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All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Exposing and Reducing Biases of Simulating Mixed-Phase Clouds in the Convection-Permitting E3SM Atmosphere Model: Lessons From an Arctic Cold-Air Outbreak

Mixed-phase clouds modulate the water and energy cycles of high-latitude regions, yet their liquid-ice phase partitioning has long been poorly simulated in climate models. Here, simulations of Arctic mixed-phase clouds by the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) are assessed against large-eddy simulations, satellite data, and ground-based observations during the Cold-Air Outbreaks in the Marine Boundary Layer Experiment field campaign. SCREAM simulates nearly completely frozen clouds, which is attributed largely to the unreasonably strong Wegener–Bergeron–Findeisen (WBF) process that converts liquid to ice excessively and partly to the early over-abundant ice production at cold temperatures from a temperature-deterministic deposition ice nucleation scheme. Assuming no subgrid variation for the WBF process in the original formulation particularly conflicts with the instantaneous saturation adjustment assumption in the condensation scheme that assumes subgrid variability, leading to exaggerated WBF process rates. A proposed simple physically-based improvement on the treatment of subgrid cloud overlap substantially increases supercooled liquid water content and notably improves cloud-top phase partitioning, aligning better with observations. Improvement of supercooled liquid water content also converges with increasing horizontal resolution. The deposition ice nucleation scheme is found responsible for a falsely-produced ice cloud aloft that is not observed, biasing the simulated cloud radiative effects and top-of-atmosphere radiative fluxes. This study identifies key deficiencies in cloud parameterizations that continue to challenge convection-permitting models.

Geosciences↗
DOE OSTI2020

Deciphering Degradation: Machine Learning on Real-World Performance Data

The solar investment community is in need of up-to-date and data-derived system degradation rates to use in financial models that calculate the risk and expectation of energy revenue. To date, financial models choose degradation rates from past studies that are limited and may not be representative of solar projects in development today. As the solar industry expands in all sectors (residential, commercial, and utility), there is a growing amount of energy generation data available to analyze. With the help of the open source degradation analysis code (RdTools) it is now possible to derive degradation on an ongoing basis and continuously provide up-to-date durability statistics to the investment community. Ongoing and accurate statistics of fielded photovoltaic systems allows financial stakeholders to constrain energy revenue projections, lower financial risk, and increase the bankability of solar. This presentation will provide a mid-term progress update on a 2 year SETO-funded degradation project that involves applying RdTools analysis on kWh Analytics’ industry database. With results from the RdTools analysis in hand, machine learning methods are employed to quantify how much variance of system performance degradation can be explained by predictor variables such as environmental factors, equipment bill of materials characteristics, and system design information. The goal of this presentation is to promote the usage of RdTools on an ongoing to data owners and to solicit feedback from researchers and stakeholders on how to maximize impact of the SETO-funded kWh Analytics degradation project.

14 SOLAR ENERGY↗
DOE OSTI2023

Deep reinforcement learning assisted co-optimization of Volt-VAR grid service in distribution networks

With the increasing penetration of distributed energy resources in distribution networks, Volt-VAR control and optimization (VVC/VVO) have become very important to ensure an acceptable quality of service to all customers. System operators can rely on slow-responding utility devices, including capacitor banks and on-load tap changing transformers, along with fast-responding battery and photovoltaic (PV) inverters for the VVC/VVO implementation. Because of variations in response time of these two classes of devices, and different control actions (discrete versus continuous), coordinated and optimal scheduling and operation have become of utmost importance. Here, this paper develops a look-ahead deep reinforcement learning (DRL)-based multi-objective VVO technique to improve the voltage profile of active distribution networks, decrease network and inverter power loss, and save the operational cost of the grid. It proposes a deep deterministic policy gradient (DDPG)-based approach to schedule the optimal reactive and/or active power set-points of fast-responding inverters, and a deep Q-network (DQN)-based DRL agent to schedule the discrete decisions variables of slow-responding assets. The reactive power output of PV and battery smart inverters are scheduled at 30-minute intervals and the capacitors’ commitment status is scheduled with several hour intervals. The proposed framework is validated on the modified IEEE 34-bus and 123-bus test cases with embedded PV and PV-plus-storage. To validate the efficacy of the proposed VVO, it is compared with several scenarios, including the base case without VVO, localized droop control of DERs, DDPG-only, and twin delayed DDPG (TD3) agent-based DRL techniques. The results justify the superior performance of the proposed method to improve the voltage profile, reduce network power loss, and minimize the look-ahead grid operational cost while minimizing the undesirable power losses in inverters as a result of power factor adjustments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Surface-to-Interior Transport Timescales and Ventilation Patterns in a Time-Dependent Circulation Driven by Sustained Climate Warming

