Results for “quality monitoring” — Page 10

NASA NTRS1988

PI-in-a-box: Intelligent onboard assistance for spaceborne experiments in vestibular physiology

In construction is a knowledge-based system that will aid astronauts in the performance of vestibular experiments in two ways: it will provide real-time monitoring and control of signals and it will optimize the quality of the data obtained, by helping the mission specialists and payload specialists make decisions that are normally the province of a principal investigator, hence the name PI-in-a-box. An important and desirable side-effect of this tool will be to make the astronauts more productive and better integrated members of the scientific team. The vestibular experiments are planned by Prof. Larry Young of MIT, whose team has already performed similar experiments in Spacelab missions SL-1 and D-1, and has experiments planned for SLS-1 and SLS-2. The knowledge-based system development work, performed in collaboration with MIT, Stanford University, and the NASA-Ames Research Center, addresses six major related functions: (1) signal quality monitoring; (2) fault diagnosis; (3) signal analysis; (4) interesting-case detection; (5) experiment replanning; and (6) integration of all of these functions within a real-time data acquisition environment. Initial prototyping work has been done in functions (1) through (4).

Colombano, Silvano↗

NASA NTRS2016

Watershed-Scale Modeling of Land-Use and Altered Environment Impacts on Aquatic Weed Growth in the Delta

The California Sacramento-San Joaquin River Delta is the hub for California's water supply, conveying water from Northern to Southern California agriculture and communities while supporting important ecosystem services, agriculture, and communities in the Delta. Changes in climate, long-term drought, and water quality have all been suspected as playing role in the dramatic expansion of invasive aquatic plants and their impact on ecosystems of the San Francisco Bay / California Delta complex. NASA Ames Research Center, USDA-Agricultural Research Service, the State of California, UC Davis, and local governments have partnered under a USDA sponsored project (DRAAWP) to develop science-based, adaptive-management strategies for invasive aquatic plants in Sacramento-San Joaquin Delta. Critical to developing management strategies is to understand how the Delta is affected by both the magnitude of fluctuations in land-use and climate / drought induced altered environments and how the plants respond to these altered environments. We utilize the Soil Water Assessment Tool (SWAT), a watershed-scale model developed to quantify the impact of land management practices in large and complex watersheds on water quality, as the backbone for a customized Delta model - Delta-SWAT. The model uses land-use, soils, elevation, and hydrologic routing to characterize pesticide and nutrient transport from the Sacramento and San Joaquin rivers watersheds and loading into the Delta. Land-use within the Delta, as well as water extraction to supply those functions, and the resulting return of water to Delta waterways are included in Delta-SWAT. Hydrologic transport within the Delta has required significant attention to address the lack of elevation driven transport processes. Delta-SWAT water quality trend estimates are compared with water quality monitoring conducted throughout the Delta. Aquatic plant response to water quality and other environmental factors is carried out using a customized model component. Plant response to the range of water quality factors, response times, and altered temperature and light regimes of the Delta have required gap-filling studies to provide model parameters. Delta-SWAT provides a tool for evaluating temporal and spatial effects of land-use and altered environments in the Delta and contributing watersheds on aquatic weed growth. Using Delta-SWAT for simulation modeling allows evaluation of historic and current conditions as well as consideration potential climate change and management practice outcomes. Delta-SWAT adds to the scientific understanding of dynamics in the Delta and enhances development of science-informed, management strategies and practices.

Ecosystem↗

NASA NTRSDate not supplied

Effects of Ambient Alcohol Levels on the Real-time Monitoring of the Atmosphere of the International Space Station

Monitoring of the spacecraft environment is required to ensure the health of the crew and the vehicle systems. For the ISS atmosphere, routine volatile organic compound (VOC) monitoring has been performed for almost a decade by Air Quality Monitors (AQMs). The target compounds measured by the AQMs include three types of chemicals: 1) those compounds that would be harmful to crew, 2) those compounds that have been detected regularly in archival samples, and 3) compounds that, while not necessarily harmful to crew health, could present problems for Environmental Control and Life Support Systems (ECLSS). Following the docking of SpaceX-Demo1 (SpX-DM1), the AQMs began to report high levels of isopropanol (IPA). While elevated IPA is routinely observed with visiting vehicles, the level measured by the AQM, and its continued presence following multiple days of scrubbing, caused concerns regarding the U.S. Water Recovery System. Following the departure of SpX-DM1, the IPA levels decreased to nominal levels, allowing the team to investigate the cause of the elevated measurements. Based on the changes in the shape of the gas chromatograph (GC) traces in the IPA region during docked operations, it appeared that an unknown coeluting species was causing problems with quantification. However, with the docking of Northrup-Grumman-11 (NG-11), the elevated IPA returned, as well as the changes in GC traces. In contrast to the SpX-DM1 results, the AQM IPA results did not return to nominal levels following the departure of NG-11, suggesting that the changes could not be tied directly to the visiting vehicle. In this paper, we will discuss a number of potential causes for both the genuine (measured in archival samples) increases in IPA as well as the much higher levels measured by the AQM. Additionally, we will discuss methods being explored to decrease the potential for a reoccurrence in the future.

