Search NASA⌕ Search

SEARCH · Search NASA

Results for “environmental sensing”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Development of self-sensing materials for extreme environments based on metamaterial concept and additive manufacturing

Structural health monitoring of an engineered component in a harsh environment is critical for multiple DOE missions including nuclear fuel cycle, subsurface energy production/storage, and energy conversion. Supported by a seeding Laboratory Directed Research & Development (LDRD) project, we have explored a new concept for structural health monitoring by introducing a self-sensing capability into structural components. The concept is based on two recent technological advances: metamaterials and additive manufacturing. A self-sensing capability can be engineered by embedding a metastructure, for example, a sheet of electromagnetic resonators, either metallic or dielectric, into a material component. This embedment can now be realized using 3-D printing. The precise geometry of the embedded metastructure determines how the material interacts with an incident electromagnetic wave. Any change in the structure of the material (e.g., straining, degradation, etc.) would inevitably perturbate the embedded metastructures or metasurface array and therefore alter the electromagnetic response of the material, thus resulting in a frequency shift of a reflection spectrum that can be detected passively and remotely. This new sensing approach eliminates complicated environmental shielding, in-situ power supply, and wire routing that are generally required by the existing active-circuit-based sensors. The work documented in this report has preliminarily demonstrated the feasibility of the proposed concept. The work has established the needed simulation tools and experimental capabilities for future studies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unique optical excitations in topological insulators (Final Technical Report)

The overall objective of this research is to understand how light interacts with topological insulator (TI) films and layered structures. Unlike normal materials, the electrons in TI films are trapped at the top and bottom surfaces of the film. These electrons have unusual properties, including low mass and high velocity. Light shining on these trapped electrons will excite electron density waves, called plasmons, which inherit the unusual properties of the electrons. This project aims to understand how these plasmons interact with each other and how the plasmon properties change as the film dimensions change. By controlling the physical properties of the films, the optical response of the film can also be controlled. In addition to single TI films, the project will also investigate the properties of stacks of TI films layered with normal insulating films. Stacking these materials results in multiple layers of trapped electrons whose plasmons can interact in ever more complex ways. After these interactions are understood, we can begin to engineer complex TI structures to obtain designer optical phenomena in the far-infrared and THz, wavelength ranges of interest for environmental monitoring and chemical sensing. This research directly addresses DOE Grand Challenges, including understanding how properties of matter emerge from complex electronic correlations and learning how to control these properties as well as the mission of the Basic Energy Sciences program to understand and control matter at the electronic/atomic level.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Structural complexity and primary production resistance are coupled in a temperate forest

The capacity of forests to resist structural change and retain material legacies–the biotic and abiotic resources that persist through disturbance–is crucial to sustaining ecosystem function after disturbance. However, the role of forest structure as both a material legacy and feature supporting carbon (C) cycling stability following disturbance has not been widely investigated. We used a large-scale disturbance manipulation to ask whether legacies of lidar-derived canopy structures drive 3-year primary production responses to disturbance. As part of the Forest Resilience Threshold Experiment (FoRTE) in northern Michigan, USA we simulated phloem-disrupting disturbances producing a range of severities and affecting canopy trees of different sizes. We quantified the legacies of forest structure using two approaches: one measuring the change in structure and primary production from pre-to post-disturbance and the second estimating resistance as log transformed ratios of control and treatment values. We found that total aboveground wood net primary production (ANPP w ) was similar across disturbance severities as legacy trees rapidly increased rates of primary production. Experiment-wide, the disturbance had limited effects on change in mean structural complexity values; however, high variance underscored large differences in the magnitude and direction of complexity's response at the plot-scale. Plot-scale structural complexity, but not vegetation area index (VAI), resistance strongly predicted ANPP w resistance while temporal VAI and structural complexity changes did not. We conclude that the presence of material legacies in the form of forest structure may affect primary production stability following disturbance and that how legacies are quantified may affect the interpretation of disturbance response.

lidar—remote sensing↗
DOE OSTI2022

Implementing Spatio-Temporal 3D-Convolution Neural Networks and UAV Time Series Imagery to Better Predict Lodging Damage in Sorghum