Abstract The effect of climate warming in response to rising atmospheric CO 2 on the ventilation of the ocean remains uncertain. Here we make theoretical advances in elucidating the relationship between ideal age and transit time distribution (TTD) in a time-dependent flow. Subsequently, we develop an offline tracer-transport model to characterize the ventilation patterns and time scales in the time-evolving circulation for the 1850–2300 period as simulated with the Community Earth System Model version 1 (CESMv1) under a business-as-usual warming scenario. We found that by 2300 2.1% less water originates from the high-latitude deep water formation regions (both hemispheres) compared to 1850. In compensation, there is an increase in the water originating from the subantarctic. We also found that slowing meridional overturning circulation causes a gradual increase in mean age during the 1850–2300 period, with a globally averaged mean-age increase of ∼110 years in 2300. Where and when the water will be re-exposed to the atmosphere depends on the post-2300 circulation. For example, if we assume that the circulation persists in its year-2300 state (scenario 1), the mean interior-to-surface transit time in year 1850 is ∼1140 years. In contrast, if we assume that the circulation abruptly recovers to its year-1850 state (scenario 2), the mean interior-to-surface transit time in 1850 is only ∼740 years. By 2300, these differences become even larger; in scenario 1, the mean interior-to-surface transit time increases by ∼200 years, whereas scenario 2 decreases by ∼80 years. The dependence of interior-to-surface transit time on the future ocean circulation produces an additional unavoidable uncertainty in the long-term durability of marine carbon dioxide removal strategies. Significance Statement The ocean’s circulation, when altered by climate warming, can affect its capacity to absorb heat and CO 2 , which are crucial for the global climate. In our study, we investigated how global warming, caused by rising CO 2 levels, might impact the ocean circulation—the way water moves from deep ocean to the surface and vice versa. We discovered that by 2300, if we continue on our current warming trajectory, the origins of water within the ocean will shift, with less coming from deep, cold zones near the poles and more from subantarctic regions. As a result, deep water will take longer time before it resurfaces than shallow water. How quickly this water travels from deep regions to the surface could change, depending on the state of future ocean circulation. If the circulation remains as predicted in 2300, this journey will take longer. Conversely, if it reverts to the pattern in 1850, the process will be quicker. This variability introduces added uncertainty to strategies aimed at mitigating climate change by storing CO 2 in the ocean. Our work highlights the intricate ways in which climate change can influence our oceans, potentially affecting our plans to mitigate global warming.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Increasing evapotranspiration decouples the positive correlation between vegetation cover and warming in the Tibetan plateau

Plant growth generally responds positively to an increase in ambient temperature. Hence, most Earth system models project a continuous increase in vegetation cover in the future due to elevated temperatures. Over the last 40 years, a considerable warming trend has affected the alpine ecosystem across the Tibetan Plateau. However, we found vegetation growth in the moderately vegetated areas of the plateau were negatively related to the warming temperatures, thus resulting in a significant degradation of the vegetative cover (LAI: slope = -0.0026 per year, p < 0.05). The underlying mechanisms that caused the decoupling of the relationship between vegetation growth and warming in the region were elaborated with the analysis of water and energy variables in the ecosystem. Results indicate that high temperatures stimulated evapotranspiration and increased the water consumption of the ecosystem (with an influence coefficient of 0.34) in these degrading areas, significantly reducing water availability (with an influence coefficient of -0.68) and limiting vegetation growth. Moreover, the negative warming effect on vegetation was only observed in the moderately vegetated areas, as evapotranspiration there predominantly occupied a larger proportion of available water (compared to the wet and highly vegetated areas) and resulted in a greater increase in total water consumption in a warmer condition (compared to dry areas with lower levels of vegetation cover). These findings highlight the risk of vegetation degradation in semi-arid areas, with the degree of vulnerability depending on the level of vegetation cover. Furthermore, results demonstrate the central role of evapotranspiration in regulating water stress intensity on vegetation under elevated temperatures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Toward Drilling the Perfect Geothermal Well: An International Research Coordination Network for Geothermal Drilling Optimization Supported by Deep Machine Learning and Cloud Based Data Aggregation

