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The project “Intelligence for Soil” (I4S) aims at the design of an integrated system for improvement of soil functions and fertilizer recommendations. This system is composed by different sensors that will provide a detailed assessment of soil properties and processes, which are prerequisites for a site-specific, resource-saving and ecofriendly soil management, considering the soil as a sustainable resource for the bioeconomy. One of these sensors will be an energy-dispersive X-Ray Fluorescence spectrometer. It is a non-destructive technique suitable for in-situ measurements due to a minimum sample preparation and it allows fast multielement analyses. In this work, an automatic system has been developed using a handheld equipment from Olympus (Vanta C series). A polypropylene (PP) film was used to protect the measuring window of the device from dust and possible cross-contamination. To control the stepper motor that unrolls the PP film, a microcontroller was used to ensure that a piece of clean PP is in front of the measuring window for each new analysis.
Preliminary calibration studies using pre-defined methods, Geochem and Soil Methods, were performed with the following Certified Reference Materials (CRMs): NRC Till 1-3, NIST 2710, BAM U110, ERM CC141, BCR 142R, IAEA Soil 7. The CRMs were measured 10 times at different spots. The spot size was 10 mm in diameter, irradiation time was 60 ? s. The averaged data from X ray characteristic emission line intensities for Al, P, Si, Ca, Fe, Mn, Zn, Cu and Ni Kα were in close agreement with certified mass fraction data. The linear correlation coefficients (r) ranged from 0.852 for P to 0.999 for Mn. A second round of calibration studies were performed with the following CRMs: GBW07402, GBW 07405, NCS DC 73023, NCS DC 73030, NCS DC 85109, NCS DC 87104, NIST 1646a, NIST 2704, NIST 2710, NRC Till 1-3, VS 2498-83, and the same pre-defined methods and experimental parameters were employed. The results of the 13 CRMs were now compared with the values obtained by a validated WDXRF method. The linear correlation coefficients (r) ranged from 0.998 for Ca to 0.999 for Zn. Other elements such as S, K and Ti can also be properly determined, but validation still requires more robust calibration models.
Further calibration studies will be performed in order to circumvent matrix effects and to guarantee reliable results. Besides that, the automatic system will be placed on a mobile sensor platform and the system will be tested in the field. A guidance for on site-specific fertilization integrating the results obtained from different sensors placed on the platform is expected.
Soil acts as a final sink for pollutants. Microplastics from different sources such as plastic mulching, littering, compost, sewage sludge, sedimentary deposition, and tyre abrasion are expected to be found in soil. However, representative and comprehensive information is missing on the sources, transport, and fate. Therefore, a reliable analysis method for microplastics in soils needs to be developed.
The presented work describes the development of a procedure for microplastics analysis in soils. A representative sampling based on the on-site conditions and a sample preparation method was established and comprised of a drying step, the separation of microplastic particles > 1 mm, and a density separation for particles < 1 mm. The detection of the large microplastic particles (> 1 mm) was conducted with Attenuated Total Reflection - Fourier Transform infrared Spectroscopy (ATR-FTIR) , while Thermal Extraction Desorption - Gas Chromatography / Mass Spectrometry (TED-GC/MS) was applied for particles < 1 mm, gaining information on the type of polymer and mass fraction. Based on the established method, 14 environmental soil samples ? with different exposure of microplastics from agriculture, industrial sites, roads, and floods were investigated.
Due to the arbitrary microplastic particle distribution among the samples for large microplastics, it was reasoned that the found particles were unrepresentative. In contrast, microplastic particles < 1 mm were discovered in a high mass in soil samples exposed to plastic mulching or fertilization with sewage sludge or compost (0 – 115 mg/kg). On average, microplastic contents detected in soil samples taken from a construction site and an inner-city lake were higher (13 – 238 mg/kg). As expected, microplastic content in soil sampled in proximity to roads was more pronounced in the upper soil layers. In contrast, very remote sampling sites, expectably uncontaminated, did not lead to the detection of microplastic regarding to thermoanalytical detection method.
In a proof of concept experiment several in vivo and in vitro ecotoxicological tests were applied to evaluate the effect of microplastics (tyre abrasion, polystyrene containing hexabromocyclododecane) in natural soils. In summary, while no effects of the examined probes could be detected on higher levels of biological organization after exposures to earthworm E. andrei, significant changes in several oxidative stress related biomarkers were observed.
Development of a robust calibration model for determination of nutrients in soils using EDXRF
(2018)
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online Analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils. Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
As part of the BonaRes research initiative, funded by the BMBF, strategies are being developed to use soil as a sustainable resource in the bio economy. The interdisciplinary subproject I4S - “Intelligence for soil” - is responsible for the development of an integrated system for site-specific management of soil fertility. For this purpose, a platform is constructed and various sensors are installed. Real-time data will be summarised in models and decision-making algorithms will be used to control fertilisation and accordingly improve soil functions. Aim of the BAM in the frame of I4S is the characterisation of an energy-dispersive X-ray fluorescence (EDXRF) based sensor for robust determination of plant essential nutrients in soil.