Unmanned aerial vehicle (UAV)-based remote sensing is gaining momentum in a variety of agricultural and environmental applications. Very-high-resolution remote sensing image sets collected repeatedly throughout a crop growing season are becoming increasingly common. Analytical methods able to learn from both spatial and time dimensions of the data may allow for an improved estimation of crop traits, as well as the effects of genetics and the environment on these traits. Multispectral and geometric time series imagery was collected by UAV on 11 dates, along with ground-truth data, in a field trial of 866 genetically diverse biomass sorghum accessions. We compared the performance of Convolution Neural Network (CNN) architectures that used image data from single dates (two spatial dimensions, 2D) versus multiple dates (two spatial dimensions + temporal dimension, 3D) to estimate lodging detection and severity. Lodging was detected with 3D-CNN analysis of time series imagery with 0.88 accuracy, 0.92 Precision, and 0.83 Recall. This outperformed the best 2D-CNN on a single date with 0.85 accuracy, 0.84 Precision, and 0.76 Recall. The variation in lodging severity was estimated by the best 3D-CNN analysis with 9.4% mean absolute error (MAE), 11.9% root mean square error (RMSE), and goodness-of-fit (R2) of 0.76. This was a significant improvement over the best 2D-CNN analysis with 11.84% MAE, 14.91% RMSE, and 0.63 R2. The success of the improved 3D-CNN analysis approach depended on the inclusion of “before and after” data, i.e., images collected on dates before and after the lodging event. The integration of geometric and spectral features with 3D-CNN architecture was also key to the improved assessment of lodging severity, which is an important and difficult-to-assess phenomenon in bioenergy feedstocks such as biomass sorghum. This demonstrates that spatio-temporal CNN architectures based on UAV time series imagery have significant potential to enhance plant phenotyping capabilities in crop breeding and Precision agriculture applications.

3D-convolution neural networks↗
DOE OSTI2020

The SrrAB two-component system regulates Staphylococcus aureus pathogenicity through redox sensitive cysteines

Staphylococcus aureus infections can lead to diseases that range from localized skin abscess to life-threatening toxic shock syndrome. The SrrAB two-component system (TCS) is a global regulator of S. aureus virulence and critical for survival under environmental conditions such as hypoxic, oxidative, and nitrosative stress found at sites of infection. Despite the critical role of SrrAB in S. aureus pathogenicity, the mechanism by which the SrrAB TCS senses and responds to these environmental signals remains unknown. Bioinformatics analysis showed that the SrrB histidine kinase contains several domains, including an extracellular Cache domain and a cytoplasmic HAMP-PAS-DHp-CA region. In this work, we show that the PAS domain regulates both kinase and phosphatase enzyme activity of SrrB and present the structure of the DHp-CA catalytic core. Importantly, this structure shows a unique intramolecular cysteine disulfide bond in the ATP-binding domain that significantly affects autophosphorylation kinetics. In vitro data show that the redox state of the disulfide bond affects S. aureus biofilm formation and toxic shock syndrome toxin-1 production. Moreover, with the use of the rabbit infective endocarditis model, we demonstrate that the disulfide bond is a critical regulatory element of SrrB function during S. aureus infection. Our data support a model whereby the disulfide bond and PAS domain of SrrB sense and respond to the cellular redox environment to regulate S. aureus survival and pathogenesis.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Electronic and Optical Properties of NV Center in Diamonds for Sensing Applications: Experimental and First Principles Density Functional Theory Approach

The nitrogen-vacancy (NV) center in a nanodiamond (ND) crystal is one of the best candidate materials for quantum sensing and metrology applications at elevated environmental conditions. The NV center can be used to achieve an unprecedented level of sensitivity for sensing applications at high temperatures and pressures. The NV center is also proven to be useful for magnetic field sensing. Furthermore, it can be used for applications in the sensing of valuable minerals such as rare earth elements (REEs) from the crude and waste subsurface materials. This work presents computational and experimental studies on electronic and optical properties of bulk diamond with N and NV defects. ND in MOF/polymer materials will be experimentally encapsulated. The transverse spin relaxation time of the NV center will be measured in bare and encapsulated ND. Different doping elements will be introduced on the surfaces and present surface electronic properties to study the surfaces with shallow NV center for quantum sensing applications.