The EDGE project, supported by the U.S. Department of Energy Geothermal Technologies Office under award DE-EE0008793, established a data-driven framework for improving the efficiency, cost-effectiveness, and reliability of geothermal well drilling. The project focused on developing scalable data infrastructure, advanced machine learning and probabilistic models, and integrated analytics tools to support continuous drilling optimization. A central objective was to reduce geothermal drilling costs by up to seventy percent while minimizing the risk of well failure through predictive diagnostics and adaptive planning. Over the project period, a comprehensive data repository was designed and deployed, incorporating records from over one hundred geothermal wells across varied geological settings. This repository supported both structured and unstructured data and adhered to FAIR data principles, enabling provenance tracking, quality control, and standardized metadata. The project introduced automated ingestion pipelines and a cloud-hosted platform that facilitated access to raw, processed, and derived datasets. This infrastructure served as the foundation for model development and analysis. Machine learning workflows were developed to predict key drilling metrics including rate of penetration, non-productive time, and total drilling costs. Self-organizing maps and dimensionality reduction methods were used to uncover operational patterns and outliers, while supervised learning algorithms such as random forests and deep neural networks were applied to forecast performance outcomes. The models were validated on heterogeneous datasets from both U.S. and Icelandic fields, demonstrating variable but significant predictive accuracy. The results indicated that finer temporal resolution, inclusion of lithological data, and consistency in operational annotations could substantially improve model performance. The project also implemented process mining techniques to reconstruct state-transition models from drilling event logs. These models enabled the identification of deviations from optimal workflows and provided insights into recurring failure modes. Analysis of non-productive time highlighted the impact of equipment failures, geological challenges, and human factors, offering opportunities for targeted mitigation strategies. The EDGE Dashboard was developed as a web-based expert system integrating data visualization, model outputs, and user-driven queries. It provided an accessible interface for operators to explore historical data, evaluate predicted outcomes, and compare drilling scenarios. Initial feedback from project partners suggested that the dashboard could serve as a foundation for more advanced advisory and optimization tools. Overall, the EDGE project demonstrated the feasibility and value of applying modern data science techniques to geothermal drilling. It delivered a set of interoperable tools and models that can support more efficient, lower-risk well development. The findings point toward a viable path for transitioning from advisory analytics to semi-autonomous drilling systems, contingent on continued collaboration, expanded datasets, and field validation. The project results have immediate relevance for drilling operations, data management practices, and future geothermal R&D efforts aimed at achieving reliable, cost-competitive geothermal energy at scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2025

Compact in situ probe for magnetotransport measurements of 2D materials under variable tensile strain

The recent development of freestanding oxide membranes has opened new opportunities for strain engineering of transition metal oxides beyond values accessible in bulk samples. While a number of studies have been performed with fixed strain, the ability to dynamically control the strain state during measurement would be greatly enabling. To this end, we present an in situ uniaxial strain probe optimized for transport measurements of tensile-strained 2D or quasi-2D materials down to 2 K. Utilizing a flexible polyimide substrate as the stress transfer medium, our platform simplifies the sample preparation process and allows precise alignment of strain fields relative to the crystalline axes. An in situ optical microscope monitors the macroscopic strain state operando and makes it possible to complete an entire magnetotransport study at cryogenic temperatures under continuous strain variations. We demonstrate the capabilities of the probe on a freestanding LaNiO 3 membrane, where we induce tensile strain up to 8% and observe a corresponding strong transport anisotropy. In view of the rapid developments in low-dimensional materials synthesis and the plethora of novel quantum phenomena they exhibit, this strain probe provides general instrumentation for examining and controlling these properties via strain.

2D materials↗
DOE OSTI2020

Doubling of U.S. Population Exposure to Climate Extremes by 2050

We quantify historical and projected trends in the population exposure to climate extremes as measured by the United States National Center for Environmental Information Climate Extremes Index (CEI). Based on the analyses of the historical observations, we find that the U.S. has already experienced a rise in the occurrence of aggregated extremes in recent decades, consistent with the climate response to historical increases in radiative forcing. Additionally, we find that exposure can be expected to intensify under the Representative Concentration Pathway 8.5, with all counties permanently exceeding the baseline variability in the occurrence of extreme hot days, warm nights, and drought conditions by 2050. As a result, every county in the U.S. is projected to permanently exceed the historical CEI variability (as measured by one standard deviation during the 1981–2005 period). Based on the current population distribution, this unprecedented change implies a yearly exposure to extreme conditions for one in every three people. We find that the increasing trend in exposure to the aggregated extremes is already detectable over much of the U.S., and particularly in the central and eastern U.S. The high correspondence between the pattern of trends in our simulations and observations increases confidence in the projected amplification of population exposure to unprecedented combinations of extreme climate conditions, should greenhouse gas concentrations continue to escalate along their current trajectory.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Neighborhood sociome factors and pediatric asthma exacerbations: Protective role of tree crown density and importance of pharmacy access in Chicago's south side

