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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Great Salt Lake Health & Air Quality: Monitoring Lakebed Exposure and its Impact on Air Quality and Environmental Hazards in the Great Salt Lake Watershed

The Great Salt Lake (GSL) is a terminal saline lake located in northern Utah, USA. Water inflow has declined rapidly over the last forty years due to human withdrawals and climate change. Lake levels have been lowered which has exposed over 50% of the lakebed to erosion. There is a public health risk tied to the subsequent increased frequency and intensity of regional dust storms caused by airborne lakebed dust under specific meteorological conditions.

Piper Christian↗
NASA NTRS2023

Great Salt Lake Health & Air Quality: Monitoring Lakebed Exposure and its Impact on Air Quality and Environmental Hazards in the Great Salt Lake Watershed

The Great Salt Lake (GSL) is a terminal saline lake located in northern Utah, USA. Water inflow has declined rapidly over the last forty years due to human withdrawals and climate change. Lake levels have been lowered which has exposed over 50% of the lakebed to erosion. There is a public health risk tied to the subsequent increased frequency and intensity of regional dust storms caused by airborne lakebed dust under specific meteorological conditions.

Piper Christian↗
NASA NTRS2023

Great Salt Lake Health and Air Quality: Monitoring Lakebed Exposure and its Impact on Air Quality and Environmental Hazards in the Great Salt Lake Watershed

Water flow into the Great Salt Lake has declined rapidly over the last forty years due to human withdrawals and climate change. As a result of declining lake levels, over 50% of the lakebed is now exposed. Dust storms may grow in frequency and intensity across Northern Utah as lakebed dust becomes airborne under specific meteorological conditions. In our research project, we utilized satellite imagery from Terra and Aqua, Sentinel-5P, CALIPSO, Landsat 5 TM, Landsat 7 ETM+, Landsat 8 OLI-2, Suomi NPP, ground sensor environmental data, and demographic data to understand the relationship between lake desiccation and dust, and the impact of pollution upon the communities surrounding the Great Salt Lake. By plotting changes in Lake Surface Area against Aerosol Optical Depth (AOD) over our study period (2010-2022), we found an inverse relationship (R2=0.3423) between lake surface area and dust levels within our study area. We conducted a Vertical Feature Mask (VFM) and Extinction Coefficient Plot, from which we identified that during dust events, the aerosol type is mainly polluted dust and the aerosol height is 200 meters from the surface. Lastly, we created bivariate choropleth maps, which demonstrate which census tracts within our study area are most vulnerable to AOD (a proxy for PM2.5 from dust), NO2 and HCHO (precursors to ozone). In summary, our findings revealed that declining lake levels are associated with an increase in intensity of dust events, and these dust events will particularly impact residents of Tooele County and the west side of Salt Lake City. Project resources support partner needs by informing targeted air monitoring efforts, lakebed management practices, and advocacy efforts for GSL stewardship.

Terminal Saline Lake↗
NASA NTRS2005

Implementation of Programmatic Quality and the Impact on Safety

The purpose of this paper is to discuss the implementation of a programmatic quality assurance discipline within the International Space Station Program and the resulting impact on safety. NASA culture has continued to stress safety at the expense of quality when both are extremely important and both can equally influence the success or failure of a Program or Mission. Although safety was heavily criticized in the media after Col~imbiaa, strong case can be made that it was the failure of quality processes and quality assurance in all processes that eventually led to the Columbia accident. Consequently, it is possible to have good quality processes without safety, but it is impossible to have good safety processes without quality. The ISS Program quality assurance function was analyzed as representative of the long-term manned missions that are consistent with the President s Vision for Space Exploration. Background topics are as follows: The quality assurance organizational structure within the ISS Program and the interrelationships between various internal and external organizations. ISS Program quality roles and responsibilities with respect to internal Program Offices and other external organizations such as the Shuttle Program, JSC Directorates, NASA Headquarters, NASA Contractors, other NASA Centers, and International Partner/participants will be addressed. A detailed analysis of implemented quality assurance responsibilities and functions with respect to NASA Headquarters, the JSC S&MA Directorate, and the ISS Program will be presented. Discussions topics are as follows: A comparison of quality and safety resources in terms of staffing, training, experience, and certifications. A benchmark assessment of the lessons learned from the Columbia Accident Investigation (CAB) Report (and follow-up reports and assessments), NASA Benchmarking, and traditional quality assurance activities against ISS quality procedures and practices. The lack of a coherent operational and sustaining quality assurance strategy for long-term manned space flight. An analysis of the ISS waiver processes and the Problem Reporting and Corrective Action (PRACA) process implemented as quality functions. Impact of current ISS Program procedures and practices with regards to operational safety and risk A discussion regarding a "defense-in-depth" approach to quality functions will be provided to address the issue of "integration vs independence" with respect to the roles of Programs, NASA Centers, and NASA Headquarters. Generic recommendations are offered to address the inadequacies identified in the implementation of ISS quality assurance. A reassessment by the NASA community regarding the importance of a "quality culture" as a component within a larger "safety culture" will generate a more effective and value-added functionality that will ultimately enhance safety.