Paudel, Hari P.↗
DOE OSTI2021

Sensor Domain of Histidine Kinase VxrA of Vibrio cholerae : Hairpin-Swapped Dimer and Its Conformational Change

VxrA and VxrB are cognate histidine kinase (HK)-response regulator (RR) pairs of a two-component signaling system (TCS) found in Vibrio cholerae, a bacterial pathogen that causes cholera. The VxrAB TCS positively regulates virulence, the type VI secretion system, biofilm formation, and cell wall homeostasis in V. cholerae, providing protection from environmental stresses and contributing to the transmission and virulence of the pathogen. The VxrA HK has a unique periplasmic sensor domain (SD) and, remarkably, lacks a cytoplasmic linker domain between the second transmembrane helix and the dimerization and histidine phosphotransfer (DHp) domain, indicating that this system may utilize a potentially unique signal sensing and transmission TCS mechanism. Here, we have determined several crystal structures of VxrA-SD and its mutants. These structures reveal a novel structural fold forming an unusual β hairpin-swapped dimer. A conformational change caused by relative rotation of the two monomers in a VxrA-SD dimer could potentially change the association of transmembrane helices and, subsequently, the pairing of cytoplasmic DHp domains. Based on the structural observation, we propose a putative scissor-like closing regulation mechanism for the VxrA HK. IMPORTANCE V. cholerae has a dynamic life cycle, which requires rapid adaptation to changing external conditions. Two-component signal transduction (TCS) systems allow V. cholerae to sense and respond to these environmental changes. The VxrAB TCS positively regulates a number of important V. cholerae phenotypes, including virulence, the type six secretion system, biofilm formation, and cell wall homeostasis. Here, we provide the crystal structures of the VxrA sensor histidine kinase sensing domain and propose a mechanism for signal transduction. The cognate signal for VxrAB remains unknown; however, in this work we couple our structural analysis with functional assessments of key residues to further our understanding of this important TCS.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Remote Sensing and Fluxes Upscaling for Real-world Impact (Workshop Report)

The "Remote Sensing and Fluxes Upscaling for Real-world Impact" workshop, held on July 9-10, 2024, at Lawrence Berkeley National Lab, was a collaborative effort led by the AmeriFlux Management Project, NEON, and the Carbon Dew Community of Practice. The event brought together over 200 registrants and approximately 100 attendees each day, including leading experts, researchers, and practitioners. The primary focus was on bridging the gap between cutting-edge research and practical applications in environmental monitoring by integrating remote sensing and flux data. Key themes included the importance of site-level measurements for validating remote sensing products, providing nature-based climate solutions, and addressing challenges such as instrument costs and the need for standardized methods. At the regional scale, discussions centered on addressing spatial heterogeneity and using high-resolution remote sensing and machine learning methods to enhance data interpretation. Global scale challenges included data consistency, gap filling, and accurate emission source identification, with opportunities for international collaboration and standardized practices to improve global carbon budget assessments. The workshop emphasized the critical need for integrating data across local, regional, and global scales through explicit scale-matching and developed a workflow for scaling flux data using "straight shot" and "explicit nesting" approaches. The event highlighted the importance of connecting scientific research with real-world applications in carbon, energy, and water management, ensuring that advancements translate into tangible societal benefits. These insights will guide future research, technology transfer, and collaboration, maximizing the potential of environmental fluxes to address real-world challenges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Remote sensing of seasonal variation of LAI and fAPAR in a deciduous broadleaf forest