First a principal component analysis (PCA) was used to identify outliers and to observe the largest variance within the German soil samples. It could be monitored that splitting of the samples was due to their iron content. Given that clay samples contain high amounts of iron and sandy samples low amounts, a classification of the samples by their soil texture according to VD LUFA was possible. Considering the complex composition of soil, a matrix-specific calibration was carried out by univariate and multivariate data analysis. The figures of merit demonstrated that a more robust calibration model with negligible matrix effects can be obtained by a multivariate approach using partial least squares regression (PLSR). A better correlation between predicted values compared to reference values for German soil samples was observed for the chemometric calibration model than for the univariate one.
Different factors can affect the received calibration models such as moisture and particle size distribution which is especially important due to later online analysis.
In first studies the influence of moisture on the detection of plant essential nutrients was investigated. With increasing water content, the characteristic fluorescence peaks decrease and start to increase again at a water content of 15 %. With lower moisture content the soil agglomerates which leads to lower packing of the sample, resulting in a rougher surface which negatively influence the signals. Whereas, agglomerates are not formed at higher water content. This allows the sample to be packed more tightly thus a smoother surface and a better homogeneity is obtained.
Furthermore, particle size distribution leads to significantly higher uncertainties and lower signals when comparing grounded (< 500 μm) and not grounded (< 2 mm) samples. This can be explained by amplifying of the already known inhomogeneity of soils.
Both factors must be included in the chemometric PLSR to obtain robust calibration models for each macro and micro nutrient.
Influence of moisture and grain sizes on the analysis of nutrients in agricultural soils using LIBS
(2018)
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression(PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Over the last few years, there has been a growing interest to apply spectroscopic methods to the agricultural field for better understanding of soil properties and for efficient, sustainable management of arable land. Within the project I4S (intelligence for soil), funded by the BMBF, an integrated system for site-specific soil fertility management is developed, consisting of different sensors like X-Ray fluorescence analysis (XRF), near-infrared spectroscopy (NIR) and laser-induced breakdown spectroscopy (LIBS). LIBS provides a fast and simultaneous multi-element analysis with little to no sample preparation, which makes it a suitable method for real-time analysis on the field.
The quantification of macro and micro nutrients in soils with LIBS is challenging due to matrix effects, different levels of moisture content and varying grain sizes. First studies revealed that the problems with matrix effects can be overcome by using well characterised soils as reference materials and chemometric tools like Partial Least Squares Regression (PLSR) for calibration.
The next step was to investigate the influence of moisture and grain sizes on the LIBS signal, which is a big issue when measuring directly on the field. The results showed that the LIBS signal decreases exponentially with increasing moisture content, as most of the laser energy is used for vaporising the water. With moisture contents of 30 % or higher almost no signal can be detected. This decrease is more severe for sandy soils than for clay soils. First tests of different grain size distributions indicate that the variation of the LIBS signal increases with growing amounts of larger grains. This results in a higher standard deviation, because of a poorer reproducibility of the plasma formation and plasma characteristic. With the help of chemometric tools the influence of moisture and grain sizes should be implemented in the calibration model for accurate analysis of nutrient composition in agricultural soils.
Reliable analysis of chemical indicators in water, sediment and soil samples for the purpose of environmental pollution assessment poses one of the greatest analytical challenges, having in mind the complexity of sample matrix and low concentrations of pollutants. Organics (pesticides, PAHs, PCBs, etc.) and heavy metals (Hg, Cd, Ni, Pb and As) represent target parameters. Laboratories performing sampling and tests in this field regulated by respective EU directives, need strong support in terms of providing them with appropriate matrix CRMs enabling the process of quality control. NMIs and DIs with proven metrological capabilities for the production and certification of such materials are necessary for the provision of quality data. This project is aiming to develop capacity to produce CRMs for environmental analysis by transferring the theoretical and practical know-how between the partners and combining their skills to focus on environmental CRM production in accordance with ISO Guide 34. Production process includes good manufacturing practices for processing materials, method development and validation for homogeneity, stability and characterisation tests, characterisation of selected analytes together with additional information about matrix constituents, the calculation of individual uncertainties (between units inhomogeneity, long term stability, characterisation) and combination of uncertainties to determine overall uncertainty of the matrix reference materials. Inter laboratory comparison registered as EURAMET project is set as the ultimate project outcome, confirming the partners’ capabilities in applying newly acquired skills.
