Volume 17 - 2015

The aim of this paper is to highlight the way towards the exploitation of treated wastewater for food crops production in order to cope with the acute water scarcity and its hazardous consequences. Reclaimed water reuse has not been practiced in Tobruk, and hence, lack of relevant data and information was the major difficulty in carrying out this research. The existing sewage treatment plant in Tobruk city produces about 7,000 cubic meters per day of suitable effluent for agricultural irrigation and safe discharge. This proportion is worth twice as much as the water being currently used for irrigation. Therefore, with proper planning and management, treated wastewater reuse schemes, in addition to their economical and environmental benefits, can be an efficient mechanism to cope with water shortage and reducing the high burden placed on the desalination plant. Wastewater reuse is considered as a reliable, secure, and drought-resistant water source. Owing to the lack of disinfection processes and routinely monitoring data, there is uncertainty concerning the microbiological safety and quality of the treated effluent. Public perception might be the major challenge facing the planning for a reuse scheme as 51 % of the population of Tobruk have shown their reluctance to consume food crops irrigated with treated wastewater. Nonetheless, public attitudes can be fostered by awareness through the media as some people, when the issue was discussed, have shown a positive response. In fact their main concerns were related to public health safety and potential negative environmental impacts which can be addressed through good planning and prudent management.

The natural environment endures natural and human-induced changes. Remote sensing has been providing monitoring oriented solutions by a series of methodologies which contribute to prudent environmental management. Analysis of multi-temporal satellite images for the observation of the land changes often includes error prone classification and change-detection techniques. The present study takes advantage of the temporal continuity of multi-temporal classified images, in order to model change trends and reduce classification uncertainty, based on reasoning rules. By revealing misclassification prone areas, training site selection is targeted. Moreover, computational tools are developed in order to disclose the alterations in land use dynamics and offer spatial reference to the pressures that land use classes impose. The proposed procedures are tested upon Landsat time series imagery, depicting the National Park of Ainos in Kefallinia, Greece.

Phthalic acid esters, commonly called as phthalates, are of a common use in the industrial activities and are known with their hazardous impact on the environment and on humans such as endocrine disrupting agents, carcinogenic and toxic effects. These adverse effects have led to increasing interest and research on the treatment and control of phthalates. Over the past two decades, there has been growing interest in the use of electrochemical techniques such as electrocoagulation (EC) for the treatment of organic pollutants particularly toxic organics. During the course of EC, where iron or stainless steel electrodes are used as electrodes, different mechanisms are operative for the elimination of organic matter, namely (a) adsorption (b) direct anodic oxidation, and (c) indirect oxidation of pollutants in the bulk solution. A statistical-based technique named as response surface methodology (RSM) is a powerful tool for modeling the complex systems (such as EC), evaluating the simultaneous effects of several factors (independent variables), and thus searching optimum conditions for desirable responses (dependent variables). Until now, RSM has not been used as a modeling and optimization tool for the EC treatment of phthalates. In this study, EC treatment, using stainless steel anode, of dimethyl phthalate (DMP), was investigated and optimized via RSM, central composite design (CCD). Initial DMP concentration (DMP_o; 20-100 mg L^-1), current density (J_c; 4.5-22.5 mA cm^-2), electrolyte concentration (NaCl, 750-1750 mg L^-1), treatment time (t_r; 60-180 min) were selected as critical process parameters while DMP, total organic carbon (TOC) removals and electrical energy consumption (EEC, in kWh m^-3) values were selected as the responses. The EC process was optimized to improve the abatements of DMP and TOC and to reduce the EEC values. Separate validation experiments were conducted for each initial DMP concentrations at optimum EC conditions established by the software module to check the goodness of fit and quality of the regression models. According to the established second-order polynomial regression models, DMP, TOC removal efficiencies and EEC values were affected by the process variables in the following decreasing order; J_c>t_r>DMP_o>NaCl (negative impact), t_r>DMP_o (negative impact)>J_c>NaCl (negative impact) and t_r>J_c>NaCl (negative impact)>DMP_o (negative impact), respectively. Analysis of variance indicated that the experimental design models obtained for the EC treatment of aqueous DMP solutions in terms of the model pollutant and mineralization were statistically significant. The response surfaces of DMP established between initial DMP concentration and current density showed that DMP removal efficiencies can be enhanced by increasing the current density to a certain value indicating that an optimum value of current density exists for maximum DMP removal.