THERMAL RUNAWAY OF NITRIC ACID-SOAKED KITTY LITTER IN TRANSURANIC WASTE
Explore the source record for details and available documents.
Hobbs, Michael L.↗
SEARCH · Search NASA
Results for “Root cause analysis”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Suppression of electroconvective and morphological instabilities by an imposed cross flow of the electrolyte
Electroconvective (EC) instability and its influence on surface morphological perturbations (Morph) are important in many applications, including electrodialysis, batteries, and fuel cells. In this work, we study the effects of a two-dimensional channel flow on the EC and Morph instabilities using two approaches. In the bulk analysis, we derive the asymptotic solutions for small and large wave numbers by neglecting the space charge layer and imposing a second kind electroosmosis slip velocity boundary condition on the electroneutral bulk region. In the full analysis, the instability of the entire region of the liquid electrolyte is analyzed using the ultraspherical spectral method. Both studies show that the flow significantly affects the EC instability. The imposed flow distorts the concentration field, causing a sheltering effect which hinders the ion transport from low- to high-concentration regions, and therefore suppresses the EC instability below a certain wave number. In combination with the viscous stabilization of the high wave number modes by the space charge layer, a sufficiently strong imposed flow can fully suppress the EC instability. The increment in the critical voltage for the instability onset is roughly proportional to the square root of the product of imposed velocity and double layer thickness. The imposed flow has a smaller effect on the Morph instability, except that it may remove the morphological modes resulting from the EC instability and thereby change the wave number of the most unstable mode.
42 ENGINEERING↗
Quantitative insights for diagnosing performance bottlenecks in lithium–sulfur batteries
Lithium–sulfur (Li–S) batteries hold significant promise for electric vehicles and aviation due to their high energy density and cost-effectiveness. However, understanding the root causes of performance degradation remains a formidable challenge, as the interplay of multiple factors obscures key failure mechanisms. A major limitation has been the inability to quantify soluble sulfur species within practical detection limits accurately and to correlate electrochemical processes with associated physical inventory changes. Here, we introduce the high-performance liquid chromatography-ultraviolet spectroscopy and gas chromatography sequential characterization (HUGS) toolkit, capable of precisely quantifying seven distinct sulfur and polysulfide species at concentrations as low as 40 ppb. HUGS has been successfully applied to practical coin and pouch cells without requiring cell modification. Furthermore, our self-developed software, Dr HUGS, enhanced the data analysis speed by over 30 times, enabling multi-source data integration and delivering comprehensive analysis results within minutes. Using HUGS, we identify significant capacity losses from inactive lithium and sulfur during initial cycles and sulfide-rich solid–electrolyte interphase (SEI) formation on the anode during later cycles. Notably, our findings reveal that soluble polysulfides have minimal contributions to capacity loss, challenging long-standing assumptions. Moreover, HUGS demonstrates that constant-pressure setups in Li–S pouch cells improve compositional uniformity compared to constant-gap configurations. For sulfurized polyacrylonitrile (SPAN) cathodes, unique issues such as non-sulfide SEI formation and lithium pulverization are observed, which can be mitigated through localized high-concentration electrolytes to enhance lithium inventory retention. By enabling precise quantification of critical inventory components, HUGS provides transformative insights into failure mechanisms across various electrolytes and cathode chemistries, guiding rational design strategies for next-generation energy storage systems.
