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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.

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At least 397 records · Page 22

Robustness of the Stochastic Parameterization of Subgrid-Scale Wind Variability in Sea Surface Fluxes

Abstract High-resolution numerical models have been used to develop statistical models of the enhancement of sea surface fluxes resulting from spatial variability of sea surface wind. In particular, studies have shown that flux enhancement is not a deterministic function of the resolved state. Previous studies focused on single geographical areas or used a single high-resolution numerical model. This study extends the development of such statistical models by considering six different high-resolution models, four different geographical regions, and three different 10-day periods, allowing for a systematic investigation of the robustness of both the deterministic and stochastic parts of the data-driven parameterization. Results indicate that the deterministic part, based on regressing the unresolved normalized flux onto resolved-scale normalized flux and precipitation, is broadly robust across different models, regions, and time periods. The statistical features of the stochastic part of the model (spatial and temporal autocorrelation and parameters of a Gaussian process fit to the regression residual) are also found to be robust and not strongly sensitive to the underlying model, modeled geographical region, or time period studied. Best-fit Gaussian process parameters display robust spatial heterogeneity across models, indicating potential for improvements to the statistical model. These results illustrate the potential for the development of a generic, explicitly stochastic parameterization of sea surface flux enhancements dependent on wind variability.

Endo, Kota↗

Storage Conditions of Human Kidney Tissue Sections Affect Spatial Lipidomics Analysis Reproducibility

Lipids often are labile, unstable, and tend to degrade overtime, so it is of the upmost importance to study these molecules in their most native state. In this study, we sought to understand the optimal storage conditions for spatial lipidomic analysis of human kidney tissue sections. Specifically, we evaluated human kidney tissue sections on several different days throughout the span of a week using our established protocol for elucidating lipids using high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). We studied kidney tissue sections stored under five different conditions: open stored at –80 °C, vacuumed sealed and stored at –80 °C, with matrix preapplied before storage at –80 °C, under a nitrogen atmosphere and stored at –80 °C, and at room temperature in a desiccator. Results were compared to data obtained from kidney tissue sections that were prepared and analyzed immediately after cryosectioning. Data was processed using METASPACE. After a week of storage, the sections stored at room temperature showed the largest amount of lipid degradation, while sections stored under nitrogen and at –80 °C retained the greatest number of overlapping annotations in relation to freshly cut tissue. Overall, we found that molecular degradation of the tissue sections was unavoidable over time, regardless of storage conditions, but storing tissue sections in an inert gas at low temperatures can curtail molecular degradation within tissue sections.

60 APPLIED LIFE SCIENCES↗

The importance of temporal and spatial incoherence in quantitative interpretation of 4D-STEM

Recent developments in pixelated detectors, when combined with aberration correction of probe forming optics have greatly enhanced the field of scanning electron diffraction. Differential phase contrast is now routine and deep learning has been proposed as a method to extract maximum information from diffraction patterns. This study examines the effects of temporal and spatial incoherence on convergent beam electron diffraction patterns and demonstrates that simple center of mass measurements cannot be naively interpreted. The inclusion of incoherence in deep learning data sets is also discussed.

36 MATERIALS SCIENCE↗

Detecting Coastal Wetland Degradation by Combining Remote Sensing and Hydrologic Modeling

Sea-level rise and climate change stresses pose increasing threats to coastal wetlands that are vital to wildlife habitats, carbon sequestration, water supply, and other ecosystem services with global significance. However, existing studies are limited in individual sites, and large-scale mapping of coastal wetland degradation patterns over a long period is rare. Our study developed a new framework to detect spatial and temporal patterns of coastal wetland degradation by analyzing fine-scale, long-term remotely sensed Normalized Difference Vegetation Index (NDVI) data. Then, this framework was tested to track the degradation of coastal wetlands at the Alligator River National Wildlife Refuge (ARNWR) in North Carolina, United States, during the period from 1995 to 2019. We identified six types of coastal wetland degradation in the study area. Most of the detected degradation was located within 2 km from the shoreline and occurred in the past five years. Further, we used a state-of-the-art coastal hydrologic model, PIHM-Wetland, to investigate key hydrologic processes/variables that control the coastal wetland degradation. The temporal and spatial distributions of simulated coastal flooding and saltwater intrusion confirmed the location and timing of wetland degradation detected by remote sensing. The combined method also quantified the possible critical thresholds of water tables for wetland degradation. The remote sensing–hydrologic model integrated scheme proposed in this study provides a new tool for detecting and understanding coastal wetland degradation mechanisms. Our study approach can also be extended to other coastal wetland regions to understand how climate change and sea-level rise impact wetland transformations.

