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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
NASA NTRS2011

MIRO Continuum Calibration for Asteroid Mode

MIRO (Microwave Instrument for the Rosetta Orbiter) is a lightweight, uncooled, dual-frequency heterodyne radiometer. The MIRO encountered asteroid Steins in 2008, and during the flyby, MIRO used the Asteroid Mode to measure the emission spectrum of Steins. The Asteroid Mode is one of the seven modes of the MIRO operation, and is designed to increase the length of time that a spectral line is in the MIRO pass-band during a flyby of an object. This software is used to calibrate the continuum measurement of Steins emission power during the asteroid flyby. The MIRO raw measurement data need to be calibrated in order to obtain physically meaningful data. This software calibrates the MIRO raw measurements in digital units to the brightness temperature in Kelvin. The software uses two calibration sequences that are included in the Asteroid Mode. One sequence is at the beginning of the mode, and the other at the end. The first six frames contain the measurement of a cold calibration target, while the last six frames measure a warm calibration target. The targets have known temperatures and are used to provide reference power and gain, which can be used to convert MIRO measurements into brightness temperature. The software was developed to calibrate MIRO continuum measurements from Asteroid Mode. The software determines the relationship between the raw digital unit measured by MIRO and the equivalent brightness temperature by analyzing data from calibration frames. The found relationship is applied to non-calibration frames, which are the measurements of an object of interest such as asteroids and other planetary objects that MIRO encounters during its operation. This software characterizes the gain fluctuations statistically and determines which method to estimate gain between calibration frames. For example, if the fluctuation is lower than a statistically significant level, the averaging method is used to estimate the gain between the calibration frames. If the fluctuation is found to be statistically significant, a linear interpolation of gain and reference power is used to estimate the gain between the calibration frames.

Lee, Seungwon↗
DOE OSTI2020

Southeast Regional CO 2 Utilization and Storage Acceleration Partnership (SECARB-USA): Needs Assessment Framework

The goal of this task is to develop an assessment to identify data needed to advance geologic storage projects, with a focus on the SECARB region. The GCCC team considers the needs of CO 2 source, storage, and utilization operators and stakeholders, finance and insurance institutions, state and local government relators and agencies, property owners, local stakeholders at prospective storage complexes, and environmental non-governmental organizations (NGOs). Many of these needs are spelled out in the requirements of the Class VI permit. However, at the initial stages of project development, the workflow will not start with permit writing. A number of other factors must be evaluated prior to this major investment. The team’s goal in this report is to inventory the range of typical needs of for a wide variety of projects. For subsequent tasks, the team will consider how these needs vary among projects and through the stages of investment, so that project developers can plan the early stages of capitalization.

42 ENGINEERING↗
DOE OSTI2023

Lithium-Ion Battery Diagnostics Using Electrochemical Impedance via Machine-Learning

Diagnosing battery states such as health, state-of-charge, or temperature is crucial for ensuring the safety and reliability of electrochemical energy storage systems. While some states, such as temperature, may be measured using cheap sensors, accurate diagnosis of battery health metrics usually requires time-consuming performance measurements, making them infeasible for use in real-world operation. These health metrics can be measured during lab-testing and then estimated on-line using predictive life models or via state observer algorithms such as Kalman filters, but these predictive methods should be supplemented by actual measurement of battery health whenever possible to ensure reliability. Rapid measurement of battery health may be done by various types of fast diagnostic techniques such as electrochemical impedance spectroscopy (EIS), which can be performed in only a few minutes and require only a fraction of the energy and power needed for a full charge and discharge measurement. But there is a substantial challenge for estimating battery health using EIS data, as EIS is sensitive to cell temperature, state-of-charge, current, and resting time in addition to health. Thus, utilizing EIS data to predict battery capacity requires correcting for all these additional variables, a task that is extremely difficult to handle analytically. This talk utilizes machine-learning methods to estimate the effectiveness of battery capacity prediction from EIS data, leveraging a data set of hundreds of EIS measurements recorded at varying temperature and state-of-charge throughout a 500-day aging study of 32 commercial, large-format NMC-Graphite lithium-ion batteries. Using EIS as input to machine-learning models is complicated by the nonlinear response of impedance to battery health, temperature, and state-of-charge, as well as the collinearity between the impedance response at neighboring frequencies, which can easily lead to overfit models. To train robust models, features from EIS data need to be extracted from the data or some subset of critical frequencies selected. Many approaches for extracting and selecting features from EIS data from electrochemical analysis and machine-learning fields were identified for analysis: using the entire raw spectra; selection of one, two, or many frequencies from the entire spectra; selecting interesting points from the EIS measurement using domain knowledge; fitting EIS with an equivalent-circuit model; calculating statistics on the raw impedance values; and reducing the dimensionality of the data using unsupervised linear (principal component analysis) and non-linear (uniform manifold approximation and projection) methods. These approaches were rigorously compared using a machine-learning pipeline approach, training linear, Gaussian process, and random forest regression models and quantifying performance using cross-validation as well as a held-out test set. An artificial neural network model trained on the raw spectra was also tested. Promising pipelines were fine-tuned via Bayesian hyperparameter optimization using cross-validation loss and training with class-specific weights to counter data set imbalance. The most reliable method for utilizing impedance in this work was the selection of two optimal frequencies through an exhaustive search, resulting in about 2% mean absolute error on test data for both Gaussian process and random forest model architectures. Interrogation of a variety of models reveals critical frequencies of 100 Hz and 103 Hz for this data set, though the optimal set of frequencies is not necessarily intuitive, i.e., the best performing models are not simply those that use impedance at frequencies that have the highest correlation to the relative discharge capacity. The best performing model is an ensemble model, which is able to predict battery capacity with 1.9% mean absolute error for unseen cells using impedance recorded at a variety of temperatures and states-of-charge.

