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At least 145 records · Page 8

Observed and Projected Changes of Large‐Scale Environments Conducive to Spring MCS Initiation Over the US Great Plains

Abstract Mesoscale convective systems (MCSs) are frequent over the US Great Plains during spring. The link between large‐scale environments and spring MCS initiation were well established. Here, historical and future changes of spring large‐scale environments favorable for MCS initiation are investigated using an MCS tracking data set, ERA5 reanalysis, and 20 Coupled Model Intercomparison Project Phase 6 (CMIP6) models. The frequency of Great Plains low‐level jet (GPLLJ)‐related MCS environments is found to have increased by ∼41% from 1979 to 2019, consistent with the enhanced GPLLJ and more frequent MCSs. Comparing CMIP6 AMIP and historical experiments, we find that the observed GPLLJ strengthening and more frequent MCS environments are mainly due to the decadal sea‐surface temperature variations rather than external forcings. Under a high emission scenario, the frequency of GPLLJ‐related environments favorable for MCS initiation will increase by ∼65% during 2015–2100, along with a stronger GPLLJ, suggesting more frequent MCSs over the US Great Plains in a warming world.

54 ENVIRONMENTAL SCIENCES↗

Mesophyll conductance response to short-term changes in p CO 2 is related to leaf anatomy and biochemistry in diverse C 4 grasses

Mesophyll CO 2 conductance (g m ) in C 3 species responds to short-term (minutes) changes in environment potentially due to changes in leaf anatomical and biochemical properties and measurement artefacts. Compared with C 3 species, there is less information on g m responses to short-term changes in environmental conditions such as partial pressure of CO 2 (pCO 2 ) across diverse C 4 species and the potential determinants of these responses. Using 16 C 4 grasses we investigated the response of g m to short-term changes in pCO 2 and its relationship with leaf anatomy and biochemistry. In general, g m increased as pCO 2 decreased (statistically significant increase in 12 species), with percentage increases in gm ranging from +13% to +250%. Greater increase in g m at low pCO 2 was observed in species exhibiting relatively thinner mesophyll cell walls along with greater mesophyll surface area exposed to intercellular air spaces, leaf N, photosynthetic capacity and activities of phosphoenolpyruvate carboxylase and Rubisco. Species with greater CO 2 responses of g m were also able to maintain their leaf water-use efficiencies (TE i ) under low CO 2 . Our study advances understanding of CO 2 response of g m in diverse C 4 species, identifies the key leaf traits related to this response and has implications for improving C 4 photosynthetic models and TE i through modification of g m .

59 BASIC BIOLOGICAL SCIENCES↗

Mechanisms of Projected Changes in Thunderstorm Downburst Environments Across the United States

Responses of downdraft convective available potential energy (DCAPE) to global warming were investigated using the Community Earth System Model (CESM2) under a high‐emission scenario through the year 2100. DCAPE is projected to increase by 5%–12% on average in most areas, independently of wind shear. A diagnostic of downdraft buoyancy is introduced to understand the mechanisms of DCAPE responses. Much of the increase in mean DCAPE is temperature‐driven, with additional contributions from changes in relative humidity and downdraft origin heights. However, extreme values increase at much faster rates than can be explained by local warming. In winter, the latitude of significant DCAPE and CAPE shifts poleward by more than 5° due to larger changes in downburst environments within midlatitude cyclones. The projected increase in cold‐season extremes indicates an interaction between weather events and warming trends that increases the potential for downbursts and straight‐line winds in winter.

