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All sources NASA NTRS DOE OSTI

DOE OSTI2014

Contribution of Road Grade to the Energy Use of Modern Automobiles Across Large Datasets of Real-World Drive Cycles: Preprint

Understanding the real-world power demand of modern automobiles is of critical importance to engineers using modeling and simulation to inform the intelligent design of increasingly efficient powertrains. Increased use of global positioning system (GPS) devices has made large scale data collection of vehicle speed (and associated power demand) a reality. While the availability of real-world GPS data has improved the industry's understanding of in-use vehicle power demand, relatively little attention has been paid to the incremental power requirements imposed by road grade. This analysis quantifies the incremental efficiency impacts of real-world road grade by appending high fidelity elevation profiles to GPS speed traces and performing a large simulation study. Employing a large real-world dataset from the National Renewable Energy Laboratory's Transportation Secure Data Center, vehicle powertrain simulations are performed with and without road grade under five vehicle models. Aggregate results of this study suggest that road grade could be responsible for 1% to 3% of fuel use in light-duty automobiles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Grid Connected Functionality

Dataset demonstrating the potential benefits that residential buildings can provide for frequency regulation services in the electric power grid. In a hardware-in-the-loop (HIL) implementation, simulated homes along with a physical laboratory home are coordinated via a grid aggregator, and it is shown that their aggregate response has the potential to follow the regulation signal on a timescale of seconds. Connected (communication-enabled), devices in the National Renewable Energy Laboratory's (NREL's) Energy Systems Integration Facility (ESIF) received demand response (DR) requests from a grid aggregator, and the devices responded accordingly to meet the signal while satisfying user comfort bounds and physical hardware limitations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2018

ICESat-2 Photon Classification: Finding Signal Photons in the ATL03 Geolocated Photon Data Product

ICESat-2 carries NASA's next-generation laser altimeter, ATLAS, (Advanced Topographic Laser Altimeter System), designed to measure changes in ice sheet height, sea ice freeboard, and vegetation canopy height. ATLAS contains a photon-counting lidar which transmits green (532-nm) pulses at 10kHz. Each pulse is split into 3 pairs of beams (one strong and one weak). Approximately 1014 photons per pulse travel from ATLAS through the atmosphere to reflect off the Earth's surface. Some return back into the ATLAS telescope where they are recorded. Photons from sunlight and instrument noise at the same wavelength are also recorded. The flight software time tags all photons within a 500m to 6 km range window and generates histograms. Using the histograms, it selects a telemetry window which varies from 20m over flat surfaces to hundreds of meters over rougher terrain. ATL03 contains the time, height (relative to the WGS-84 ellipsoid), latitude and longitude of every photon within the telemetry window. The basic challenge is to determine which of these photons were reflected off the surface. We have developed an algorithm that identifies these signal photons and assigns a confidence level (low, medium, or high) to each signal photon based on the signal to noise ratio. We present an overview of the signal identification algorithm and show the results on actual ICESat-2 data over ice sheet, sea ice, vegetated, and water surfaces. Higher level ATLAS products work with aggregations of the photons in order to determine the ellipsoidal height of the Earth, canopy height and structure, and other quantities of geophysical interest.

Remote Sensing↗

NASA NTRSDate not supplied

Analysis of Alternative Architectures for a 2024 Lunar Sortie

This study focused on performing a broad analysis of lunar architectures to identify key alternatives capable of performing a 2024 human lunar landing mission. The goal of the study was to identify architecture alternatives which exhibit robustness, as measured by launch vehicle payload margin, to absorb the natural growth that occurs during design maturation. The study consisted of up to three element architectures which perform the mission from cis-lunar aggregation, through the surface sortie, and return to cis-lunar space. More than 600,000 data points were evaluated utilizing a novel architecture synthesis framework currently being developed by NASA/MSFC. Key findings from the study indicate that architecture complexity can be reduced by minimizing the number of discrete, unique architecture elements, which in turn translates into reduced design, development, test, and evaluation (DDT&E) complexity. It was identified that heavy lift launch vehicles enable such architectures while also providing significant increases to launch vehicle margin over existing and near-term commercial launch vehicle (CLV) performance expected in the 2024 time frame. Additionally, the study identified storable propellant based lander architectures as leading alternatives in achieving a 2024 human lunar landing by providing reduced technology needs and development schedules.

