Simplifying Sample Preparation for Soil Fertility Analysis by X-ray Fluorescence Spectrometry

Mixing uniformity effect on leaching behaviour of cement-based solidified contaminated clay

Mixing uniformity is essential for the quality control of the contaminated clay's solidification. To investigate the effect of the mixing uniformity on the leaching behaviour of the cement-based solidified contaminated clay, this study proposed a quantitative method to characterize the mixing uniformity and investigated the leaching behaviour by the leaching toxicity tests and semi-dynamic leaching tests. X-ray computed tomography (X-CT) was employed to reveal the internal mesoscopic structure. In this case, Pb2+ was selected as a tagged pollutant because of the widespread attention at heavy metal-contaminated sites. The leaching toxicity indicates the significant Pb2+ concentration deviation among the parallel specimens and non-association with the mixing uniformity. However, the Pb2+ cumulative leaching mass and observed diffusion coefficient by the semi-dynamic leaching tests both decrease with the mixing uniformity. X-CT image analysis reveals that the high cement zones wrap the low cement zones with different dimensions in the heterogeneous solidified matrix. Moreover, the specimen pretreatment method in the existing leaching toxicity standards may be inadequate because of the overall encapsulation destruction by the crushing process and representativeness uncertainty when sampling. However, for semi-dynamic leaching, the Pb2+ migration depends on the uniformity, reflecting the continuous distribution of high cement zones.

Exploration of pristine plate-tectonic plains and mining exposure areas for indigenous microbial communities and its impact on the mineral-microbial geochemical weathering process in ultramafic setting

Heavy metal release from harsh ultramafic settings influences microbial diversity and function in soil ecology. This study aimed to determine how serpentine mineralosphere bacterial assemblies and their functions differed in two different plate-tectonic plains and mining exposure sites under heavy metal release conditions. The results showed that the Proteobacteria, Actinobacteria, Cyanobacteria, Planctomycetes, and Chloroflexi were the most abundant bacterial groups among all the sites. The log10-based LDA scores highlighted that some specific groups of bacterial assemblies were enriched in plate-tectonic plains and mining activity areas of the serpentine mineralosphere. Functional prediction revealed that the abundance of heavy metal (Cr and Ni) resistance and biogeochemical cycles involving functional KEGG orthology varied in samples from plate-tectonic plains and mining activity sites. The bipartite plot showed that the enrichment of the biogeochemical cycle and heavy metal resistance functional genes correlated with the abundance of serpentine mineralosphere bacterial groups at a 0.005% confidence level. The co-occurrence network plot revealed that the interconnection pattern of the indigenous bacterial assemblies changed in different plate-tectonic plains and mining exposure areas. Finally, this study concluded that due to heavy metal release, the variation in bacterial assemblies, their functioning, and intercommunity co-occurrence patterns were clarified the synergetic effect of mineral-microbial geochemical weathering process in serpentine mining areas.

X-ray fluorescence spectrometry for environmental analysis: Basic principles, instrumentation, applications and recent trends

In recent years, the conceptual advancement on green analytical chemistry (GAC) has moved in parallel with efforts to incorporate new screening or quantitative low-cost analytical tools to solve analytical problems. In this sense, the role of solid state techniques that allow the non-invasive analysis (or with a minimum sample treatment) of solid samples cannot be neglected. This review describes the basic principles, instrumentation and advances in the application of X-ray fluorescence instrumentation to the environmental sciences research topics, published between 2006 and 2020. Obviously, and because of the enormous number of works that can be found in the literature, it is not possible to exhaustively cover all published articles and the diversity of topics related to the environment in which a solid state technique like XRF has been applied successfully. It is a question of making a compilation of the instrumentation in use, the significant advances in XRF spectrometry and sample treatment strategies to highlight the potential of its implementation for environmental assessment.

Spectra Fusion of Mid-Infrared (MIR) and X-ray Fluorescence (XRF) Spectroscopy for Estimation of Selected Soil Fertility Attributes