Williams, Ian N. [Iowa State Univ., Ames, IA (Unit↗

Using Computer Simulations to Optimize Biofuel Production

The DOE strives to ensure America's security and prosperity by addressing energy challenges. NREL shares this goal and tries to achieve a clean energy world. Fossil fuels are problematic for both organizations. Using them endangers American security. Their supply is finite and burning them causes environmental damage. Biofuels are a good alternative to fossil fuels. They are renewably produced on American soil and can lower greenhouse gas emissions. Also, cars and planes need no costly mechanical adjustments to use biofuels. However, the fuels themselves are expensive. For my SULI project, I reduced the cost of biofuels by optimizing the production process through computer simulations. Existing simulations were accurate but slow. One simulation takes up to eight hours, and researchers must do hundreds. My solution reduces the computing time. I treated the biomass particles in the simulation as one-dimensional. That simplified the simulation equations, making them easier for the computer to solve. Still, biomass particles are three-dimensional. The 1D assumption was wrong and produced inaccurate results. To maintain accuracy while increasing speed, I developed a method to convert 1D simulation results into usable 3D data. I adjusted the 1D simulation until the output matched the 3D results for a specific environment. I found out how much the simulation changed when the environment changed. Machine learning algorithms defined a relationship between 1D and 3D data for all environments. This lets scientists convert fast 1D simulation results into valid 3D data.

1D↗

Projected changes to severe thunderstorm environments as a result of twenty-first century warming from RegCM CORDEX-CORE simulations

Hazardous weather related to the occurrence of severe thunderstorms including tornadoes, high-winds, and hail cause significant damage globally to life and property every year. Yet the impact on these storms from a warming climate remains a difficult task due to their transient nature. Here, we investigate the change in large-scale environments in which severe thunderstorms form during twenty-first century warming (RCP2.6 and RCP8.5) in a group of RegCM CORDEX-CORE simulations. Severe potential is measured in terms of Convective Available Potential Energy (CAPE) and vertical wind-shear during the severe seasons in three regions which are known to currently be prone to severe hazards: North America, subtropical South America, and eastern India and Bangladesh. In every region, environments supportive for severe thunderstorms are projected to increase during the warm season months in both the RCP2.6 and RCP8.5 scenarios during the twenty-first century. The number of days supportive for severe thunderstorms increases by several days per season over the vast majority of each region by the end of the century. Analyzing the CAPE and shear trends during the twenty-first century, we find seasonally and regionally specific changes driving the increased severe potential. Twenty-first century surface warming is clearly driving a robust increase in CAPE in all regions, however poleward displacement of vertical shear in the future leads to the displacement of severe environments over North America and South America. The results found here relate that severe impacts in the future cannot be generalized globally, and that regionally specific changes in vertical shear may drive future movement of regions prone to severe weather.

54 ENVIRONMENTAL SCIENCES↗

Spontaneous generation of athermal phonon bursts within bulk silicon causing excess noise, low energy background events, and quasiparticle poisoning in superconducting sensors

Solid state phonon detectors used in the search for dark matter and coherent neutrino nucleus interactions (CE $v$ NS) require excellent energy resolution (eV-scale or below) and low backgrounds. An unknown source of phonon bursts, the low energy excess (LEE), dominates other above-threshold backgrounds and generates excess shot noise from subthreshold bursts. Here, in this paper, we measure these phonon bursts for 12 days after cooldown in two nearly identical 1 cm 2 silicon detectors that differ only in the thickness of their substrate (1 vs 4 mm thick). We find that both the channel-correlated shot noise and near-threshold shared LEE relax with time since cooldown. Additionally, both the correlated shot noise and LEE rates scale linearly with substrate thickness. When combined with previous measurements of other silicon phonon detectors with different substrate geometries and mechanical support strategies, these measurements strongly suggest that the dominant source of both above and below threshold LEE is the bulk substrate. By monitoring the relation between bias power and excess phonon shot noise, we estimate that the energy scale for subthreshold noise events is 0.68 ± 0.38 meV. In our final dataset, we report a world-leading energy resolution of 258.5 ± 0.4 meV in the 1 mm thick detector. Simple calculations suggest that these silicon substrate phonon bursts are likely a significant source of quasiparticle poisoning in superconducting qubits operated in well shielded and vibration free environments.

