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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Sustainable Development in Algeria’s Urban Areas: Population Growth and Land Consumption

We analyzed the urban development sustainability in five major urban areas of Algeria by the standard of the UN Sustainability Development Goal indicator SDG 11.3.1, which focuses on the ratio of land consumption rate to population growth rate. We utilized the annual global artificial impervious area (GAIA) dataset to characterize land-use and population data from the two censuses carried out by the National Office of Statistics (ONS) for 2008 and 2018. We discuss the prevailing relationship between urban land consumption rate and population growth rate at the smallest territorial and population census unit scale. We confirm that the indicator SDG 11.3.1 is nonlinear and that while, for example, the wilaya of Tlemcen as a whole appears to be on a sustainable path, twenty-one of its communes are not. We found that overall, and for most of its communes, the wilaya of Oran seems to have an urban land use commensurable to its population growth, but in the wilaya of Algiers, out of fifty-seven communes, only fourteen have a tendency towards sustain-able development. However, the latter wilaya hosts the country’s capital and includes government buildings that are uninhabited but are accounted for as land consumed, and as such, the relationship between urban land consumption and population growth is biased. The wilaya of Annaba showed large discrepancies in terms of land use and population growth rates, and the evolution of these quantities is not homogenous across communes and not sustainable. In the Saharan wilaya of Ghardaia, the development is not homogeneous in all communes, with smaller communes undergoing buildup increases of more than 150% over the decade. Finally, in all communes where population growth exceeded urban land growth, there will be overcrowding, an aspect neither the SDG 11.3.1 nor the impervious surface per capita indicator captures. This result, in addition to other limitations, makes SDG 11.3.1 incomplete for the determination of the sustainable development in urban areas.

Algeria↗
DOE OSTI2025

The Interplay Between Climate and Urban Expansion on Building Energy Demand in Morocco

Understanding building energy demand is critical for addressing climate uncertainty challenges and ensuring sustainable urban growth. This study develops a building energy demand (BED) model to explore how climate variation and urban expansion affect residential and commercial space heating and cooling demands in Morocco for three scenarios, namely, 2005, 2018, and 2018 + 1.5 °C. The results show that coastal cities have lower heating and cooling needs due to the oceanic influence, while interior cities require significantly higher heating demand per-unit-floorspace. Between 2005 and 2018, urban growth increased total heating and cooling demand by 218.8 GWh, particularly in northern and coastal regions, despite per-unit-floorspace reductions in milder climates and improved building efficiency in 2018. Residential heating remains the dominant energy use, though commercial demand is significant in urban centers. Under the 2018 + 1.5 °C hypothetical scenario, heating demand across Morocco declines by 335.8 GWh compared to 2018, with urban areas amplifying this trend. Meanwhile, cooling demand increases slightly by 44.4 GWh, with major cities experiencing relative increases of up to 50%. These findings highlight a trade-off where reduced winter heating needs are partly offset by increased summer cooling demands in densely urbanized areas. In conclusion, the study identifies key urban hotspots for targeted interventions, emphasizing the need for energy-efficient building designs, climate-adaptive urban planning, and resilient energy management strategies to sustainably address shifting seasonal energy patterns.

Morocco↗
DOE OSTI2024

WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology

This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."

integrated multisector multiscale modeling↗
DOE OSTI2023

Exploring the Meteorological Impacts of Surface and Rooftop Heat Mitigation Strategies Over a Tropical City

Different heat mitigation technologies have been developed to improve the thermal environment in cities. However, the regional impacts of such technologies, especially in the context of a tropical city, remain unclear. The deployment of heat mitigation technologies at city-scale can change the radiation balance, advective flow, and energy balance between urban areas and the overlying atmosphere. We used the mesoscale Weather Research and Forecasting model coupled with a physically based single-layer urban canopy model to assess the impacts of five different heat mitigation technologies on surface energy balance, standard surface meteorological fields, and planetary boundary layer (PBL) dynamics for premonsoon typical hot summer days over a tropical coastal city in the month of April in 2018, 2019, and 2020. Results indicate that the regional impacts of cool materials (CMs), super-cool broadband radiative coolers, green roofs (GRs), vegetation fraction change, and a combination of CMs and GRs (i.e., “Cool city (CC)”) on the lower atmosphere are different at diurnal scale. Results showed that super-cool materials have the maximum potential of ambient temperature reduction of 1.6°C during peak hour (14:00 LT) compared to other technologies in the study. During the daytime hours, the PBL height was considerably lower than the reference scenario with no implementation of strategies by 700 m for super-cool materials and 500 m for both CMs and CC cases; however, the green roofing system underwent nominal changes over the urban area. During the nighttime hours, the PBL height increased by CMs and the CC strategies compared to the reference scenario, but minimal changes were evident for super-cool materials. The changes of temperature on the vertical profile of the heat mitigation implemented city reveal a stable PBL over the urban domain and a reduction of the vertical mixing associated with a pollution dome. This would lead to crossover phenomena above the PBL due to the decrease in vertical wind speed. Therefore, assessing the coupled regional impact of urban heat mitigation over the lower atmosphere at city-scale is urgent for sustainable urban planning.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

