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Olive mill wastewater (OMW) is a compound originating from oil mills during oil extraction processes. In the Mediterranean area, more than 30 million m3 of OMW are produced each year, which represents 95-97% of the world production. Such volumes of untreated OMW are usually directly disposed of into drainage systems, water bodies (such as streams, lagoons and ponds) or are sprinkled on soils, causing potentially severe environmental problems to soils and groundwater. Consequently, there is a serious waste management problem related to the olive oil industry, because these practices no longer being acceptable. In the case of on-land OMW disposal, the characterization and the identification of this contaminant in soils is a fundamental task especially with a view to maintaining the integrity and quality of agroecosystems. In recent years, soils have been extensively studied to detect contaminants by using various geophysical methods. Among such techniques, time domain reflectometry (TDR) has shown, in different contexts, evident sensitivity and resolution capability for the characterization of contaminated soil sites. In order to further exploit the potential of the TDR technique, in this study we conducted a series of laboratory-controlled tests to explore how OMW influences the dielectric response of contaminated soils. This investigation led to the development of an empirical dielectric model to estimate the presence of OMW in variably saturated-contaminated soils with different textures and pedological features. In particular, the soils selected belong to two typical pedological units in southern Italy which account for approximately 90% of the Italian olive plantations and one of the highest concentrations of the olive oil industry in Italy. In these districts, as well as in other European and Mediterranean countries, there is the controversial habit to spread OMW on soils.

Management of saline water irrigation at the field scale requires electrical conductivity to be monitored regularly in the generally shallow soil layer explored by plant roots. Non-invasive electromagnetic induction (EMI) sensors might be valuable for evaluating large-scale soil salinity. However, obtaining information on soil surface salinity from depth-integrated EMI measurements requires either an inversion of the electromagnetic signal or an empirical calibration. We opted for an empirical calibration of an EM38 sensor, which requires a reference dataset of local bulk electrical conductivity, σb, for comparison with EMI readings for estimating regression coefficients. We used time-domain reflectometry (TDR) to replace direct sampling for local σbmeasurements. With empirical approaches, the different soil volumes involved with the EMI and TDR sensors become problematic. We resolved this issue by analysing large EMI and TDR datasets recorded at several times along three transects irrigated with water at 1, 3 and 6 dS m−1degrees of salinity. A Fourier filtering technique was applied to remove the high frequency part (at small spatial scales) of the variation in the original data, which was the main source of dissimilarity between the two datasets. Therefore, calibration focused on the lower frequency information only; that is, information at a spatial scale larger than the observation volume of the sensors. We show that information from the TDR observations derives from a combination of local and larger scale heterogeneities, and how it should be managed for calibration of the EMI sensor. Analysis enabled us to identify characteristics of the calibration data that should be included to improve prediction. Highlights: EMI and TDR sensors have different observation volumes and are not immediately comparable. With Fourier filtering the different observation volumes were accounted for in calibration of the EMI data. We improved the empirical calibration between EMI and TDR datasets. We identified criteria to select data series for EMI sensor calibration.

The term non-aqueous phase liquid (NAPL) refers to a group of organic compounds with scarce solubility in water. They are the products of various human activities and may be accidentally introduced into the soil system. Given their toxicity level and high mobility, NAPLs constitute a serious geo-environmental problem. Contaminant distribution in the soil and groundwater entails fundamental information for the remediation of polluted soil sites. The present research explored the possible employment of time domain reflectometry (TDR) to estimate pollutant removal in a silt-loam soil that was primarily contaminated with a light hydrocarbon and then flushed with diverse washing solutions. Known mixtures of soil and NAPL were prepared in the laboratory to achieve soil specimens with diverse pollution levels. The prepared soil samples were repacked into plastic cylinders and then placed in testing cells. Washing solutions were then injected upward into the contaminated sample, and both the quantity of remediated oil and the bulk dielectric permittivity of the soil sample were determined. The above data was also used to develop a dielectric model (the α mixing model) which permits the volumetric NAPL content (θNAPL) within the contaminated sample to be determined and quantified during the different decontamination stages. Our results demonstrate that during a decontamination process, the TDR device is NAPL-sensitive: the dielectric permittivity of the medium increases as the NAPL volume decreases. Moreover, decontamination progression can be monitored using a simple (one-parameter) mixing model.