Previous works indicate that data fusion, compared to single data modelling can improve the assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal soil sensing techniques i.e., mid-infrared (MIR) and X-ray fluorescence (XRF) spectroscopy were evaluated, for assessment of seven fertility attributes. These soil attributes include pH, organic carbon (OC), phosphorous (P), potassium (K), magnesium (Mg), calcium (Ca) and moisture contents (MC). Three kinds of spectra fusion (SF) (spectra concatenation) approaches of MIR and XRF spectra were compared, namely, spectra fusion-Partial least square (SF-PLS), spectra fusion-Sequential Orthogonalized Partial least square (SF-SOPLS) and spectra fusion-Variable Importance Projection-Sequential Orthogonalized Partial least square (SF-VIP-SOPLS). Furthermore, the performance of SF models was compared with the developed single sensor model (based on individual spectra of MIR and XRF). Compared with the results obtained from single sensor model, SF models showed improvement in the prediction performance for all studied attributes, except for OC, Mg, and K prediction. More specifically, the highest improvement was observed with SF-SOPLS model for pH [R2p = 0.90, root mean square error prediction (RMSEP) = 0.15, residual prediction deviation (RPD) = 3.30, and ratio of performance inter-quantile (RPIQ) = 3.59], successively followed by P (R2p = 0.91, RMSEP = 4.45 mg/100 g, RPD = 3.53, and RPIQ = 4.90), Ca (R2p = 0.92, RMSEP = 177.11 mg/100 g, RPD = 3.66, and RPIQ = 3.22) and MC (R2p = 0.80, RMSEP = 1.91%, RPD = 2.31, RPIQ = 2.62). Overall the study concluded that SF approach with SOPLS attained better performance over the traditional model developed with the single sensor spectra, hence, SF is recommended as the best SF method for improving the prediction accuracy of studied soil attributes. Moreover, the multi-sensor spectra fusion approach is not limited for only MIR and XRF data but in general can be extended for complementary information fusion in order to improve the model performance in precision agriculture (PA) applications.

Spectral data of tropical soils using dry-chemistry techniques (VNIR, XRF, and LIBS): A dataset for soil fertility prediction

Proximal soil sensing technologies, such as visible and near infrared diffuse reflectance spectroscopy (VNIR), X-ray fluorescence spectroscopy (XRF), and laser-induced breakdown spectroscopy (LIBS), are dry-chemistry techniques that enable rapid and environmentally friendly soil fertility analyses. The application of XRF and LIBS sensors in an individual or combined manner for soil fertility prediction is quite recent, especially in tropical soils. The shared dataset presents spectral data of VNIR, XRF, and LIBS sensors, even as the characterization of key soil fertility attributes (clay, organic matter, cation exchange capacity, pH, base saturation, and exchangeable P, K, Ca, and Mg) of 102 soil samples. The samples were obtained from two Brazilian agricultural areas and have a wide variation of chemical and textural attributes. This is a pioneer dataset of tropical soils, with potential to be reused for comparative studies with other datasets, e.g., comparing the performance of sensors, instrumental conditions, and/or predictive models on different soil types, soil origin, concentration range, and agricultural practices. Moreover, it can also be applied to compose soil spectral libraries that use spectral data collected under similar instrumental conditions.

Development of a rapid field testing method for metals in horizontal directional drilling residuals with XRF sensor

Portable X-ray fluorescence (pXRF) spectrometer allows fast in-situ elemental determination without wet digestion for soils or geological materials, but the use of XRF on wet materials is not well documented. Our objective was to develop a rapid field method using pXRF to measure metals in the residues from horizontal directional drilling (HDD) operations so that proper disposal decisions can be made in-situ. To establish the procedure, we spiked soil samples with 4 concentrations of Cr, Ni, Cu, Zn, As, Cd, and Pb up to 1000 mg kg⁻¹, and then the metal concentrations were determined by wet chemical method after drying and acid digestion (standard method), and by pXRF, also at laboratory conditions, after drying and at two different moisture conditions. The measurements by pXRF and standard method after drying and after removal of excess water (AREW) were highly correlated with slopes ranging from 0.83 ± 0.01 to 1.08 ± 0.01 (P < 0.001) for all metals. The relationship was better AREW than the saturated paste without removal of excess water and the moisture content affected only the accuracy of As, Cd, and Pb. The procedure established was successfully used for HDD residues collected from 26 states of US with moisture content ranging from 14 to 83% AREW. The pXRF was proven to be a reliable tool for fast detection of common metals in dried soils and HDD residues, and samples containing < 30% moisture content without needing to correct for moisture. If the moisture is > 30%, excess water in samples need to be removed with a commercially available filter press to achieve high accuracy. The developed procedures reduce time of metal detection from days to about an hour which allows drilling operators to make quick decisions on soil or HDD disposal.

Combined Use of Vis-NIR and XRF Sensors for Tropical Soil Fertility Analysis: Assessing Different Data Fusion Approaches

