1 Analytische Chemie; Referenzmaterialien
Filtern
Dokumenttyp
- Zeitschriftenartikel (10) (entfernen)
Sprache
- Englisch (10)
Referierte Publikation
- ja (10)
Schlagworte
- Soil (10) (entfernen)
Organisationseinheit der BAM
- 1 Analytische Chemie; Referenzmaterialien (10)
- 1.4 Prozessanalytik (7)
- 1.7 Organische Spuren- und Lebensmittelanalytik (2)
- 1.0 Abteilungsleitung und andere (1)
- 1.1 Anorganische Spurenanalytik (1)
- 1.6 Anorganische Referenzmaterialien (1)
- 1.8 Umweltanalytik (1)
- 4 Material und Umwelt (1)
- 4.3 Schadstofftransfer und Umwelttechnologien (1)
- 5 Werkstofftechnik (1)
Paper des Monats
- ja (2)
Soil certified reference material (CRM), UME EnvCRM 03 was produced by a collaborative approach among national metrology institutes, designated institutes and university research laboratories within the scope of the EMPIR project: Matrix Reference Materials for Environmental Analysis. This paper presents the sampling and processing methodology, homogeneity, stability, characterization campaign, the assignment of property values and their associated uncertainties in compliance with ISO 17034:2016. The material processing methodology involves blending a natural soil sample with a contaminated soil sample obtained by spiking elemental solutions for 8 elements (Cd, Co, Cu, Hg, Ni, Pb, Sb and Zn) to reach the level of warning risk monitoring values specified for metals and metalloids of soils in Europe. Comparative homogeneity and stability test data were obtained by two different institutes, ensuring the reliability and back up of the data. The certified values and associated expanded uncertainties for the total mass fractions of thirteen elements (As, Cd, Co, Cr, Cu, Fe, Hg, Mn, Ni, Pb, Sb, V and Zn) are established. The developed CRM can be used for the development and validation of measurement procedures for the determination of the total mass fractions of elements in soil and also for quality control/assurance purposes. The developed CRM is the first example of a soil material originating from Türkiye.
In precision agriculture, the estimation of soil parameters via sensors and the creation of nutrient maps are a prerequisite for farmers to take targeted measures such as spatially resolved fertilization. In this work, 68 soil samples uniformly distributed over a field near Bonn are investigated using laser-induced breakdown spectroscopy (LIBS). These investigations include the determination of the total contents of macro- and micronutrients as well as further soil parameters such as soil pH, soil organic matter (SOM) content, and soil texture. The applied LIBS instruments are a handheld and a platform spectrometer, which potentially allows for the single-point measurement and scanning of whole fields, respectively. Their results are compared with a high-resolution lab spectrometer.
The prediction of soil parameters was based on multivariate methods. Different feature selection methods and regression methods like PLS, PCR, SVM, Lasso, and Gaussian processes were tested and compared. While good predictions were obtained for Ca, Mg, P, Mn, Cu, and silt content, excellent predictions were obtained for K, Fe, and clay content. The comparison of the three different spectrometers showed that although the lab spectrometer gives the best results, measurements with both field spectrometers also yield good results. This allows for a method transfer to the in-field measurements
For the first time, µ-X-ray fluorescence (µ-XRF) mapping combined with fluorine K-edge µ-X-ray absorption near-edge structure (µ-XANES) spectroscopy was applied to depict per- and polyfluoroalkyl substances (PFAS) contamination and inorganic fluoride in samples concentrations down to 100 µg/kg fluoride. To demonstrate the matrix tolerance of the method, several PFAS contaminated soil and sludge samples as well as selected consumer product samples (textiles, food contact paper and permanent baking sheet) were investigated. µ-XRF mapping allows for a unique element-specific visualisation at the sample surface and enables localisation of fluorine containing compounds to a depth of 1 µm. Manually selected fluorine rich spots were subsequently analysed via fluorine K-edge µ-XANES spectroscopy. To support spectral interpretation with respect to inorganic and organic chemical distribution and compound class determination, linear combination (LC) fitting was applied to all recorded µ-XANES spectra. Complementarily, solvent extracts of all samples were target-analysed via LC-MS/MS spectrometry. The detected PFAS sum values range from 20 to 1136 µg/kg dry weight (dw). All environmentally exposed samples revealed higher concentration of PFAS with a chain length >C8 (e.g. 580 µg/kg dw PFOS for Soil1), whereas the consumer product samples showed a more uniform distribution with regard to chain lengths from C4 to C8. Independent from quantified PFAS amounts via target analysis, µ-XRF mapping combined with µ-XANES spectroscopy was successfully applied to detect both point-specific concentration maxima and evenly distributed surface coatings of fluorinated organic contaminants in the corresponding samples.
