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NASA NTRS2005

Urban Landscape Characterization Using Remote Sensing Data For Input into Air Quality Modeling

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models that are used to assess whether urban areas are in attainment of EPA air quality standards, particularly for ground level ozone. This inadequacy of air quality models to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well these models predict ozone pollutant levels over metropolitan areas and ultimately, whether cities exceed EPA ozone air quality standards. We are exploring the utility of high-resolution remote sensing data and urban growth projections as improved inputs to meteorological and air quality models focusing on the Atlanta, Georgia metropolitan area as a case study. The National Land Cover Dataset at 30m resolution is being used as the land use/land cover input and aggregated to the 4km scale for the MM5 mesoscale meteorological model and the Community Multiscale Air Quality (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for future scenarios to 2030 is being facilitated by land use projections using a spatial growth model. Land use projections were developed using the 2030 Regional Transportation Plan developed by the Atlanta Regional Commission. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality.

Quattrochi, Dale A.↗

NASA NTRS2013

Data Quality Screening Service

A report describes the Data Quality Screening Service (DQSS), which is designed to help automate the filtering of remote sensing data on behalf of science users. Whereas this process often involves much research through quality documents followed by laborious coding, the DQSS is a Web Service that provides data users with data pre-filtered to their particular criteria, while at the same time guiding the user with filtering recommendations of the cognizant data experts. The DQSS design is based on a formal semantic Web ontology that describes data fields and the quality fields for applying quality control within a data product. The accompanying code base handles several remote sensing datasets and quality control schemes for data products stored in Hierarchical Data Format (HDF), a common format for NASA remote sensing data. Together, the ontology and code support a variety of quality control schemes through the implementation of the Boolean expression with simple, reusable conditional expressions as operands. Additional datasets are added to the DQSS simply by registering instances in the ontology if they follow a quality scheme that is already modeled in the ontology. New quality schemes are added by extending the ontology and adding code for each new scheme.

Strub, Richard↗

NASA NTRS2023

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Carl Malings↗

NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗

NASA NTRSDate not supplied

Integrating Low-Cost Sensor Systems and Networks to Enhance Air Quality Applications

Recent advances in low-cost air quality sensor systems are rapidly increasing the accessibility of air quality information around the world. At the same time, there are many technical challenges to appropriately and effectively using of the information they provide. A key opportunity to increase the applicability and actionability of low-cost sensor data is to use these devices at network scale and combine their information with insights from other air quality data sources such as numerical models and satellite remote sensing. When the limitations of low-cost sensors are understood and acknowledged, and appropriate complementary data are used to overcome these limitations, low cost sensors can support a variety of applications such as improving location-specific air quality forecasting and estimation, quantifying local source impacts, identifying air quality disparities, assessing the benefits of mitigation actions, and promoting community engagement with air quality issues. This presentation will first provide an overview and summarize key findings from a recently released World Meteorological Organization report discussing how networks of low-cost air quality sensor systems can be integrated to effectively support such applications. The presentation will also give a brief overview of an ongoing NASA-funded effort to create accessible tools for integrating multiple sources of air quality information, including global forecasting models, satellite remote sensing data, and in-situ information from both regulatory and low-cost monitors.

