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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

United States cool surfaces deployment plan

Solar-reflective building envelope surfaces, such as cool roofs and walls, can be especially helpful in disadvantaged communities that often have poorly insulated older homes, aging or absent air conditioning units, steep utility bills, polluted air, and high vulnerability and exposure to extreme heat. With support from the U.S. Department of Energy, our project seeks to dramatically increase the climate-appropriate deployment of cool surfaces across the United States with an emphasis on their application to disadvantaged communities. First, we sought to identify cool-surface deployment barriers, opportunities, and models by (a) reviewing the history of cool-surface deployment activities, (b) interviewing cool-surface stakeholders, (c) researching successful energy-efficiency/green building deployment models; and (d) interviewing the actors who have implemented these models. Second, we conducted a workshop to engage stakeholders in development of a deployment plan. Third, we asked several U.S. federal agencies (a) how cool surfaces and cool surface stakeholders could support their missions and (b) how agency activities could support cool-surface deployment. Fourth, we identified a set of transformative ideas that form the core of the deployment plan. Transformative ideas include but are not limited to initiatives to (a) launch an educational campaign to make the general public and building professionals aware of how cool roofs and walls exclude unwanted solar heat; (b) create a “Cool Roof Prize” stimulating the development of affordable, high-performance cool asphalt roofing shingles; (c) conduct high-profile, large-scale demonstration programs that bring cool surfaces to disadvantaged heat-vulnerable communities; and (d) support local, regional, and state climate action (heat mitigation) plans with cool surfaces.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2021

Temporal and Spatial Distribution of Health, Labor, and Crop Benefits of Climate Change Mitigation in the United States

Societal benefits from climate change mitigation accrue via multiple pathways. We examine the US impacts of emission changes on several factors that are affected by both climate and air quality responses. Nationwide benefits through midcentury stem primarily from air quality improvements, which are realized rapidly, and include human health, labor productivity, and crop yield benefits. Benefits from reduced heat exposure become large around 2060, thereafter often dominating over those from improved air quality. Monetized benefits are in the tens of trillions of dollars for avoided deaths and tens of billions for labor productivity and crop yield increases and reduced hospital expenditures. Total monetized benefits this century are dominated by health and are much larger than in previous analyses due to improved understanding of the human health impacts of exposure to both heat and air pollution. Benefit–cost ratios are therefore much larger than in prior studies, especially those that neglected clean air benefits. Specifically, benefits from clean air exceed costs in the first decade, whereas benefits from climate alone exceed costs in the latter half of the century. Furthermore, monetized US benefits largely stem from US emissions reductions. Increased emphasis on the localized, near-term air quality–related impacts would better align policies with societal benefits and, by reducing the mismatch between perception of climate as a risk distant in space and time and the need for rapid action to mitigate long-term climate change, might help increase acceptance of mitigation policies.

climate change↗
DOE OSTI2022

Stress Drivers of Glucose Dynamics during Ozone Exposure Measured Using Radiotelemetry in Rats

Inhaled irritant air pollutants may trigger stress-related metabolic dysfunction associated with altered circulating adrenal-derived hormones. We used implantable telemetry in rats to assess real-time changes in circulating glucose during and after exposure to ozone and mechanistically linked responses to neuroendocrine stress hormones. First, using a cross-over design, we monitored glucose during ozone exposures (0.0, 0.2, 0.4, and 0.8 ppm) and nonexposure periods in male Wistar Kyoto rats implanted with glucose telemeters. A second cohort of unimplanted rats was exposed to ozone (0.0, 0.4 or 0.8 ppm) for 30 min, 1 h, 2 h, or 4 h with hormones measured immediately post exposure. We assessed glucose metabolism in sham and adrenalectomized rats, with or without supplementation of adrenergic/glucocorticoid receptor agonists, and in a separate cohort, antagonists. Ozone (0.8 ppm) was associated with significantly higher blood glucose and lower core body temperature beginning 90 min into exposure, with reversal of effects 4–6 h post exposure. Glucose monitoring during four daily 4-h ozone exposures revealed duration of glucose increases, adaptation, and diurnal variations. Ozone-induced glucose changes were preceded by higher levels of adrenocorticotropic hormone, corticosterone, and epinephrine but lower levels of thyroid-stimulating hormone, prolactin, and luteinizing hormones. Higher glucose and glucose intolerance were inhibited in rats that were adrenalectomized or treated with adrenergic plus glucocorticoid receptor antagonists but exacerbated by agonists. We demonstrated the temporality of neuroendocrine-stress–mediated biological sequalae responsible for ozone-induced glucose metabolic dysfunction and mechanism in a rodent model.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
NASA NTRS2017