Abstract Background Pediatric asthma exacerbations remain a critical public health concern, particularly in historically underserved urban settings. Objective This study investigates sociome factors—the social context of disease—associated with asthma exacerbations among children living in Chicago's South Side, leveraging clinical and publicly available generalizable census tract‐level datasets from agencies including ChiVes, the City of Chicago Data Portal, EPA, Census Bureau, HUD, NOAA, and more. The aim is to uncover novel hypotheses for potential new interventions. Methods A generalized linear model assessed associations with the outcome of asthma exacerbations while accounting for clustering at the patient level. Predictors included all variables from the Sociome Data Commons, including social, environmental, behavioral, economic, housing, and school variables. Results Predictors of decreased risk included patient age (+4.8 years, −22%), tree crown density (+6% coverage, −17%), parks per acre (+0.41, −8%), and labor market engagement (+0.8 points, −9%). Conversely, predictors of increased risk included increased distance to the nearest pharmacy (+0.28 miles, +12%), limited English skills (+2.3%, +10%), higher inequality (+0.08 points, +8%), and visits in the Spring (+11%) and Fall (+20%). Conclusion The results suggest that tree crown density, a novel finding in the context of asthma exacerbations, may play a protective role. Limited access to health care facilities such as pharmacies continues to complicate care. Clinical Implications These findings provide hypotheses for future interventions for long‐standing asthma disparities.

Allergy↗
DOE OSTI2022

Cooling Egypt: Cost and benefits of room air conditioner efficiency improvement in Egypt

Egypt is one of the largest countries in the Middle East and North Africa region with a population of a total of 106 million (as of July 2022). Increasing urbanization, population growth, and increasing temperatures will likely continue to drive demand for electricity from air conditioning, with corresponding negative impacts on the peak load, air quality, and greenhouse gas emissions from refrigerant use and energy consumption. Since 2003, Egypt has adopted a standards and labeling program for various appliances, and has improved its room air conditioner (RAC) energy efficiency requirements in 2016 and 2017. However, its market is still dominated by inefficient fixed-speed drive (FSD) RACs due to a price-sensitive market. Seasonal metrics such as Cooling Season Performance Factor (CSPF) measure the energy efficiency of FSD and variable-speed drive (VSD) RACs on the same scale. Leading markets, most notably China, have already adopted a new MEPS in 2020 and 2022, which align largely with the United for Efficiency (U4E) Model Regulations Guidelines. India has also adopted a CSPF metric. Both India and China are now experiencing (1) increased demand and consumption of higher efficiency VSD RACs, as well as (2) a corresponding drop in prices from the economies of scale of production of these higher efficiency RACs. Because China is a leading manufacturer in the global air conditioner (AC) market, its adoption of the model U4E regulations have effectively raised the energy efficiency of the ACs in the global market. To achieve the benefits of taking a similar approach to Egypt’s market, we offer the following recommendations: 1. Adopt a common metric to measure the efficiency of both FSD and VSD RACs 2. Raise the stringency of MEPS to the level of the U4E Model Regulation Guidelines 3. Restructure the labeling program from the “A++” labeling scheme to a simpler A through E or a star-rating program 4. Strengthen the compliance regime governing the Egyptian AC MEPS 5. Assist small and medium AC manufacturers in Egypt to transition manufacturing lines to produce low-cost, high-efficiency VSD RACs. Adopting these recommendations would reduce Egypt’s electricity consumption to 11 TWh in 2030 and 89 TWh in 2050—with corresponding carbon dioxide (CO2) emissions reductions and avoided generation capacity requirements compared to the baseline.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Advanced Solar and Load Forecasting Incorporating HD Sky Imaging (Phase III)