battery↗
DOE OSTI2023

Agilent CRADA (Abstract)

The CRADA between Agilent Technologies Inc. and Battelle will focus on five software components as listed below: Prototype 4D Feature Finding functionality with a particular focus on recovering low level features and extending the bottom end dynamic range of IM-MS technology. Compare and contrast developments to current 4D Feature Finding capabilities. Highlight important algorithmic aspects employed. Implement the PNNL saturation correction algorithm. Agilent will give PNNL the needed data file access API and assistance in understanding it implementation and any needed instrumental aspects. Supported high resolution products to include Agilent’s TOF, QTOF and IM-QTOF mass spectrometers. PNNL will then work with Agilent to benchmark performance. Implementation of the PNNL Hadamard de-multiplexing algorithm. Agilent will give provide PNNL the needed date file access API access and as needed assistance in understanding the current Agilent multiplexed IM offering. PNNL will then work with Agilent on benchmark performance. Add ion mobility collision cross sections to existing and new metabolomic libraries for data analysis with Agilent’s informatics program MPP/ID Browser. PNNL will work with Agilent to create a software pipeline that takes data from chemical and metabolic standards and properly formats it for inclusion in MPP accessible libraries, using the collision cross section as a new separation dimension. Improvements of MPP multidimensional matching to identify metabolomic features using multiple characteristics beyond retention time and accurate mass. Most significantly matching will include analyte collision cross section with proposed support for sample fraction or RapidFire cartridge and fragmentation spectra. PNNL will work with Agilent to modify and improve the current MPP analysis pipeline to allow for creating, aligning, and identifying MS features defined by accurate mass, collision cross section and chromatographic retention time. As additional criteria such as fraction or RapidFire cartridge type are supported in the identification process, then they also will become part of the automation workflow. This includes the automation of said system to work with command line program (i.e. not a GUI) sufficient for programmatic execution in a pipeline.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Report on High Energy Arcing Fault Experiments: Experimental Results from Open Box Enclosures

This report documents an experimental program designed to investigate High Energy Arcing Fault (HEAF) phenomena. The experiments focus on providing data to better characterize the arc to improve the prediction of arc energy emitted during a HEAF event. An open box experiment allow for direct observation of the arc, which allows diagnostic instrumentation to record the phenomenological data needed for better characterization of the arc energy source term. The data collected supports characterization of the arc and arc jet, enclosure breach, material loss, and electrical properties. These results will be used to better characterizing the hazard for improvements in fire probabilistic risk assessment (PRA) realism. The experiments were performed at KEMA Labs located in Chalfont, Pennsylvania. The experimental design, setup, and execution were completed by staff from the NRC, the National Institute of Standards and Technology (NIST), Sandia National Laboratories (SNL) and KEMA Labs. In addition, representatives from the Electric Power Research Institute (EPRI) observed some of the experimental setup and execution. The HEAF experiments were performed between August 22, 2020 and September 18, 2020 on near-identical 51 cm (20 in) cube metal boxes suspended from a Unistrut support structure. The three-phase arcing fault was initiated at the ends of the conductors oriented vertically and located at the center of the box. Either aluminum or copper conductors were used for the conductors. The low-voltage experiments used 1 000 volts AC, while the medium-voltage experiments used 6 900 volts AC consistent with other recently completed experiments. Durations of the experiment ranged from 1 s to 5 s with fault currents ranging from 1 kA to 30 kA. Real-time electrical operating conditions, including voltage, current and frequency, were measured during the experiments. Heat fluxes and incident energies were measured with plate thermometers, radiometers, and slug calorimeters at various locations around the electrical enclosures. The experiments were documented with normal and high-speed videography, infrared imaging and photography.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1971