William T. Wallace↗

NASA NTRS1973

Instrumentation for air quality measurements.

Comparison of the new generation of air quality monitoring instruments with some more traditional methods. The first generation of air quality measurement instruments, based on the use of oxidant coulometric cells, nitrogen oxide colorimetry, carbon monoxide infrared analyzers, and other types of detectors, is compared with new techniques now coming into wide use in the air monitoring field and involving the use of chemiluminescent reactions, optical absorption detectors, a refinement of the carbon monoxide infrared analyzer, electrochemical cells based on solid electrolytes, and laser detectors.

Loewenstein, M.↗

NASA NTRS1976

Technology transfer potential of an automated water monitoring system

The nature and characteristics of the potential economic need (markets) for a highly integrated water quality monitoring system were investigated. The technological, institutional and marketing factors that would influence the transfer and adoption of an automated system were studied for application to public and private water supply, public and private wastewater treatment and environmental monitoring of rivers and lakes.

Jamieson, W. M.↗

NASA NTRS2022

Hybrid data-driven and model-informed online tool wear detection in milling machines

Precision machining tool wear is responsible for low product throughput and quality. Monitoring the tool wear online is vital to prevent degradation in machining quality. However, direct real-time tool wear measurement is not practical. This paper presents residual-based anomaly detection models, combining a hybrid model comprised of a physics-based model and a data-driven model (a decision tree or a neural network) to predict signals of interest (e.g., power or forces) under nominal conditions, followed by Page’s cumulative sum test for detecting tool wear on-line using the computer numerical control machine measurements. The most informative features are ranked using dynamic programming and its approximation variants from real-time measurements and machine settings, such as the width of cut, depth of cut, feed rate and spindle speed, that serve as inputs to the predictive models. The baseline nominal model is incrementally updated with experimental data via a gradient boosted adaptation model to generate the residuals that account for discrepancies between the actual machine data under normal conditions and the baseline nominal model predictions. The hybrid model is validated against 20 Mazak milling machine experimental tests and one Haas run-to-failure experiment. The proposed anomaly detector is applied to synthetic data from simulations of the physics-based model at different operating conditions, measurement noise levels, and tool wear levels, and the methods were able to achieve an overall 92% accuracy in data with 1% noise. The anomaly detection methods based on hybrid model reduced the false alarms of either the data-driven or physical-based models alone, and are found to be capable of good online detection of tool wear.

Online anomaly detection↗

NASA NTRSDate not supplied

Impact of Canadian Wildfires on Mid Atlantic’s Region Air Quality: An Analysis Using ASDC Data

Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.

Ingrid Garcia-Solera↗

NASA NTRSDate not supplied

Analyzing the Impact of Canadian Wildfires on Air Quality in the U.S. Mid-Atlantic: with Data and Tools from NASA’s Atmospheric Sciences Data Center

Wildfires pose a growing concern in North America due to their harmful impacts on air quality and public health, with increased wildfire activity in recent years leading to widespread smoke plumes that can transcend borders. The exposure of New York City (NYC), the most populous city in North America, to Canadian wildfire smoke highlights the substantial implications for public health and urban environments. To better understand the impact of Canadian wildfires on air quality in NYC, satellite data from the NASA Atmospheric Science Data Center (ASDC) at Langley Research Center, along with ground-based measurements and atmospheric modeling results, are analyzed. We examine concentrations of atmospheric aerosols—particularly PM2.5 particulate matter originating from Canadian wildfires—their dispersion patterns, and the duration and intensity of smoke events impacting NYC. Data from multiple satellites, such as those from the Earth Polychromatic Imaging Camera (EPIC), are synergistically used to identify regions affected by wildfires and estimate aerosol loading. Ground-based measurements, including data from air quality monitoring stations, provide localized information for validation and calibration purposes. The findings of this study contribute to our understanding of the impact of Canadian wildfires on NYC's air quality and emphasize the importance of monitoring and prediction of transboundary smoke events using data synthesized from multiple sources, such as those provided by the ASDC. This information is crucial for policymakers, public health officials, and residents in affected areas to develop effective strategies for mitigating the health risks associated with wildfire smoke and improving air quality during wildfire seasons. The utilization of ASDC data in this research highlights the critical role of atmospheric remote sensing in addressing the challenges posed by wildfires and their consequences on regional scales.

