Identification of Uranyl Minerals Using Oxygen K‐Edge X‐Ray Absorption Spectroscopy

Although most of the world's uranium exists as pitchblende or uraninite, this mineral can be weathered to a great variety of secondary uranium minerals, most containing the uranyl cation. Anthropogenic uranium compounds can also react in the environment, leading to spatial–chemical alterations that could be useful for nuclear forensics analyses. Soft X‐ray absorption spectroscopy (XAS) has the advantages of being non‐destructive, element‐specific and sensitive to electronic and physical structure. The soft X‐ray probe can also be focused to a spot size on the order of tens of nanometres, providing chemical information with high spatial resolution. However, before XAS can be applied at high spatial resolution, it is necessary to find spectroscopic signatures for a variety of uranium compounds in the soft X‐ray spectral region. To that end, we collected the near edge X‐ray absorption fine structure (NEXAFS) spectra of a variety of common uranyl‐bearing minerals, including uranyl carbonates, oxyhydroxides, phosphates and silicates. We find that uranyl compounds can be distinguished by class (carbonate, oxyhydroxide, phosphate or silicate) based on their oxygen K‐edge absorption spectra. This work establishes a database of reference spectra for future spatially resolved analyses. We proceed to show scanning X‐ray transmission microscopy (STXM) data from a schoepite particle in the presence of an unknown contaminant.     

Portability of semantic and spatial–temporal machine learning methods to analyse social media for near-real-time disaster monitoring

Natural Hazards - Up-to-date information about an emergency is crucial for effective disaster management. However, severe restrictions impede the creation of spatiotemporal information by current...     

Spatial crime distribution and prediction for sporting events using social media

ABSTRACT

Sporting events attract high volumes of people, which in turn leads to increased use of social media. In addition, research shows that sporting events may trigger violent behavior that can lead to crime. This study analyses the spatial relationships between crime occurrences, demographic, socio-economic and environmental variables, together with geo-located Twitter messages and their ‘violent’ subsets. The analysis compares basketball and hockey game days and non-game days. Moreover, this research aims to analyze crime prediction models using historical crime data as a basis and then introducing tweets and additional variables in their role as covariates of crime. First, this study investigates the spatial distribution of and correlation between crime and tweets during the same temporal periods. Feature selection models are applied in order to identify the best explanatory variables. Then, we apply localized kernel density estimation model for crime prediction during basketball and hockey games, and on non-game days. Findings from this study show that Twitter data, and a subset of violent tweets, are useful in building prediction models for the seven investigated crime types for home and away sporting events, and non-game days, with different levels of improvement.     

Heterogeneous reduction of Tc(VII) by Fe(II) at the solid-water interface

Experiments were performed herein to investigate the rates and products of heterogeneous reduction of Tc(VII) by Fe(II) adsorbed to hematite and goethite, and by Fe(II) associated with a dithionite-citrate-bicarbonate (DCB) reduced natural phyllosilicate mixture [structural, ion-exchangeable, and edge-complexed Fe(II)] containing vermiculite, illite, and muscovite. The heterogeneous reduction of Tc(VII) by Fe(II) adsorbed to the Fe(III) oxides increased with increasing pH and was coincident with a second event of adsorption. The reaction was almost instantaneous above pH 7. In contrast, the reduction rates of Tc(VII) by DCB-reduced phyllosilicates were not sensitive to pH or to added that adsorbed to the clay. The reduction kinetics were orders of magnitude slower than observed for the Fe(III) oxides, and appeared to be controlled by structural Fe(II). The following affinity series for heterogeneous Tc(VII) reduction by Fe(II) was suggested by the experimental results: aqueous Fe(II) ∼ adsorbed Fe(II) in phyllosilicates [ion-exchangeable and some edge-complexed Fe(II)] ? structural Fe(II) in phyllosilicates ? Fe(II) adsorbed on Fe(III) oxides. Tc-EXAFS spectroscopy revealed that the reduction products were virtually identical on hematite and goethite that were comprised primarily of sorbed octahedral TcO₂ monomers and dimers with significant Fe(III) in the second coordination shell. The nature of heterogeneous Fe(III) resulting from the redox reaction was ambiguous as probed by Tc-EXAFS spectroscopy, although Mössbauer spectroscopy applied to an experiment with ⁵⁶Fe-goethite with adsorbed ⁵⁷Fe(II) implied that redox product Fe(III) was goethite-like. The Tc(IV) reduction product formed on the DCB-reduced phyllosilicates was different from the Fe(III) oxides, and was more similar to Tc(IV) oxyhydroxide in its second coordination shell. The heterogeneous reduction of Tc(VII) to less soluble forms by Fe(III) oxide-adsorbed Fe(II) and structural Fe(II) in phyllosilicates may be an important geochemical process that will proceed at very different rates and that will yield different surface species depending on subsurface pH and mineralogy.     

Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks

ABSTRACT

The investigation of human activity patterns from location-based social networks like Twitter is an established approach of how to infer relationships and latent information that characterize urban structures. Researchers from various disciplines have performed geospatial analysis on social media data despite the data’s high dimensionality, complexity and heterogeneity. However, user-generated datasets are of multi-scale nature, which results in limited applicability of commonly known geospatial analysis methods. Therefore in this paper, we propose a geographic, hierarchical self-organizing map (Geo-H-SOM) to analyze geospatial, temporal and semantic characteristics of georeferenced tweets. The results of our method, which we validate in a case study, demonstrate the ability to explore, abstract and cluster high-dimensional geospatial and semantic information from crowdsourced data.     

Multilevel samplers as microcosms to assess microbial response to biostimulation

Passive multilevel samplers (MLS) containing a solid matrix for microbial colonization were used as in situ microcosms in conjunction with a push-pull biostimulation experiment designed to promote biological U(VI) and Tc(VII) reduction. MLS were deployed at 24 elevations in the injection well and two downgradient wells to investigate the spatial variability in microbial community composition and growth prior to and following biostimulation. The microbial community was characterized by real-time quantitative polymerase chain reaction (Q-PCR) quantification of bacteria, NO(3)(-)-reducing bacteria (nirS and nirK), delta-proteobacteria, Geobacter sp., and methanogens (mcrA). Pretest cell densities were low overall but varied substantially with significantly greater bacterial populations detected at circumneutral pH (t-test, alpha= 0.05), suggesting carbon substrate and low pH limitations of microbial activity. Although pretest cell densities were low, denitrifying bacteria were dominant members of the microbial community. Biostimulation with an ethanol-amended ground water resulted in concurrent NO(3)(-) and Tc(VII) reduction, followed by U(VI) reduction. Q-PCR analysis of MLS revealed significant (1 to 2 orders of magnitude, Mann-Whitney U-test, alpha= 0.05) increases in cell densities of bacteria, denitrifiers, delta-proteobacteria, Geobacter sp., and methanogens in response to biostimulation. Traditionally, characterization of sediment samples has been used to investigate the microbial community response to biostimulation; however, collection of sediment samples is expensive and not conducive to deep aquifers or temporal studies. The results presented demonstrate that push-pull tests with passive MLS provide an inexpensive approach to determine the effect of biostimulation on contaminant concentrations, geochemical conditions, and the microbial community composition and function.     

Determining individual mineral contributions to U(VI) adsorption in a contaminated aquifer sediment: A fluorescence spectroscopy study

The adsorption and speciation of U(VI) was investigated on contaminated, fine grained sediment materials from the Hanford 300 area (SPP1 GWF) in simulated groundwater using cryogenic laser-induced U(VI) fluorescence spectroscopy combined with chemometric analysis. A series of reference minerals (montmorillonite, illite, Michigan chlorite, North Carolina chlorite, California clinochlore, quartz and synthetic 6-line ferrihydrite) was used for comparison that represents the mineralogical constituents of SPP1 GWF. Surface area-normalized K_d values were measured at U(VI) concentrations of 5 × 10⁻⁷ and 5 × 10⁻⁶ mol L⁻¹ that displayed the following affinity series: 6-line-ferrihydrite > North Carolina chlorite ≈ California clinochlore > quartz ≈ Michigan chlorite > illite > montmorillonite. Both time-resolved spectra and asynchronous two-dimensional (2D) correlation analysis of SPP1 GWF at different delay times indicated that two major adsorbed U(VI) species were present in the sediment that resembled U(VI) adsorbed on quartz and phyllosilicates. Simulations of the normalized fluorescence spectra confirmed that the speciation of SPP1 GWF was best represented by a linear combination of U(VI) adsorbed on quartz (90%) and phyllosilicates (10%). However, the fluorescence quantum yield for U(VI) adsorbed on phyllosilicates was lower than quartz and, consequently, its fractional contribution to speciation may be underestimated. Spectral comparison with literature data suggested that U(VI) exist primarily as inner-sphere complexes with surface silanol groups on quartz and as surface U(VI) tricarbonate complexes on phyllosilicates.     