Determination of stratospheric temperature and height gradients from nimbus 3 radiation data

To improve the specification of stratospheric horizontal temperature and geopotential height fields from satellite radiation data, needed for high flying aircraft, a technique was derived to estimate data between satellite tracks using interpolated IRIS 15-micron data from Nimbus III. The interpolation is based on the observed gradients of the MRIR 15-micron radiances between subsatellite tracks. The technique was verified with radiosonde data taken within 6 hours of the satellite data. The sample varied from 1126 pairs at low levels to 383 pairs at 10 mb using northern hemisphere data for June 15 to July 20, 1969. The data were separated into five latitude bands. The Rms temperature differences were generally from 2 to 5 C for all levels above 300 mb. From 500 to 300 mb RMS differences vary from 4 to 9C except at high latitudes which show values near 3C. The RMS differences between radiosonde heights and those calculated hydrostatically from the surface were from 30 to 280 meters increasing from the surface to 10 mb. Integration starting at 100 mb reduced the RMS difference in the stratosphere to 20 to 120 meters from 70 to 10 mb. From a comparison with actual operational maps at 50 and 10 mb, it appears the techniques developed produce analyses in general agreement with those from radiosonde data. In addition, they are able to indicate details over areas of sparse data not shown by conventional techniques.

Nicholas, G. W.↗
NASA NTRS1976

A plan for application system verification tests: The value of improved meteorological information, volume 1

The framework within which the Applications Systems Verification Tests (ASVTs) are performed and the economic consequences of improved meteorological information demonstrated is described. This framework considers the impact of improved information on decision processes, the data needs to demonstrate the economic impact of the improved information, the data availability, the methodology for determining and analyzing the collected data and demonstrating the economic impact of the improved information, and the possible methods of data collection. Three ASVTs are considered and program outlines and plans are developed for performing experiments to demonstrate the economic consequences of improved meteorological information. The ASVTs are concerned with the citrus crop in Florida, the cotton crop in Mississippi and a group of diverse crops in Oregon. The program outlines and plans include schedules, manpower estimates and funding requirements.

Source record↗
NASA NTRS2008

LEOMAC: A Future 'Global Atmospheric Composition Mission' (CACM) Concept

Resolution of important outstanding questions in air quality, climate change and ozone layer stability demands global observations of multiple chemical species with high horizontal and vertical resolution from the boundary layer to the stratopause. We present a mission concept that delivers the needed atmospheric composition observations, along with cloud ice and water vapor data needed for improvements in climate and weather forecasting models. The mission comprises ultraviolet and infrared nadir and microwave limb viewing instruments observing wide swaths each orbit. We review the scientific goals of the mission and the measurement capabilities this concept will deliver. We describe how precessing orbits offer significant improvements in temporal resolution and diurnal coverage compared to sun-synchronous orbits. Such improvements are needed to quantify the impact of critical 'fast processes' such as deep convection on the composition and radiative properties of the upper troposphere, a region where water vapor and ozone are strong but poorly understood greenhouse gases. This concept can serve as the 'Global Atmospheric Composition Mission' (GACM) recently recommended by the National Academy of Sciences decadal survey as one of 17 priority earth science missions for the coming decade.