54 ENVIRONMENTAL SCIENCES↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

2022 Next-Generation National Household Travel Survey - Origin-Destination Data Addendum for the Oahu Region

# 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region The 2022 Next-Generation National Household Travel Survey – Origin-Destination Data Addendum for the Oahu Region covers the Hawaiian island of Oahu and five counties in surrounding islands. An addendum to the nationwide version of the study conducted in 2019, this regional survey provided additional information and spatial granularity about people’s movements to, from, and within the areas under study. ## Data Collection Agency The survey was conducted for the Oahu Metropolitan Planning Organization. ## Survey Methodology Data were collected in two zones: the core area (census blocks in the island of Oahu) and the halo area (five counties in adjacent Hawaiian islands). All trips were assigned an origin zone and a destination zone, as well as a travel mode, purpose, and distance. Sociodemographic information related to age, income, and gender was also collected. ## Survey Records, Data, and Documentation Survey records include 1,154,167,619 trips.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Characterization factors and other air quality impact metrics: Case study for PM 2.5 -emitting area sources from biofuel feedstock supply

In this paper, we develop a framework and metrics for estimating the impact of emission sources on regulatory compliance and human health for applications in air quality planning and life cycle impact assessment (LCIA). Our framework is based on a pollutant's characterization factor (CF) and three new metrics: Available Regulatory Capacity for Incremental Emissions (ARCIE), Source CF Ratio, and Activity Health Impact (AHI) Ratio. ARCIE can be used to assess whether a receptor location has capacity to accommodate additional source emissions while complying with regulatory limits. We present CF as a midpoint indicator of health impacts per unit mass of emitted pollutant. Source CF Ratio enables comparison of potential new-source locations based on human health impacts. The AHI Ratio estimates the health impacts of a pollutant in relation to the utilization of the source for each unit of product or service. These metrics can be applied to any pollutant, energy source sector (e.g., agriculture, electricity), source type (point, line, area), and spatial modeling domain (nation, state, city, region). We demonstrate these metrics through a case study of fine particulate (PM 2.5 ) emissions from U.S. corn stover harvesting and local processing at various scales, representing steps in the biofuel production process. We model PM 2.5 formation in the atmosphere using a novel reduced-complexity chemical transport model called the Intervention Model for Air Pollution (InMAP). Through this case study, we present the first area-source PM 2.5 CFs that address the recommendations of several LCIA studies to establish spatially explicit CFs specific to an energy source sector or type. Overall, the framework developed in this work provides multiple new ways to consider the potential impacts of air emissions through spatially differentiated metrics.

09 BIOMASS FUELS↗

Distribution of genetic variation underlying adult migration timing in steelhead of the Columbia River basin

Abstract Fish migrations are energetically costly, especially when moving between freshwater and saltwater, but are a viable strategy for Pacific salmon and trout ( Oncorhynchus spp.) due to the advantageous resources available at various life stages. Anadromous steelhead ( O. mykiss ) migrate vast distances and exhibit variation for adult migration phenotypes that have a genetic basis at candidate genes known as greb1L and rock1 . We examined the distribution of genetic variation at 13 candidate markers spanning greb1L , intergenic, and rock1 regions versus 226 neutral markers for 113 populations ( n = 9,471) of steelhead from inland and coastal lineages in the Columbia River. Patterns of population structure with neutral markers reflected genetic similarity by geographic region as demonstrated in previous studies, but candidate markers clustered populations by genetic variation associated with adult migration timing. Mature alleles for late migration had the highest frequency overall in steelhead populations throughout the Columbia River, with only 9 of 113 populations that had a higher frequency of premature alleles for early migration. While a single haplotype block was evident for the coastal lineage, we identified multiple haplotype blocks for the inland lineage. The inland lineage had one haplotype block that corresponded to candidate markers within the greb1L gene and immediately upstream in the intergenic region, and the second block only contained candidate markers from the intergenic region. Haplotype frequencies had similar patterns of geographic distribution as single markers, but there were distinct differences in frequency between the two haplotype blocks for the inland lineage. This may represent multiple recombination events that differed between lineages where phenotypic differences exist between freshwater entry versus arrival timing as indicated by Micheletti et al. (2018a). Redundancy analyses were used to model environmental effects on allelic frequencies of candidate markers, and significant variables were migration distance, temperature, isothermality, and annual precipitation. This study improves our understanding of the spatial distribution of genetic variation underlying adult migration timing in steelhead as well as associated environmental factors and has direct conservation and management implications.

