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At least 235 records · Page 13

Long-Duration High-Resolution Large-Eddy Simulations of Hurricane Laura (2020) for Infrastructure Design

This dataset provides long-duration large-eddy simulations (LES) of Hurricane Laura (2020), capturing the turbulent boundary-layer wind field during the storm's passage at horizontal grid spacings of 55.55 m and 10 m. Hurricane Laura made landfall as a Category 4 storm in southwestern Louisiana at 06:00 UTC on 27 August 2020, producing widespread damage to coastal infrastructure. The simulations characterize the spatial and temporal variability of the turbulent wind at heights spanning the lowest 300 m of the boundary layer, throughout the approach and passage of the storm's eyewall over a fixed location. To resolve the spatial heterogeneity of wind and turbulence conditions across the storm, seven independent refined LES runs are performed at radial locations spanning the storm relative to its direction of motion. The refinement positions are defined on a normalized radial coordinate r ̂=r/R, where R≈21 km is Laura's radius of maximum wind, and lie between r ̂=-1.5 and r ̂=+1.5 in ∆r ̂=0.5 increments along a line perpendicular to the storm's track at the midpoint of the simulation. Negative r ̂ corresponds to locations south-southwest of the storm center and positive r ̂ to locations north-northeast. The dataset is segmented into seven radial refinement groups.

17 WIND ENERGY↗

Simulation of high-frequency dissolved oxygen dynamics in a shallow estuary, the Corsica River, Chesapeake Bay

Understanding shallow water biogeochemical dynamics is a challenge in coastal regions, due to the presence of highly variable land-water interface fluxes, tight coupling with sediment processes, tidal dynamics, and diurnal variability in biogeochemical processes. While the deployment of continuous monitoring devices has improved our understanding of high-frequency (12 - 24 hours) variability and spatial heterogeneity in shallow regions, mechanistic modeling of these dynamics has lagged behind conceptual and empirical models. The inherent complexity of shallow water systems is represented in the Corsica River estuary, a small basin within the Chesapeake Bay ecosystem, where abundant monitoring data have been collected from long-term monitoring stations, continuous monitoring sensors, synoptic sensor surveys, and measurements of sediment-water fluxes. A state-of-the-art modeling system, the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM), was applied to the Corsica domain with a high-resolution grid and nutrient loads from the most recent version of the Chesapeake Bay watershed model. The Corsica SCHISM model reproduced observed high-frequency variability in dissolved oxygen, as well as seasonal variability in chlorophyll-a and sediment-water fluxes. Time-series signal analyses using Empirical Model Decomposition and spectral analysis revealed that the diurnal and M2 tide frequencies are the dominant high-frequency modes and physical transport contributes a larger share to dissolved oxygen budgets than biogeochemical processes on an hourly time scale. Heterogeneity and patchiness in dissolved oxygen resulting from phytoplankton distributions and geometry-driven eddies amplify the physical transport effect, and on longer time scales oxygen is controlled more by photosynthesis and respiration. Our simulation demonstrates that interactions among physical and biological dynamics generate complex high-frequency variability in water quality and non-linear reposes to nutrient loading and environmental forcing in shallow water systems.

54 ENVIRONMENTAL SCIENCES↗

Coupled Biotic-Abiotic Processes Control Biogeochemical Cycling of Dissolved Organic Matter in the Columbia River Hyporheic Zone

A critical component of assessing the impacts of climate change on watershed ecosystems involves understanding the role that dissolved organic matter (DOM) plays in driving whole ecosystem metabolism. The hyporheic zone—a biogeochemical control point where ground water and river water mix—is characterized by high DOM turnover and microbial activity and is responsible for a large fraction of lotic respiration. Yet, the dynamic nature of this ecotone provides a challenging but important environment to parse out different DOM influences on watershed function and net carbon and nutrient fluxes. We used high-resolution Fourier-transform ion cyclotron resonance mass spectrometry to provide a detailed molecular characterization of DOM and its transformation pathways in the Columbia river watershed. Samples were collected from ground water (adjacent unconfined aquifer underlying the Hanford 300 Area), Columbia river water, and its hyporheic zone. The hyporheic zone was sampled at five locations to capture spatial heterogeneity within the hyporheic zone. Our results revealed that abiotic transformation pathways (e.g., carboxylation), potentially driven by abiotic factors such as sunlight, in both the ground water and river water are likely influencing DOM availability to the hyporheic zone, which could then be coupled with biotic processes for enhanced microbial activity. The ground water profile revealed high rates of N and S transformations via abiotic reactions. The river profile showed enhanced abiotic photodegradation of lignin-like molecules that subsequently entered the hyporheic zone as low molecular weight, more degraded compounds. While the compounds in river water were in part bio-unavailable, some were further shown to increase rates of microbial respiration. Together, river water and ground water enhance microbial activity within the hyporheic zone, regardless of river stage, as shown by elevated putative amino-acid transformations and the abundance of amino-sugar and protein-like compounds. This enhanced microbial activity is further dependent on the composition of ground water and river water inputs. Our results further suggest that abiotic controls on DOM should be incorporated into predictive modeling for understanding watershed dynamics, especially as climate variability and land use could affect light exposure and changes to ground water essential elements, both shown to impact the Columbia river hyporheic zone.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Model and remote-sensing-guided experimental design and hypothesis generation for monitoring snow-soil–plant interactions