Detailed knowledge about soil composition is an important prerequisite for many applications, for example precision agriculture. Current standard laboratory methods are complex and time-consuming but could be complemented by non-invasive optical techniques. Its capability to provide a molecular fingerprint of individual soil components makes Raman spectroscopy a very promising candidate. A major challenge is strong fluorescence interference inherent to soil, but this issue can be overcome effectively using shifted excitation Raman difference spectroscopy (SERDS). A customized dual-wavelength diode laser emitting at 785.2 and 784.6 nm was used to investigate 117 soil samples collected from an agricultural field along a distance of 624 m and down to depths of 1 m. To address soil spatial heterogeneity, a raster scan approach comprising 100 measurement spots per sample was applied. Based on the Raman spectroscopic fingerprint extracted from intense fluorescence interference by SERDS, 13 mineral soil constituents were identified, and even closely related molecular species could be discriminated, for example polymorphs of titanium dioxide and calcium carbonate. For the first time, the capability of SERDS is demonstrated to predict the calcium carbonate content as an important soil parameter using partial least squares regression (R2 = 0.94, root mean square error of cross-validation RMSECV = 2.1%). Our findings demonstrate that SERDS can extract a wealth of spectroscopic information from disturbing backgrounds enabling qualitative and quantitative soil analysis. This highlights the large potential of SERDS for precision agriculture but also in further application areas, for example geology, cultural heritage and planetary exploration.
In this study, we employed nitrogen microwave inductively coupled atmospheric-pressure plasma (MICAP) combined with quadrupole mass spectrometry (MS) and a liquid sample introduction system to analyze heavy metals in soils. The vanadium, cobalt, nickel, zinc, copper, chromium, arsenic, lead, and cadmium contents in seven reference and three environmental soil samples determined using MICAP-MS were within the uncertainty of the reference values, indicating that MICAP-MS is promising for soil analysis similar to the conventional inductively coupled plasma mass spectrometry (ICP-MS) technique. In addition, the limits of detection (LODs) and sensitivity of both techniques using N2 and Ar plasma were of the same order of magnitude. Furthermore, the performance of MICAP-MS under different N2 purity was investigated, and we found that the plasma formation and ionization efficiency were not influenced by the impurities in the gas. A prominent advantage of MICAP-MS is the low operating cost associated with gas consumption. In this work, MICAP-MS used nitrogen, which is cheaper than argon, and consumed 25% less gas than ICP-MS. Using low-purity N2 can further reduce the gas cost, making MICAP-MS more cost effective than ICP-MS. These results suggest that MICAP-MS is a promising alternative to ICP-MS for the analysis of heavy metals in the soil.
Near-infrared (NIR) spectroscopy is a promising candidate for low-cost, nondestructive, and high-throughput mass quantification of micro¬plastics in environmental samples. Widespread application of the technique is currently hampered mainly by the low sensitivity of NIR spectroscopy compared to thermo-analytical approaches commonly used for this type of analysis. This study shows how the application of NIR spectroscopy for mass quantification of microplastics can be extended to smaller analyte levels by combining it with a simple and rapid microplastic enrichment protocol. For this purpose, the widely used flotation of microplastics in a NaCl solution, accelerated by centrifugation, was chosen which allowed to remove up to 99 % of the matrix at recovery rates of 83–104 %. The spectroscopic measurements took place directly on the stainless-steel filters used to collect the extracted particles to reduce sample handling to a minimum. Partial least squares regression (PLSR) models were used to identify and quantify the extracted microplastics in the mass range of 1–10 mg. The simple and fast extraction procedure was systematically optimized to meet the requirements for the quantification of microplastics from common PE-, PP-, and PS-based packaging materials with a particle size < 1 mm found in compost or soils with high natural organic matter content (> 10 % determined by loss on ignition). Microplastics could be detected in model samples at a mass fraction of 1 mg g-1. The detectable microplastic mass fraction is about an order of magnitude lower compared to previous studies using NIR spectroscopy without additional enrichment. To emphasize the cost-effectiveness of the method, it was implemented using some of the cheapest and most compact NIR spectrometers available.
Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization.
Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements.
A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2 s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2 s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation Analysis for liquid samples (original and filtered) resulted in lower R2 s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
Within the framework of precision agriculture, the determination of various soil properties is moving into focus, especially the demand for sensors suitable for in-situ measurements. Energy-dispersive X-ray fluorescence (EDXRF) can be a powerful tool for this purpose. In this study a huge diverse soil set (n = 598) from 12 different study sites in Germany was analysed with EDXRF. First, a principal component analysis (PCA) was performed to identify possible similarities among the sample set.
Clustering was observed within the four texture classes clay, loam, silt and sand, as clay samples contain high and sandy soils low iron mass fractions. Furthermore, the potential of uni- and multivariate data evaluation with partial least squares regression (PLSR) was assessed for accurate Determination of nutrients in German agricultural samples using two calibration sample sets. Potassium and iron were chosen for testing the performance of both models. Prediction of these nutrients in 598 German soil samples with EDXRF was more accurate using PLSR which is confirmed by a better overall averaged deviation and PLSR should therefore be preferred.
The increasing pollution of terrestrial and aquatic ecosystems with plastic debris leads to the accumulation of microscopic plastic particles of still unknown amount. To monitor the degree of contamination analytical methods are urgently needed, which help to quantify microplastics (MP). Currently, time-costly purified materials enriched on filters are investigated both by micro-infrared spectroscopy and/or micro-Raman. Although yielding precise results, these techniques are time consuming, and are restricted to the analysis of a small part of the sample in the order of few micrograms. To overcome these problems, here we tested a macroscopic dimensioned NIR process-spectroscopic method in combination with chemometrics. For calibration, artificial MP/soil mixtures containing defined ratios of polyethylene, polyethylene terephthalate, polypropylene, and polystyrene with diameters < 125 µm were prepared and measured by a process FT-NIR spectrometer equipped with a fiber optic reflection probe. The resulting spectra were processed by chemometric models including support vector machine regression (SVR), and partial least squares discriminant analysis (PLS-DA). Validation of models by MP mixtures, MP-free soils and real-world samples, e.g. and fermenter residue, suggest a reliable detection and a possible classification of MP at levels above 0.5 to 1.0 mass% depending on the polymer. The benefit of the combined NIRS chemometric approach lies in the rapid assessment whether soil contains MP, without any chemical pre-treatment. The method can be used with larger sample volumes and even allows for an online prediction and thus meets the demand of a high-throughput method.
The quantification of the elemental content in soils with laser-induced breakdown spectroscopy (LIBS) is challenging because of matrix effects strongly influencing the plasma formation and LIBS signal. Furthermore, soil heterogeneity at the micrometre scale can affect the accuracy of analytical results. In this paper, the impact of univariate and multivariate data evaluation approaches on the quantification of nutrients in soil is discussed. Exemplarily, results for calcium are shown, which reflect trends also observed for other elements like magnesium, silicon and iron. For the calibration models, 16 certified reference soils were used. With univariate and multivariate approaches, the calcium mass fractions in 60 soils from different testing grounds in Germany were calculated. The latter approach consisted of a principal component analysis (PCA) of adequately pre-treated data for classification and identification of outliers, followed by partial least squares regression (PLSR) for quantification. For validation, the soils were also characterised with inductively coupled plasma optical emission spectroscopy (ICP OES) and X-ray fluorescence (XRF) analysis. Deviations between the LIBS quantification results and the reference analytical results are discussed.