WRF Output from 270m domain running simulation with no 3D morphology, NUDAPT 3D morphology, 100m resolution 3D morphology and 10m resolution 3D morphology

This is a group of four datasets that were run for an experiment testing the effect of the resolution and the coverage of 3D urban morphological inputs on meteorological output. This data is associated with the paper "Sensitivity of Urban Morphology to the Resolution of Urban Morphological Feature Inputs: Implications for Characterizing Urban Sustainability."

integrated multisector multiscale modeling↗
DOE OSTI2026

Urban agrivoltaics enhance crop resilience and food-energy synergies in a changing climate

Urban agrivoltaics, the synergistic integration of solar photovoltaics with urban agriculture, offers a transformative solution to food and energy insecurity, which are major barriers to sustainable urban development, especially in low-income urban areas facing intensified heat and water stress due to climate change. With 70% of the global population projected to live in cities by 2050, maximizing underutilized urban spaces is critical. Our study presents the first evaluation of ground-based agrivoltaics in an urban context, demonstrating that, while early-season yields may decline due to light reduction in temperate climates, productivity rebounds during periods of extreme heat, extending harvest windows and enhancing crop resilience. As cities seek climate-adaptive infrastructure, converting just a fraction of vacant land and rooftops to urban agrivoltaics can yield significant co-benefits such as generating renewable energy for thousands of households while supplying fresh produce to help alleviate food deserts.

14 SOLAR ENERGY↗
DOE OSTI2023

Assessing Impacts of Environmental Perturbations on Urban Biogenic Carbon Exchange in the Chicago Region

Carbon dioxide (CO 2 ) quantification is critical for assessing city-level carbon emissions and sustainable urban development. While urban vegetation has the potential to provide environmental benefits, such as heat and carbon mitigation, the CO 2 exchange from biogenic sectors and its impact from the environmental perturbations are often overlooked. It is also challenging to simulate the plant functions in the complex urban terrain. This study presents a processed-based modeling approach to assess the biogenic carbon fluxes from the vegetated areas over the Chicago Metropolitan Area (CMA) using the Weather Research and Forecast—Urban Biogenic Carbon exchange model. We investigate the change of CO 2 sink power in CMA under heatwaves and irrigation. The results indicate that the vegetation plays a significant role in the city's carbon portfolio and the landscaping management has the potential to reduce carbon emissions significantly. Furthermore, based on the competing mechanisms in the biogenic carbon balance identified in this study, we develop a novel Environmental Benefit Score metrics framework to identify the vulnerability and mitigation measures associated with nature-based solutions (NbS) within CMA. By using the generalized portable framework and our science-policy confluence analysis presented in this study, global cities can maximize the effectiveness of NbS and accelerate carbon neutrality.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2020

Assessment of Sustainability Development in Urban Areas of Morocco

In Morocco, the last census counted 70% of houses in cities, with seven cities accounting for 25% of the total population and 41% of the urban population. This paper questioned the sustainability indicator in Moroccan cities using a novel methodology based on Earth observations and census data. We estimated the indicator for the 25 largest urban areas between 2003 and 2013. A stratification based on urbanization fractions allowed for the definition and comparison of hierarchically ordered zones consistent across urban areas and scales. We found no systematic pattern of growth between cities. However, three categories of development were identified: a group of large cities with an indicator less than unity and land consumption commensurate to population growth, a group of medium-sized cities trending moderately towards unsustainability with an indicator between one and two, and a group including small cities with significant departure from sustainability. The disparate sustainability levels between urban areas appear to be related to the rapid economic growth, typical of an emerging economy. Our results indicate a continuous population growth pushing outwards the perimeters of existing urban areas. Unless regulated, land consumption is increasing faster than the population in most parts of Morocco, in line with global trends.

