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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Application of Atmospheric Gases and Particulate Matter to the Assessment of Urban Heat Island

Background: Urban heat island (UHI), where built areas are warmer compared to non-urban regions, increases human related diseases and mortality. A key challenge in UHI analysis is the designation of sites as urban or suburban/rural; however, the growing complexity of green spaces in urban areas and the predominance of the transportation sector in nonurban areas creates a dilemma for distinct delineation. Objectives: This study aims to utilize the variability of atmospheric components such as particulate matter (PM), inorganic gases, and volatile organic compounds (VOCs) as direct tracers of the degree of urbanization for ground-based measurements to fully comprehend UHI in convoluted regions with indistinct delineation of urban and nonurban environments. Methods: Atmospheric gases and aerosols were used as direct tracers of urbanization for UHI analysis. Inorganic gases and particulate matter were monitored in two sites in a southeastern US city with varying degrees of urbanization. VOCs were analyzed using a proton transfer reaction time-of-flight mass spectrometer. Results: The more-urbanized site exhibited warmer night conditions and elevated total oxidant levels, leading to the formation of nanometer-sized particles. Machine learning analysis revealed similar atmospheric pollutant profiles for both sites, suggesting comparable sources and variability. Biogenic VOCs were enhanced at the less-urbanized site; however, levels of anthropogenic aromatic VOCs were comparable for both sites. A comprehensive mass spectra analysis revealed distinct molecular backbones per site that further affirmed the applicability of VOCs as indicators of urbanization. Conclusion: This study concludes that VOCs provide more direct and accurate information than typical inorganic gases and PM parameters for characterizing the degree of urbanization. Further exploration of VOCs can enhance our understanding of UHI dynamics and its interaction with vegetation in urban green spaces.

Air quality sensor↗
DOE OSTI2016

Urban stream deserts: Mapping a legacy of urbanization in the United States

Protecting, as well as restoring, natural resources within the urban landscape has environmental and economic importance, especially as the global population continues to shift towards urban areas. One extreme legacy of rapid development and urbanization is stream burial. Riverless urban areas are mapped in 11 Megaregions within the United States, with additional focus on the Great Lakes, using a semi-automated, geoprocessing workflow completed within ArcMap10.2. Combining U.S. Census Bureau Urban Areas (UAs), Impervious Surface Coverage (ISC) from the 2011 National Land Cover Dataset (NLCD), and National Hydrography Dataset (NHD) flowlines, allowed the delineation of urban stream deserts (UrbSDs), or watershed areas where stream channels have likely been buried or removed. Approximately 6.2% of the area of UAs (or 11,490 km 2 ) within the 11 Megaregions are UrbSD, and they are most prevalent in major cities in the North California and Great Lakes Megaregions. Specifically, 537 UrbSD exist within the Great Lakes Megaregion, with Detroit (MI) and Chicago (IL) comprising some of the largest UrbSDs in the United States. Regardless of Megaregion, UrbSDs represent the most intensely urbanized components of the urban environment, as UrbSDs have higher population densities, impervious surface coverage and developed land uses than adjacent urban areas. UrbSD are unique, but poorly understood, components of the urban ecosystem that highlights the consequences of sacrificing longterm environmental sustainability (e.g., ecosystem services) for short-term economic growth (rapid development).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

The relative influence of climate and housing development on current and projected future fire patterns and structure loss across three California landscapes

Climate and land use patterns are expected to change dramatically in the coming century, raising concern about their effects on wildfire patterns and subsequent impacts to human communities. The relative influence of climate versus land use on fires and their impacts, however, remains unclear, particularly given the substantial geographical variability in fire-prone places like California. Here, we developed a modeling framework to compare the importance of climatic and human variables for explaining fire patterns and structure loss for three diverse California landscapes, then projected future large fire and structure loss probability under two different climate (hot-dry or warm-wet) and two different land use (rural or urban residential growth) scenarios. The relative importance of climate and housing pattern varied across regions and according to fire size or whether the model was for large fires or structure loss. The differing strengths of these relationships, in addition to differences in the nature and magnitude of projected climate or land use change, dictated the extent to which large fires or structure loss were projected to change in the future. Despite this variability, housing and human infrastructure were consistently more responsible for explaining fire ignitions and structure loss probability, whereas climate, topography, and fuel variables were more important for explaining large fire patterns. For all study areas, most structure loss occurred in areas with low housing density (from 0.08 to 2.01 units/ha), and expansion of rural residential land use increased structure loss probability in the future. Regardless of future climate scenario, large fire probability was only projected to increase in the northern and interior parts of the state, whereas climate change had no projected impact on fire probability in southern California. Given the variation in fire-climate relationships and land use effects, policy and management decision-making should be customized for specific geographical regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Global reconstructed daily surge levels from the 20th Century Reanalysis (1871–2010)

