Distributed Sensor-Modulated Transistors for Hydro-Biogeochemical Monitoring
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54 ENVIRONMENTAL SCIENCES↗
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2024 OES-Environmental 2024 State of the Science Report, Chapter 8: Marine Renewable Energy Data and Information Systems
As the marine renewable energy (MRE) sector grows, large amounts of environmental and technical data and information are being collected. When these data and information are openly available, they can be used to guide research and development, inform responsible siting and consenting of projects, and increase stakeholder understanding through transparency. For example, quality environmental data collected during the siting, consenting, construction, operation, and decommissioning of MRE projects can all play key roles in better characterizing baseline conditions, developing effective monitoring and mitigation strategies, and retiring environmental risks through data transferability (see Chapter 6). Ensuring that these data and information are easily discoverable and accessible will help the MRE sector make informed decisions and coexist in an increasingly busy ocean environment.
16 TIDAL AND WAVE POWER↗
Synchrotron-based X-ray fluorescence microscopy mapping the ionome of a toxic freshwater cyanobacterium
Harmful algal blooms (HABs) pose a major environmental concern across the globe. In abundance, cyanobacteria, or so-called green-blue algae can produce extremely dangerous cyanotoxins that harm humans and animals. This study focused on the mapping and distribution of intracellular macro-and micronutrients of the widespread freshwater cyanobacteria Microcystis aeruginosa (M. aeruginosa). Towards a better understanding of trace metal uptake and homeostasis throughout the cell cycle, we quantitatively mapped the spatial distribution of the elements P, K, Fe, Ca, Zn, Mn, and Cu across the ultrastructure of frozen-hydrated single cells using state-of-theart X-ray nanofluorescence imaging at the Advanced Photon Source (APS) at Argonne National Laboratory. Further, bulk cellular nutrient and trace metal content correlated well with the total intracellular elemental content in individual cells obtained by quantitative synchrotron X-ray fluorescence measurements. Multi-dimensional mappings showed P and K atoms colocalized as discrete semicircular hotspots that were analyzed with respect to their stoichiometry. Elevated Cu and Ca concentrations were detected along division plane of cells. P and K were found to have similar spatial elemental distribution with about 65% and 69% of the total cellular P and K, respectively, located at the hotspots. The P and K colocalization were refined further using nanotomography, showing a K envelope surrounding the P core. Inorganic P and organic P compounds were specified using solution-state 31 P nuclear magnetic resonance (NMR) spectroscopy from M. aeruginosa. Of the total extracted P determined by 31 P NMR spectroscopy, 47% were found to be nucleotides while only 11% were polyphosphates. Multimodal X-ray imaging provides a better understanding of intracellular biochemical processes in cyanobacteria, helping us monitor and combat an emerging environmental threat.
31P NMR spectroscopy↗
SPARTAN and IMPROVE Comparison Experiment (SPICE) Interim Campaign Report
SPICE (the SPARTAN and IMPROVE Comparison Experiment) aims to obtain and quantify comparisons between aerosol PM 2.5 mass concentration measurements from the University of Oklahoma (OU) Surface Particulate Matter Network (SPARTAN) station and the U.S. Environmental Protection Agency (EPA) Interagency Monitoring of Protected Visual Environments (IMPROVE) station hosted by the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) User Facility at ARM’s Southern Great Plains (SGP) observatory in Oklahoma. Aerosols—both natural and anthropogenic—affect human populations in multiple ways. Much of ARM’s research focuses on how aerosols influence weather and climate through their optical and radiative effects, as well as their impacts on clouds and precipitation. However, aerosols also pose direct risks to humans and other organisms through inhalation, with the severity of health impacts depending on particle size, chemical composition, and duration of exposure. The IMPROVE network was established by the U.S. EPA to monitor air quality, including visible clarity as well as total and chemically speciated aerosol mass concentrations. The ARM SGP site hosts the IMPROVE SOGP station. Separately, the SPARTAN network operates a globally distributed set of stations similar to IMPROVE but with an emphasis on remote deployment and semi-autonomous operation for use beyond the borders of the United States (IMPROVE only operates within the U.S.). The University of Oklahoma operates a SPARTAN station. To establish confidence in the OU SPARTAN instrumentation and measurement protocol relative to the EPA-certified IMPROVE station, the OU SPARTAN station is currently deployed at SGP in close proximity to the IMPROVE SOGP station. The SPICE campaign was envisioned as a contiguous calendar-year effort for 2025 to capture seasonal variation in mass loading as well as composition. However, independent of the SPICE campaign, the SPARTAN network adopted a new filter construction part-way through the year, interrupting our contiguous data set. Thus, to obtain a contiguous data set with a uniform consistent configuration, SPICE desires an extension through 2026.