Climate change is affecting the phenology of terrestrial ecosystems. In deciduous forests, phenology in leaf area index (LAI) is the primary driver of seasonal variation in the fraction of absorbed photosynthetically active radiation (fAPAR), which drives photosynthesis. Remote sensing has been widely used to estimate LAI and fAPAR. However, while many studies have examined both empirical and model-based relationships among LAI, fAPAR, and spectral vegetation indices (SVI) from remote sensing, few studies have systematically and empirically examined how relationships among these variables change over the growing season. In this study, we examine how and why seasonal-scale covariation differs among time series of remotely sensed SVIs and both LAI and fAPAR based on current understanding and theory. To do this we use newly available remote sensing data sets in combination with time series of in-situ measurements and a canopy radiative transfer model to analyze how seasonal variation in canopy and environmental conditions affect relationships among remotely sensed SVIs, LAI, and fAPAR at a temperate deciduous forest site in central Massachusetts. Our results show that accounting for seasonal variation in canopy shadowing, which is driven by variation in solar zenith angle, improved remote sensing-based estimates of LAI, fAPAR, and daily total APAR. Specifically, we show that the phenology of SVIs is strongly influenced by seasonal variation in near infrared (NIR) reflectance arising from systematic variation in the canopy shadow fraction that is independent of changes in LAI or fAPAR. Therefore, results of this work provide a refined basis for understanding how remote sensing can be used to monitor and model the phenology of LAI, fAPAR, APAR, and gross primary productivity in temperate deciduous forests.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Sensing at the Nanoscale Using Nitrogen-Vacancy Centers in Diamond: A Model for a Quantum Pressure Sensor

The sensing of stress under harsh environmental conditions with high resolution has critical importance for a range of applications including earth’s subsurface scanning, geological CO2 storage monitoring, and mineral and resource recovery. Using a first-principles density functional theory (DFT) approach combined with the theoretical modelling of the low-energy Hamiltonian, here, we investigate a novel approach to detect unprecedented levels of pressure by taking advantage of the solid-state electronic spin of nitrogen-vacancy (NV) centers in diamond. We computationally explore the effect of strain on the defect band edges and band gaps by varying the lattice parameters of a diamond supercell hosting a single NV center. A low-energy Hamiltonian is developed that includes the effect of stress on the energy level of a ±1 spin manifold at the ground state. By quantifying the energy level shift and split, we predict pressure sensing of up to 0.3 MPa/Hz using the experimentally measured spin dephasing time. We show the superiority of the quantum sensing approach over traditional optical sensing techniques by discussing our results from DFT and theoretical modelling for the frequency shift per unit pressure. Importantly, we propose a quantum manometer that could be useful to measure earth’s subsurface vibrations as well as for pressure detection and monitoring in high-temperature superconductivity studies and in material sciences. Our results open avenues for the development of a sensing technology with high sensitivity and resolution under extreme pressure limits that potentially has a wider applicability than the existing pressure sensing technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Assessing the Consistency of Estimated Ground Cover Fractions between the BLM AIM Method and Optical Remote Sensing Method

Since 2012, Argonne National Laboratory (Argonne) has supported the Bureau of Land Management (BLM) in developing remote sensing methodologies for long-term environmental monitoring of Palo Verde Mesa in eastern Riverside County, California, including methods for: detailed mapping of ephemeral streams, estimating fractional cover of desert-land surface components (e.g., trees, shrubs, litters, and bare ground), evaluating erosion risk or land stability, and characterizing vegetation alliances using spatial structure and geostatistical approaches. These studies showed the promise of remote sensing for monitoring changes in desert landscapes by providing information that would be difficult to obtain through field surveys. During this time the BLM has also worked to establish long-term monitoring protocols and compiled field-observation data collected using standardized protocols from the Assessment, Inventory, and Monitoring (AIM) strategy. The AIM data can be compared to data derived from remote sensing methods to evaluate their relative operational utility in monitoring landscape change. If ground cover estimated using remotely sensed imagery is comparable to AIM ground cover estimates, then remote sensing can be used to monitor whether any land cover change in desert landscapes may be related to solar energy development. Therefore, the goal of this study was to determine the consistency in ground cover estimates between AIM data and those derived from publicly- available remotely sensed imagery, such as that available through the U.S. Department of Agriculture, National Agricultural Imagery Program (NAIP), to examine feasibility of a remote sensing method for complementing AIM monitoring. Based on the image analysis in this study, we also provide recommendations for how small unmanned aerial system (sUAS) data may be used to complement BLM’s AIM data and NAIP imagery for future vegetation monitoring. The ground cover types we originally planned to investigate were trees, shrubs, and bare ground. However, the small sample size and a limited range of cover fraction of trees and shrubs in the AIM dataset (e.g., 33 samples with a maximum shrub cover of 18%, 14 samples with a maximum tree cover of 17%) did not allow for performing a meaningful evaluation for the remote sensing approach. Therefore, we conducted the study focusing on bare ground, foliar, and rock cover, all of which are indicators reported in the AIM remote sensing dataset.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Monitoring SEIRAS on a Graphitic Electrode for Surface-Sensitive Electrochemistry: Real-Time Electrografting

