Element contents and their seasonal dynamics in leaves of alder Alnus glutinosa (L.) Gaertn

Alnus glutinosa is an actinorhizal plant that fixes N via actinomycetes. Compared to other trees, A. glutinosa is more resistant to environmental stress and able to uptake soil nutrients more easily. Alnus glutinosa grows well not only in natural stands but also in degraded environment or soil in need of restoration. Changes in the contents of selected macro-, micro-, and non-nutrient elements in the leaves of A. glutinosa during the vegetation season were monitored in the Ore Mountains (Czech Republic), an area affected by extreme air pollution in the past. Decreased foliar content of N, P, K, and Cu, and increased content of Ca, Mn, Zn, and Al were observed; the content of other elements (S, Mg, Pb, and Cd) varied during the growing season or remained constant. From the viewpoint of nutrition, the content of N, S, Ca, and Mg macroelements was adequate; concentrations of P and K were low. Excessive amounts of Mn and Zn were measured, and the level of Cu was good. Non-nutrient elements Pb and Cd were present at the background level, and the level of Al was high. N/P, N/Ca, N/Mg, and Ca/Mg ratios were balanced, S/N value showed the lack of S, and N/K ratio indicated low content of K, which caused also suboptimal K/Ca and K/Mg values. The P/Al ratio varied from balanced to lower values. The content of individual elements and monitored changes were influenced by the amount of elements in the soil, moisture conditions, foliage phenology, and altitude.

Assessment of Soil Fertility Using Induced Fluorescence and Machine Learning

Techniques such as proximal soil sampling are investigated to increase the sampling density and hence the resolution at which nutrient prescription maps are developed. With the advent of a commercial mobile fluorescence sensor, this study assessed the potential of fluorescence to estimate soil chemical properties and fertilizer recommendations. This experiment was conducted over two years at nine sites on 168 soil samples and used random forest regression to estimate soil properties, fertility classes, and recommended N rates for maize production based on induced fluorescence of air-dried soil samples. Results showed that important soil properties such as soil organic matter, pH, and CEC can be estimated with a correlation of 0.74, 0.75, and 0.75, respectively. When attempting to predict fertility classes, this approach yielded an overall accuracy of 0.54, 0.78, and 0.69 for NO₃-N, SOM, and Zn, respectively. The N rate recommendation for maize can be directly estimated by fluorescence readings of the soil with an overall accuracy of 0.78. These results suggest that induced fluorescence is a viable approach for assessing soil fertility. More research is required to transpose these laboratory-acquired soil analysis results to in situ readings successfully.

On the Influence and Correction of Water Content on pXRF Analysis of Lateritic Nickel Ore Deposits in the Context of Open Pit Mines of New-Caledonia

In a number of applications, the use of portable X-ray fluorescence (pXRF) instruments offers a time and cost-saving alternative to standard laboratory instruments. This is particularly true in a mining context where decisions must be taken quickly in the field. However, pXRF is a technique known to be efficient, provided that samples are well prepared, i.e., dried and finely ground. On the mine face, little-to-no sample preparation is conceivable as mining vehicles must be able to operate continuously. Therefore, solutions have to be found even for raw materials and one of the most critical problems is the sample water content, in particular in the context of open pit mines in a tropical area. A large number of analysis shows that knowledge of humidity enables the measured concentration to be effectively corrected for the three instruments used (Niton, X-met, Titan). It is possible to overcome the difficulty of measuring water content in the field by fixing it to its maximum value (saturation). The results show that the saturation method is reliable, or at least, promising.