Carl Malings↗

NASA NTRS2012

Global Air Quality and Climate

Emissions of air pollutants and their precursors determine regional air quality and can alter climate. Climate change can perturb the long-range transport, chemical processing, and local meteorology that influence air pollution. We review the implications of projected changes in methane (CH4), ozone precursors (O3), and aerosols for climate (expressed in terms of the radiative forcing metric or changes in global surface temperature) and hemispheric-to-continental scale air quality. Reducing the O3 precursor CH4 would slow near-term warming by decreasing both CH4 and tropospheric O3. Uncertainty remains as to the net climate forcing from anthropogenic nitrogen oxide (NOx) emissions, which increase tropospheric O3 (warming) but also increase aerosols and decrease CH4 (both cooling). Anthropogenic emissions of carbon monoxide (CO) and non-CH4 volatile organic compounds (NMVOC) warm by increasing both O3 and CH4. Radiative impacts from secondary organic aerosols (SOA) are poorly understood. Black carbon emission controls, by reducing the absorption of sunlight in the atmosphere and on snow and ice, have the potential to slow near-term warming, but uncertainties in coincident emissions of reflective (cooling) aerosols and poorly constrained cloud indirect effects confound robust estimates of net climate impacts. Reducing sulfate and nitrate aerosols would improve air quality and lessen interference with the hydrologic cycle, but lead to warming. A holistic and balanced view is thus needed to assess how air pollution controls influence climate; a first step towards this goal involves estimating net climate impacts from individual emission sectors. Modeling and observational analyses suggest a warming climate degrades air quality (increasing surface O3 and particulate matter) in many populated regions, including during pollution episodes. Prior Intergovernmental Panel on Climate Change (IPCC) scenarios (SRES) allowed unconstrained growth, whereas the Representative Concentration Pathway (RCP) scenarios assume uniformly an aggressive reduction, of air pollutant emissions. New estimates from the current generation of chemistry-climate models with RCP emissions thus project improved air quality over the next century relative to those using the IPCC SRES scenarios. These two sets of projections likely bracket possible futures. We find that uncertainty in emission-driven changes in air quality is generally greater than uncertainty in climate-driven changes. Confidence in air quality projections is limited by the reliability of anthropogenic emission trajectories and the uncertainties in regional climate responses, feedbacks with the terrestrial biosphere, and oxidation pathways affecting O3 and SOA.

ozone↗

NASA NTRS2016

Large Gain in Air Quality Compared to an Alternative Anthropogenic Emissions Scenario

During the last 30 years, significant effort has been made to improve air quality through legislation for emissions reduction. Global three-dimensional chemistrytransport simulations of atmospheric composition over the past 3 decades have been performed to estimate what the air quality levels would have been under a scenario of stagnation of anthropogenic emissions per capita as in 1980, accounting for the population increase (BA1980) or using the standard practice of neglecting it (AE1980), and how they compare to the historical changes in air quality levels. The simulations are based on assimilated meteorology to account for the yearto- year observed climate variability and on different scenarios of anthropogenic emissions of pollutants. The ACCMIP historical emissions dataset is used as the starting point. Our sensitivity simulations provide clear indications that air quality legislation and technology developments have limited the rapid increase of air pollutants. The achieved reductions in concentrations of nitrogen oxides, carbon monoxide, black carbon, and sulfate aerosols are found to be significant when comparing to both BA1980 and AE1980 simulations that neglect any measures applied for the protection of the environment. We also show the potentially large tropospheric air quality benefit from the development of cleaner technology used by the growing global population. These 30-year hindcast sensitivity simulations demonstrate that the actual benefit in air quality due to air pollution legislation and technological advances is higher than the gain calculated by a simple comparison against a constant anthropogenic emissions simulation, as is usually done. Our results also indicate that over China and India the beneficial technological advances for the air quality may have been masked by the explosive increase in local population and the disproportional increase in energy demand partially due to the globalization of the economy.

Law (Jurisprudence)↗

NASA NTRS2016

Handling Qualities Optimization for Rotorcraft Conceptual Design

Over the past decade, NASA, under a succession of rotary-wing programs has been moving towards coupling multiple discipline analyses in a rigorous consistent manner to evaluate rotorcraft conceptual designs. Handling qualities is one of the component analyses to be included in a future NASA Multidisciplinary Analysis and Optimization framework for conceptual design of VTOL aircraft. Similarly, the future vision for the capability of the Concept Design and Assessment Technology Area (CD&A-TA) of the U.S Army Aviation Development Directorate also includes a handling qualities component. SIMPLI-FLYD is a tool jointly developed by NASA and the U.S. Army to perform modeling and analysis for the assessment of flight dynamics and control aspects of the handling qualities of rotorcraft conceptual designs. An exploration of handling qualities analysis has been carried out using SIMPLI-FLYD in illustrative scenarios of a tiltrotor in forward flight and single-main rotor helicopter at hover. Using SIMPLI-FLYD and the conceptual design tool NDARC integrated into a single process, the effects of variations of design parameters such as tail or rotor size were evaluated in the form of margins to fixed- and rotary-wing handling qualities metrics as well as the vehicle empty weight. The handling qualities design margins are shown to vary across the flight envelope due to both changing flight dynamic and control characteristics and changing handling qualities specification requirements. The current SIMPLI-FLYD capability and future developments are discussed in the context of an overall rotorcraft conceptual design process.

