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At least 325 records · Page 18

Anthropogenic uranium signatures in turtles, tortoises, and sea turtles from nuclear sites

Chelonians (turtles, tortoises, and sea turtles) grow scute keratin in sequential layers over time. Once formed, scute keratin acts as an inert reservoir of environmental information. For chelonians inhabiting areas with legacy or modern nuclear activities, their scute has the potential to act as a time-stamped record of radionuclide contamination in the environment. Here, we measure bulk (i.e., homogenized scute) and sequential samples of chelonian scute from the Republic of the Marshall Islands and throughout the United States of America, including at the Barry M. Goldwater Air Force Range, southwestern Utah, the Savannah River Site, and the Oak Ridge Reservation. We identify legacy uranium ( 235 U and 236 U) contamination in bulk and sequential chelonian scute that matches known nuclear histories at these locations during the 20th century. Our results confirm that chelonians bioaccumulate uranium radionuclides and do so sequentially over time. This technique provides both a time series approach for reconstructing nuclear histories from significant past and present contexts throughout the world and the ability to use chelonians for long-term environmental monitoring programs (e.g., sea turtles at Enewetok and Bikini Atolls in the Republic of the Marshall Islands and in Japan near the Fukushima Daiichi reactors).

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

Wetting and drying trends in the land–atmosphere reservoir of large basins around the world

Abstract. Global change is altering hydrologic regimes worldwide, including large basins that play a central role in the sustainability of human societies and ecosystems. The basin water budget is a fundamental framework for understanding these basins' sensitivity and future dynamics under changing forcings. In this budget, studies often treat atmospheric processes as external to the basin and assume that atmosphere-related water storage changes are negligible in the long term. These assumptions are potentially misleading in large basins with strong land–atmosphere feedbacks, including terrestrial moisture recycling, which is critical for global water distribution. Here, we introduce the land–atmosphere reservoir (LAR) concept, which includes atmospheric processes as a critical component of the basin water budget and use it to study long-term changes in the water storage of some of the world's largest basins. Our results show significant LAR water storage trends over the last 4 decades, with a marked latitudinal contrast: while low-latitude basins have accumulated water, high-latitude basins have been drying. If they continue, these trends will disrupt the discharge regime and compromise the sustainability of these basins, resulting in widespread impacts.

54 ENVIRONMENTAL SCIENCES↗

Environmental Impacts of Closed-Loop Pumped Storage Hydropower

The goal of this report is to help license applicants, resource agencies, and other members of the hydropower community involved in closed-loop pumped storage hydropower permitting and licensing process, focus the scope of environmental reviews, and more quickly identify impacts with project nexus and potential mitigation measures for these impacts. Pumped storage hydropower (PSH) is an energy storage technology that uses energy to pump water up from a lower reservoir to an upper reservoir where water is stored until electricity is needed and the water is released to a lower reservoir passing through turbines. Closed-loop PSH—PSH that is not continuously connected to a naturally flowing water feature—is one of the lowest greenhouse gas emitting energy storage technologies and is therefore a critical part of the transition to renewable energy (Simon et al. 2023). Proposals for closed-loop PSH facilities in the United States currently account for more than 40% of original licenses and 99% of potential generation capacity in the Federal Energy Regulatory Commission (FERC) hydropower licensing pipeline (Johnson et al. 2023). While closed-loop PSH facilities can have lower environmental impacts than open-loop PSH facilities, no closed-loop facilities have been constructed in the United States to enable direct accounting of project impacts and efficacy of mitigations. Many proposals for closed-loop PSH submitted to FERC are abandoned early in the permitting and licensing process prior to license applications and environmental assessments, so there is little documentation describing potential project impacts and proposed mitigations. The newness of closed-loop PSH proposals in the United States may mean that tribal, federal, and state agencies with authorities for cultural and natural resources protection and management involved in the FERC licensing process may not have experience with closed-loop PSH regulation. Moreover, many proposed closed-loop PSH facilities are in areas that do not have high concentrations of conventional hydropower, so these agencies may also be unfamiliar with the FERC hydropower licensing process. The goal of this report is to help license applicants, resource agencies, and other members of the hydropower community focus the scope of environmental review for the closed-loop PSH development, licensing, and federal authorization process enabling quicker identification of potential impacts, mitigations, and situations where mitigation may not be possible. We found that environmental impacts of closed-loop PSH are highly site-specific, and generalizations about the types of environmental impacts across closed-loop PSH projects are difficult to make. Environmental impacts of closed-loop PSH are like those for open-loop PSH with a few exceptions including water sourcing, which can lead to delays and contention due to potential complexities with water rights, impacts to aquatic resources, and greenhouse gas emission potential. Cultural resource impacts were commonly reported in National Environmental Policy Act (NEPA) documents reviewed and discussed in interviews, but in many cases such impacts cannot be mitigated.

