Assessing the Effect of Intensive Agriculture and Sandy Soil Properties on Groundwater Contamination by Nitrate and Potential Improvement Using Olive Pomace Biomass Slag (OPBS)

Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/ 4.0/). -- (This article belongs to the Special Issue Biomass—a Renewable Resource for Carbon Materials)The relationship between agricultural activities, soil characteristics, and groundwater quality is critical, particularly in rural areas where groundwater directly supplies local people. In this paper, three agricultural sandy soils were sampled and analyzed for physicochemical parameters such as pH, water content, bulk density, electrical conductivity (EC), organic matter (OM), cation exchange capacity (CEC), and soil grain size distribution. Major and trace elements were analyzed by inductively coupled plasma-optical emission spectrometry (ICP/OES) to determine their concentrations in the fine fraction (FF) of the soils. Afterward, the elemental composition of the soils was identified by X-ray powder diffraction (XRD) and quantified by X-ray fluorescence (XRF). The surface soil characteristics were determined by the Brunauer–Emmett–Teller (BET) method, whereas the thermal decomposition of the soils was carried out using thermogravimetric analysis and differential scanning calorimetric (TGA-DSC) measurements. The morphological characteristics were obtained by scanning electron microscopy (SEM). Afterward, column-leaching experiments were conducted to investigate the soil’s retention capacity of nitrate (NO−3). Parallelly, a chemical and physical study of olive pomace biomass slag (OPBS) residue was carried out in order to explore its potential use as a soil additive and improver in the R’mel area. The OPBS was characterized by physicochemical analysis, assessed for heavy metals toxicity, and characterized using (XRD, XRF, SEM, and BET) techniques. The results show that the R’mel soils were slightly acidic to alkaline in nature. The soils had a sandy texture with low clay and silt percentage (<5% of the total fraction), low OM content, and weak CEC. The column experiments demonstrated that the R’mel irrigated soils have a higher tendency to release large amounts of nitrate due to their texture and a higher degree of mineralization which allows water to drain quickly. The OPBS chemical characterization indicates a higher alkaline pH (12.1), higher water content (7.18%), and higher unburned carbon portion (19.97%). The trace elements were present in low concentrations in OPBS. Macronutrients in OPBS showed composition rich in Ca, K, and Mg which represent 10.59, 8.24, and 1.56%, respectively. Those nutrients were quite low in soil samples. Both XRD and XRF characterization have shown a quasi-dominance of SiO2 in soil samples revealing that quartz was the main crystalline phase dominating the R’mel soils. Oppositely, OPBS showed a reduced SiO2 percentage of 26,29% while K, Ca, and P were present in significant amounts. These results were confirmed by XRF analysis of OPBS reporting the presence of dolomite (CaMg, (CO3)2), fairchildite (K2Ca (CO3)2), and free lime (CaO). Finally, the comparison between the surface characteristic of OPBS and soils by BET and SEM indicated that OPBS has a higher surface area and pore volume compared to soils. In this context, this study suggests a potential utilization of OPBS in order to (1) increase soil fertility by the input of organic carbon and macronutrients in soil; (2) increase the water-holding capacity of soil; (3) increase soil CEC; (4) stabilize trace elements; (5) enhance the soil adsorption capacity and porosity

Une nouvelle approche pour la reconstruction statistique des images : le rapiéçage de motifs stochastiques

A patchwork approach to stochastic simulation : a route towards the analysis of morphology in multiphase systems -- Approche multi-échelle

The Direct Sampling method to perform multiple-point geostatistical simulations

Multiple-point geostatistics is a general statistical framework to model spatial fields displaying a wide range of complex structures. In particular, it allows controlling connectivity patterns that have a critical importance for groundwater flow and transport problems. This approach involves considering data events (spatial arrangements of values) derived from a training image (TI). All data events found in the TI are usually stored in a database, which is used to retrieve conditional probabilities for the simulation. Instead, we propose to sample directly the training image for a given data event, making the database unnecessary. Our method is statistically equivalent to previous implementations, but in addition it allows extending the application of multiple-point geostatistics to continuous variables and to multivariate problems. The method can be used for the simulation of geological heterogeneity, accounting or not for indirect observations such as geophysics. We show its applicability in the presence of complex features, nonlinear relationships between variables, and with various cases of nonstationarity. Computationally, it is fast, easy to parallelize, parsimonious in memory needs, and straightforward to implement

Simulation of Earth textures by conditional image quilting

Training image-based approaches for stochastic simulations have recently gained attention in surface and subsurface hydrology. This family of methods allows the creation of multiple realizations of a study domain, with a spatial continuity based on a training image (TI) that contains the variability, connectivity, and structural properties deemed realistic. A major drawback of these methods is their computational and/or memory cost, making certain applications challenging. It was found that similar methods, also based on training images or exemplars, have been proposed in computer graphics. One such method, image quilting (IQ), is introduced in this paper and adapted for hydrogeological applications. The main difficulty is that Image Quilting was originally not designed to produce conditional simulations and was restricted to 2-D images. In this paper, the original method developed in computer graphics has been modified to accommodate conditioning data and 3-D problems. This new conditional image quilting method (CIQ) is patch based, does not require constructing a pattern databases, and can be used with both categorical and continuous training images. The main concept is to optimally cut the patches such that they overlap with minimum discontinuity. The optimal cut is determined using a dynamic programming algorithm. Conditioning is accomplished by prior selection of patches that are compatible with the conditioning data. The performance of CIQ is tested for a variety of hydrogeological test cases. The results, when compared with previous multiple-point statistics (MPS) methods, indicate an improvement in CPU time by a factor of at least 50. Key Points The first use of image quilting approach in geosciences Adaptations for exact conditioning and 3-D simulation A drastic acceleration compared to existing algorithm

Integrating multiple scales of hydraulic conductivity measurements in training image-based stochastic models

Hydraulic conductivity is one of the most critical and at the same time one of the most uncertain parameters in many groundwater models. One problem commonly faced is that the data are usually not collected at the same scale as the discretized elements used in a numerical model. Moreover, it is common that different types of hydraulic conductivity measurements, corresponding to different spatial scales, coexist in a studied domain, which have to be integrated simultaneously. Here we address this issue in the context of Image Quilting, one of the recently developed multiple-point geostatistics methods. Based on a training image that represents fine-scale spatial variability, we use the simplified renormalization upscaling method to obtain a series of upscaled training images that correspond to the different scales at which measurements are available. We then apply Image Quilting with such a multiscale training image to be able to incorporate simultaneously conditioning data at several spatial scales of heterogeneity. The realizations obtained satisfy the conditioning data exactly across all scales, but it can come at the expense of a small approximation in the representation of the physical scale relationships. In order to mitigate this approximation, we iteratively apply a kriging-based correction to the finest scale that ensures local conditioning at the coarsest scales. The method is tested on a series of synthetic examples where it gives good results and shows potential for the integration of different measurement methods in real-case hydrogeological models