Apparent magnetic susceptibility (MSa) as recorded by electromagnetic induction (EMI) instruments could offer relevant information about non-soil subsurface features. It is less affected by natural soil properties than its prominent counterpart, i.e., apparent electrical conductivity (ECa). Hence, MSa is generally a promising approach to investigate artificial inclusions and structures in soil. However, while the origin depth of EMI based ECa is widely accepted, the depth sensitivity (DS) of MSa measurements remains poorly understood. The depth interpretation of MSa is particularly challenging due to negative values especially for objects that are randomly distributed over different depths. Here we assessed the performance of both multi-coil (MC) and multi-frequency (MF) EMI sensors for identifying and determining the DS of MSa measurements in shallow soils through detection of buried small targets of known conductivity. Two experiments were conducted in a sandy loam podzolic soil in western Newfoundland, Canada. Materials of different conductivities, including metal and plastic targets, were buried at depths between 20 and 80 cm. Three inter-coil separations (32, 71 and 118 cm) of the MC sensor and four factory-calibrated frequencies (18, 38, 49 and 80 kHz) of the MF sensor were tested in both horizontal and vertical coil orientations. The MC sensor clearly detected all four metal targets from three coil separations in both coil orientations while the MF sensor identified more anomalies than targets limiting its information value. Based on the measurements from MC and the theoretical DS function, a criterion was developed and validated to assess the potential depth origin of MSa. We found that negative or less than the background values occur, if the depth of the target is shallower than 0.36 times the coil distance of the employed EMI sensor. According to this criterion, the depth origins of metallic targets were correctly identified under the assumption of low induction numbers, even if values were negative.
Electromagnetic induction (EMI) technique is an established method to measure the apparent electrical conductivity (ECa) of soil as a proxy for its physicochemical properties. Multi-frequency (MF) and multi-coil (MC) are the two types of commercially available EMI sensors. Although the working principles are similar, their theoretical and effective depth of investigation and their resolution capacity can vary. Given the recent emphasis on non-invasive mapping of soil properties, the selection of the most appropriate instrument is critical to support robust relationships between ECa and the targeted properties. In this study, we compared the performance of MC and MF sensors by their ability to define relationships between ECa (i.e., MF–ECa and MC–ECa) and shallow soil properties. Field experiments were conducted under wet and dry conditions on a silage-corn field in western Newfoundland, Canada. Relationships between temporally stable properties, such as texture and bulk density, and temporally variable properties, such as soil water content (SWC), cation exchange capacity (CEC) and pore water electrical conductivity (ECw) were investigated. Results revealed significant (p < 0.05) positive correlations of ECa to silt content, SWC and CEC for both sensors under dry conditions, higher correlated for MC–ECa. Under wet conditions, correlation of MF–ECa to temporally variable properties decreased, particularly to SWC, while the correlations to sand and silt increased. We concluded that the MF sensor is more sensitive to changes in SWC which influenced its ability to map temporally variable properties. The performance of the MC sensor was less affected by variable weather conditions, providing overall stronger correlations to both, temporally stable or variable soil properties for the tested Podzol and hence the more suitable sensor toward various precision agricultural practices.
Nitrates, when found in excess in the groundwater, are a health hazard. Nitrates can easily be transported by groundwater movement and may eventually reach aquifers located far away from the contamination location. Recent research has focused on developing innovative techniques to remediate nitrate-contaminated soils. It is important to monitor the nitrate movement in the groundwater to predict the extent of nitrate contamination. Traditionally, nitrate movement is monitored by sampling soil pore water and analysing it in the laboratory. In this study a non-destructive method, using time domain reflectometry (TDR) to monitor nitrate movement in laboratory flow experiments, is presented. Three flow cells (replicates) packed with a silty loam soil were subjected to a constant hydraulic gradient inducing saturated water flow through the flow cells. A source of potassium nitrate solution, containing 500 ppm NO3-N, was connected at the inflow end, and the nitrate concentration change with time was monitored along the length of the flow cells by sampling the soil solution and analysing it in the laboratory for NO3-N concentration. At the time of sampling, TDR wave forms were recorded using the TDR mini-probes inserted at regular intervals along the length of the flow cells. The bulk soil electrical conductivity and the water content values extracted from the TDR wave forms were used to predict the nitrate concentrations at different locations. The nitrate concentration values predicted from the TDR-measured bulk electrical conductivity and water content data correlated well with the nitrate concentrations obtained by soil solution sampling method.
Hydrocarbon contaminated soil and groundwater is considered to be a leading cause for increased health risk and environmental contamination. Therefore, an efficient technique is needed to retard the movement or enhance the removal of the contaminant depending on the remediation objective. The goals of this study were to evaluate the impact of the addition of a cationic surfactant on the movement of hydrocarbons within a contaminated clay soil subjected to electrokinetic treatment. Water-flushing and surfactant-flushing experiments were conducted on one-dimensional soil columns. The model diesel fuel was composed of a mixture of benzene, toluene, ethylbenzene, xylenes [BTEX] and three selected polycyclic hydrocarbons [PAHs]. In the water-flushing experiments, the application of an electrokinetic treatment was found to enhance the removal of PAHs from the clay columns by about 20%. In contrast, the application of an electrokinetic treatment, when coupled with cationic surfactant-flushing, retarded the movement of BTEX and the three selected PAHs in the clay columns. Hydraulic columns with surfactant (CTAB) removed 17% more naphthalene and 11% more 2-methylnaphthalene compared to columns subjected to electrokinetic treatment with CTAB. The flux through the electrokinetic columns during water flushing as well as surfactant flushing was higher than the flux due to hydraulic gradient alone. As the solubility of hydrocarbons increased, they moved farther with electrokinetic treatment without CTAB. However, with CTAB the electrokinetic treatment tends to retard the movement. Use of a cationic surfactant coupled with electrokinetic treatment was found to retard the movement of contaminants.
Identifying and characterizing the spatial patterns in soil moisture variability under different land use conditions is crucial for agriculture, forestry, civil and environmental engineering. Yet employing multi-frequency electromagnetic induction (EMI) techniques to carry out this task is under-represented in boreal podzolic soils. This study: (i) compared four frequencies (2.8 ~ 80 kHz) for shallow mapping of soil moisture measured with a time-domain reflectometry at 0 – 20 cm soil depth under three different land-use conditions (agricultural land, field road, and a recently cleared natural forest), (ii) developed a relationship between apparent electrical conductivity (ECa) measured using multi-frequency EMI (GEM-2) and soil moisture and (iii) assessed the effectiveness of ECa as an auxiliary variable in predicting soil moisture variations under different land use conditions. The means of ECa measurements were calculated for the exact sampling location (ground truth data) in each land use condition at a research site, Pasadena, Newfoundland. Soil moisture–ECa linear regression models for the three land-use conditions were only statistically significant for 38.3 kHz frequency and were further analyzed. Further statistical analysis revealed that ECa was primarily controlled by soil moisture for the three land-use conditions, with the natural forest possessing the highest mean ECa and soil moisture. Geostatistical analysis revealed that cokriging ECa with less densely collected soil moisture improved the characterization accuracy of soil moisture variability across the different land use conditions. These results reveal the effectiveness of the georeferenced MF–EMI technique to rapidly assess intra-field soil moisture variability under different land uses.
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