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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Robust historical evapotranspiration trends across climate regimes

Evapotranspiration (ET) links the hydrological, energy and carbon cycles on the land surface. Quantifying ET and its spatio-temporal changes is also key to understanding climate extremes such as droughts, heatwaves and flooding. Regional ET estimates require reliable observation-based gridded ET datasets, and while many have been developed using physically based, empirically based and hybrid techniques, their efficacy, and particularly the efficacy of their uncertainty estimates, is difficult to verify. In this work, we extend the methodology used in Hobeichi et al. (2018) to derive two new versions of the Derived Optimal Linear Combination Evapotranspiration (DOLCE) product, with observationally constrained spatio-temporally varying uncertainty estimates, higher spatial resolution, more constituent products and extended temporal coverage (1980–2018). After demonstrating the efficacy of these uncertainty estimates with out-of-sample testing, we derive novel ET climatology clusters for the land surface, based on the magnitude and variability of ET at each location on land. The new clusters include three wet and three dry regimes and provide an approximation of Köppen–Geiger climate classes. The verified uncertainty estimates and extended time period then allow us to examine the robustness of historical trends spatially and in each of these six ET climatology clusters. We find that despite robust decreasing ET trends in some regions these do not correlate with behavioural ET clusters. Each cluster, and the majority of the Earth's surface, shows clear robust increases in ET over the recent historical period. The new datasets DOLCE V2.1 and DOLCE V3 can be used for benchmarking global ET estimates and for examining ET trends respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Scalable Gradient Free Method for Bayesian Experimental Design with Implicit Models

Bayesian experimental design (BED) is to answer the question that how to choose designs that maximize the information gathering. For implicit models, where the likelihood is intractable but sampling is possible, conventional BED methods have difficulties in efficiently estimating the posterior distribution and maximizing the mutual information (MI) between data and parameters. Recent work proposed the use of gradient ascent to maximize a lower bound on MI to deal with these issues. However, the approach requires a sampling path to compute the pathwise gradient of the MI lower bound with respect to the design variables, and such a pathwise gradient is usually inaccessible for implicit models. In this paper, we propose a novel approach that leverages recent advances in stochastic approximate gradient ascent incorporated with a smoothed variational MI estimator for efficient and robust BED. Without the necessity of pathwise gradients, our approach allows the design process to be achieved through a unified procedure with an approximate gradient for implicit models. Several experiments show that our approach outperforms baseline methods, and significantly improves the scalability of BED in high-dimensional problems.

Zhang, Jiaxin↗
DOE OSTI2023

Sample Preparation Method for Low-Level Total 129 I Measurements by ICP-MS

Trace-level measurements of iodine’s isotopic ( 129 I and 127 I) and chemical species distributions are needed for an accurate understanding of radioiodine migration in the Hanford subsurface. Pacific Northwest National Laboratory (PNNL) previously developed a novel analytical method for iodine characterization that uses ion chromatography (IC) coupled to inductively coupled mass spectrometry (ICP-MS). While the method can measure speciated forms of 129 I at levels below the drinking water standard, an interference from molybdenum (Mo) prevents the assay from quantifying the $\underline{total}$ 129 I concentration in many Hanford sample matrices. In this work, solvent extraction was evaluated as a sample preparation method for eliminating the Mo interference. A series of 10 experiments was conducted in which solutions containing known amounts of iodate or iodide were treated by solvent extraction, and the extracted solutions were analyzed for total iodine concentrations by ICP MS. Several extraction parameters such as reagent concentrations and chemical reaction times were systematically adjusted in attempts to optimize the extraction process. While solvent extraction was shown to be effective at removing Mo, there was a consistent inability to recover more than approximately 75% of the total iodine in most experiments. This would reduce the ability to detect 129 I at levels near the drinking water standard. Additionally, the extraction efficiencies in several experiments were highly variable, suggesting that solvent extraction could add significant uncertainty to radioiodine measurements. We recommend evaluating ion exchange as an alternative sample preparation approach in fiscal year (FY) 2024.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Stochastic modeling of phosphorus transport in the Three Gorges Reservoir by incorporating variability associated with the phosphorus partition coefficient

