Results for “sample consumption” — Page 41

DOE OSTI2023

Linear Viscoelastic Properties of Putative Cyclic Polymers Synthesized by Reversible Radical Recombination Polymerization (R3P)

Linear viscoelastic properties in both melt and solution states are reported for a series of poly(3,6-dioxa-1,8-octanedithiol) (polyDODT) made by reversible radical recombination polymerization (R 3 P) under conditions designed to produce linear (LDODT), cyclic (RDODT) and linear-cyclic mixtures (LRDODT). PolyDODT is amorphous (T g < -50 °C) and highly flexible (entanglement molecular weight M e,lin ≈ 1850 g/mol for LDODT). PolyDODT’s low T g and low M e,lin enable characterization over a wide dynamic range and a wide range of dimensionless weight-average molecular weight Z w = M w /M e,lin . Measurements at temperatures from -57 °C to 100 °C provide up to 18 deades of reduced frequency, which is necessary to characterize RDODT melts with Z w from 23 to 300. The two highest molecular weight polymers in the present RDODT series have such high M w (406k and 556k g/mol) that mass spectrometry, NMR spectroscopy, and even chemical assays for chain ends are unable to rule out up to 2 mol% linear contaminant. By studying the samples in solution (using dilution to reduce Z w ) we could compare their dynamics with those of previously established high-purity polystyrene (PS) rings (limited to Z w ≤ 13.6). RDODT solutions with Z w < 15 (concentrations <5wt% for RDODT-406k and 556k) have dynamic moduli G^* that accord with LCCC-purified PS rings in terms of the frequency dependence (including the absence of a plateau), the progression of shapes of G* as a function of Z w , and the linear scaling of their zero-shear viscosity η0 with M w . The shape of G* as a function of Z w for solutions of RDODT-406k and -556k also accord with lower M w RDODT melts (which have ≤ 1.3mol% linear contaminant). Thus, measurement of the linear viscoelastic properties of appropriate concentrations of high M w (>200k g/mol) putative cyclic polymers, in which linear chains evade spectroscopic detection, may provide an alternative means (though not fully proven) of validation of sample purity. When Z w > 15 (including all seven RDODT melts and eight of their solutions), G* has a rubbery plateau. This suggests that the onset of entanglement-like behavior in rings requires 4-5-fold greater Z w than is required for linear chains. Further, the plateau moduli of RDODT samples are indistinguishable from G N o of the corresponding LDODT (melt or matched-concentration solutions). In entangled linear polymers, the observation that G N o is independent of Z w follows from limitations on lateral fluctuations due to neighboring chains becoming independent of position along a given chain. The present results for RDODT suggest that this holds for sufficiently long endless chains, too. While the RDODT have the same G N o as entangled LDODT, when Z w > 60 the terminal relaxation, if reached at all, of RDODT extends to orders of magnitude lower frequency than an entangled linear polymer of the same Z w . Consequently, the viscosity of RDODT with Z w > 60 increases with Z w much more strongly than the 3.4-power observed for entangled linear polymers. Lastly, these novel polymers, with disulfide-linked backbone and broad relaxation time distribution may prove important in relation to biodegradable elastomers and materials with exceptional low-frequency dissipation, extending at least 12 decades below the onset of the rubbery plateau.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Black carbon emissions from modern automated cordwood stoves for space heating

Wood burning stove design is improving as regulation become stricter for particulate matter emissions. Here, this study describes results of black carbon (BC) emissions from three modern automated wood stoves when tested in common failure modes using a test protocol that included cold-starts, high and low fuel loading densities, and use of overly large pieces of fuel. Emission sampling was performed using a dilution sampling system in tandem with an aethalometer to measure BC with high time resolution. Results show that the time-weighted average BC emission rates were 0.91, 4.30, and 1.74 g/h for the three stoves tested (A, B, and C respectively), the values, both higher and lower than the United States 2020 certification test emission limit of 2.5 g/h. The results demonstrate good performance overall, given the rigorous nature of the testing protocol used. The aethalometer data showed that the performance was relatively poor during cold-start, when the fuel was ignited and combusted without a coalbed, and when stoves failed to optimize air delivery due to specific loading and output conditions. During the cold-start test section, the average BC emission rate was seven times higher than the overall average for Stove A, 10 times above the average for Stove B, and two times above the average emission rate for Stove C. Calculations of the Average Angstrom Absorption Exponent (AAE) of the exhaust aerosol showed little correlation with BC concentration. The results show that automation results in a quantifiable reduction in particular BC emissions.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS1996