Seasonal Impact of Regional Outdoor Biomass Burning on Air Pollution in Three Indian Cities: Delhi, Bengaluru, and Pune

Air pollution in many of India's cities exceeds national and international standards, and effective pollution control strategies require knowledge of the sources that contribute to air pollution and their spatiotemporal variability. In this study, we examine the influence of a single pollution source, outdoor biomass burning, on particulate matter (PM) concentrations, surface visibility, and aerosol optical depth (AOD) from 2007 to 2013 in three of the most populous Indian cities. We define the upwind regions, or "airsheds," for the cities by using atmospheric back trajectories from the HYSPLIT model. Using satellite fire radiative power (FRP) observations as a measure of fire activity, we target pre-monsoon and post-monsoon fires upwind of the Delhi National Capital Region and pre-monsoon fires surrounding Bengaluru and Pune. We find varying contributions of outdoor fires to different air quality metrics. For the post-monsoon burning season, we find that a subset of local meteorological variables (air temperature, humidity, sea level pressure, wind speed and direction) and FRP as the only pollution source explained 39% of variance in Delhi station PM(sub 10) anomalies, 77% in visibility, and 30% in satellite AOD; additionally, per unit increase in FRP within the daily airshed (1000 MW), PM(sub 10) increases by 16.34 micrograms per cubic meter, visibility decreases by 0.097 km, and satellite AOD increases by 0.07. In contrast, for the pre-monsoon burning season, we find less significant contributions from FRP to air quality in all three cities. Further, we attribute 99% of FRP from post-monsoon outdoor fires within Delhi's average airshed to agricultural burning. Our work suggests that although outdoor fires are not the dominant air pollution source in India throughout the year, post-monsoon fires contribute substantially to regional air pollution and high levels of population exposure around Delhi. During 3-day blocks of extreme PM(sub 2.5) in the 2013 post-monsoon burning season, which coincided with statistically significant high fire activity, concentrations in Delhi averaged 304 micrograms per cubic meter, or more than 1000% above the 24-h PM(sub 2.5) guideline (25 micrograms per cubic meter) of the World Health Organization. These results suggest that providing viable alternatives to agricultural residue burning could help improve post-monsoon air quality for a growing population of 63 million (39% in urban areas) within Delhi's airshed.

Air quality; Outdoor fires; Crop residue burning; ↗
NASA NTRS2020

Spatiotemporal Characteristics of the Association between AOD and PM over the California Central Valley

Many air pollution health effects studies rely on exposure estimates of particulate matter (PM) concentrations derived from remote sensing observations of aerosol optical depth (AOD). Simple but robust calibration models between AOD and PM are therefore important for generating reliable PM exposures. We conduct an in-depth examination of the spatial and temporal characteristics of the AOD-PM2.5 relationship by leveraging data from the Distributed Regional Aerosol Gridded Observation Networks (DRAGON) field campaign where eight NASA Aerosol Robotic Network (AERONET) sites were co-located with EPA Air Quality System (AQS) monitoring sites in California’s Central Valley from November 2012 to April 2013. With this spatiotemporally rich data we found that linear calibration models (R(exp 2) = 0.35, RMSE = 10.38 µg/cu.m) were significantly improved when spatial (R(exp 2) = 0.45, RMSE = 9.54 µg/cu.m), temporal (R(exp 2) = 0.62, RMSE = 8.30 µg/cu.m), and spatiotemporal (R(exp 2) = 0.65, RMSE = 7.58 µg/cu.m) functions were included. As a use-case we applied the best spatiotemporal model to convert space-borne MultiAngle Imaging Spectroradiometer (MISR) AOD observations to predict PM2.5 over the region (R(exp 2) = 0.60, RMSE = 8.42 µg/cu.m). Our results imply that simple AERONET AOD-PM2.5 calibrations are robust and can be reliably applied to space-borne AOD observations, resulting in PM2.5 prediction surfaces for use in downstream applications.