13 HYDRO ENERGY↗

Produced Water DNA Database (PW-DNA): Utilizing KBase to generate an environmental specific curated molecular database

The deep subsurface is estimated to host the majority of Earth’s microbial biomass yet remains one of the most challenging environments to access and study. One common approach to investigate these microbial communities is through the analysis of produced water from subsurface reservoirs, where researchers can assess water and gas chemistry along with molecular (DNA/RNA) sequence data. Advances in high-throughput sequencing have greatly expanded our understanding of these environments and their biotechnological potential. However, further progress requires large-scale, integrative meta-analyses across diverse datasets. To address this need, we developed the Produced Water-DNA (PW-DNA) Database, a curated, publicly available resource that consolidates microbial DNA/RNA sequences, geochemical data, and relevant metadata from in situ hydrocarbon environments such as coal beds, oil reservoirs, and natural gas systems. The PW-DNA database delivers three core benefits to the research community: (1) it improves data sharing by linking environmental microbial datasets with corresponding geochemical parameters, enabling more robust filtering and analysis; (2) it connects with complementary research databases to promote broader dissemination and interoperability; and (3) it supports technological innovation by serving as a resource for identifying microbial trends and exploring genetic potential. While individual studies have highlighted basin-specific microbial communities and functional redundancy in biogeochemical cycling, a comprehensive, system-wide perspective is needed to better understand connectivity and novelty across subsurface ecosystems. By designing the PW-DNA in the KBase platform, we provide a reproducible, visual framework for integrating large-scale genomic and geochemical data, enabling researchers to perform more informed analyses and experimental design. Ultimately, this resource enhances the ability to identify, characterize, and interpret microbial functions across diverse subsurface environments, thereby accelerating discovery in subsurface microbiology and biotechnology.

59 BASIC BIOLOGICAL SCIENCES↗

wmpy-power: A Python package for process-based regional hydropower simulation

Hydropower is an important source of renewable energy in many parts of the world. The generation potential for a hydropower facility can vary greatly due to fluctuations in precipitation and snowmelt patterns impacting streamflow and reservoir storage. Human activities such as irrigation, manufacturing, and hydration can also influence water availability at nearby and downstream facilities. wmpy-power--the hydropower model described in this work--is process-based, leveraging explicit reservoir storage and release data to address impacts on hydropower from climate change and human adaptive behaviors to inform long-term planning and resource-adequacy considerations.

13 HYDRO ENERGY↗

Establishing an Early CO 2 Storage Complex in Kemper County, Mississippi: Project ECO 2 S (Phase III) (Final Progress Report)

“Establishing an Early CO 2 Storage Complex in Kemper County, Mississippi (Project ECO 2 S)” is an early mover project within the U.S. Department of Energy (DOE) and National Energy Technology Laboratory’s Carbon Storage Assurance Facility Enterprise, or CarbonSAFE, initiative. Phase III of Project ECO2S advanced the foundational work of Phase II, which confirmed the potential for safely, permanently, and economically storing commercial volumes of carbon dioxide (CO 2 ) in the regionally significant saline reservoir system near Mississippi Power Company’s (MPC) Kemper County Energy Facility. The primary objective of Phase III was to complete detailed site characterization necessary to obtain an Underground Injection Control (UIC) Class VI Permit to Construct from the U.S. Environmental Protection Agency. This involved transitioning from regional geologic assessments to high-resolution site-specific analyses.