Ingrid Garcia-Solera↗

NASA NTRS1971

Water Pollution Detection by Reflectance Measurements

Measurement of the intensity of light reflected from various planar liquid surfaces has been performed. The results of this brief study show that the presence of a film of foreign material floating on a reference substrate is easily detected by reflectance measurement if the two liquids possess significantly different refractive indices, for example, oil (n = 1.40) and water (n = 1.33). Additional study of various optical configurations, and the building and testing of a prototype monitoring device revealed that the method is sufficiently practical for application to continuous water quality monitoring.

Goolsby, A. D.↗

NASA NTRS1996

Test Structures for Rapid Prototyping of Gas and Pressure Sensors

A multi-project ceramic substrate was used in developing a gas sensor and pressure sensor. The ceramic substrate cantained 36 chips with six variants including sensors, process control monitors, and an interconnect ship. Tha gas sensor is being developed as an air quality monitor and the pressure gauge as a barometer.

ceramics ceramic substrate gas sensor pressure sen↗

NASA NTRS2014

Satellite Monitoring Over the Canadian Oil Sands: Highlights from Aura OMI and TES

Satellite remote sensing provides a unique perspective for air quality monitoring in and around the Canadian Oil Sands as a result of its spatial and temporal coverage. Presented are Aura satellite observations of key pollutants including nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), ammonia (NH3), methanol (CH3OH), and formic acid (HCOOH) over the Canadian Oil Sands. Some of the highlights include: (i) the evolution of NO2 and SO2 from the Ozone Monitoring Instrument (OMI), including comparisons with other nearby sources, (ii) two years of ammonia, carbon monoxide, methanol, and formic acid observations from 240 km North-South Tropospheric Emission Spectrometer (TES) transects through the oils sands, and (iii) preliminary insights into emissions derived from these observations.

NO2↗

NASA NTRSDate not supplied

Effects of Ambient Alcohol Levels on the Real-time Monitoring of the Atmosphere of the International Space Station

Effects of Ambient Alcohol Levels on the Real-time Monitoring of the Atmosphere of the International Space StationWilliam T. Wallace,1 Thomas F. Limero,1 Kenneth W. Clark,2 and Daniel B. Gazda31KBR, 2400 NASA Pkwy., Houston, TX 770582JES Tech, 16870 Royal Crest Dr., Houston, TX 770583NASA Lyndon B. Johnson Space Center, 2101 NASA Pkwy., Houston, TX 77058Monitoring of the spacecraft environment is required to ensure the safety of the crew and the health of the vehicle systems. For the ISS atmosphere, routine monitoring has been performed for many years by the Major Constituent Analyzer (MCA) and two Air Quality Monitors (AQMs), which, together, are used to monitor for trace (ppb) levels of 22 volatile organic compounds (VOCs). The target compounds measured by the AQMs include three types of chemicals: 1) those compounds that would be harmful to crew, 2) those compounds that have been detected regularly in archival samples, and 3) compounds that, while not necessarily harmful to crew health, could present problems for Environmental Control and Life Support Systems (ECLSS).In March 2019, following the docking of SpaceX-Demo1 (SpX-DM1), the on-board AQMs began to report extremely high levels of isopropyl alcohol (IPA). While elevated IPA is not abnormal following the docking of visiting vehicles (due to cleaning of the cargo prior to flight), the level measured by the AQM, and its continued presence following multiple days of scrubbing, caused concerns regarding the U.S. Water Recovery System. Following the departure of SpX-DM1, the IPA levels decreased to nominal levels, allowing the team to investigate the cause of the elevated measurements. Initially, based on the changes in the shape of the gas chromatograph (GC) traces in the IPA region during docked operations, it appeared that some unknown coeluting species was causing problems with quantification. However, with the docking of Northrup-Grumman-11 (NG-11), the elevated IPA concentrations returned, as well as the changes in GC traces. In contrast to the SpX-DM1 results, though, the AQM IPA results did not return to nominal levels following the departure of NG-11, suggesting that the changes could not be tied directly to the visiting vehicle.In this paper, we will discuss a number of potential causes for both the genuine (measured in archival samples) increases in IPA as well as the much higher levels measured by the AQM. Additionally, we will discuss methods being explored to decrease the potential for a reoccurrence in the future.