25 ENERGY STORAGE↗
A novel in vivo mouse intervertebral disc degeneration model induced by compressive suture
Intervertebral disc degeneration (IDD) is the root cause of many musculoskeletal disorders of the spine. However, the etiology of IDD is complex and still not well understood. Animal models of IDD would be useful in deciphering the underlying mechanisms. But the existing animal models have their limitations. Therefore, to establish a novel mouse model that can simulate the human IDD process in vivo, we proposed to carefully circumcise the 2 mm-wide tail skin and then compressively sutured the defect with a simple end-to-end suture to exert excessive pressure on the disc. After 1-week, 2-week, and 4-week compression, the mice were sacrificed and the intervertebral discs were harvested for tissue analysis. The radiological, morphological, and molecular modifications of intervertebral discs were measured to characterize this model. Radiologically, the water content of the intervertebral disc decreased significantly after 2-week compression. Morphologically, the nucleus pulposus showed a decrease in volume and the number of notochordal cells. The compressive suture also broke the balance between anabolic and catabolic enzymes in nucleus pulposus, which led to the remodeling of the extracellular matrix in nucleus pulposus as the content of aggrecan and collagen II decreased. The compressive suture could induce intervertebral discs degeneration in a more reasonable way, which was solely influenced by mechanical loading, as the mice caudal vertebrae still moved freely after the operation. This kind of animal model could be adapted as a reliable in vivo mouse IDD model for the research regarding the etiology and treatments of IDD.
60 APPLIED LIFE SCIENCES↗
Potential Impacts of Assimilating All-sky Satellite Infrared Radiances on Convection-Permitting Analysis and Prediction of Tropical Convection
Geostationary infrared satellite observations are spatially dense [< 1/(20 km)2] and temporally frequent (< 1/hour). These suggest the possibility of using these observations to constrain sub-synoptic features over data sparse regions, such as tropical oceans. In this study, the potential impacts of assimilating water vapor channel brightness temperature (WV-BT) observations from the geostationary Meteorological Satellite 7 (Meteosat-7) on tropical convection analysis and prediction were systematically examined through a series of ensemble data assimilation experiments. WV-BT observations were assimilated hourly into convection-permitting ensembles using Penn State’s ensemble square root filter (EnSRF). Comparisons against independently observed Meteosat-7 window channel brightness temperature (Window-BT) show that the assimilation of WV-BT generally improved the intensities and locations of large-scale cloud patterns at spatial scales larger than 100 km. However, comparisons against independent soundings indicate that the EnSRF analysis produced a much stronger dry bias than the no data assimilation experiment. This strong dry bias is associated with the use of the simulated WV-BT from the prior mean during the EnSRF analysis step. A stochastic variant of the ensemble Kalman filter (NoMeanSF) is proposed. The NoMeanSF algorithm was able to assimilate the WV-BT without causing such a strong dry bias and the quality of the analyses’ horizontal cloud pattern is similar to EnSRF’s analyses. Finally, deterministic forecasts initiated from the NoMeanSF analyses possess better horizontal cloud patterns above 500 km than those of the EnSRF. These results suggest that it might be better to assimilate all-sky WV-BT through the NoMeanSF algorithm than EnSRF algorithm.
Chan, Man-Yau↗
Local and global approaches to treat the torsional barriers of 4-methylacetophenone using microwave spectroscopy
The Fourier transform microwave spectrum of 4-methylacetophenone recorded from 8 GHz to 18 GHz under jet-cooled conditions has revealed large tunneling splittings arising from a low barrier to internal rotation of the ring methyl group and small splittings from a high torsional barrier of the acetyl methyl group. The large splittings are especially challenging to model, while the small splittings are difficult to analyze due to the resolution limit of 120 kHz. The combination of two methyl groups undergoing internal rotations caused each rotational transition to split into five torsional species, which were resolved and fitted using a modified version of the XIAM code and the newly devel- oped ntop code to a root-mean-square deviation close to measurement accuracy, providing an estimate of the V 3 potential barriers of about 22 cm -1 and 584–588 cm -1 for the ring and the acetyl methyl groups, respectively. The assignment was aided by separately fitting the five torsional species using odd-power order operators. Only one conformer in which all heavy atoms are located on a symmetry plane could be identified in the spectrum, in agreement with results from conformation analysis using quantum chemical calculations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Gamma Spectrometry Code Rodeo for Uranium Enrichment—FY 2021 Report