Lahouari Bounoua↗
DOE OSTI2024

Leveraging generative AI for urban digital twins: a scoping review on the autonomous generation of urban data, scenarios, designs, and 3D city models for smart city advancement

The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management. Despite their effectiveness, these applications often rely on massive amounts of high-dimensional and multi-domain data for monitoring and characterizing different urban sub-systems, presenting challenges in application areas that are limited by data quality and availability, as well as costly efforts for generating urban scenarios and design alternatives. As an emerging research area in deep learning, Generative Artificial Intelligence (GenAI) models have demonstrated their unique values in content generation. This paper aims to explore the innovative integration of GenAI techniques and urban digital twins to address challenges in the planning and management of built environments with focuses on various urban sub-systems, such as transportation, energy, water, and building and infrastructure. The survey starts with the introduction of cutting-edge generative AI models, such as the Generative Adversarial Networks (GAN), Variational Autoencoders (VAEs), Generative Pre-trained Transformer (GPT), followed by a scoping review of the existing urban science applications that leverage the intelligent and autonomous capability of these techniques to facilitate the research, operations, and management of critical urban subsystems, as well as the holistic planning and design of the built environment. Based on the review, we discuss potential opportunities and technical strategies that integrate GenAI models into the next-generation urban digital twins for more intelligent, scalable, and automated smart city development and management.

3D city modeling↗
DOE OSTI2024

Street-level temperature estimation using graph neural networks: Performance, feature embedding and interpretability

Estimating street-level air temperature is a challenging task due to the highly heterogeneous urban surfaces, canyon-like street morphology, and the diverse physical processes in the built environment. Though pioneering studies have embarked on investigations via data-driven approaches, many questions remain to be answered. Here, in this study, we leveraged an innovative framework and redefined the street-level temperature estimation problem using Graph Neural Networks (GNN) with spatial embedding techniques. The results showed that GNN models are more capable and consistent of estimating street-level temperature among tested locations, benefiting from its unique strength in handling extensive data over unstructured graph topology. In addition, we conducted in-depth analysis of feature importance to enhance the model interpretability. Among the urban features analyzed in this study, the time-variant canopy density and meter-level land use data emerge as crucial factors. Our findings highlight GNN 's high potential in capturing the complex dynamics between urban elements and their impacts on microclimate, thus offering valuable insights for comprehensive urban data collection and urban climate modeling in general. Collectively, this study also contributes to urban planning and policy by providing avenues to enhance city resilience against climate change, thereby advancing the agenda for environmental stewardship and urban sustainability.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

K in an Urban World: New Contexts for Hydraulic Conductivity

Abstract Hydraulic conductivity ( K ) is a key hydrologic parameter widely recognized to be difficult to estimate and constrain, with little consistent assessment in disturbed, urbanized soils. To estimate K , it is either measured, or simulated by pedotransfer functions, which relate K to easily measured soil properties. We measured K in urbanized soils by double‐ring infiltrometer ( K dring ), near‐saturated tension infiltrometry ( K minidisk ), and constant head borehole permeametry ( K borehole ), along with other soil properties across the major soil orders in 12 United States cities. We compared measured K with that predicted from the pedotransfer function, ROSETTA. We found that regardless of soil texture, K dring was consistently larger than K minidisk ; with the latter having slightly less sample variance. K borehole was dependent upon specific subsurface conditions, and contrary to common expectations, did not always decrease with depth. Based on either soil textural class, or percent textural separates (sand, silt clay), ROSETTA did not accurately predict measured K for surface nor subsurface soils. We go on to discuss how K varies in urban landscapes, the role of measurement methods and artifacts in the perception of this metric, and implications for hydrologic modeling. Overall, we aim to inspire consistency and coherence when addressing K ‐related challenges in sustainable urban water management.

Shuster, W. D.↗
NASA NTRS2007

Gray Wave of the Great Transformation: A Satellite View of Urbanization, Climate, and Food Security

Land cover change driven by human activity is profoundly affecting Earth's natural systems with impacts ranging from a loss of biological productivity to changes in atmospheric chemistry and regional and global climate. This change has been so pervasive and progressed so rapidly, compared to natural processes, scientists refer to it as 'the great transformation'. Urbanization or the 'gray wave' of this transformation is being increasingly recognized as an important process in global climate change. A hallmark of our success as a species, large urban conglomerates do in fact alter their environments so profoundly that the local climate, atmospheric composition, and the basic ecology of the landscape are affected in ways that have consequences to human health and economic well-being. Fortunately we have incredible new tools to observe and understand these processes in ways that can be used to plan and develop enjoyable and sustainable urban places. A suite of Earth observing satellites is making it possible to study the interactions between urbanization, biological processes, and the atmosphere including weather and climate. Using these Earth Observatories we are learning how urban heat islands form and potentially ameliorate them, how urbanization can affect rainfall, pollution, surface water recharge at the local level, and climate and food security globally.