chemistry↗
NASA NTRS2015

Magnetospheric Multiscale Instrument Suite Operations and Data System

The four Magnetospheric Multiscale (MMS) spacecraft will collect a combined volume of approximately 100 gigabits per day of particle and field data. On average, only 4 gigabits of that volume can be transmitted to the ground. To maximize the scientific value of each transmitted data segment, MMS has developed the Science Operations Center (SOC) to manage science operations, instrument operations, and selection, downlink, distribution, and archiving of MMS science data sets. The SOC is managed by the Laboratory for Atmospheric and Space Physics (LASP) in Boulder, Colorado and serves as the primary point of contact for community participation in the mission. MMS instrument teams conduct their operations through the SOC, and utilize the SOC's Science Data Center (SOC) for data management and distribution. The SOC provides a single mission data archive for the housekeeping and science data, calibration data, ephemerides, attitude and other ancillary data needed to support the scientific use and interpretation. All levels of data products will reside at and be publicly disseminated from the SDC. Documentation and metadata describing data products, algorithms, instrument calibrations, validation, and data quality will be provided. Arguably, the most important innovation developed by the SOC is the MMS burst data management and selection system. With nested automation and 'Scientist-in-the-Loop' (SITL) processes, these systems are designed to maximize the value of the burst data by prioritizing the data segments selected for transmission to the ground. This paper describes the MMS science operations approach, processes and data systems, including the burst system and the SITL concept.

Baker, D. N.↗
NASA NTRSDate not supplied

From NASA's EOS to ESO: Advancing Applications of the Future Atmosphere Observing (AOS) Mission

The NASA Earth System Observatory (ESO) Atmosphere Observing System (AOS) is being designed to explore the fundamental questions of how interconnections between aerosols, clouds and precipitation impact our weather and climate, addressing real-world challenges to benefit society. A fundamental component of the AOS mission is ensuring that applications for economic and societal benefit are considered to the greatest extent possible in mission design. As a result, the AOS Applications Impact Team (AIT) was formed to address this objective. The overarching goal of the AIT is to help improve capacity for transitioning science to applications to make it possible to more quickly and effectively inform decisions that will directly benefit society. We seek to maximize AOS benefit to impact decisions through early engagement in the mission development phase in order to prepare stakeholders to apply observations as soon as AOS mission data become available. A critical component of preparing future users of AOS mission data is building on the successes of applications of NASA’s existing Earth Observing System (EOS), A-Train, and sub-orbital campaigns with the goal of advancing current mission applications activities and preparing for innovative AOS mission observations. NASA’s GPM mission forms a framework to enhance AOS precipitation applications while AOS health and air quality applications benefit from the heritage of CALIPSO and MODIS. Additionally, AOS will likely benefit from current and future missions such as TROPICS, MAIA, TEMPO, and PACE which launch before AOS. Additionally, current sub-orbital field campaigns, such as NASA IMPACTS and ACTIVATE, provide rich data sources to highlight future AOS capabilities. Engaging with existing missions and sub-orbital field campaigns helps to identify and understand data needs, gaps, and opportunities for current and future stakeholders, determine what data products are of highest value and use, and connect stakeholders with current mission data that can serve as AOS proxy data, among others. In this presentation, we provide an overview of AOS AIT activities, initiatives, and the AOS Applications Seminar Series to highlight how existing EOS, A-Train, and sub-orbital missions can play a critical role in advancing AOS applications prior to launch.

Emily B. Berndt↗
DOE OSTI2024

Geospatial Data Platform for All

Spatiotemporal data has evolved in scale due to augmented use in cross-domain applications. Simultaneously, there is substantial growth in the availability of Geographic Information Systems (GIS) data provided by the United States Geological Survey (USGS) along with other federal, state, county, or local agencies through open-data portals and public access APIs. However, data availability does not equate with accessibility. Large-scale analyses and applications require robust, performant data management with co-location of data storage and computing. The insufficiency of data management infrastructure compels researchers to adopt ad hoc project- specific GIS data storage solutions (e.g., copying data to High-Performance computer file systems). As an ad hoc storage strategy does not scale, it hampers cross-domain analyses causing difficulty in data reuse and utilizing existing code bases. Furthermore, GIS data is complex and requires expertise to analyze and manipulate due to its intricate data structures and data-specific projection transformations. Despite the challenges, we recognize that derived GIS data products, e.g., satellite or LIDAR-based images, can be used in downstream applications such as AI by domain, but non-GIS experts. To address the data needs and overcome the challenges, we are working towards a GIS Data Platform focused on efficient data storage, data discovery and access, and an API to enable common workflows. We propose a knowledge-graph (KG) approach for data discovery, whereby datasets are semantically linked to higher- level constructs such as projects and research areas. The semantic data links enable researchers to explore datasets in a top-down approach by specifying relevant and meaningful terms (assists in finding hidden data). An advantage is that the nodes and edges in a knowledge graph create built-in semantic documentation. Deeper spatiotemporal connections between data sources can be encoded via Graph Neural Networks (GNN) (Zhang et al., 2021). The KG approach can be extended to integrate the data itself in a Virtual KG (VKG). Our work will derive inspiration from large-scale VKG efforts that have been undertaken or are currently underway as part of the OpenStreetMap project (Ding et al., 2021). For DOE Data Days, we share the proposed geospatial data platform hybrid (cloud/on-prem) architecture, our work-to-date on storing, retrieving, and transforming LiDAR and raster data relevant to two important NREL use-cases, including the Renewable Energy Potential (reV) Model, and present our proposal for a KG based data discovery engine.