In FY22, the participating Department of Energy (DOE) laboratories continued the work on the project “Gamma Spectrometry Code Rodeo for Uranium Enrichment”. Oak Ridge National Laboratory (ORNL) and Lawrence Livermore National Laboratory (LLNL) continued their efforts to acquire spectra using uranium sources. A collimator (with side and back shields) was custom designed and constructed at ORNL. Spectra were acquired at ORNL using the uranium enrichment standards supplied by New Brunswick Laboratory (NBL) and the collimated M400 detector. Spectra were also acquired using steel absorbers of different thickness placed between the source and the collimated M400 detector. The plan was to ship the collimator to LLNL so that spectra could be acquired using the LLNL detector in collimated geometries. This activity was suspended since problems were noticed with the performance of the LLNL M400 detector. Also, it was brought to light to the project team that some of the aspects of the M400 detector had been updated by the vendor H3D in response to requests by the IAEA. In February 2021, the ORNL and LLNL M400 detectors were returned to the H3D factory in order to resolve the problems with the LLNL detector and to update ORNL and LLNL detectors to the same configuration as the M400 supplied to the IAEA. H3D repaired the LLNL M400 and performed the IAEA updates on both the ORNL and LLNL detectors. ORNL and LLNL received the detectors in April 2021. Data acquisition was continued using the upgraded detectors. ORNL re-collected the spectra using point sources and the NBL standards in uncollimated and collimated geometries. The collimator was shipped from ORNL to LLNL. Spectra were acquired at LLNL through Q4 of FY22. When the ORNL spectra were examined closely, it became apparent that there were spurious artifacts present in some of the spectra. This rendered suspect the ORNL spectra collected in June/July 2021 time frame. ORNL contacted H3D and per H3D’s advice, the M400 detector was returned to H3D for repairs in mid-August 2021. H3D diagnosed the problem and established that the root cause was the extra tight packaging that created a strain on the CZT crystals, causing one of them to break down. The repaired M400 was returned to ORNL in early September 2021. ORNL re-started data acquisition for the third time. Data was continued to be acquired at ORNL and LLNL through September 2021. The ORNL and LLNL data will be shared with the principal investigators from the analyzing laboratories. The GADRAS code and FRAM have been developed to have the capability to analyze the M400 spectra. Good quality spectra from FY21 have been analyzed using modified versions of GADRAS and FRAM. The spectra have also been analyzed using the GEM code. The analysis results from GADRAS, FRAM and GEM codes are presented and discussed in this report. Some preliminary results from FRAM and GEM analysis were presented in February 2021 at the U-Pu Isotopics Workshop sponsored by the IAEA. CZTU code is being modified to analyze M400 spectra. Data collection, code development and analysis will be continued in FY22 as per the FY22 Project Work Plan (PWP).
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Low-pass spectral analysis of time-resolved serial femtosecond crystallography data
Low-pass spectral analysis (LPSA) is a recently developed dynamics retrieval algorithm showing excellent retrieval properties when applied to model data affected by extreme incompleteness and stochastic weighting. In this work, we apply LPSA to an experimental time-resolved serial femtosecond crystallography (TR-SFX) dataset from the membrane protein bacteriorhodopsin (bR) and analyze its parametric sensitivity. While most dynamical modes are contaminated by nonphysical high-frequency features, we identify two dominant modes, which are little affected by spurious frequencies. The dynamics retrieved using these modes shows an isomerization signal compatible with previous findings. We employ synthetic data with increasing timing uncertainty, increasing incompleteness level, pixel-dependent incompleteness, and photon counting errors to investigate the root cause of the high-frequency contamination of our TR-SFX modes. By testing a range of methods, we show that timing errors comparable to the dynamical periods to be retrieved produce a smearing of dynamical features, hampering dynamics retrieval, but with no introduction of spurious components in the solution, when convergence criteria are met. Using model data, we are able to attribute the high-frequency contamination of low-order dynamical modes to the high levels of noise present in the data. Finally, we propose a method to handle missing observations that produces a substantial dynamics retrieval improvement from synthetic data with a significant static component. Reprocessing of the bR TR-SFX data using the improved method yields dynamical movies with strong isomerization signals compatible with previous findings.