Architecture Analysis↗

DOE OSTI2021

Advancing Solar Innovation for Low and Moderate-Income Households (Final Technical Report)

The overarching goal of this project was to deepen the understanding of the social and economic aspects of solar adoption, with a specific eye towards any pathways or barriers that could be identified that directly impact low and moderate-income households. Specific objectives included (1) identifying and assessing significant non-economic barriers that complicate the ability of LMI households to adopt and benefit from solar, even under scenarios with low economic barriers; (2) assessing the differential ability of groups to benefit from solar adoption based on their ability to invest in different solar innovations; (3) explaining why some LMI communities nonetheless experience significantly higher rates of solar adoption and benefit than others; (4) developing a methodological model for generating data addressing key issues to the project; and (5) designing and developing an interactive web portal that aggregates existing datasets, provides researchers trend analysis capabilities, standardizes project protocols for other states and communities; provides the opportunities for researchers using the same protocol in other communities to upload their results; and engages with the general public by providing them the ability to upload their own perspectives and stories of solar technology adoption. Through a common framework for conducting semi-structured field interviews. In partnership with cooperative extension services, in conjunction with survey research and the aggregation of existing solar adoption datasets, this study has identified that currently ,there is a strong values alignment and knowledge access challenge that continues to affect solar adoption. Participants in this study – many of whom are either low or moderate-income and/or live in underserved rural communities do not believe solar is an option where they live or believe solar is appropriate for their lifestyle. This perception challenge is further exacerbated by the absolute dearth of adopters in most communities and a total lack of local marketing for solar energy. What marketing that does exist is geared towards audiences other than those in the participant's community, reinforcing a perception that 22% of survey respondents (n = 1,551) have: that solar energy is not meant for them and their community. Overcoming these serious human challenges pertaining to knowledge and marketing are critical to reducing the customer acquisition costs incurred amongst LMI communities and especially those in rural America where the opportunities solar provides to increase local energy resilience, security, all while engaging in personal stewardship of the planet could align with local values. Community-Based Social Marketing, built in partnership with community leaders and geared towards meeting local visions of ideal life, is a near term opportunity to bridge the gap between local priorities and national market and policy imperatives for solar while moving towards the administration's goals for renewable energy as part of a just and equitable decarbonization program. The tools and methods developed for this study can serve as a template for identifying other specific local challenges for solar adoption in other state, and provide the opportunity for a common data framework for understanding system-wide trends and barriers to achieving uptake of residential solar in communities that may, ultimately, benefit the most from the technology.

14 SOLAR ENERGY↗

NASA NTRS2016

Ice Cloud Backscatter Study and Comparison with CALIPSO and MODIS Satellite Data

An invariant imbedding T-matrix (II-TM) method is used to calculate the single-scattering properties of 8-column aggregate ice crystals. The II-TM based backscatter values are compared with those calculated by the improved geometric-optics method (IGOM) to refine the backscattering properties of the ice cloud radiative model used in the MODIS Collection 6 cloud optical property product. The integrated attenuated backscatter-to-cloud optical depth (IAB-ICOD) relation is derived from simulations using a CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite) lidar simulator based on a Monte Carlo radiative transfer model. By comparing the simulation results and co-located CALIPSO and MODIS (Moderate Resolution Imaging Spectroradiometer) observations, the non-uniform zonal distribution of ice clouds over ocean is characterized in terms of a mixture of smooth and rough ice particles. The percentage of the smooth particles is approximately 6 percent and 9 percent for tropical and mid-latitude ice clouds, respectively.

MODIS↗

NASA NTRS2005

Remote Sensing Characterization of the Urban Landscape for Improvement of Air Quality Modeling

The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts 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 the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, in moderating ground-level ozone and air temperature, compared to "business as usual" simulations in which heat island mitigation strategies are not applied. 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 (CMAQ) modeling schemes. Use of these data has been found to better characterize low densityhburban development as compared with USGS 1 km land use/land cover data that have traditionally been used in modeling. Air quality prediction for fiture 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, the regional planning agency for the area. This allows the state Environmental Protection agency to evaluate how these transportation plans will affect fbture air quality.

