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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.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Modeling Water Quality in Watersheds: From Here to the Next Generation

In this synthesis, we assess present research and anticipate future development needs in modeling water quality in watersheds. We first discuss areas of potential improvement in the representation of freshwater systems pertaining to water quality, including representation of environmental interfaces, in-stream water quality and process interactions, soil health and land management, and (peri-)urban areas. In addition, we provide insights into the contemporary challenges in the practices of watershed water quality modeling, including quality control of monitoring data, model parameterization and calibration, uncertainty management, scale mismatches and provisioning of modeling tools. Finally, we make three recommendations to provide a path forward for improving watershed water quality modeling science, infrastructure and practices. These include building stronger collaborations between experimentalists and modelers, bridging gaps between modelers and stakeholders, and cultivating and applying procedural knowledge to better govern and support water quality modeling processes within organizations.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Space View Issues for Hyperspectral Sounders

The expectation for climate quality measurements from hyperspectral sounders is absolute calibration accuracy at the 100 mK level and stability at the < 40 mK/decade level. The Atmospheric InfraRed Sounder (AIRS)1, Cross-track Infrared Sounder (CrIS), and Infrared Atmospheric Sounding Interferometer (IASI) hyperspectral sounders currently in orbit have been shown to agree well over most of their brightness temperature range. Some larger discrepancies are seen, however, at the coldest scene temperatures, such as those seen in Antarctic winter and deep convective clouds. A key limiting factor for the calibrated scene radiance accuracy for cold scenes is how well the effective radiance of the cold space view pertains to the scene views. The space view signal is composed of external sources and instrument thermal emission at about 270 K from the scan mirror, external baffles, etc. Any difference in any of these contributions between space views and scene views will impact the absolute calibration accuracy, and the impact can be critical for cold scenes. Any change over time in these will show up as an apparent trend in calibrated radiances. We use AIRS data to investigate the validity of the space view assumption in view of the 100 mK accuracy and 40 mK/decade trend expectations. We show that the space views used for the cold calibration point for AIRS v5 Level-1B products meet these standards except under special circumstances and that AIRS v6 Level-1B products will meet them under all circumstances. This analysis also shows the value of having multiple distinct space views to give operational redundancy and analytic data, and that reaching climate quality requires continuing monitoring of aging instruments and adjustment of calibration.

trends↗
NASA NTRS2023

Development of a Photoacoustic Formaldehyde Monitor

Key indoor air quality pollutant formaldehyde (H2CO) is tracked on International Space Station (ISS) using passive badges returned to the ground periodically for analysis. The process is time-consuming both in preparation and for analysis upon return 6-12 months later. Badges also require precious crew time for deploy, retrieval and stow. As NASA’s focus in space exploration shifts to the Moon and Mars, archival sample return becomes increasingly impractical, so the aim of this project is to develop a highly reliable real-time analyzer for H2CO at low concentrations with data downlinked. Potential sources of H2CO include materials off gassing, use of formalin as a tissue fixative in biological payloads and overheating of acetal polymers. The Spacecraft Maximum Allowable Concentration (SMAC) for H2CO is 100 ppb for exposures of 7 days or longer. ISS concentrations recently run only 10 - 30 ppb but have spiked as high as 60 ppb in the past. Gateway real time monitoring requirements call for a range of 8 - 140 ppb. For this project, a concentration range of 5 - 500 ppb H2CO is targeted. The core tunable diode laser spectroscopy (TDLS) technology was developed by Vista Photonics through the NASA and US Navy Small Business Innovation Research (SBIR) programs. Monitors based on this technology have been demonstrated on ISS, trialed on a nuclear submarine and are in production as Anomaly Gas Analyzers for both ISS and Orion. Initially, direct absorption TDLS was used exclusively in these monitors, however, the H2CO target concentration is much lower, and a longer wavelength required, so a photoacoustic spectroscopy (PAS) technique was adapted, where the laser excitation is detected by a sensitive microphone vs. a conventional photodetector. This paper will discuss the results of NASA-JSC laboratory testing of a prototype PAS based formaldehyde monitor and explore potential adaptations for Gateway missions and beyond.

