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Olive Mill Solid Wastes (OMSW) released in nature without any treatment is a major environmental problem in the Mediterranean region. In this work, the catalyzed pyrolysis of OMSW has been investigated. A catalyst based on SBA-15 mesoporous silica doped with chromium ferrite nanoparticles was prepared by the double solvent technique (DS). The prepared catalyst was characterized by scanning electron microscopy (SEM), Wide and Small Angle X-ray Scattering (WAXS, SAXS), Energy Dispersive X-ray (EDX) and FT-IR spectroscopies. Reverse spinel chromium ferrite nanoparticles were located inside the SBA-15 pores as confirmed by SEM images. The obtained catalyst was tested for pyrolysis reactions of OMSW. Several parameters were studied to optimize the conditions of the pyrolysis reaction in order to increase the bio-oil conversion yield. The GC-MS results demonstrated that the quality of the obtained bio-oil was improved by decreasing the quantity of phenolic and oxygenated components as well as the size of the obtained molecules. The produced bio-oil from pyrolysis of OMSW is identical to that obtained from the pyrolysis of commercial cellulose under the same conditions. A 37% conversion yield of bio-oil was obtained for the best conditions.

Activated Montmorillonite (AM) reveals as a low-cost and efficient adsorbent for the adsorption of nicotine and pyridine from aqueous solutions. In this study, the influence of several operation conditions (initial compounds concentration, volumetric flow rate, and height of bed) on the shape of breakthrough curves and the mass transfer resistance was evaluated. Adsorption experiments were developed in order to determine the adsorption isotherm of the system, then the adsorption of pyridine and nicotine onto activated Montmorillonite in single and binary system has been studied using fixed bed reactor. In continuous adsorption, Results show that the maximum nicotine uptake 110 mg/g of AM was achieved through electrostatic attraction and hydrogen bond at a pH = 6.3, a flow rate of 1 ml/min and a height of bed equal to 12 mm. In binary mixtures, zeolites adsorption is governed primarily by the size of pollutants present in water. Thus, the bigger compound (in this case, Nicotine), was adsorbed more easily than the pyridine present in the mixture. Experimental data were fitted according to Fowler Guggenheim for the isotherms and Wolborska model for the breakthroughs. AM was regenerated by ethanol and the results show that about 94% of the adsorption capacity is maintained after three times of cyclic adsorption-desorption process.

This paper investigates the feasibility of predicting nitrate contamination from agricultural sources using multi-layer perceptron artificial neural networks (MLP-ANNs). The approach consists in training an MLP-ANN to predict nitrate concentrations based on a set of indirect measurements, such as pH, electrical conductivity, temperature and groundwater level. These are simpler and more economical than direct measurements, and they can be continuously collected on-site, rather than by performing laboratory tests. The approach has been validated in the nitrate vulnerable zone of the Arborea plain (central western Sardinia, Italy) by comparing the results obtained with different MLP-ANN models in order to find the most efficient model. The results show that the MLP-ANN-based model is a time- and cost-efficient method for predicting nitrate concentration.

The phenomenon of seawater intrusion is one of the major problems in Sardinian coastal aquifers (Italy). In particular, it has been detected in the reclamation area of Arborea plain (west Sardinia) where intensive agriculture and dairy farming are the mainstays of the local economy.In this research SINTACS and GALDIT vulnerability indexes and the numerical model for simulating groundwater flow have been applied for evaluating respectively the intrinsic vulnerability to pollution, seawater intrusion and groundwater flow for a typical Mediterranean phreatic sandy aquifer such as Arborea plain aquifer.This research may offer a valuable contribution to the team of existing tools in the field of seawater intrusion and groundwater quality modelling.

Activated Montmorillonite was applied for the sorption of pyridine as a health-toxic substance. Fourier-transform Infrared spectroscopy (FTIR), zeta potential analysis, thermogravimetric analysis (TGA), N2 physisorption analysis and X-ray diffraction (XRD) method were used to investigate the structure and composition of Montmorillonite. The batch sorption system was used to study the effect of different parameters on the adsorption. Results had shown that neutral pH had a significant effect on the sorption of pyridine; thermodynamic calculations indicated that the adsorption of pyridine over Montmorillonite was exothermic reaction and the equilibrium capacity was slightly increased by using H2SO4 as activating agent by comparison with that obtained by using HCl and H3PO4. Fitting the experimental data to isotherms models indicated that the experimental data were well fitted by Fowler Guggenheim isotherm models. Finally the results obtained by FTIR and TGA proved that the relative affinity of the pyridine toward the Montmorillonite was related to the electron donor-acceptor complexes that were formed between the basic sites (nitrogen or oxygen) and hydrogens (acidic site).

The problem of identifying an unknown pollution source in polluted aquifers, based on known contaminant concentration measurements, is part of the broader group of issues called inverse problems. This paper investigates the feasibility of solving the groundwater pollution inverse problem by using artificial neural networks (ANNs). The approach consists first in training an ANN to solve the direct problem, in which the pollutant concentration in a set of monitoring wells is calculated for a known pollutant source. Successively, the trained ANN is frozen and is used to solve the inverse problem, where the pollutant source is calculated which corresponds to a set of concentrations in the monitoring wells. The approach has been applied for a real case which deals with the contamination of the Rhine aquifer by carbon tetrachloride (CCl4) due to a tanker accident. The obtained results are compared with the solution obtained with a different approach retrieved from literature. The results show the suitability of ANN-based methods for solving inverse non-linear problems.

This paper describes three different methods used to evaluate the nitrate contamination from agricultural practices in a study area located in the Nitrate Vulnerable Zone (NVZ) of the Arborea plain (Sardinia - Italy). Potential risk of contamination and concentration of nitrate pollution in groundwater has been estimated by using Parametric, Numerical and Artificial Neural Networks methods. Parametric methods consider the combination of intrinsic aquifer vulnerability to contamination index (SINTACS) and agricultural nitrates hazard index (IPNOA). The transport numerical model is based on flow model, obtained with modular three dimensional finite difference groundwater flow model (MODFLOW), and it is made applying 3-D Multi-Species Transport Model (MT3D). Artificial Neural Networks (ANNs) are used for the estimation of the nitrate concentration in monitoring well.