16 research outputs found

    A comprehensive study of three different portable XRF scanners to assess the soil geochemistry of an extensive sample dataset

    Get PDF
    The assessment of soil elemental concentrations nowadays mainly occurs through conventional laboratory analyses. However, proximal soil sensing (PSS) techniques such as X-ray fluorescence (XRF) spectrometry are proving to reduce analysis time and costs, and thus offer a worthy alternative to laboratory analyses. Moreover, XRF scanners are non-destructive and can be directly employed in the field. Although the use of XRF for soil elemental analysis is becoming widely accepted, most previous studies were limited to one scanner, a few samples, a few elements, or a non-diverse sample database. Here, an extensive and diverse soil database was used to compare the performance of three different XRF scanners with results obtained through conventional laboratory analyses. Scanners were used in benchtop mode with built-in soil calibrations to measure the concentrations of 15 elements. Although in many samples Cu, S, P, and Mg concentrations were up to 6, 12, 13, and 5 times overestimated by XRF, and empirical recalibration is recommended, all scanners produced acceptable results, even for lighter elements. Unexpectedly, XRF performance did not seem to depend on soil characteristics such as CaCO3 content. While performances will be worse when expanding to the field, our results show that XRF can easily be applied by non-experts to measure soil elemental concentrations reliably in widely different environments

    Can spectral analyses improve measurement of key soil fertility parameters with X-ray fluorescence spectrometry?

    Get PDF
    While laboratory methods of elemental analysis of soil nutrients are used frequently to support soil studies, the implementation of more portable and cost-efficient methods lingers behind. The portable (handheld) X-ray fluorescence spectrometer (XRF) is one such tool enabling onsite elemental analysis in a straightforward manner. However, in soil studies the use of XRF often remains cumbersome, following the poor performance of the method for low-Z elemental analysis and the complex nature of the soil matrix, introducing background noise. Here, we therefore evaluate how the potential use of a portable XRF for predicting potassium (K), phosphorus (P), magnesium (Mg) and calcium (Ca) can be improved through the analysis of XRF spectral data with the Random Forest (RF) machine learning method. A total of 105 soil samples from a wide range of soils collected from 10 different countries (D.R. Congo, Belgium, Ivory Coast, Italy, The Netherlands, Saudi-Arabia, South Africa, Spain, Switzerland, and Zimbabwe) were scanned using an Oxford XMET8000 XRF spectrometer (Oxford Instruments, UK). Spectral data of the calibration set (n = 74) were pulled in one matrix alongside measured elemental concentrations and subjected to RF analysis. Resulting models were validated using an independent validation set (n = 31). The best RF prediction result was obtained for K followed successively by Ca, Mg and P with coefficient of determination (R-2) values of 0.83, 0.76, 0.69, and 0.47, and root mean squared error of prediction (RMSEP) of 2283.8, 6818.7, 1511.8, and 538.08 mg kg(-1), respectively. The RF modelling procedure provided improved prediction performance compared to the calibration models provided by the manufacturer (R-2 = 0.65, 0.75, 0.65, and 0.22, for K, Ca, Mg and P, respectively). Our results suggest that portable XRF instruments coupled with spectral data analysis by RF allows for rapid and low-cost analysis of soil K and Ca with remarkable accuracy. Still, the lower measurement accuracy for P and Mg suggests further work is needed to test whether the prediction can be improved by better calibration models, and how such approaches can help overcoming instrumental limitations

    High-resolution surveying with small-loop requency-domain electromagnetic systems : efficient survey design and adaptive processing

    No full text
    The range of near-surface applications whereby the electrical and magnetic properties of the subsurface are investigated expands continually [1], and, particularly for terrestrial applications, increasingly higher spatial sampling densities are deployed [2]. Within the broad range of geophysical methods, small-loop frequency-domain electromagnetic (FDEM) instrumentation is often used to record variations of multiple EM properties simultaneously [3]-[7]. Such instruments typically incorporate at least one transmitter in combination with one or more receivers. The resulting transmitter-receiver pairs are commonly configured in either a horizontal coplanar (HCP), vertical coplanar (VCP), vertical coaxial (VCA), or perpendicular (PRP) orientation [8], [9]. A distinction can be made between symmetrical (e.g., HCP, VCP, and VCA) and asymmetrical (e.g., PRP) coil configurations [10], [11] in relation to survey heading. Although the former render an isotropic response unaffected by the acquisition direction, an asymmetrical configuration will produce different responses based on survey heading

    An integrated magnetic approach to assess spatiotemporal airborne pollution impacts related to different stages in the steel production process

    No full text
    Particulate matter (PM) emitted in the steel production and cement industry, as well as in the transport and energy production sector, possesses enhanced magnetic properties which enable to delineate the impact of airborne pollution once the particles have deposited onto the Earth’s surface. Topsoil magnetic susceptibility, for instance, has been shown a suitable parameter for airborne pollution monitoring in areas with homogeneous soil type and land use. However, when study areas compri se multiple types of land use , the distribution of magnetic particles down t he soil profile can vary strongly and im pede reliable investigation of pollution impact s. Here, we demonstrate how an adaptive approach involving depth-integrated magnetic susceptibility records can mediate adverse effects of varied land use on topsoil magnetic records for pollution studies (Declercq et al., 2020). Furthermore, in tandem with other receptors of magnetic PM (strawberry and grass leaves, plastic coated cardboards (PCCs) and wiped anthropogenic surfaces), long-term variations captured in soil magnetic records were compared to short-term pollution impacts. Although this approach enables to reliably discriminate spatiotemporal variations in airborne pollution impacts, a straightforward means to relate environmental magnetic signatures more directly to specific emission outputs, and pollutant sources - facilitating the development of control measure strategies - remains lacking. To this end, we characterised the physicochemical properties of PM generated by the largest emitter of magnetic PM in our study area, a steel mill. Particular PM loads involved in different steps of the steel production process (crude iron ore, sinter plant, blast furnace and steel mill), as well as ambient PM collected on PCCs nearby the factory, were magnetically, morphologically and chemically characterized and mutually compared. Source-specific fingerprints facilitated the interpretation of magnetic pollution impacts and revealed how emissions from the blast furnace and steel mill strongly affected the observed magnetic signatures in the environment. We show how the presented magnetic approach supports spatial investigation of source-specific airborne pollution impacts, both on the short and long term
    corecore