pXRF Data Research Articles

Green tech soil analysis: a comparison of potentially toxic element contents among urban, agricultural, and gold mining areas in the Eastern Brazilian Amazon.

Artisanal gold mining can lead to soil contamination with potentially toxic elements (PTEs), necessitating soil quality monitoring due to environmental and human health risks. However, determining PTE levels through acid digestion is time-consuming, generates chemical waste, and requires significant resources. As an alternative, portable X-ray fluorescence (pXRF) offers a faster, more cost-effective, and sustainable analysis. This study compared total As, Ba, Cr, Cu, Fe, Mn, Ni, Pb, Sr, Ti, V, and Zn obtained from pXRF with their pseudo-total contents obtained through acid digestion (USEPA method 3051A) in areas influenced by artisanal gold mining in the Eastern Amazon, Brazil. pXRF data and machine learning algorithms were used to predict extractable Cu, Fe, Mn, and Zn. Linear regression models were fitted to compare the two methods, and random forest and support vector machine techniques were used to predict extractable contents. The best regression model fits for the pseudo-total PTE contents were those for Cu, Fe, Mn and Pb in agricultural areas (R2 > 0.80); Fe and Mn in gold mining residue (R2 > 0.70); and Ba, Cu and Mn in urban areas (R2 > 0.80). The best models for predicting the extractable PTE contents were those for Cu (R2 = 0.72; RMSE = 2.58mgdm-3) and Zn (R2 = 0.71; RMSE = 1.44mgdm-3) in agricultural areas and for Zn (R2 = 0.72; RMSE = 0.43mgdm-3) in gold mining residue. The results demonstrated that pXRF can characterize and predict PTE contents in mining-impacted areas, offering a sustainable approach to soil quality analysis.

Advancing Leaf Nutritional Characterization of Blueberry Varieties Adapted to Warm Climates Enhanced by Proximal Sensing

Blueberries offer multiple health benefits, and their cultivation has expanded to warm tropical regions. However, references for foliar nutritional content are lacking in the literature. Proximal sensing may enhance nutritional characterization to optimize blueberry production. We aimed (i) to characterize the nutrient contents of healthy plants of three blueberry varieties adapted to warm climates (Emerald, Jewel, and Biloxi) using a reference method for foliar analysis (inductively coupled plasma (ICP)) and a portable X-ray fluorescence (pXRF) spectrometer on fresh and dry leaves and (ii) to differentiate blueberry varieties based on their nutrient composition. Nutrient content was statistically compared per leaf moisture condition (fresh or dry) with ICP results and used to differentiate the varieties via the random forest algorithm. P and Zn contents (ICP) in leaves were different among varieties. Dry leaf results (pXRF) were strongly correlated with ICP results. Most nutrients determined using ICP presented good correlation with pXRF data (R2 from 0.66 to 0.93). The three varieties were accurately differentiated by pXRF results (accuracy: 87%; kappa: 0.80). Predictions of nutrient contents based on dry leaves analyzed by pXRF outperformed those based on fresh leaves. This approach can also be applied to other crops to facilitate nutrient assessment in leaves.

Clean and accurate soil quality monitoring in mining areas under environmental rehabilitation in the Eastern Brazilian Amazon.