Due to rapidly changing sky conditions, the available solar irradiance for energy generation is subject to wide swings in amplitude. As the market penetration of solar energy continues to increase rapidly, these variations in solar power generation are beginning to have an impact on grid stability and increased wear on power switches. Solar power forecasting plays a critical role in operations for Independent System Operators (ISOs) and utilities. Accurate forecasts help maintain grid reliability, optimize generation from renewables, and reduce operating costs. Of particular interest to the ISOs and utilities are sudden changes in solar irradiance, termed “ramp events,” due to the movement of clouds. One significant impact of ramp events on the grid is additional ancillary service requirements necessary to manage such variability. Ramp events can also cause voltage fluctuations in the distribution grids and trigger actions of automated line equipment (e.g., tap changers), leading to additional maintenance costs. In high penetration solar regions, forecasts must be made for both transmission-connected and distribution-connected resources – either behind the meter or in front of it. Particularly for distributed solar energy resources, forecasting can be a challenge due to the lack of visibility of the energy resource. Brookhaven National Laboratory (BNL) has been working towards nowcasting Global Horizontal Irradiance (GHI) using low-cost technologies for several years now. The Solar NowCasting technology being developed by BNL is a 0 – 30 min solar “nowcasting” technology applicable to a scale covering both large generating facilities and residential, distributed solar installations that relies on a network of ground-based high-definition (HD) cameras and surface pyranometers. GHI forecasts are being produced for both regions using 8 ground-based HD cameras and at least 2 surface pyranometers. When stitched together, the camera images can be used to forecast the impact of clouds on available solar irradiance over a domain of ~50 km 2 . Phase I of the project was the engineering scale up conceptual design stage. Phase II was the initial field test and demonstration, in which the technology was scaled up by a factor of 20 times and successfully demonstrated in eastern Long Island. In Phase III, an additional forecasting network was added in upstate NY and both networks are currently being operated until at least a full year of data is gathered by the Albany network to allow for the collection of sufficient data to evaluate performance against the persistence and smart persistence models using the Solar Arbiter.

14 SOLAR ENERGY↗
DOE OSTI2023

Effects of grain size and small-scale bedform architecture on CO 2 saturation from buoyancy-driven flow

Small-scale (mm-dm scale) heterogeneity has been shown to significantly impact CO 2 migration and trapping. To investigate how and why different aspects of small-scale heterogeneity affect the amount of capillary trapping during buoyancy-driven upward migration of CO 2 , we conducted modified invasion percolation simulations on heterogeneous domains. Realistic simulation domains are constructed by varying two important aspects of small-scale geologic heterogeneity: sedimentary bedform architecture and grain size contrast between the matrix and the laminae facies. Buoyancy-driven flow simulation runs cover 59 bedform architecture and 40 grain size contrast cases. Simulation results show that the domain effective CO 2 saturation is strongly affected by both grain size and bedform architecture. At high grain size contrasts, bedforms with continuous ripple lamination at the cm scale tend to retain higher CO 2 saturation than bedforms with discontinuous or cross lamination. In addition, the “extremely well sorted” grain sorting cases tend to have lower CO 2 saturation than expected for cross-laminated domains. Finally, both a denser CO 2 phase and greater interfacial tension increase CO 2 saturation. Again, variation in fluid properties seems to have a greater effect on CO 2 saturation for cross-laminated domains. This result suggests that differences in bedform architecture can impact how CO 2 saturation values respond to other variables such as grain sorting and fluid properties.