Meytar Sorek-Hamer↗
DOE OSTI2023

The effect of ambient ozone exposure on three types of diabetes: a meta-analysis

Background: Ozone as an air pollutant is gradually becoming a threat to people's health. However, the effect of ozone exposure on risk of developing diabetes, a fast-growing global metabolic disease, remains controversial. Objective: To evaluate the impact of ambient ozone exposure on the incidence rate of type 1, type 2 and gestational diabetes mellitus. Method: We systematically searched PubMed, Web of Science, and Cochrane Library databases before July 9, 2022, to determine relevant literature. Data were extracted after quality evaluation according to the Newcastle Ottawa Scale (NOS) and the agency for healthcare research and quality (AHRQ) standards, and a meta-analysis was used to evaluate the correlation between ozone exposure and type 1 diabetes mellitus (T1D), type 2 diabetes mellitus (T2D), and gestational diabetes mellitus (GDM). The heterogeneity test, sensitivity analysis, and publication bias were performed using Stata 16.0. Results: Our search identified 667 studies from three databases, 19 of which were included in our analysis after removing duplicate and ineligible studies. Among the remaining studies, three were on T1D, five were on T2D, and eleven were on GDM. The result showed that ozone exposure was positively correlated with T2D [effect size (ES) = 1.06, 95% CI: 1.02, 1.11] and GDM [pooled odds ratio (OR) = 1.01, 95% CI: 1.00, 1.03]. Subgroup analysis demonstrated that ozone exposure in the first trimester of pregnancy might raise the risk of GDM. However, no significant association was observed between ozone exposure and T1D. Conclusion: Long-term exposure to ozone may increase the risk of T2D, and daily ozone exposure during pregnancy was a hazard factor for developing GDM. Decreasing ambient ozone pollution may reduce the burden of both diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Factors Impacting Range Hood Use in California Houses and Low-Income Apartments

Venting range hoods can control indoor air pollutants emitted during residential cooktop and oven cooking. To quantify their potential benefits, it is important to know how frequently and under what conditions range hoods are operated during cooking. We analyzed data from 54 single family houses and 17 low-income apartments in California in which cooking activities, range hood use, and fine particulate matter (PM 2.5 ) were monitored for one week per home. Range hoods were used for 36% of cooking events in houses and 28% in apartments. The frequency of hood use increased with cooking frequency across homes. In both houses and apartments, the likelihood of hood use during a cooking event increased with the duration of cooktop burner use, but not with the duration of oven use. Actual hood use rates were higher in the homes of participants who self-reported more frequent use in a pre-study survey, but actual use was far lower than self-reported frequency. Residents in single family houses used range hoods more often when cooking caused a discernible increase in PM 2.5 . In apartments, residents used their range hood more often only when high concentrations of PM 2.5 were generated during cooking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1984

Effect of space exposure on pyroelectric infrared detectors (A0135)

The effects of long-duration space exposure and launch environment on the performance of pyroelectric detectors which is important for the prediction of performance degradation, setting exposure limits, or determining shielding requirements was investigated. Air pollution monitoring and thermal mapping of the Earth, which includes the remote sensing of aerosols and limb scanning infrared radiometer projects, requires photodetection in the 6- to 20 micro m region of the spectrum. Pyroelectric detectors can detect radiation in the 1- to 100 micro m region while operating at room temperature. This makes tahe pyroelectric detector a prime candidate to fill the thermal infrared detector requirements.

Robertson, J. B.↗
NASA NTRS2018

NASA's GEOS Composition Model Assessment of PM2.5 During Wildfires: Inferring the Impact of PM2.5 Exposure on Adverse Respiratory & Cardiovascular Conditions

Particulate matter pollution is a mixture of solid and liquid droplets floating in the air that can lead to reduced air quality and increased adverse health impact. Fine particulate matter (PM2.5) can be emitted into the air from anthropogenic sources such as the burning of fossil fuels, motor vehicles, and powerplant emissions. Exposure to PM2.5 can aggravate pre-existing respiratory and cardiovascular conditions. When PM2.5 is inhaled it can cause damage to the lungs such as reduced lung function and shortness of breath. After being inhaled PM2.5 can enter the bloodstream and cause harm to the heart. One major natural source of PM2.5 exposure is from wildfire smoke. The particulates within the smoke from the wildfires can spread from the initial source region, potentially impacting communities both near and far. During and after wildfire events, PM2.5 levels can exceed the WHO air quality guidelines (10 m.g/m^3 annual mean; 25 m.g/m^3 daily mean), becoming hazardous to an individual's health. Global models can be used to simulate the emission and transport of these particulates and subsequently they can be valuable to air quality forecasting in highly polluted areas. The NASA Goddard Earth Observing System (GEOS) Composition Forecast (GEOS-CF) system has been used to produce near-real time air quality forecasts of atmospheric composition at a high global resolution of 25x25 km2. The GEOS-CF system utilizes the GEOS weather forecast model coupled with GEOS-Chem (version 11) chemistry module to provide analyses and forecasts of various toxic air pollutants, including PM2.5. The GEOS-CF simulated high levels of PM2.5 (40 m.g/m^3 to 250 m.g/m^3 ), exceeding the WHO guidelines, during multiple recent regional and global wildfire seasons, including the 2017 Washington State and Northern and Southern California wildfire seasons. Furthermore, the GEOS-CF simulated PM2.5 applied to a human health assessment model, BenMAP (The Environmental Benefits Mapping and Analysis Program, version 1.3), estimates the impact on adverse respiratory health conditions due to PM2.5 exposure from wildfires. The GEOS-CF predicted PM2.5 during the wildfire season with the corresponding BenMAP results provides an assessment of the human health impact of PM2.5 exposure.