54 ENVIRONMENTAL SCIENCES↗
Compilation and Evaluation of Data from Groundwater Impounded within the U12n and U12t Tunnels, Rainier and Aqueduct Mesas, Nevada National Security Site
The Department of Defense (DOD) Defense Threat Reduction Agency (DTRA) and the U.S. Department of Energy (DOE) Office of Environmental Management Nevada Program (EM NV) conducted routine physical and geochemical monitoring of water impounded behind impermeable structures placed within the U12n and U12t tunnels on the Nevada National Security Site (NNSS) (formerly the Nevada Test Site [NTS]). Desert Research Institute (DRI) conducted a semiquantitative evaluation of pressure data to determine the extent to which the tunnels have been inundated and a qualitative evaluation of monitored geochemical parameters to determine if trends exist that were indicative of hydrologic and/or geochemical processes operating behind the tunnel plugs. Data were also evaluated to determine if monitored radiological parameters exceeded primary standards associated with the Environmental Protection Agency’s (EPA) Safe Drinking Water Act (SDWA) (40 C.F.R. § 141.66).
54 ENVIRONMENTAL SCIENCES↗
Townsend's Ground Squirrel Conservation on the Hanford Site for Calendar Year 2025
The U.S. Department of Energy (DOE), Hanford Field Office (HFO) conducts ecological monitoring on the DOE-managed portion of Hanford Site (hereby referred to as Central Hanford) to collect and track data needed to ensure compliance with an array of environmental laws, regulations, and policies governing HFO activities. Ecological monitoring data provides baseline information about the plants, animals, and habitat under HFO stewardship on the Hanford Site, which is required for accurate ecological impact assessment decision making under the National Environmental Policy Act and the Comprehensive Environmental Response, Compensation, and Liability Act. In addition, ecological monitoring helps ensure that HFO, its contractors, and other entities conducting activities on the Hanford Site are in compliance with DOE/EIS-0222-F, Final Hanford Comprehensive Land Use Plan Environmental Impact Statement. HFO places priority on monitoring plant and animal species or habitats that have specific regulatory protections or requirements; that are rare and/or declining (federal or state listed endangered, threatened, or sensitive species); or are of significant interest to federal, state, or tribal governments or to the public.
54 ENVIRONMENTAL SCIENCES↗
Ionic-Based Electrochemical Gas Sensors for Low-Cost, High-Sensitivity SO2 Detection
Sulfur dioxide (SO2) is a toxic gas associated with adverse health and environmental effects that necessitate reliable monitoring techniques. Here, we report the development of an all-solid-state electrochemical sensor utilizing a lithium borate (Li3BO3) solid electrolyte capable of subppm of SO2 detection. While subppm of SO2 sensing has been previously demonstrated in other solid-state electrolyte systems─such as stabilized zirconia, natrium super ionic conductors (NASICON) under mixed-potential conditions─here we establish Li3BO3 as an alternative solid electrolyte enabling equilibrium potentiometric sensing in an all-solid architecture. This sensor demonstrates a detection limit of at least 0.25 ppm, surpassing the human-olfactory threshold and meeting the rigorous requirements for industrial and personal monitoring applications. The sensing mechanism relies on the formation of Li2SO4 on the electrode surface, as evidenced by multimodal characterization techniques, including Raman spectroscopy, scanning electron microscopy (SEM), and scanning transmission electron microscopy (STEM). The strong linear correlation between the open-circuit potential (OCV) and the logarithm of SO2 concentration between 0.25 and 2 ppm indicates that the response is Nernstian in nature.
Lagunas, Francisco (ORCID:000000026377683X)↗
Atomistic mechanisms of water vapor–induced surface passivation
The microscopic mechanisms underpinning the spontaneous surface passivation of metals from ubiquitous water have remained largely elusive. Here, using in situ environmental electron microscopy to atomically monitor the reaction dynamics between aluminum surfaces and water vapor, we provide direct experimental evidence that the surface passivation results in a bilayer oxide film consisting of a crystalline-like Al(OH) 3 top layer and an inner layer of amorphous Al 2 O 3 . The Al(OH) 3 layer maintains a constant thickness of ~5.0 Å, while the inner Al 2 O 3 layer grows at the Al 2 O 3 /Al interface to a limiting thickness. On the basis of experimental data and atomistic modeling, we show the tunability of the dissociation pathways of H 2 O molecules with the Al, Al 2 O 3 , and Al(OH) 3 surface terminations. The fundamental insights may have practical significance for the design of materials and reactions for two seemingly disparate but fundamentally related disciplines of surface passivation and catalytic H 2 production from water.