data platform↗
NASA NTRS2010

Real-Time On-Board Processing Validation of MSPI Ground Camera Images

The Earth Sciences Decadal Survey identifies a multiangle, multispectral, high-accuracy polarization imager as one requirement for the Aerosol-Cloud-Ecosystem (ACE) mission. JPL has been developing a Multiangle SpectroPolarimetric Imager (MSPI) as a candidate to fill this need. A key technology development needed for MSPI is on-board signal processing to calculate polarimetry data as imaged by each of the 9 cameras forming the instrument. With funding from NASA's Advanced Information Systems Technology (AIST) Program, JPL is solving the real-time data processing requirements to demonstrate, for the first time, how signal data at 95 Mbytes/sec over 16-channels for each of the 9 multiangle cameras in the spaceborne instrument can be reduced on-board to 0.45 Mbytes/sec. This will produce the intensity and polarization data needed to characterize aerosol and cloud microphysical properties. Using the Xilinx Virtex-5 FPGA including PowerPC440 processors we have implemented a least squares fitting algorithm that extracts intensity and polarimetric parameters in real-time, thereby substantially reducing the image data volume for spacecraft downlink without loss of science information.

Earth Sciences Decadal Survey↗
NASA NTRSDate not supplied

Communication and Social Science in the Satellite Needs Working Group (SNWG) Assessment

Every two years, NASA conducts an in-depth assessment of the satellite Earth observation data needs of U.S. federal civilian agencies submitted through the Satellite Needs Working Group (SNWG) survey. The SNWG assessment occurs at the nexus of science and people: in the 2022 assessment, over 100 scientists across NASA, NOAA, and USGS were organized to interview over 165 end users at 29 agencies about their unique satellite needs, brainstorm a range of solutions to meet those needs, and communicate back to agencies about resources available for meeting their needs. The innovative approaches to communication, organization, and team make-up that will be described in this talk are vital to the success of the SNWG assessment. As the first major step in evaluating a satellite need, the tri-agency assessment team interviews the agency SMEs who submitted the survey to understand how satellite data could help inform their decision-making process or enable them to fulfill their key responsibilities. In preparation, NASA utilizes social scientists to provide training to all assessment participants on how to hold a discovery-centered interview, including starting with a purpose, creating a welcoming space, exploring all aspects and edges of the need, and brainstorming possible solutions to meet the need. After the interviews, assessment participants propose and review solutions across all thematic areas, seeking those that will help multiple agencies. During the selection process, agencies expected to benefit from a new solution have an opportunity to provide feedback on the proposed activity and are invited to co-design the solution with NASA, should it be implemented. The organization of needs and solutions takes place in Asana, a workflow management tool adapted for the SNWG assessment, and the Report Generation Tool (RGT) enables assessment teams to collaboratively write reports that are returned to each agency with information on current and upcoming resources that help meet their needs.

Katrina Virts↗
DOE OSTI2021

Making a Water Data System Responsive to Information Needs of Decision Makers

Evidence-based environmental management requires data that are sufficient, accessible, useful and used. A mismatch between data, data systems, and data needs for decision making can result in inefficient and inequitable capital investments, resource allocations, environmental protection, hazard mitigation, and quality of life. In this paper, we examine the relationship between data and decision making in environmental management, with a focus on water management. We focus on the concept of decision-driven data systems —data systems that incorporate an assessment of decision-makers' data needs into their design. The aim of the research was to examine the process of translating data into effective decision making by engaging stakeholders in the development of a water data system. Using California's legislative mandate for state agencies to integrate existing water and other environmental data as a case study, we developed and applied a participatory approach to inform data-system design and identify unmet data needs. Using workshops and focused stakeholder meetings, we developed 20 diverse use cases to assess data sources, availability, characteristics, gaps, and other attributes of data used for representative decisions. Federal and state agencies made up about 90% of the data sources, and could readily adapt to a federated data system, our recommended model for the state. The remaining 10% of more-specialized data, central to important decisions across multiple use cases, would require additional investment or incentives to achieve data consistency, interoperability, and compatibility with a federated system. Based on this assessment, we propose a typology of different types of data limitations and gaps described by stakeholders. We also propose technical, governance, and stakeholder engagement evaluation criteria to guide planning and building environmental data systems. Data-system governance involving both producers and users of data was seen as essential to achieving workable standards, stable funding, convenient data availability, resilience to institutional change, and long-term buy-in by stakeholders. Our work provides a replicable lesson for using decision-maker and stakeholder engagement to shape the design of an environmental data system, and inform a technical design that addresses both user and producer needs.