A comparison of XRFS and ICP-OES methods for soil trace metal analyses in a mining impacted agricultural watershed

Field portable X-ray fluorescence spectroscopy (XRFS) has become increasingly prevalent for in situ detection of trace metals, as it is both rapid and cost effective. The accuracy of in situ XRFS analyses has been questioned due to possible interferences from elevated soil moisture and organic content. In this study, three metal analysis protocols (Cd, Pb, Zn) were compared for surface soil samples collected near the Tar Creek Superfund Site in northeastern Oklahoma. The protocols included the use of a field portable XRF spectrometer for in situ analyses and on homogenized, pulverized, air-dried soil samples sieved to < 250-µm fraction in the laboratory. A subset of soil samples was also analyzed after microwave-assisted hot HNO₃ digestion followed by inductively coupled plasma-optical emission spectrometry (ICP-OES) analyses. Moisture content and loss-on-ignition (as a surrogate for organic matter) were determined for each sample. Soil moisture exceeding 10% in situ caused underreporting of field XRF readings when compared to the laboratory XRF readings. Relationships between concentrations determined by laboratory XRFS and ICP-OES for Pb (r² = 0.96) and Zn (r² = 0.91) were not statistically different (p < 0.025 for both analytes). A strong relationship between ICP-OES analyses of Zn and Cd (r² = 0.93) allowed prediction of Cd concentrations for additional samples not analyzed by ICP-OES. This study recommends that XRFS field readings be used for initial screening only and that samples analyzed via field portable XRFS be homogenized, air dried, sieved and re-analyzed in the laboratory to yield the most accurate results.

On-stream mineral identification of tailing slurries of tungsten via NIR and XRF data fusion measurement techniques

Mineral flotation processes are controlled by monitoring the grade of the present minerals. The economy of the flotation process can be significantly improved by on-line analysis of minerals in a slurry. However, online and quantitative mineral identification of slurries is challenging. Industrial developers are demanding novel ideas enabling differentiation between minerals with similar elemental contents, such as scheelite and fluorite or gangue minerals, since they have different flotation properties. The primary focus of this research is the measurement of mineral contents from the elemental concentrations acquired by an on-stream slurry analyser based on X-ray fluorescence (XRF) and near-infrared spectroscopy (NIR). In this work, the samples in the test were obtained from a tungsten dressing plant. It is vital to master the mineral grade for controlling the flotation plant. The XRF parameters were optimised by Monte Carlo simulation, and the XRF and NIR data fusion was discussed. A multivariate statistical method called the least squares support vector machine (LS-SVM) was employed to perform the element-to-mineral conversion. The results show that such data integrations enable on-stream and quantitative identification of slurry mineral contents, especially for scheelite, wolframite, fluorite and calcite, which are essential minerals in tungsten ore beneficiation. This technique can lead to many benefits, such as rapid control of concentrate quality, enhanced recovery and savings in money, time, energy and workforce.

Elemental composition and moisture prediction in manure by portable X-ray fluorescence spectroscopy using random forest regression

Manure elemental composition determination is essential to develop farm nutrient budgets and assess environmental risk. Portable X-ray fluorescence (PXRF) spectrometers could facilitate hazardous waste-free, rapid, and cost-effective elemental concentration determinations. However, sample moisture is a problem for elemental concentration determination by X-ray methods. The objective of this study was to quantify the effect of sample moisture content, predict moisture content, and correct for moisture effect on elemental concentration determinations in livestock manure. Oven-dried manure samples (n = 40) were ground and adjusted to five moisture ranges of (w/w moisture) <10%, 10-20%, 20-30%, 40-50%, and 60-70%. Samples were scanned by PXRF for 180 s using a vacuum (<1,333 Pa) and without a filter. The presence of moisture negatively affected elemental determination in manure samples. Calibrations (n = 200) were prepared using random forest regression with detector channel counts as independent variables. A three-step validation was performed using all the data, random cross-validation and external validation. The back end of the spectrum (14-15 keV) had strong predictive power (r²  = .98) for moisture content. The random forest approach increased r² between PXRF and wet chemical methods from <.66 to >.90 for P, K, and Mg and from .78 to .98 for Fe, compared with linear, nonlinear, and Lucas-Tooth and Price equations. These results indicated that elemental concentration can accurately be measured in dried and moist manure samples using PXRF and expands the potential applications of PXRF to in situ elemental determinations for agricultural and environmental samples.