Paul D Mudgett↗
NASA NTRS2007

Flight Dynamics Performances of the MetOp A Satellite during the First Months of Operations

The 19th of October 2006 at 16:28 UTC the first MetOp satellite (MetOp A) was successfully launched from the Baykonur cosmodrome by a Soyuz/Fregat launcher. After only three days of LEOP operations, performed by ESOC, the satellite was handed over to EUMETSAT, who is since then taking care of all satellite operations. MetOp A is the first European operational satellite for meteorology flying in a Low Earth Orbit (LEO), all previous satellites operated by EUMETSAT, belonging to the METEOSAT family, being located in the Geo-stationary orbit. To ensure safe operations for a LEO satellite accurate and continuous commanding from ground of the on-board AOCS is required. That makes the operational transition at the end of the LEOP quite challenging, as the continuity of the Flight Dynamics operations is to be maintained. That means that the main functions of the Flight Dynamics have to be fully validated on-flight during the LEOP, before taking over the operational responsibility on the spacecraft, and continuously monitored during the entire mission. Due to the nature of a meteorological operational mission, very stringent requirements in terms of overall service availability (99 % of the collected data), timeliness of processing of the observation data (3 hours after sensing) and accuracy of the geo-location of the meteorological products (1 km) are to be fulfilled. That translates in tight requirements imposed to the Flight Dynamics facility (FDF) in terms of accuracy, timeliness and availability of the generated orbit and clock solutions; a detailed monitoring of the quality of these products is thus mandatory. Besides, being the accuracy of the image geo-location strongly related with the pointing performance of the platform and with the on-board timing stability, monitoring from ground of the behaviour of the on-board sensors and clock is needed. This paper presents an overview of the Flight Dynamics operations performed during the different phases of the MetOp A mission up to routine. The activities performed to validate all the Flight Dynamics functions, characterize the behaviour of the satellite and monitor the performances of the Flight Dynamics facility will be highlighted. The MetOp Flight Dynamics Operations team is led by Anders Meier Soerensen and composed by Pier Luigi Righetti, Francisco Sancho, Antimo Damiano and David Lazaro. The team is supported by Hilda Meixner, responsible for all Flight Dynamics validation activities.

Righetti, Pier Luigi↗
NASA NTRS2017

Harmonized Landsat/Sentinel-2 Reflectance Products for Land Monitoring (Invited)

Many land applications require more frequent observations than can be obtained from a single 'Landsat class' sensor. Agricultural monitoring, inland water quality assessment, stand-scale phenology, and numerous other applications all require near-daily imagery at better than 1ha resolution. Thus the land science community has begun expressing a desire for a '30-meter MODIS' global monitoring capability. One cost-effective way to achieve this goal is via merging data from multiple, international observatories into a single virtual constellation. The Harmonized Landsat/Sentinel-2 (HLS) project has been working to generate a seamless surface reflectance product by combining observations from USGS/NASA Landsat-8 and ESA Sentinel-2. Harmonization in this context requires a series of radiometric and geometric transforms to create a single surface reflectance time series agnostic to sensor origin. Radiometric corrections include a common atmospheric correction using the Landsat-8 LaSRC/6S approach, a simple BRDF adjustment to constant solar and nadir view angle, and spectral bandpass adjustments to fit the Landsat-8 OLI reference. Data are then resampled to a consistent 30m UTM grid, using the Sentinel-2 global tile system. Cloud and shadow masking are also implemented. Quality assurance (QA) involves comparison of the output 30m HLS products with near-simultaneous MODIS nadir-adjusted observations. Prototoype HLS products have been processed for approximately 7% of the global land area using the NASA Earth Exchange (NEX) compute environment at NASA Ames, and can be downloaded from the HLS web site (https://hls.gsfc.nasa.gov). A wall-to-wall North America data set is being prepared for 2018. This talk will review the objectives and status of the HLS project, and illustrate applications of high-density optical time series data for agriculture and ecology. We also discuss lessons learned from HLS in the general context of implementing virtual constellations.