Phosphorus (P) fate and transport plays a crucial role in the ecology of rivers and reservoirs in which eutrophication is limited by P. A key uncertainty in models used to help manage P in such systems is the partitioning of P to suspended and bed sediments. By analyzing data from field and laboratory experiments, we stochastically characterize the variability of the partition coefficient (Kd) and derive spatio-temporal solutions for P transport in the Three Gorges Reservoir (TGR). We formulate a set of stochastic partial different equations (SPDEs) to simulate P transport by randomly sampling Kd from the measured distributions, to obtain statistical descriptions of the P concentration and retention in the TGR. The correspondence between predicted and observed P concentrations and P retention in the TGR combined with the ability to effectively characterize uncertainty suggests that a model that incorporates the observed variability can better describe P dynamics and more effectively serve as a tool for P management in the system. This study highlights the importance of considering parametric uncertainty in estimating uncertainty/variability associated with simulated P transport.

Partition coefficient↗
DOE OSTI2019

Ongoing solution reproducibility of earth system models as they progress toward exascale computing

We present a methodology for solution reproducibility for the Energy Exascale Earth System Model during its ongoing software infrastructure development to prepare for exascale computers. The nonlinear chaotic nature of climate system simulations precludes traditional model verification approaches since machine precision differences—resulting from code refactoring, changes in software environment, and so on—grow exponentially to a different weather state. Here, we leverage the nature of climate as a statistical description of the atmosphere in order to establish model reproducibility. We evaluate the degree to which two-sample equality of distribution tests can confidently detect the change in climate from minor tuning parameter changes on model output variables in order to establish the level of difference that indicates a new climate. To apply this (baselined test), we target a section of the model’s development cycle wherein no intentional science changes have been applied to its source code. We compare an ensemble of short simulations that were conducted using a verified model configuration against a new ensemble with the same configuration but with the latest software infrastructure (Common Infrastructure for Modeling the Earth, CIME5.0), compiler versions, and software libraries. We also compare these against ensemble simulations conducted using the original version of the software infrastructure (CIME4.0) of the earlier model configuration, but with the latest compilers and software libraries, to test the impact of new compilers and libraries in isolation from additional software infrastructure. The two-sample equality of distribution tests indicates that these ensembles indeed represent the same climate.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Prediction of the J C (B) Behavior of Bi-2212 Wires at High Field

Bi-2212 wires have been produced for much of the last decade with varying architectures, powder sources and great variability of final J c (B) properties. As Bi-2212 transitions from an R&D materials development to a magnet technology, means to predictably forecast J c (B) become increasingly valuable. Here we report on characterizations of short samples having J c (15T, 4.2 K) varying from 1210 to 6560 A/mm 2 , measured in fields up to 31 T, and drawn from 10 billets. Using both 4 K transport and variable temperature VSM measurements capable of probing the 20 K irreversibility field, B irr = μ 0 H k , we discovered that the vortex pinning properties are very stable across these wires, which all used the same standard powder composition (Bi 2.17 Sr 1.94 Ca 0.89 Cu 2.00 O 8+x ), even though it has been made by three different manufacturers over nine years. We conclude that a power-law fit J c ∝ B -α , where α = 0.280 (σ = 0.015), works well at 4.2 K over the field range of at least 3-30 T and that μ 0 H k (20 K) remains stable at 8.6 ± 0.4 T. We conclude that the dominant current limiting mechanism predicting J c (B) in Bi-2212 is the effective filament connectivity. J c (B) at high fields may thus be predicted by simple I c measurements at easily accessible fields in the 3-15 T range.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Key process parameters to modify the porosity of cerium dioxide microspheres formed in the internal gelation process