Soil quality monitoring in mining rehabilitation areas is a crucial step to validate the effectiveness of the adopted recovery strategy, especially in critical areas for environmental conservation, such as the Brazilian Amazon. The use of portable X-ray fluorescence (pXRF) spectrometry allows a rapid quantification of several soil chemical elements, with low cost and without residue generation, being an alternative for clean and accurate environmental monitoring. Thus, this work aimed to assess soil quality in mining areas with different stages of environmental rehabilitation based on predictions of soil fertility properties through pXRF along with four machine learning algorithms (projection pursuit regression, PPR; support vector machine, SVM; cubist regression, CR; and random forest, RF) in the Eastern Brazilian Amazon. Sandstone and iron mines in different chronological stages of rehabilitation (initial, intermediate, and advanced) were evaluated, in addition to non-rehabilitated and native forest areas. A total of 81 soil samples (26 from sandstone mine and 55 from iron mine) were analyzed by both traditional wet-chemistry methods and pXRF. The available/exchangeable contents of K, Ca, B, Fe, and Al, in addition to H+Al, cation exchange capacity at pH = 7, Al saturation, soil organic matter, pH, sum of bases, base saturation, clay, and sand were accurately predicted (R2 > 0.70) using pXRF data, with emphasis on the prediction of Fe (R2 = 0.93), clay content (R2 = 0.81), H+Al (R2 = 0.81), and K+ (R2 = 0.85). The best predictive models were developed by RF and CR (86%) and when considering pXRF data + mining area + stage of rehabilitation (73%). The results highlight the potential of pXRF to accurately assess soil properties in environmental rehabilitation areas in the Amazon region (yet scarcely evaluated under this approach), promoting a more agile and cheaper preliminary diagnosis compared to traditional methods.

Portable XRF applied to regional bedrock mapping in Quebec, Canada

Whole-rock geochemistry yields better geological maps of the bedrock, since it allows, for example, visually similar lithologies to be distinguished. However, conventional laboratory geochemistry is not available in the field during a mapping campaign. Portable X-ray fluorescence (pXRF) analyzers can produce fit-for-purpose data rapidly, and constitute a useful complement to conventional geochemistry in a bedrock mapping project. The pXRF data can be employed (i) while still in the field, to orient the mapping campaign; (ii) to prepare a preliminary geological map back in the office; and (iii) to interpolate between conventional whole-rock analyses on the final map. The latter application requires a matrix-matched secondary calibration of the pXRF data to improve its accuracy. Attention must also be given to precision issues, as well as potential instrument drift and analytical interferences.This paper summarizes recent efforts to systematically integrate quantitative pXRF data in government mapping projects within the Province of Quebec (Canada). We first demonstrate how an Olympus Vanta series M pXRF analyzer was commissioned and calibrated. We then show how the pXRF data was systematically acquired and utilized during a 1:50,000 to 1:20,000 mapping campaign in a Precambrian greenstone belt setting (Abitibi Subprovince). Before the mapping began in 2021, volcanic rocks in the study area were supposed to consist of only two mafic tholeiitic formations, called Obatogamau and Bruneau. Integrating pXRF into this project meant that while still in the field and immediately afterwards during preparation of the preliminary map: (1) a marker horizon representing the top of the Obatogamau Formation was chemically identified and then traced laterally; (2) the felsic Waconichi Formation was recognized despite the high metamorphic grade and also traced laterally just above the marker horizon; (3) the Bruneau Formation was shown to be much more continuous than previously known and to have Mg-tholeiites at its base, like in the type locality; (4) the Blondeau Formation was shown to occur above the Bruneau Formation. Within the volumetrically dominant Obatogamau Formation, Fe-tholeiites were also distinguished from visually similar Mg-tholeiites, which allowed these subunits to be traced while still in the field, leading to a more detailed map. Later, after the conventional laboratory chemistry had been received, the final geological map was prepared, but it was very similar to the preliminary map, confirming the satisfactory performance of the pXRF method for this application. However, we do note some issues and limitations even with the latest generation of analyzers.

Portable X-ray fluorescence spectrometry: a cost-effective method for analysing trace metals in deposited dust.