In this study, we develop a machine-learning (ML)-enabled strategy for selecting hillslope-scale ecohydrological monitoring sites within snow-dominated mountainous watersheds, with a particular focus on snow-soil–plant interactions. Data layers rely on spatial data layers from both remote sensing and hydrological model simulations. Specifically, a Landsat-based foresummer drought sensitivity index is used to define the dependency of the annual peak plant productivity on the Palmer drought severity index in the early growing season. Hydrological simulations provide the spatiotemporal dynamics of near-surface soil moisture and snow depth. In this framework, a regression analysis identifies the key hydrological variables relevant to the spatial heterogeneity of drought sensitivity. We then apply unsupervised clustering to these key variables, using the Gaussian mixture model, to group hillslopes into several zones that have divergent relationships regarding soil moisture, snow dynamics, and drought sensitivity. Using the datasets collected in the East River Watershed (Crested Butte, Colorado, United States), results show that drought sensitivity is significantly correlated with model-derived soil moisture and snow-free timing over space and time. The relationship is, however, non-linear, such that the correlation decreases above a threshold elevation and in a heavy snow year due to large snowpacks, lateral flow, and soil storage limitations. Clustering is then able to define the zones that have high or low sensitivity to drought, as well as the mid-elevation regions where sensitivity is associated with the topographic aspect and net potential radiation. In addition, the algorithm identifies the most representative hillslopes with road/trail access within each zone for installing monitoring sites. Our method also aims to significantly increase the use of ML and model-simulation results to guide critical zone and watershed monitoring activities.

54 ENVIRONMENTAL SCIENCES↗

Intensified Positive Arctic–Methane Feedback under IPCC Climate Scenarios in the 21st Century

The positive Arctic–methane (CH 4 ) feedback forms when more CH 4 is released from the Arctic tundra to warm the climate, further stimulating the Arctic to emit CH 4 . This study utilized the CLM-Microbe model to project CH 4 emissions across five distinct Arctic tundra ecosystems on the Alaska North Slope, considering three Shared Socioeconomic Pathway (SSP) scenarios using climate data from three climate models from 2016 to 2100. Employing a hyper-resolution of 5 m × 5 m within 40,000 m 2 domains accounted for the Arctic tundra’s high spatial heterogeneity; three sites were near $Utqia\dot{g}vik$ (US-Beo, US-Bes, and US-Brw), with one each in Atqasuk (US-Atq) and Ivotuk (US-Ivo). Simulated CH 4 emissions substantially increased by a factor of 5.3 to 7.5 under the SSP5–8.5 scenario compared to the SSP1–2.6 and SSP2–4.5 scenarios. The projected CH 4 emissions exhibited a stronger response to rising temperature under the SSP5–8.5 scenario than under the SSP1–2.6 and SSP2–4.5 scenarios, primarily due to strong temperature dependence and the enhanced precipitation-induced expansion of anoxic conditions that promoted methanogenesis. The CH 4 transport via ebullition and plant-mediated transport is projected to increase under all three SSP scenarios, and ebullition dominated CH 4 transport by 2100 across five sites. Projected CH 4 emissions varied in temperature sensitivity, with a Q 10 range of 2.7 to 60.9 under SSP1–2.6, 3.8 to 17.6 under SSP2–4.5, and 5.7 to 17.2 under SSP5–8.5. Compared with the other three sites, US-Atq and US-Ivo were estimated to have greater increases in CH 4 emissions due to warmer temperatures and higher precipitation. The fact that warmer sites and warmer climate scenarios had higher CH 4 emissions suggests an intensified positive Arctic–CH 4 feedback in the 21st century. Microbial physiology and substrate availability dominated the enhanced CH 4 production. The simulated intensified positive feedback underscores the urgent need for a more mechanistic understanding of CH 4 dynamics and the development of strategies to mitigate CH 4 across the Arctic.