Changes in aerobic power of women, ages 20-64 yr

This study quantified and compared the cross-sectional and longitudinal influence of age, self-report physical activity (SR-PA), and body composition (%fat) on the decline of maximal aerobic power (VO2peak) of women. The cross-sectional sample consisted of 409 healthy women, ages 20-64 yr. The 43 women of the longitudinal sample were from the same population and examined twice, the mean time between tests was 3.7 (+/-2.2) yr. Peak oxygen uptake was determined by indirect calorimetry during a maximal treadmill test. The zero-order correlation of -0.742 between VO2peak and %fat was significantly (P < 0.05) higher then the SR-PA (r = 0.626) and age correlations (r = -0.633). Linear regression defined the cross-sectional age-related decline in VO2peak at 0.537 ml.kg-1.min-1.yr-1. Multiple regression analysis (R = 0.851) showed that adding %fat and SR-PA and their interaction to the regression model reduced the age regression weight of -0.537, to -0.265 ml.kg-1.min-1.yr-1. Statistically controlling for time differences between tests, general linear models analysis showed that longitudinal changes in aerobic power were due to independent changes in %fat and SR-PA, confirming the cross-sectional results. These findings are consistent with men's data from the same lab showing that about 50% of the cross-sectional age-related decline in VO2peak was due to %fat and SR-PA.

Aging/physiology↗

DOE OSTI2017

Molecular Outflows in Local ULIRGs: Energetics from Multitransition OH Analysis

We report on the energetics of molecular outflows in 14 local ultraluminous infrared galaxies (ULIRGs) that show unambiguous outflow signatures (P Cygni profiles or high-velocity absorption wings) in the far-infrared lines of OH measured with the Herschel/PACS spectrometer. All sample galaxies are gas-rich mergers at various stages of the merging process. Detection of both ground-state (at 119 and 79 μm) and one or more radiatively excited (at 65 and 84 μm) lines allows us to model the nuclear gas (≲300 pc) and the more extended components using spherically symmetric radiative transfer models. Reliable models and the corresponding energetics are found in 12 of the 14 sources. The highest molecular outflow velocities are found in buried sources, in which slower but massive expansion of the nuclear gas is also observed. With the exception of a few outliers, the outflows have momentum fluxes of (2–5) × L {sub IR}/c and mechanical luminosities of (0.1–0.3)% of L {sub IR}. The moderate momentum boosts in these sources (≲3) suggest that the outflows are mostly momentum driven by the combined effects of active galactic nuclei (AGNs) and nuclear starbursts, as a result of radiation pressure, winds, and supernova remnants. In some sources (∼20%), however, powerful (10{sup 10.5–11} L {sub ⊙}) AGN feedback and (partially) energy-conserving phases are required, with momentum boosts in the range of 3–20. These outflows appear to be stochastic, strong AGN feedback events that occur throughout the merging process. In a few sources, the outflow activity in the innermost regions has subsided in the past ∼1 Myr. While OH traces the molecular outflows at subkiloparsec scales, comparison of the masses traced by OH with those previously inferred from tracers of more extended outflowing gas suggests that most mass is loaded (with loading factors of M-dot /SFR=1--10) from the central galactic cores (a few × 100 pc), qualitatively consistent with an ongoing inside-out quenching of star formation. Outflow depletion timescales are <10{sup 8} yr, shorter than the gas consumption timescales by factors of 1.1–15, and are anticorrelated with the AGN luminosity.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2017

Energy Efficiency Potential in the U.S. Single-Family Housing Stock

Typical approaches for assessing energy efficiency potential in buildings use a limited number of prototypes, and therefore suffer from inadequate resolution when pass-fail cost-effectiveness tests are applied, which can significantly underestimate or overestimate the economic potential of energy efficiency technologies. This analysis applies a new approach to large-scale residential energy analysis, combining the use of large public and private data sources, statistical sampling, detailed building simulations, and high-performance computing to achieve unprecedented granularity - and therefore accuracy - in modeling the diversity of the single-family housing stock. The result is a comprehensive set of maps, tables, and figures showing the technical and economic potential of 50 plus residential energy efficiency upgrades and packages for each state. Policymakers, program designers, and manufacturers can use these results to identify upgrades with the highest potential for cost-effective savings in a particular state or region, as well as help identify customer segments for targeted marketing and deployment. The primary finding of this analysis is that there is significant technical and economic potential to save electricity and on-site fuel use in the single-family housing stock. However, the economic potential is very sensitive to the cost-effectiveness criteria used for analysis. Additionally, the savings of particular energy efficiency upgrades is situation-specific within the housing stock (depending on climate, building vintage, heating fuel type, building physical characteristics, etc.).