59 BASIC BIOLOGICAL SCIENCES↗
Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign
Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.
54 ENVIRONMENTAL SCIENCES↗
Are Atmospheric Models Too Cold in the Mountains? The State of Science and Insights from the SAIL Field Campaign
Mountains play an outsized role in water resource availability, and the amount and timing of water they provide depend strongly on temperature. To that end, we ask the question: How well are atmospheric models capturing mountain temperatures? We synthesize results showing that high-resolution, regionally relevant climate models produce 2-m air temperature (T2m) measurements colder than what is observed (a “cold bias”), particularly in snow-covered midlatitude mountain ranges during winter. We find common cold biases in 44 studies across global mountain ranges, including single-model and multimodel ensembles. We explore the factors driving these biases and examine the physical mechanisms, data limitations, and observational uncertainties behind T2m. Our analysis suggests that the biases are genuine and not due to observation sparsity or resolution mismatches. Cold biases occur primarily on mountain peaks and ridges, whereas valleys are often warm biased. Our literature review suggests that increasing model resolution does not clearly mitigate the bias. By analyzing data from the Surface Atmosphere Integrated Field Laboratory (SAIL) field campaign in the Colorado Rocky Mountains, we test various hypotheses related to cold biases and find that local wind circulations, longwave (LW) radiation, and surface-layer parameterizations contribute to the T2m biases in this particular location. We conclude by emphasizing the value of coordinated model evaluation and development efforts in heavily instrumented mountain locations for addressing the root cause(s) of T2m biases and improving predictive understanding of mountain climates.
54 ENVIRONMENTAL SCIENCES↗
Disruption of the endogenous indole glucosinolate pathway impacts the Arabidopsis thaliana root exudation profile and rhizobacterial community
Root exudates are composed of primary and secondary metabolites known to modulate the rhizosphere microbiota. Glucosinolates are defense compounds present in the Brassicaceae family capable of deterring pathogens, herbivores and biotic stressors in the phyllosphere. In addition, traces of glucosinolates and their hydrolyzed byproducts have been found in the soil, suggesting that these secondary metabolites could play a role in the modulation and establishment of the rhizosphere microbial community associated with this family. Here, we used Arabidopsis thaliana mutant lines, including the cyp79B2cyp79B3 double mutant line with a disruption in the indole glucosinolate pathway and atr1D, which overexpresses ATR1 and increases glucosinolate production. These lines were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and 16S rRNA amplicon sequencing to evaluate how genetic modifications to the indole glucosinolate pathway affects the root exudate profile of Arabidopsis thaliana, and, in turn, impacts the rhizosphere microbial community. Metabolic analysis of root exudates from the wild-type Columbia (Col-0), along with the mutant lines, confirmed that alterations to the indole glucosinolate biosynthetic pathway result in shifts in the root exudate profile of the plant. We observed changes in the relative abundance of exuded metabolites. Moreover, 16S rRNA amplicon sequencing results provided evidence that the rhizobacterial communities associated with the plant lines used were directly impacted in diversity and community composition. Here, this work provides further information on the involvement of secondary metabolites and their role in modulating the rhizobacterial community. Root metabolites dictate the presence of different bacterial species, including plant growth-promoting rhizobacteria (PGPR). Our results suggest that genetic alterations in the indole glucosinolate pathway cause disruptions beyond the endogenous levels of the plant, significantly changing the abundance and presence of different metabolites in the root exudates of the plants as well as the microbial rhizosphere community.