58 GEOSCIENCES↗
DOE OSTI2020

Risk Model for EM Decision Support Toolsets

The Government Office of Accountability (GAO) has published several reports identifying the need for the Department of Energy (DOE) Office of Environmental Management (EM) to address mounting costs for DoE's cleanup program. DOEEM could greatly benefit from independent decision tool-sets/models that would allow them to evaluate options and inform business decision at the enterprise level considering site-specific life cycle costs and system plans. Program goals: Develop a tool-set that enables EM to independently evaluate alternatives, assess outcomes from different contracting strategies, and inform critical decisions for the enterprise. Project goals: Adaption of a Risk Model for integration with complimentary tool-sets for project level decision making. Operational Events: Discrete event model that evaluates operational variables (e.g. capacity, throughput, maintenance constraints) and identify bottlenecks. Identifying and Bounding Project Risk: Identify risks that impact confidence in meeting goals (e.g. production). Life cycle Cost: Evaluate impacts of staffing levels, inventory, and capital investments on life cycle costs. Methods and approach: Monte Carlo Analysis is being executed to generate results: Input derives from Risk Register Data; Tied to Projected and Target Schedules; Incorporates float duration within the model. Assumes associated risk mitigation actions being completed within a timeline of five fiscal years. Metrics include: Confidence in Meeting Production Goal; Confidence in Safety Standards; Confidence in Continuous Operation; Other Metrics can be added as appropriate regarding specific site needs. The adapted risk model can be used as a stand alone decision tool or can be integrated complementary tool-sets (i.e. process and cost models) for project-specific decisions. These support tool-sets can then be integrated with others for site- and complex- level evaluations. Future work includes designing an adaptable and modular framework that would allow integration of multiple tool-sets for holistic and/or targeted evaluation of alternative strategies for decision making that could lead to risk and cost reduction across the enterprise.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Evaluating Entrainment–Mixing Characteristics through Direct Comparisons of Drop Size Distributions Using In Situ Observations from ACE-ENA

Abstract Constraining the impacts of entrainment and associated mixing (i.e., entrainment–mixing) on cloud properties continues to be difficult, partly due to observational uncertainties as well as a lacking consensus of which methodologies for diagnosing entrainment–mixing are most appropriate. This study introduces a novel method to evaluate the presence and degree of inhomogeneous and homogeneous mixing using ∼100 h of in situ observations from a research aircraft over the northeastern Atlantic. Specifically, drop size distributions are compared between regions containing negligible and significant entrainment for select flight legs, making a direct characterization of the degree of homogeneous and inhomogeneous mixing possible. A measure of drop concentration variance is used as a proxy variable to diagnose entrainment–mixing. Results correspond well with entrainment–mixing metrics, showing lower Damköhler numbers where drop size distributions shift toward smaller drop sizes (i.e., inhomogeneous mixing) and greater transition length scales where drop size distributions do not (i.e., homogeneous mixing). Inhomogeneous mixing occurs in most samples from Aerosol and Cloud Experiment in the Eastern North Atlantic (ACE-ENA) (regardless of homogeneous mixing frequencies increasing with increasing spatial resolution from ∼100 to ∼10 m) and is associated with decreased drop size relative dispersion and both greater aerosol and drop concentrations compared with homogeneous mixing. Precipitating clouds have a greater frequency of homogeneous mixing compared with nonprecipitating clouds. The proposed methodology is similarly applied to in situ observations of southeast Pacific stratocumulus, shallow convective clouds over central Oklahoma and low-level clouds over the Southern Ocean. All four locations are primarily dominated by inhomogeneous mixing with minimal variability among each region.

Clouds↗
DOE OSTI2026

Atmospheric Gases as Quantitative Indicators of Urbanization Across Land-Use Gradients

The urban heat island (UHI) effect is a significant phenomenon characterized by higher surface temperatures in cities compared to surrounding rural areas. This change in the Earth's energy balance presents considerable challenges for urban populations, increasing heat exposure, straining energy infrastructure, and exacerbating health vulnerabilities. Quantifying UHI intensity is challenging due to blurred distinctions between urban and rural areas as land-use patterns evolve. To address this challenge, the present study builds on the framework established by Salvador et al. (2025) and investigates the use of atmospheric constituents, specifically volatile organic compounds (VOCs) and inorganic gases, as tracers of urbanization levels. A key improvement in this approach is the inclusion of a designated true rural site for comparison. Field measurements were conducted at two locations in the Southeastern United States during the summer of 2025. Inorganic gases were monitored continuously, while VOC samples were collected biweekly and analyzed using a proton transfer reaction time-of-flight mass spectrometer. The urban site exhibited elevated concentrations of anthropogenic inorganic gases, with carbon monoxide and nitrogen dioxide averaging 878 ppb and 17.99 ppb, respectively. In contrast, the rural site showed lower levels of these pollutants, 796 ppb for carbon monoxide and 12.46 ppb for nitrogen dioxide, while displaying higher concentrations of biogenic VOCs, including isoprene and monoterpenes at 1.24 ppb and 0.08 ppb, respectively. Extended mass spectral analyses revealed distinct chemical differences between the two sites, supporting the observed variability of major gases. Overall, these findings are consistent with our previous research and highlight the value of atmospheric constituents as effective indicators of urbanization, thereby improving our understanding of UHI dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Post-fire time series photos from five sites across the Oak Creek watershed, Washington