Recently, an internal gelation study demonstrated that the use of heated urea and hexamethylenetetramine can have a pronounced impact on the porosity and sintering characteristics of cerium dioxide (CeO 2 ) microspheres. This effort has identified process variables that can significantly change the initial porosity of the CeO 2 microspheres with slight modifications. A relatively small difference in the sample preparation of cerium ammonium nitrate and ammonium hydroxide solution had a large reproducible impact on the porosity and slow pour density of the produced microspheres. Increases in the gelation temperature as small as 0.5 K also produced a noticeable increase in the slow pour density. If the gelation temperature was increased too high, the use of the heated hexamethylenetetramine and urea was no longer observed to be effective in increasing the porosity of the CeO 2 microspheres. In conclusion, the final process variable was the amount of dispersing agent, Span™ 80, which can increase the slow pour density and produce significantly smaller microspheres.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Assessment of the Idaho National Laboratory Remote Handled Low Level Waste Disposal Facility Hydraulic Performance

This document provides a summary of the expected hydraulic performance of the vault drainage system installed at Idaho National Laboratory’s Remote Handled Low-Level Waste disposal facility. Hydraulic performance was assessed through analysis of the mechanical properties of the drainage system and backfill materials, including material gradation and proctor data; laboratory data, including bulk density, porosity, unsaturated hydraulic conductivity, moisture-retention data, and van Genuchten parameters; and field-data collected using in-situ instrumentation with data collected during a vault-scale infiltration characterization test. Hydraulic performance of the vault drainage system was designed to promote a greater than 500-year vault concrete longevity. The predicted hydraulic performance is, in part, a function of the materials used in construction, sequencing of the hydraulic drainage materials, and the as-built condition of the materials following compaction necessary to ensure vault stability. Specifying specific as-built hydraulic properties is exceedingly difficult at the facility specification/design phase. Instead, material mechanical properties were specified and confirmed early during the construction process. As the drainage materials were installed, samples were sent to a laboratory for hydraulic property testing and, after drainage system installation was complete around the performance assessment vaults, a field-scale infiltration test was conducted with data collection to verify the as-built hydraulic performance. This report provides summary data for the measured mechanical properties, laboratory data, and field infiltration characterization test data. Mechanical data have relatively little variability because of the sieve system used when the materials were crushed onsite. Laboratory data were obtained for a relatively small number of material samples, but the data obtained for each sample are extensive. However, laboratory data provide the moisture retention relationships and relative hydraulic conductivity-moisture content relationships for uninstalled materials. Conducting field-scale infiltration tests with collection of moisture retention data and wetting front propagation data allows extension of laboratory data to the as-installed conditions of the vault drainage system. These combined data types are integrated using a numerical simulation of the infiltration characterization test to refine model parameters. The infiltration characterization model will be used to support the hydraulic and concrete performance of the vault system provided in the facility performance assessment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

Comparison of High-Frequency Solar Irradiance: Ground Measured vs. Satellite-Derived

High-frequency solar variability is an important to grid integration studies, but ground measurements are scarce. The high resolution irradiance algorithm (HRIA) has the ability to produce 4-sceond resolution global horizontal irradiance (GHI) samples, at locations across North America. However, the HRIA has not been extensively validated. In this work, we evaluate the HRIA against a database of 10 high-frequency ground-based measurements of irradiance. The evaluation focuses on variability-based metrics. This results in a greater understanding of the errors in the HRIA as well as suggestions for improvement to the HRIA.