Chang, C. L. [Argonne National Laboratory 1 , 9700↗

Spatiotemporal Learning in Power Modules: Wavelet-Enhanced Forecasting of Thermomechanical Degradation

Detecting internal defects in power electronics packages is critical for their performance and reliability, especially under extreme operating conditions, as these defects can lead to catastrophic failure if not properly addressed. Confocal scanning acoustic microscopy (C-SAM) plays a key role in the nondestructive evaluation of bond layer degradation within a power electronics package by detecting defects such as delamination, voids, and cracks. However, accurately quantifying and predicting these defects from C-SAM images remains a significant challenge due to the low noise-to-signal ratio, which typically arises from both imaging process and bond patterns itself. In this paper, we explore machine learning strategies for processing C-SAM images and providing predictive models of defect growth. We use C-SAM images of sintered copper and sintered silver samples, which are obtained under accelerated thermal experiments, as the representative dataset for our study. We investigate the effect of Fourier transforms and wavelet transforms on these datasets to remove high-frequency noise and address noise across multiple scales with histogram equalization to enhance the contrast and improve the visibility of defects. As a result, defect boundaries can be clearly distinguished, enabling more accurate tracking of their growth over time. We then employ different time-series forecasting algorithms on the denoised images to formulate an image-based lifetime prediction model. Statistical models and deep-learning techniques are trained on images obtained in the early stages of thermal shock, and defect growth in the later stages is predicted. Our work serves as a preliminary attempt to improve the accuracy of lifetime prediction models of power electronics packages, which is critical under extreme operating environments.

24 POWER TRANSMISSION AND DISTRIBUTION↗

High-affinity amide-lanthanide adsorption to gram-positive soil bacteria

The gram-positive soil bacterium, Arthrobacter nicotianae, uses multiple organic acid functional groups to adsorb lanthanides onto its cell surface. At relevant soil pH conditions of 4.0–6.0, many of these functional groups are de-protonated and available for cation sorption and metal immobilization. However, among the plethora of naturally occurring site types, A. nicotianae is shown to possess high-affinity amide and phosphate sites that disproportionately affect lanthanide adsorption to the cell wall. We quantify neodymium (Nd)-selective site types, reporting an amide-Nd stability constant of log 10 K = 6.41 ± 0.23 that is comparable to sorption via phosphate-based moieties. These sites are two to three orders of magnitude more selective for Nd than the adsorption of divalent metals to ubiquitous carboxyl-based moieties. This implies the importance of lanthanide biosorption in the context of metal transport in subsurface systems despite trace concentrations of lanthanides found in the natural environment.

59 BASIC BIOLOGICAL SCIENCES↗

Impact of the built, social, and food environment on long‐term weight loss within a behavioral weight loss intervention

Abstract Background Behavioral weight loss interventions can lead to an average weight loss of 5%–10% of initial body weight, however there is wide individual variability in treatment response. Although built, social, and community food environments can have potential direct and indirect influences on body weight (through their influence on physical activity and energy intake), these environmental factors are rarely considered as predictors of variation in weight loss. Objective Evaluate the association between built, social, and community food environments and changes in weight, moderate‐to‐vigorous physical activity (MVPA), and dietary intake among adults who completed an 18‐month behavioral weight loss intervention. Methods Participants included 93 adults (mean ± SD; 41.5 ± 8.3 years, 34.4 ± 4.2 kg/m 2 , 82% female, 75% white). Environmental variables included urbanicity, walkability, crime, Neighborhood Deprivation Index (includes 13 social economic status factors), and density of convenience stores, grocery stores, and limited‐service restaurants at the tract level. Linear regressions examined associations between environment and changes in body weight, waist circumference (WC), MVPA (SenseWear device), and dietary intake (3‐day diet records) from baseline to 18 months. Results Grocery store density was inversely associated with change in weight ( β = −0.95; p = 0.02; R 2 = 0.062) and WC ( β = −1.23; p < 0.01; R 2 = 0.109). Participants living in tracts with lower walkability demonstrated lower baseline MVPA and greater increases in MVPA versus participants with higher walkability (interaction p = 0.03). Participants living in tracts with the most deprivation demonstrated greater increases in average daily steps ( β = 2048.27; p = 0.02; R 2 = 0.039) versus participants with the least deprivation. Limited‐service restaurant density was associated with change in % protein intake ( β = 0.39; p = 0.046; R 2 = 0.051). Conclusion Environmental factors accounted for some of the variability (<11%) in response to a behavioral weight loss intervention. Grocery store density was positively associated with weight loss at 18 months. Additional studies and/or pooled analyses, encompassing greater environmental variation, are required to further evaluate whether environment contributes to weight loss variability.