54 ENVIRONMENTAL SCIENCES↗

U-Surf: a global 1 km spatially continuous urban surface property dataset for kilometer-scale urban-resolving Earth system modeling

High-resolution urban climate modeling has faced substantial challenges due to the absence of a globally consistent, spatially continuous, and accurate dataset to represent the spatial heterogeneity of urban surfaces and their biophysical properties. This deficiency has long obstructed the development of urban-resolving Earth system models (ESMs) and ultra-high-resolution urban climate modeling, over large domains. Here, we present U-Surf, a first-of-its-kind 1 km resolution present-day (circa 2020) global continuous urban surface parameter dataset. Using the urban canopy model (UCM) in the Community Earth System Model as a base model for satisfying dataset requirements, U-Surf leverages the latest advances in remote sensing, machine learning, and cloud computing to provide the most relevant urban surface biophysical parameters, including radiative, morphological, and thermal properties, for UCMs at the facet and canopy level. Generated using a systematically unified workflow, U-Surf ensures internal consistency among key parameters, making it the first globally coherent urban canopy surface dataset. U-Surf significantly improves the representation of the urban land heterogeneity both within and across cities globally; provides essential, high-fidelity surface biophysical constraints to urban-resolving ESMs; enables detailed city-to-city comparisons across the globe; and supports next-generation kilometer-resolution Earth system modeling across scales. U-Surf parameters can be easily converted or adapted to various types of UCMs, such as those embedded in weather and regional climate models, as well as air quality models. The fundamental urban surface constraints provided by U-Surf can also be used as features for machine learning models and can have other broad-scale applications for socioeconomic, public health, and urban planning contexts. We expect U-Surf to advance the research frontier of urban system science, climate-sensitive urban design, and coupled human–Earth systems in the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.11247598 (Cheng et al., 2024).

Cheng, Yifan [Univ. of Illinois at Urbana-Champaig↗

When Do Different Scenarios of Future Electricity Demand Start to Meaningfully Diverge?

Climate and population change are known to influence electricity demand, but what is the impact of uncertainty in climate and population projections on electricity demand in the United States (U.S.) over the next 30 years? This question has important implications for investment decisions in the energy sector, which are typically made using a 15- to 30-year time horizon. Simply put, if future climate and population scenarios do not lead to meteorological conditions and subsequent energy demands that are distinctly different within the first 30 years then, for the purposes of investment decisions, it may not matter which potential pathway we are most likely on. The Integrated, Multisector, Multiscale Modeling (IM3) project has generated a wide, yet plausible range of 21st century high-resolution climate and population scenarios for the U.S. The IM3 projections span two climate scenarios (Representative Concentration Pathways 4.5 and 8.5) and two population scenarios (Shared Socioeconomic Pathways 3 and 5). For each of the climate scenarios, we reflect a range of climate model uncertainty by using boundary conditions from a set of climate models that are hotter and colder than the multi-model mean. In total there are eight (2 x 2 x 2) IM3 scenarios. This work explores a basic question: When do future climate and energy outcomes start to meaningfully diverge across the eight scenarios? The analysis is done on both the raw meteorology time-series from each scenario as well as a time-series of projected total electricity demand generated using IM3's Total ELectricity Loads (TELL) model. We also explore the spatial heterogeneity of the divergence signal to identify regions of the country that may be more or less path dependent.

Climate Change↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Supporting Information - How Can Crop Production Adapt to growing groundwater restrictions in the West?

Abstract This data plan outlines the structure and content of datasets generated and utilized in the research presented in the manuscript abstract. Groundwater overdraft has led to serious water supply issues in the US West. Most western states manage groundwater use through a permitting system, but California has only recently begun to restrict groundwater use statewide with the 2014 passage of groundwater restrictions which target the elimination of groundwater overdraft practices by 2042. With groundwater extraction curtailed, crop production in the US West (a $95 billion industry annually) will be affected, and appropriate response strategies will be needed to ensure minimal disruption to food production and the regional economy. In this paper, we explore the adoption of alternative adaptive responses: (a) deficit irrigation; (b) switching to less water-intensive crops; (c) changing the extent of irrigated land (including fallowing); and (d) geographically shifting crop production. Employing an integrated modeling approach, we explicitly capture the interactions and feedbacks between local hydrology, changes in crop yields, crop and land use decision-making, changes in crop prices, and regional shifts in crop production. We find that the optimal adaptive response is spatially heterogeneous and comprises a portfolio of strategies. Southwestern states and California will be the most impacted by groundwater restrictions. The optimal responses in these states are to both adopt deficit irrigation strategies and reduce a portion of their irrigated croplands, resulting in a shift in crop production to northwestern states with a larger supply of water. The datasets detailed in this repository represent the output from these integrated models, specifically designed to support the analysis and visualization presented in the manuscript and supplemental information. These datasets, used in conjunction with the scripts available at our associated GitHub repository (https://github.com/pches/Femeena_etal_How_can_crop_production_adapt), enable the reproduction of figures and facilitate a comprehensive understanding of the optimal adaptive strategies in agriculture for mitigating water stress under varying groundwater extraction scenarios, as outlined in the abstract.

