exceed the maximum permissible as well.

Particulate matter (PM), classified according to aerodynamic diameter, is one of the harmful pollutants causing health damaging effects. It is considered as cancerogenic by the World Health Organization (WHO) because of the substances found in the chemical composition of PM. In this study, short-term prediction of PM2.5 pollution at 1, 2 and 3 hours was modelled using deep learning methods. Three deep learning algorithms and the combination thereof were evaluated: Long-short term memory units (LSTM), recurrent neural networks (RNN) and gated recurrent unit (GRU). Air Quality Monitoring Stations of the Ministry of Environment and Urbanization of Turkey were utilized to obtain the data. Specifically, meteorological and air pollution data were obtained from a monitoring station located in Keçiören District of Ankara. Several trials were conducted using different combinations of RNN, GRU and LSTM models. Pollutant concentrations and meteorological factors were integrated into the model as input parameters to predict PM2.5 concentration for 1,2 and 3 hours. Best results with R2 of 0.83, 0.7 and 0.63 for 1-, 2-, and 3-hour predictions, respectively, were obtained by using a combination of GRU and RNN models. The results of this study are promising for explaining the effect of different deep learning models on prediction performance.

An experimental study was carried out to investigate the energy dissipation characteristics of sea grass in a dam break flume. Dam break flows were generated in a flume. A steel ball was subjected to the dam break flow, and then climbed on a slope. Artificial sea grass was used as the energy dissipation plant. In the study, the characteristics (such as wave height, wave velocity, Froude number) of the dam break flow were investigated, the ball subjected to the dam break flow was observed, some impact factors of ball climbing height were explored. Results show that, the ball climbing height increases with increasing reservoir water level and decreasing ball diameter. The relative ball climbing height decreases with increasing relative distance and increasing relative grass height. The relative ball climbing height shows logarithmic correlation with relative reservoir water level. The energy dissipation coefficient of the sea grass changed with reservoir water level, with the average value of 0.34 in this study.

The identification of PM sources and their contribution to measured PM concentrations is crucial for the environmental policy making since the findings be able to conduce to the development of relevant legislation in order to achieve effective air quality manage. In this study, the sources of PM10 at three receptors with different characteristics, within the Western Macedonia (WM) in NW Greece, were investigated: S1 in the center of Kozani, a medium sized city located at the southern edge of the industrial axis of WM, where urban activities and traffic density occur. S2 in the city of Ptolemaida, a medium sized city located in the centre of industrial zone, and S3 in the village of Eratyra, a rural residential district outside of the industrial area. For this purpose the multivariate Positive Matrix Factorization (EPA PMF 5.0) receptor model was applied on elemental data. Specifically, PM10 samples, obtained by filtration during 1-year sampling campaign, were analyzed by ICP-MS instrument. Twenty five elements were detected at quantifiable concentrations in the investigated PM samples. For the particle samples obtained in areas within and proximal from mining and power station operations, a six-factor model gave source profiles that attributed to be vehicle exhaust, road dust, soil dust, coal combustion, oil combustion and biomass burning. Furthermore, at the background site, the major contributors were biomass burning, soil dust and oil burning while no distant transport from industrial axis was recorded.