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

Disturbed, diffuse, or just missing? A global study of the H I content of Hickson compact groups

Hickson compact groups (HCGs) are dense configurations of four to ten galaxies, whose H I morphology appears to follow an evolutionary sequence of three phases, with gas initially confined to galaxies, then significant amounts spread throughout the intra-group medium, and finally with almost no gas remaining in the galaxies themselves. It has also been suggested that several groups may harbour a diffuse H I component that is resolved out by interferometric observations. The H I deficiency of HCGs is expected to increase as the H I morphological phase progresses along the evolutionary sequence. If this is the case, H I deficiency would be a rough proxy for the age and evolutionary state of a HCG. We aim to test this hypothesis for the first time using a large sample of HCGs and to investigate the evidence for diffuse H I in HCGs. We performed a uniform reduction of all publicly available VLA H I observations (38 HCGs) with a purpose-built pipeline that also maximises the reproducibility of this study. The resulting H I data cubes were then analysed with the latest software tools to perform a manual separation of emission features into those belonging to galaxies and those extending into the intra-group medium. We thereby classified the H I morphological phase of each group as well as quantified their H I deficiency compared to galaxies in isolation. We find little evidence that H I deficiency can be used as a proxy for the evolutionary phase of a compact group in either of the first two phases, with the distribution of H I deficiency being consistent in both. However, for the final phase, the distribution clearly shifts to high H I deficiencies, with more than 90% of the expected H I content typically missing. Across all HCGs studied, we identify a few cases where there is strong evidence for a diffuse gas component in the intra-group medium, which might be detectable with improved observations. We also classify a new sub-phase where groups contain a lone H I -bearing galaxy, but are otherwise devoid of gas. The new morphological phase we have identified is likely the result of an evolved, gas-poor group acquiring a new, gas-rich member. The large spread of H I deficiencies in the first two morphological phases suggests that there is a broad range of initial H I content in HCGs, which is perhaps influenced by large-scale environment, and that the timescale for morphological changes is, in general, considerably shorter than the timescale for the destruction or consumption of neutral gas in these systems.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

A dataset for understanding self-reported patterns influencing residential energy decisions

Household occupant behavior and decision-making dynamics substantially impact technology uptake and residential building energy performance. Although significant research underscores the importance of social science in energy studies, few public data with representative samples on household energy decision-making patterns are available. The dataset (UPGRADE-E: Understanding Patterns Guiding Residential Adoption and Decisions about Energy Efficiency) presents 9,919 responses from U.S. residents of single-family and small multifamily homes. Derived from a national-scale internet survey, the dataset contains 391 variables: demographics, building characteristics, home modifications, willingness to adopt new technologies, motivations for making changes, barriers, program participation, trusted information sources, and energy scenarios. Responses were validated via internal consistency checks and comparison with other U.S. national scale datasets. UPGRADE-E advances knowledge of household energy related decision-making, tying demographics, home modifications, and self-reported cognitive drivers together at a scale and breadth that has not been previously achieved. Policymakers and researchers at local, regional, and national levels may leverage this dataset to understand drivers influencing the adoption of key technologies in U.S. homes.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2018

Preparation for Pick-and-Eat Food Production on the International Space Station: Flight Definition for the VEG-04 and VEG-05 Missions