DNDCe↗

Spatial and unobserved heterogeneity in consumer preferences for adoption of electric and hybrid vehicles: A Bayesian hierarchical modeling approach

The transition to low carbon vehicles known as alternative fuel vehicles (AFVs) is well underway. This transition has been motivated partly by consumer demand and partly by legislation such as the Zero Emission Vehicle mandate, which requires manufacturers to sell a certain percentage of their vehicles as AFVs. While the long-term adoption of AFVs (specifically, electric and hybrids) may take several years, there is a need to understand consumer preferences for AFV adoption and the pathways of AFV adoption from a national perspective. Therefore, this study sought to provide information about consumer preferences regarding AFV ownership while considering spatial and unobserved heterogeneity in consumer preferences, which can potentially impact societal transition to low carbon fueled vehicles. The 2017 National Household Travel Survey was used to calibrate Bayesian logit and hierarchical models. Here, the findings of these models reveal that higher gasoline prices contribute toward the adoption of battery electric vehicles. The results also reveal that the perceived disadvantages of AFVs for long commutes are the key barrier in wider adoption of AFVs. Interestingly, frequent use of the internet by consumers revealed a higher likelihood for purchase of hybrid vehicles. Furthermore, West Coast residents are observed to be a large portion of the early adopters and are more likely to purchase hybrids as compared to battery electric vehicles. The knowledge generated by this study has implications for making better informed decisions about AFV adoption and developing incentives to promote wider adoption of AFVs by overcoming their perceived disadvantages.

33 ADVANCED PROPULSION SYSTEMS↗

Spatial and chemical heterogeneity in aqueous Zn/MnO 2 batteries: role of Zn and Mn containing complexes

Aqueous Zn/MnO 2 batteries are a promising, safe alternative for grid-scale energy storage, owing to their environmentally safe and low-cost nature. The dissolution–deposition reaction mechanism in a mild aqueous pH regime has recently gained significance due to its relevance in battery design. Comprehending both the specific locations and the way reaction progresses is crucial for efficient batteries. This study demonstrates that Zinc Hydroxy Sulfate (ZHS) formed during discharge primarily near the dissolved MnO2 particles. Acting as a host for charge reactants in subsequent cycles, the charge product morphology was visualized using operando X-ray fluorescence microscopy. After ∼400 hours of cycling, capacity fade was linked to the formation of a Zn–Mn core–shell phase which is attributed to an irreversible core phase in the electrode, visualized through three-dimensional chemical mapping. Altogether, this research underscores the importance of understanding local morphological evolution in designing electrodes and chemistries for advanced grid-scale energy storage technologies.

36 MATERIALS SCIENCE↗

Automated characterization of spatial and dynamical heterogeneity in supercooled liquids via implementation of machine learning

Abstract A computational approach by an implementation of the principle component analysis (PCA) with K -means and Gaussian mixture (GM) clustering methods from machine learning algorithms to identify structural and dynamical heterogeneities of supercooled liquids is developed. In this method, a collection of the average weighted coordination numbers ( W C N s ‾ ) of particles calculated from particles’ positions are used as an order parameter to build a low-dimensional representation of feature (structural) space for K -means clustering to sort the particles in the system into few meso-states using PCA. Nano-domains or aggregated clusters are also formed in configurational (real) space from a direct mapping using associated meso-states’ particle identities with some misclassified interfacial particles. These classification uncertainties can be improved by a co-learning strategy which utilizes the probabilistic GM clustering and the information transfer between the structural space and configurational space iteratively until convergence. A final classification of meso-states in structural space and domains in configurational space are stable over long times and measured to have dynamical heterogeneities. Armed with such a classification protocol, various studies over the thermodynamic and dynamical properties of these domains indicate that the observed heterogeneity is the result of liquid–liquid phase separation after quenching to a supercooled state.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Empirical evidence that mean soil contaminant concentration is not a conservative indicator of external exposure to wildlife