14 SOLAR ENERGY↗
DOE OSTI2017

IN-PILE MEASUREMENTS OF FUEL ROD DIMENSIONAL CHANGES UTILIZING THE TEST REACTOR LOOP PRESSURE FOR MOTION

Different types of materials are being considered for fuel, cladding, and structures in existing and advanced nuclear reactors. These materials can undergo significant dimensional and physical changes during irradiation. Currently in the US, such changes are measured with the ‘cook and look’ method (i.e. repeatedly irradiating a specimen for a specified period of time and then removing it from the reactor for evaluation). The time and labor to remove, examine, and return irradiated samples for each measurement makes this approach expensive. In addition, such techniques provide limited data, and handling may disturb the phenomena of interest. Therefore, in-pile detection of changes in geometry is sorely needed to understand real-time behavior during irradiation testing of fuels and materials in high flux US Material and Test Reactors (MTRs). This paper presents development results of an advanced Linear Variable Differential Transformer (LVDT)-based test rig capable of detecting real-time changes in diameter or length of fuel rods or other material samples during irradiation in US MTRs. This test rig is being developed at the Idaho National Laboratory (INL), and will provide experimenters with a unique capability to measure dimensional changes associated with fuel and clad swelling, pellet-clad interaction, and crud buildup. This novel LVDT design uses the MTR’s loop pressure to drive the test specimen at a constant rate. This leads to a simplified construction, that is, the use of fewer components than other diameter gauge test rigs. This simplified design only requires three pressure boundary penetrations. Further, a key component of this test rig is a leak tight hydraulic ram capable of actuating under low differential pressure, and is able to survive the high irradiation tests at Pressurized Water Reactor (PWR) conditions. The sensor has been proven on a benchtop scale, and shows repeatability and reliability. Measurement accuracies of ±3 µm for diameter and ±90 µm for length have been achieved.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Machine-learning based approach to examine ecological processes influencing the diversity of riverine dissolved organic matter composition

Dissolved organic matter (DOM) assemblages in freshwater rivers are formed from mixtures of simple to complex compounds that are highly variable across time and space. These mixtures largely form due to the environmental heterogeneity of river networks and the contribution of diverse allochthonous and autochthonous DOM sources. Most studies are, however, confined to local and regional scales, which precludes an understanding of how these mixtures arise at large, e.g., continental, spatial scales. The processes contributing to these mixtures are also difficult to study because of the complex interactions between various environmental factors and DOM. Here we propose the use of machine learning (ML) approaches to identify ecological processes contributing toward mixtures of DOM at a continental-scale. We related a dataset that characterized the molecular composition of DOM from river water and sediment with Fourier-transform ion cyclotron resonance mass spectrometry to explanatory physicochemical variables such as nutrient concentrations and stable water isotopes ( 2 H and 18 O). Using unsupervised ML, distinctive clusters for sediment and water samples were identified, with unique molecular compositions influenced by environmental factors like terrestrial input and microbial activity. Sediment clusters showed a higher proportion of protein-like and unclassified compounds than water clusters, while water clusters exhibited a more diversified chemical composition. We then applied a supervised ML approach, involving a two-stage use of SHapley Additive exPlanations (SHAP) values. In the first stage, SHAP values were obtained and used to identify key physicochemical variables. These parameters were employed to train models using both the default and subsequently tuned hyperparameters of the Histogram-based Gradient Boosting (HGB) algorithm. The supervised ML approach, using HGB and SHAP values, highlighted complex relationships between environmental factors and DOM diversity, in particular the existence of dams upstream, precipitation events, and other watershed characteristics were important in predicting higher chemical diversity in DOM. Our data-driven approach can now be used more generally to reveal the interplay between physical, chemical, and biological factors in determining the diversity of DOM in other ecosystems.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Enabling probabilistic learning on manifolds through double diffusion maps

Here, we present a generative learning framework for probabilistic sampling that extends Probabilistic Learning on Manifolds (PLoM), which is designed to generate statistically consistent realizations of a random vector in a finite-dimensional Euclidean space, informed by a (representative) set of observations. In its original form, PLoM constructs a reduced-order probabilistic model by combining three main components: (a) kernel density estimation to approximate the underlying probability measure, (b) Diffusion Maps to characterize the manifold of the data, and (c) a reduced-order Itô Stochastic Differential Equation (ISDE) to sample from the learned distribution. However, its sampling dynamics are posed in the ambient space and the retained number of reduced coordinates is chosen by projection-reconstruction error. In practice, this often (i) requires more coordinates than the data’s intrinsic dimension to achieve stable sampling and (ii) lacks a smooth, basis-independent lifting back to the data domain; moreover, standard Diffusion Maps emphasize harmonic eigenfunctions and can miss non-harmonic latent structure. We address these limitations by decoupling geometry learning from sampling: a first Diffusion Maps pass identifies non-harmonic coordinates on which we formulate a full-order ISDE directly in the latent space, while Double Diffusion Maps captures multiscale geometric features and Geometric Harmonics (GH) learns a smooth lifting map to the ambient variables that is independent of the particular diffusion basis. This hybrid design preserves the system’s dynamical richness with a compact geometric representation and enables principled out-of-sample inference. The effectiveness and robustness of the proposed method are illustrated through two numerical studies: one based on data generated from two-dimensional Hermite polynomial functions and another based on high-fidelity simulations of a detonation wave in a reactive flow.