For projects requiring extensive environmental sampling and rapid decision-making to identify trace metal contamination using dust wipes, the cost and time required for wet chemistry analysis can be prohibitive. Under such circumstances there is a need for a suitable screening method that is cost-effective, efficient, and portable. To address this need, this study investigated the utility of portable X-ray fluorescence (pXRF) for the analysis of trace metals in dust wipes. Here, 316 dust wipe samples from three different geographical settings co-located with mining and smelting operations were investigated for their trace metal loadings (μg m-2) of arsenic (As), chromium (Cr), copper (Cu), iron (Fe), manganese (Mn), nickel (Ni), lead (Pb), and zinc (Zn) using pXRF. Results collected using pXRF were compared against inductively coupled plasma mass spectrometry (ICP-MS) concentrations using matched dust wipes (n = 87) to assess reproducibility. A subset of dust wipes (n = 4) were subject to different pXRF analytical scenarios (ranging from 1 to 12 pXRF measurements) using a standardised test duration of 30 seconds to identify the most efficient number of tests for analytical precision. Conducting four pXRF tests on a single wipe (total exposure time of 120 seconds) returned comparable results to ICP-MS and was adopted for analysis of all samples. Results from dust wipes analysed with both ICP-MS and pXRF (n = 87) showed moderate to strong Spearman Rho correlations (rs = 0.489-0.956, p < 0.01) and linear regression coefficients of variation demonstrated good agreement between methods (R2 = 0.432-0.989, p < 0.05). Linear regression equations were used to correct pXRF data to the ICP-MS dust wipe data for samples analysed by both approaches, and applied to pXRF data that were not subject to ICP-MS analysis (n = 229). Application of the correction formula resulted in a substantial improvement of pXRF's accuracy and precision, confirming its effectiveness for assessing trace metals in dust wipes.

Feasibility of using Vis-NIR spectroscopy and PXRF spectrometry to estimate regional soil cadmium concentration

Conventionally, soil cadmium (Cd) measurements in the laboratory are expensive and time-consuming, involving complex processes of sample preparation and chemical analysis. This study aimed to identify the feasibility of using sensor data of visible near-infrared reflectance (Vis-NIR) spectroscopy and portable X-ray fluorescence spectrometry (PXRF) to estimate regional soil Cd concentration in a time- and cost-saving manner. The sensor data of Vis-NIR and PXRF, and Cd concentrations of 128 surface soils from Yunnan Province, China, were measured. Outer-product analysis (OPA) was used for synthesizing the sensor data and Granger-Ramanathan averaging (GRA) was applied to fuse the model results. Artificial neural network (ANN) models were built using Vis-NIR data, PXRF data, and OPA data, respectively. Results showed that: (1) ANN model based on PXRF data performed better than that based on Vis-NIR data for soil Cd estimation; (2) Fusion methods of both OPA and GRA had higher predictive power (R2) = 0.89, ratios of performance to interquartile range (RPIQ) = 4.14, and lower root mean squared error (RMSE) = 0.06, in ANN model based on OPA fusion; R2 = 0.88, RMSE = 0.06, and RPIQ = 3.53 in GRA model) than those based on either Vis-NIR data or PXRF data. In conclusion, there exists a great potential for the combination of OPA fusion and ANN to estimate soil Cd concentration rapidly and accurately.

Predictive modeling of total Hg background concentration in soils of the Amazon Rainforest biome with support of proximal sensors and auxiliary variables

Mercury (Hg) is a very well-recognized potential pollutant worldwide, and in the Amazon biome illegal artisanal gold mining practices tend to maximize this environmental problem. Considering the large extent of this biome and the evidences of Hg contamination, the main objective of this work was to predict the total Hg background concentration in Amazon soils using proximal sensors with aid of Random Forest (RF) and Support Vector Machine (SVM) algorithms, taking the total Hg content by direct analyzer as a reference. Soil texture, fertility properties and terrain attributes (elevation and slope) were considered as auxiliary variables in the modeling process. For that, nine contrasting sites of the Amazon rainforest biome were selected. In each site, eight locations (30 m apart) were carefully selected to collect composite soil samples at three depths: 0–20 cm, 20–40 cm, and 40–60 cm, totaling 216 soil samples. The pXRF data were separated into calibration (70%) and validation (30%) datasets for validation purposes. Total Hg in the studied soils ranged from 21.5 to 208 μg kg−1. RF models had better performance than SVM ones in Hg content prediction, showing the highest R2 and lowest RMSE values. Clay fraction content, total Al2O3, low degree of crystallinity Al forms, and stable elements (Nb, Zr, and Ti) positively correlated with total Hg. The combination of pXRF + magnetic susceptibility + soil texture data provided the best prediction models, however, pXRF data alone successfully predicted total Hg with R2 of 0.83. Saving time and cost and non-generating chemical effluents in prediction of total Hg background concentration in Amazon soils may be performed, constituting a secure basis for environmental regulation.