Leaching tests are fundamental tools for the assessment of groundwater impact by contaminated soils concerning the soil-groundwater pathway. Such procedures are supposed to serve as the basis for a reliable leachate prognosis. They can be applied to determine the short and long term leaching behaviour as well as the source term of contaminated soils. For this purpose two types of leaching procedures have been validated in Germany for the examination of the leaching behaviour of frequently occurring organic substances (DIN 19528 - column test and DIN 19529 - batch test). A liquid-to-solid ratio (L/S) of 2 L/kg and 10 L/kg) is the basis for the risk assessment which is implemented in different German regulations. The equivalence of test results for both tests for the same material under investigation has been investigated for a variety of pollutants in order to assess their reliability in compliance testing. However, for emerging pollutants there is hardly data available on this issue. Leaching tests on soils contaminated with emerging pollutants such as PFC (Perfluorinated Surfactants) are currently coming more into consideration due to the increasing detection of contaminated sites. Therefore, two soils were investigated in this study from different contamination source (paper sludge containing compost and fire distinguishing foam) using both leaching tests and both liquid-to-solid ratios. The leachability of the various perfluorinated compounds in relation to their content in solid matter was considered. Furthermore the eluate pre-treatment prior analysis (in particular liquid/solid separation step needed for batch tests) has been taken into account.
The comparability of the results from batch and column is dependent on the solubility of the various compounds, on the L/S and on the turbidity in the eluates.
Despite abundant evidence of the occurrence of microplastics (MP) – these are particles smaller 5 mm – in aquatic environments, little is known about the accumulation of plastic in terrestrial environments, especially in soils.
Possible major input pathways could be the use of plastic mulching, the use of compost, sewage sludge or residues from biogas facilities as fertilizers, as well as littering in urban areas. To estimate the MP pollution, the development of reliable, fast methods for sampling, sample preparation, and detection is needed. The obtained data must be representative of the sampled environmental compartment and measurements from different environmental compartments must be comparable. A first breakthrough is an application of ThermoExtractionDesorption-Gas Chromatography-MassSpectrometry (TED-GC-MS) for the detection of MP, including tire abrasives. This method allows the determination of mass content within a few hours and only a minimum of sample preparation for samples from aquatic environments is needed.
However, in contrast to filtrate samples from aquatic environments, sediment or soil samples need an enrichment of MP. Whereas MP concentration from marine sediments can be obtained by floatation and density Separation techniques using NaCl solutions, the extraction or separation from soils proves to be more difficult, as plastic particles are often part of organo-mineral aggregates within the soil matrix.
The aim of this study is the development of a practicable processing guideline for representatively taken soil samples in order to concentrate microplastics, without complex and time-consuming treatment steps. Dispersants or detergents can be applied to decompose the soil matrix, but each preparation step carries the risk of crosscontamination of the sample and prolongs the preparation procedure.
For this reason, we choose ZnCl2-solution with a density of 1.7 g/cm3, which include the densities of relevant MP types (0.9-1.7 g/cm3). It was tested to achieve both, disaggregation and separation as it decomposes organic material and dissolves carbonates. Also, ZnCl2 is inert to the precipitation of undesirable salts and Carbonates during the process of density separation, as polytungstate solution does. ZnCl2 can be reused after stepwise filtering (7 µm, 1.5 µm, 0.7 µm). Thus, disposal costs can be reduced. Efficiency and reproducibility of the sample preparation as well as the degradation behavior of MP under the present conditions were demonstrated with model samples.
Real sampling campaigns were conducted at several agricultural sites and floodplains in south-west Germany. The sampling was performed according to practice for soil sampling, using adequate sampling strategies (pattern of sampling, number of field samples, homogenization, etc). The lab sample was fractioned into three size classes (5-100 µm, 100-1000 µm, and 1-5 mm). The identification and determination of mass fraction were done using TED-GC-MS.
The ubiquitous presence of unintended plastics in the environment has been an issue in scientific studies and public debate. Recent studies on MP findings are focused mainly on aquatic systems, while little is known about MP in terrestrial ecosystems. Fermentation residues, sewage sludge and compost are secondary raw material fertilizers and represent a possible input path of plastics in soils. Soils are final sinks for micro plastics. In this context, samples were taken in a combined fermentation and composting plant in Germany to get real compost samples which were investigated. Existing regulations include requirements for total contents of plastics in combination with visual determination methods. In order to avoid possible underdeterminations, precise detection methods should be used in the future from a scientific point of view. For this reason, the use of thermoanalytical detection is an appropriate way. Spectroscopic methods such as Raman or FTIR are not suitable for determining the mass content of microplastic, as these output a particle number. In Germany, compost is a potential vector for MP in soil due to its use as fertilizer. Therefore, we show the application of TThermoExtractionDesorption-GasChromatography-MassSpectrometry (TED-GC-MS) as a fast, integral analytical technique, which in contrast to the spectroscopic methods does not measure the number of particles but a mass content. In a nitrogen atmosphere the sample is pyrolyzed to 600 °C and an excerpt of the pyrolysis gases is collected on a solid phase adsorber. Afterwards, the decomposition gases are desorbed and measured in a GC-MS system. Characteristic pyrolysis products of each polymer can be used to identify the polymer type and determine the mass contents in the present sample. For the first time the work represents a routine procedure for the determination of plastics in composts and fermentation residues. This current study will also give inside in various important aspects of sample preparation, which include a meaningful size fractionation, a necessary density separation regarding the removal of inorganic contents and at finally a homogenization.