59 BASIC BIOLOGICAL SCIENCES↗
Symbiosis Under Stress: How Arbuscular Mycorrhizal Fungi and Sorghum Metabolism Shift Under Drought
As drought becomes more prevalent across the globe, causing billions of dollars in agricultural loss, the need to maintain crop health and productivity grows increasingly important. Out of the most important cereal crops, sorghum shows the greatest drought tolerance, and plant-microbiome interactions at the root region play a crucial role in this. A key microbial player is arbuscular mycorrhizal fungi (AMF), which deliver water and nutrients to plants in exchange for nutrients they cannot produce. Using sorghum as a model plant, we combine mass-spectrometry based proteomics and metabolomics to examine metabolic interactions between sorghum roots and AMF under drought stress. With AMF, sorghum downregulated lipid-related biological processes involving fatty acid biosynthesis and metabolism. Similarly, amino acid biosynthesis pathways were also suppressed; however, metabolite analysis revealed increased amino acid abundance related to the phenylpropanoid pathway. AMF also enhanced the upregulation of drought-protective osmolytes, such as mannitol and sorbitol, suggesting their key role in mediating sorghum’s response to drought stress. AMF hyphal biomass also had an increased abundance of key osmoprotectant amino acids, indicating similar mechanisms of drought tolerance between sorghum and AMF. Metabolomic evidence also suggested that carbohydrate exchange between sorghum and AMF shifted under stress, indicating an altered exudation pattern likely driven by drought response. Our results demonstrate the molecular mechanisms through which AMF modulate sorghum metabolism under drought conditions, highlighting their promising role in improving crop resilience. By identifying the molecular targets that can improve drought tolerance, we can begin engineering drought resistant agricultural biosystems.
60 APPLIED LIFE SCIENCES↗
A Divergent Clade KAI2 Protein in the Root Parasitic Plant Orobanche minor Is a Highly Sensitive Strigolactone Receptor and Is Involved in the Perception of Sesquiterpene Lactones
Abstract Strigolactones (SLs) were initially discovered as germination inducers for root parasitic plants. In 2015, three groups independently reported the characterization of the SL receptor in the root parasitic plant Striga hermonthica, which causes significant damage to crop production, particularly in sub-Saharan Africa. The characterized receptors belong to HYPOSENSITIVE TO LIGHT/KARRIKIN INSENSITIVE2 (HTL/KAI2), which is a member of the α/β-hydrolase protein superfamily. In non-parasitic plants, HTL/KAI2 perceives the smoke-derived germination inducer karrikin and a yet-unidentified endogenous ligand. However, root parasitic plants evolved a specific clade of HTL/KAI2 that has diverged from the KAI2 clade of non-parasitic plants. The S. hermonthica SL receptors are included in this specific clade, which is called KAI2 divergent (KAI2d). Orobanche minor is an obligate root holoparasitic plant that grows completely dependent on the host for water and nutrients because of a lack of photosynthetic ability. Previous phylogenetic analysis of KAI2 proteins in O. minor has demonstrated the presence of at least five KAI2d clade genes. Here, we report that KAI2d3 and KAI2d4 in O. minor have the ability to act as the SL receptors. They directly interact with SLs in vitro, and when expressed in Arabidopsis, they rescue thermo-inhibited germination in response to the synthetic SL analog GR24. In particular, KAI2d3 showed high sensitivity to GR24 when expressed in Arabidopsis, suggesting that this receptor enables highly sensitive SL recognition in O. minor. Furthermore, we provide evidence that these KAI2d receptors are involved in the perception of sesquiterpene lactones, non-strigolactone-type germination inducers.
