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Quantum computing research and development is growing worldwide; yet the energy and physical resource demands of future quantum-accelerated data centres are unknown. Planning for quantum computing requires strong collaboration between research communities across engineering, physics, environmental sciences, economics, policy, and energy systems and scenario modelling.
Hydrogen is a versatile resource with critical roles in decarbonization, industrial manufacturing, and energy integration. However, most hydrogen today is produced from fossil fuels, resulting in high emissions and energy consumption. Although low-carbon hydrogen production methods, such as steam methane reforming with carbon capture and renewable-powered electrolysis, are advancing, their high costs hinder large-scale deployment. Identifying alternative pathways for producing low-cost, low-emission hydrogen is therefore essential. Geologic hydrogen, referring to natural and stimulated hydrogen generated in the Earth's subsurface, has attracted growing attention as a potential source of sustainable, economically viable, and environmentally favorable hydrogen. This paper provides a comprehensive review of geologic hydrogen, covering its resource potential, origins, migration and trapping mechanisms, exploration techniques, production strategies, and pipeline transportation. It also identifies key knowledge gaps and proposes a roadmap for future research. The review indicates that geologic hydrogen has vast resource potential and can leverage existing subsurface technologies and geophysical exploration methods. However, major challenges persist, including uncertain hydrogen generation rates, limited understanding and control of serpentinization processes, costly transportation infrastructure, the lack of validated techno-economic analysis, and potential social and environmental issues. As the field is still in its early stages, progress will require interdisciplinary collaboration spanning geoscience, engineering, economics, environmental science, and policy and regulation.
Achieving tunable controls on the adsorption and self-assembly of nanoscale building blocks at immiscible fluid interfaces is essential for synthesizing advanced materials, stabilizing emulsions, and the sustainable storage and recovery of fluids. Although extensive efforts have been directed toward resolving the assembly of spherical nanoparticles at water–hydrocarbon interfaces, 2D nanoparticles have received far less attention. In this study, we developed novel controls to direct the adsorption and assembly of 2D laponite nano-discs (diameter = ∼30 nm and thickness = ∼1 nm) at water–heptane interfaces using traces of sodium dodecyl sulfate (SDS) surfactant, demonstrated by advanced in situ small-angle X-ray scattering, atomic force microscopy, spinning drop tensiometer measurements and molecular dynamics simulations. The results show that SDS surfactant displaces the adsorbed laponite nano-discs from the interface toward the aqueous phase. The extent of this displacement increases with SDS concentration such that high SDS concentrations convert laponite-rich interfaces to SDS-rich interfaces free of laponite nano-discs. This transformation is associated with significant alterations in the rheological and nanomechanical properties of the host interface. In this context, reduction in interfacial tension and interfacial stiffness and an increase in the interfacial deformation is observed on increasing the SDS concentration from 0 to 0.1 wt%. The displacement of laponite nano-discs from the interface is driven by strong electrostatic interactions between the hydrophilic SDS group and interfacial water molecules. These studies unlock new insights into the adsorption and assembly of 2D nanoscale particles at water–hydrocarbons interfaces that are relevant for various applications related to energy and environmental science and engineering.
Natural disasters have increased in the United States in recent decades. At the same time, there has been a shift in population away from the states in the Northeast and Midwest to areas in the Sun Belt, many of which face increased risks from natural disasters. Spatial equilibrium theory predicts that households trade off risk for income in making location decisions. This study estimates a spatial equilibrium model of household location choice to understand these trade-offs. The results show that households require as much as 0.40% of annual household income to endure an additional disaster over the course of a decade. They also show that these values differ substantially depending on household skill level with higher-skill, higher-income households willing to pay three times more in annual income to avoid an additional natural disaster. Furthermore, these results have important implications for policymakers thinking about climate change adaptation and environmental justice.
Many cities are experiencing increases in extreme heat because of global temperature rise combined with the urban heat island effect. The heterogeneity of urban morphology also leads to fine-scale variability in potential for heat exposure. Yet, how this rise in temperature and local variability together impacts urban residents differently at exposure-relevant scales is still not clear. Here we map the Universal Thermal Climate Index, a more complete indicator of human heat stress at an unprecedentedly fine spatial resolution (1 m), for 14 major cities in the United States using urban microclimate modeling. We examined the different heat exposure levels across different socioeconomic and racial/ethnic groups in these cities, finding that income level is most consistently associated with heat stress. We further conducted scenario simulations for a hypothetical 1 °C increase of air temperature in all cities. Results show that a 1 °C increase would have a substantial impact on human heat stress, with impacts that differ across cities. The results of this study can help us better evaluate the impact of extreme heat on urban residents at decision-relevant scales.