The ubiquity of graphitic materials in electrochemistry makes it highly desirable to probe their interfacial behavior under electrochemical control. Probing the dynamics of molecules at the electrode/electrolyte interface is possible through spectroelectrochemical approaches involving surface-enhanced infrared absorption spectroscopy (SEIRAS). Usually, this technique can only be done on plasmonic metals such as gold or carbon nanoribbons, but a more convenient substrate for carbon electrochemical studies is needed. Here, we expanded the scope of SEIRAS by introducing a robust hybrid graphene-on-gold substrate, where we monitored electrografting processes occurring at the graphene/electrolyte interface. These electrodes consist of graphene deposited onto a roughened gold-sputtered internal reflection element (IRE) for attenuated total reflectance (ATR) SEIRAS. The capabilities of the graphene-gold IRE were demonstrated by successfully monitoring the electrografting of 4-amino-2,2,6,6-tetramethyl-1-piperidine N -oxyl (4-amino-TEMPO) and 4-nitrobenzene diazonium (4-NBD) in real time. These grafts were characterized using cyclic voltammetry and ATR-SEIRAS, clearly showing the 1520 and 1350 cm –1 NO 2 stretches for 4-NBD and the 1240 cm –1 C–C, C–C–H, and N–Ȯ stretch for 4-amino-TEMPO. Successful grafts on graphene did not show the SEIRAS effect, while grafting on gold was not stable for TEMPO and had poorer resolution than on graphene-gold for 4-NBD, highlighting the uniqueness of our approach. The graphene-gold IRE is proficient at resolving the spectral responses of redox transformations, unambiguously demonstrating the real-time detection of surface processes on a graphitic electrode. This work provides ample future directions for real-time spectroelectrochemical investigations of carbon electrodes used for sensing, energy storage, electrocatalysis, and environmental applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Slow-Light Mid-IR Silicon Photonic Chips for NO 2 and CH 4 Gas Detection

A compact, chip-scale mid-infrared gas sensor is demonstrated, leveraging a two-dimensional photonic crystal waveguide (PCW) fabricated on a silicon-on-insulator (SOI) platform. The PCW comprises a hexagonal lattice with lattice constant a = 860 nm and hole radius r = 0.22a, incorporating a central line defect of reduced-radius holes (r s = 0.7r) to induce slow-light propagation near the photonic band edge with a group index of approximately 73, thereby enhancing light-matter interaction. The sensor operates at fundamental absorption wavelengths of 3.42 μm for nitrogen dioxide (NO 2 ) and 3.40 μm for methane (CH 4 ), utilizing the strongest molecular vibrational transitions for maximum sensitivity. Experimental validation was conducted using dynamically diluted gas mixtures generated by mass flow controllers, with signal acquisition performed by a liquid nitrogen-cooled InSb detector. For NO 2 , the sensor exhibited excellent linear response over 5–25 ppm (part per million) with coefficient of determination R 2 = 0.9934, achieving a detection limit of 210 ppb (part per billion)─representing the first reported silicon photonic-based NO 2 detection. For CH 4 , exposure to 25 ppm resulted in a 6.4% decrease in transmitted intensity, demonstrating multigas sensing capability. The CMOS-compatible fabrication process and compact 3 mm device footprint establish this SOI-PCW platform as a scalable, low-power solution for integrated mid-infrared gas sensing, with significant potential for environmental monitoring and industrial safety applications.