Reflectance↗
NASA NTRS2012

Validation of MODIS FLH and In Situ Chlorophyll a from Tampa Bay, Florida (USA)

Satellite observation of phytoplankton concentration or chlorophyll-a (chla) is an important characteristic, critically integral to monitoring coastal water quality. However, the optical properties of estuarine and coastal waters are highly variable and complex and pose a great challenge for accurate analysis. Constituents such as suspended solids and dissolved organic matter and the overlapping and uncorrelated absorptions in the blue region of the spectrum renders the blue-green ratio algorithms for estimating chl-a inaccurate. Measurement of suninduced chlorophyll fluorescence, on the other hand, which utilizes the near infrared portion of the electromagnetic spectrum may, provide a better estimate of phytoplankton concentrations. While modelling and laboratory studies have illustrated both the utility and limitations of satellite algorithms based on the sun induced chlorophyll fluorescence signal, few have examined the empirical validity of these algorithms or compared their accuracy against bluegreen ratio algorithms . In an unprecedented analysis using a long term (2003-2011) in situ monitoring data set from Tampa Bay, Florida (USA), we assess the validity of the FLH product from the Moderate Resolution Imaging Spectrometer against a suite of water quality parameters taken in a variety of conditions throughout this large optically complex estuarine system. . Overall, the results show a 106% increase in the validity of chla concentration estimation using FLH over the standard chla estimate from the blue-green OC3M algorithm. Additionally, a systematic analysis of sampling sites throughout the bay is undertaken to understand how the FLH product responds to varying conditions in the estuary and correlations are conducted to see how the relationships between satellite FLH and in situ chlorophyll-a change with depth, distance from shore, from structures like bridges, and nutrient concentrations and turbidity. Such analysis illustrates that the correlations between FLH and in situ chla measurements increases with increasing distance between monitoring sites and structures like bridges and shore. Due probably to confounding factors, expected improvement in the FLH- chla relationship was not clearly noted when increasing depth and distance from shore alone (not including bridges). Correlations between turbidity and nutrient concentrations are discussed further and principle component analyses are employed to address the relationships between the multivariate data sets. A thorough understanding of how satellite FLH algorithms relate to in situ water quality parameters will enhance our understanding of how MODIS s global FLH algorithm can be used empirically to monitor coastal waters worldwide.

Fischer, Andrew↗
DOE OSTI2022

Quantifying Nitrogen Bioextraction by Seaweed Farms – A Real-time Modeling-Monitoring Case Study in Hood Canal, WA

Anthropogenic nitrogen input is the leading cause of increasing marine eutrophication. Cultivating seaweeds at large scales may provide a viable solution for mitigating eutrophication especially when nitrogen extraction is achieved as an added environmental benefit of biofuel and food production. As the first step to promoting and implementing this solution, it is critical to quantify and assess the effectiveness of nitrogen removal by existing seaweed farms. Pacific Northwest National Laboratory and Hood Canal Mariculture Inc. conducted a case study by applying a coupled modeling-monitoring approach to quantify nitrogen bioextraction by a one-hectare commercial sugar kelp farm in Hood Canal, WA. Through this two-year effort, the project team conducted intensive field monitoring of water quality parameters at the farm site and subsequent laboratory analysis. Meanwhile, the team applied a coupled hydrodynamic-macroalgal growth model to simulate kelp growth and nitrogen bioextraction over the two kelp growing seasons. The model results showed an overall good comparison with field monitoring data and showed the effectiveness of nitrogen removal by kelp farms. The established modeling-monitoring approach can also be transferable for supporting broader research activities in macroalgal farming and eutrophication mitigation.

09 BIOMASS FUELS↗
NASA NTRS2010

Evaluation of the VIIRS Land Algorithms at Land PEATE

The Land Product Evaluation and Algorithm Testing Element (Land PEATE), a component of the Science Data Segment of the National Polar-orbiting Operational Environmental Satellite System (NPOESS) Preparatory Project (NPP), is being developed at the NASA Goddard Space Flight Center (GSFC). The primary task of the Land PEATE is to assess the quality of the Visible Infrared Imaging Radiometer Suite (VIIRS) Land data products made by the Interface Data Processing System (IDPS) using the Operational (OPS) Code during the NPP era and to recommend improvements to the algorithms in the IDPS OPS code. The Land PEATE uses a version of the MODIS Adaptive Processing System (MODAPS), NPPDAPS, that has been modified to produce products from the IDPS OPS code and software provided by the VIIRS Science Team, and uses the MODIS Land Data Operational Product Evaluation (LDOPE) team for evaluation of the data records generated by the NPPDAPS. Land PEATE evaluates the algorithms by comparing data products generated using different versions of the algorithm and also by comparing to heritage products generated from different instrument such as MODIS using various quality assessment tools developed at LDOPE. This paper describes the Land PEATE system and some of the approaches used by the Land PEATE for evaluating the VIIRS Land algorithms during the pre-launch period of the NPP mission and the proposed plan for long term monitoring of the quality of the VIIRS Land products post-launch.

