The simulation-optimisation models of groundwater and contaminant transport can be a powerful tool in the management of groundwater resources and remediation design. In this study, using Multiquadratic Radial Basis Function (MRBF) a coupled groundwater flow and reactive transport of contaminant and oxidant was developed in the framework of the Meshfree method. The parameter analysis has determined the optimum shape parameter (0.97), and the results of the model were compared with a physical sandbox model which were in good agreement. The genetic algorithm approach was used to find the optimum design of the remediation using permanganate as an oxidant. To find the optimum design we considered two objectives and two constraints. The results revealed that the breakthrough of contaminant to the downstream area of interest and the concentration of the contaminant in this area is reduced significantly with optimisation.

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.

This work demonstrates the potential of Fe-modified biochar for the treatment of arsenic (As) simulated wastewater and the monitoring of adsorption in real-time. Specifically, we propose the utilization of date-palm leaves for the production of biochar, further modified with Fe in order to improve its adsorption function against inorganic pollutants, such as As. Both the original biochar and the Fe-modified biochar were used for adsorption of As in laboratory batch and column experiments. The monitoring of the biochar(s) performance and As treatment was also enhanced by using the spectral induced polarization (SIP) method, offering real-time monitoring, in addition to standard chemical monitoring. Both the original and the Fe-modified biochar achieved high removal rates with Fe-modified biochar achieving up to 98% removal of As compared to the 17% by sand only (control). In addition, a correlation was found between post-adsorption measurements and SIP measurements.

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.