Growth of fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the dietary experience as we move toward longer-duration missions. Tending plants also may serve as a countermeasure for crew psychological stresses associated with long duration spaceflight. However, requirements to support consistent growth of a variety of high quality, nutritious crops under spaceflight environmental conditions is unknown. This study is exploring the potential to grow plants for food production on the International Space Station (ISS) using the Veggie vegetable production system. Ground testing is underway to compare the impacts of several fertilizer and lighting treatments on growth, quality, and nutritional composition of the leafy green crop mizuna, and the dwarf tomato crop Red Robin when subjected to Veggie ISS environmental conditions. Early testing focused on the leafy crop Tokyo Bekana Chinese cabbage, but ground tests indicated that this plant suffered from stress responses when grown under LEDs and the chronically elevated CO2 levels found on the ISS. Mizuna, a related leafy variety that grows well in the presence of high CO2, and has excellent organoleptic characteristics, was selected as an alternate crop. Tomato crops have been grown using two fertilizer formulations and two pollination techniques, and growth tests using different red:blue lighting environments are underway. Chemical analysis is also being conducted and these data, when coupled with the growth results, will be used to down-select to the two best lighting treatments and best fertilizer treatment for future testing of each crop on the ISS. Additionally, seed-source testing has become important, with mizuna seeds from two different vendors growing very differently. A seed source has been selected, and seed-surface-sanitizing methods have been confirmed for mizuna, but these remain under development for tomato. A crop-handling protocol is also being evaluated to support food safety. All harvests reserve a subset of samples for microbial analysis to determine baseline microbial levels and help establish critical control points for food safety. Testing was initially conducted in hardware analogs of the standard Veggie plant pillows. However, a new Veggie watering system, the Passive Orbital Nutrient Delivery System or PONDS, has been designed and is being prepared for future flight experiments. With the selection of this growth system, ground tests have shifted to analog PONDS systems. Crop tests on ISS, designated VEG-04 for mizuna and VEG-05 for tomato, are planned in 2018 to evaluate any additional impacts of spaceflight on the light and fertilizer conditions down-selected from ground tests. A set of Veggie-specific questions has been developed to characterize the psychological impacts of plant growth and plant-care activities during spaceflight. Organoleptic questionnaires have been developed to assess produce attributes in microgravity taste sessions. These tests for plants growing in the Veggie hardware on ISS will help to mitigate the risk of an inadequate food supply for long duration missions by developing methods and determining hardware requirements to integrate fresh vegetables as a dietary supplement. This research was co-funded by the Human Research Program and Space Biology (MTL1075) in the ILSRA 2015 NRA call.

Veggie↗

NASA NTRS2018

Preparation for Pick-and-Eat Food Production on the International Space Station: Flight Definition for the VEG-04 and VEG-05 Missions

Growth of fresh, nutritious, palatable produce for crew consumption during spaceflight may provide health-promoting, bioavailable nutrients and enhance the dietary experience as we move toward longer-duration missions. Tending plants also may serve as a countermeasure for crew psychological stresses associated with long duration spaceflight. However, requirements to support consistent growth of a variety of high quality, nutritious crops under spaceflight environmental conditions is unknown. This study is exploring the potential to grow plants for food production on the International Space Station (ISS) using the Veggie vegetable production system. Ground testing is underway to compare the impacts of several fertilizer and lighting treatments on growth, quality, and nutritional composition of the leafy green crop mizuna, and the dwarf tomato crop "Red Robin" when subjected to Veggie ISS environmental conditions. Early testing focused on the leafy crop "Tokyo Bekana" Chinese cabbage, but ground tests indicated that this plant suffered from stress responses when grown under LEDs and the chronically elevated CO2 levels found on the ISS. Mizuna, a related leafy variety that grows well in the presence of high CO2, and has excellent organoleptic characteristics, was selected as an alternate crop. Tomato crops have been grown using two fertilizer formulations and two pollination techniques, and growth tests using different red:blue lighting environments are underway. Chemical analysis is also being conducted and these data, when coupled with the growth results, will be used to down-select to the two best lighting treatments and best fertilizer treatment for future testing of each crop on the ISS. Additionally, seed-source testing has become important, with mizuna seeds from two different vendors growing very differently. A seed source has been selected, and seed-surface-sanitizing methods have been confirmed for mizuna, but these remain under development for tomato. A crop-handling protocol is also being evaluated to support food safety. All harvests reserve a subset of samples for microbial analysis to determine baseline microbial levels and help establish critical control points for food safety. Testing was initially conducted in hardware analogs of the standard Veggie plant pillows. However, a new Veggie watering system, the Passive Orbital Nutrient Delivery System or PONDS, has been designed and is being prepared for future flight experiments. With the selection of this growth system, ground tests have shifted to analog PONDS systems. Crop tests on ISS, designated VEG-04 for mizuna and VEG-05 for tomato, are planned in 2018 to evaluate any additional impacts of spaceflight on the light and fertilizer conditions down-selected from ground tests. A set of Veggie-specific questions has been developed to characterize the psychological impacts of plant growth and plant-care activities during spaceflight. Organoleptic questionnaires have been developed to assess produce attributes in microgravity taste sessions. These tests for plants growing in the Veggie hardware on ISS will help to mitigate the risk of an inadequate food supply for long duration missions by developing methods and determining hardware requirements to integrate fresh vegetables as a dietary supplement. This research was co-funded by the Human Research Program and Space Biology (MTL#1075) in the ILSRA 2015 NRA call.