Saunders, Emily↗
DOE OSTI2023

Face mask integrated with flexible and wearable manganite oxide respiration sensor

Face masks are key personal protective equipment for reducing exposure to viruses and other environmental hazards such as air pollution. Integrating flexible and wearable sensors into face masks can provide valuable insights into personal and public health. The advantages that a breath-monitoring face mask requires, including multi-functional sensing ability and continuous, long-term dynamic breathing process monitoring, have been underdeveloped to date. Here, we design an effective human breath monitoring face mask based on a flexible La 0.7 Sr 0.3 MnO 3 (LSMO)/Mica respiration sensor. The sensor’s capabilities and systematic measurements are investigated under two application scenes, namely clinical monitoring mode and daily monitoring mode, to monitor, recognise, and analyse different human breath status, i.e., cough, normal breath, and deep breath. This sensing system exhibits super-stability and multi-modal capabilities in continuous and long-time monitoring of the human breath. We determine that during monitoring human breath, thermal diffusion in LSMO is responsible for the change of resistance in flexible LSMO/Mica sensor. Both simulated and experimental results demonstrate good discernibility of the flexible LSMO/Mica sensor operating at different breath status. Our work opens a route for the design of novel flexible and wearable electronic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Host tree species mediate corticolous lichen responses to elevated CO 2 and O 3 after 10 years exposure in the Aspen-FACE system

Lichens contribute significantly to the biodiversity and functioning of many ecosystems. Although lichens are useful air pollution bioindicators and may respond in significant ways to global change, they are studied infrequently under field conditions in chamberless exposure systems. We surveyed corticolous lichens on paper birch ( Betula papyrifera ) and trembling aspen ( Populus tremuloides ) after 10 years exposure (1998–2007) to elevated CO 2 (eCO 2 ) and O 3 (eO 3 ) in the Aspen-FACE experiment in Rhinelander, WI, USA. This experiment utilized chamberless exposure rings, 30 m in diameter, with both host trees planted together in one quadrant. Four treatments were allocated among 12 rings: ambient, eCO 2 , eO 3 , and the combination of eCO 2 + eO 3 , each replicated once in each of three blocks. Over the course of the experiment, ambient CO 2 increased from 343 to 386 ppm while eCO 2 averaged ~530 ppm CO 2 . Ambient ozone concentrations averaged ~37 ppb and ~49 ppb for eO 3 although exposures decreased with time. Tree growth and leaf area index were negatively affected by eO 3 and stimulated by eCO 2 , resulting in higher photosynthetically active radiation (PAR) in eO 3 and lower in eCO 2 . We assessed lichen richness and cover on five host trees per ring on the north-facing side of the trunks, which were higher on birch than on aspen. Additionally, neither of the lichen measures on birch responded to the exposure treatments, while on aspen lichen cover was highest in eO 3 and lowest in eCO 2 . On aspen, lichen cover was positively related to PAR and dominated by Caloplaca . No relationship was found for birch, although Lecanora exhibited a negative relationship with PAR. These lichens were insensitive to direct effects of eCO 2 and eO 3 at the levels applied. Instead, they responded to indirect effects, such as host tree species, and changes in understory PAR, resulting from direct effects of eO 3 and eCO 2 on the host trees.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Secondhand Exposure to Simulated Cannabis Vaping Aerosols

