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Paleosol weathering commonly is characterized by an index of alteration determined from bulk rock elemental abundances. Although a variety of indices exist, they all essentially compare the proportion of immobile major elemental oxides associated with refractory minerals (mainly Al2O3 in phyllosilicate clays) to mobile major elements associated with labile minerals (i.e., CaO, Na2O). Higher proportions of immobile major elemental oxides reflects more intense chemical weathering, which is used to infer warmer, wetter climate conditions. However, bulk rock alteration indices are known to be influenced by variability in particle size, effects of quartz dilution, and authigenic mineralization, which are challenging to account for when destructive analytical approaches are used. We tested a non-destructive method of assessing chemical weathering intensity using automated mineralogy analysis, which relies on SEM BSE imaging and EDS spectrum acquisition with automated matching of whole spectra to reference spectra to generate quantitative mineralogy estimates. Our case study focused on 9 Upper Pennsylvanian Spodosol samples from the Appalachian basin, and the dominant mineralogic group identified by automated mineralogy in all samples were phyllosilicate clays (50-85%). Six samples showed >20% feldspar: however, grain shape analysis indicates often micron-scale clay-sized grains with prismatic crystal habit were assigned a potassium feldspar spectra. This is due to the automated mineralogy algorithm identifying a mixed signal from sub-micron quartz/amorphous silica and illite in the X-ray volume as a feldspar x-ray spectrum. Furthermore, the Chemical Index of Alteration calculated from bulk elemental estimates of automated mineralogy results ranged from ~60-65, and A-CN-K ternary plots indicate samples were influenced by K-metasomatism. Using an algorithm that assigns automated mineral identification on the basis of both whole-spectrum matching and particle morphology attributes (including grain shape/crystal habit and size), we can better constrain mineralogical interpretation of fine-grained sedimentary lithologies. This allows automated mineralogy analysis to be used in a new approach for assessing weathering intensity by analyzing the proportion of mobile versus immobile elements in prismatic grains versus equant grains across a distribution of grain sizes in a way that can mitigate or minimize known limitations to the accuracy of alteration indices (e.g., authigenic skins, K-metasomatism). 

Advancements in automated mineralogy offer an opportunity to develop new approaches to the study of fine-grained sedimentary lithologies including paleosols and pedogenic minerals that hold valuable paleoclimate information. Automated mineralogy is a non-destructive analytical technique that relies on BSE imaging with spectral data to output multimodal hypermaps. Spatial domains are delineated and assigned mineral phases using whole spectrum best matching to reference spectra, providing quantitative sample composition estimates with high throughput data collection. We targeted a Spodosol in the lower part of the Upper Pennsylvanian Casselman Fm. of the Appalachian basin to evaluate the utility of automated mineralogy in determining paleosol composition. During deposition of the lower Casselman Fm., tropical climate during the Late Paleozoic Ice Age began a return to a more humid regime following the Kasimovian–Gzhelian boundary (~304 Ma) warming event. The Spodosol is a composite paleosol approximately 1.4 m thick that displays redoximorphic mottling, small scale (≤ 3 cm) slickensides and weak angular platy ped development. We performed automated mineralogy analysis on 9 paleosol samples, which were formed into 25 mm polished epoxy mounts of disaggregated peds, and generated complete mineralogical maps of the samples. These results indicate that phyllosilicate clays, mainly illite, formed the dominant mineralogic group (50-85%) with lesser amounts of quartz (~5-23%), feldspar (12-30%), carbonate (0-12%) and Fe-oxides (0-9%). Estimates of Al, Ca, Na and K from were used to determine Chemical Index of Alteration, with values ranging from 59-67. These CIA estimates tend to be quite low compared to CIA estimates determined from previous work using bulk elemental abundances by WDS-XRF (CIA >67). Further interrogation of these preliminary results revealed that interphase quartz-illite analyses were assigned a potassium feldspar interpretation. Ultimately we will combine image analysis (e.g., particle shape/habit) with new reference spectra for paleosol interphase matrix material, which together with WDS-XRF and XRD mineralogy calibration can be used to develop a robust methodology for automated mineralogy analysis of paleosols. 

Fine-grained sedimentary lithologies can be challenging to characterize but often preserve vital environmental indicators. We used cm-scale outcrop description, bulk geochemistry, and micro- to nano-scale automated mineral identification SEM and characterization analysis to investigate attributes of paleosols in the central Appalachian basin. Paleosols were selected that developed under varying climatic conditions of the latest Pennsylvanian (earliest Permian? Late Paleozoic Ice Age. In the upper Casselman Fm., paleosols exhibit redoximorphic texture (in a suspect spodosol? with cm-scale slickensides and weak ped development, with mineralogy that includes small amounts of gypsum, barite, and pyrite within the predominantly illite matrix. A histosol “underclayˮ in the Casselman shows nodular calcite within an illite matrix that is cross-cut by micro-veins of gypsum. In the overlying Monongahela Fm., within interbedded clastic and carbonate lacustrine deposits is a composite vertisol with large vertic structures and gilgai microtopography. The vertisol contains carbonate nodules in an illite/quartz matrix with disseminated dolomite and rare pyrite. Upward in the Monongahela Fm., lacustrine carbonates of the Benwood Member show evidence for pedogenesis, such as rootlets and auto-brecciation/fracturing, and weak argillan development. These carbonate paleosols show successive upward decrease in illite and quartz content, with carbonate minerals becoming increasingly dolomitic. Microfabrics and mineralogical relationships indicate a primary origin of the dolomite. Status of presenter N/A ⌘K Sign in The integration of soil macro- and micro-morphology along with mineralogy provides more comprehensive climatic and environmental indicators for this succession, and reflects a transition from paleosol interaction with sulfate-rich water toward interaction with more alkaline water over time 

Abstract A catalog containing details of the highest-energy cosmic rays recorded through the detection of extensive air showers at the Pierre Auger Observatory is presented with the aim of opening the data to detailed examination. Descriptions of the 100 showers created by the highest-energy particles recorded between 2004 January 1 and 2020 December 31 are given for cosmic rays that have energies in the range 78–166 EeV. Details are also given on a further nine very energetic events that have been used in the calibration procedure adopted to determine the energy of each primary. A sky plot of the arrival directions of the most energetic particles is shown. No interpretations of the data are offered. 

Abstract Novel observation techniques (e.g., smart tracers) for characterizing coupled hydrological and biogeochemical processes are improving understanding of stream network transport and transformation dynamics. In turn, these observations are thought to enable increasingly sophisticated representations within transient storage models (TSMs). However, TSM parameter estimation is prone to issues with insensitivity and equifinality, which grow as parameters are added to model formulations. Currently, it is unclear whether (or not) observations from different tracers may lead to greater process inference and reduced parameter uncertainty in the context of TSM. Herein, we aim to unravel the role of in‐stream processes alongside metabolically active (MATS) and inactive storage zones (MITS) using variable TSM formulations. Models with one (1SZ) and two storage zones (2SZ) and with and without reactivity were applied to simulate conservative and smart tracer observations obtained experimentally for two reaches with differing morphologies. As we show, smart tracers are unsurprisingly superior to conservative tracers when it comes to partitioning MITS and MATS. However, when transient storage is lumped within a 1SZ formulation, little improvement in parameter uncertainty is gained by using a smart tracer, suggesting the addition of observations should scale with model complexity. Importantly, our work identifies several inconsistencies and open questions related to reconciling time scales of tracer observation with conceptual processes (parameters) estimated within TSM. Approaching TSM with multiple models and tracer observations may be key to gaining improved insight into transient storage simulation as well as advancing feedback loops between models and observations within hydrologic science.