This dataset supports a broader study examining wildfire impacts on hydrologic connectivity across 5 sites within the Oak Creek watershed and the resulting biogeochemical impacts. Sites were selected using the Advanced Terrestrial Simulator (ATS) hydrologic model to identify locations with varying groundwater contributions and hydrologic responses across different burn severity scenarios. The Retreat Fire burned from July 23 to August 2, 2024, affecting all five sites. This dataset provides time series game camera photos, while the broader study includes continuous water quality monitoring, biogeochemical sampling of water and soils, precipitation data, and organic matter analysis. The other data types and additional metadata (include site environmental information) can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018020. Because this study is ongoing, this data package will be updated regularly to include newly collected photos. For details on how to navigate data packages generated by this project, see https://data.ess-dive.lbl.gov/portals/PNNLRiverCorridorSFA/About. In addition to a readme, this data package also includes a file-level metadata (FLMD) file that describes each file and a data dictionary (DD) that describes all column/row headers and variable definitions. This dataset is comprised of (1) file-level metadata; (2) data dictionary; (3) field metadata; (4) readme; (5) field protocol; and (6) folders of game camera photos. The game camera photos are organized by site with subfolders by month of collection. The field metadata contains a subset of the information collected that is most relevant to photo-processing. The full set of field metadata can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/3018020. All files are .csv, .pdf, or .jpg.

Burn severity↗
DOE OSTI2026

Distributed optical fibre sensing in physical oceanography: emergence and future prospects

Distributed optical fibre sensing (DOFS) is a technology that enables continuous, real-time measurements of a range of environmental parameters along the length of a fibre optic cable. In this article, we review the recently emerged applications of DOFS techniques in physical oceanography and offer a perspective on the technology's potential for future growth within the field. The introduction of DOFS to physical oceanography is contextualised with a brief history of the technology, which spun off primarily from the use of seafloor-laid optical fibres for telecommunications purposes. The key components and underpinning physics of a DOFS system are outlined and, on their basis, the suite of variables that are observable with DOFS are discussed. The implementation factors of DOFS, which include trade-offs between measurement accuracy and spatio-temporal resolutions and ranges, are also examined. The physical oceanographic applications of DOFS to date are then illustrated with case examples of four distinct DOFS techniques: distributed temperature sensing (DTS), which can provide ocean temperature observations; distributed static strain sensing (DSS) and distributed acoustic sensing (DAS), which are sensitive to temperature, cable strain and strain-associated variables, such as pressure and ocean velocity; and ultra-long-range observations of ocean currents with optical interferometry. The forthcoming prospects of DOFS in physical oceanography are considered, and are proposed to include new fibre optic-based approaches to sense ocean salinity and measure through the water column. We conclude with reflections on the future role of DOFS within the Global Ocean Observing System, and highlight the opportunities provided by the existing world-wide network of seafloor-laid optical fibres.

Naveira Garabato, Alberto C. [Univ. of Southampton↗
DOE OSTI2020

Ink-substrate interactions during 3D printing revealed by time-resolved coherent X-ray scattering

Additive printing techniques are regarded as revolutionary and versatile methods of advanced device manufacturing, stemming from the possibility to pattern materials on a custom-based approach and the potential to create novel microstructures and achieve new functionalities. Despite these advantages, the inherent anisotropy of the printing process is a source of property gradients within the printed materials, often associated with variable and/or poor performance. Up to date, the evolutionary pathways associated with printing have largely remained unaddressed, mainly owing to the difficulty to study the transformations induced in the material during processing. Time-resolved coherent X-ray scattering techniques, such as X-ray photon correlation spectroscopy, enable the in situ study of transient nanoscale and mesoscale states in a large variety of materials, including amorphous ones, by directly accessing the most relevant timescales and length scales of their nanoscale and mesoscale dynamics, self-assembly, and mesostructure evolution. In this work, we conduct in operando studies of continuous-flow direct writing with colloidal inks, focusing on how the ink formulation and ink-substrate interactions affect the processes that determine the macroscopic properties of the printed materials. We find fundamental differences in the ink structural relaxations emerging from the primary colloid properties (monodisperse versus aggregated colloids) and the substrates surface energy and mechanical properties. Our work helps to reveal and quantify the basic science governing the evolution of 3D-printed materials during processing, ultimately improving engineering criteria for the design of printable materials.

36 MATERIALS SCIENCE↗