Quattrochi, Dale A.↗

DOE OSTI2022

Cortical Proteins and Individual Differences in Cognitive Resilience in Older Adults

Background and objectives: Cognitive resilience is a well-recognized concept, but knowledge gaps about its underlying mechanisms have made it difficult to develop instruments that identify older adults with high or low resilience. We tested whether aggregating cortical peptides associated with cognitive resilience into an index can identify adults with higher or lower cognitive resilience. Methods: We used data from 1,192 older decedents, including annual clinical testing, indices of 10 Alzheimer disease (AD) and related dementia (ADRD) pathologies, and 226 proteotypic peptides measured in the dorsal lateral prefrontal cortex. We used linear mixed-effects models to identify peptides that were related to cognitive resilience (i.e., cognitive decline not explained by ADRD pathologies [false discovery rate <0.05]). We aggregated the expression levels of these resilience peptides into a person-specific cognitive resilience index and examined its association with AD clinical and pathologic phenotypes. Results: We constructed a resilience index from 52 of 226 peptides related to cognitive resilience. A higher index was associated with slower cognitive decline (estimate 0.05, SE 0.003, p < 0.001) and slower motor decline (estimate 0.005, SE 0.001, p < 0.001). Most resilience peptides (70%) were specific to cognitive decline, but 30% also provided resilience for motor decline. A higher index was also related to a lower burden of AD pathologies (odds ratio [OR] 0.41, SE 0.01, p < 0.001) and modified the association of AD pathology with cognition in that a higher index modified the negative effects of AD pathology on AD dementia proximate to death (OR 0.70, SE 0.14, p = 0.010). Up to 90% of cognitive resilience peptides were related to AD pathologic phenotypes. Discussion: Cortical proteins may provide some degree of cognitive resilience. These multifunctional proteins also seem to provide resilience to other AD clinical phenotypes and have independent associations with ADRD pathologies. Resilience proteins may be high-value therapeutic targets for drug discovery of interventions that maintain brain health in aging adults via multiple pathways.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2006

Land-Cover and Land-Use Change in the Brazilian Amazon: Smallholders, Ranchers and Frontier Stratification

Tropical deforestation is a significant driver of global environmental change, given its impacts on the carbon cycle and biodiversity. Loss of the Amazon forest, the focus of this article, is of particular concern because of the size and the rapid rate at which the forest is being converted to agricultural use. In this article, we identify what has been the most important driver of deforestation in a specific colonization frontier in the Brazilian Amazon. To this end, we consider (1) the land-use dynamics of smallholder households, (2) the formation of pasture by large-scale ranchers, and (3) structural processes of land aggregation by ranchers. Much has been written about relations between smallholders and ranchers in the Brazilian Amazon, particularly those involving conflict over land, and this article explicates the implications of such social processes for land cover. Toward this end, we draw on panel data (1996-2002) and satellite imagery (1986-1999) to show the deforestation that is attributable to small- and largeholders, and the deforestation that is attributable to aggregations of property arising from a process that we refer to as frontier stratification. Evidently, most of the recent deforestation in the study area has resulted from the household processes of smallholders, not from conversions to pasture pursuant to the appropriations of smallholders' property by well-capitalized ranchers or speculators.

Aldrich, Stephen P.↗

DOE OSTI2020

Data-Driven Linear Parameter-Varying Modeling and Control of Flexible Loads for Grid Services: Preprint

Flexible loads have great potential to improve the power grid's flexibility and stability. To effectively control large ensembles of heterogeneous loads, reliable models thereof are required. The paper presents a data-driven modeling and control approach to manage flexible loads for providing grid services. We leverage a linear parameter-varying autoregressive moving average (LPV-ARMA) model to describe the aggregate load response, where the parameters in the model are used to capture external environment impacts (e.g., weather). A gain-scheduling feedback controller is then developed to adapt to environmental variations. This data-driven approach can be easily applied to different types of loads in various environmental conditions. In addition to the ensemble controller, distributed load controllers are designed to deliver grid services, while maintaining the quality of service of inherent load tasks. We demonstrate the work on the IEEE 37-node distribution system for real-time power regulation services through control of thermostatically controlled loads.

control↗

NASA NTRS2006

Spatial Growth Modeling and High Resolution Remote Sensing Data Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 60 percent of the world s population will live in cities. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes within an urban ecosystems perspective. A reduction in air quality over cities is a major result of these impacts. Because of its complexity, the urban landscape is not adequately captured in air quality models such as the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts ozone 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 spatial growth modeling (SGM) projections as improved inputs to a meteorological/air quality modeling system focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include business as usual and smart growth scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. 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 CMAQ modeling schemes. Use of these data has been found to better characterize low density/suburban development as compared with USGS lkm 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, the regional planning agency for the area. This allows the Georgia Environmental Protection Division to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rational decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗

DOE OSTI2025

Poster Abstract: Leveraging Large Language Models to Reveal Interpretable Cooling Behaviors from Smart Thermostat Data

Frequent heatwaves and hot summers increasingly challenge occupant comfort, health, and energy grid stability. Addressing these challenges requires a detailed understanding of household cooling behaviors, such as thermostat adjustments and adaptive responses to extreme conditions. Traditional analyses often rely on aggregated numerical metrics that overlook subtle but important household-specific variations. In this study, we introduce a generalizable methodology that integrates large language models (LLMs) with vision capabilities to enable scalable and detailed analysis of residential thermostat data. Using Ecobee's Donate Your Data (DYD) dataset—which provides five-minute records of indoor temperatures, thermostat setpoints, and HVAC runtimes—we focus on two U.S. cities with contrasting summer climates : Austin (TX) and Phoenix (AZ). Because raw time-series data are not well suited for direct LLM analysis, we transform them into visual representations, such as daily indoor temperature trajectories and weekly runtime histograms, to better capture behavioral variations. Leveraging LLMs' visual interpretation, we extract descriptive behavioral features, including temperature preferences, time-of-day cooling orientation, anticipatory versus reactive heatwave responses, and behavioral consistency. These semantic features support unsupervised clustering to identify distinct occupant archetypes at scale, revealing differences—such as morning-centric anticipatory coolers versus households that shift toward warmer setpoints during heatwaves—that can inform demand response, resilience planning, and health-aware interventions. By converting raw numerical data into interpretable behavioral patterns, this methodology enables scalable and practical analysis of occupant behavior, supporting actionable insights for comfort, resilience, and energy management.

Nihar, Kopal↗

NASA NTRS2006

Remote Sensing and Spatial Growth Modeling Coupled with Air Quality Modeling to Assess the Impact of Atlanta, Georgia on the Local and Regional Environment

The growth of cities, both in population and areal extent, appears as an inexorable process. Urbanization continues at a rapid rate, and it is estimated that by the year 2025, 80 percent of the world s population will live in cities. Directly aligned with the expansion of cities is urban sprawl. Urban expansion has profound impacts on a host of biophysical, environmental, and atmospheric processes. A reduction in air quality over cities is a major result of these impacts. Strategies that can be directly or indirectly implemented to help remediate air quality problems in cities and that can be accepted by political decision makers and the general public are now being explored to help bring down air pollutants and improve air quality. The urban landscape is inherently complex and this complexity is not adequately captured in air quality models, particularly the Community Multiscale Air Quality (CMAQ) model that is used to assess whether urban areas are in attainment of EPA air quality standards, primarily for ground level ozone. This inadequacy of the CMAQ model to sufficiently respond to the heterogeneous nature of the urban landscape can impact how well the model predicts 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 spatial growth modeling (SGM) projections as improved inputs to the meteorology component of the CMAQ model focusing on the Atlanta, Georgia metropolitan area as a case study. These growth projections include "business as usual" and "smart growth" scenarios out to 2030. The growth projections illustrate the effects of employing urban heat island mitigation strategies, such as increasing tree canopy and albedo across the Atlanta metro area, which in turn, are used to model how ozone and air temperature can potentially be moderated as impacts on elevating ground-level ozone, as opposed to not utilizing heat island mitigation strategies. 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 (CMAQ) modeling schemes. Use of these data have been found to better characterize low density/suburban development as compared with USGS 1km 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, the regional planning agency for the area. This allows the State Environmental Protection agency to evaluate how these transportation plans will affect future air quality. The coupled SGM and air quality modeling approach provides insight on what the impacts of Atlanta s growth will be on the local and regional environment and exists as a mechanism that can be used by policy makers to make rationale decisions on urban growth and sustainability for the metropolitan area in the future.

Quattrochi, Dale A.↗

DOE OSTI2025

IM3 Phase 2 Official Simulations: GCAM-Demeter-SELECT Annualized Land Use and Land Cover, Wood Harvest and Fertilization Data with Dynamic Urbanization Harmonized to CLM Land Definitions at 0.125 Degrees