Studying the effect of global patterns of wind and pressure gradients on the sea level variation (storm surge) is a key issue in understanding the recent climate change effect on the dynamical state of the ocean. The analysis of the spatial and temporal variability of storm surges from observations is a difficult task to accomplish since observations are not homogeneous in time, scarce in space, and moreover, their temporal coverage is limited. A recent global surge database developed by AVISO (DAC, Dynamic Atmospheric Correction) fulfilled the lack of data in terms of spatial coverage, but not regarding time extent, since it only includes the last two decades (1992–2014). In this work, we use the 20th Century Reanalysis V2 (20CR), which spans the years 1871 to 2010, to statistically reconstruct daily maximum surge levels at a global scale. A multivariate linear regression model is fitted between daily mean ERA-interim sea level pressure fields and daily maximum surge levels from DAC. Following, the statistical model is used to reconstruct daily surges using mean sea level pressure fields from 20CR. The verification of the statistical model shows good agreements between DAC levels and the reconstructed surge levels from the 20CR. The validation of the reconstructed surge with tide gauges, distributed throughout the domain, shows good accuracy both in terms of high correlations and small errors. A time series comparison is also depicted at specific tide gauges for the beginning of the 20th century, showing a high concordance. Therefore, this work provides to the scientific community, a daily database of maximum surge levels; which correspond to an extension of the DAC database, from 1871 to 2010. This database can be used to improve the knowledge on historical storm surge conditions, allowing the study of their temporal and spatial variability.

Geology↗
DOE OSTI2021

Tightly-coupled camera/LiDAR integration for point cloud generation from GNSS/INS-assisted UAV mapping systems

Unmanned aerial vehicles (UAVs) equipped with integrated global navigation satellite systems/inertial navigation systems (GNSS/INS) together with cameras and/or LiDAR sensors are being widely used for topographic mapping in a variety of applications such as precision agriculture, coastal monitoring, and archaeological documentation. Integration of image-based and LiDAR point clouds can provide a comprehensive 3D model of the area of interest. For such integration, ensuring a good alignment between data from the different sources is critical. Although many works have been conducted on this topic, there is still a need for a rigorous integration approach that minimizes the discrepancy between camera and LiDAR data caused by inaccurate system calibration parameters and/or trajectory artifacts. This study proposes an automated tightly-coupled camera/LiDAR integration workflow for GNSS/INS-assisted UAV systems. The proposed strategy is conducted in three main steps. First, an image-based point cloud is generated using a LiDAR/GNSS/INS-assisted structure from motion (SfM) strategy. Then, feature correspondences between image-based and LiDAR point clouds are automatically identified. Finally, an integrated-bundle adjustment procedure including image points, LiDAR raw measurements, and GNSS/INS information is conducted to minimize the discrepancy between point clouds from different sensors while estimating system calibration parameters and refining the trajectory information. The proposed SfM strategy and integration framework are evaluated using five datasets. The SfM results show that using LiDAR data can facilitate feature matching and further increase the number of reconstructed 3D points. The experimental results also illustrate that the developed automated camera/LiDAR integration strategy is capable of accurately estimating system calibration parameters to achieve good alignment among camera/LiDAR data from single/multiple systems. Finally, an absolute accuracy in the range of 3–5 cm is achieved for the image/LiDAR point clouds after the integration process.