Huls, Dale Thomas↗
NASA NTRS2010

Ambiguity of Quality in Remote Sensing Data

This slide presentation reviews some of the issues in quality of remote sensing data. Data "quality" is used in several different contexts in remote sensing data, with quite different meanings. At the pixel level, quality typically refers to a quality control process exercised by the processing algorithm, not an explicit declaration of accuracy or precision. File level quality is usually a statistical summary of the pixel-level quality but is of doubtful use for scenes covering large areal extents. Quality at the dataset or product level, on the other hand, usually refers to how accurately the dataset is believed to represent the physical quantities it purports to measure. This assessment often bears but an indirect relationship at best to pixel level quality. In addition to ambiguity at different levels of granularity, ambiguity is endemic within levels. Pixel-level quality terms vary widely, as do recommendations for use of these flags. At the dataset/product level, quality for low-resolution gridded products is often extrapolated from validation campaigns using high spatial resolution swath data, a suspect practice at best. Making use of quality at all levels is complicated by the dependence on application needs. We will present examples of the various meanings of quality in remote sensing data and possible ways forward toward a more unified and usable quality framework.

Lynnes, Christopher↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRSDate not supplied

Supporting Global Air Quality Management Needs With A Flexible Data Fusion Tool for Estimation and Forecasting in Google Earth Engine

High spatial and temporal resolution air quality estimation and forecasting can be enhanced by combining global data sources, like chemical transport models and satellite remote sensing, with local information from regulatory and low-cost air quality monitors. Successful integration of data from these diverse sources is complicated by many factors, however, including differences in spatial and temporal resolution, data availability and latency issues, varying data quality, and large computational and data storage requirements. This presentation will provide an overview of a NASA-funded effort to develop the foundation for future operationalization of air quality forecasting for world-wide end-users and integration into their air quality management decision processes, which will be achieved in future phases of this multi-year project. We will summarize our progress in developing a data fusion system using the Google Earth Engine platform which can integrate model, satellite, and surface-level monitoring datasets to enhance estimation and forecasting of air-quality-relevant pollutants at sub-daily and sub-city scales. The tool is being developed in close cooperation with several city- and regional-level air quality managers in the USA and around the world. Our end-goal is to provide these air quality managers with the information they need to assess and anticipate the impacts of poor air quality, track changes in air quality due to ongoing mitigation efforts and land use changes, and identify ways to improve their air quality monitoring strategies. This presentation will focus on recent advances achieved through the project, including integration of multiple air quality datasets in a prototype data fusion system in Google Earth Engine, the quantification of uncertainties associated with our data fusion approach, and the development of user interfaces and visualization tools to convey air quality information in a way which best meets end-user needs.

Carl Malings↗
NASA NTRS2018

Information Quality Cluster and Usability

The Information Quality Cluster (IQC) of the Federation of Earth Science Information Partners (ESIP) has been active since 2014 with membership from multiple organizations including NASA and NOAA. The purpose of this presentation is to foster collaboration between the IQC and the ESIP Usability Cluster. The IQC's activities are motivated partly by the guidelines on information quality from several federal agencies. The agencies developed the guidelines complying with a request in 2002 from the Office of Management and Budget (OMB). The OMB request resulted from a congressional mandate, namely, Section 515 of the Treasury and General Government Appropriations Act for Fiscal Year 2001 (Public Law 106-554; H.R. 5658). NASA's guidelines, for example, emphasize the need for high information quality indicating the various types of public users of information from NASA's missions and programs. The IQC's vision is to become an authoritative and responsive resource of information and guidance to data providers on how best to implement data quality standards and best practices, so that the implementations comply with the various agencies' guidelines, as well as provide users with the best quality of information possible. The IQC interacts with various national and international organizations and encourages collaboration for exchange of information. The IQC considers four aspects of information quality: Scientific Quality, Product Quality, Stewardship Quality and Service Quality. The IQC has considered several use cases to identify issues in capturing, describing, providing access to, and enabling use of information on quality. Several of these use cases point to issues about the usability of information. Collaboration between the IQC and Usability Cluster will be beneficial for arriving at solutions to such issues.

Remote Sensing; Data Systems; Information Quality;↗