Proximal sensing provides clean, fast, and accurate quality control of organic and mineral fertilizers

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint.

Towards direct and eco-friendly analysis of plants using portable X-ray fluorescence spectrometry: A methodological approach

The assessment of the nutritional status of plants is traditionally performed by wet-digestion methods using oven-dried and ground samples. This process requires sampling, takes time, and it is non-environmentally friendly. Agricultural and environmental science have been greatly benefited by in-field, ecofriendly methods, and real-time element measurements. This work employed the portable X-ray fluorescence spectrometry (pXRF) to analyze intact and fresh leaves of crops aiming to assess the effect of water content and leaf surface (adaxial and abaxial) on pXRF results. Also, pXRF data were used to predict the real concentration of macro- and micronutrients. Eight crops (bean, castor plant, coffee, eucalyptus, guava tree, maize, mango, and soybean) with contrasting water contents were used. Intact leaf fragments (∼2 × 2 cm), fresh or oven-dried (60 °C) were obtained to be analyzed via pXRF on both adaxial and abaxial surface. Conventional wet-digestion method was also performed on powdered material to obtain the concentration of macro- and micronutrients via ICP-OES. The data were subjected to descriptive statistics, principal component analysis (PCA) and random forest (RF) algorithm regression. RF was used to predict the real concentration of macro- and micronutrients based on pXRF measurements obtained directly on intact leaves. Water content had a significant effect on pXRF results. However, a positive correlation between the concentration of macro- and micronutrients obtained via pXRF directly on intact leaves and conventional analysis performed on powdered samples was obtained. PCA analysis allowed a clear differentiation of crops based on elemental composition. The concentrations of macro- and micronutrients were very accurately predicted via RF. Even elements not detected by pXRF (N and B) were satisfactory predicted. From this pilot study, it is possible to concluded that pXRF is feasible for in-field assessment of nutritional status of plants. Further studies are needed to obtain specific and robust calibrations for each crop.

Determining the Provenance of Traded Wildlife in the Philippines.

The illegal wildlife trade is a significant threat to global biodiversity, often targeting already threatened species. In combating the trade, it is critical to know the provenance of the traded animal or part to facilitate targeted conservation actions, such as education and enforcement. Here, we present and compare two methods, portable X-ray fluorescence (pXRF) and stable isotope analysis (SIA), to determine both the geographic and source provenance (captive or wild) of traded animals and their parts. Using three critically endangered, frequently illegally traded Philippine species, the Palawan forest turtle (Siebenrockiella leytensis), the Philippine cockatoo (Cacatua haematuropygia), and the Philippine pangolin (Manis culionensisis), we demonstrate that using these methods, we can more accurately assign provenance using pXRF data (x¯ = 83%) than SIA data (x¯ = 47%). Our results indicate that these methods provide a valuable forensic tool that can be used in combating the illegal wildlife trade.

Sensor-based field methods for pedology and soil surveys: Protocol suggestions for Brazilian tropical soils