Cantor, Alida↗
NASA NTRS2002

Laboratory Astrophysics White Paper: Summary of Laboratory Astrophysics Needs

The NASA Laboratory Astrophysics Workshop (NASA LAW) met at NASA Ames Research Center from 1-3 May 2002 to assess the role that laboratory astrophysics plays in the optimization of NASA missions, both at the science conception level and at the science return level. Space missions provide understanding of fundamental questions regarding the origin and evolution of galaxies, stars, and planetary systems. In all of these areas the interpretation of results from NASA's space missions relies crucially upon data obtained from the laboratory. We stress that Laboratory Astrophysics is important not only in the interpretation of data, but also in the design and planning of future missions. We recognize a symbiosis between missions to explore the universe and the underlying basic data needed to interpret the data from those missions. In the following we provide a summary of the consensus results from our Workshop, starting with general programmatic findings and followed by a list of more specific scientific areas that need attention. We stress that this is a 'living document' and that these lists are subject to change as new missions or new areas of research rise to the fore.

Source record↗
NASA NTRS1973

Interdisciplinary research on the application of ERTS-1 data to the regional land use planning process.

Although the degree to which ERTS-1 imagery can satisfy regional land use planning data needs is not yet known, it appears to offer means by which the data acquisition process can be immeasurably improved. This paper documents the initial experiences of an interdisciplinary group attempting to formulate ways of analyzing the effectiveness of ERTS-1 imagery as a base for environmental monitoring and the resolution of regional land allocation problems. Because of the need to describe and depict regional resource complexity in an interrelatable state, certain resources within the geographical regions have been inventoried and stored in a two-dimensional computer-based map form. Computer oriented processes were developed to provide for the economical storage, analysis and spatial display of natural and cultural data for regional land use planning purposes. Statistical programs have been developed that correlate interpreted data with stored data, both spatially and numerically.

Clapp, J. L.↗
DOE OSTI2021

Pollution inequality 50 years after the Clean Air Act: the need for hyperlocal data and action

Fifty years ago the Clean Air Act amendments of 1970 were the first major US legislation that authorized regulation of air pollutants, creating National Ambient Air Quality Standards (NAAQSs) to protect public health and the environment. While US air quality has improved, with average PM 2.5 concentrations in 2016 a third of 1981 levels, air pollution remains a major health risk in the US and globally. Moreover, air pollution impacts are still uneven, with the most polluted US communities of 50 years ago still so today. Air pollution “hot spots” result in disproportionate exposure at neighborhood scales within cities, particularly in disadvantaged communities, but an in-depth understanding at these scales is lacking. This policy perspective discusses how trends in sensor technology, spatial data collection, analytics and retrieval are converging to enable the production of hyperlocal air pollution data, and that this needs to be done in a manner that enables marginalized communities to shape decision making.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS1972

Interdisciplinary research on the application of ERTS-1 data to the regional land use planning process

The author has identified the following significant results. Although the degree to which ERTS-1 imagery can satisfy regional land use planning data needs is not yet known, it appears to offer means by which the data acquisition process can be immeasurably improved. The initial experiences of an interdisciplinary group attempting to formulate ways of analyzing the effectiveness of ERTS-1 imagery as a base for environmental monitoring and the resolution of regional land allocation problems are documented. Application of imagery to the regional planning process consists of utilizing representative geographical regions within the state of Wisconsin. Because of the need to describe and depict regional resource complexity in an interrelatable state, certain resources within the geographical regions have been inventoried and stored in a two-dimensional computer-based map form. Computer oriented processes were developed to provide for the economical storage, analysis, and spatial display of natural and cultural data for regional land use planning purposes. The authors are optimistic that the imagery will provide revelant data for land use decision making at regional levels.

Clapp, J. L.↗