This study conducted a comparative proteomic analysis to identify potential genetic markers for the biological function of chemolithoautotrophic iron oxidation in the marine bacterium Ghiorsea bivora. To date, this is the only characterized species in the class Zetaproteobacteria that is not an obligate iron-oxidizer, providing a unique opportunity to investigate differential protein expression to identify key genes involved in iron-oxidation at circumneutral pH. Over 1000 proteins were identified under both iron- and hydrogen-oxidizing conditions, with differentially expressed proteins found in both treatments. Notably, a gene cluster upregulated during iron oxidation was identified. This cluster contains genes encoding for cytochromes that share sequence similarity with the known iron-oxidase, Cyc2. Interestingly, these cytochromes, conserved in both Bacteria and Archaea, do not exhibit the typical β-barrel structure of Cyc2. This cluster potentially encodes a biological nanowire-like transmembrane complex containing multiple redox proteins spanning the inner membrane, periplasm, outer membrane, and extracellular space. The upregulation of key genes associated with this complex during iron-oxidizing conditions was confirmed by quantitative reverse transcription-PCR. These findings were further supported by electromicrobiological methods, which demonstrated negative current production by G. bivora in a three-electrode system poised at a cathodic potential. This research provides significant insights into the biological function of chemolithoautotrophic iron oxidation.
Drought effects are pervasive in terrestrial ecosystems, yet there is limited understanding of how drought impacts the transformation of plant carbon (C) inputs to mineral-associated organic matter (MAOM)—the largest and slowest-cycling pool of soil organic carbon (SOC). In a 12-week 13 C-CO 2 greenhouse labeling experiment, we tracked the formation of MAOM derived from the two dominant sources of plant C input to the mineral soil—living root inputs ( 13 C-rhizodeposits) and decaying root inputs ( 13 C-root detritus)—under normal moisture and droughted conditions in a semiarid grassland soil. At the end of the 12-week period, we also measured the persistence of 13 C-MAOM formed from rhizodeposits versus root detritus via a subsequent persistence assay. Drought reduced the formation of MAOM derived from living roots by decreasing rhizodeposits, reducing microbial growth rates, and altering the composition of organic matter, lipids, and metabolites. Drought initially delayed the formation of MAOM derived from root detritus by slowing the early stages of root litter decomposition (week 4–8), but did not decrease total MAOM formation by the end of the 12-week period. Notably, drought enhanced the persistence of MAOM derived from root detritus, but did not influence the persistence of MAOM derived from rhizodeposits. Our results provide some of the first direct evidence that drought can reduce the formation of MAOM in a grassland soil, but may enhance its persistence, based on the source of plant input from which MAOM is derived.
In May 2025, nearly 700 participants from all around the world coalesced at 10 regional nodes and a few satellite nodes to take part in a global hackathon of kilometer-scale (horizontal grid spacing < 10 km) regional and global Earth system models. Exciting science is emerging from these efforts, ranging across novel model analysis, new ways of integrating with satellite data, and emulation with machine learning. New technologies were trialed that enable the community to work in new and complementary ways to democratize access to global information at a local scale from a set of the world’s highest-resolution climate models. The hackathon demonstrated how exascale data can be organized to be accessible to anyone. Fundamentally, the community could apply these techniques and technologies to move toward more participative models for coproduction and delivery of diverse sources of climate information for climate scientists and citizens alike.
NOTE: The manuscript associated with this data package is currently in review. The data/scripts may be revised based on reviewer feedback. Upon manuscript acceptance, this data package will be updated with the final scripts and additional metadata. This data package is associated with the publication “Revisiting controls on hyporheic respiration with knowledge-guided machine learning at continental scale” submitted to Environmental Science & Technology (Zheng et al. 2026). The project combines mechanistic process modeling with knowledge-guided machine learning (KGML) to evaluate how organic matter chemistry, microbial biomass, and physical substrate accessibility regulate realized respiration rates across river corridors. All data used in this paper have been previously published and can be accessed at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719 (Goldman et al., 2020). This data package contains 3 R-markdown (Rmd) preprocessing scripts for the previously published data and subsequent modelling workflows. The full workflow with input and output data can be found in the associated GitHub repository at https://github.com/jianqiuz/KGML-WHONDRS.