Although limited alternatives have been proposed to describe soils by using proximal sensors under natural conditions, efforts must be made to apply the newest technologies to assist in soil characterization and mapping. Our main objective was: to create a protocol incorporating technology that assists pedologists: (i) to compare soil horizonation by using visible, near, and shortwave infrared (Vis-NIR-SWIR) spectroscopy, mid-infrared spectroscopy (MIR), portable X-ray fluorescence (pXRF) indices, and conventional field observations; (ii) to identify mineralogical and physico-chemical variations between soil horizons by using proximal sensors, with inferences about pedogenic processes; and (iii) to expand punctual information to spatial dimensions with RGB images and terrain attributes. Seven soil profiles located through a toposequence evaluation were analyzed on a farm in the São Paulo State, Brazil. First, when the area consisted of exposed soils, images were obtained by Landsat and an unmanned aerial vehicle (UAV), and a digital surface model (DSM) was then built by stereoscopy, which was subsequently used to calculate thirteen terrain attributes. Each soil profile was evaluated with devices directly in the field (except for the MIR analyses, for which soil samples were brought to the laboratory and dried). Several indices were calculated from the pXRF data during the profile analysis, while principal component analyses (PCA) were performed to reduce the dimensions of the spectral data. Four datasets for each soil profile (VIS-NIR-SWIR, pXRF, MIR, and a combination of all the data) were submitted for k-means clustering analysis and the similarities among the layers were evaluated. Following the profile evaluation, spatialization was performed based on single bands from the RGB image and PCA using terrain attributes. The data were sampled to a punctual view of 370 points, classified, and then interpolated with the spline method. The Vis-NIR-SWIR spectral range, together with soil color, provided better correspondence with conventional field observations. We observed that the combined use of Vis-NIR-SWIR, MIR, and pXRF helped to identify and describe the soil profiles appropriately. Proximal sensors also allowed us to make more robust inferences regarding pedogenesis. We delineated soil information profiles in soil units by using the spatial dimension's terrain attributes and, RGB images from a UAV.

Detailed characterization of iron-rich tailings after the Fundão dam failure, Brazil, with inclusion of proximal sensors data, as a secure basis for environmental and agricultural restoration

Following the Fundão dam failure in Brazil, 60 million m3 of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R2 and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fundão dam failure can be in detail characterized via pXRF ex situ, providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes.

Portable x-ray fluorescence calibrations: Workflow and guidelines for optimizing the analysis of geological samples

The use of portable Energy Dispersive X-Ray Fluorescence (pXRF) spectrometers has exploded within the last decade for e.g., environmental, geological, and archaeological field research. Producing reliable geochemical data using a pXRF is a challenging process that requires the development of dedicated and matrix-matched empirical calibrations. Although a large field of applications, several research papers are still published using unquantified pXRF data, using manufacturers' built-in calibrations unsuitable for targeted rocks, or interpreting chemical concentrations without checking the limit of quantification of the pXRF spectrometer and its calibration model. In this paper, we present a general workflow for building an empirical calibration model for a pXRF spectrometer, from choosing reference materials to validating calibration's efficiency and quantifying its limit of quantification. We also compare the geochemical results on a sedimentary and an igneous rocks suite obtained from thirty-two calibration models that were built using different software available, including a simple homemade spreadsheet, the EasyCal calibration software, a free and open-source calibration app named CloudCal, and the built-in GeoMining and GeoExplorer calibrations provided by Bruker manufacturer on the Tracer 5 pXRF. This decoupled approach allows (i) to explore the pros and cons of each calibration model and determine which empirical calibration model brings the most accurate chemical concentration for a sedimentary and an igneous rock suite, (ii) to weigh the impact of each calibration parameter (type of regression line, spectral interferences, normalization, influence coefficients, multiple conditions of excitation and acquisition) on the accuracy of geochemical concentrations on rock samples and (iii) to bring innovative tools to assess the limits of quantification of a newly built calibration model.

Tracing the origin of deposited sediments: A study applying proximal sensing in a drainage subbasin

Erosion is a major cause of soil degradation. Understanding the sources of sediment and the processes that exacerbate this problem is crucial for soil conservation. Remote and proximal sensing technologies have contributed to advance our understanding of soil erosion processes and support soil conservation research. The objective of this study is to use two proximal sensors, a portable X-ray fluorescence spectrometer (pXRF) and a magnetic susceptibilimeter, to identify the origin of sediments deposited in the lower portion of a drainage subbasin by using a fingerprinting approach. Five sediment tracing elements were selected: Fe, Si, Al, Ti, and Zr. The sediment source area is composed of gabbro, granite, and felsic orthogneiss, upon which two soil classes were formed: Rhodic Ferralsol (Dystric) (Latossolo Vermelho Distroférrico típico, LV) and Haplic Acrisol (Argissolo Vermelho-Amarelo Distrófico típico, PVA). In the source area, 35 samples were collected from the topsoil layer (0–2 cm of depth) using a regular grid of 50 × 50 m2. In the deposition area, 24 samples were collected in a regular grid of 7 × 7 m2. The source of the sediments was determined using cluster analysis with data provided by the proximal sensors and topographic attributes. Results indicated that the Haplic Acrisol, which is more susceptible to erosion, contributed most to sediment deposition. Analysis of pXRF data showed its potential to identify and differentiate sediments and their sources. Cluster models based on the abundance of Fe, Si and Al, as well as the Ti/Zr ratio, demonstrated the best results with a global accuracy of 88% and a Kappa coefficient of 0.74. The use of the susceptibilimeter data alone did not yield satisfactory results. Results indicate the usefulness of pXRF in identifying the source of sediments in drainage basins, making it a valuable tool in soil conservation and erosion assessments.