Annualized land use land cover data, including wood harvest and fertilizer use data from the Global Change Analysis Model (GCAM) downscaled to 0.125 degrees for couping with the Community Land Model (CLM). GCAM here refers to GCAM-USA v5.3.im3 which has an enhanced electricity sector and an updated data system needed to represent regional to local scale dynamics. Data is also harmonized with future urbanization projections from the Spatially-Explicit, Long-term, Empirical City developmenT (SELECT) model. Projections/Data are generated using the demeter land use and land cover downscaling model. Original projections were generated at 0.05 degrees before being aggregared to 0.125 degrees. Projections are available for 8 alternative scenarios. Two versions of final data are included- one with managed forests or harvested forest area per pixel broken out and one with the same aggregated into total forests. Following folders are included: demeter_78_PFT_output:This is the final output of dynamic land use land cover change for 78 PFTs as required by CLM raw_outputs_incl_managed_forest: This is the final output but with managed forests broken out as a different PFT. Essentially a 79th PFT is added. wood_harvest_outputs: Wood harvest output per pixel in gC/m2 fertilization_outputs: Fertilizer use per pixel in gN/m2 Each NetCDF file in each folder represents a projection for a separate year, scenario. Land use outputs are organized as PFT level data saved as subdata. Link to GCAM version used- https://data.msdlive.org/records/yb23g-44274 Link to SELECT documentation -https://www.sciencedirect.com/science/article/pii/S1364815219301707 Link to CLM documentation- - https://www.cesm.ucar.edu/models/clm In case of questions contact- kanishka.narayan@pnnl.gov

GCAM-USA↗

DOE OSTI2015

Enhanced Luminescent Stability through Particle Interactions in Silicon Nanocrystal Aggregates

Close-packed assemblies of ligand-passivated colloidal nanocrystals can exhibit enhanced photoluminescent stability, but the origin of this effect is unclear. Here, we use experiment, simulation, and ab initio computation to examine the influence of interparticle interactions on the photoluminescent stability of silicon nanocrystal aggregates. The time-dependent photoluminescence emitted by structures ranging in size from a single quantum dot to agglomerates of more than a thousand is compared with Monte Carlo simulations of noninteracting ensembles using measured single-particle blinking data as input. In contrast to the behavior typically exhibited by the metal chalcogenides, the measured photoluminescent stability shows an enhancement with respect to the noninteracting scenario with increasing aggregate size. We model this behavior using time-dependent density functional theory calculations of energy transfer between neighboring nanocrystals as a function of nanocrystal size, separation, and the presence of charge and/or surface-passivation defects. In conclusion, our results suggest that rapid exciton transfer from “bright” nanocrystals to surface trap states in nearest-neighbors can efficiently fill such traps and enhance the stability of emission by promoting the radiative recombination of slowly diffusing excited electrons.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

NASA NTRS2020

Wildfire Smoke Particle Properties and Evolution, From Space-Based Multi-Angle Imaging II: The Williams Flats Fire during the FIREX-AQ Campaign

Although the characteristics of biomass burning events and the ambient ecosystem determine emitted smoke composition, the conditions that modulate the partitioning of black carbon (BC) and brown carbon (BrC) formation are not well understood, nor are the spatial or temporal frequency of factors driving smoke particle evolution, such as hydration, coagulation, and oxidation, all of which impact smoke radiative forcing. In situ data from surface observation sites and aircraft field campaigns offer deep insight into the optical, chemical, and microphysical traits of biomass burning (BB) smoke aerosols, such as single scattering albedo (SSA) and size distribution, but cannot by themselves provide robust statistical characterization of both emitted and evolved particles. Data from the NASA Earth Observing System’s Multi-Angle Imaging SpectroRadiometer (MISR) instrument can provide at least a partial picture of BB particle properties and their evolution downwind, once properly validated. Here we use in situ data from the joint NOAA/NASA 2019 Fire Influence on Regional to Global Environments Experiment-Air Quality (FIREX-AQ) field campaign to assess the strengths and limitations of MISR-derived constraints on particle size, shape, light-absorption, and its spectral slope, as well as plume height and associated wind vectors. Based on the satellite observations, we also offer inferences about aging mechanisms effecting downwind particle evolution, such as gravitational settling, oxidation, secondary particle formation, and the combination of particle aggregation and condensational growth. This work builds upon our previous study, adding confidence to our interpretation of the remote-sensing data based on an expanded suite of in situ measurements for validation. The satellite and in situ measurements offer similar characterizations of particle property evolution as a function of smoke age for the 06 August Williams Flats Fire, and most of the key differences in particle size and absorption can be attributed to differences in sampling and changes in the plume geometry between sampling times. Whereas the aircraft data provide validation for the MISR retrievals, the satellite data offer a spatially continuous mapping of particle properties over the plume, which helps identify trends in particle property downwind evolution that are ambiguous in the sparsely sampled aircraft transects. The MISR data record is more than two decades long, offering future opportunities to study regional wildfire plume behavior statistically, where aircraft data are limited or entirely lacking.