LED light↗

DOE OSTI2024

Reducing the cost of home energy upgrades in the US: An industry survey

Decarbonizing the US residential building stock requires a substantial acceleration in home energy upgrades. Numerous barriers exist to accelerating adoption of efficient and electric building technologies, but foremost among these is high upfront costs. This study uses an industry survey delivered to a sample of home energy professionals to examine promising cost reduction strategies across a range of project types, including HVAC, water heating, and envelope/insulation projects. The survey included quantitative and qualitative questions to collect evidence on the estimated cost reduction potential of these strategies and their likelihood of use in the construction industry. The 167 survey respondents included contractors, energy consultants, architects, manufacturers, and others with experience in delivering energy upgrades in single-family and multifamily buildings in the US. Results show that significant cost reductions are achievable by minimizing additional infrastructure costs (such as replacing electric panels), streamlining project planning/management, and deploying innovations that simplify installation. We find that for a typical deep retrofit project, including heat pumps for space and water heating in addition to envelope upgrades, the strategies could result in a total installed cost reduction of nearly 50%, dramatically improving the customer economics of such a project. This research makes a novel contribution to the literature on strategies to reduce the costs of residential retrofits. We discuss how our study's insights on the highest-value cost reduction strategies for home energy upgrades can further accelerate their uptake in the US housing stock.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

Dataset 1: A National and City Dataset on Human Factors in Pooled Rideshare, 2021

Dataset 1: A National and City Dataset on Human Factors in Pooled Rideshare, 2021. Dataset Description: Pooled Rideshare Acceptance Survey - Phase 1 (2021, N = 5,385). This dataset captures responses from a nationally representative sample of 5,385 adults across the United States to understand public acceptance, preferences, and behavioral intentions related to pooled rideshare (PR) services. The primary objective of this research is to provide actionable insights to inform the design, deployment, and policy development of sustainable shared mobility systems. Data was collected via an online survey administered through a national panel provider. Participants ranged in age from 18 to 95 years, and representation from all U.S. regions. The survey instrument was designed to explore numerous dimensions related to PR adoption including demographic traits, current travel habits, rideshare familiarity, trust, safety, environmental attitudes, and user experience preferences. Both rideshare users and non-users were included, offering a diverse range of perspectives. - Phase_1_Final - The dataset includes survey items developed from literature reviews, and prior field studies. Each row represents an individual respondent, and each column corresponds to a variable such as willingness to use pooled rideshare, attitudes toward specific service features, and sociodemographic data. The data is available in both .CSV and .SAV formats. - Phase_1_Final_MapFile - The accompanying data dictionary explains all variable labels, response scales, and codes. An .XLSX format of the full survey instrument is also included to support interpretation and reuse of the dataset.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2025

2013-2014 Greater Fairbanks, Alaska, Transportation Survey

# 2013–2014 Greater Fairbanks, Alaska, Transportation Survey The 2013–2014 Greater Fairbanks Transportation Survey obtained behavior data for regional travel demand modeling. The planning region in Alaska comprised the North Star Borough—known as the PM2.5 nonattainment region—and included the cities of Fairbanks and North Pole. ## Data Collection Agency The Alaska Department of Transportation and Public Facilities conducted the survey. ## Methodology Data collection occurred in two phases: the first in fall 2013 and the second in winter 2014. The first phase employed address-based sampling to recruit more than 1,700 households for a one-day personal travel survey, and a sub-sample participated with global position system (GPS) and on-board diagnostic (OBD) loggers installed in their vehicles (282 vehicles) for one week. The purpose of phase one was to better understand the impact of vehicle emissions on air quality in the PM2.5 nonattainment region. Many of the households participating in the vehicle GPS/OBD portion of phase one were asked to participate in phase two. ## Drive Cycle Processing and Filtering NREL has developed a GPS data filtration routine to filter erroneous data points in individual drive cycles sourced from GPS devices mounted in vehicles. Second-by-second drive cycle data collected from GPS-instrumented vehicles during this survey have passed through NREL's drive cycle processing and filtering routines. ## Survey Records Survey records include 135 households. ## More Information For more information about the survey, see the [Greater Fairbanks Transportation Survey Final Report](https://www.nrel.gov/media/docs/libraries/tsdc/greater-fairbanks-transportation-survey-final-report.pdf?sfvrsn=16ecd45b_1). ## Transportation Data For details on available travel survey data and variable definitions, see the [data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/akdot_data_dictionary.pdf?sfvrsn=16778e11_1). NREL-generated drive cycle data are also available for this survey. For details on available data and variable definitions, see the [drive cycle data dictionary](https://www.nrel.gov/media/docs/libraries/tsdc/drive_cycles_data_dictionary.pdf?sfvrsn=7de7e888_1). Transportation data are available as zipped files. [Download Winzip](http://www.winzip.com/downwz.htm).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