Earth & Environmental Sciences

Undocumented Orphaned Wells (UOWs) are wells without an operator that have limited or no documentation with regulatory authorities. An estimated 310,000 to 800,000 UOWs exist in the United States (US), whose locations are largely unknown. These wells can potentially leak methane and other volatile organic compounds to the atmosphere, and contaminate groundwater. In this study, we developed a novel framework utilizing a state-of-the-art computer vision neural network model to identify the precise locations of potential UOWs. The U-Net model is trained to detect oil and gas well symbols in georeferenced historical topographic maps, and potential UOWs are identified as symbols that are further than 100 m from any documented well. A custom tool was developed to rapidly validate the potential UOW locations. We applied this framework to four counties in California and Oklahoma, leading to the discovery of 1301 potential UOWs across >40,000 km². We confirmed the presence of 29 UOWs from satellite images and 15 UOWs from magnetic surveys in the field with a spatial accuracy on the order of 10 m. This framework can be scaled to identify potential UOWs across the US since the historical maps are available for the entire nation.

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Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed. Here, we analyzed R_dark variability and its associations with V_cmax and other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative. We found that leaf magnesium and calcium concentrations were more significant in explaining cross-site R_dark than commonly used traits like LMA, N and P concentrations, but univariate trait-R_dark relationships were always weak (r² ≤ 0.15) and forest-specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait-R_dark relationships, accurately predicted cross-site R_dark (r² = 0.65) and pinpointed the factors contributing to R_dark variability. Our findings reveal a few novel traits with greater cross-site scalability regarding R_dark, challenging the use of empirical trait-R_dark relationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimating R_dark, which could ultimately improve process modeling of terrestrial plant respiration.

In the United States, hundreds of thousands of undocumented orphan wells have been abandoned, leaving the burden of managing environmental hazards to governmental agencies or the public. These wells, a result of over a century of fossil fuel extraction without adequate regulation, lack basic information like location and depth, emit greenhouse gases, and leak toxic substances into groundwater. For most of these wells, basic information such as well location and depth is unknown or unverified. Addressing this issue necessitates innovative and interdisciplinary approaches for locating, characterizing, and mitigating their environmental impacts. Our survey of the United States revealed the need for tools to identify well locations and assess conditions, prompting the development of technologies including machine learning to automatically extract information from old records (95%+ accuracy), remote sensing technologies like aero-magnetometers to find buried wells, and cost-effective methods for estimating methane emissions. Notably, fixed-wing drones equipped with magnetometers have emerged as cost-effective and efficient for discovering unknown wells, offering advantages over helicopters and quadcopters. Efforts also involved leveraging local knowledge through outreach to state and tribal governments as well as citizen science initiatives. These initiatives aim to significantly contribute to environmental sustainability by reducing greenhouse gases and improving air and water quality.

This study investigates the enhancement of disposal efficiency for deep geological repositories (DGRs) based on three design factors: decay heat optimization, increased thermal limit of the buffer, and double-layer concept using coupled thermo-hydro-mechanical (THM) numerical simulations. Decay heat optimization is achieved by iteratively emplacing spent nuclear fuels having the maximum and minimum decay heat in a canister. Disposal areas can be reduced by 20 % to 40 % compared to the current reference disposal system in Korea (KRS+) in accordance with the combinations of the three design factors, alleviating challenges in site selection for the DGR. This study additionally identifies an optimal layer spacing of 500 m for the double-layer concept in the viewpoint of the buffer temperature, where thermal interaction between the upper and lower layers nearly disappears. However, determining the ultimate disposal and layer spacing requires engineering judgement, considering not only the thermal performance of the DGR but also various factors such as cost and difficulties of the construction and rock mass stability. DGRs designed with an increased thermal limit of the buffer poses a greater probability of rock mass failure around disposal tunnels and deposition holes due to elevated thermal stresses. Densely arranged heat sources for the DGRs with enhanced disposal efficiency lead to larger temperature increase even at the far-field scale, raising a possibility of thermally driven fracture shear activation with associated hydraulic, mechanical, and seismic changes.

Our ability to predict, control, or design biological function is fundamentally limited by poorly annotated gene function. This can be particularly challenging in non-model systems. Accordingly, there is motivation for new high-throughput methods for accurate functional annotation. Here, we used complementation of auxotrophs and DNA barcode sequencing (Coaux-Seq) to enable high-throughput characterization of protein function. Fragment libraries from eleven genetically diverse bacteria were tested in twenty different auxotrophic strains of Escherichia coli to identify genes that complement missing biochemical activity. We recovered 41% of expected hits, with effectiveness ranging per source genome, and observed success even with distant E. coli relatives like Bacillus subtilis and Bacteroides thetaiotaomicron. Coaux-Seq provided the first experimental validation for 53 proteins, of which 11 are less than 40% identical to an experimentally characterized protein. Among the unexpected function identified was a sulfate uptake transporter, an O-succinylhomoserine sulfhydrylase for methionine synthesis, and an aminotransferase. We also identified instances of cross-feeding wherein protein overexpression and nearby non-auxotrophic strains enabled growth. Altogether, Coaux-Seq's utility is demonstrated, with future applications in ecology, health, and engineering.