A Proximal Sensor-Based Approach for Clean, Fast, and Accurate Assessment of the Eucalyptus spp. Nutritional Status and Differentiation of Clones.

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops.

A pXRF analysis of historic brick chemical data throughout the lake Michigan Morainic region, United States

Prior research has identified and grouped localized brick samples utilizing portable X-ray fluorescence (pXRF). This work asks whether historic brick samples across a multiple-state regional area can be reliably grouped based upon pXRF data. To answer this, pXRF readings (n = 718) were taken from sites or extant buildings (n = 36) in Milwaukee, Wisconsin; Chicago, Illinois; Hobart, Indiana; South Bend, Indiana; The University of Notre Dame, Indiana; and Zeeland, Michigan. Results indicate distinct chemical groupings that correspond with and represent different brick production sites. In some cases, it is possible to identify the use of brick of more than one chemical type within the same structure. This work is important because of the historic significance of bricks as an artifact class. A rich history of brick production has become a crucial factor in the community identity for many of these sites and the results of this project indicate that matching brick samples found in archaeological sites to their production site with pXRF is promising. The results from this work also indicate that historic bricks that are to be reused for contemporary repairs and construction projects can be accurately sourced based upon chemical data supplied by pXRF analysis.

Rapid elemental prediction of heterogeneous tropical soils from pXRF data: a comparison of models via linear regressions and machine learning algorithms

Context USEPA 3051a is a standard analytical methodology for the extraction of inorganic substances in soils. However, these analyses are expensive, time-consuming and produce chemical residues. Conversely, proximal sensors such as portable X-ray fluorescence (pXRF) spectrometry reduce analysis time, costs and consequently offer a valuable alternative to laboratory analyses. Aim We aimed to investigate the feasibility to predict the results of the USEPA 3051a method for 28 chemical elements from pXRF data. Methods Samples (n = 179) representing a large area from Brazil were analysed for elemental composition using the USEPA 3051a method and pXRF scanning (Al, Ca, Cr, Cu, Fe, K, Mn, Ni, P, Pb, Sr, Ti, V, Zn and Zr). Linear regressions (simple linear regression – SLR and stepwise multiple linear regressions – SMLR) and machine learning algorithms (support vector machine – SVM and random forest – RF) were tested and compared. Modelling was developed with 70% of the data, while the remaining 30% were used for validation. Key results Results demonstrated that SVM and RF performed better than SLR and SMLR for the prediction of Al, Ba, Bi, Ca, Cd, Ce, Co, Cr, Cu, Fe, Mg, Mn, Mo, P, Pb, Sn, Sr, Ti, Tl, V, Zn and Zr; R2 and RPD values ranged from 0.52 to 0.94 and 1.43 to 3.62, respectively, as well as the lowest values of RMSE and NRMSE values (0.28 to 0.70 mg kg−1). Conclusions and implications Most USEPA 3051a results can be accurately predicted from pXRF data saving cost, time, and ensuring large-scale routine geochemical characterisation of tropical soils in an environmentally friendly way.