03 NATURAL GAS↗

Influence of extreme fracture flow channels on the thermal performance of open-loop geothermal systems at commercial scale

Abstract Adequate stewardship of geothermal resources requires accurate forecasting of long-term thermal performance. In enhanced geothermal systems and other fracture-dominated reservoirs, predictive models commonly assume constant-aperture fractures, although spatial variations in aperture can greatly affect reservoir permeability, fluid flow distribution, and heat transport. Whereas previous authors have investigated the effects of theoretical random aperture distributions on thermal performance, here we further explore the influence of permeability heterogeneity considering field-constrained aperture distributions from a meso-scale field site in northern New York, USA. Using numerical models of coupled fluid flow and heat transport, we conduct thermal–hydraulic simulations for a hypothetical reservoir consisting of a relatively impervious porous matrix and a single, horizontal fracture. Our results indicate that in highly channelized fields, most well design configurations and operating conditions result in extreme rates of thermal drawdown (e.g., 50% drop in production well temperatures in under 2 years). However, some other scenarios that account for the risks of short-circuiting can potentially enhance heat extraction when mass flow rate is not excessively high, and the direction of geothermal extraction is not aligned with the most permeable features in the reservoir. Through a parametric approach, we illustrate that well separation distance and relative positioning play a major role in the long-term performance of highly channelized fields, and both can be used to help mitigate premature thermal breakthrough.

Rangel-Jurado, Nicolás↗

Optimal carbon storage reservoir management through deep reinforcement learning

Model-based optimization plays a central role in energy system design and management. The complexity and high-dimensionality of many process-level models, especially those used for geosystem energy exploration and utilization, often lead to formidable computational costs when the dimension of decision space is also large. This work adopts elements of recently advanced deep learning techniques to solve a sequential decision-making problem in applied geosystem management. Specifically, a deep reinforcement learning framework was formed for optimal multiperiod planning, in which a deep Q-learning network (DQN) agent was trained to maximize rewards by learning from high-dimensional inputs and from exploitation of its past experiences. To expedite computation, deep multitask learning was used to approximate high-dimensional, multistate transition functions. Both DQN and deep multitask learning are pattern based. As a demonstration, the framework was applied to optimal carbon sequestration reservoir planning using two different types of management strategies: monitoring only and brine extraction. Both strategies are designed to mitigate potential risks due to pressure buildup. Results show that the DQN agent can identify the optimal policies to maximize the reward for given risk and cost constraints. Finally, experiments also show that knowledge the agent gained from interacting with one environment is largely preserved when deploying the same agent in other similar environments.

15 GEOTHERMAL ENERGY↗

Laboratory and pilot-scale studies of membrane distillation for desalination of produced water from Permian Basin

Substantial quantities of produced water generated during the extraction of oil and gas from unconventional reservoirs present significant environmental concern and increase the operating cost for this industry. Membrane Distillation (MD) can serve as a potential solution for beneficial reuse of produced water by generating high-quality permeate and reducing the volume of produced water requiring disposal. This study investigated treatment of produced water from Permian Basin in both laboratory and pilot-scale studies. Laboratory tests revealed the potential to successfully recover 50% of produced water although some increase in permeate conductivity was observed due to the passage of ammonia from the feed side. Initial pilot-scale test with filtration of raw produced water as the only pre-treatment step led to precipitation of SrSO 4 , NaCl, and Fe in the system once the solubility limits for these salts were exceeded. However, chemical pretreatment that included pH adjustment, aeration, and barite precipitation, allowed successful steady-state operation of the AGMD pilot system for 5 days where the produced water was concentrated from 127 g/L to 255 g/L (~50% water recovery) while recovering high quality permeate. Overall, greater than 99.7 % salt rejection was achieved with the average permeate flux of 1.86 LMH. Finally, mass balance analysis suggested potential Ba and Ca precipitation in the system but there was no impact on AGMD performance and permeate quality during 5 days of continuous operation in the field.