42 ENGINEERING↗
DOE OSTI2023

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. In conclusion, the proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

EPIsembleVis: A geo-visual analysis and comparison of the prediction ensembles of multiple COVID-19 models

In this work, we present EPIsembleVis, a web-based comparative visual analysis tool for evaluating the consistency of multiple COVID-19 prediction models. Our approach analyzes a collection of COVID-19 predictions from different epidemiological models as an ensemble and utilizes two metrics to quantify model performance. These metrics include (a) prediction uncertainty (represented as the dispersion of predictions in each ensemble) and (b) prediction error (calculated by comparing individual model predictions with the recorded data). Through an interactive visual interface, our approach provides a data-driven workflow for (a) selecting and constructing the COVID-19 model prediction ensemble based on the spatiotemporal overlap of available predictions of multiple epidemiological models, (b) quantifying the model performance using both the uncertainty of each model prediction ensemble, and the error of each ensemble member that represents individual model predictions, and (c) visualizing the spatiotemporal variability in the projection performance of individual models using a suite of novel ensemble visualization techniques, such as the data availability map, a spatiotemporal textured-tile calendar, multivariate rose chart, and time-series leaflet glyph. We demonstrate the capability of our ensemble visual interface through a case study that investigates the performance of weekly COVID-19 predictions, which are provided through the COVID-19 Forecast Hub UMass-Amherst Influenza Forecasting Center of Excellence [47] for the United States and United States Territories. The EPIsembleVis tool is implemented using open-source web technologies and adaptive system design, rendering it interoperable with Elasticsearch and Kibana for automatically ingesting COVID-19 predictions from online repositories, and it is generalizable for analyzing worldwide projections from more epidemiological models.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Geospatial analysis of freight accessibility and job attraction: The role of interstate ramps, airports, ports, and rail

The number of jobs within an industry is significantly influenced by geographical location, with transportation infrastructure playing a key role. While previous research has largely focused on how access to jobs affects employment, less attention has been given to how transportation infrastructure impacts business operations and job attraction. Here, this study addresses this gap by examining how the ease of transporting products to key transportation facilities affects job numbers in freight-intensive industries. Using job data from the Longitudinal Employment Household Dynamics dataset at the Census Tract level, we applied a non-parametric model to assess the impact of proximity to interstate ramps, rail intermodals, ports, and airports. Our analysis revealed that closer transportation infrastructure generally has a greater impact on employment. Specifically, interstate ramps are crucial for attracting jobs, particularly in rural areas, while airport proximity is essential for industries dealing with high-value, time-sensitive goods, as seen notably in Massachusetts. The importance of transportation facilities varies considerably across states and industries. The findings and method in this study can be used by transportation agencies for freight planning.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Detrital U-Pb zircon and 40 Ar/ 39 Ar muscovite geochronology from Middle Pennsylvanian strata in the Anadarko Basin, Texas Panhandle, USA

The Late Mississippian-Permian Anadarko Basin formed in Texas and Oklahoma, USA as the result of inversion of Neoproterozic and Cambrian rift structures. Subsidence was driven by flexural loading of the Amarillo-Wichita Uplift, and this uplift may represent the easternmost element of the Ancestral Rocky Mountains system. The northwestern part of this basin has generally been interpreted to have been filled by sediment derived from the Ancestral Front Range Uplift, ~475 km to the northwest during the early stages of basin filling. We test this model using U-Pb detrital zircon and 40 Ar/ 39 Ar detrital muscovite results from three subsurface samples of the Morrow B sandstone in the northwestern part of the Anadarko Basin. We provide a new maximum depositional age of 310.9 ± 4.9 Ma that indicates the age of the Morrow B to be no older than late Atokan to early Desmoinesian Age (Moscovian), ~10 Myr younger than previously interpreted. In contrast to some previous interpretations, we propose that the most likely source for the sediment in the Morrow B is the Amarillo Uplift to the south. Detrital zircon and detrital muscovite data have age peaks at 900–1300 Ma, 1370 Ma and 1600–1800 Ma corresponding to derivation from Grenville, Granite-Rhyolite and Yavapai-Mazatzal basement provinces, respectively. A dominant detrital zircon peak at 1370 Ma suggests that Mesoproterozoic granites in the Amarillo Uplift were exposed by Middle Pennsylvanian time, and detritus eroded from the Amarillo Uplift dominated the lowstand sediment budget of the Texas Panhandle during this time; small volumes of sediment were likely sourced from the Ancestral Front Range. In conclusion, this study presents the first detrital geochronology data from the subsurface Anadarko Basin and the first detrital muscovite data from late Paleozoic southwestern Laurentia. The results presented here highlight the interpretive power of combined detrital zircon and muscovite datasets.

58 GEOSCIENCES↗