Cell Biology↗
Application of Orthogonal Defect Classification for Software Reliability Analysis
The modernization of existing and new nuclear power plants with digital instrumentation and control systems (DI&C) is a recent and highly trending topic. However, there lacks strong consensus on best-estimate reliability methodologies by both the United States (U.S.) Nuclear Regulatory Commission (NRC) and the industry. This has resulted in hesitation for further modernization projects until a more unified methodology is realized. In this work, we develop an approach called Orthogonal-defect Classification for Assessing Software Reliability (ORCAS) to quantify probabilities of various software failure modes in a DI&C system. The method utilizes accepted industry methodologies for software quality assurance that are also verified by experimental or mathematical formulations. In essence, the approach combines a semantic failure classification model with a reliability growth model to predict (and quantify) the potential failure modes of a DI&C software system. The semantic classification model is used to address the question: How do latent defects in software contribute to different software failure root causes? The use of reliability growth models is then used to address the question: Given the connection between latent defects and software failure root causes, how can we quantify the reliability of the software? A case study was conducted on a representative I&C platform (ChibiOS) running a smart sensor acquisition software developed by Virginia Commonwealth University (VCU). The testing and evidence collection guidance in ORCAS was applied, and defects were uncovered in the software. Qualitative evidence, such as condition coverage, was used to gauge the completeness and trustworthiness of the assessment while quantitative evidence was used to determine the software failure probabilities. The reliability of the software was then estimated and compared to existing operational data of the sensor device. It is demonstrated that by using ORCAS, a semantic reasoning framework can be developed to justify if the software is reliable (or unreliable) while still leveraging the strength of the existing methods.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Horizontal gene transfer of the Mer operon is associated with large effects on the transcriptome and increased tolerance to mercury in nitrogen-fixing bacteria
Abstract Background Mercury (Hg) is highly toxic and has the potential to cause severe health problems for humans and foraging animals when transported into edible plant parts. Soil rhizobia that form symbiosis with legumes may possess mechanisms to prevent heavy metal translocation from roots to shoots in plants by exporting metals from nodules or compartmentalizing metal ions inside nodules. Horizontal gene transfer has potential to confer immediate de novo adaptations to stress. We used comparative genomics of high quality de novo assemblies to identify structural differences in the genomes of nitrogen-fixing rhizobia that were isolated from a mercury (Hg) mine site that show high variation in their tolerance to Hg. Results Our analyses identified multiple structurally conserved merA homologs in the genomes of Sinorhizobium medicae and Rhizobium leguminosarum but only the strains that possessed a Mer operon exhibited 10-fold increased tolerance to Hg. RNAseq analysis revealed nearly all genes in the Mer operon were significantly up-regulated in response to Hg stress in free-living conditions and in nodules. In both free-living and nodule environments, we found the Hg-tolerant strains with a Mer operon exhibited the fewest number of differentially expressed genes (DEGs) in the genome, indicating a rapid and efficient detoxification of Hg from the cells that reduced general stress responses to the Hg-treatment. Expression changes in S. medicae while in bacteroids showed that both rhizobia strain and host-plant tolerance affected the number of DEGs. Aside from Mer operon genes, nif genes which are involved in nitrogenase activity in S. medicae showed significant up-regulation in the most Hg-tolerant strain while inside the most Hg-accumulating host-plant. Transfer of a plasmid containing the Mer operon from the most tolerant strain to low-tolerant strains resulted in an immediate increase in Hg tolerance, indicating that the Mer operon is able to confer hyper tolerance to Hg. Conclusions Mer operons have not been previously reported in nitrogen-fixing rhizobia. This study demonstrates a pivotal role of the Mer operon in effective mercury detoxification and hypertolerance in nitrogen-fixing rhizobia. This finding has major implications not only for soil bioremediation, but also host plants growing in mercury contaminated soils.