Handling Qualities↗

NASA NTRS2020

Climate and air quality impacts due to mitigation of non-methane near-term climate forcers

It is important to understand how future environmental policies will impact both climate change and air pollution. Although targeting near-term climate forcers (NTCFs), defined here as aerosols, tropospheric ozone, and precursor gases, should improve air quality, NTCF reductions will also impact climate. Prior assessments of the impact of NTCF mitigation on air quality and climate have been limited. This is related to the idealized nature of some prior studies, simplified treatment of aerosols and chemically reactive gases, as well as a lack of a sufficiently large number of models to quantify model diversity and robust responses. Here, we quantify the 2015–2055 climate and air quality effects of non-methane NTCFs using nine state-of-the-art chemistry–climate model simulations conducted for the Aerosol and Chemistry Model Intercomparison Project (AerChemMIP). Simulations are driven by two future scenarios featuring similar increases in greenhouse gases (GHGs) but with “weak” (SSP3-7.0) versus “strong” (SSP3-7.0-lowNTCF) levels of air quality control measures. As SSP3-7.0 lacks climate policy and has the highest levels of NTCFs, our results (e.g., surface warming) represent an upper bound. Unsurprisingly, we find significant improvements in air quality under NTCF mitigation (strong versus weak air quality controls). Surface fine particulate matter (PM2.5) and ozone (O3) decrease by −2.2±0.32 µg m−3 and −4.6±0.88 ppb, respectively (changes quoted here are for the entire 2015–2055 time period; uncertainty represents the 95 % confidence interval), over global land surfaces, with larger reductions in some regions including south and southeast Asia. Non-methane NTCF mitigation, however, leads to additional climate change due to the removal of aerosol which causes a net warming effect, including global mean surface temperature and precipitation increases of 0.25±0.12 K and 0.03±0.012 mm d−1, respectively. Similarly, increases in extreme weather indices, including the hottest and wettest days, also occur. Regionally, the largest warming and wetting occurs over Asia, including central and north Asia (0.66±0.20 K and 0.03±0.02 mm d−1), south Asia (0.47±0.16 K and 0.17±0.09 mm d−1), and east Asia (0.46±0.20 K and 0.15±0.06 mm d−1). Relatively large warming and wetting of the Arctic also occur at 0.59±0.36 K and 0.04±0.02 mm d−1, respectively. Similar surface warming occurs in model simulations with aerosol-only mitigation, implying weak cooling due to ozone reductions. Our findings suggest that future policies that aggressively target non-methane NTCF reductions will improve air quality but will lead to additional surface warming, particularly in Asia and the Arctic. Policies that address other NTCFs including methane, as well as carbon dioxide emissions, must also be adopted to meet climate mitigation goals.

Climate↗

NASA NTRSDate not supplied

Real Time Nitrogen Monitoring and Quality Control System for NASA Astromaterials Collection

The Nitrogen system is a vital utility supporting the curation office within NASA's Astromaterials Re-search and Exploration Science (ARES) Division at the Johnson Space center (JSC). Since the Apollo samples were returned in 1969. It has played an essential role in preserving NASA’s astromaterial collections (now nine collections) by maintaining contamination-free, controlled environments required for long term curation and advanced scientific research. To meet the stringent purity requirements for contamination control, high purity modified Grade C Liquid Nitrogen (LN2) is delivered weekly by an industrial gas vendor to a 15,000-gallon tank located outside at NASA JSC near the ARES facility. This nitrogen is vital for creating an inert atmosphere to store the collection in, ensuring they are continuously shielded from contaminants and alterations through a constant N2purge. For most of the past 65 years deliveries of LN2 have required a CoA (Certificate of Analysis) prior to delivery. If those were not available, then LN2 deliver were sent to third-party laboratories for verification, creating an operational bottleneck and quality gap. To address this challenge and maintain rigorous quality control, NASA JSC Infrastructure and Astromaterials Acquisition & Curation Office initiated the development of an on-site Analytical Gas Sampling (AGS) Laboratory. Designed to streamline quality verification if a CoA unavailable, the AGS lab was constructed by a subcontractor in Newton, NJ. After a successful factory acceptance test conducted by the NASA ARES Infrastructure team, the lab was trans-ported to NASA JSC, where it was anchored and integrated into the site’s nitrogen pressure system using stainless steel, oxygen-cleaned tubing. The ARES nitrogen system was modified to provide parallel flows to the AGS lab for quality control and to the astromaterials collection labs for maintaining an inert environment. The AGS lab features two operational modes: (1)Primary: Gaseous Nitrogen (GN2) Quality Monitoring –The AGS system continuously samples the site’s GN2 supply every 15 minutes to ensure compliance with required purity levels. (2) Secondary: LN2 Quality Verification when a comprehensive CoA is not available, a cryogenic vaporizer converts LN2 to GN2, which is then analyzed for impurities: H₂, Ar, O₂, CO, CO₂, H₂O, and total hydrocarbons (THC). By significantly enhancing on-site analytical capabilities, the AGS lab eliminates reliance on external testing, ensures uninterrupted quality monitoring of the nitrogen pressure system, and reinforces NASA’s commitment to preserving pristine astromaterials under the highest standards of curation and research integrity.