Visible and near infrared (vis-NIR) diffuse reflectance and X-ray fluorescence (XRF) sensors are promising proximal soil sensing (PSS) tools for predicting soil key fertility attributes. This work aimed at assessing the performance of the individual and combined use of vis-NIR and XRF sensors to predict clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable (ex-) nutrients (ex-P, ex-K, ex-Ca, and ex-Mg) in Brazilian tropical soils. Individual models using the data of each sensor alone were calibrated using multiple linear regressions (MLR) for the XRF data, and partial least squares (PLS) regressions for the vis-NIR data. Six data fusion approaches were evaluated and compared against individual models using relative improvement (RI). The data fusion approaches included (i) two spectra fusion approaches, which simply combined the data of both sensors in a merged dataset, followed by support vector machine (SF-SVM) and PLS (SF-PLS) regression analysis; (ii) two model averaging approaches using the Granger and Ramanathan (GR) method; and (iii) two data fusion methods based on least squares (LS) modeling. For the GR and LS approaches, two different combinations of inputs were used for MLR. The GR2 and LS2 used the prediction of individual sensors, whereas the GR3 and LS3 used the individual sensors prediction plus the SF-PLS prediction. The individual vis-NIR models showed the best results for clay and OM prediction (RPD ≥ 2.61), while the individual XRF models exhibited the best predictive models for CEC, V, ex-K, ex-Ca, and ex-Mg (RPD ≥ 2.57). For eight out of nine soil attributes studied (clay, CEC, pH, V, ex-P, ex-K, ex-Ca, and ex-Mg), the combined use of vis-NIR and XRF sensors using at least one of the six data fusion approaches improved the accuracy of the predictions (with RI ranging from 1 to 21%). In general, the LS3 model averaging approach stood out as the data fusion method with the greatest number of attributes with positive RI (six attributes; namely, clay, CEC, pH, ex-P, ex-K, and ex-Mg). Meanwhile, no single approach was capable of exploiting the synergism between sensors for all attributes of interest, suggesting that the selection of the best data fusion approach should be attribute-specific. The results presented in this work evidenced the complementarity of XRF and vis-NIR sensors to predict fertility attributes in tropical soils, and encourage further research to find a generalized method of data fusion of both sensors data.

Effect of terminal temperature on the morphology and potentially toxic metals concentrations of biochars derived from paper and kitchen waste

This study investigated the morphology and potentially toxic metal concentrations of paper waste-based biochar (PB) and kitchen waste-based biochar (KB) obtained at 500 and 700 °C. The morphology and potentially toxic metals (Cr, Mn, Cu, Cd, Pb, Zn, Ag, and Ba) concentrations in the biochars were determined by SEM and FT-IR analysis. The Cr, Mn, Cu, and Cd concentrations in PB were low, while the Ba content was relatively high at 0.1 mg∙kg^-1. An increase in the terminal temperature led to an increase in the concentrations of Fe/Mn oxide-bound potentially toxic metals of PB, and a decrease in the concentrations of organic matter-bound potentially toxic metals. The Fe/Mn oxide-bound Cr, Mn, Cu, Pb, and Zn concentrations of KB decreased with an increase in the terminal temperature. Therefore, increasing the terminal temperature could reduce the bioavailability of potentially toxic metals in PB and KB. The environmental risk of the different biochars when used for soil remediation was assessed by the potential ecological risk index (RI), and a case study of a Tibetan soil was also conducted. The potentially toxic metal concentrations leached from both PB and KB were lower than the relevant standards. The findings showed that both PB and KB can be safely used for soil remediation.

Effect of X-Ray Tube Configuration on Measurement of Key Soil Fertility Attributes with XRF

The successful use of energy-dispersive X-ray fluorescence (ED-XRF) sensors for soil analysis requires the selection of an optimal procedure of data acquisition and a simple modelling approach. This work aimed at assessing the performance of a portable XRF (XRF) sensor set up with two different X-ray tube configurations (combinations of voltage and current) to predict nine key soil fertility attributes: (clay, organic matter (OM), cation exchange capacity (CEC), pH, base saturation (V), and extractable nutrients (P, K, Ca, and Mg). An XRF, operated at a voltage of 15 kV (and current of 23 μA) and 35 kV (and current of 7 μA), was used for analyzing 102 soil samples collected from two agricultural fields in Brazil. Two different XRF data analysis scenarios were used to build the predictive models: (i) 10 emission lines of 15 keV spectra (EL-15), and (ii) 12 emission lines of 35 keV spectra (EL-35). Multiple linear regressions (MLR) were used for model calibration, and the models’ prediction performance was evaluated using different figures of merit. The results show that although X-ray tube configuration affected the intensity of the emission lines of the different elements detected, it did not influence the prediction accuracy of the studied key fertility attributes, suggesting that both X-ray tube configurations tested can be used for future analyses. Satisfactory predictions with residual prediction deviation (RPD) ≥ 1.54 and coefficient of determination (R2) ≥ 0.61 were obtained for eight out of the ten studied soil fertility attributes (clay, OM, CEC, V, and extractable K, Ca, and Mg). In addition, simple MLR models with a limited number of emission lines was effective for practical soil analysis of the key soil fertility attributes (except pH and extractable P) using XRF. The simple and transparent methodology suggested also enables future researches that seek to optimize the XRF scanning time in order to speed up the XRF analysis in soil samples.