biomass burning↗

NASA NTRS1975

Dry heat effects on survival of indigenous soil particle microflora and particle viability studies of Kennedy Space Center soil

Research efforts were concentrated on attempts to obtain data concerning the dry heat resistance of particle microflora in Kennedy Space Center soil samples. The in situ dry heat resistance profiles at selected temperatures for the aggregate microflora on soil particles of certain size ranges were determined. Viability profiles of older soil samples were compared with more recently stored soil samples. The effect of increased particle numbers on viability profiles after dry heat treatment was investigated. These soil particle viability data for various temperatures and times provide information on the soil microflora response to heat treatment and are useful in making selections for spacecraft sterilization cycles.

Ruschmeyer, O. R.↗

DOE OSTI2015

Wildlife Densities and Habitat Use Across Temporal and Spatial Scales on the Mid-Atlantic Outer Continental Shelf. Final Report to the Department of Energy EERE Wind & Water Power Technologies Office

The Mid-Atlantic Baseline Studies Project helped address environmental barriers to offshore wind energy development in the mid-Atlantic region by providing regulators, developers, and other stakeholders with comprehensive baseline ecological data and analyses. Project funders and collaborators from a range of academic institutions, non-governmental organizations, federal agencies, foundations, and private companies came together to study bird, sea turtle, and marine mammal distributions, densities, and movements on the mid-Atlantic Outer Continental Shelf between 2012 and 2014. Specific project activities and goals included the following: (1) Conduct standardized surveys to quantify bird, sea turtle, and marine mammal densities seasonally and annually throughout the study region and identify important habitat use or aggregation areas. (2) Develop statistical models to help understand the drivers of wildlife distribution and abundance patterns. (3) Use individual tracking data for several focal bird species to provide information on population connectivity and individual movements that is complementary to survey data. (4) Identify species that are likely to be exposed to offshore wind energy development activities in the mid-Atlantic study area. (5) Develop U.S.-based technological resources and assessment methods for future monitoring efforts, including a comparison of high resolution digital video aerial surveys to boat-based surveys. (6) Help meet data needs associated with National Environmental Policy Act (NEPA), Marine Mammal Protection Act, and Endangered Species Act requirements, by contributing several years of data and analysis towards future Environmental Impact Statements. This report consists of six parts: Project overview (executive summary and Chapters 1-2); Examining wildlife distributions and relative abundance from a digital video aerial survey platform (Chapters 3-6); Examining wildlife distributions and abundance using boat-based surveys (Chapters 7-12); Integrating data across survey platforms (Chapters 13-19); Individual movements and habitat use for focal bird species (Chapters 20-25); and Nocturnal avian migration monitoring (Chapters 26-27). Boat-based and digital video aerial surveys each had specific advantages and disadvantages, but were largely complementary (Chapters 1, 5, 13-14). Digital aerial surveys were particularly useful for covering offshore areas at broad scales, where general distributions of taxonomic groups were a priority; boat surveys could provide more detailed data on species identities and behaviors, but were more limited in geographic scope due to their slower survey pace. The mid-Atlantic study area was important for wintering and breeding taxa, and its location also made it a key migratory corridor. There was considerable variation in species composition and spatial patterns by season, largely driven by dynamic environmental conditions (Chapters 12, 15, and 20-22). Habitat gradients in nearshore waters, however, were reliable influences on productivity and patterns of species distributions and abundance. Areas within about 30-40 km of the coast offshore of the mouths of Chesapeake and Delaware Bays, as well as to the south of Delaware Bay along the coast, were consistent hotspots of abundance and species diversity, regardless of survey methodology or analytical approach (Chapters 2, 12, 17). Inter-annual variation was substantial, and the importance of certain environmental variables in predicting animal distributions indicates that these species may well respond to future environmental shifts brought about by anthropogenic effects and climatic change. This study is an important first step, however, towards understanding how bird, marine mammal, and sea turtle populations in the mid-Atlantic may be exposed to offshore wind energy development and other anthropogenic activities. The results of this study provide insight to help address environmental permitting requirements for current and future offshore development projects, and serve as a starting point for more site-specific studies, risk analyses, and evaluation of potential measures to avoid and minimize those risks.

17 WIND ENERGY↗