Double diffusion maps↗
DOE OSTI2021

Convolutional neural network identification of galaxy post-mergers in UNIONS using IllustrisTNG

ABSTRACT The Canada–France Imaging Survey (CFIS) will consist of deep, high-resolution r-band imaging over ∼5000 deg2 of the sky, representing a first-rate opportunity to identify recently merged galaxies. Because of the large number of galaxies in CFIS, we investigate the use of a convolutional neural network (CNN) for automated merger classification. Training samples of post-merger and isolated galaxy images are generated from the IllustrisTNG simulation processed with the observational realism code RealSim. The CNN’s overall classification accuracy is 88 per cent, remaining stable over a wide range of intrinsic and environmental parameters. We generate a mock galaxy survey from IllustrisTNG in order to explore the expected purity of post-merger samples identified by the CNN. Despite the CNN’s good performance in training, the intrinsic rarity of post-mergers leads to a sample that is only ∼6 per cent pure when the default decision threshold is used. We investigate trade-offs in purity and completeness with a variable decision threshold and find that we recover the statistical distribution of merger-induced star formation rate enhancements. Finally, the performance of the CNN is compared with both traditional automated methods and human classifiers. The CNN is shown to outperform Gini–M20 and asymmetry methods by an order of magnitude in post-merger sample purity on the mock survey data. Although the CNN outperforms the human classifiers on sample completeness, the purity of the post-merger sample identified by humans is frequently higher, indicating that a hybrid approach to classifications may be an effective solution to merger classifications in large surveys.

Bickley, Robert W.↗
DOE OSTI2023

Multisensor Agile Adaptive Sampling (MAAS): A Methodology to Collect Radar Observations of Convective Cell Life Cycle

Abstract Multisensor Agile Adaptive Sampling (MAAS), a smart sensing framework, was adapted to increase the likelihood of observing the vertical structure (with little to no gaps), spatial variability (at subkilometer scale), and temporal evolution (at ∼2-min resolution) of convective cells. This adaptation of MAAS guided two mechanically scanning C-band radars (CSAPR2 and CHIVO) by automatically analyzing the latest NEXRAD data to identify, characterize, track, and nowcast the location of all convective cells forming in the Houston domain. MAAS used either a list of predetermined rules or real-time user input to select a convective cell to be tracked and sampled by the C-band radars. The CSAPR2 tracking radar was first tasked to collect three sector plan position indicator (PPI) scans toward the selected cell. Edge computer processing of the PPI scans was used to identify additional targets within the selected cell. In less than 2 min, both the CSAPR2 and CHIVO radars were able to collect bundles of three to six range–height indicator (RHI) scans toward different targets of interest within the selected cell. Bundles were successively collected along the path of cell advection for as long as the cell met a predetermined set of criteria. Between 1 June and 30 September 2022 over 315 000 vertical cross-section observations were collected by the C-band radars through ∼1300 unique isolated convective cells, most of which were observed for over 15 min of their life cycle. To the best of our knowledge, this dataset, collected primarily through automatic means, constitutes the largest dataset of its kind.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

SCEC/USGS Community Stress-Drop Validation Study: How Spectral Fitting Approaches Influence Measured Source Parameters