Stress changes due to changes in fluid pressure and temperature in a faulted formation may lead to the opening/shearing of the fault. This can be due to subsurface (geo)engineering activities such as fluid injections and geologic disposal of nuclear waste. Such activities are expected to rise in the future making it necessary to assess their short- and long-term safety. Here, a new machine learning (ML) approach to model pore pressure and fault displacements in response to high-pressure fluid injection cycles is developed. The focus is on fault behavior near the injection borehole. To capture the temporal dependencies in the data, long short-term memory (LSTM) networks are utilized. To prevent error accumulation within the forecast window, four critical measures to train a robust LSTM model for predicting fault response are highlighted: (i) setting an appropriate value of LSTM lag, (ii) calibrating the LSTM cell dimension, (iii) learning rate reduction during weight optimization, and (iv) not adopting an independent injection cycle as a validation set. Several numerical experiments were conducted, which demonstrated that the ML model can capture peaks in pressure and associated fault displacement that accompany an increase in fluid injection. The model also captured the decay in pressure and displacement during the injection shut-in period. Further, the ability of an ML model to highlight key changes in fault hydromechanical activation processes was investigated, which shows that ML can be used to monitor risk of fault activation and leakage during high pressure fluid injections.

We conduct coupled thermo-hydro-mechanical modeling of a KBS-3V repository design in crystalline rocks, using data and conditions from the Forsmark in Olkiluoto repository sites in Sweden and Finland. The study focuses on repository performance related to the impact of thermal and hydraulic evolution on the potential for thermal–mechanical damage to underground repository excavations. For the designs and conditions considered at the Forsmark and Olkiluoto repository sites, the simulations show a peak temperature well under the adopted performance target of a 100°C maximum temperature, whereas there is still a high potential for thermal–mechanical damage to the KBS-3V waste deposition holes. The thermal–mechanical damage is much more likely if rock permeability is so low that it delays saturation and swelling of bentonite-clay-based backfill beyond the time for the thermal–mechanical peak, which occurs 50 to 100 years after nuclear waste deposition. We also found that sidewalls of the KBS-3V emplacement tunnels are vulnerable to tensile fracturing due to the combined effect of thermal stressing and backfill swelling. The study highlights a strong interaction between bentonite-based backfill and host rock through capillary suction along with induced rock desaturation. A careful design and selection of the bentonite-clay-based backfill materials for KBS-3V tunnels and deposition holes can facilitate a timely saturation and backfill swelling that in turn can minimize thermal–mechanical damage.

Radionuclide transport in smectite clay barrier systems used for nuclear waste disposal is controlled by diffusion, with adsorption significantly retarding transport rates. While a relatively minor component of spent nuclear fuel, 79Se is a major driver of the safety case for spent fuel disposal due to its long half-life (3.3 × 105 yr) and its low adsorption to clay (KD < 10 L/kg), thus a thorough understanding of Se diffusion through clay is critical for understanding the long-term safety of spent fuel disposal systems. Through-diffusion experiments with tritiated water (HTO, conservative tracer) and Se(VI) were conducted with a well-characterized, purified montmorillonite source clay (SWy-2) under a constant ionic strength (0.1 M) and three different electrolyte compositions: Na+, Ca2+, and a Na + -Ca2+ mixture at pH 6.5 in order to probe the effects of electrolyte composition and interlayer cation composition on clay microstructure, Se(VI) aqueous speciation, and ultimately diffusion. The results were modeled using a reactive transport modeling approach to determine values of porosity (ε), De (effective diffusion coefficient), and KD (distribution coefficient for adsorption). HTO diffusive flux was higher in Ca-montmorillonite (De = 1.68 × 10−10 m2 s−1) compared to Na-montmorillonite (De = 7.83 × 10−11 m2 s−1). This increase in flux is likely due to a greater degree of clay layer stacking in the presence of Ca2+ compared to Na+, which leads to larger inter-particle pores. Overall, the Se(VI) flux was much lower than the HTO flux due to anion exclusion, with Se(VI) flux following the order Ca (De = 1.03 × 10−11 m2 s−1) > Na–Ca (De = 2.12 × 10−12 m2 s−1) > Na (De = 1.28 × 10−12 m2 s−1). These differences in Se(VI) flux are due to a combination of factors, including (1) larger accessible porosity in Ca-montmorillonite due to clay layer stacking and smaller electrostatic effects compared to Na-montmorillonite, (2) larger accessible porosity for neutral-charge CaSeO4 species which makes up 32% of aqueous Se(VI) in the pure Ca system, and (3) possibly higher Se(VI) adsorption for Ca-montmorillonite. Through a combination of experimental and modeling work, this study highlights the compounding effects that electrolyte and counterion compositions can have on radionuclide transport through clay. Diffusion models that neglect these effects are not transferable from laboratory experimental conditions to in situ repository conditions.