Wolfe, Robert E.↗
DOE OSTI2018

Marine Corps Base Camp Lejeune New River Estuary (NRE) - Autonomous Vertical Profiler (AVP) Morgan Bay Data 2008-2016

Critical military training and testing on lands along the nation’s coastal and estuarine shorelines are increasingly placed at risk because of encroachment pressures in surrounding areas, impairments due to other anthropogenic disturbances, and changes in climate and sea level. The U.S. Department of Defense (DoD) intends to enhance and sustain its training and testing assets and also optimize its stewardship of natural resources through the development and application of an ecosystem-based management approach on DoD installations. To accomplish this goal, particularly for installations in estuarine/coastal environments, the Strategic Environmental Research and Development Program (SERDP) launched the Defense Coastal/Estuarine Research Program (DCERP) as a 10-year effort at Marine Corps Base Camp Lejeune (MCBCL) in North Carolina. The results of the second 5 years of the program (DCERP2) are presented in the DCERP2 Final Report.There were four overarching objectives of DCERP2. The first objective was to understand the effects of climate change impacts, including warming temperatures, variability in the hydrological cycle, storm events, and sea level rise on the coastal ecosystems at MCBCL from observations and measurements made over the 10-year program. The second objective was to understand the carbon cycle of the coastal and terrestrial ecosystems at MCBCL through a highly integrated sampling program. The third objective was to develop models, tools, and indicators to evaluate current and projected future ecosystem state changes and translate scientific findings into actionable information for installation managers. The last objective was to recommend adaptive management strategies to sustain ecosystem natural resources within the context of an active military installation.This data package includes a zipfile containing the results of continuous (30 minute interval) water quality samples and wind speed and direction anemometer (MET) data from Morgan Bay upstream of Station 5 (AVP2): Autonomous vertical profilers (AVPs) were deployed from June 2008 through October 2016 in central Stones Bay at Station 3, and in Morgan Bay upstream of Station 5 and were used to continuously monitor key water quality parameters. The AVPs consist of a moored floating platform housing a computer controlled winch mechanism that lowers and raises a YSI Model 6600 multi-parameter water quality datasonde at programmable time intervals (Reynolds-Fleming et al., 2002). Winch speed and sampling frequency of the AVP system and datasonde were configured to produce full water column profiles of temperature, salinity, DO concentration, chl concentration (by fluorescence), and turbidity every 30 minutes and achieve vertical depth resolution of approximately 10 cm. An anemometer (RM Young) affixed to the mast of the AVP measured six minute averages of wind speed and direction every 30 minutes. Data were stored locally on the AVP computers, and telemetered back to the laboratory each night. Datasondes were exchanged with freshly cleaned and calibrated datasondes as necessary but at least on a monthly basis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A systematic review of machine learning in groundwater monitoring

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.

AI/ML↗
NASA NTRS1975

Remote sensing of effects of land-use practices on water quality

Research efforts are presented for the use of remote sensing in environmental surveys in Kentucky. Ground truth parameters were established that represent the vegetative cover of disturbed and undisturbed watersheds in the Cumberland Plateau of eastern Kentucky. Several water quality parameters were monitored of the watersheds utilized in the establishment of ground truth data. The capabilities of multistage-multispectral aerial photography and satellite imagery were evaluated in detecting various land use practices. The use of photographic signatures of known land use areas utilizing manually-operated spot densitometers was studied. The correlation of imagery signature data to water quality data was examined. Potential water quality predictions were developed from forested and nonforested watersheds based upon the above correlations. The cost effectiveness of predicting water quality values was evaluated using multistage and satellite imagery sampling techniques.