42 ENGINEERING↗

Novel candidate taxa contribute to key metabolic processes in Fennoscandian Shield deep groundwaters

The continental deep biosphere contains a vast reservoir of microorganisms, although a large proportion of its diversity remains both uncultured and undescribed. In this study, the metabolic potential (metagenomes) and activity (metatranscriptomes) of the microbial communities in Fennoscandian Shield deep subsurface groundwaters were characterized with a focus on novel taxa. DNA sequencing generated 1270 de-replicated metagenome-assembled genomes and single-amplified genomes, containing 7 novel classes, 34 orders, and 72 families. The majority of novel taxa were affiliated with Patescibacteria, whereas among novel archaea taxa, Thermoproteota and Nanoarchaeota representatives dominated. Metatranscriptomes revealed that 30 of the 112 novel taxa at the class, order, and family levels were active in at least one investigated groundwater sample, implying that novel taxa represent a partially active but hitherto uncharacterized deep biosphere component. The novel taxa genomes coded for carbon fixation predominantly via the Wood–Ljungdahl pathway, nitrogen fixation, sulfur plus hydrogen oxidation, and fermentative pathways, including acetogenesis. These metabolic processes contributed significantly to the total community’s capacity, with up to 9.9% of fermentation, 6.4% of the Wood–Ljungdahl pathway, 6.8% of sulfur plus 8.6% of hydrogen oxidation, and energy conservation via nitrate (4.4%) and sulfate (6.0%) reduction. Key novel taxa included the UBA9089 phylum, with representatives having a prominent role in carbon fixation, nitrate and sulfate reduction, and organic and inorganic electron donor oxidation. These data provided insights into deep biosphere microbial diversity and their contribution to nutrient and energy cycling in this ecosystem.

Candidatus↗

PSH Assessment and Site Identification [Slides]

NLR's Pumped Storage Hydropower (PSH) geospatial and cost model algorithms are applied to the Chemehuevi Reservation to assess the potential for PSH within the reservation. The algorithm identifies both "open-loop" PSH opportunities formed by constructing a new reservoir within the reservation paired with bordering Lake Havasu and "closed-loop" systems formed by two new reservoirs within the reservation. Options range from 28 to 538 MW of electrical generation power at maximum generation and 10 hours of storage. CAPEX is estimates as 4422 2022 $\$$/kW of generating power, which is meaningfully higher than the lowest cost systems identified in NLR's national scale assessments. Further economic analysis is needed to fully evaluate whether this would be an attractive option to meet the Chemehuevi Reservation's goals.

25 ENERGY STORAGE↗

Surfactant-Aided Low-Salinity Waterflooding for Low-Temperature Carbonate Reservoirs

Carbonate reservoirs tend to be oil-wet/mixed-wet and heterogeneous because of mineralogy and diagenesis. The objective of this study is to improve oil recovery in low-temperature dolomite reservoirs using low-salinity and surfactant-aided spontaneous imbibition. The low-salinity brine composition was optimized using ζ-potential measurements, contact-angle (CA) experiments, and a novel wettability-alteration measure. Significant wettability alteration was observed on dolomite rocks at a salinity of 2,500 ppm. We evaluated 37 surfactants by performing CA, interfacial-tension (IFT), and spontaneous-imbibition experiments. Here, three (quaternary ammonium) cationic and one (sulfonate) anionic surfactants showed significant wettability alteration and produced 43–63% of original oil in place (OOIP) by spontaneous imbibition. At a low temperature (35°C), oil recovery by low-salinity effect is small compared with that by wettability-altering surfactants. Coreflood tests were performed with a selected low-salinity cationic surfactant solution. A novel coreflood was proposed that modeled heterogeneity and dynamic imbibition into low-permeability regions. The results of the “heterogeneous” coreflood were consistent with that of spontaneous-imbibition tests. These experiments demonstrated that a combination of low-salinity brine and surfactants can make originally oil-wet dolomite rocks more water-wet and improve oil recovery from regions bypassed by waterflood at a low temperature of 35°C.

02 PETROLEUM↗

Time-lapse seismic data inversion for estimating reservoir parameters using deep learning