Crystals↗
DOE OSTI2022

Going mobile to address emerging climate equity needs in the heterogeneous urban environment

We report the Brookhaven National Laboratory Center for Multiscale Applied Sensing (CMAS) aims to address environmental equity needs in the context of a changing climate. As a first step towards this goal, the center developed a one-of-a-kind observatory tailored to the study of highly heterogeneous urban environments. This article describes the features of the mobile observatory that enable its rapid deployment either on or off the power grid, as well as its instrument payload. Beyond its unique design, the observatory optimizes data collection within the obstacle-laden urban environment using a new smart sampling paradigm. This setup facilitated the collection of previously poorly documented environmental properties including wind profiles throughout the atmospheric column. The mobile observatory captured unique observations during its first few intensive observation periods (IOPs).. Vertical air motion and infrared temperature measurements collected along the faces of the supertall One Vanderbilt skyscraper in Manhattan, NY reveal how solar and anthropogenic heating affect wind flow and thus the venting of heat, pollution, and contaminants in urban street canyons. Also, air temperature measurements collected during travel along a 150-km transect between Upton and Manhattan, NY offer a high-resolution view of the urban heat island and reveal that temperature disparities also exist within the city across different neighborhoods. Ultimately, the datasets collected by CMAS are poised to help guide equitable urban planning by highlighting existing disparities and characterizing the impact of urban features on the urban microclimate with the goal of improving human comfort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Quantum Sensing and its Potential for Nuclear Safeguards

Quantum sensing has the potential to provide ultrasensitive measurements of physical phenomenon. Unlike quantum computing, quantum sensing is available now, though in general at research laboratories. A notable commercially-available quantum sensing device is the ubiquitous Superconducting Quantum Interference Device (SQUID), which can measure faint magnetic fields such as found in the human brain. Quantum sensing is used for direct measurement of environmental phenomenon, such as electromagnetic fields and accelerations, which then are used for certain applications. For example, quantum sensing of accelerations is useful for Position, Navigation, and Timing (PNT) applications. It is not clear, however, how quantum sensing can be useful for nuclear safeguards. This report provides first a background in quantum sensing, followed by a survey of potential safeguards utilizations of quantum sensing. Several potential safeguards applications are identified and explored.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Coupling Remote Sensing With a Process Model for the Simulation of Rangeland Carbon Dynamics

Rangelands provide significant environmental benefits through many ecosystem services, which may include soil organic carbon (SOC) sequestration. However, quantifying SOC stocks and monitoring carbon (C) fluxes in rangelands are challenging due to the considerable spatial and temporal variability tied to rangeland C dynamics as well as limited data availability. We developed the Rangeland Carbon Tracking and Management (RCTM) system to track long-term changes in SOC and ecosystem C fluxes by leveraging remote sensing inputs and environmental variable data sets with algorithms representing terrestrial C-cycle processes. Bayesian calibration was conducted using quality-controlled C flux data sets obtained from 61 Ameriflux and NEON flux tower sites from Western and Midwestern US rangelands to parameterize the model according to dominant vegetation classes (perennial and/or annual grass, grass-shrub mixture, and grass-tree mixture). The resulting RCTM system produced higher model accuracy for estimating annual cumulative gross primary productivity (GPP) (R 2 > 0.6, RMSE <390 g C m -2 ) relative to net ecosystem exchange of CO 2 (NEE) (R 2 > 0.4, RMSE <180 g C m -2 ). Model performance in estimating rangeland C fluxes varied by season and vegetation type. The RCTM captured the spatial variability of SOC stocks with R 2 = 0.6 when validated against SOC measurements across 13 NEON sites. Model simulations indicated slightly enhanced SOC stocks for the flux tower sites during the past decade, which is mainly driven by an increase in precipitation. Future efforts to refine the RCTM system will benefit from long-term network-based monitoring of vegetation biomass, C fluxes, and SOC stocks.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Compressed sensing methods with applications to advanced air sampling

Environmental sampling methods developed by the Savannah River National Laboratory (SRNL) employ collectors with sorbent media tubes set at various locations to collect airborne emissions. Laboratory analyses of these tubes results in one-dimensional signals regarding what chemicals are being released and transported within the atmosphere. The analysis process is time consuming especially when analyzing a full year’s worth of tubes (hourly sample collection results in nearly 9,000 tubes per year). Using a signal processing method such as compressed sensing allows for recreation of the full signal while greatly reducing the number of analyzed samples required. Due to the sparsity of data retrieved from the air tubes, it is possible to use measurements a fraction of the size of the original data to gain much of the same information. This would improve the overall time and cost of analysis when modeling one-dimensional sampling signals.

54 ENVIRONMENTAL SCIENCES↗