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et al., Optimal remediation design and simulation groundwater flow coupled to contaminant transport using genetic algorithm and radial point collocation method (RPCM), Science of the Total Environment,

In this work, we explore the use of biochar as a remediation agent, and the sensitivity of the spectral‐induced polarization method as a remediation monitoring aid. Biochar amended columns were fully saturated with industrial wastewater (olive oil mill waste) with very high concentration of phenols (∼2485 mg/L) and other substances. The biochar‐amended columns achieved very high removal rates of phenols compared to the control (sand only). Geophysical monitoring over the duration of the experiment (10 days) showed changes in the spectral‐induced polarization signal (imaginary conductivity) consistent with phenol removal as confirmed by geochemical monitoring. This experiment confirmed the utility of biochar as a remediation agent. Furthermore, spectral‐induced polarization can serve as long‐term, high resolution, monitoring aid in organic contaminant degradation processes.

Here, we show the electrical response, bacterial community, and remediation of hydrocarbon-contaminated groundwater from a gasworks site using a graphite-chambered bio-electrochemical system (BES) that utilizes granular activated carbon (GAC) as both sorption agent and high surface area anode. Our innovative concept is the design of a graphite electrode chamber system rather than a classic non-conductive BES chamber coupled with GAC as part of the BES. The GAC BES is a good candidate as a sustainable remediation technology that provides improved degradation over GAC, and near real-time observation of associated electrical output. The BES chambers were effectively colonized by the bacterial communities from the contaminated groundwater. Principal coordinate analysis (PCoA) of UniFrac Observed Taxonomic Units shows distinct grouping of microbial types that are associated with the presence of GAC, and grouping of microbial types associated with electroactivity. Bacterial community analysis showed that β-proteobacteria (particularly the PAH-degrading Pseudomonadaceae ) dominate all the samples. Rhodocyclaceae - and Comamonadaceae -related OTU were observed to increase in BES cells. The GAC BES (99% removal) outperformed the control graphite GAC chamber, as well as a graphite BES and a control chamber both filled with glass beads. Electronic supplementary material The online version of this article (10.1007/s11356-019-04297-w) contains supplementary material, which is available to authorized users.

Sustainable agriculture management typically requires detailed characterization of physical, chemical, and biological aspects of soil properties. These properties are essential for agriculture and should be determined before any decision for crop type selection and cultivation practices. Moreover, the implementation of soil characterization at the beginning could avoid unsustainable soil management that might lead to gradual soil degradation. This is the only way to develop appropriate agricultural practices that will ensure the necessary soil treatment in an accurate and targeted way. Remote sensing and geophysical surveys have great opportunities to characterize agronomic soil attributes non-invasively and efficiently from point to field scale. Remote sensing can provide information about the soil surface (or even a few centimeters below), while near-surface geophysics can characterize the subsoil. Results from the methods mentioned above can be used as an input model for soil and/or soil/water interaction modeling. The soil modeling can offer a better explanation of complex physicochemical processes in the vadose zone. Considering their potential to support sustainable agriculture in the future, this paper aims to explore different methods and approaches, such as the applications of remote sensing, geophysics, and modeling in soil studies.

No abstract

In situ chemical oxidation using permanganate as an oxidant is a remediation technique often used to treat contaminated groundwater. In this paper, groundwater flow with a full hydraulic conductivity tensor and remediation process through in situ chemical oxidation are simulated. The numerical approach was verified with a physical sandbox experiment and analytical solution for 2D advection-diffusion with a first-order decay rate constant. The numerical results were in good agreement with the results of physical sandbox model and the analytical solution. The developed model was applied to two different studies, using multi-objective genetic algorithm to optimise remediation design. In order to reach the optimised design, three objectives considering three constraints were defined. The time to reach the desired concentration and remediation cost regarding the number of required oxidant sources in the optimised design was less than any arbitrary design.

Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.