36 MATERIALS SCIENCE↗
Improving the performance of cryogenic calorimeters with nonlinear multivariate noise cancellation algorithms
Abstract State-of-the-art physics experiments require high-resolution, low-noise, and low-threshold detectors to achieve competitive scientific results. However, experimental environments invariably introduce sources of noise, such as electrical interference or microphonics. The sources of this environmental noise can often be monitored by adding specially designed “auxiliary devices” (e.g. microphones, accelerometers, seismometers, magnetometers, and antennae). A model can then be constructed to predict the detector noise based on the auxiliary device information, which can then be subtracted from the true detector signal. Here, we present a multivariate noise cancellation algorithm which can be used in a variety of settings to improve the performance of detectors using multiple auxiliary devices. To validate this approach, we apply it to simulated data to remove noise due to electromagnetic interference and microphonic vibrations. We then employ the algorithm to a cryogenic light detector in the laboratory and show an improvement in the detector performance. Finally, we motivate the use of nonlinear terms to better model vibrational contributions to the noise in thermal detectors. We show a further improvement in the performance of a particular channel of the CUORE detector when using the nonlinear algorithm in combination with optimal filtering techniques.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Investigating effects of soil chemicals on density of small mammal bioindicators using spatial capture-recapture models
Monitoring the ecological impacts of environmental pollution and the effectiveness of remediation efforts requires identifying relationships between contaminants and the disruption of biological processes in populations, communities, or ecosystems. Wildlife are useful bioindicators, but traditional comparative experimental approaches rely on a staunch and typically unverifiable assumption that, in the absence of contaminants, reference and contaminated sites would support the same densities of bioindicators, thereby inferring direct causation from indirect data. We demonstrate the utility of spatial capture-recapture (SCR) models for overcoming these issues, testing if community density of common small mammal bioindicators was directly influenced by soil chemical concentrations. By modeling density as an inhomogeneous Poisson point process, we found evidence for an inverse spatial relationship between Peromyscus density and soil mercury concentrations, but not other chemicals, such as polychlorinated biphenyls, at a site formerly occupied by a nuclear reactor. Although the coefficient point estimate supported Peromyscus density being lower where mercury concentrations were higher (β = –0.44), the 95% confidence interval overlapped zero, suggesting no effect was also compatible with our data. Estimated density from the most parsimonious model (2.88 mice/ha; 95% CI = 1.63–5.08), which did not support a density-chemical relationship, was within the range of reported densities for Peromyscus that did not inhabit contaminated sites elsewhere. Environmental pollution remains a global threat to biodiversity and ecosystem and human health, and our study provides an illustrative example of the utility of SCR models for investigating the effects that chemicals may have on wildlife bioindicator populations and communities.
36 MATERIALS SCIENCE↗
Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques
Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.
Chai, Chengping [Oak Ridge National Laboratory (OR↗
Engineering Methanogenic Microbiomes to Redirect Flux to Biomass
In this study, we present a method for acquiring and characterizing novel microbial consortia that regulates methanogens and methanotrophs through selective cultivation and metagenomic analysis of indigenous microorganisms in the environment. In addition, we present the work performed as part of this project to model the pathways that act as limiting factors in microbial methane metabolism based on a carbon cycle model. In this report, we describe the methods for selective cultivation of methane-metabolism-related microorganisms from environmental samples, the method for monitoring their methane consumption performance, and the method and results for verifying their functions using quantitative PCR and metagenomics techniques. The microbial consortia containing methanotrophs were obtained through selective cultivation and molecular biological verification, and their methane consumption performance was evaluated. In addition, the potential of the existence of bacteriophages interacting with methane metabolism-related microorganisms was identified through metagenomic sequencing.
09 BIOMASS FUELS↗
UNCERTAINTY-AWARE DEEP LEARNING FRAMEWORK FOR FORECASTING COASTAL WATER LEVEL IN VIRGINIA BEACH
Coastal areas like Virginia Beach, USA, are increasingly vulnerable to flooding. To mitigate the impact of flooding, it is crucial for the City of Virginia Beach to have reliable 72-hour-ahead (3 days) forecasts of water levels at key gauge locations. To support this effort, several sensors have been installed throughout the city to monitor water levels and other environmental parameters such as wind speed, precipitation, and atmospheric pressure. Leveraging sensor data from one of these locations, we developed an uncertainty-aware deep learning model to forecast water levels. We employed deep quantile regression (DQR) to quantify variability in the predictions and examined the performance of three different model architectures. In addition to exclusively including historical data, we investigated the improvement wind forecasts provide to the accuracy of 72-hour-ahead water level predictions. The results show a twelvefold improvement in the flood forecast for a real flooding event.
