This work aims at investigating the possibility of a wireless sensor network powered by an energy harvesting technology, such as a microbial fuel cell (MFC). An MFC is a bioreactor that transforms energy stored in chemical bonds of organic compounds into electrical energy. This process takes place through catalytic reactions of microorganisms under anaerobic conditions. An anode chamber together with a cathode chamber composes a conventional MFC reactor. The protons generated in the anode chamber are then transferred into the cathode chamber through a proton exchange membrane (PEM). A possible option is to use the soil itself as the membrane. In this case, we are referring to, more properly, a terrestrial microbial fuel cell (TMFC). This research examines the sustainability of a wireless sensor network powered by TMFC for land monitoring and precision agriculture. Acting on several factors, such as pH, temperature, humidity and type of soil used, we obtained minimum performance requirements in terms of the output power of the TMFC. In order to identify some of the different network node configurations and to compare the resulting performance, we investigated the energy consumption of the core components of a node, e.g., the transceiver and microcontroller, looking for the best performance.

Microbial fuel cells (MFC) are an emerging technology for waste, wastewater and polluted soil treatment. In this manuscript, pollutants that can be treated using MFC systems producing energy are presented. Furthermore, the applicability of MFC in environmental monitoring is described. Common microbial species used, release of genome sequences, and gene regulation mechanisms, are discussed. However, although scaling-up is the key to improving MFC systems, it is still a difficult challenge. Mathematical models for MFCs are used for their design, control and optimization. Such models representing the system are presented here. In such comprehensive models, microbial growth kinetic approaches are essential to designing and predicting a biosystem. The empirical and unstructured Monod and Monod-type models, which are traditionally used, are also described here. Understanding and modelling of the gene regulatory network could be a solution for enhancing knowledge and designing more efficient MFC processes, useful for scaling it up. An advanced bio-based modelling concept connecting gene regulation modelling of specific metabolic pathways to microbial growth kinetic models is presented here; it enables a more accurate prediction and estimation of substrate biodegradation, microbial growth kinetics, and necessary gene and enzyme expression. The gene and enzyme expression prediction can also be used in synthetic and systems biology for process optimization. Moreover, various MFC applications as a bioreactor and bioremediator, and in soil pollutant removal and monitoring, are explored.

Oil contamination is a major source of pollution in the environment. It may take decades for oil-contaminated soils to be remedied. This study models oil-contaminated soils using one of the world’s greatest environmental disasters, the onshore oil spill in the desert of Kuwait in 1991. This work uses state-of-art remote sensing technologies and machine learning to investigate the oil spills during the first Gulf War. We were able to identify oil-contaminated and clear locations in Kuwait using unsupervised classification over pre- and post-oil spill data. The research area’s pre-war and post-war circumstances, in terms of oil spills, were discovered by developing spectral signatures with different wavelengths and several spectral indices utilized for oil-contamination detection. Following that, we use this data for sampling and training to model various oil-contaminated soil levels. In addition, we analyze two separate datasets and used three modeling methodologies, Random Tree (RT), Support Vector Machine (SVM) and Random Forest (RF). The results show that the suggested approach is effective in detecting oil-contaminated soil. As a result, the location and degree of contamination may be established. The results of this analysis can be a valid support to the studies of an appropriate remediation.

El crecimiento de un área industrial en la parte este de la Llanura de Catania en Sicilia ha causado una intensa explotación del agua subterránea, con consecuencias negativas en su equilibrio hidrodinámico y en la degradación de la calidad del agua. En este trabajo, la porción este de la llanura localizada entre el Monte Etna y la meseta Hyblean, es analizada por medio de las características litológicas de perforaciones y datos de resistividad, los cuales han permitido la reconstrucción de las relaciones geométricas en el depósito y sus variaciones de espesor. La variabilidad granulométrica de los depósitos aluviales (arena, cieno y grava) y su permeabilidad relativa son características de un típico acuífero multicapa que es libre en la parte superior y parcialmente confinado en su porción inferior. Descansa sobre un susbstrato de arcillas marinas cuya morfología es afectada por estructuras tectónicas controlando el espesor del acuífero. La piezometría muestra una pronunciada depresión causada por el descenso del nivel estático cercano a los 2m anuales desde 1962 debido al bombeo de los pozos industriales. Esto también ha inducido infiltración de agua de mala calidad desde el acuífero contaminado sobreyaciente, mientras que perforaciones exploratorias profundas han causado una extensa contaminación del acuífero debido a los cloruros contenidos en el agua de las lentes arenosas dentro de las arcillas marinas subyacentes.doi: sin doi

This paper aims to show different techniques for identifying brownfields and for monitoring based on remote sensing. If brownfields are associated...

This paper presents the optical analysis on how the light coupling performance between optical channel waveguides and gold periodic nanoarrays in the visible spectrum can be improved using an indium tin oxide (ITO) thin film as buffer layer. Coupling efficiency, out-of-plane scattering and far-field projected beam focus and diffraction angle are evaluated using Finite-Difference Time-Domain (FDTD) method and scattering theory for different nanogratings and nanocylinders array patterns. The implementation of ITO enables up to a 12 fold increase in coupling efficiency, a 6.4 fold enhancement of light scattered out of the array plane and consequent improvements in the diffracted beam radiation intensity peaks, without compromising the beam' focus or altering its deflection angle. This configuration provides a cheap and simple solution to increase the device performance without changing the array features, for integrated biosensing and scattering-related applications.