Could energy equilibrium and greenhouse gas emissions in agroecosystems play a key role in crop replacement? A case study in orange and kiwi orchards

Environmental Science and Pollution Research - The development of agriculture is linked to energy resources. Consequently, energy analysis in agroecosystems could be a useful tool for monitoring...     

Quantification and site-specification of the support practice factor when mapping soil erosion risk associated with olive plantations in the Mediterranean island of Crete

Due to inappropriate agricultural management practices, soil erosion is becoming one of the most dangerous forms of soil degradation in many olive farming areas in the Mediterranean region, leading to significant decrease of soil fertility and yield. In order to prevent further soil degradation, proper measures are necessary to be locally implemented. In this perspective, an increase in the spatial accuracy of remote sensing datasets and advanced image analysis are significant tools necessary and efficient for mapping soil erosion risk on a fine scale. In this study, the Revised Universal Soil Loss Equation (RUSLE) was implemented in the spatial domain using GIS, while a very high resolution satellite image, namely a QuickBird image, was used for deriving cover management (C) and support practice (P) factors, in order to map the risk of soil erosion in Kolymvari, a typical olive farming area in the island of Crete, Greece. The results comprised a risk map of soil erosion when P factor was taken uniform (conventional approach) and a risk map when P factor was quantified site-specifically using object-oriented image analysis. The results showed that the QuickBird image was necessary in order to achieve site-specificity of the P factor and therefore to support fine scale mapping of soil erosion risk in an olive cultivation area, such as the one of Kolymvari in Crete. Increasing the accuracy of the QB image classification will further improve the resulted soil erosion mapping.     

Assessment and prediction of exposure to benzene of filling station employees

In the present study, the exposure to benzene of employees working in two filling stations (one urban and one rural) was estimated, through the method of passive sampling. Additional data (30′ measurements of benzene exposure through active sampling to employees dealing with different activities, meteorological and traffic data) were collected. The measurements campaign was performed in both summer and wintertime to determine the seasonal variation of the exposure pattern.In addition, a set of artificial neural networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees based on active sampling data and the parameters related to the employees’ exposure. The quantification of the contribution of each parameter to the overall exposure pattern was also attempted.The results showed that although vapour recovery technologies are installed in the refuelling systems and benzene emissions are significantly reduced compared to the past, filling station employees are still highly exposed to benzene (52–15 μg m⁻³). Benzene exposure is strongly correlated to car refuelling (exposure levels up to 85 μg m⁻³), while activities like car washing or working in cash machine inside an office contribute to lower exposure levels (up to 44 and 24 μg m⁻³ respectively). In rural filling station, exposure levels were in general lower compared to the urban ones, due to the smaller amount of gasoline that was traded and the absence of any significant traffic effect or urban background concentration. The developed ANN seemed to be a promising technique in the prediction of the exposure pattern giving very good results, and the quantification of the parameters affirmed the importance of the refueling procedure to the exposure levels.     

Analysis of the recycling potential of used automotive oil filters using the Life Cycle Assessment approach

The aim of this study is to evaluate whether a specific recycling system for used oil filters (UOFs) is environmentally viable by considering all steps of the product's life cycle. In that aspect an analysis of the environmental impacts regarding different waste management scenarios of UOFs in Greece is presented using the Life Cycle Assessment (LCA) approach. Waste scenarios varied from maximum feasible recycling and recovery of metals and used lubricant oil, to disposal of UOFs to landfills without any prior treatment. In order to perform this analysis, the principles of ISO 14040 were followed and a relevant LCA software was used (SimaPro 7.2). Additionally, the results of a previous work conducted by the authors were deployed, including some experimental measurements undertaken so as to evaluate and quantify the factors affecting the recovery of the lubricant oil contained in used automotive filters. Indicatively, it was estimated that a maximum of 1340 tons of used oil and 1810 tons of steel are disposed every year in Greece, as a result of the non-effective management of used automotive filters.     

Greenhouse gas emissions from two hydroelectric reservoirs in Mediterranean region

Water reservoirs are used for many purposes, such as water supply, irrigation, flood mitigation, and hydroelectric energy generation. Although hydroelectric energy is considered “green,” many studies show that the construction of a reservoir enhances greenhouse gas (GHG) emissions at the transformed area. These emissions, mainly of CO₂, CH₄, and N₂O gases, depend on the age of the reservoir, landscape and soil composition, fauna and flora remnants of the impounded area, climatic conditions, and basin runoffs. Consequently, GHG emissions significantly vary between reservoirs and depending on local specificities. Several studies have investigated GHG emissions from reservoirs around the world, focusing mainly on reservoirs located in cold regions, temperate regions, and tropical regions. Research is lacking for reservoirs in Mediterranean countries, like Greece, and similar regions. This work initially assesses the net GHG emissions of a newly created reservoir (Ilarion est. 2012) in Western Macedonia, Greece. The methodology for net GHG emission calculation was based on the use of literature data concerning pre-impoundment emission factors and local specificities of the reservoir (terrain type, canopy cover), as well as on the 2-year measurement data that were collected using a “static floating chamber.” Furthermore, in this work, the gross GHG emissions of an older, in-line reservoir (Polyfytos est. 1974) were also calculated, based on 2-year measurement data. The results show that the global warming potential (GWP) of the reservoirs is dictated by methane emissions; it minimizes during winter and spring and maximizes during summer and autumn. Hydroelectric energy production at Ilarion Reservoir results in 32 to 97 times less total CO₂ equivalent emissions in comparison to fossil fuels, while at Polyfytos Reservoir only 8 to 24 times less (based on gross emissions). It appears that the impact of a reservoir’s morphology on GHG emissions is more significant than that of a reservoir’s age.     

Spatial planning framework,a challenge for marine tourism development: location of diving parks on Rhodes island,Greece

Environment, Development and Sustainability - Dive tourism has been growing constantly in recent years, contributing to socio-economic development of coastal zones and islands. In Greece new users...     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Spatial typologies of Greek coastal zones and unregulated Urban growth

Coastal zones attract a large number of people and productive activities. This puts pressure on ecosystems and affects the organization of local economies, having an impact on social cohesion and coherence. The complexity of coastal socio-spatial systems and the fact that coastal zones, and, more particularly, islands that are considered vulnerable areas (that is, parameters: such as sea level rising, desertification, built-up areas excessive expansion etc) and important for the overall operation of the state imply the significance of a further study of the subject. The paper deals with the principal socio-spatial pressures on coastal zones and islands that are caused by unregulated urban growth. The incompetent spatial planning framework, the “loose” political system, related to urban growth, and the socio-economical characteristics are considered as the principal reasons for built-up areas excessive dispersion. Here, delineation of the coastal zone is proposed, using physico-geographical and man-made variables. Soil Sealing dataset is used to illustrate where built-up areas dispersion and basic metrics can be applied. Furthermore, an overview of the planning spatial framework is attempted, so that the particularities of urban growth can be interpreted.