Tewahade, Selam↗

Annual Site Environmental Report (2019)

Los Alamos National Laboratory’s (the Laboratory’s) annual site environmental reports are prepared by the Laboratory’s environmental organizations, as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 3, Radiation Protection of the Public and the Environment. The following chapters in this report discuss our success in complying with environmental laws, regulations, and orders (Chapter 2, Compliance Summary); how we manage the Laboratory’s environmental performance (Chapter 3, Environmental Programs); how we monitor for air emissions of radioactive materials and climate conditions (Chapter 4, Air Quality); how we monitor for effects of Laboratory operations on groundwater quality (Chapter 5, Groundwater Protection); how we monitor the movement of chemicals and radionuclides by storm water runoff and the levels of chemicals and radionuclides in deposited sediment (Chapter 6, Watershed Quality); how we monitor for the presence, levels, and effects of chemicals and radionuclides in plants, animals, and soil (Chapter 7, Ecosystem Health); and finally, what radionuclide dose or risk from chemical exposure members of the public may experience as a result of Laboratory operations (Chapter 8, Public Dose and Risk Assessment).

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

ASER Annual Site Environmental Report 2020

Los Alamos National Laboratory’s (the Laboratory’s) annual site environmental reports are prepared each year by the Laboratory’s environmental organizations, as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 4, Radiation Protection of the Public and the Environment. The following chapters in this report discuss our success in complying with environmental laws, regulations, and orders (Chapter 2, Compliance Summary); how we manage the Laboratory’s environmental performance (Chapter 3, Environmental Programs and Analytical Data Quality); how we monitor for air emissions of radioactive materials and climate conditions (Chapter 4, Air Quality); how we monitor for effects of Laboratory operations on groundwater quality (Chapter 5, Groundwater Protection); how we monitor the movement of chemicals and radionuclides by storm water runoff and the levels of chemicals and radionuclides in deposited sediment (Chapter 6, Watershed Quality); how we monitor for the presence, levels, and effects of chemicals and radionuclides in plants, animals, soil, and vegetation (Chapter 7, Ecosystem Health); and finally, what radionuclide dose or risk from chemical exposure members of the public may experience as a result of Laboratory operations (Chapter 8, Public Dose and Risk Assessment).

54 ENVIRONMENTAL SCIENCES↗

Los Alamos National Laboratory 2021 Annual Site Environmental Report

Los Alamos National Laboratory’s (LANL’s or the Laboratory’s) annual site environmental reports are prepared each year by the Laboratory’s environmental organizations as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 4, Radiation Protection of the Public and the Environment.

54 ENVIRONMENTAL SCIENCES↗

Los Alamos National Laboratory 2022 Annual Site Environmental Report (Rev. 2)

Los Alamos National Laboratory (Laboratory) annual site environmental reports are prepared each year by the Laboratory’s environmental organizations as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 4, Radiation Protection of the Public and the Environment. The chapters in this report discuss our compliance with environmental laws, regulations, and orders (Chapter 2, Compliance Summary); how we manage the Laboratory’s environmental performance and assure the quality of data from analysis of environmental samples (Chapter 3, Environmental Programs and Analytical Data Quality); how we monitor for air emissions of radioactive materials and for weather conditions (Chapter 4, Air Quality); how we monitor for effects of Laboratory operations on groundwater quality (Chapter 5, Groundwater Protection); how we monitor the levels of chemicals and radionuclides in storm water runoff and sediment (Chapter 6, Watershed Quality); how we monitor for the levels and effects of chemicals and radionuclides in plants, animals, soil, and vegetation (Chapter 7, Ecosystem Health); and finally, what radioactive dose or risk from chemical exposure that members of the public could experience as a result of Laboratory operations (Chapter 8, Public Dose and Risk Assessment).