Hasan, Mahmud [Thomas Jefferson National Accelerat↗
Modular Subsurface Sensors and Integrated Software for Advanced Subsurface Characterization and Monitoring using Unoccupied Vehicles
The advent and subsequent proliferation of autonomous airborne, waterborne, and groundbased vehicles (i.e., “drones”) promises to broadly transform the geosciences and associated industries, including fossil energy exploration and development, mineral resource exploration and development, water-resource management, and environmental remediation. For geophysical characterization and monitoring, the prospect of programming highly repeatable and low-cost drone missions for subsurface imaging will allow for deployments in hazardous and previously inaccessible areas. Coupled with autonomous workflows for data processing, management, and visualization, drone-based geophysical characterization and monitoring will enable unprecedented, real-time insight into diverse subsurface properties and processes of scientific and engineering importance. Toward this end, the objectives of this Lab Directed Research and Development (LDRD) project were to develop new (1) instrumentation for dronebased electromagnetic induction (EMI) geophysical imaging, including separated transmitter and receivers and associated electronics, (2) software for real-time data telemetry, processing, management, and visualization. Although EMI has been previously deployed using unoccupied aerial systems (UASs), these applications failed to capitalize on the game-changing capabilities of drone platforms. Whereas drone-based data acquisition allows for collection of rich, three-dimensional (3D) multi-offset/multi-angle configurations between transmitters and receivers, past efforts have relied on conventional instrumentation that was designed for ground-based data collection with the transmitter and a single receiver housed in the same unit; nor did these previous applications demonstrate real-time delivery of results to support rapid management decisions in the field. In this 1-year project, we (1) designed and constructed new lightweight independent transmitter and receiver antenna platforms that communicate with a laptop computer; (2) developed software to control data acquisition, manage/transfer data, and visualize data as its collected; and (3) demonstrated the operation of the new hardware and software systems in a ground-based field test. Our work entails major technological advances for EMI and established a foundation on which to build a new drone-based, real-time geophysical EMI imaging capability to support diverse challenges facing the nation.
47 OTHER INSTRUMENTATION↗
NNSS Drinking Water Systems and Wastewater Systems Monitoring
Presentation of NNSS Drinking Water and Wastewater 2021 monitoring
54 ENVIRONMENTAL SCIENCES↗
Y-12 Groundwater Protection Program Groundwater and Surface Water Sampling and Analysis Plan (CY 2021)
This plan provides a description of the groundwater and surface water quality monitoring activities planned for calendar year (CY) 2021 at the U.S. Department of Energy Y-12 National Security Complex (Y-12) that will be managed by the Y-12 Groundwater Protection Program (GWPP). Groundwater and surface water monitoring is performed by the GWPP. Groundwater and surface water monitoring will be performed in three hydrogeologic regimes at Y-12: the Bear Creek Hydrogeologic Regime (Bear Creek Regime), the Upper East Fork Poplar Creek Hydrogeologic Regime (East Fork Regime), and the Chestnut Ridge Hydrogeologic Regime (Chestnut Ridge Regime). The Bear Creek and East Fork regimes are located in Bear Creek Valley and the Chestnut Ridge Regime is located south of Y-12. Additional surface water monitoring will be performed north of Pine Ridge along the boundary of the Oak Ridge Reservation. The following sections of this report provide details regarding the CY 2021 groundwater and surface water monitoring activities. Section 2 describes the monitoring locations in each regime and the processes used to select the sampling locations. A description of the field measurements and laboratory analytes is provided in Section 3. Sample collection methods and procedures are described in Section 4, and Section 5 lists the documents cited for more detailed operational and technical information. The narrative sections of the report reference several appendices. Figures (maps and diagrams) and tables (excluding a data summary table presented in Section 4) are in Appendix A and Appendix B, respectively. Groundwater Monitoring Schedules (when issued throughout CY 2021) will be inserted in Appendix C, and addenda to this plan (if issued) will be inserted in Appendix D. Laboratory requirements (bottle lists, holding times, etc.) are provided in Appendix E, and an approved Waste Management Plan is provided in Appendix F. Modifications to the CY 2021 monitoring program may be necessary during implementation. Changes in programmatic requirements may alter the analytes specified for selected monitoring wells or may add or remove wells from the planned monitoring network. Each modification to the monitoring program will be approved by the Y-12 GWPP manager and documented as an addendum to this sampling and analysis plan.
54 ENVIRONMENTAL SCIENCES↗
Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?
Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.
54 ENVIRONMENTAL SCIENCES↗
Low Cost Hydrogen Monitor for Continuous Quantification of Facility Emissions
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54 ENVIRONMENTAL SCIENCES↗