Imhoff, Marc L.↗
DOE OSTI2024

Biofuels Production and Development in the United States

Addressing transportation emissions will require a combination of policy interventions, technological innovations, and behavioral changes. To decrease transportation-related carbon emissions, we recommend transitioning to cleaner and more efficient transport, expanding public transportation, and promoting sustainable urban planning. Transitioning to biofuels in the U.S. transportation sector presents a host of environmental, economic, and strategic advantages. Environmentally, biofuels offer a promising avenue for reducing greenhouse gas (GHG) emissions compared to conventional fossil fuels. Biofuels produce fewer harmful emissions, such as particulate matter and nitrogen oxides, compared to conventional fuels, thereby improving local air quality, and reducing health risks associated with air pollution. The U.S. government issued a "Sustainable Aviation Fuel (SAF) Grand Challenge" to promote the development of SAF and address the decarbonization of commercial aviation. The targets are to expand current domestic SAF production to 3 billion gallons per year by 2030, and then to 35 billion gallons by 2050, while achieving life cycle GHG reduction of at least 50% relative to fossil Jet A. The United States' biofuel production capacity rose to 23.8 billion gallons per year in 2023, a growth of more than 1.7 billion gallons since 2022. Ethanol nameplate capacity has reached 17.8 billion gallons per year since 2023, the ethanol production is 15.6 billion gallons in 2023 as capped by EPA regulation. Biodiesel production capacity stands at 2.1 billion gallons, and renewable diesel capacity is now 3.9 billion gallons. This newsletter keeps you informed on the state of biofuels in the United States, policy and regulations, technology advancements, and environmental and social impacts.

biodiesel↗
DOE OSTI2024

Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments

The project titled “Developing an Energy-Conscious Traffic Signal Control System for Optimized Fuel Consumption in Connected Vehicle Environments” addresses energy-related challenges associated with adaptive traffic control systems by integrating connected vehicles (CV) and connected infrastructure (CI). The system developed in this project, a CV-based adaptive traffic control system, aims to improve fuel consumption in mixed traffic environments by capitalizing on emerging CV and CI communication technologies, as well as leveraging recent advances in Artificial Intelligence (AI), optimization, and edge computing. The system was tested at the MLK Smart Corridor, an urban testbed managed by the University of Tennessee at Chattanooga (UTC) and the City of Chattanooga. The system was validated through extensive simulations, both Software-in-the-Loop (SILS) and Hardware-in-the-Loop (HILS), and was further implemented and tested in real-world conditions at several intersections along the corridor. The Fuel Consumption Performance Index (FC-PI) and the Ecological Performance Index (Eco-PI) were developed as the key components for evaluating the system’s impact on fuel consumption and emissions. These metrics provided a comprehensive means of understanding the impact of traffic signal control optimization in mixed traffic environments. The report presents an in-depth analysis of the Eco-PI, FC-PI, adaptive traffic control system integration, and the testing and field implementation of the system. The results demonstrate significant reductions in fuel consumption and emissions, showcasing the system’s capability to contribute to more sustainable urban traffic management. The report also documents the challenges encountered and recommendations for scaling and further improving the system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

We collect vehicle trajectory data from major transportation network companies (TNCs) in New York City (NYC) in 2017 and 2019, and we use the trajectory data to understand how the growth of TNCs has impacted traffic congestion and emission in urban areas. By mining the large-scale trajectory data and conduct the case study in NYC, we confirm that the rise of TNC is the major contributing factor that makes urban traffic congestion worse. From 2017 to 2019, the number of for-hire vehicles (FHV) has increased by over 48% and served 90% more daily trips. These resulted in an average citywide speed reduction of 22.5% on weekdays, and the average speed in Manhattan decreased from 11.76 km/h in April 2017 to 9.56 km/h in March 2019. The heavier traffic congestion may have led to 136% more NOx, 152% more CO and 157% more HC emission per kilometer traveled by the FHV sector. Our results show that the traffic condition is consistently worse across different times of the day and at different locations in NYC. And we build the connection between the number of available FHVs and the reduction in travel speed between the two years of data and explain how the rise of TNC may impact traffic congestion in terms of moving speed and congestion time. Our findings provide valuable insights for different stakeholders and decision-makers in framing regulation and operation policies towards more effective and sustainable urban mobility.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Imputation of urban environmental sensor data using gated attention bidirectional long short-term memory (GA-BiLSTM): methods, performance, and implications

Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.

Data imputation↗
DOE OSTI2024

Optimizing dynamic wireless charging for electric buses: A data-driven approach to infrastructure planning

The network configuration significantly impacts the performance of dynamic wireless charging (DWC) technology for electric buses. Here, this study presents a novel approach to planning charging infrastructure for public transit using data-driven nonconvex mixed-integer optimization. Integrating DWC and charging station technologies reveals a trade-off between enroute and stationary charging times. Our framework optimizes bus frequency settings and transmitter coil arrangements to minimize operational and infrastructure costs. A case study in Chattanooga, Tennessee, demonstrates the method's effectiveness in mitigating range anxiety and reducing charging expenses. This research implies that integrating DWC technology into public transit systems can enhance the feasibility and cost-effectiveness of electric bus operations, promoting sustainable urban mobility.

33 ADVANCED PROPULSION SYSTEMS↗