This paper deals with the issue of monitoring the spatial distribution of bulk electrical conductivity, σb, in the soil root zone by using electromagnetic induction (EMI) sensors under different water and salinity conditions. To deduce the actual distribution of depth-specific σb from EMI apparent electrical conductivity (ECa) measurements, we inverted the data by using a regularized 1-D inversion procedure designed to manage nonlinear multiple EMI-depth responses. The inversion technique is based on the coupling of the damped Gauss–Newton method with truncated generalized singular value decomposition (TGSVD). The ill-posedness of the EMI data inversion is addressed by using a sharp stabilizer term in the objective function. This specific stabilizer promotes the reconstruction of blocky targets, thereby contributing to enhance the spatial resolution of the EMI results in the presence of sharp boundaries (otherwise smeared out after the application of more standard Occam-like regularization strategies searching for smooth solutions). Time-domain reflectometry (TDR) data are used as ground-truth data for calibration of the inversion results. An experimental field was divided into four transects 30 m long and 2.8 m wide, cultivated with green bean, and irrigated with water at two different salinity levels and using two different irrigation volumes. Clearly, this induces different salinity and water contents within the soil profiles. For each transect, 26 regularly spaced monitoring soundings (1 m apart) were selected for the collection of (i) Geonics EM-38 and (ii) Tektronix reflectometer data. Despite the original discrepancies in the EMI and TDR data, we found a significant correlation of the means and standard deviations of the two data series; in particular, after a low-pass spatial filtering of the TDR data. Based on these findings, this paper introduces a novel methodology to calibrate EMI-based electrical conductivities via TDR direct measurements. This calibration strategy consists of a linear mapping of the original inversion results into a new conductivity spatial distribution with the coefficients of the transformation uniquely based on the statistics of the two original measurement datasets (EMI and TDR conductivities).

This paper dealt with the calibration of an EMI sensor for monitoring the time dynamics of root zone salinity under irrigation with saline water. Calibration was based on an empirical multiple regression approach largely adopted in the past and still applied in practice for its relative simplicity. Compared to the more complex inversion approaches, it requires an independent dataset of local σb measured within discrete depth intervals, to be compared to horizontal and vertical electrical conductivity (ECaH and ECaV) readings for estimating the parameters of the empirical regression equations. In this paper, we used time domain reflectometry (TDR) readings to replace direct sampling for these local σb measurements. When using this approach, there is the important issue of taking into account the effect of the different sensor observation volumes, making the readings not immediately comparable for empirical calibration. Accordingly, a classical Fourier’s filtering technique was applied to remove the high frequency part (at small spatial scale) of the original data variability, which, due to the different observation volume, was the main source of dissimilarity between the two datasets. Thus, calibration focused only on the lower frequency information, that is, the information at a spatial scale larger than the observation volume of the sensors. By this analysis, we showed and quantified the degree to which the information of the set of TDR readings came from a combination of local and larger scale heterogeneities and how they have to be manipulated for use in EMI electromagnetic induction sensor calibration.

Contamination of soils with non-aqueous phase liquids (NAPL) constitutes a serious geo-environmental problem, given the toxicity level and high mobility of these organic compounds. To develop effective decontamination methods, characterisation and identification of contaminated soils are needed. The objective of this work is to explore the potential of dielectric permittivity measurements to detect the presence of NAPLs in soils. The dielectric permittivity was measured by Time Domain Reflectometry method (TDR) in soil samples with either different volumetric content of water (θw) and NAPL (θNAPL) or at different stages during immiscible displacement test carried out with two different flushing solutions. A mixing model proposed by Francisca and Montoro, was calibrated to estimate the volume fraction of contaminant present in soil. Obtained results, showed that soil contamination with NAPL and the monitoring of immiscible fluid displacement, during soil remediation processes, can be clearly identified from dielectric measurements. © 2013, Copyright A. Comegna et al.