Spectral source parameters used to estimate an earthquake’s stress drop (⁠Δσ⁠) can vary significantly across measurement approaches. The Statewide California Earthquake Center/U.S. Geological Survey Community Stress‐Drop Validation Study was initiated to compare source parameter estimates, focusing initially on a dataset from the 2019 Ridgecrest earthquake sequence. As part of that validation effort, here we focus on one potential source of uncertainty: whether spectral fitting approaches alone, applied to a common set of spectra from the 2019 Ridgecrest sequence result in different source parameter estimates. By using a common set of benchmark spectra analyzed across a consistent frequency band of 1–40 Hz, we eliminate many sources of variability. A subgroup of validation study participants volunteered to estimate the low‐frequency displacement (⁠Ω 0 ⁠) and corner frequency (⁠ƒ c ⁠) by fitting a smooth function to benchmark displacement spectra. Participants used linear‐ or log‐sampled spectra, assumed a Brune or Boatwright spectral model, and applied different misfit criteria. We compare 17 approaches used to estimate ⁠Ω 0 ⁠, ƒ c ⁠, and Δσ for 54 earthquake spectra. Our results reveal that 35% of events have Δσ estimates within a factor of two, whereas others exhibit variations exceeding an order of magnitude. The variability in and can largely be attributed to whether a spectrum is consistent with the smooth function of an idealized simple crack model. The trade‐off between Ω 0 and ƒ c may be more pronounced when using linearly sampled spectra, as higher frequency spectral bumps control the fits. As expected, methods that assumed a Boatwright model tended to have lower Ω 0 and somewhat higher ƒ c compared to those assuming a Brune model, although resulting Δσ estimates are similar. Finally, when compared to the overall validation study results, the fitting approach alone may account for between 5% and 90% (25% on average) of the total variability in spectral Δσ⁠.

58 GEOSCIENCES↗
DOE OSTI2018

Assessing rare earth element concentrations in geothermal and oil and gas produced waters: A potential domestic source of strategic mineral commodities (Final Report)

The project team collected and analyzed 224 water samples and 101 matching rock samples. INL's improved method of measuring aqueous REEs allows study of samples previously thought too volume limited to measure. The study found that aqueous REEs occur at trace levels in all analyzed samples, and sometimes exceed ocean REE concentrations by a factor of 1,000. No significant predictive relationship to lithology, reservoir temperature, nor salinity was discovered, but aqueous REE concentration appears spatially controlled. Future work is needed to find the spatially-dependent variable that controls aqueous REE concentration.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Data and scripts associated with “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments”

This data package is associated with the publication “Moisture content modulates DOM thermodynamic regulation of oxygen consumption in drying streambed sediments” published in Scientific Reports (Garayburu-Caruso et al., 2026). The package contains processed data products and scripts used to quantify how drying and re-inundation of riverbed sediments influence dissolved organic matter (DOM) thermodynamic properties and their relationship with sediment oxygen (O₂) consumption across 33 stream sites in the contiguous United States. The data package contains DOM thermodynamic metrics (e.g., Gibbs free energy of carbon oxidation and thermodynamic efficiency), and O₂ consumption along with watershed-scale climate and land-cover metrics used as explanatory variables in the analyses. Underlying unprocessed and processed ultrahigh-resolution mass spectrometry data, oxygen consumption rates from laboratory moisture-manipulation experiments, within-sample environmental properties, sediment moisture content and contextual field measurements are archived separately at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/2428003 (Laan et al., 2024) and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1923689 (Forbes et al.,2023). A preliminary version of this data package was published in February 2026 at the time of manuscript submission. It was updated in June 2026, at the time of manuscript acceptance, to include the finalized data and additional metadata (readme, data dictionary, and file level metadata). For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. At the top level, the data package is organized into five main folders: (1) Data, (2)Figures, (3) Map, (4) GAM_Reulsts, and (5) src. The Data folder contains analysis-ready tabular files with oxygen consumption rates, DOM thermodynamic properties by site and treatment, site-level environmental variables, watershed-scale metrics, and other derived variables referenced in the manuscript. The Figures folder contains static image files associated with the main text and supplemental figures, while the Map folder includes spatial data and map-layer files used to create the sampling-location map. The GAM results folder contains the results for each of the general additive model (GAM).The src folder contains R scripts used to perform data processing, statistical analyses (including clustering, generalized additive models, and threshold analysis), and figure generation. This data package is associated with a GitHub repository found at https://github.com/WHONDRS-Hub/ECA_DOM_Thermodynamics.