Geologic carbon sequestration involves the injection of captured carbon dioxide ([Formula: see text]) into subsurface formations for long-term storage. The movement and fate of the injected [Formula: see text] plume is of great concern to regulators because monitoring helps to identify potential leakage zones and determines the possibility of safe long-term storage. To address this concern, we design a deep-learning framework for [Formula: see text] saturation monitoring to determine the geologic controls on the storage of the injected [Formula: see text]. We use different combinations of porosities and permeabilities for a given reservoir to generate saturation and velocity models. We train the deep-learning model with a few time-lapse seismic images and their corresponding changes in saturation values for a particular [Formula: see text] injection site. The deep-learning model learns the mapping from the change in the time-lapse seismic response to the change in [Formula: see text] saturation during the training phase. We then apply the trained model to data sets comprising different time-lapse seismic image slices (corresponding to different time instances) generated using different porosity and permeability distributions that are not part of the training to estimate the [Formula: see text] saturation values along with the plume extent. Our algorithm provides a deep-learning assisted framework for the direct estimation of [Formula: see text] saturation values and plume migration in heterogeneous formations using the time-lapse seismic data. Our method improves the efficiency of time-lapse inversion by streamlining the large number of intermediate steps in the conventional time-lapse inversion workflow. This method also helps to incorporate the geologic uncertainty for a given reservoir by accounting for the statistical distribution of porosity and permeability during the training phase. Tests on different examples verify the effectiveness of our approach.

Geochemistry & Geophysics↗

A machine learning model for predicting the minimum miscibility pressure of CO 2 and crude oil system based on a support vector machine algorithm approach

CO 2 enhanced oil recovery (EOR) is a potential way for carbon capture, utilization and storage (CCUS). Though, the effect of CO 2 injection is greatly influenced by the reservoir conditions. Typically, Minimum miscible pressure (MMP) is selected as one of the key parameters for the screening and evaluation of prospective CO 2 flooding. Conventional slim tube test is both accurate and widely accepted but it is inefficient. Existing empirical formulas for MMPs are easy to be used but have been proved inaccurate and unreliable. Machine learning-based methods have great advantages in predicting MMP. However, only predication accuracy is discussed for most models without the screening of the main control factors and further validation of the model reliability. In this paper, a new prediction model based on support vector machine (SVM) was developed for pure/impure CO 2 and crude oil system. This study was based on 147 sets of MMP data from the literature with full information on reservoir temperature, oil composition and gas composition. The main control factors were screened by several statistical methods. Unlike the conventional prediction models that verified by only prediction accuracy, learning curve and single factor control variable analysis are further validated to obtain the optimum model.

02 PETROLEUM↗

Testing the efficacy of active pressure management as a tool to mitigate induced seismicity

Manipulation of subsurface stresses through fluid injection/extraction may induce seismicity through modulation of overpressure along faults. Globally, most industrial operations do not induce significant earthquakes. However, the possibility of inducing moderate to large earthquakes is recognized as a significant risk for basin-scale carbon dioxide (CO 2 ) storage operations (Niass et al., 2017). One possible method to reduce the occurrence of induced seismicity is to maintain only small (or zero) fluid overpressure changes in the subsurface through extraction of formation brine. For active pressure management (APM), operators may employ different procedures including extraction of various brine volumes and different configurations of injection/extraction well locations. However, the impact each these operational procedures on induced seismicity must be understood in order to develop management strategies. Here, we describe a numerical modeling workflow to evaluate the efficacy of APM techniques, that when applied to a specific site may be used to inform operational management strategies. We couple the multiphase reservoir model, NUFT, with the earthquake simulator, RSQSim and simulate seismicity induced by CO 2 injection into a hypothetical brine saturated reservoir. Reservoir overpressure is modified by co-extraction of brine at various fractions of the injected CO 2 volume from five potential wells. Our goal is to understand how the well-pair configuration and the extracted brine volume impact the total number and the maximum event magnitude. Additionally, the performance of each scenario is also evaluated in terms of the volume of CO 2 that can be stored before an earthquake above a tolerance threshold is induced.

58 GEOSCIENCES↗

Occurrence and molecular characterization of Enterocytozoon bieneusi in wild and domestic animal species in Portugal