Graves, D. H.↗
NASA NTRS1977

Viking 1975 Mars lander interactive computerized video stereophotogrammetry

A novel computerized interactive video stereophotogrammetry system has been developed for analysis of Viking 1975 lander imaging data. Prompt, accurate, and versatile performance is achieved. Earth-returned digital imagery data are driven from a computer to a pair of video monitors. Powerful computer support enables a photogrammetrist, stereoscopically viewing the video displays, to create diverse topographic products. Profiles, representing the intersection of any definable surface with the Martian relief, are readily generated. Vertical profiles and elevation contour maps, including stereo versions, are produced. Computer overlays of map products on stereo images aid map interpretation and permit independent quality evaluation. Slaved monitors enable parallel viewing. Maps span from the immediate foreground to the remote limits of ranging capability. Surface sampler arm specific vertical profiles enable direct reading of arm commands required for sample acquisition, rock rolling, and trenching. The ranging accuracy of plus or minus 2 cm throughout the sample area degrades to plus or minus 20 m at 100-m range.

Liebes, S., Jr.↗
DOE OSTI2018

Aerosol sources in subway environments

Highlights: • PM{sub 2.5} concentrations varied widely among subway platforms (26–86 µg m{sup −3}). • Main PM{sub 2.5} component in metro microenvironment is iron oxide. • Subway sources contribution account for up to 91% of ambient subway PM{sub 2.5}. • From the five subway-specific sources identified, brakes contributed the most. • Elements related with health issues are apportioned mostly by subway sources (> 60%). Millions of people use rail subway public transport around the world, despite the relatively high particulate matter (PM) concentrations in these underground environments, requiring the identification and quantification of the aerosol source contributions to improve the air quality. An extensive aerosol monitoring campaign was carried out in eleven subway stations in the Barcelona metro system, belonging to seven subway lines. PM{sub 2.5} samples were collected during the metro operating hours and chemically analysed to determine major and trace elements, inorganic ions, and total carbon. The chemical compositions of subway components such as brake pads, rail tracks and pantographs were also determined. The mean PM{sub 2.5} concentrations varied widely among stations, ranging from 26 µg m{sup −3} to 86 µg m{sup −3}. Subway PM{sub 2.5} was mainly constituted by Fe{sub 2}O{sub 3} (30–66%), followed by carbonaceous matter (18–37%) for the old stations, while for new stations equipped with Platform Screen Doors (PSD) these percentages go down to 21–44% and 15–30%, respectively. Both the absolute concentrations and the relative abundance of key species differed for each subway station, although with common patterns within a given subway line. This is a result of the different emission chemical profiles in different subway lines (using diverse types of brakes and/or pantographs). The co-emission of different sources poses a problem for their separation by receptor models. Nevertheless, receptor modelling (Positive Matrix Factorization) was applied resulting in ten sources, five of them subway-specific: RailWheel, RailWheel+Brake, Brake{sub A}, Brake{sub B}, Pb. The sum of their contributions accounted for 43–91% of bulk PM{sub 2.5} for the old stations and 21–52% for the stations with PSD. The decrease of the activity during the weekends resulted in a decrease (up to 56%) in the subway-specific sources contribution to the –already lower– bulk PM{sub 2.5} concentrations compared to weekdays. The health-related elements are mainly apportioned (> 60%) by subway sources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

ammonia↗
DOE OSTI2019

Continuous Water Quality Sensing for FGD Wastewater

The coal fired plant power industry requires new solutions for on-line monitoring of flue gas desulfurization (FGD) wastewaters for regulated trace metals to reduce recurring operating and off-site laboratory analysis costs and provide a high level of confidence of compliance with state and federal discharge guidelines. The University of Alabama at Birmingham (UAB) developed an integrated water sensor package for continuous water quality monitoring of flue gas desulfurization (FGD) wastewaters for concentration measurements of Selenium (Se). UAB utilized a diverse team of University Professors/Staff Members/Student(s) (Engineering and Innovative Technology Development Group), a University Affiliated Research Institution from the local area (Southern Research), and a cost sharing Industry Partner (Metrohm). The team developed and validated a proprietary wet chemistry batch process for water sample preparation; and implemented this process as a fully automated on-line monitor prototype system. This prototype system’s performance was evaluated in both the laboratory and during in-field deployment to a coal fired power plant. In both environments it was found that the prototype system was unable to recreate the batch process’s success at preparing FGD water samples for total Se measurements with a commercial-off-the-shelf (COTS) cyclic anodic stripping voltammetry (CSV) system. Through this experience, UAB has provided insight into a number of specific areas where improvements can be made with respect to the COTS systems used in this project to yield a more complete and competitive industry solution for on-line monitoring of total Se in industrial wastewater.

20 FOSSIL-FUELED POWER PLANTS↗