glove box↗

DOE OSTI2024

Monitoring water quality in the lower Kansas River using remote sensing

Abstract We demonstrate how to combine remote sensing data from satellite imagery (Sentinel‐2) with in situ water quality gauging (USGS Super Gages and the Gybe hyperspectral radiometer) to create spatially dense maps of water quality parameters (chlorophyll‐a concentration, turbidity, and nitrate plus nitrite concentration) along the lower Kansas River. The water quality maps are created using locally tuned models of the target water quality parameters, and this study describes the steps used to design, calibrate, and validate the empirical correlations. Water quality parameters such as chlorophyll‐a concentration are correlated with well‐studied absorption and scattering features in the visible spectrum (roughly 400–700 nm). Nutrients (such as nitrate plus nitrite concentration) lack strong absorption features in the visible spectrum, and in those cases we describe a novel surrogate data modeling approach that identifies overlapping water parcels between the in situ gauging and the remote sensing imagery. Measurements from the overlapping water parcels yield excellent correlations () for the target water quality parameters for limited windows of time (or limited sections of river reaches). Examples are provided illustrating how the water quality maps can be used to track river inputs from ungauged sources (such as creeks), or reveal the mixing patterns at the confluences.

Tufillaro, Nicholas↗

DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

Resistance Spot Welding (RSW) is a critical process in the automotive industry, valued for its cost-effectiveness, short cycle time, and robustness. However, achieving consistent high-quality joints remains challenging due to the complex interplay of various factors, like materials, processes, and manufacturing uncertainties, etc. Under the collaborative project between Oak Ridge National Laboratory (ORNL) and General Motors (GM), we have developed a robust and expansible machine learning (ML) framework aimed at enhancing quality control in RSW. By harnessing the power of machine learning, we have developed the ability to ensure every aspect of the welding process, from the initial process design stage to the final weld joint quality. The framework operates by analyzing a variety of data streams, including in-line process signals, process parameters, materials, and postprocessed weld joint data. Through this analysis, the models have been trained to detect deviations from optimal quality standards, leveraging their ability to identify signature data patterns and anomalies within in-line signals and construct complex correlations between these signals and weld quality parameters. Meanwhile, the machine learning framework is designed to adapt to a variety of materials, including high strength steels and aluminum alloys, etc. Its flexible architecture facilitates the incorporation of diverse data sources and features, enabling precise modeling and prediction across a broad range of material properties and weld quality variables. The expansible ML frameworks represent a promising transformation in weld quality monitoring and control, empowering industry to achieve high levels of efficiency, consistency, and reliability in manufacturing.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Machine Learning for Automated Weld Quality Monitoring and Control

42 ENGINEERING↗

NASA NTRS1977

Effects of control laws and relaxed static stability on vertical ride quality of flexible aircraft

State variable techniques are utilized to generate the RMS vertical load factors for the B-52H and B-1 bombers at low level, mission critical, cruise conditions. A ride quality index is proposed to provide meaningful comparisons between different controls or conditions. Ride quality is shown to be relatively invariant under various popular control laws. Handling quality variations are shown to be major contributors to ride quality variations on both vehicles. Relaxed static stability is artificially implemented on the study vehicles to investigate its effects on ride quality. The B-52H ride quality is generally degraded when handling characteristics are automatically restored by a feedback control to the original values from relaxed stability conditions. The B-1 airplane shows little ride quality sensitivity to the same analysis due to the small rigid body contribution to load factors at the flight condition investigated.