Mineralogy, elemental composition, and organic matter prediction of wetland soils in the Cerrado biome, Brazil

Context The Cerrado, an area of savannah and wetlands in Brazil, is an important wet ecosystem characterised by organic-rich soils that has important environmental functions. In this work, we hypothesised that the total elemental composition obtained via portable X-ray fluorescence spectrometry (pXRF) could be useful a tool to predict soil organic matter. Aims We aimed to predict the total organic carbon (TOC) content and its fractions (carbon fulvic acid, C-FA; carbon humic acid, C-HA; carbon humin, C-HU) of soils occurring in representative Veredas biome from the Cerrado, Brazil. Methods We collected 144 soil samples from surface (0–20 cm) and subsurface (40–70 cm) layers from six representative Veredas environment located in the Cerrado bome, Triângulo Mineiro region, Brazil. These samples were analysed using a pXRF to obtain the total elemental composition. TOC, C-HA, C-FA, and C-HU fractions were determined by wet-oxidation and titration methods. The pXRF data was correlated with TOC and humic substances and later subjected to simple and multiple linear regression (MLR). Key results The major elements corroborated the mineralogy of wetland soils. TOC and humic substances, mainly C-HU, were reasonably predicted using pXRF data. All humic substances and TOC were strongly correlated to Pb. Thus, by parsimony, TOC could be reasonable predicted exclusively based on Pb concentration. Conclusions We demonstrated that pXRF can be an efficient tool to assess the elemental composition of the Cerrado wetlands soils contributing to still scarce inventory. Further, the elements germane to soil organic matter compounds can be used to predict TOC and its fractions. Implications A detailed characterisation of the Cerrado wetland soils in situ can be successfully performed using pXRF in further studies.

Archaeometric Investigation of Gold Coins (Byzantine Period, Romanus Iii) By Means of Portable Xrf: Characterization and Comparison

Some of the ancient materials in museums can only be examined on-site, which requires in-situ analysis techniques. Coins made of precious metals are one of such materials and portable X-ray fluorescence (p-XRF) spectrometry is one of the most preferred technique for gold and silver-based coins that need to be characterized in situ and non-destructively. In this study, a detailed archaeometric investigation was carried out for the gold coins which were unearthed after the excavations in Melik Ahmet Street (Diyarbakır, 1992) and brought to Directorate of Diyarbakır Museum through confiscation. The chemical compositions of the samples were specified by means of portable XRF which was applied on 45 gold coins of the Romanus III period. The main element in the composition was found as gold detected as 93.89% in average. Silver was the most abundant element after gold, however the highest silver rate did not exceed 9% (5.25% in average). Copper and iron were identified respectively in 12 coins (Cu: 1.05-2.46%) and 18 coins (Fe: 0.87-6.5%). Manganese, nickel, titanium, lead, iridium and osmium were seen in one sample each. The hierarchical clustering analysis applied with gold-silver-copper-iron and gold-silver suggested three different groups. The hierarchical clustering analysis was also applied considering the p-XRF data achieved in this study (belonging to the Romanus III period), solidus samples belonging to six Byzantine period emperors, and for the hollow coins belonging to four Byzantine period emperors. Taking into account the distribution, classification and the variations, the coins of different emperor periods were compared.

Moisture, organic matter, and large particle correction for accurate Pb portable X‐ray fluorescence assessment in urban soils

AbstractThe need for accurate quantification of lead concentrations in urban centers around the world is turning attention towards rapid methods for contamination assessment. Portable X‐ray Fluorescence (pXRF) has been recognized as a more accessible alternative to traditional digestion methods. This study was conducted to produce measurement corrections for in‐situ pXRF measurements conducted on field soils under varying conditions of moisture, organic matter (OM) content, and particle size heterogeneity. Urban forest soils were analyzed for Pb under increasing soil moisture, OM, and coarse fraction contents with two different pXRF and the results were compared to reverse aqua regia digestions. While both instruments showed a strong correlation despite using different calibration methods, correction of pXRF data to digestion values improved accuracy but potentially underestimated Pb concentrations. We propose an empirical correction equation derived from the regression of variance between theoretical and observed concentrations to correct for moisture effects. OM and particle size heterogeneity did not produce a significant effect on Pb measurement and did not warrant correction. Our study confirms other work showing that pXRF provides an accurate and precise alternative to traditional digestion methods for Pb screening, provided that moisture is corrected.