This dataset has results and the model associated with the publication Ciulla et al., (2024). It contains a U-Net semantic segmentation model (unet_model.h5) and associated code implemented in tensorflow 2.0 for the model training and identification of oil and gas well symbols in USGS historical topographic maps (HTMC). Given a quadrangle map (7.5 minutes), downloadable at this url: https://ngmdb.usgs.gov/topoview/, and a list of coordinates of the documented wells present in the area, the model returns the coordinates of oil and gas symbols in the HTMC maps. For reproducibility of our workflow, we provide a sample map in California and the documented well locations for the entire State of California (CalGEM_AllWells_20231128.csv) downloaded from https://www.conservation.ca.gov/calgem/maps/Pages/GISMapping2.aspx. Additionally, the locations of 1,301 potential undocumented orphaned wells identified using our deep learning framework or the counties of Los Angeles and Kern in California, and Osage and Oklahoma in Oklahoma are provided in the file found_potential_UOWs.zip. The results of the visual inspection of satellite imagery in Osage County is in the file visible_potential_UOWs.zip. The dataset also includes a custom tool to validate the detected symbols in the HTMC maps (vetting_tool.py). More details about the methodology can be found in the associated paper: Ciulla, F., Santos, A., Jordan, P., Kneafsey, T., Biraud, S.C., and Varadharajan, C. (2024) A Deep Learning Based Framework to Identify Undocumented Orphaned Oil and Gas Wells from Historical Maps: a Case Study for California and Oklahoma. Accepted for publication in Environmental Science and Technology. The geographical coordinates provided correspond to the locations of potential undocumented orphaned oil and gas wells (UOWs) extracted from historical maps. The actual presence of wells need to be confirmed with on-the-ground investigations. For your safety, do not attempt to visit or investigate these sites without appropriate safety training, proper equipment, and authorization from local authorities. Approaching these well sites without proper personal protective equipment (PPE) may pose significant health and safety risks. Oil and gas wells can emit hazardous gasses including methane, which is flammable, odorless and colorless, as well as hydrogen sulfide, which can be fatal even at low concentrations. Additionally, there may be unstable ground near the wellhead that may collapse around the wellbore. This dataset was prepared as an account of work sponsored by the United States Government. While this document is believed to contain correct information, neither the United States Government nor any agency thereof, nor the Regents of the University of California, nor any of their employees, makes any warranty, express or implied, or assumes any legal responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by its trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or the Regents of the University of California. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof or the Regents of the University of California.
In December 2020 and March 2021, EPA issued two MS4 strawman permit drafts specific to the Los Alamos area. EPA made text changes in the 2 nd strawman permit based on stakeholder comments related to the initial draft. During the public meeting on the Los Alamos area designation decision held on February 13, 2024, EPA staff stated that comments from the past strawman permits would be incorporated into the new MS4 permit to be issued state-wide. However, it does not appear that any of the text changes from the 2 nd Los Alamos area strawman permit were included in this state-wide general MS4 strawman permit. For reference, the 2 nd Los Alamos area revised strawman permit is included with these comments as Attachment 2. Many of the following comments refer to or state EPA’s updated text included in the 2 nd Los Alamos area strawman permit. The updated text from this 2 nd strawman permit that are referenced in the following comments should be applicable state-wide and it is suggested that this past work be reconsidered and incorporated.
Our project sought to explore the ability of seaweed (Ulva lactuca) farms to clean polluted waterways of excess nitrogen and phosphorus through bioremediation. We addressed this goal in three primary ways. First, we worked with an undergraduate student from the Environmental Sciences Department at SDSU (Emily Bews) to examine how Ulva would perform under elevated nutrients and decreased salinity conditions, such as would be expected on the seaweed farms during periods of high rainwater runoff. This would show whether Ulva could indeed be grown on farms during these periods. We conducted laboratory experiments at SDSU’s CMIL (marine laboratory) using orthogonal combinations of two salinities and three nutrient loadings, and measured several aspects of Ulva physiology, namely growth, photosynthetic rates, chlorophyll fluorescence, and stable isotope analyses of Ulva’s tissues, and tissue uptake of phosphorus and nitrogen. Our results clearly show Ulva is an ideal candidate for using on farms during periods of heavy rains and takes up excess nutrients. The results of this were published in Marine Pollution Bulletin, with undergraduate Bews as lead author (Bews, E., L. Booher, T. Polizzi, C. Long, J-H Kim, MS Edwards. 2021. Effects of salinity and nutrients on metabolism and growth of Ulva lactuca: implications for bioremediation of coastal watersheds. Marine Pollution Bulletin 166: 121299.).