Emissions from cannabis vaping degrade indoor air quality and expose non-users to secondhand pollutants. We investigated how the vaping mixture composition affects indoor aerosol characteristics and exposures. Simulated cannabis vaping aerosol was produced by flash evaporation in a 20 m3 chamber of mixtures containing terpenoids, cannabinoids, cannabis extract constituents, and the adulterant vitamin E acetate (VEA). Aerosol time- and size-resolved concentrations (8 nm-2.5 μm at 1 Hz) were measured, and a dosimetry model was used to evaluate the intake of secondhand aerosols. The results showed peak particle number (PN) concentrations between 0.7 × 106 and 13 × 106 cm-3 and peak mass concentration (PM1.0) between 65 and 1191 μg m-3 at t = 5 min after emission. Concentrations decreased to 21-57% of peak PN and 33-69% of peak PM1.0 at t = 60 min. The PM1.0 yield was 0.06 for a terpenoid-only mixture, 0.22-0.36 for tetrahydrocannabinol (THC)-terpenoid mixtures, and >1 for mixtures containing high concentrations of cannabidiol (CBD) or VEA. For intake deposition, the highest aerosol fraction was deposited in the pulmonary region, followed by the tracheobronchial and head regions. Deposition increased in the presence of THC, CBD, or VEA, with aerosols <100 nm contributing the majority of particles deposited in all regions.

Tang, Xiaochen↗
DOE OSTI2023

Cleaner Cooking: Exploring Tools to Measure and Understand the Long-term Adoption and Environmental Significance of Cookstoves in India

About 40% of the world’s population, or roughly 3 billion people, rely on solid biomass fuels like coal, wood, dung, and crop residues to cook and meet their household energy needs. This outdated energy system has severe social, health, and environmental implications. Women are disproportionately affected as they predominantly bear the burden of cooking and collecting fuelwood, which exacerbates the “time poverty” trap that restricts them from participating in economic and educational activities. Exposure to indoor solid fuel combustion, also known as household air pollution, is responsible for 3-4 million premature deaths per year and is a leading risk factor for chronic obstructive pulmonary disease, childhood pneumonia, stroke, ischemic heart disease, and lung cancer. Solid-fuel cooking contributes to 16% of global ambient air pollution, emitting CO 2 and other climate-forcing pollutants like carbon monoxide, black carbon, and methane.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS1969

Environmental health program activities

Activities reported include studies on toxic air contaminants, excessive noise, poor lighting, food sanitation, water pollution, and exposure to nonionizing radiation as health hazards. Formulations for a radiological health manual provide guidance to personnel in the procurement and safe handling of radiation producing equipment and Apollo mission planning. A literature search and development of a water analysis laboratory are outlined to obtain information regarding microbiological problems involving potable water, waste management, and personal hygiene.

Bergtholdt, C. P.↗
DOE OSTI2021

Weather responsive smart ventilation system using multiple optimization parameters

A smart ventilation system which uses outdoor temperature and moisture to optimize control of the system. The main principle is to shift ventilation from time periods that have large indoor-outdoor temperature and moisture differences to periods when these differences are smaller, and their energy and comfort impacts are expected to be less. Fan flow rates are reduced when the outside temperature and moisture falls outside of optimum levels, yet overall air exchange is maintained to ensure chronic and acute exposure to pollutants remains relative to best practice. Online weather and smart thermostat data can be used as control inputs, so no specific measurement devices are needed to control ventilation fans.

Parker, Danny↗
NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗
NASA NTRS2022

Combining Machine Learning and Numerical Simulation for High-Resolution PM2.5 Concentration Forecast

Forecasting ambient PM2.5 concentrations with spatiotemporal coverage is key to alerting decision-makers of pollution episodes and preventing detrimental public exposure, especially in regions with limited ground air monitoring stations. The existing methods either rely on chemical transport models (CTMs) to forecast spatial distribution of PM2.5 with nontrivial uncertainty or statistical algorithms to forecast PM2.5 concentration time-series at air monitoring locations without continuous spatial coverage. In this study, we developed a PM2.5 forecast framework by combining the robust Random Forest algorithm with a publicly accessible global CTM forecast product – NASA’s Goddard Earth Observing System “Composition Forecasting” (GEOS-CF), providing spatiotemporally continuous PM2.5 concentration forecasts for the next five days at a 1-km spatial resolution. Our forecast experiment was conducted for a region in Central China including the populous and polluted Fenwei Plain. The forecast for the next two days had overall validation R2 of 0.76 and 0.64, respectively; the R2 was around 0.5 for the following three forecast days. Spatial cross-validation showed similar validation metrics. Our forecast model, with validation normalized mean bias close to zero, substantially reduced the large biases in GEOS-CF. The proposed framework requires minimal computational resources compared to running CTMs at urban scales, enabling near-real-time PM2.5 forecast in resource-restricted environments.

PM2.5↗