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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Potential benefits of cool walls on residential and commercial buildings across California and the United States: Conserving energy, saving money, and reducing emission of greenhouse gases and air pollutants

Solar-reflective “cool” walls reduce absorption of sunlight by the building envelope, which may decrease cooling load in warm weather and increase heating load in cool weather. Changes to annual heating, ventilation, and air conditioning (HVAC) energy use depend on climate, wall construction, wall orientation, building geometry, HVAC efficiency, and operating schedule. Changes to annual energy cost and energy-related emissions further vary with local energy prices and emission factors. In this work, we used EnergyPlus to perform over 100,000 whole-building energy simulations, spanning 10 different building categories, three building vintages, 16 California climate zones, and 15 United States (U.S.) climate zones. We find that cool walls yielded annual source energy, energy cost, and emission savings in all California climate zones and in warm U.S. (ASHRAE) climate zones. In California, annual whole-building HVAC energy cost savings were 4.0–27% in single-family homes, 0.5–3.8% in medium office buildings, and 0.0–8.5% in stand-alone retail stores. In warm U.S. climates—zones 1A (Miami, FL) through 4B (Albuquerque, NM)—annual HVAC energy cost savings were 1.8–8.3% in single-family homes, 0.3–4.6% in medium office buildings, and 0.5–11% in stand-alone retail stores. California and U.S. fractional source energy and emission savings were comparable to fractional energy cost savings. Per unit surface area modified, cool-wall savings often exceeded cool-roof savings because building codes typically prescribe much less wall insulation than roof insulation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Urban mining from biomass, brine, sewage sludge, phosphogypsum and e-waste for reducing the environmental pollution: Current status of availability, potential, and technologies with a focus on LCA and TEA

We discuss how rapid industrialization, improved standards of living, growing economies and ever-increasing population has led to the unprecedented exploitation of the finite and non-renewable resources of minerals in past years. It was observed that out of 100 BMT of raw materials processed annually only 10% is recycled back. This has resulted in a strenuous burden on natural or primary resources of minerals (such as ores) having limited availability. Moreover, severe environmental concerns have been raised by the huge piles of waste generated at landfill sites. To resolve these issues, ‘Urban Mining’ from waste or secondary resources in a Circular Economy’ concept is the only sustainable solution. The objective of this review is to critically examine the availability, elemental composition, and the market potential of the selected secondary resources such as lignocellulosic/algal biomass, desalination water, sewage sludge, phosphogypsum, and e-waste for minerals sequestration. This review showed that, secondary resources have potential to partially replace the minerals required in different sectors such as macro and microelements in agriculture, rare earth elements (REEs) in electrical and electronics industry, metals in manufacturing sector and precious elements such as gold and platinum in ornamental industry. Further, inputs from the selected life cycle analysis (LCA) & techno economic analysis (TEA) were discussed which showed that although, urban mining has a potential to reduce the greenhouse gaseous (GHG) emissions in a sustainable manner however, process improvements through innovative, novel and cost-effective pathways are essentially required for its large-scale deployment at industrial scale in future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Toward equitable environmental exposure modeling through convergence of data, open, and citizen sciences: an example of air pollution exposure modeling amidst increasing wildfire smoke

Exposure modeling is critical in environmental epidemiology and human health but may face challenges (e.g., skewed data, unequal error, context-insensitive validation, and computational demands). Modeling decisions reflect the intended use of the models and the values that modelers prioritize. We aimed to provide a conceptual framework and machine learning (ML) modeling protocols that address these issues. With 500m-gridded hourly PM 2.5 and O 3 levels in Illinois before, during, and after the 2023 Canadian wildfire season as a motivating example, we conducted modeling experiments to evaluate modeling methods, guided by three domains we propose based on theories of science: 1) Data Diversity, leveraging open and citizen science data to enhance inclusivity, parsimony, and representativeness; 2) Equitable Accuracy, ensuring fairly distributed uncertainties across subpopulations; and 3) Sustainable Modeling, balancing accuracy with reducing computational demands to promote accessibility for under-resourced researchers. Here, we found that ML with publicly available data can achieve high accuracy. Depending on methods, performance may vary substantially, even with identical input data. Large but skewed data may reduce performance. Misuse of cross-validation protocols can underestimate prediction error; although we observed R 2 s of ∼98 %, the modeled estimates varied significantly, indicating the need for careful model validation. By using new modeling protocols including representativeness-considered training and validation data and a new loss function, we achieved high agreement between estimates and ground-based measurements (e.g., R 2 = ∼90 % for PM 2.5 ; ∼80 % for O 3 ), equally distributed errors across sociodemographic strata and urban–rural divides, and reduction in computation time—from several weeks or months to a few days.

Exposure assessment↗