59 BASIC BIOLOGICAL SCIENCES↗
Simulated plant-mediated oxygen input has strong impacts on fine-scale porewater biogeochemistry and weak impacts on integrated methane fluxes in coastal wetlands
Methane (CH 4 ) emissions from wetland ecosystems are controlled by redox conditions in the soil, which are currently underrepresented in Earth system models. Plant-mediated radial oxygen loss (ROL) can increase soil O 2 availability, affect local redox conditions, and cause heterogeneous distribution of redox-sensitive chemical species at the root scale, which would affect CH 4 emissions integrated over larger scales. In this study, we used a subsurface geochemical simulator (PFLOTRAN) to quantify the effects of incorporating either spatially homogeneous ROL or more complex heterogeneous ROL on model predictions of porewater solute concentration depth profiles (dissolved organic carbon, methane, sulfate, sulfide) and column integrated CH 4 fluxes for a tidal coastal wetland. From the heterogeneous ROL simulation, we obtained 18% higher column averaged CH 4 concentration at the rooting zone but 5% lower total CH 4 flux compared to simulations of the homogeneous ROL or without ROL. This difference is because lower CH 4 concentrations occurred in the same rhizosphere volume that was directly connected with plant-mediated transport of CH 4 from the rooting zone to the atmosphere. Sensitivity analysis indicated that the impacts of heterogeneous ROL on model predictions of porewater oxygen and sulfide concentrations will be more important under conditions of higher ROL fluxes or more heterogeneous root distribution (lower root densities). Despite the small impact on predicted CH 4 emissions, the simulated ROL drastically reduced porewater concentrations of sulfide, an effective phytotoxin, indicating that incorporating ROL combined with sulfur cycling into ecosystem models could potentially improve predictions of plant productivity in coastal wetland ecosystems.
54 ENVIRONMENTAL SCIENCES↗
Causal relationships of vegetation productivity with root zone water availability and atmospheric dryness at the catchment scale
Abstract. This study explores the causal relationships between catchment water availability, vapor pressure deficit, and gross primary productivity (GPP) across 341 catchments in the contiguous US. Seasonal climatic, hydrological, and vegetation characteristics were represented using the Horton index, ecological aridity index, evaporative fraction index, and carbon uptake efficiency. Statistical methods, including circularity statistics, correlation analysis, and causality tests, were employed to determine the complex interactions between catchment wetness, atmospheric dryness, and vegetation carbon uptake. The results revealed a maximum lag of 2 months in the intra-annual variability of catchment water supply–productivity and atmospheric water demand–productivity relationships, with hysteresis patterns varying with the catchment's hydrological characteristics. In catchments not permanently under water-limited or energy-limited conditions, vegetation experiences hydrological stress during the peak growing period, coinciding with the highest gross primary productivity and carbon uptake efficiency being out of phase with the Horton index and in phase with the evaporative fraction index. Causality analysis highlights strong temporal continuity in GPP seasonal characteristics, with a cause–effect relationship between catchment water supply, atmospheric demand, and vegetation productivity spanning a maximum of 2 months. These findings underscore the need for a comprehensive functional framework that integrates catchment water supply, atmospheric demand, and vegetation productivity to enhance our understanding and predictive capabilities with regard to ecosystem responses to climate change.
54 ENVIRONMENTAL SCIENCES↗
Validation of density pump-out by pedestal-foot magnetic island formation prior to ELM suppression in KSTAR and DIII-D tokamaks
Abstract TM1 nonlinear two-fluid simulations reveal key characteristics of density pump-out caused by pedestal-foot island formation when applying resonant magnetic perturbation (RMP). These characteristics include a bifurcation in pump-out with a low RMP coil current threshold, a sensitivity of pump-out magnitude to q 95 , and the magnitude of pump-out scaling as the square root of the RMP coil current. Dedicated experiments are carried out in the KSTAR and DIII-D tokamaks to validate these features and show that: (1) a staircase bifurcation in density pump-out is observed when slowly ramping up the n = 1 RMP current, and the density fluctuations are found to be slightly decreased from the pedestal-foot to the pedestal-top; (2) the magnitude of density pump-out becomes weaker when decreasing q 95 from 5.5 to 4.9, and a partial recovery of density pump-out is observed when q 95 is ramped down to lower than 4.9; and (3) analysis of a DIII-D database of n = 3 RMP edge-localized mode control experiments finds that the magnitude of density pump-out is proportional to the square root of RMP coil current Δ n e / n e ∝ I RMP 0.5 . These experimental observations are consistent with TM1 simulations.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