Dissolved organic matter↗
DOE OSTI2022

High manganese redox variability and manganate predominance in temperate soil profiles as determined by X-ray absorption spectroscopy

Manganese speciation is a key to understanding the fate of contaminants, nutrients, and organic matter in soils. To date, quantification of Mn species in bulk soils has been performed mainly by sequential extraction methods and rarely supported by spectroscopic analysis. In order to obtain quantitative information on the Mn species inventory of soils, we investigated 46 soil horizons (<2-mm fraction, 45.1–2,280 mg/kg Mn) of nine typical Central European soils (Cambisols, Chernozems, Luvisols, Podzol, Stagnosol) by chemical Mn analyses and Mn K-edge X-ray absorption spectroscopy, and related speciation results to major soil properties. Amounts of Mn 2+ , Mn 3+ , and Mn 4+ and the average oxidation state of Mn were evaluated by linear combination fitting (LCF) of X-ray absorption near edge structure (XANES) spectra. Additionally, we used extended X-ray absorption fine structure (EXAFS) spectroscopy to identify and quantify major Mn species. For this, EXAFS spectra of 20 organic and mineral soil samples from five soils (Cambisols, Chernozem, Luvisol) were analyzed by LCF and shell fitting. XANES analyses revealed a high Mn redox variability in organic surface layers, with Mn 2+ being most abundant (≤100%, $\overline{x}$ = 54%), followed by Mn 3+ (≤80%, $\overline{x}$ = 32%) and Mn 4+ (≤55%, $\overline{x}$ = 14%). Mineral soil horizons contained significantly less Mn 2+ (≤56%, $\overline{x}$ = 23%), about equal quantities of Mn 3+ (≤68%, $\overline{x}$ = 31%), and were enriched in Mn 4+ (≤89%, $\overline{x}$ = 46%). EXAFS analyses implied the presence of six major Mn species groups: manganates, organically complexed Mn, Mn(III) oxyhydroxides, silicate-bound Mn, Mn oxides without tunnel- or layer structure, and physisorbed Mn. In litter horizons, Mn was mainly present in organic complexes (58–91%, $\overline{x}$ = 78%) and as physisorbed Mn (≤15%), but individual horizons also comprised manganates, Mn(III) oxyhydroxides, and silicate-bound Mn. Manganates, likely mixtures of phyllomanganates with hexagonal layer symmetry and tectomanganates, dominated in all mineral soil horizons (37–94%, $\overline{x}$ = 67%). Correlation analysis showed that manganates dissolve completely during dithionite-citrate and acid ammonium oxalate extractions, and suggested that Mn 4+ -rich manganates preferentially form under less acidic soil conditions, partly by oxidation of organically complexed Mn(II), and that they are enriched in the soil clay fraction. Mineral soil horizons also contained minor quantities of organically complexed Mn (≤39%, $\overline{x}$ = 11%), silicate-bound Mn (≤30%, $\overline{x}$ = 8%), Mn(III) oxyhydroxides (≤37%, $\overline{x}$ = 7%), Mn oxides without tunnel- or layer structure (≤18%, $\overline{x}$ = 5%), and physisorbed Mn (≤14%, $\overline{x}$ <1%). The detection of Mn(III) oxyhydroxides such as feitknechtite (β-MnOOH) or groutite (α-MnOOH) as well as the spinel hausmannite (Mn 3 O 4 ) in acidic soils is remarkable, since their formation is normally linked to neutral or alkaline pH conditions. Minor contributions of silicate-bound Mn indicate the release of Mn from primary minerals already at early stages of soil formation, and low concentrations of physisorbed Mn suggest that exchangeable Mn is rapidly converted into manganates in oxic soils. In conclusion, the predominance of manganates in mineral soils has far-reaching implications for the functioning of soils and biogeochemical element cycles, as these minerals play an important role in metal binding, plant nutrition, and redox-related processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