54 ENVIRONMENTAL SCIENCES↗

Los Alamos National Laboratory 2023 Annual Site Environmental Report

Los Alamos National Laboratory (Laboratory) annual site environmental reports are prepared each year by the Laboratory’s environmental organizations as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 4, Radiation Protection of the Public and the Environment.

54 ENVIRONMENTAL SCIENCES↗

Los Alamos National Laboratory 2024 Annual Site Environmental Report

Los Alamos National Laboratory (Laboratory) annual site environmental reports are prepared each year by the Laboratory’s environmental organizations as required by U.S. Department of Energy Order 231.1B, Administrative Change 1, Environment, Safety, and Health Reporting, and Order 458.1, Administrative Change 4, Radiation Protection of the Public and the Environment.

54 ENVIRONMENTAL SCIENCES↗

Characterizing Complex Gas–Solid Interfaces with in Situ Spectroscopy: Oxygen Adsorption Behavior on Fe–N–C Catalysts

Electrocatalysts for the oxygen reduction reaction within polymer electrolyte membrane fuel cells based on iron, nitrogen, and carbon elements (Fe–N–C) are receiving significant research attention as they offer an inexpensive alternative to catalysts based on platinum-group metals. Although both the performance and the fundamental understanding of Fe–N–C catalysts have improved over the past decade, there remains a need to differentiate the relative activity of different active sites. Toward this goal, our study is focused on characterizing the interactions between O 2 and a set of five structurally different Fe–N–C materials. Detailed characterization of the Fe speciation was performed with 57 Fe Mössbauer spectroscopy and soft X-ray absorption spectroscopy of the Fe L 3,2 -edge, whereas nitrogen chemical states were investigated with X-ray photoelectron spectroscopy (XPS). In addition to initial sXAS and XPS measurements performed in ultra-high vacuum (UHV), measurements were also performed (at the identical location) in an atmosphere of 100 mTorr of O 2 at 80 °C (O 2 -rich). XPS and sXAS results reveal the presence of several types of FeNxCy adsorption sites. FeNxCy sites that are proposed as the most active ones do not show significant change (based on the techniques used in this study) when their environment is changed from UHV to O 2 -rich. Correlation with Mössbauer and sXAS results suggests that this is most likely due to the persistence of strongly adsorbed O 2 molecules from their previous exposure to air. However, other species do show spectroscopic changes from UHV conditions to O 2 -rich. This implies that these sites have a weaker interaction with O 2 that results in their desorption in vacuum conditions and re-adsorption when exposed to the O 2 -rich environment. The nature of these weakly and strongly O 2 -adsorbing FeN x C y sites is discussed in the context of different synthetic and processing parameters employed to fabricate each of these five Fe–N–C materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Performance of a Spectral Wave Model at Predicting Wave Farm Impacts

For renewable ocean wave energy to support global energy demands, wave energy converters (WECs) will likely be deployed in large numbers (farms), which will necessarily change the nearshore environment. Wave farm induced changes can be both helpful (e.g., beneficial habitat and coastal protection) and potentially harmful (e.g., degraded habitat, recreational, and commercial use) to existing users of the coastal environment. It is essential to estimate this impact through modeling prior to the development of a farm, and to that end, many researchers have used spectral wave models, such as Simulating WAves Nearshore (SWAN), to assess wave farm impacts. However, the validity of the approaches used within SWAN have not been thoroughly verified or validated. Herein, a version of SWAN, called Sandia National Laboratories (SNL)-SWAN, which has a specialized WEC implementation, is verified by comparing its wave field outputs to those of linear wave interaction theory (LWIT), where LWIT is theoretically more appropriate for modeling wave-body interactions and wave field effects. The focus is on medium-sized arrays of 27 WECs, wave periods, and directional spreading representative of likely conditions, as well as the impact on the nearshore. A quantitative metric, the Mean Squared Skill Score, is used. Results show that the performance of SNL-SWAN as compared to LWIT is “Good” to “Excellent”.

environmental impacts↗