A local scale-sensitive indicator of spatial autocorrelation for assessing high- and low-value clusters in multiscale datasets

Georeferenced user-generated datasets like those extracted from Twitter are increasingly gaining the interest of spatial analysts. Such datasets oftentimes reflect a wide array of real-world phenomena. However, each of these phenomena takes place at a certain spatial scale. Therefore, user-generated datasets are of multiscale nature. Such datasets cannot be properly dealt with using the most common analysis methods, because these are typically designed for single-scale datasets where all observations are expected to reflect one single phenomenon (e.g., crime incidents). In this paper, we focus on the popular local G statistics. We propose a modified scale-sensitive version of a local G statistic. Furthermore, our approach comprises an alternative neighbourhood definition that enables to extract certain scales of interest. We compared our method with the original one on a real-world Twitter dataset. Our experiments show that our approach is able to better detect spatial autocorrelation at specific scales, as opposed to the original method. Based on the findings of our research, we identified a number of scale-related issues that our approach is able to overcome. Thus, we demonstrate the multiscale suitability of the proposed solution.     

Influence of Carbon and Microbial Community Priming on the Attenuation of Uranium in a Contaminated Floodplain Aquifer

The capacity for subsurface sediments to sequester radionuclide contaminants, such as uranium (U), and retain them after bioremediation efforts are completed is critical to the long‐term stewardship of re‐mediated sites. In U bioremediation strategies, carbon amendment stimulates bioreduction of U(VI) to U(IV), immobilizing it within the sediments. Sediments enriched in natural organic matter are naturally capable of sequestering significant U, but may serve as sources to the aquifer, contributing to plume persistence. Two types of organic‐rich sediments were compared to better understand U release mechanisms. Sediments that were artificially primed for U removal were retrieved from an area previously biostimulated while detrital‐rich sediments were collected from a location never subject to amendment. Batch incubations demonstrated that primed sediments rapidly removed uranium from the groundwater, whereas naturally reduced sediments released a sizeable portion of U before U(VI)‐reduction commenced. Column experiments confirmed that U release persisted for 65 pore volumes in naturally reduced sediments, demonstrating their sink‐source behavior. Acetate addition to primed sediments shifted the microbial community from sulfate‐reducing bacteria within Desulfobacteraceae to the iron‐reducing Geobacteraceae and Firmicutes, associated with efficient U(VI) removal and retention, respectively. In contrast, Geobacteraceae communities in naturally reduced sediments were replaced by sequences with similarity to Pseudomonas spp. during U release, while U(VI) removal only occurred with enrichment of Firmicutes. These investigations stress the importance of characterizing zones with heterogeneous carbon pools at U‐contaminated sites prior to the determination of a remedial strategy to identify areas, which may contribute to long‐term sourcing of the contaminants.     

Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment

Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal and spatial resolution. This paper presents an approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection. Our results demonstrate that earthquake footprints can be reliably and accurately identified in our use case. More, a number of relevant semantic topics can be automatically identified without a priori knowledge, revealing clearly differing temporal and spatial signatures. Furthermore, we are able to generate a damage map that indicates where significant losses have occurred. The validation of our results using statistical measures, complemented by the official earthquake footprint by US Geological Survey and the results of the HAZUS loss model, shows that our approach produces valid and reliable outputs. Thus, our approach may improve current disaster management procedures through generating a new and unseen information layer in near real time.