Roberts, P. A.↗

NASA NTRS1980

Exploratory piloted simulator study of the effects of winglets on handling qualities of a representative agricultural airplane

The effects on handling qualities of adding winglets to a representative agricultural aircraft configuration during swath-run maneuvering were evaluated. Aerodynamic data used in the simulation were based on low-speed wind tunnel tests of a full scale airplane and a subscale model. The Cooper-Harper handling qualities rating scale, supplementary pilot comments, and pilot vehicle performance data were used to describe the handling qualities of the airplane with the different wing-tip configurations. Results showed that the lateral-directional handling qualities of the airplane were greatly affected by the application of winglets and winglet cant angle. The airplane with winglets canted out 20 deg exhibited severely degraded lateral directional handling qualities in comparison to the basic airplane. When the winglets were canted inward 10 deg, the flying qualities of the configuration were markedly improved over those of the winglet-canted-out configuration or the basic configuration without winglets, indicating that proper tailoring of the winglet design may afford a potential benefit in the area of handling qualities.

Ogburn, M. E.↗

NASA NTRS1986

Flared landing approach flying qualities. Volume 1: Experiment design and analysis

An inflight research study was conducted utilizing the USAF Total Inflight Simulator (TIFS) to investigate longitudinal flying qualities for the flared landing approach phase of flight. The purpose of the experiment was to generate a consistent set of data for: (1) determining what kind of commanded response the pilot prefers in order to flare and land an airplane with precision, and (2) refining a time history criterion that took into account all the necessary variables and their characteristics that would accurately predict flying qualities. The result of the first part provides guidelines to the flight control system designer, using MIL-F-8785-(C) as a guide, that yield the dynamic behavior pilots perfer in flared landings. The results of the second part provides the flying qualities engineer with a newly derived flying qualities predictive tool which appears to be highly accurate. This time domain predictive flying qualities criterion was applied to the flight data as well as six previous flying qualities studies, and the results indicate that the criterion predicted the flying qualities level 81% of the time and the Cooper-Harper pilot rating, within + or - 1, 60% of the time.

Weingarten, Norman C.↗

NASA NTRS1986

Flared landing approach flying qualities. Volume 2: Appendices

An in-flight research study was conducted utilizing the USAF/Total In-Flight Simulator (TIFS) to investigate longitudinal flying qualities for the flared landing approach phase of flight. A consistent set of data were generated for: determining what kind of command response the pilot prefers/requires in order to flare and land an aircraft with precision, and refining a time history criterion that took into account all the necessary variables and the characteristics that would accurately predict flying qualities. Seven evaluation pilots participated representing NASA Langley, NASA Dryden, Calspan, Boeing, Lockheed, and DFVLR (Braunschweig, Germany). The results of the first part of the study provide guidelines to the flight control system designer, using MIL-F-8785-(C) as a guide, that yield the dynamic behavior pilots prefer in flared landings. The results of the second part provide the flying qualities engineer with a derived flying qualities predictive tool which appears to be highly accurate. This time-domain predictive flying qualities criterion was applied to the flight data as well as six previous flying qualities studies, and the results indicate that the criterion predicted the flying qualities level 81% of the time and the Cooper-Harper pilot rating, within + or - 1%, 60% of the time.

Weingarten, Norman C.↗

NASA NTRS1991

Flying quality analysis and flight evaluation of a highly augmented combat rotorcraft

This paper discusses implications of digital flight control system design for rotorcraft and illustrates the analysis of the resulting handling qualities obtained with the Advanced Digital Optical Control System demonstrator in the context of the proposed new handling-qualities specification for rotorcraft. Topics covered are digital flight control design and analysis methods, flight testing techniques, handling-qualities evaluation results, and correlation of flight test results with analytical models and the proposed handling-qualities specification. The evaluation of the demonstrator system indicates desirable response characteristics based on equivalent damping and frequency, but undersirably large effective time delays (exceeding 240 ms in all axes). Piloted handling qualities are found to be desirable or adequate for all low, medium, and high pilot gain tasks, but handling qualities are inadequate for ultrahigh gain tasks such as slope and running landings. Correlation of these results with the proposed handling-qualities specification indicates good agreement for the bandwidth boundaries, but suggests the need for more stringent limits on allowable phase delay. Analytical models based on emulation (s-plane) techniques compare favorably with flight-extracted frequency-domain characteristics of the overall (end-to-end) system responses.

Tischler, Mark B.↗