Adequate information on external exposure is often a limiting factor in ecological risk assessments because of a lack of exposure data for free-ranging animals as they interact with a heterogeneous distribution of contaminants. Instead, most wildlife exposure data are estimated from model simulations. However, empirical exposure data are rarely available to verify model results, or to test fundamental assumptions inherent in exposure assessments. We used novel, GPS-coupled radiation dosimeters on reindeer (Rangifer tarandus) to quantify their external exposure for five months in a Chernobyl fallout affected mountainous area of Norway. The exposure data were then used to test a fundamental assumption in screening-level risk assessments: Mean soil contaminant concentrations can be used to conservatively estimate chronic external exposures to representative animals. External exposure and GPS coordinates were obtained hourly and compared to model simulations of exposure based on airborne surveys of 137 Cs soil contamination. Our objectives were to determine 1) if modelled dose estimates based on mean 137 Cs soil concentrations were conservative, and 2) if modelled dose estimates could be improved by adjusting for spatial–temporal heterogeneity of contaminants using 137 Cs concentrations within reindeers’ GPS-defined home ranges and core areas. We found that external exposure modelled from mean soil contaminant concentrations underestimated exposure by 70% compared to empirical measurements from GPS-dosimeter collars worn on individual animals. Using area-weighted mean contaminant levels within the animals’ home range improved the model estimates, but still underestimated the field dosimeter data by 53%. The best estimate of external exposure was obtained when high-use areas within their home range, along with spatial heterogeneity of the contaminant, was accounted for by using area-weighted mean soil contaminant levels within GPS-derived core areas. Accounting for temporal use of core areas within animals’ home range provided a 24% improvement. Here, a comparison of our analyses to similar work performed on Chernobyl wolves found the two studies concur that risk assessors should be cautious in relying on the paradigm of using averaged soil contaminant levels to provide conservative estimates of external exposure to representative individuals. Results of modelled external exposure based on mean soil contaminant concentrations could misguide assessors and cause inappropriate management decisions.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Decoding crops one cell at a time: from cell atlases to single-cell genetics

Understanding the mechanisms underlying key agricultural traits remains a central challenge in crop research, but recent advances in technologies are providing powerful tools to address this issue. Among these, single-cell and spatial transcriptomics have revealed tissue heterogeneity and spatial organization, offering unique insights into cellular gene expression dynamics and the coordinated activity of multiple cell types. These approaches help uncover how specific cell types contribute to agricultural traits and refine candidate loci lists through integration with trait-associated loci. Additionally, single-cell and spatial transcriptomics have the potential to serve as cell-level readout platforms integrating cellular perturbations, enabling high-throughput discovery of causal relationships between genotype and gene expression at the cellular level in plants. Successful implementation will accelerate the identification of key genetic variants for crop improvement. Furthermore we review lessons learned from application of single-cell screening in mammalian cells, highlight major technical and biological barriers to its use in plants, and outline potential strategies to overcome these challenges. Together, the widespread application and integration of single-cell and spatial transcriptomics with other technologies enable not only the descriptive cataloging of cell states but also the causal interrogation of sequence functions and regulatory networks at cell type resolution, ultimately advancing gene function studies and accelerating crop improvement.

Cellular heterogeneity↗

Community vulnerability is the key determinant of diverse energy burdens in the United States

Low-income households generally experience a high energy burden; however, the factors influencing energy burdens are beyond socio-economics. This study explores the relationships between the multidimensionality of community vulnerability factors and energy burden across multiple geospatial levels in the United States. Our study found the distribution of energy burden in 2020 showed a great deal of variety, ranging from a minimum of 2.93 % to a maximum of 30.45 % across 3142 counties. The results of non-spatial and spatial regressions showed that the vulnerability ranks of socioeconomic, household composition and disability, minority and language, household type and transportation, and COVID mortality rate are significant predictors of energy burdens at the national level. However, at the regional level, only socioeconomic, minority and language significantly influence energy burdens. Minority and language negatively impact energy burdens except for the South East-Central region. Additionally, our analyses highlight the need to consider community vulnerability indicators' spatial homogeneity and heterogeneity. At the national level, only the epidemiological factors index is a spatially homogeneous predictor; on the regional and state level, the spatially homogeneous predictors such as socioeconomic status, household composition and disability, and household type and transportation vary by region. Such a region-sensitive relationship between energy burden and the predictors indicates spatial heterogeneity. Here this study suggests policy recommendations through the lens of the multidimensionality of community vulnerability factors. Implementing flexible national energy policies while making particular energy assistance policies for the vulnerable population at the regional or state levels is essential.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