William T Wallace↗

NASA NTRS2021

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Ghada Y.H. El Serafy↗

NASA NTRS2004

Autonomous Multi-sensor Coordination: The Science Goal Monitor

Next-generation science and exploration systems will employ new observation strategies that will use multiple sensors in a dynamic environment to provide high quality monitoring, self-consistent analyses and informed decision making. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing of earth phenomenon. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe the dynamic phenomena. The SGM system enables users to specify events of interest and how to react when an event is detected. The system monitors streams of data to identify occurrences of the key events previously specified by the scientist/user. When an event occurs, the system autonomously coordinates the execution of the users desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems Aqua/Terra spacecrafts MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM is being used to investigate forest fires, floods and volcanic eruptions. We are now identifying new earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

Koratkar, Anuradha↗

NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles Using Python

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗

NASA NTRS2020

Development of a Display Tool to Quality Control Weather Balloon Data for Space Launch Vehicles

Continuous atmospheric data analysis is an important factor for space launch vehicle design and operations. The balloon quality control tool was developed by NASA’s Marshall Space Flight Center (MSFC) Natural Environments Branch (NEB) for monitoring quality control processes and verifying the automated flags created on the balloon data sets analyzed. The data sets currently analyzed are comprised of high-resolution and low-resolution balloon data from NASA Kennedy Space Center (KSC), co-located on the United States Air Force’s Eastern range (ER) at the Cape Canaveral Air Force Station. The NEB was tasked to perform a quality assessment of these data sets and needed a tool to confirm the quality control (QC) flags produced from an automated process and add additional QC flags if necessary. This Graphical User Interface (GUI) was developed to visualize all of the data from these balloon sets, display any flags from the automated QC process, and add additional flags to variables if necessary. The GUI was developed in Python 3.6 utilizing different packages available such as pandas for data analysis and manipulation, NumPy for high-performance multidimensional array and tools to compute with and manipulate arrays, Matplotlib for plotting data and Tkinter to build the GUI.

Jessica K Headley↗

NASA NTRS1992

Evaluation of capillary electrophoresis for in-flight ionic contaminant monitoring of SSF potable water

Until 1989, ion chromatography (IC) was the baseline technology selected for the Specific Ion Analyzer, an in-flight inorganic water quality monitor being designed for Space Station Freedom. Recent developments in capillary electrophoresis (CE) may offer significant savings of consumables, power consumption, and weight/volume allocation, relative to IC technology. A thorough evaluation of CE's analytical capability, however, is necessary before one of the two techniques is chosen. Unfortunately, analytical methods currently available for inorganic CE are unproven for NASA's target list of anions and cations. Thus, CE electrolyte chemistry and methods to measure the target contaminants must be first identified and optimized. This paper reports the status of a study to evaluate CE's capability with regard to inorganic and carboxylate anions, alkali and alkaline earth cations, and transition metal cations. Preliminary results indicate that CE has an impressive selectivity and trace sensitivity, although considerable methods development remains to be performed.

Mudgett, Paul D.↗

NASA NTRSDate not supplied

In Situ Water Quality Data for the Chesapeake Bay

This paper examines in situ water quality datameasured during2020-2021in the Chesapeake Bay for comparison with optical satellite data. Thiscollection was performed as part of a NASA project aiming to develop new methods for water quality monitoring from satellite remote sensingusing artificial intelligence. Our objective is to use insitu data as ground-truth to provide water quality classifications, or labels,to their overlapping (in time and location)satellite imagery. Having such labeled data, can help us achieve our project’s longer-termgoal:to train artificial intelligencemodelsto recognize features in spectral informationfor monitoringwater qualityfrom satellites. Because routine monitoring by state agencies is conducted at discrete locations, we obtained a flow-through system operated from small boats to measure waterquality parameters along transects for comparison with two-dimensional maps collected from space, with an initial focus on low oxygenevents, due to their large spatial extent and regular occurrence each summer.We also evaluated similar in situ data collected during 1984-2021by the Chesapeake Program.

Nargess Memarsadeghi↗