Los Alamos National Laboratory (LANL) operates seven mesa-top instrumented meteorology towers: Technical Area (TA) 6, TA-49, TA-53, TA-54, TA-63, TA-54B, and TA-16B. An additional instrumented tower is located in Mortandad Canyon (TA-5 MDCN), and there is a rain gauge at North Community (NCOM), located within the town of Los Alamos. The 10 meter (m) towers at TA-63, TA-54B, and TA-16B have been in testing since they were installed in 2021, and will be included in a future data completeness report. A description of the meteorology monitoring network, prior to the installation of the TA-63, TA-54B, and TA-16B is found in Dewart and Boggs (2014). Four of the mesa-top towers (e.g., TA-6, TA-49, TA-53, and TA-54) are instrumented at the 1.2 m, 11.5 m, 23 m, and 46 m levels. In addition, the TA-6 tower is instrumented at the 92 m level. The TA-5 MDCN tower is 10 m in height and is instrumented at 1.2 m and 10 m. Data are collected and averaged every 15 minutes. Range checking is performed on each measurement every 15 minutes; data that are beyond normal ranges are eliminated from the data set and replaced by a code for missing data. In addition, data are reviewed weekly by qualified meteorologists to identify bad data not identified by the range checking technique. The data steward eliminates these data from the data set and replaces them with a code for missing data. The instrument technicians also review that data and schedule instrument replacement, as required. All instruments are calibrated at a frequency that meets the criteria identified in ANSI/ANS-3.11-2015. Data completeness is determined by the number of total 15-minute records available versus the number of possible measurements for the entire year. As a rule, the meteorologists do not attempt to estimate data that are eliminated as bad data. Original datalogger records, including bad data, can be recalled from program archival storage.
The DOE Meteorological Coordinating Council (DMCC) conducted an initial LANL Meteorological (Met) Program Assist Visit in August 2006 (DMCC 2006). A follow-up assist visit in August 2015 assessed progress (DMCC 2015), and in June 2023, the DOE Meteorological Subcommittee (DMSC), successor to the DMCC, conducted a second follow-up assist visit (DMSC 2023), in which the Met Program was evaluated relative to the following 8 high-level questions.
A presentation given at WTAMU on 04/17/2025 about the Environmental Remediation at Pantex.
An aquifer pumping test was conducted on the Lost Lake Aquifer Zone (LLAZ) at the recently installed recovery well RWM001R in accordance with the approved test plan (Dixon, 2024). The objective of the testing was to determine baseline well performance parameters and aquifer hydraulic conductivity. This testing consisted of two step-drawdown tests to determine well performance properties and two constant pumping rate aquifer tests with recovery periods to determine aquifer hydraulic properties. Well performance parameters determined included specific capacity, well efficiency, and head loss coefficients. The results of the step-drawdown testing are presented in Tables ES1 and ES2 whereas the results of the constant pumping rate testing are presented in Table ES3. The average specific capacity of RWM001R operating at pump capacity (~54 gpm) was 2.2 gpm/ft using data from the final step of both tests. The average well efficiency determined from both step-drawdown tests was 56% using data from the final step of both tests. For both step-drawdown tests, well efficiency was inversely related to the pumping rate with the average efficiency decreasing from approximately 79% to 56%. Aquifer head loss coefficients from both tests were comparable (Table ES2).
Slug tests were performed on 21 wells screened within the Upper Three Runs aquifer (UTRA) at the Chemical, Metals, and Pesticide (CMP) Pits Operable Unit (OU) to determine unit-specific hydraulic properties. The UTRA is the water table aquifer at CMP Pits and is comprised of three discrete zones that are separated by two semi-continuous confining zones. These zones include the transmissive zone (TZ, unconfined), the middle aquifer zone (MAZ, semi-confined), and the lower aquifer zone (LAZ, semi-confined). The LAZ, the most substantial portion of the UTRA in the area, extends vertically to the green clay confining zone (GCCZ). A subset of wells from each zone was selected for slug testing. The objective of the project was to determine hydraulic properties of the three zones of the UTRA for use in future updates to the CMP pits groundwater flow and contaminant transport modeling.
The National Environmental Policy Act of 1969, as amended (NEPA), mandates that federal agencies assess and document potential environmental impacts before deciding on proposed actions. While significant progress has been made in cataloging and standardizing NEPA documents themselves, the legal challenges that frequently arise from these decisions remain poorly cataloged and largely inaccessible for systematic analysis. Litigation challenging NEPA compliance can substantially delay project timelines, reshape agency decision-making, and establish precedents that influence future environmental reviews — yet no standardized, machine-readable corpus exists that links litigation records to the NEPA projects they contest.