Collins, Erin E.↗

Metal concentration and leaf spatial distribution assessed by synchrotron µXRF in Brazilian nickel hyperaccumulators

Background and Aims: The limited number of known hyperaccumulator species in tropical regions, including Brazil, has hampered the development of nickel (Ni) agromining. In addition, the mechanisms underlying metal accumulation and distribution in these species remain poorly understood, despite their pivotal role in identifying species with economic potential. Furthermore, this study assessed the accumulation potential and foliar spatial distribution of metals in three Brazilian hyperaccumulator species (Pfaffia sarcophylla, Justicia lanstyakii, and Lippia lupulina) growing in one of the world’s largest mafic–ultramafic complexes. Methods Concentrations of Ni and other metals in leaves were determined using portable X-ray fluorescence (pXRF) and inductively coupled plasma optical emission spectroscopy (ICP-OES). Synchrotron-based X-ray microfluorescence (SR-µXRF) was employed to map the spatial distribution of metals across intact leaves. Results: The results revealed leaf Ni concentrations ranging from 320 to 1,950 mg kg⁻ 1 in P. sarcophylla, 1,640 to 6,810 mg kg⁻ 1 in L. lupulina, and 1,990 to 4,900 mg kg⁻ 1 in J. lanstyakii. Nickel, Mn, and Co exhibited similar distribution patterns, concentrating mainly in the leaf margins and veins across all species, while Ca, K, and Mg co-localization suggests regulatory mechanisms for adaptation. Conclusion: Although these plants exhibited Ni hyperaccumulation, our findings suggest limited commercial potential for agromining. Further research on genetic variability and nutrient assimilation, particularly in P. sarcophylla, is recommended to elucidate the mechanisms underlying Ni accumulation and intraspecific variability.

36 MATERIALS SCIENCE↗

Global impacts of vegetation clumping on regulating land surface heat fluxes

The clumping index (CI) quantifies the non-random distribution of vegetation across space, which regulates the canopy radiative transfer processes and land surface carbon, water, and energy cycles. However, its impact on global surface energy budget, particularly sensible heat fluxes and surface temperature, is not well understood. Additionally, while there have been studies showing significant seasonal variations in CI, the impacts of these variations on surface energy fluxes remain unclear. In this study, we incorporated satellite-derived spatially and temporally explicit CI data into the Community Land Model version 5 (CLM5) to evaluate the effects of CI on global land energy fluxes. Our results showed that including CI increased the global mean sensible heat flux dissipated from ground by 3.9 W m -2 (~18%), while decreasing the global mean vegetation sensible heat flux by 4.9 W m -2 (~65%), resulting in a total sensible heat decrease of 1.0 W m -2 (~3%). In contrast, CI increased the global mean latent heat flux by 0.8 W m -2 (~2%), primarily due to increased evapotranspiration (up to 11 W m -2 ) in tropical regions. We also found considerable impacts of seasonal variations in CI, particularly on sensible heat fluxes from ground and vegetation in evergreen needleleaf forests and deciduous needleleaf forests. Using constant CI rather than considering seasonal variations resulted in significant overestimation and underestimation of the sensible heat fluxes from vegetation and ground, respectively, in boreal summer. In conclusion, these changes in surface energy fluxes caused by CI and its seasonal variations led to up to 1.7 and 0.5 K differences in simulated mean ground temperature. These findings highlight the importance of including CI and considering its seasonal variations in modeling land surface energy fluxes.

54 ENVIRONMENTAL SCIENCES↗