Abstract The phylum Microsporidia encompasses a diverse group of obligate, intracellular, and spore-forming organisms able to infect a wide range of animal hosts. Among them, Enterocytozoon bieneusi is the most frequently reported species in humans and animals. Little is known about the presence and epidemiology of E. bieneusi in wildlife. We investigated E. bieneusi occurrence and genetic diversity in wild and domestic mammals, through molecular-detection methods, from different regions across Portugal. A total of 756 samples were collected from 288, 242, and 226 wild carnivores, wild ungulates, and domestic animals, respectively. Overall, eight specimens were E. bieneusi-positive (1.1%, 8/756) obtained from five wild (Iberian lynx, Iberian wolf, red fox, stone marten, and wild boar) and one domestic (sheep) host. Nucleotide sequence analysis identified four genotypes of E. bieneusi, Type IV, Wildboar3, BEB6, and PtEbIX. Three of those genotypes belong to Groups 1 (Type IV and Wildboar3) and 2 (BEB6), which are known to contain genotypes capable of infecting a variety of hosts, including humans, highlighting their public health importance. PtEbIX belongs to the dog-specific Group 11. This study represents the first, largest, and most comprehensive molecular-based epidemiology survey carried out in Portugal in wild and domestic animals to date and the first worldwide identification of E. bieneusi in wolf species. Our study showed that wild carnivores and ungulates may act as reservoirs of zoonotic genotypes of E. bieneusi, establishing their role in maintaining the sylvatic cycle of this parasite while representing a potential source of infection for humans and domestic animals.

Figueiredo, Ana M. (ORCID:0000000226236340)↗

Ab initio quantum scattering calculations and a new potential energy surface for the HCl( X 1Σ+)–O2(X3Σg−) system: Collision-induced line shape parameters for O2-perturbed R(0) 0–0 line in H35Cl

The remote sensing of abundance and properties of HCl—the main atmospheric reservoir of Cl atoms that directly participate in ozone depletion—is important for monitoring the partitioning of chlorine between “ozone-depleting” and “reservoir” species. Such remote studies require knowledge of the shapes of molecular resonances of HCl, which are perturbed by collisions with the molecules of the surrounding air. In this work, we report the first fully quantum calculations of collisional perturbations of the shape of a pure rotational line in H35Cl perturbed by an air-relevant molecule [as the first model system we choose the R(0) line in HCl perturbed by O2]. The calculations are performed on our new highly accurate HCl(X1Σ+)–O2(X3Σg−) potential energy surface. In addition to pressure broadening and shift, we also determine their speed dependencies and the complex Dicke parameter. This gives important input to the community discussion on the physical meaning of the complex Dicke parameter and its relevance for atmospheric spectra (previously, the complex Dicke parameter for such systems was mainly determined from phenomenological fits to experimental spectra and the physical meaning of its value in that context is questionable). We also calculate the temperature dependence of the line shape parameters and obtain agreement with the available experimental data. We estimate the total combined uncertainties of our calculations at 2% relative root-mean-square error in the simulated line shape at 296 K. This result constitutes an important step toward computational population of spectroscopic databases with accurate ab initio line shape parameters for molecular systems of terrestrial atmospheric importance.

Chemistry↗

Nanoparticle-Surfactant Stabilized Strong Foam for Enhanced Oil Recovery in High-Salinity Fractured Carbonate Reservoirs

Summary Foam flooding can minimize bypassing in gasfloods in fractured reservoirs. Finding a foam formulation effective in high-salinity brine is challenging, especially with divalent cations, e.g., American Petroleum Institute (API) brine (8% NaCl with 2% CaCl2). When formulating with nanoparticles, the colloidal dispersion stability is difficult due to the dramatic reduction in zeta potential and the Debye length at high salinity. The aim of this work was to develop a strong foam in API brine at the ambient temperature, using a nonionic surfactant and ethyl cellulose nanoparticles (ECNP), for gasflooding in fractured carbonate reservoirs. ECNPs was synthesized and dispersed in API brine using a nonionic surfactant (also denoted as SF). SF and SF/ECNP foams were generated, and their stability was studied at atmospheric pressure and 950 psi. Foam mobility was measured in a sandpack at high pressure. Foam flood experiments were conducted in oil-saturated fractured carbonate cores. The nonionic surfactant proved to be a good dispersion agent for ECNP in API brine. The SF/ECNP mixture stabilized foam in API brine, even in the presence of oil. Injecting a partially miscible gas (below its minimum miscibility pressure) as an SF foam into a fractured core more than doubles the oil recovery over injection of the gas alone. The injection of the strong foam (SF/ECNP) further improves the oil recovery over that of the SF foam, indicating the synergy between ECNP and surfactant. ECNP accumulates in the foam lamella and induces larger pressure gradients in the fracture to divert more gas into the matrix for oil displacement.

Engineering↗